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Xie H, Illapani VSP, Vezina LG, Gholipour T, Oluigbo C, Gaillard WD, Cohen NT. Mapping Functional Connectivity Signatures of Pharmacoresistant Focal Cortical Dysplasia-Related Epilepsy. Ann Neurol 2024. [PMID: 39192492 DOI: 10.1002/ana.27069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 08/06/2024] [Accepted: 08/09/2024] [Indexed: 08/29/2024]
Abstract
OBJECTIVE To determine common network alterations in focal cortical dysplasia pharmacoresistant epilepsy (FCD-PRE) using functional connectivity analysis of resting-state functional magnetic resonance imaging (rsfMRI). METHODS This is a retrospective imaging cohort from Children's National Hospital (Washington, DC, USA) from January, 2011 to January, 2022. Patients with 3-T MRI-confirmed FCD-PRE underwent rsfMRI as part of routine clinical care. Patients were included if they were age 5-22 years at the time of the scan, and had a minimum of 18 months of follow-up. Healthy, typically-developing controls were included from Children's National Hospital (n = 16) and matched from Human Connectome Project-Development public dataset (n = 100). RESULTS A total of 42 FCD-PRE patients (20 M:22 F, aged 14.2 ± 4.1 years) and 116 healthy controls (56 M:60 F, aged 13.7 ± 3.3 years) with rsfMRI were included. Seed-based functional connectivity maps were generated for each FCD, and each seed was used to generate a patient-specific z-scored connectivity map on 116 controls. FCD-PRE patients had mutual altered connectivity in regions of dorsal attention, default mode, and control networks. Functional connectivity was diminished within the FCD dominant functional network, as well as in homotopic regions. Cluster specific connectivity patterns varied by pathological subtype. Higher FCD connectivity to the limbic network was associated with increased odds of Engel I outcome. INTERPRETATION This study demonstrates diminished functional connectivity patterns in FCD-PRE, which may represent a neuromarker for the disease, independent of FCD location, involving the dorsal attention, default mode, and control functional networks. Higher connectivity to the limbic network is associated with a seizure-free outcome. Future multicenter, prospective studies are needed to allow for much earlier detection of signatures of treatment-resistant epilepsy. ANN NEUROL 2024.
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Affiliation(s)
- Hua Xie
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, USA
| | - Venkata Sita Priyanka Illapani
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, USA
| | - L Gilbert Vezina
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, USA
| | - Taha Gholipour
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | - Chima Oluigbo
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, USA
| | - William D Gaillard
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, USA
| | - Nathan T Cohen
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC, USA
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Lee HM, Fadaie F, Gill RS, Caldairou B, Sziklas V, Crane J, Hong SJ, Bernhardt BC, Bernasconi A, Bernasconi N. MRI-Derived Modeling of Disease Progression Patterns in Patients With Temporal Lobe Epilepsy. Neurology 2024; 103:e209524. [PMID: 38981074 DOI: 10.1212/wnl.0000000000209524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Temporal lobe epilepsy (TLE) is assumed to follow a steady course that is similar across patients. To date, phenotypic and temporal diversities of TLE evolution remain unknown. In this study, we aimed at simultaneously characterizing these sources of variability based on cross-sectional data. METHODS We studied consecutive patients with TLE referred for evaluation by neurologists to the Montreal Neurological Institute epilepsy clinic, who underwent in-patient video EEG monitoring and multimodal imaging at 3 Tesla, comprising 3D T1 and fluid-attenuated inversion recovery and 2D diffusion-weighted MRI. The cohort included patients with drug-resistant epilepsy and patients with drug-responsive epilepsy. The neuropsychological evaluation included Wechsler Adult Intelligence Scale-III and Leonard tapping task. The control group consisted of participants without TLE recruited through advertisement and who underwent the same MRI acquisition as patients. Based on surface-based analysis of key MRI markers of pathology (gray matter morphology and white matter microstructure), the Subtype and Stage Inference algorithm estimated subtypes and stages of brain pathology to which individual patients were assigned. The number of subtypes was determined by running the algorithm 100 times and estimating mean and SD of disease trajectories and the consistency of patients' assignments based on 1,000 bootstrap samples. Effect of normal aging was subtracted from patients. We examined associations with clinical and cognitive parameters and utility for individualized predictions. RESULTS We studied 82 patients with TLE (52 female, mean age 35 ± 10 years; 11 drug-responsive) and 41 control participants (23 male, mean age 32 ± 8 years). Among 57 operated, 43/37/20 had Engel-I outcome/hippocampal sclerosis/hippocampal isolated gliosis, respectively. We identified 3 trajectory subtypes: S1 (n = 35), led by ipsilateral hippocampal atrophy and gliosis, followed by white-matter damage; S2 (n = 27), characterized by bilateral neocortical atrophy, followed by ipsilateral hippocampal atrophy and gliosis; and S3 (n = 20), typified by bilateral limbic white-matter damage, followed by bilateral hippocampal gliosis. Patients showed high assignability to their subtypes and stages (>90% bootstrap agreement). S1 had the highest proportions of patients with early disease onset (effect size d = 0.27 vs S2, d = 0.73 vs S3), febrile convulsions (χ2 = 3.70), drug resistance (χ2 = 2.94), a positive MRI (χ2 = 8.42), hippocampal sclerosis (χ2 = 7.57), and Engel-I outcome (χ2 = 1.51), pFDR < 0.05 across all comparisons. S2 and S3 exhibited the intermediate and lowest proportions, respectively. Verbal IQ and digit span were lower in S1 (d = 0.65 and d = 0.50, pFDR < 0.05) and S2 (d = 0.76 and d = 1.09, pFDR < 0.05), compared with S3. We observed progressive decline in sequential motor tapping in S1 and S3 (T = -3.38 and T = -4.94, pFDR = 0.027), compared with S2 (T = 2.14, pFDR = 0.035). S3 showed progressive decline in digit span (T = -5.83, p = 0.021). Supervised classifiers trained on subtype and stage outperformed subtype-only and stage-only models predicting drug response in 73% ± 1.0% (vs 70% ± 1.4% and 63% ± 1.3%) and 76% ± 1.6% for Engel-I outcome (vs 71% ± 0.8% and 72% ± 1.1%), pFDR < 0.05 across all comparisons. DISCUSSION Cross-sectional MRI-derived models provide reliable prognostic markers of TLE disease evolution, which follows distinct trajectories, each associated with divergent patterns of hippocampal and whole-brain structural alterations, as well as cognitive and clinical profiles.
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Affiliation(s)
- Hyo M Lee
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Fatemeh Fadaie
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Ravnoor S Gill
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Benoit Caldairou
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Viviane Sziklas
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Joelle Crane
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Seok-Jun Hong
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (H.M.L., F.F., R.S.G., B.C., S.-J.H., A.B., N.B.), and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute (V.S., J.C.), Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
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Billaud CHA, Yu J. The hippocampus as a structural and functional network epicentre for distant cortical thinning in neurocognitive aging. Neurobiol Aging 2024; 139:82-89. [PMID: 38657394 DOI: 10.1016/j.neurobiolaging.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/05/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024]
Abstract
Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ± 8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (β=-0.150,p=<.001) and RHIP (β=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (β=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (β=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (β=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (β=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (β=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.
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Affiliation(s)
- Charly Hugo Alexandre Billaud
- Nanyang Technological University, Psychology, School of Social Sciences, 48 Nanyang Avenue, Singapore City 639798, Singapore.
| | - Junhong Yu
- Nanyang Technological University, Psychology, School of Social Sciences, 48 Nanyang Avenue, Singapore City 639798, Singapore
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Karadag N, Hagen E, Shadrin AA, van der Meer D, O'Connell KS, Rahman Z, Kutrolli G, Parker N, Bahrami S, Fominykh V, Heuser K, Taubøll E, Steen NE, Djurovic S, Dale AM, Frei O, Andreassen OA, Smeland OB. Dissecting the Shared Genetic Architecture of Common Epilepsies With Cortical Brain Morphology. Neurol Genet 2024; 10:e200143. [PMID: 38817246 PMCID: PMC11139015 DOI: 10.1212/nxg.0000000000200143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/27/2024] [Indexed: 06/01/2024]
Abstract
Background and Objectives Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences. Methods We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples. Results The epilepsies (2.4 k-2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples. Discussion Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.
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Affiliation(s)
- Naz Karadag
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Espen Hagen
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Alexey A Shadrin
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Dennis van der Meer
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Kevin S O'Connell
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Zillur Rahman
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Gleda Kutrolli
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Nadine Parker
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Shahram Bahrami
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Vera Fominykh
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Kjell Heuser
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Erik Taubøll
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Nils Eiel Steen
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Srdjan Djurovic
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Anders M Dale
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Oleksandr Frei
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Ole A Andreassen
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
| | - Olav B Smeland
- From the Institute of Clinical Medicine (N.K., E.H., A.A.S., D.M., K.S.O.C., Z.R., G.K., N.P., S.B., V.F., N.E.S., O.F., O.A.A., O.B.S.), NORMENT, University of Oslo; K.G. Jebsen Centre for Neurodevelopmental Disorders (A.A.S., O.A.A.), University of Oslo and Oslo University Hospital, Norway; Faculty of Health (D.M.), School of Mental Health and Neuroscience, Maastricht University, Netherlands; Department of Neurology (K.H., E.T.), Oslo University Hospital; Faculty of Medicine (E.T.), University of Oslo; Division of Mental Health and Addiction (N.E.S., O.A.A., O.B.S.), Oslo University Hospital; Department of Psychiatric Research (N.E.S.), Diakonhjemmet Hospital; Department of Medical Genetics (S.D.), Oslo University Hospital, Norway; Department of Clinical Science (S.D.), NORMENT, University of Bergen, Norway; Department of Cognitive Science (A.M.D.); Multimodal Imaging Laboratory (A.M.D.); Department of Psychiatry (A.M.D.); Department of Neurosciences (A.M.D.), University of California, San Diego; and Department of Informatics (O.F.), Center for Bioinformatics, University of Oslo, Norway
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5
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Arafat T, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. Prog Neurobiol 2024; 236:102604. [PMID: 38604584 DOI: 10.1016/j.pneurobio.2024.102604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/18/2023] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Thaera Arafat
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada; Department of Neurology, Duke University School of Medicine and Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC 27705, USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3 BG, United Kingdom
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Queretaro, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.
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6
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Han S, Fang K, Zheng R, Li S, Zhou B, Sheng W, Wen B, Liu L, Wei Y, Chen Y, Chen H, Cui Q, Cheng J, Zhang Y. Gray matter atrophy is constrained by normal structural brain network architecture in depression. Psychol Med 2024; 54:1318-1328. [PMID: 37947212 DOI: 10.1017/s0033291723003161] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression. METHODS In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis. RESULTS In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset. CONCLUSIONS These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Huafu Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Henan Engineering Research Center of Brain Function Development and Application, Zhengzhou, China
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7
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Miron G, Müller PM, Hohmann L, Oltmanns F, Holtkamp M, Meisel C, Chien C. Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug-Resistant Temporal Lobe Epilepsy. Ann Neurol 2024; 95:984-997. [PMID: 38391006 DOI: 10.1002/ana.26893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/26/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024]
Abstract
OBJECTIVE In temporal lobe epilepsy (TLE), a taxonomy classifying patients into 3 cognitive phenotypes has been adopted: minimally, focally, or multidomain cognitively impaired (CI). We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug-resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes. METHODS TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance F-statistics from comparisons of GM regions within each TLE cognitive phenotype and age- and sex-matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome. RESULTS A total of 124 patients (63 females, mean age ± standard deviation [SD] = 36.0 ± 12.0 years) and 117 healthy controls (63 females, mean age ± SD = 36.1 ± 12.0 years) were analyzed. In the multidomain CI group (n = 66, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n = 37, 29.8%) showed 13 regions, whereas minimally impaired patients (n = 21, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age ± SD = 33.6 ± 18.0 years) patients who underwent surgery, LRMs based on network-identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70 ± 0.15. INTERPRETATION A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug-resistant TLE. ANN NEUROL 2024;95:984-997.
