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Ngo A, Royer J, Rodriguez-Cruces R, Xie K, DeKraker J, Auer H, Tavakol S, Lam J, Schrader DV, Dudley RWR, Bernasconi A, Bernasconi N, Frauscher B, Lariviere S, Bernhardt BC. Associations of Cerebral Blood Flow Patterns With Gray and White Matter Structure in Patients With Temporal Lobe Epilepsy. Neurology 2024; 103:e209528. [PMID: 39008785 PMCID: PMC11314957 DOI: 10.1212/wnl.0000000000209528] [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: 10/23/2023] [Accepted: 04/08/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Neuroimaging studies in patients with temporal lobe epilepsy (TLE) show widespread brain network alterations beyond the mesiotemporal lobe. Despite the critical role of the cerebrovascular system in maintaining whole-brain structure and function, changes in cerebral blood flow (CBF) remain incompletely understood in the disease. Here, we studied whole-brain perfusion and vascular network alterations in TLE and assessed its associations with gray and white matter compromises and various clinical variables. METHODS We included individuals with and without pharmaco-resistant TLE who underwent multimodal 3T MRI, including arterial spin labelling, structural, and diffusion-weighted imaging. Using surface-based MRI mapping, we generated individualized cortico-subcortical profiles of perfusion, morphology, and microstructure. Linear models compared regional CBF in patients with controls and related alterations to morphological and microstructural metrics. We further probed interregional vascular networks in TLE, using graph theoretical CBF covariance analysis. The effects of disease duration were explored to better understand the progressive changes in perfusion. We assessed the utility of perfusion in separating patients with TLE from controls using supervised machine learning. RESULTS Compared with control participants (n = 38; mean ± SD age 34.8 ± 9.3 years; 20 females), patients with TLE (n = 24; mean ± SD age 35.8 ± 10.6 years; 12 females) showed widespread CBF reductions predominantly in fronto-temporal regions (Cohen d -0.69, 95% CI -1.21 to -0.16), consistent in a subgroup of patients who remained seizure-free after surgical resection of the seizure focus. Parallel structural profiling and network-based models showed that cerebral hypoperfusion may be partially constrained by gray and white matter changes (8.11% reduction in Cohen d) and topologically segregated from whole-brain perfusion networks (area under the curve -0.17, p < 0.05). Negative effects of progressive disease duration further targeted regional CBF profiles in patients (r = -0.54, 95% CI -0.77 to -0.16). Perfusion-derived classifiers discriminated patients from controls with high accuracy (71% [70%-82%]). Findings were robust when controlling for several methodological confounds. DISCUSSION Our multimodal findings provide insights into vascular contributions to TLE pathophysiology affecting and extending beyond mesiotemporal structures and highlight their clinical potential in epilepsy diagnosis. As our work was cross-sectional and based on a single site, it motivates future longitudinal studies to confirm progressive effects, ideally in a multicentric setting.
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Affiliation(s)
- Alexander Ngo
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jessica Royer
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Raul Rodriguez-Cruces
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Ke Xie
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jordan DeKraker
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Hans Auer
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Shahin Tavakol
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Jack Lam
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Dewi V Schrader
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Roy W R Dudley
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Andrea Bernasconi
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Neda Bernasconi
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Birgit Frauscher
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Sara Lariviere
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Boris C Bernhardt
- From the Department of Neurology and Neurosurgery (A.N., J.R., R.R.-C., K.X., J.D., H.A., S.T., J.L., A.B., N.B., B.F., B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Québec; Department of Pediatrics (D.V.S.), University of British Columbia, Vancouver; Department of Pediatric Surgery (R.W.R.D.), Montreal Children's Hospital, McGill University, Montreal, Québec, Canada; and Center for Brain Circuit Therapeutics (S.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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Ellsay AC, Winston GP. Advances in MRI-based diagnosis of temporal lobe epilepsy: Correlating hippocampal subfield volumes with histopathology. J Neuroimaging 2024. [PMID: 39092876 DOI: 10.1111/jon.13225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/27/2024] [Accepted: 07/17/2024] [Indexed: 08/04/2024] Open
Abstract
Epilepsy, affecting 0.5%-1% of the global population, presents a significant challenge with 30% of patients resistant to medical treatment. Temporal lobe epilepsy, a common cause of medically refractory epilepsy, is often caused by hippocampal sclerosis (HS). HS can be divided further by subtype, as defined by the International League Against Epilepsy (ILAE). Type 1 HS, the most prevalent form (60%-80% of all cases), is characterized by cell loss and gliosis predominantly in the subfields cornu ammonis (CA1) and CA4. Type 2 HS features cell loss and gliosis primarily in the CA1 sector, and type 3 HS features cell loss and gliosis predominantly in the CA4 subfield. This literature review evaluates studies on hippocampal subfields, exploring whether observable atrophy patterns from in vivo and ex vivo magnetic resonance imaging (MRI) scans correlate with histopathological examinations with manual or automated segmentation techniques. Our findings suggest only ex vivo 1.5 Tesla (T) or 3T MRI with manual segmentation or in vivo 7T MRI with manual or automated segmentations can consistently correlate subfield patterns with