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Affiliation(s)
- Gadi Miron
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Paul Manuel Müller
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Louisa Hohmann
- Epilepsy Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
| | - Frank Oltmanns
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
| | - Martin Holtkamp
- Epilepsy Center Berlin-Brandenburg, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Epilepsy Center Berlin-Brandenburg, Institute for Diagnostics of Epilepsy, Berlin, Germany
| | - Christian Meisel
- Computational Neurology, Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
- NeuroCure Cluster of Excellence, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Center for Stroke Research Berlin, Berlin, Germany
| | - Claudia Chien
- Experimental Clinical and Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Neuroscience Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Neuroscience, Charité-Universitätsmedizin Berlin, Berlin, Germany
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8
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Zhou C, Xie F, Wang D, Huang X, Guo D, Du Y, Xiao L, Liu D, Xiao B, Yang Z, Feng L. Preoperative structural-functional coupling at the default mode network predicts surgical outcomes of temporal lobe epilepsy. Epilepsia 2024; 65:1115-1127. [PMID: 38393301 DOI: 10.1111/epi.17921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVE Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features. METHODS This study analyzed presurgical diffusion and functional magnetic resonance imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patients were categorized into seizure-free (SF) and non-seizure-free (nSF) groups based on postsurgical recurrence. Individual functional connectivity (FC), structural connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning model to classify surgical outcomes. RESULTS Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p < .05). Multiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p < .05). Models trained using the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteristic curve of .893 with an overall accuracy of .887. SIGNIFICANCE Presurgical hyper-SFC in the DMN was strongly associated with postoperative seizure recurrence. Furthermore, our results introduce a novel SFC-based machine learning model to precisely classify the surgical outcomes of TLE.
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Affiliation(s)
- Chunyao Zhou
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Xiaoting Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yangsa Du
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Ling Xiao
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, Xiangya Hospital, Central South University (Jiangxi Branch), Nanchang, China
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9
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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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10
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Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, Zyriax BC, Zeller T, Kühn S, Gallinat J, Fiehler J, Twerenbold R, Omidvarnia A, Patil KR, Eickhoff SB, Thomalla G, Cheng B. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. eLife 2024; 12:RP93246. [PMID: 38512127 PMCID: PMC10957178 DOI: 10.7554/elife.93246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
The link between metabolic syndrome (MetS) and neurodegenerative as well as cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, brain morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis, we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Felix L Nägele
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Maximilian Schell
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - D Leander Rimmele
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Birgit-Christiane Zyriax
- Midwifery Science-Health Services Research and Prevention, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-EppendorfHamburgGermany
| | - Tanja Zeller
- Department of Cardiology, University Heart and Vascular CenterHamburgGermany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/LuebeckHamburgGermany
- University Center of Cardiovascular Science, University Heart and Vascular CenterHamburgGermany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart and Vascular CenterHamburgGermany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/LuebeckHamburgGermany
- University Center of Cardiovascular Science, University Heart and Vascular CenterHamburgGermany
- Epidemiological Study Center, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Amir Omidvarnia
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Kaustubh R Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Goetz Thomalla
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
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11
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Chen J, Ngo A, Rodríguez-Cruces R, Royer J, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda CL, Alvim MKM, Bonilha L, Gleichgerrcht E, Focke NK, Kreilkamp B, Domin M, von Podewils F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, Bender B, O’Brien TJ, Sinclair B, Vivash L, Kwan P, Desmond PM, Lui E, Duma GM, Bonanni P, Ballerini A, Vaudano AE, Meletti S, Tondelli M, Alhusaini S, Doherty CP, Cavalleri GL, Delanty N, Kälviäinen R, Jackson GD, Kowalczyk M, Mascalchi M, Semmelroch M, Thomas RH, Soltanian-Zadeh H, Davoodi-Bojd E, Zhang J, Lenge M, Guerrini R, Bartolini E, Hamandi K, Foley S, Rüber T, Bauer T, Weber B, Caldairou B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Pardoe H, Severino M, Striano P, Tortora D, Kaestner E, Hatton SN, Arienzo D, Vos SB, Ryten M, Taylor PN, Duncan JS, Whelan CD, Galovic M, Winston GP, Thomopoulos SI, Thompson PM, Sisodiya SM, Labate A, McDonald CR, Caciagli L, Bernasconi N, Bernasconi A, Larivière S, Schrader D, Bernhardt BC. A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.02.583073. [PMID: 38496668 PMCID: PMC10942350 DOI: 10.1101/2024.03.02.583073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Objectives Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.
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Affiliation(s)
- Judy Chen
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Raúl Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | | | - Antonio Gambardella
- Neuroscience Research Center, University Magna Græcia, Catanzaro, CZ, Italy
- Institute of Neurology, University Magna Græcia, Catanzaro, CZ, Italy
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Querétaro, México
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Fernando Cendes
- Department of Neurology, University of Campinas-–UNICAMP, Campinas, São Paulo, Brazil
| | - Clarissa L. Yasuda
- Department of Neurology, University of Campinas-–UNICAMP, Campinas, São Paulo, Brazil
| | - Marina K. M. Alvim
- Department of Neurology, University of Campinas-–UNICAMP, Campinas, São Paulo, Brazil
| | | | | | - Niels K. Focke
- Department of Neurology, University of Medicine Göttingen, Göttingen, Germany
| | - Barbara Kreilkamp
- Department of Neurology, University of Medicine Göttingen, Göttingen, Germany
| | - Martin Domin
- Institute of Diagnostic Radiology and Neuroradiology, Functional Imaging Unit, University Medicine Greifswald, Greifswald, Germany
| | - Felix von Podewils
- Department of Neurology, University Medicine Greifswald, Epilepsy Center, Greifswald, Germany
| | - Soenke Langner
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Bern, Switzerland
| | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Raviteja Kotikalapudi
- Department of Neurology, University of Medicine Göttingen, Göttingen, Germany
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Benjamin Bender
- Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Germany
| | - Terence J. O’Brien
- Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Victoria, Australia
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Sinclair
- Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Victoria, Australia
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Victoria, Australia
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Patrick Kwan
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Patricia M. Desmond
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Elaine Lui
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Gian Marco Duma
- Scientific Institute IRCCS E.Medea, Epilepsy Unit, Conegliano (TV), Italy
| | - Paolo Bonanni
- Scientific Institute IRCCS E.Medea, Epilepsy Unit, Conegliano (TV), Italy
| | - Alice Ballerini
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy
| | - Anna Elisabetta Vaudano
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria, Modena, Italy
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy
| | - Stefano Meletti
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria, Modena, Italy
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy
| | - Manuela Tondelli
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy
- Primary Care Department, Azienda Sanitaria Locale di Modena, Modena, Italy
| | - Saud Alhusaini
- Department of Molecular and Cellular Therapeutics, The Royal College of Surgeons in Ireland, Dublin, Ireland
- Department of Neurology, Alpert Medical School of Brown University, Providence, RI, USA
| | - Colin P. Doherty
- Department of Neurology, St James’ Hospital, Dublin, Ireland
- FutureNeuro SFI Research Centre, Dublin, Ireland
| | - Gianpiero L. Cavalleri
- Department of Molecular and Cellular Therapeutics, The Royal College of Surgeons in Ireland, Dublin, Ireland
- FutureNeuro SFI Research Centre, Dublin, Ireland
| | - Norman Delanty
- Department of Molecular and Cellular Therapeutics, The Royal College of Surgeons in Ireland, Dublin, Ireland
- FutureNeuro SFI Research Centre, Dublin, Ireland
| | - Reetta Kälviäinen
- Epilepsy Center, Neuro Center, Kuopio University Hospital, Member of the European Reference Network for Rare and Complex Epilepsies EpiCARE, Kuopio, Finland
- Faculty of Health Sciences, School of Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Graeme D. Jackson
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne VIC 3010, Australia
| | - Magdalena Kowalczyk
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne VIC 3010, Australia
| | - Mario Mascalchi
- Neuroradiology Research Program, Meyer Children Hospital of Florence, University of Florence, Florence, Italy
| | - Mira Semmelroch
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne VIC 3010, Australia
| | - Rhys H. Thomas
- Transitional and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Hamid Soltanian-Zadeh
- Contol and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
- Departments of Research Administration and Radiology, Henry Ford Health System, Detroit, MI, USA
| | | | - Junsong Zhang
- Cognitive Science Department, Xiamen University, Xiamen, China
| | - Matteo Lenge
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children’s Hospital IRCCS, Florence, Italy
| | - Renzo Guerrini
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children’s Hospital IRCCS, Florence, Italy
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Italy
| | | | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK
| | - Theodor Rüber
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Tobias Bauer
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Chantal Depondt
- Department of Neurology, Hôpital Erasme, Hôpital Universitaire de Bruxelles (HUB), Université Libre de Bruxelles, Brussels, Belgium
| | - Julie Absil
- Department of Radiology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Sarah J. A. Carr
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | - Eugenio Abela
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
| | - Orrin Devinsky
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | - Heath Pardoe
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, United States
| | | | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genova, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Domenico Tortora
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Erik Kaestner
- Department of Radiation Medicine and Applied Sciences; Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
| | - Sean N. Hatton
- Department of Neurosciences, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
| | - Donatello Arienzo
- Department of Radiation Medicine and Applied Sciences; Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
| | - Sjoerd B. Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Mina Ryten
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Chalfont St Peter, Bucks, UK
| | - Peter N. Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- CNNP Lab, ICOS group, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Christopher D. Whelan
- Department of Molecular and Cellular Therapeutics, The Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Marian Galovic
- Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zürich, Zürich, Switzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
- Department of Medicine, Division of Neurology, Queen’s University, Kingston, ON, Canada
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Angelo Labate
- Neurophysiopathology and Movement Disorders Clinic, Regional Epilepsy Center, University of Messina, Italy
| | - Carrie R. McDonald
- Department of Radiation Medicine and Applied Sciences; Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, United States
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Chalfont St Peter, Bucks, UK
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Center for Brain Circuit Therapeutics, Brigham and Women’s Hospital, Harvard University, Boston, MA, USA
| | - Dewi Schrader
- BC Children’s Hospital, University of British Columbia, Vancouver, BC, Canada
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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12
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Li J, Long Z, Sheng W, Du L, Qiu J, Chen H, Liao W. Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes. Biol Psychiatry 2024; 95:414-425. [PMID: 37573006 DOI: 10.1016/j.biopsych.2023.08.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/31/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied. METHODS Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes. RESULTS We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies. CONCLUSIONS Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.
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Affiliation(s)
- Jiao Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Zhiliang Long
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Wei Sheng
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Lian Du
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, P.R. China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Wei Liao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, P.R. China.
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13
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Sheng W, Cui Q, Guo Y, Tang Q, Fan YS, Wang C, Guo J, Lu F, He Z, Chen H. Cortical thickness reductions associate with brain network architecture in major depressive disorder. J Affect Disord 2024; 347:175-182. [PMID: 38000466 DOI: 10.1016/j.jad.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND Cortical thickness reductions in major depressive disorder are distributed across multiple regions. Research has indicated that cortical atrophy is influenced by connectome architecture on a range of neurological and psychiatric diseases. However, whether connectome architecture contributes to changes in cortical thickness in the same manner as it does in depression is unclear. This study aims to explain the distribution of cortical thickness reductions across the cortex in depression by brain connectome architecture. METHODS Here, we calculated a differential map of cortical thickness between 110 depression patients and 88 age-, gender-, and education level-matched healthy controls by using T1-weighted images and a structural network reconstructed through the diffusion tensor imaging of control group. We then used a neighborhood deformation model to explore how cortical thickness change in an area is influenced by areas structurally connected to it. RESULTS We found that cortical thickness in the frontoparietal and default networks decreased in depression, regional cortical thickness changes were related to reductions in their neighbors and were mainly limited by the frontoparietal and default networks, and the epicenter was in the prefrontal lobe. CONCLUSION Current findings suggest that connectome architecture contributes to the irregular topographic distribution of cortical thickness reductions in depression and cortical atrophy is restricted by and dependent on structural foundation.
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Affiliation(s)
- Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China.
| | - YuanHong Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; MOE Key Lab for Neuroinformation, HighField Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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14
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Devinsky O, Elder C, Sivathamboo S, Scheffer IE, Koepp MJ. Idiopathic Generalized Epilepsy: Misunderstandings, Challenges, and Opportunities. Neurology 2024; 102:e208076. [PMID: 38165295 PMCID: PMC11097769 DOI: 10.1212/wnl.0000000000208076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/19/2023] [Indexed: 01/03/2024] Open
Abstract
The idiopathic generalized epilepsies (IGE) make up a fifth of all epilepsies, but <1% of epilepsy research. This skew reflects misperceptions: diagnosis is straightforward, pathophysiology is understood, seizures are easily controlled, epilepsy is outgrown, morbidity and mortality are low, and surgical interventions are impossible. Emerging evidence reveals that patients with IGE may go undiagnosed or misdiagnosed with focal epilepsy if EEG or semiology have asymmetric or focal features. Genetic, electrophysiologic, and neuroimaging studies provide insights into pathophysiology, including overlaps and differences from focal epilepsies. IGE can begin in adulthood and patients have chronic and drug-resistant seizures. Neuromodulatory interventions for drug-resistant IGE are emerging. Rates of psychiatric and other comorbidities, including sudden unexpected death in epilepsy, parallel those in focal epilepsy. IGE is an understudied spectrum for which our diagnostic sensitivity and specificity, scientific understanding, and therapies remain inadequate.
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Affiliation(s)
- Orrin Devinsky
- From the Comprehensive Epilepsy Center (O.D., C.E.), New York University School of Medicine, New York, Department of Neuroscience (S.S.), Central Clinical School, Monash University, Melbourne, Department of Neurology (S.S.), Alfred Health, Melbourne; Departments of Medicine and Neurology, The Royal Melbourne Hospital (S.S.), Epilepsy Research Centre, Department of Medicine, Austin Health (I.E.S.), Murdoch Children's Research Institute (I.E.S.), and Department of Pediatrics (I.E.S.), Royal Children's Hospital, The University of Melbourne; The Florey Institute of Neuroscience and Mental Health (I.E.S.), Melbourne, Victoria, Australia; and Department of Clinical and Experimental Epilepsy (M.J.K.), University College London Institute of Neurology, United Kingdom
| | - Christopher Elder
- From the Comprehensive Epilepsy Center (O.D., C.E.), New York University School of Medicine, New York, Department of Neuroscience (S.S.), Central Clinical School, Monash University, Melbourne, Department of Neurology (S.S.), Alfred Health, Melbourne; Departments of Medicine and Neurology, The Royal Melbourne Hospital (S.S.), Epilepsy Research Centre, Department of Medicine, Austin Health (I.E.S.), Murdoch Children's Research Institute (I.E.S.), and Department of Pediatrics (I.E.S.), Royal Children's Hospital, The University of Melbourne; The Florey Institute of Neuroscience and Mental Health (I.E.S.), Melbourne, Victoria, Australia; and Department of Clinical and Experimental Epilepsy (M.J.K.), University College London Institute of Neurology, United Kingdom
| | - Shobi Sivathamboo
- From the Comprehensive Epilepsy Center (O.D., C.E.), New York University School of Medicine, New York, Department of Neuroscience (S.S.), Central Clinical School, Monash University, Melbourne, Department of Neurology (S.S.), Alfred Health, Melbourne; Departments of Medicine and Neurology, The Royal Melbourne Hospital (S.S.), Epilepsy Research Centre, Department of Medicine, Austin Health (I.E.S.), Murdoch Children's Research Institute (I.E.S.), and Department of Pediatrics (I.E.S.), Royal Children's Hospital, The University of Melbourne; The Florey Institute of Neuroscience and Mental Health (I.E.S.), Melbourne, Victoria, Australia; and Department of Clinical and Experimental Epilepsy (M.J.K.), University College London Institute of Neurology, United Kingdom
| | - Ingrid E Scheffer
- From the Comprehensive Epilepsy Center (O.D., C.E.), New York University School of Medicine, New York, Department of Neuroscience (S.S.), Central Clinical School, Monash University, Melbourne, Department of Neurology (S.S.), Alfred Health, Melbourne; Departments of Medicine and Neurology, The Royal Melbourne Hospital (S.S.), Epilepsy Research Centre, Department of Medicine, Austin Health (I.E.S.), Murdoch Children's Research Institute (I.E.S.), and Department of Pediatrics (I.E.S.), Royal Children's Hospital, The University of Melbourne; The Florey Institute of Neuroscience and Mental Health (I.E.S.), Melbourne, Victoria, Australia; and Department of Clinical and Experimental Epilepsy (M.J.K.), University College London Institute of Neurology, United Kingdom
| | - Matthias J Koepp
- From the Comprehensive Epilepsy Center (O.D., C.E.), New York University School of Medicine, New York, Department of Neuroscience (S.S.), Central Clinical School, Monash University, Melbourne, Department of Neurology (S.S.), Alfred Health, Melbourne; Departments of Medicine and Neurology, The Royal Melbourne Hospital (S.S.), Epilepsy Research Centre, Department of Medicine, Austin Health (I.E.S.), Murdoch Children's Research Institute (I.E.S.), and Department of Pediatrics (I.E.S.), Royal Children's Hospital, The University of Melbourne; The Florey Institute of Neuroscience and Mental Health (I.E.S.), Melbourne, Victoria, Australia; and Department of Clinical and Experimental Epilepsy (M.J.K.), University College London Institute of Neurology, United Kingdom
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15
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Georgiadis F, Larivière S, Glahn D, Hong LE, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens FA, Green MJ, Cairns MJ, Michie PT, Rasser PE, Catts S, Tooney P, Scott RJ, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite TD, Karuk A, Pomarol-Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay IS, Sponheim SR, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein DJ, Howells F, Temmingh HS, Diaz Zuluaga AM, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk SL, Thompson P, van Erp TGM, Turner JA, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Mol Psychiatry 2024:10.1038/s41380-024-02442-7. [PMID: 38336840 DOI: 10.1038/s41380-024-02442-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/08/2024] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.
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Affiliation(s)
- Foivos Georgiadis
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
| | - Sara Larivière
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - David Glahn
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, US
| | - Bryan Mowry
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia
| | - Carmel Loughland
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, USA
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Frans A Henskens
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Patricia T Michie
- School of Psychological Sciences, University of Newcastle, Newcastle, NSW, Australia
| | - Paul E Rasser
- School of Medicine and Public Health, College of Health, Medicine, and Wellbeing, The University of Newcastle, Callaghan, NSW, Australia
| | - Stanley Catts
- Faculty of Medicine, University of Queensland, St Lucia, QLD, Australia
| | - Paul Tooney
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Rodney J Scott
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Ulrich Schall
- Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Vaughan Carr
- School of Clinical Medicine, Discipline of Psychiatry, UNSW Sydney, Sydney, NSW, Australia
| | - Yann Quidé
- School of Clinical Medicine, Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, NSW, Australia
| | - Axel Krug
- University Hospital Bonn, Department of Psychiatry and Psychotherapy, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Frederike Stein
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Igor Nenadić
- Department. of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Tilo Kircher
- Department of Psychiatry, University of Marburg, Rudolf Bultmann Str. 8, 35039, Marburg, Germany
| | - Raquel Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruben Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Andriana Karuk
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation & CIBERSAM, ISCIII, Barcelona, Spain
| | | | - Aristotle Voineskos
- School of Biomedical Science and Pharmacy, University of Newcastle, Newcastle, NSW, Australia
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore, Singapore
| | | | - Diana Tordesillas Gutiérrez
- Department of Radiology, Marqués de Valdecilla University Hospital, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain
| | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental Neurosciences, Technischen Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
| | - Nicolas Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Kelly Rootes-Murdy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, USA
| | - Caroline Demro
- University of Minnesota Department of Psychology, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Ian S Ramsay
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Scott R Sponheim
- Minneapolis VA Health Care System, Minneapolis, MN, USA
- University of Minnesota Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA
| | - Andre Schmidt
- University of Basel, Department of Psychiatry, Basel, Switzerland
| | | | | | - Irina Lebedeva
- Mental Health Research Center, Moscow, Russian Federation
| | - Cyril Höschl
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Filip Spaniel
- National Institute of Mental Health, Topolova 748, 250 67, Klecany, Czech Republic
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Dana Nguyen
- Department of Pediatric Neurology, University of California Irvine, Irvine, CA, USA
| | - Anne Uhlmann
- Department of child and adolescent psychiatry, TU Dresden, Dresden, Germany
| | - Dan J Stein
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Fleur Howells
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Henk S Temmingh
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Ana M Diaz Zuluaga
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Carlos López Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, School of Medicine, Universidad de Antioquia, Medellin, Colombia
| | - Felice Iasevoli
- University of Naples, Department of Neuroscience, Naples, Italy
| | - Ellen Ji
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stephanie Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Wolfgang Omlor
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Philipp Homan
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Stefan Kaiser
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland
| | - Bratislav Misic
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Sofie L Valk
- Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, the Ohio State University, Columbus, OH, USA
| | - Boris Bernhardt
- McGill University, Montreal Neurological Institute, Montreal, QC, Canada
| | - Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital University of Zurich, Zurich, Switzerland.
- Division of Adult Psychiatry, Department of Psychiatry, Geneva University Hospitals, Geneva, Switzerland.
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16
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Lenge M, Balestrini S, Napolitano A, Mei D, Conti V, Baldassarri G, Trivisano M, Pellacani S, Macconi L, Longo D, Rossi Espagnet MC, Cappelletti S, D'Incerti L, Barba C, Specchio N, Guerrini R. Morphometric network-based abnormalities correlate with psychiatric comorbidities and gene expression in PCDH19-related developmental and epileptic encephalopathy. Transl Psychiatry 2024; 14:35. [PMID: 38238304 PMCID: PMC10796344 DOI: 10.1038/s41398-024-02753-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Protocadherin-19 (PCDH19) developmental and epileptic encephalopathy causes an early-onset epilepsy syndrome with limbic seizures, typically occurring in clusters and variably associated with intellectual disability and a range of psychiatric disorders including hyperactive, obsessive-compulsive and autistic features. Previous quantitative neuroimaging studies revealed abnormal cortical areas in the limbic formation (parahippocampal and fusiform gyri) and underlying white-matter fibers. In this study, we adopted morphometric, network-based and multivariate statistical methods to examine the cortex and substructure of the hippocampus and amygdala in a cohort of 20 PCDH19-mutated patients and evaluated the relation between structural patterns and clinical variables at individual level. We also correlated morphometric alterations with known patterns of PCDH19 expression levels. We found patients to exhibit high-significant reductions of cortical surface area at a whole-brain level (left/right pvalue = 0.045/0.084), and particularly in the regions of the limbic network (left/right parahippocampal gyri pvalue = 0.230/0.016; left/right entorhinal gyri pvalue = 0.002/0.327), and bilateral atrophy of several subunits of the amygdala and hippocampus, particularly in the CA regions (head of the left CA3 pvalue = 0.002; body of the right CA3 pvalue = 0.004), and differences in the shape of hippocampal structures. More severe psychiatric comorbidities correlated with more significant altered patterns, with the entorhinal gyrus (pvalue = 0.013) and body of hippocampus (pvalue = 0.048) being more severely affected. Morphometric alterations correlated significantly with the known expression patterns of PCDH19 (rvalue = -0.26, pspin = 0.092). PCDH19 encephalopathy represents a model of genetically determined neural network based neuropsychiatric disease in which quantitative MRI-based findings correlate with the severity of clinical manifestations and had have a potential predictive value if analyzed early.
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Affiliation(s)
- Matteo Lenge
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Simona Balestrini
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, 00100, Rome, Italy
| | - Davide Mei
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Valerio Conti
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Giulia Baldassarri
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, 00100, Rome, Italy
| | - Marina Trivisano
- Neurology, Epilepsy and Movement Disorders, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Simona Pellacani
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Letizia Macconi
- Pediatric Radiology Unit, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Daniela Longo
- Functional and Interventional Neuroimaging Unit, Bambino Gesù Children's Hospital, IRCCS, 00165, Rome, Italy
| | | | - Simona Cappelletti
- Neurology, Epilepsy and Movement Disorders, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Ludovico D'Incerti
- Pediatric Radiology Unit, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Carmen Barba
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Renzo Guerrini
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy.
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17
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Bröhl T, Rings T, Pukropski J, von Wrede R, Lehnertz K. The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1338864. [PMID: 38293249 PMCID: PMC10825060 DOI: 10.3389/fnetp.2023.1338864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 12/19/2023] [Indexed: 02/01/2024]
Abstract
Epilepsy is now considered a network disease that affects the brain across multiple levels of spatial and temporal scales. The paradigm shift from an epileptic focus-a discrete cortical area from which seizures originate-to a widespread epileptic network-spanning lobes and hemispheres-considerably advanced our understanding of epilepsy and continues to influence both research and clinical treatment of this multi-faceted high-impact neurological disorder. The epileptic network, however, is not static but evolves in time which requires novel approaches for an in-depth characterization. In this review, we discuss conceptual basics of network theory and critically examine state-of-the-art recording techniques and analysis tools used to assess and characterize a time-evolving human epileptic brain network. We give an account on current shortcomings and highlight potential developments towards an improved clinical management of epilepsy.
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Affiliation(s)
- Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Thorsten Rings
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
| | - Jan Pukropski
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Bonn, Germany
- Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Bonn, Germany
- Interdisciplinary Center for Complex Systems, University of Bonn, Bonn, Germany
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18
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Warren AEL, Tobochnik S, Chua MMJ, Singh H, Stamm MA, Rolston JD. Neurostimulation for Generalized Epilepsy: Should Therapy be Syndrome-specific? Neurosurg Clin N Am 2024; 35:27-48. [PMID: 38000840 PMCID: PMC10676463 DOI: 10.1016/j.nec.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2023]
Abstract
Current applications of neurostimulation for generalized epilepsy use a one-target-fits-all approach that is agnostic to the specific epilepsy syndrome and seizure type being treated. The authors describe similarities and differences between the 2 "archetypes" of generalized epilepsy-Lennox-Gastaut syndrome and Idiopathic Generalized Epilepsy-and review recent neuroimaging evidence for syndrome-specific brain networks underlying seizures. Implications for stimulation targeting and programming are discussed using 5 clinical questions: What epilepsy syndrome does the patient have? What brain networks are involved? What is the optimal stimulation target? What is the optimal stimulation paradigm? What is the plan for adjusting stimulation over time?
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Affiliation(s)
- Aaron E L Warren
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Steven Tobochnik
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Melissa M J Chua
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hargunbir Singh
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michaela A Stamm
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - John D Rolston
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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19
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Micalizzi E, Ballerini A, Giovannini G, Cioclu MC, Scolastico S, Pugnaghi M, Orlandi N, Malagoli M, Genovese M, Todeschini A, Giunta L, Villani F, Meletti S, Vaudano AE. The role of the amygdala in ictal central apnea: insights from brain MRI morphometry. Ann Clin Transl Neurol 2024; 11:121-132. [PMID: 37936526 PMCID: PMC10791031 DOI: 10.1002/acn3.51938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/09/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023] Open
Abstract
OBJECTIVE Ictal central apnea (ICA) is a frequent correlate of focal seizures, particularly in temporal lobe epilepsy (TLE), and regarded as a potential electroclinical biomarker of sudden unexpected death in epilepsy (SUDEP). Aims of this study are to investigate morphometric changes of subcortical structures in ICA patients and to find neuroimaging biomarkers of ICA in patients with focal epilepsy. METHODS We prospectively recruited focal epilepsy patients with recorded seizures during a video-EEG long-term monitoring with cardiorespiratory polygraphic recordings from April 2020 to September 2022. Participants were accordingly subdivided into two groups: patients with focal seizures with ICA (ICA) and without (noICA). A pool of 30 controls matched by age and sex was collected. All the participants underwent MRI scans with volumetric high-resolution T1-weighted images. Post-processing analyses included a whole-brain VBM analysis and segmentation algorithms performed with FreeSurfer. RESULTS Forty-six patients were recruited (aged 15-60 years): 16 ICA and 30 noICA. The whole-brain VBM analysis showed an increased gray matter volume of the amygdala ipsilateral to the epileptogenic zone (EZ) in the ICA group compared to the noICA patients. Amygdala sub-segmentation analysis revealed an increased volume of the whole amygdala, ipsilateral to the EZ compared to controls [F(1, 76) = 5.383, pFDR = 0.042] and to noICA patients ([F(1, 76) = 5.383, pFDR = 0.038], specifically of the basolateral complex (respectively F(1, 76) = 6.160, pFDR = 0.037; F(1, 76) = 5.121, pFDR = 0.034). INTERPRETATION Our findings, while confirming the key role of the amygdala in participating in ictal respiratory modifications, suggest that structural modifications of the amygdala and its subnuclei may be valuable morphological biomarkers of ICA.
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Affiliation(s)
- Elisa Micalizzi
- Department of NeuroscienceIRCCS San Martino HospitalGenoaItaly
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Alice Ballerini
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Giada Giovannini
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Maria Cristina Cioclu
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Simona Scolastico
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Matteo Pugnaghi
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Niccolò Orlandi
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | | | | | | | - Leandra Giunta
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Flavio Villani
- Department of NeuroscienceIRCCS San Martino HospitalGenoaItaly
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
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20
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Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, Zyriax BC, Zeller T, Kühn S, Gallinat J, Fiehler J, Twerenbold R, Omidvarnia A, Patil KR, Eickhoff SB, Thomalla G, Cheng B. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.22.529531. [PMID: 36865285 PMCID: PMC9980040 DOI: 10.1101/2023.02.22.529531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
The link between metabolic syndrome (MetS) and neurodegenerative as well cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, cortical morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.
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Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Felix L. Nägele
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Maximilian Schell
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - D. Leander Rimmele
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Birgit-Christiane Zyriax
- Midwifery Science-Health Services Research and Prevention, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Tanja Zeller
- Department of Cardiology, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/Luebeck, Martinistraße 52, 20251 Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/Luebeck, Martinistraße 52, 20251 Hamburg, Germany
- University Center of Cardiovascular Science, University Heart and Vascular Center, Martinistraße 52, 20251 Hamburg, Germany
- Epidemiological Study Center, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Amir Omidvarnia
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Kaustubh R. Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Simon B. Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center Ju lich, Wilhelm-Johnen-Straße, 52425 Ju lich, Germany
| | - Götz Thomalla
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20251 Hamburg, Germany
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Khan SU, Jan SU, Koo I. Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:9572. [PMID: 38067944 PMCID: PMC10708722 DOI: 10.3390/s23239572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.
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Affiliation(s)
- Shafi Ullah Khan
- Department of Electrical Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
| | - Sana Ullah Jan
- School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;
| | - Insoo Koo
- Department of Electrical Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
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22
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Bou Assi E, Schindler K, de Bézenac C, Denison T, Desai S, Keller SS, Lemoine É, Rahimi A, Shoaran M, Rummel C. From basic sciences and engineering to epileptology: A translational approach. Epilepsia 2023; 64 Suppl 3:S72-S84. [PMID: 36861368 DOI: 10.1111/epi.17566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/03/2023]
Abstract
Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.
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Affiliation(s)
- Elie Bou Assi
- Department of Neuroscience, Université de Montréal, Montréal, Canada
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, Bern University, Bern, Switzerland
| | - Christophe de Bézenac
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Timothy Denison
- MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Émile Lemoine
- Centre de Recherche du CHUM (CRCHUM), Montréal, Canada
- Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Canada
| | | | - Mahsa Shoaran
- Institute of Electrical and Micro Engineering, Neuro-X Institute, EPFL, Lausanne, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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23
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Lin Q, Li W, Li Y, Liu P, Zhang Y, Gong Q, Zhou D, An D. Aberrant structural rich club organization in temporal lobe epilepsy with focal to bilateral tonic-clonic seizures. Neuroimage Clin 2023; 40:103536. [PMID: 37944396 PMCID: PMC10663961 DOI: 10.1016/j.nicl.2023.103536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/19/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVE The purpose of this study was to assess the differences of topological characteristic and rich club organization between temporal lobe epilepsy (TLE) patients with focal seizure (FS) only and those with focal to bilateral tonic-clonic seizures (FBTCS). METHODS We recruited 130 unilateral TLE patients, of which 57 patients with FS only and 73 patients with both FS and FBTCS, and 68 age- and gender-matched healthy controls (HC). Whole-brain networks were constructed based on diffusion weighted imaging data. Graph theory was applied to quantify the topological network metrics and rich club organization. Network-based statistic (NBS) analysis was administered to investigate the difference in edge-wise connectivity strength. The non-parametric permutation test was applied to evaluate the differences between groups. Benjamini-Hochberg FDR at the alpha of 5% was carried out for multiple comparations. RESULTS In comparison with HC, both the FS and FBTCS group displayed a significant reduction in whole-brain connectivity strength and global efficiency. The FBTCS group showed lower connectivity strength both in the rich club and feeder connections compared to HC. The FS group had lower connectivity strength in the feeder and local connections compared to HC. NBS analysis revealed a wider range of decreased connectivity strength in the FBTCS group, involving 90% of the rich club regions, mainly affecting temporal-subcortical, frontal-parietal, and frontal-temporal lobe, the majority decreasing connections were between temporal lobe and stratum. While the decreased connectivity strength in the FS group were relatively local, involving 50% of rich club regions, mainly concentrated on the temporal-subcortical lobe. CONCLUSIONS Network integration was reduced in TLE. TLE with FBTCS selectively disrupted the rich club regions, while TLE with FS only were more likely to affect the non-rich club regions, emphasizing the contribution of rich club organization to seizure generalization.
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Affiliation(s)
- Qiuxing Lin
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuming Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Peiwen Liu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yingying Zhang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dongmei An
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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24
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Xiao F, Caciagli L, Wandschneider B, Sone D, Young AL, Vos SB, Winston GP, Zhang Y, Liu W, An D, Kanber B, Zhou D, Sander JW, Thom M, Duncan JS, Alexander DC, Galovic M, Koepp MJ. Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference. Brain 2023; 146:4702-4716. [PMID: 37807084 PMCID: PMC10629797 DOI: 10.1093/brain/awad284] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/30/2023] [Accepted: 08/02/2023] [Indexed: 10/10/2023] Open
Abstract
Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.
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Affiliation(s)
- Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Britta Wandschneider
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
| | - Daichi Sone
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Psychiatry, The Jikei University School of Medicine, Tokyo, 105-8461, Japan
| | - Alexandra L Young
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
- Centre for Microscopy, Characterisation, and Analysis, University of Western Australia, Perth, WA 6009, Australia
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Medicine, Division of Neurology, Queen’s University, Kingston, K7L 3N6, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, K7L 3N6, Canada
| | - Yingying Zhang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Wenyu Liu
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Dongmei An
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Baris Kanber
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
| | - Dong Zhou
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
| | - Josemir W Sander
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041, China
- Stichting Epilepsie Instellingen Nederland – (SEIN), Heemstede, 2103SW, The Netherlands
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Departments of Computer Science, Medical Physics, and Biomedical Engineering, UCL, London, WC1E 6BT, UK
| | - Marian Galovic
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, Zurich, CH-8091, Switzerland
| | - Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
- UCL-Epilepsy Society MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, SL9 0RJ, UK
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25
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Chen H, Wang Y, Ji T, Jiang Y, Zhou X. Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy. CNS Neurosci Ther 2023; 29:3259-3268. [PMID: 37170486 PMCID: PMC10580342 DOI: 10.1111/cns.14257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/13/2023] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
OBJECTIVE Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data. METHODS Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes. RESULTS The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level. SIGNIFICANCE This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.
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Affiliation(s)
- Hao Chen
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
| | - Yi Wang
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
| | - Taoyun Ji
- Department of Pediatrics and Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Yuwu Jiang
- Department of Pediatrics and Pediatric Epilepsy CenterPeking University First HospitalBeijingChina
| | - Xiao‐Hua Zhou
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
- Department of Biostatistics, School of Public HealthPeking UniversityBeijingChina
- Pazhou LabGuangzhouChina
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Kotikalapudi R, Kincses B, Zunhammer M, Schlitt F, Asan L, Schmidt-Wilcke T, Kincses ZT, Bingel U, Spisak T. Brain morphology predicts individual sensitivity to pain: a multicenter machine learning approach. Pain 2023; 164:2516-2527. [PMID: 37318027 PMCID: PMC10578427 DOI: 10.1097/j.pain.0000000000002958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 02/18/2023] [Accepted: 03/23/2023] [Indexed: 06/16/2023]
Abstract
ABSTRACT Sensitivity to pain shows a remarkable interindividual variance that has been reported to both forecast and accompany various clinical pain conditions. Although pain thresholds have been reported to be associated to brain morphology, it is still unclear how well these findings replicate in independent data and whether they are powerful enough to provide reliable pain sensitivity predictions on the individual level. In this study, we constructed a predictive model of pain sensitivity (as measured with pain thresholds) using structural magnetic resonance imaging-based cortical thickness data from a multicentre data set (3 centres and 131 healthy participants). Cross-validated estimates revealed a statistically significant and clinically relevant predictive performance (Pearson r = 0.36, P < 0.0002, R2 = 0.13). The predictions were found to be specific to physical pain thresholds and not biased towards potential confounding effects (eg, anxiety, stress, depression, centre effects, and pain self-evaluation). Analysis of model coefficients suggests that the most robust cortical thickness predictors of pain sensitivity are the right rostral anterior cingulate gyrus, left parahippocampal gyrus, and left temporal pole. Cortical thickness in these regions was negatively correlated to pain sensitivity. Our results can be considered as a proof-of-concept for the capacity of brain morphology to predict pain sensitivity, paving the way towards future multimodal brain-based biomarkers of pain.
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Affiliation(s)
- Raviteja Kotikalapudi
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
| | - Balint Kincses
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
- Department of Neurology, Center for Translational Neuro- and Behavioural Sciences, University Medicine Essen, Essen, Germany
| | - Matthias Zunhammer
- Department of Neurology, Center for Translational Neuro- and Behavioural Sciences, University Medicine Essen, Essen, Germany
| | - Frederik Schlitt
- Department of Neurology, Center for Translational Neuro- and Behavioural Sciences, University Medicine Essen, Essen, Germany
| | - Livia Asan
- Department of Neurology, Center for Translational Neuro- and Behavioural Sciences, University Medicine Essen, Essen, Germany
| | - Tobias Schmidt-Wilcke
- Institute for Clinical Neuroscience and Medical Psychology, Heinrich Heine University, Düsseldorf, Germany
- Neurocenter, District Hospital Mainkofen, Deggendorf, Germany
| | - Zsigmond T. Kincses
- Departments of Neurology and
- Radiology, University of Szeged, Szeged, Hungary
| | - Ulrike Bingel
- Department of Neurology, Center for Translational Neuro- and Behavioural Sciences, University Medicine Essen, Essen, Germany
| | - Tamas Spisak
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen, Essen, Germany
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27
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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28
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Royer J, Larivière S, Rodriguez-Cruces R, Cabalo DG, Tavakol S, Auer H, Ngo A, Park BY, Paquola C, Smallwood J, Jefferies E, Caciagli L, Bernasconi A, Bernasconi N, Frauscher B, Bernhardt BC. Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy. Brain 2023; 146:3923-3937. [PMID: 37082950 PMCID: PMC10473569 DOI: 10.1093/brain/awad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/21/2023] [Accepted: 03/23/2023] [Indexed: 04/22/2023] Open
Abstract
Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.
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Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Bo-yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Data Science, Inha University, Incheon 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
| | - Casey Paquola
- Multiscale Neuroanatomy Lab, INM-1, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | | | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, MA 19104, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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29
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Lenge M, Balestrini S, Mei D, Macconi L, Caligiuri ME, Cuccarini V, Aquino D, Mazzi F, d’Incerti L, Darra F, Bernardina BD, Guerrini R. Morphometry and network-based atrophy patterns in SCN1A-related Dravet syndrome. Cereb Cortex 2023; 33:9532-9541. [PMID: 37344172 PMCID: PMC10431750 DOI: 10.1093/cercor/bhad224] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023] Open
Abstract
Mutations of the voltage-gated sodium channel SCN1A gene (MIM#182389) are among the most clinically relevant epilepsy-related genetic mutations and present variable phenotypes, from the milder genetic epilepsy with febrile seizures plus to Dravet syndrome, a severe developmental and epileptic encephalopathy. Qualitative neuroimaging studies have identified malformations of cortical development in some patients and mild atrophic changes, partially confirmed by quantitative studies. Precise correlations between MRI findings and clinical variables have not been addressed. We used morphometric methods and network-based models to detect abnormal brain structural patterns in 34 patients with SCN1A-related epilepsy, including 22 with Dravet syndrome. By measuring the morphometric characteristics of the cortical mantle and volume of subcortical structures, we found bilateral atrophic changes in the hippocampus, amygdala, and the temporo-limbic cortex (P-value < 0.05). By correlating atrophic patterns with brain connectivity profiles, we found the region of the hippocampal formation as the epicenter of the structural changes. We also observed that Dravet syndrome was associated with more severe atrophy patterns with respect to the genetic epilepsy with febrile seizures plus phenotype (r = -0.0613, P-value = 0.03), thus suggesting that both the underlying mutation and seizure severity contribute to determine atrophic changes.
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Affiliation(s)
- Matteo Lenge
- Neuroscience Department, Meyer Children’s Hospital IRCCS, 50139, Florence, Italy
| | - Simona Balestrini
- Neuroscience Department, Meyer Children’s Hospital IRCCS, 50139, Florence, Italy
| | - Davide Mei
- Neuroscience Department, Meyer Children’s Hospital IRCCS, 50139, Florence, Italy
| | - Letizia Macconi
- Neuroradiology Unit, Meyer Children’s Hospital IRCCS, 50139, Florence, Italy
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Grecia University, 88100, Catanzaro, Italy
| | - Valeria Cuccarini
- Neuroradiology Unit, Fondazione IRCCS Neurologico Carlo Besta, 20100, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Neurologico Carlo Besta, 20100, Milan, Italy
| | - Federica Mazzi
- Neuroradiology Unit, Fondazione IRCCS Neurologico Carlo Besta, 20100, Milan, Italy
| | - Ludovico d’Incerti
- Neuroradiology Unit, Meyer Children’s Hospital IRCCS, 50139, Florence, Italy
| | - Francesca Darra
- Child Neuropsychiatry Unit, Department of Engineering for Innovation Medicine University of Verona, 37100, Verona, Italy
| | - Bernardo Dalla Bernardina
- Child Neuropsychiatry Unit, Department of Engineering for Innovation Medicine University of Verona, 37100, Verona, Italy
- Pediatric Epilepsy Research Center (CREP), Azienda Ospedaliera Universitaria Integrata, 37100, Verona, Italy
| | - Renzo Guerrini
- Neuroscience Department, Meyer Children’s Hospital IRCCS, 50139, Florence, Italy
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30
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Wang G, Liu X, Zhang M, Wang K, Liu C, Chen Y, Wu W, Zhao H, Xiao B, Wan L, Long L. Structural and functional changes of the cerebellum in temporal lobe epilepsy. Front Neurol 2023; 14:1213224. [PMID: 37602268 PMCID: PMC10435757 DOI: 10.3389/fneur.2023.1213224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Aims This study aimed to comprehensively explore the cerebellar structural and functional changes in temporal lobe epilepsy (TLE) and its association with clinical information. Methods The SUIT toolbox was utilized to perform cerebellar volume and diffusion analysis. In addition, we extracted the average diffusion values of cerebellar peduncle tracts to investigate microstructure alterations. Seed-based whole-brain analysis was used to investigate cerebellar-cerebral functional connectivity (FC). Subgroup analyses were performed to identify the cerebellar participation in TLE with/without hippocampal sclerosis (HS)/focal-to-bilateral tonic-clonic seizure (FBTCS) and TLE with different lateralization. Results TLE showed widespread gray matter atrophy in bilateral crusII, VIIb, VIIIb, left crusI, and left VIIIa. Both voxel and tract analysis observed diffusion abnormalities in cerebellar afferent peduncles. Reduced FC between the right crus II and the left parahippocampal cortex was found in TLE. Additionally, TLE showed increased FCs between left lobules VI-VIII and cortical nodes of the dorsal attention and visual networks. Across all patients, decreased FC was associated with poorer cognitive function, while increased FCs appeared to reflect compensatory effects. The cerebellar structural changes were mainly observed in HS and FBTCS subgroups and were regardless of seizure lateralization, while cerebellar-cerebral FC alterations were similar in all subgroups. Conclusion TLE exhibited microstructural changes in the cerebellum, mainly related to HS and FBTCS. In addition, altered cerebellar-cerebral functional connectivity is associated with common cognitive alterations in TLE.
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Affiliation(s)
- Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Xianghe Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Min Zhang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Kangrun Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Chaorong Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Yayu Chen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of Infectious Diseases, Xiangya Hospital, Central South University, Changsha, China
| | - Wenyue Wu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Haiting Zhao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Lily Wan
- Department of Anatomy and Neurobiology, Central South University Xiangya Medical School, Changsha, Hunan, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
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31
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Xie K, Royer J, Larivière S, Rodriguez-Cruces R, Frässle S, Cabalo DG, Ngo A, DeKraker J, Auer H, Tavakol S, Weng Y, Abdallah C, Horwood L, Frauscher B, Caciagli L, Bernasconi A, Bernasconi N, Zhang Z, Concha L, Bernhardt BC. Atypical connectome topography and signal flow in temporal lobe epilepsy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.23.541934. [PMID: 37292996 PMCID: PMC10245853 DOI: 10.1101/2023.05.23.541934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Linda Horwood
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Lorenzo Caciagli
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Luis Concha
- Brain Connectivity Laboratory, Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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32
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Hampel H, Gao P, Cummings J, Toschi N, Thompson PM, Hu Y, Cho M, Vergallo A. The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci 2023; 46:176-198. [PMID: 36642626 PMCID: PMC10720395 DOI: 10.1016/j.tins.2022.12.004] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/18/2022] [Accepted: 12/14/2022] [Indexed: 01/15/2023]
Abstract
Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.
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Affiliation(s)
- Harald Hampel
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA.
| | - Peng Gao
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas (UNLV), Las Vegas, NV, USA
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yan Hu
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Min Cho
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
| | - Andrea Vergallo
- Alzheimer's Disease & Brain Health, Eisai Inc., Nutley, NJ, USA
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33
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McKavanagh A, Ridzuan-Allen A, Kreilkamp BAK, Chen Y, Manjón JV, Coupé P, Bracewell M, Das K, Taylor PN, Marson AG, Keller SS. Midbrain structure volume, estimated myelin and functional connectivity in idiopathic generalised epilepsy. Epilepsy Behav 2023; 140:109084. [PMID: 36702054 DOI: 10.1016/j.yebeh.2023.109084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/01/2023] [Accepted: 01/01/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Structural and functional neuroimaging studies often overlook lower basal ganglia structures located in and adjacent to the midbrain due to poor contrast on clinically acquired T1-weighted scans. Here, we acquired T1-weighted, T2-weighted, and resting-state fMRI scans to investigate differences in volume, estimated myelin content and functional connectivity of the substantia nigra (SN), subthalamic nuclei (SubTN) and red nuclei (RN) of the midbrain in IGE. METHODS Thirty-three patients with IGE (23 refractory, 10 non-refractory) and 39 age and sex-matched healthy controls underwent MR imaging. Midbrain structures were automatically segmented from T2-weighted images and structural volumes were calculated. The estimated myelin content for each structure was determined using a T1-weighted/T2-weighted ratio method. Resting-state functional connectivity analysis of midbrain structures (seed-based) was performed using the CONN toolbox. RESULTS An increased volume of the right RN was found in IGE and structural volumes of the right SubTN differed between patients with non-refractory and refractory IGE. However, no volume findings survived corrections for multiple comparisons. No myelin alterations of midbrain structures were found for any subject groups. We found functional connectivity alterations including significantly decreased connectivity between the left SN and the thalamus and significantly increased connectivity between the right SubTN and the superior frontal gyrus in IGE. CONCLUSIONS We report volumetric and functional connectivity alterations of the midbrain in patients with IGE. We postulate that potential increases in structural volumes are due to increased iron deposition that impacts T2-weighted contrast. These findings are consistent with previous studies demonstrating pathophysiological abnormalities of the lower basal ganglia in animal models of generalised epilepsy.
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Affiliation(s)
- Andrea McKavanagh
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK.
| | - Adam Ridzuan-Allen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK; Department of Neurology, University Medical Centre Göttingen, Göttingen, Germany
| | - Yachin Chen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital', United States
| | - José V Manjón
- Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain
| | - Pierrick Coupé
- Pictura Research Group, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR 5800), Laboratoire Bordelais de Recherche en Informatique, Bordeaux, France
| | - Martyn Bracewell
- The Walton Centre NHS Foundation Trust, Liverpool, UK; Schools of Medical Sciences and Psychology, Bangor University, Bangor, UK
| | - Kumar Das
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK
| | - Anthony G Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK; The Walton Centre NHS Foundation Trust, Liverpool, UK
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A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD. Mol Psychiatry 2023; 28:1210-1218. [PMID: 36575304 PMCID: PMC10005951 DOI: 10.1038/s41380-022-01916-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 11/21/2022] [Accepted: 12/09/2022] [Indexed: 12/28/2022]
Abstract
Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.
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Taylor JJ, Lin C, Talmasov D, Ferguson MA, Schaper FLWVJ, Jiang J, Goodkind M, Grafman J, Etkin A, Siddiqi SH, Fox MD. A transdiagnostic network for psychiatric illness derived from atrophy and lesions. Nat Hum Behav 2023; 7:420-429. [PMID: 36635585 PMCID: PMC10236501 DOI: 10.1038/s41562-022-01501-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 11/23/2022] [Indexed: 01/13/2023]
Abstract
Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.
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Affiliation(s)
- Joseph J Taylor
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Christopher Lin
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Talmasov
- Departments of Neurology and Psychiatry, Columbia University Medical Center, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Michael A Ferguson
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for the Study of World Religions, Harvard Divinity School, Cambridge, MA, USA
| | - Frederic L W V J Schaper
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jing Jiang
- Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Madeleine Goodkind
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
- New Mexico Veterans Affairs Healthcare System, Albuquerque, NM, USA
| | - Jordan Grafman
- Departments of Physical Medicine and Rehabilitation, Neurology, & Psychiatry, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute at Stanford, Stanford University School of Medicine, Stanford, CA, USA
- Alto Neuroscience, Los Altos, CA, USA
| | - Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Bonkhoff AK, Schirmer MD, Bretzner M, Hong S, Regenhardt RW, Donahue KL, Nardin MJ, Dalca AV, Giese A, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez‐Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah C, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Wu O, Rost NS. The relevance of rich club regions for functional outcome post-stroke is enhanced in women. Hum Brain Mapp 2023; 44:1579-1592. [PMID: 36440953 PMCID: PMC9921242 DOI: 10.1002/hbm.26159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 10/24/2022] [Accepted: 11/11/2022] [Indexed: 11/30/2022] Open
Abstract
This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Univ. Lille, Inserm, CHU Lille, U1171 – LilNCog (JPARC) – Lille Neurosciences & CognitionLilleFrance
| | - Sungmin Hong
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Marco J. Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Anne‐Katrin Giese
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Brandon L. Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Steven J. T. Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Elissa C. McIntosh
- Department of PsychiatryJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - John Attia
- Hunter Medical Research InstituteNewcastleNew South WalesAustralia
- School of Medicine and Public HealthUniversity of NewcastleNewcastleNew South WalesAustralia
| | - John W. Cole
- Department of NeurologyUniversity of Maryland School of Medicine and Veterans Affairs Maryland Health Care SystemBaltimoreMarylandUSA
| | - Amanda Donatti
- School of Medical SciencesUniversity of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN)CampinasSão PauloBrazil
| | - Christoph J. Griessenauer
- Department of NeurosurgeryGeisingerDanvillePennsylvaniaUSA
- Research Institute of NeurointerventionParacelsus Medical UniversitySalzburgAustria
| | - Laura Heitsch
- Department of Emergency MedicineWashington University School of MedicineSt LouisMissouriUSA
- Department of NeurologyWashington University School of Medicine & Barnes‐Jewish HospitalSt LouisMissouriUSA
| | - Lukas Holmegaard
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Katarina Jood
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Jordi Jimenez‐Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM‐Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques). Department of Medicine and Life Sciences (MELIS)Universitat Pompeu FabraBarcelonaSpain
| | - Steven J. Kittner
- Department of NeurologyUniversity of Maryland School of Medicine and Veterans Affairs Maryland Health Care SystemBaltimoreMarylandUSA
| | - Robin Lemmens
- Department of NeurosciencesKU Leuven – University of Leuven, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND)LeuvenBelgium
- Department of Neurology, VIB, Vesalius Research CenterLaboratory of Neurobiology, University Hospitals LeuvenLeuvenBelgium
| | - Christopher R. Levi
- School of Medicine and Public HealthUniversity of NewcastleNewcastleNew South WalesAustralia
- Department of NeurologyJohn Hunter HospitalNewcastleNew South WalesAustralia
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for PharmacogenomicsUniversity of FloridaGainesvilleFloridaUSA
| | | | - Chia‐Ling Phuah
- Department of NeurologyWashington University School of Medicine & Barnes‐Jewish HospitalSt LouisMissouriUSA
| | - Stefan Ropele
- Department of Neurology, Clinical Division of NeurogeriatricsMedical University GrazGrazAustria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Henry and Allison McCance Center for Brain HealthMassachusetts General HospitalBostonMassachusettsUSA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM‐Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques). Department of Medicine and Life Sciences (MELIS)Universitat Pompeu FabraBarcelonaSpain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Ralph L. Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of NeurogeriatricsMedical University GrazGrazAustria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL)EghamUK
- St Peter's and Ashford HospitalsAshfordUK
| | - Agnieszka Slowik
- Department of NeurologyJagiellonian University Medical CollegeKrakowPoland
| | - Alessandro Sousa
- School of Medical SciencesUniversity of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN)CampinasSão PauloBrazil
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Daniel Strbian
- Department of NeurologyHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Turgut Tatlisumak
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Vincent Thijs
- Stroke DivisionFlorey Institute of Neuroscience and Mental HealthHeidelbergAustralia
- Department of NeurologyAustin HealthHeidelbergAustralia
| | - Achala Vagal
- Department of RadiologyUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
- Department of Radiology, NeuroradiologySkåne University HospitalLundSweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation MedicineUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Ramin Zand
- Department of NeurologyPennsylvania State UniversityHersheyPennsylvaniaUSA
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Bradford B. Worrall
- Departments of Neurology and Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Christina Jern
- Department of NeurologyJagiellonian University Medical CollegeKrakowPoland
- Department of Clinical Genetics and GenomicsSahlgrenska University HospitalGothenburgSweden
| | - Arne G. Lindgren
- Department of NeurologySkåne University HospitalLundSweden
- Department of Clinical Sciences Lund, NeurologyLund UniversityLundSweden
| | | | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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Lehnertz K, Bröhl T, Wrede RV. Epileptic-network-based prediction and control of seizures in humans. Neurobiol Dis 2023; 181:106098. [PMID: 36997129 DOI: 10.1016/j.nbd.2023.106098] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/08/2023] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
Epilepsy is now conceptualized as a network disease. The epileptic brain network comprises structurally and functionally connected cortical and subcortical brain regions - spanning lobes and hemispheres -, whose connections and dynamics evolve in time. With this concept, focal and generalized seizures as well as other related pathophysiological phenomena are thought to emerge from, spread via, and be terminated by network vertices and edges that also generate and sustain normal, physiological brain dynamics. Research over the last years has advanced concepts and techniques to identify and characterize the evolving epileptic brain network and its constituents on various spatial and temporal scales. Network-based approaches further our understanding of how seizures emerge from the evolving epileptic brain network, and they provide both novel insights into pre-seizure dynamics and important clues for success or failure of measures for network-based seizure control and prevention. In this review, we summarize the current state of knowledge and address several important challenges that would need to be addressed to move network-based prediction and control of seizures closer to clinical translation.
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Affiliation(s)
- Klaus Lehnertz
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany; Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany.
| | - Timo Bröhl
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany
| | - Randi von Wrede
- Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany
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Xie K, Royer J, Lariviere S, Rodriguez-Cruces R, de Wael RV, Park BY, Auer H, Tavakol S, DeKraker J, Abdallah C, Caciagli L, Bassett DS, Bernasconi A, Bernasconi N, Frauscher B, Concha L, Bernhardt BC. Atypical intrinsic neural timescales in temporal lobe epilepsy. Epilepsia 2023; 64:998-1011. [PMID: 36764677 DOI: 10.1111/epi.17541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is the most common pharmacoresistant epilepsy in adults. Here we profiled local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using resting-state functional magnetic resonance imaging (rs-fMRI), we mapped the whole-brain intrinsic neural timescales (INT), which reflect temporal hierarchies of neural processing. Parallel analysis of structural and diffusion MRI data examined associations with TLE-related structural compromise. Finally, we evaluated the clinical utility of INT. METHODS We studied 46 patients with TLE and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored the effects of age and epilepsy duration. Supervised machine learning assessed the utility of INT for identifying patients with TLE vs controls and left- vs right-sided seizure onset. RESULTS Relative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in the ipsilateral medial and lateral temporal regions, and bilateral sensorimotor cortices as well as thalamus and hippocampus. Findings were similar, albeit with reduced effect sizes, when correcting for structural alterations. INT reductions in TLE increased with advancing disease duration, yet findings differed from the aging effects seen in controls. INT-derived classifiers discriminated patients vs controls (balanced accuracy, 5-fold: 76% ± 2.65%; cross-site, 72%-83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96% ± 2.10%; cross-site, 95%-97%), with high accuracy and cross-site generalizability. Findings were consistent across both acquisition sites and robust when controlling for motion and several methodological confounds. SIGNIFICANCE Our findings demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which emphasizes promising clinical utility.
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Affiliation(s)
- Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Sara Lariviere
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Data Science, Inha University, Incheon, Republic of Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Chifaou Abdallah
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Lorenzo Caciagli
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dani S Bassett
- Department of Biomedical Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Luis Concha
- Brain Connectivity Laboratory, Institute of Neurobiology, Universidad Nacional Autónoma de Mexico (UNAM), Juriquilla, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Li J, Keller SS, Seidlitz J, Chen H, Li B, Weng Y, Meng Y, Yang S, Xu Q, Zhang Q, Yang F, Lu G, Bernhardt BC, Zhang Z, Liao W. Cortical morphometric vulnerability to generalised epilepsy reflects chromosome- and cell type-specific transcriptomic signatures. Neuropathol Appl Neurobiol 2023; 49:e12857. [PMID: 36278258 DOI: 10.1111/nan.12857] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 11/30/2022]
Abstract
AIMS Generalised epilepsy is thought to involve distributed brain networks. However, the molecular and cellular factors that render different brain regions more vulnerable to epileptogenesis remain largely unknown. We aimed to investigate epilepsy-related morphometric similarity network (MSN) abnormalities at the macroscale level and their relationships with microscale gene expressions at the microscale level. METHODS We compared the MSN of genetic generalised epilepsy with generalised tonic-clonic seizure patients (GGE-GTCS, n = 101) to demographically matched healthy controls (HC, n = 150). Cortical MSNs were estimated by combining seven morphometric features derived from structural magnetic resonance imaging for each individual. Regional gene expression profiles were derived from brain-wide microarray measurements provided by the Allen Human Brain Atlas. RESULTS GGE-GTCS patients exhibited decreased regional MSNs in primary motor, prefrontal and temporal regions and increases in occipital, insular and posterior cingulate cortices, when compared with the HC. These case-control neuroimaging differences were validated using split-half analyses and were not affected by medication or drug response effects. When assessing associations with gene expression, genes associated with GGE-GTCS-related MSN differences were enriched in several biological processes, including 'synapse organisation', 'neurotransmitter transport' pathways and excitatory/inhibitory neuronal cell types. Collectively, the GGE-GTCS-related cortical vulnerabilities were associated with chromosomes 4, 5, 11 and 16 and were dispersed bottom-up at the cellular, pathway and disease levels, which contributed to epileptogenesis, suggesting diverse neurobiologically relevant enrichments in GGE-GTCS. CONCLUSIONS By bridging the gaps between transcriptional signatures and in vivo neuroimaging, we highlighted the importance of using MSN abnormalities of the human brain in GGE-GTCS patients to investigate disease-relevant genes and biological processes.
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Affiliation(s)
- Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.,Department of Radiology, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Fang Yang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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40
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Su TY, Tang Y, Choi JY, Hu S, Sakaie K, Murakami H, Jones S, Blümcke I, Najm I, Ma D, Wang ZI. Evaluating whole-brain tissue-property changes in MRI-negative pharmacoresistant focal epilepsies using MR fingerprinting. Epilepsia 2023; 64:430-442. [PMID: 36507762 PMCID: PMC10107443 DOI: 10.1111/epi.17488] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 12/09/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVE We aim to quantify whole-brain tissue-property changes in patients with magnetic resonance imaging (MRI)-negative pharmacoresistant focal epilepsy by three-dimensional (3D) magnetic resonance fingerprinting (MRF). METHODS We included 30 patients with pharmacoresistant focal epilepsy and negative MRI by official radiology report, as well as 40 age- and gender-matched healthy controls (HCs). MRF scans were obtained with 1 mm3 isotropic resolution. Quantitative T1 and T2 relaxometry maps were reconstructed from MRF and registered to the Montreal Neurological Institute (MNI) space. A two-sample t test was performed in Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) to evaluate significant abnormalities in patients comparing to HCs, with correction by the threshold-free cluster enhancement (TFCE) method. Subgroups analyses were performed for extra-temporal epilepsy/temporal epilepsy (ETLE/TLE), and for those with/without subtle abnormalities detected by morphometric analysis program (MAP), to investigate each subgroup's pattern of MRF changes. Correlation analyses were performed between the mean MRF values in each significant cluster and seizure-related clinical variables. RESULTS Compared to HCs, patients exhibited significant group-level T1 increase ipsilateral to the epileptic origin, in the mesial temporal gray matter (GM) and white matter (WM), temporal pole GM, orbitofrontal GM, hippocampus, and amygdala, with scattered clusters in the neocortical temporal and insular GM. No significant T2 changes were detected. The ETLE subgroup showed a T1-increase pattern similar to the overall cohort, with additional involvement of the ipsilateral anterior cingulate GM. The subgroup of MAP+ patients also showed a T1-increase pattern similar to the overall cohort, with additional cluster in the ipsilateral lateral orbitofrontal GM. Higher T1 was associated with younger seizure-onset age, longer epilepsy duration, and higher seizure frequency. SIGNIFICANCE MRF revealed group-level T1 increase in limbic/paralimbic structures ipsilateral to the epileptic origin, in patients with pharmacoresistant focal epilepsy and no apparent lesions on MRI, suggesting that these regions may be commonly affected by seizures in the epileptic brain. The significant association between T1 increase and higher seizure burden may reflect progressive tissue damage.
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Affiliation(s)
- Ting-Yu Su
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yingying Tang
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Joon Yul Choi
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Ken Sakaie
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Stephen Jones
- Imaging Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Ingmar Blümcke
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
- Neuropathology, University of Erlangen, Erlangen, Germany
| | - Imad Najm
- Epilepsy Center, Cleveland Clinic, Cleveland, Ohio, USA
| | - Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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41
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Li X, Ruan C, Zibrila AI, Musa M, Wu Y, Zhang Z, Liu H, Salimeen M. Children with autism spectrum disorder present glymphatic system dysfunction evidenced by diffusion tensor imaging along the perivascular space. Medicine (Baltimore) 2022; 101:e32061. [PMID: 36482590 PMCID: PMC9726346 DOI: 10.1097/md.0000000000032061] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
This study used diffusion tensor imaging (DTI) along the perivascular space (DTI-ALPS) to assess glymphatic system function in autism spectrum disorder (ASD) compared to healthy controls. Patients with ASD may have glymphatic system dysfunction, which is related to age. We retrospectively included 30 children with ASD and 25 healthy controls in this study. 3T magnetic resonance imaging scanner was used to perform DTI magnetic resonance imaging on all participants, and the DTI-ALPS index was calculated from the DTI data. Additionally, we evaluated how the DTI-ALPS index differed between the 2 groups. Moreover, we examined the relationships between the bilateral DTI-ALPS index and the age of the participants. The DTI-ALPS index considerably differed between groups. In the left index (1.02 ± 0.12 vs. 1.27 ± 0.25, P < .001) and in the right index (1.03 ± 0.12 vs. 1.32 ± 0.20, P < .001), the DTI-ALPS in ASD patients was significantly lower than that in healthy controls. Furthermore, the DTI-ALPS index was strongly and positively associated with age. In patients with ASD, there is a glymphatic system dysfunction. This is intimately correlated to age. Our findings suggest the importance of the DTI-ALPS approach in assessing the function of the glymphatic system in ASD.
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Affiliation(s)
- Xin Li
- Department of Anaesthesiology, School of Medicine, Yan’an University, Yanan, China
| | - Cailian Ruan
- Anatomy Department, School of Medicine, Yan’an University, Yanan City, China
| | - Abdoulaye Issotina Zibrila
- Laboratory of Experimental Pharmacology, Department of Animal Physiology, Faculty of Science and Technology, University of Abomey-Calavi, Abomey-Calavi, Benin
| | - Mazen Musa
- Department of Orthodontics, Al Tegana Dental Teaching Hospital, Faculty of Dentistry, University of Science and Technology, Omdurman, Sudan
| | - Yifan Wu
- MD Undergraduate Program, School of Medicine, Yan’an University, Yan’an City, China
| | - Zhengxiang Zhang
- Department of Pharmacology, School of Medicine, Yan’an University, Yan’an City, China
| | - Heng Liu
- Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi City, China
| | - Mustafa Salimeen
- Department of Radiology, Affiliated Hospital, School of Medicine, Yan’an University, Yan’an City, China
- Department of Radiology, Dongola Teaching Hospital Faculty of Medicine and Health Sciences, University of Dongola, Dongola, Republic of Sudan, Dongola, Sudan
- * Correspondence: Mustafa Salimeen, Radiology Department, Affiliated Hospital, School of Medicine, Yana’an University, Yan’an City, China (e-mail: )
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42
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Auvin S. Paediatric epilepsy and cognition. Dev Med Child Neurol 2022; 64:1444-1452. [PMID: 35801543 DOI: 10.1111/dmcn.15337] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 06/09/2022] [Accepted: 06/09/2022] [Indexed: 01/31/2023]
Abstract
Cognitive comorbidities are more frequent in children with epilepsy than in the general population. The cognitive function of children with epilepsy should be appropriately screened, and when there is an impairment, it must be diagnosed and managed. Several factors contribute to the occurrence of this comorbidity. The underlying aetiology and epilepsy syndrome are the major risk factors. Other factors also play a role, such as seizure recurrence, antiseizure medication, and interictal abnormalities. Recent evidence also suggested that cognitive involvement is an ongoing process that interacts with the normal maturation of cognitive function in children with epilepsy. Furthermore, some patients experience rapid cognitive deterioration related to epileptic activity, resulting in epileptic encephalopathy. Further research is needed to better understand how to prevent or modify factors that affect cognitive function in children with epilepsy. WHAT THIS PAPER ADDS: Cognitive comorbidities are more frequent in children with epilepsy than in the general population. The risk factors for cognitive impairment are the underlying aetiology/syndrome, seizure recurrence, antiseizure medication, and interictal abnormalities. Advanced genetic and neuroimaging studies are useful tools to better understand cognitive impairment in children with epilepsy.
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Affiliation(s)
- Stéphane Auvin
- Assistance Publique-Hôpitaux de Paris, Service de Neurologie Pédiatrique, Hôpital Robert Debré, Paris, France.,Université Paris-Cité, Institut National de la Santé et de la Recherche Médicale NeuroDiderot, Paris, France.,Institut Universitaire de France, Paris, France
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43
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Brain network architecture constrains age-related cortical thinning. Neuroimage 2022; 264:119721. [PMID: 36341953 DOI: 10.1016/j.neuroimage.2022.119721] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/23/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including "hotspots" of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.
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Hettwer MD, Larivière S, Park BY, van den Heuvel OA, Schmaal L, Andreassen OA, Ching CRK, Hoogman M, Buitelaar J, van Rooij D, Veltman DJ, Stein DJ, Franke B, van Erp TGM, Jahanshad N, Thompson PM, Thomopoulos SI, Bethlehem RAI, Bernhardt BC, Eickhoff SB, Valk SL. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders. Nat Commun 2022; 13:6851. [PMID: 36369423 PMCID: PMC9652311 DOI: 10.1038/s41467-022-34367-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.
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Affiliation(s)
- M. D. Hettwer
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.419524.f0000 0001 0041 5028Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S. Larivière
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada
| | - B. Y. Park
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada ,grid.202119.90000 0001 2364 8385Department of Data Science, Inha University, Incheon, Republic of Korea ,grid.410720.00000 0004 1784 4496Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - O. A. van den Heuvel
- grid.484519.5Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neuroscience and Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - L. Schmaal
- grid.1008.90000 0001 2179 088XCentre for Youth Mental Health, The University of Melbourne, Melbourne, VIC Australia ,grid.488501.00000 0004 8032 6923Orygen, Parkville, VIC Australia
| | - O. A. Andreassen
- grid.5510.10000 0004 1936 8921NORMENT Centre, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - C. R. K. Ching
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - M. Hoogman
- grid.10417.330000 0004 0444 9382Departments of Psychiatry and Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - J. Buitelaar
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D. van Rooij
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - D. J. Veltman
- grid.484519.5Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Anatomy and Neuroscience and Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - D. J. Stein
- grid.7836.a0000 0004 1937 1151South African Medical Research Council Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - B. Franke
- grid.10417.330000 0004 0444 9382Departments of Psychiatry and Human Genetics, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - T. G. M. van Erp
- grid.266093.80000 0001 0668 7243Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine Hall, Irvine, CA USA ,grid.266093.80000 0001 0668 7243Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA USA
| | | | | | | | | | | | | | - N. Jahanshad
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - P. M. Thompson
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - S. I. Thomopoulos
- grid.42505.360000 0001 2156 6853Imaging Genetics Center, Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA USA
| | - R. A. I. Bethlehem
- grid.5335.00000000121885934Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK ,grid.5335.00000000121885934Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B. C. Bernhardt
- grid.416102.00000 0004 0646 3639Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC Canada
| | - S. B. Eickhoff
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany
| | - S. L. Valk
- grid.411327.20000 0001 2176 9917Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Centre Jülich, Jülich, Germany ,grid.419524.f0000 0001 0041 5028Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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Walton E, Bernardoni F, Batury VL, Bahnsen K, Larivière S, Abbate-Daga G, Andres-Perpiña S, Bang L, Bischoff-Grethe A, Brooks SJ, Campbell IC, Cascino G, Castro-Fornieles J, Collantoni E, D'Agata F, Dahmen B, Danner UN, Favaro A, Feusner JD, Frank GKW, Friederich HC, Graner JL, Herpertz-Dahlmann B, Hess A, Horndasch S, Kaplan AS, Kaufmann LK, Kaye WH, Khalsa SS, LaBar KS, Lavagnino L, Lazaro L, Manara R, Miles AE, Milos GF, Monteleone AM, Monteleone P, Mwangi B, O'Daly O, Pariente J, Roesch J, Schmidt UH, Seitz J, Shott ME, Simon JJ, Smeets PAM, Tamnes CK, Tenconi E, Thomopoulos SI, van Elburg AA, Voineskos AN, von Polier GG, Wierenga CE, Zucker NL, Jahanshad N, King JA, Thompson PM, Berner LA, Ehrlich S. Brain Structure in Acutely Underweight and Partially Weight-Restored Individuals With Anorexia Nervosa: A Coordinated Analysis by the ENIGMA Eating Disorders Working Group. Biol Psychiatry 2022; 92:730-738. [PMID: 36031441 DOI: 10.1016/j.biopsych.2022.04.022] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/01/2022] [Accepted: 04/28/2022] [Indexed: 11/15/2022]
Abstract
BACKGROUND The pattern of structural brain abnormalities in anorexia nervosa (AN) is still not well understood. While several studies report substantial deficits in gray matter volume and cortical thickness in acutely underweight patients, others find no differences, or even increases in patients compared with healthy control subjects. Recent weight regain before scanning may explain some of this heterogeneity. To clarify the extent, magnitude, and dependencies of gray matter changes in AN, we conducted a prospective, coordinated meta-analysis of multicenter neuroimaging data. METHODS We analyzed T1-weighted structural magnetic resonance imaging scans assessed with standardized methods from 685 female patients with AN and 963 female healthy control subjects across 22 sites worldwide. In addition to a case-control comparison, we conducted a 3-group analysis comparing healthy control subjects with acutely underweight AN patients (n = 466) and partially weight-restored patients in treatment (n = 251). RESULTS In AN, reductions in cortical thickness, subcortical volumes, and, to a lesser extent, cortical surface area were sizable (Cohen's d up to 0.95), widespread, and colocalized with hub regions. Highlighting the effects of undernutrition, these deficits were associated with lower body mass index in the AN sample and were less pronounced in partially weight-restored patients. CONCLUSIONS The effect sizes observed for cortical thickness deficits in acute AN are the largest of any psychiatric disorder investigated in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium to date. These results confirm the importance of considering weight loss and renutrition in biomedical research on AN and underscore the importance of treatment engagement to prevent potentially long-lasting structural brain changes in this population.
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Affiliation(s)
- Esther Walton
- Department of Psychology, University of Bath, Bath, United Kingdom
| | - Fabio Bernardoni
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Victoria-Luise Batury
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Klaas Bahnsen
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec
| | - Giovanni Abbate-Daga
- Eating Disorders Center for Treatment and Research, University of Turin, Turin, Italy
| | - Susana Andres-Perpiña
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Centro de Investigación Biomédica en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Lasse Bang
- Norwegian Institute of Public Health, Oslo; Regional Department for Eating Disorders, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Amanda Bischoff-Grethe
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Samantha J Brooks
- School of Psychology, Faculty of Health Sciences, Liverpool John Moores University, Liverpool, United Kingdom; Department of Neuroscience, Uppsala University, Sweden
| | - Iain C Campbell
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Eating Disorders Unit, Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Giammarco Cascino
- Section of Neurosciences, Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Josefina Castro-Fornieles
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Centro de Investigación Biomédica en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | | | | | - Brigitte Dahmen
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Unna N Danner
- Altrecht Eating Disorders Rintveld, Altrecht Mental Health Institute, Zeist, the Netherlands; Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
| | - Angela Favaro
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Jamie D Feusner
- Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, California
| | - Guido K W Frank
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Hans-Christoph Friederich
- Centre for Psychosocial Medicine, Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany
| | - John L Graner
- Center for Cognitive Neuroscience, Duke University, Durham, North Carolina
| | - Beate Herpertz-Dahlmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Andreas Hess
- Institute for Pharmacology and Toxicology, University Erlangen-Nuremberg, Erlangen, Germany
| | - Stefanie Horndasch
- Department of Child and Adolescent Psychiatry, University Clinic Erlangen, Erlangen, Germany
| | - Allan S Kaplan
- Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Lisa-Katrin Kaufmann
- Department of Consultation-Liaison Psychiatry and Psychosomatics, University Hospital Zurich, University of Zurich; Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Walter H Kaye
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, University of Tulsa, Tulsa, Oklahoma; Oxley College of Health Sciences, University of Tulsa, Tulsa, Oklahoma
| | - Kevin S LaBar
- Center for Cognitive Neuroscience, Duke University, Durham, North Carolina
| | - Luca Lavagnino
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston Texas
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institut Clinic de Neurociències, Hospital Clínic Universitari, Centro de Investigación Biomédica en Red de Salud Mental, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain
| | - Renzo Manara
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Amy E Miles
- Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada
| | - Gabriella F Milos
- Department of Consultation-Liaison Psychiatry and Psychosomatics, University Hospital Zurich, University of Zurich
| | | | - Palmiero Monteleone
- Section of Neurosciences, Department of Medicine, Surgery and Dentistry, Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Benson Mwangi
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston Texas
| | - Owen O'Daly
- Centre for Neuroimaging Studies, King's College London, London, United Kingdom; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Jose Pariente
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Julie Roesch
- Department of Neuroradiology, University Clinic Erlangen, Erlangen, Germany
| | - Ulrike H Schmidt
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Eating Disorders Unit, Department of Psychological Medicine, King's College London, London, United Kingdom
| | - Jochen Seitz
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Megan E Shott
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Joe J Simon
- Centre for Psychosocial Medicine, Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany
| | - Paul A M Smeets
- UMC Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands; Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Elena Tenconi
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Annemarie A van Elburg
- Altrecht Eating Disorders Rintveld, Altrecht Mental Health Institute, Zeist, the Netherlands; Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Georg G von Polier
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; Institute for Neuroscience and Medicine: Brain and Behaviour, Forschungszentrum Jülich, Jülich, Germany; Department of Child and Adolescent Psychiatry, University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Christina E Wierenga
- Department of Psychiatry, University of California San Diego, La Jolla, California; Eating Disorders Center for Treatment and Research, University of California San Diego, La Jolla, California
| | - Nancy L Zucker
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Joseph A King
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California
| | - Laura A Berner
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Stefan Ehrlich
- Translational Developmental Neuroscience Section, Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany; Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.
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Multiscale neural gradients reflect transdiagnostic effects of major psychiatric conditions on cortical morphology. Commun Biol 2022; 5:1024. [PMID: 36168040 PMCID: PMC9515219 DOI: 10.1038/s42003-022-03963-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/07/2022] [Indexed: 02/06/2023] Open
Abstract
It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.
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Kerr WT, Tatekawa H, Lee JK, Karimi AH, Sreenivasan SS, O'Neill J, Smith JM, Hickman LB, Savic I, Nasrullah N, Espinoza R, Narr K, Salamon N, Beimer NJ, Hadjiiski LM, Eliashiv DS, Stacey WC, Engel J, Feusner JD, Stern JM. Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls. Epilepsy Behav 2022; 134:108858. [PMID: 35933959 DOI: 10.1016/j.yebeh.2022.108858] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/26/2022] [Accepted: 07/15/2022] [Indexed: 11/15/2022]
Abstract
PURPOSE Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations. METHODS Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners. RESULTS After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm3, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS. CONCLUSIONS These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
| | - Hiroyuki Tatekawa
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - John K Lee
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Amir H Karimi
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Siddhika S Sreenivasan
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Joseph O'Neill
- Division of Child & Adolescent Psychiatry, Jane & Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA
| | - Jena M Smith
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - L Brian Hickman
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Ivanka Savic
- Department of Women's and Children's Health, Karolinska Institute and Neurology Clinic, Karolinksa University Hospital, Karolinska Universitetssjukhuset, Stockholm, Sweden
| | - Nilab Nasrullah
- Department of Women's and Children's Health, Karolinska Institute and Neurology Clinic, Karolinksa University Hospital, Karolinska Universitetssjukhuset, Stockholm, Sweden
| | - Randall Espinoza
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Katherine Narr
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Noriko Salamon
- Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Nicholas J Beimer
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Lubomir M Hadjiiski
- Department of Radiology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Dawn S Eliashiv
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - William C Stacey
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA; Brain Research Institute, University of California Los Angeles, Los Angeles, CA, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Jamie D Feusner
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA; Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - John M Stern
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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48
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Ballerini A, Tondelli M, Talami F, Molinari MA, Micalizzi E, Giovannini G, Turchi G, Malagoli M, Genovese M, Meletti S, Vaudano AE. Amygdala subnuclear volumes in temporal lobe epilepsy with hippocampal sclerosis and in non-lesional patients. Brain Commun 2022; 4:fcac225. [PMID: 36213310 PMCID: PMC9536297 DOI: 10.1093/braincomms/fcac225] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 05/12/2022] [Accepted: 09/05/2022] [Indexed: 11/28/2022] Open
Abstract
Together with hippocampus, the amygdala is important in the epileptogenic network of patients with temporal lobe epilepsy. Recently, an increase in amygdala volumes (i.e. amygdala enlargement) has been proposed as morphological biomarker of a subtype of temporal lobe epilepsy patients without MRI abnormalities, although other data suggest that this finding might be unspecific and not exclusive to temporal lobe epilepsy. In these studies, the amygdala is treated as a single entity, while instead it is composed of different nuclei, each with peculiar function and connection. By adopting a recently developed methodology of amygdala’s subnuclei parcellation based of high-resolution T1-weighted image, this study aims to map specific amygdalar subnuclei participation in temporal lobe epilepsy due to hippocampal sclerosis (n = 24) and non-lesional temporal lobe epilepsy (n = 24) with respect to patients with focal extratemporal lobe epilepsies (n = 20) and healthy controls (n = 30). The volumes of amygdala subnuclei were compared between groups adopting multivariate analyses of covariance and correlated with clinical variables. Additionally, a logistic regression analysis on the nuclei resulting statistically different across groups was performed. Compared with other populations, temporal lobe epilepsy with hippocampal sclerosis showed a significant atrophy of the whole amygdala (pBonferroni = 0.040), particularly the basolateral complex (pBonferroni = 0.033), while the non-lesional temporal lobe epilepsy group demonstrated an isolated hypertrophy of the medial nucleus (pBonferroni = 0.012). In both scenarios, the involved amygdala was ipsilateral to the epileptic focus. The medial nucleus demonstrated a volume increase even in extratemporal lobe epilepsies although contralateral to the seizure onset hemisphere (pBonferroni = 0.037). Non-lesional patients with psychiatric comorbidities showed a larger ipsilateral lateral nucleus compared with those without psychiatric disorders. This exploratory study corroborates the involvement of the amygdala in temporal lobe epilepsy, particularly in mesial temporal lobe epilepsy and suggests a different amygdala subnuclei engagement depending on the aetiology and lateralization of epilepsy. Furthermore, the logistic regression analysis indicated that the basolateral complex and the medial nucleus of amygdala can be helpful to differentiate temporal lobe epilepsy with hippocampal sclerosis and with MRI negative, respectively, versus controls with a consequent potential clinical yield. Finally, the present results contribute to the literature about the amygdala enlargement in temporal lobe epilepsy, suggesting that the increased volume of amygdala can be regarded as epilepsy-related structural changes common across different syndromes whose meaning should be clarified.
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Affiliation(s)
- Alice Ballerini
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia , Modena 41125 , Italy
| | | | - Francesca Talami
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia , Modena 41125 , Italy
| | | | - Elisa Micalizzi
- PhD Program in Clinical and Experimental Medicine, University of Modena and Reggio Emilia , Modena 41121 , Italy
| | - Giada Giovannini
- Neurology Unit, OCB Hospital, AOU Modena , Modena 41126 , Italy
- PhD Program in Clinical and Experimental Medicine, University of Modena and Reggio Emilia , Modena 41121 , Italy
| | - Giulia Turchi
- Neurology Unit, OCB Hospital, AOU Modena , Modena 41126 , Italy
| | - Marcella Malagoli
- Neuroradiology Unit, OCB Hospital, AOU Modena , Modena 41126 , Italy
| | - Maurilio Genovese
- Neuroradiology Unit, OCB Hospital, AOU Modena , Modena 41126 , Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia , Modena 41125 , Italy
- Neurology Unit, OCB Hospital, AOU Modena , Modena 41126 , Italy
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia , Modena 41125 , Italy
- Neurology Unit, OCB Hospital, AOU Modena , Modena 41126 , Italy
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49
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Hansen JY, Shafiei G, Vogel JW, Smart K, Bearden CE, Hoogman M, Franke B, van Rooij D, Buitelaar J, McDonald CR, Sisodiya SM, Schmaal L, Veltman DJ, van den Heuvel OA, Stein DJ, van Erp TGM, Ching CRK, Andreassen OA, Hajek T, Opel N, Modinos G, Aleman A, van der Werf Y, Jahanshad N, Thomopoulos SI, Thompson PM, Carson RE, Dagher A, Misic B. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities. Nat Commun 2022; 13:4682. [PMID: 35948562 PMCID: PMC9365855 DOI: 10.1038/s41467-022-32420-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/28/2022] [Indexed: 12/21/2022] Open
Abstract
Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.
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Affiliation(s)
- Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Jacob W Vogel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Smart
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Martine Hoogman
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Barbara Franke
- Departments of Psychiatry and Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Daan van Rooij
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jan Buitelaar
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, WC1N 3BG, UK
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Odile A van den Heuvel
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Dept of Psychiatry & Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, & Center for the Neurobiology of Leaning and Memory, University of California Irvine, 309 Qureshey Research Lab, Irvine, CA, USA
| | - Christopher R K Ching
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Nils Opel
- Institute of Translational Psychiatry, University of Münster, Münster, Germany & Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Gemma Modinos
- Department of Psychosis Studies & MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - André Aleman
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, Groningen, The Netherlands
| | - Ysbrand van der Werf
- Department of Anatomy & Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Neda Jahanshad
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Sophia I Thomopoulos
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Keck School of Medicine, Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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50
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Hale AT, Chari A, Scott RC, Cross JH, Rozzelle CJ, Blount JP, Tisdall MM. Expedited epilepsy surgery prior to drug resistance in children: a frontier worth crossing? Brain 2022; 145:3755-3762. [PMID: 35883201 DOI: 10.1093/brain/awac275] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 06/18/2022] [Accepted: 07/08/2022] [Indexed: 11/14/2022] Open
Abstract
Epilepsy surgery is an established safe and effective treatment for selected candidates with drug-resistant epilepsy. In this opinion piece, we outline the clinical and experimental evidence for selectively considering epilepsy surgery prior to drug resistance. Our rationale for expedited surgery is based on the observations that, 1) a high proportion of patients with lesional epilepsies (e.g. focal cortical dysplasia, epilepsy associated tumours) will progress to drug-resistance, 2) surgical treatment of these lesions, especially in non-eloquent areas of brain, is safe, and 3) earlier surgery may be associated with better seizure outcomes. Potential benefits beyond seizure reduction or elimination include less exposure to anti-seizure medications (ASM), which may lead to improved developmental trajectories in children and optimize long-term neurocognitive outcomes and quality of life. Further, there exists emerging experimental evidence that brain network dysfunction exists at the onset of epilepsy, where continuing dysfunctional activity could exacerbate network perturbations. This in turn could lead to expanded seizure foci and contribution to the comorbidities associated with epilepsy. Taken together, we rationalize that epilepsy surgery, in carefully selected cases, may be considered prior to drug resistance. Lastly, we outline the path forward, including the challenges associated with developing the evidence base and implementing this paradigm into clinical care.
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Affiliation(s)
- Andrew T Hale
- Division of Pediatric Neurosurgery, Children's of Alabama, Birmingham, AL, USA
| | - Aswin Chari
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK.,Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Rod C Scott
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,Department of Paediatric Neurology, Nemours Children's Hospital, Wilmington, DE, USA.,Department of Paediatric Neurology, Great Ormond Street Hospital, London, UK
| | - J Helen Cross
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,Department of Paediatric Neurology, Great Ormond Street Hospital, London, UK
| | - Curtis J Rozzelle
- Division of Pediatric Neurosurgery, Children's of Alabama, Birmingham, AL, USA
| | - Jeffrey P Blount
- Division of Pediatric Neurosurgery, Children's of Alabama, Birmingham, AL, USA
| | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK.,Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
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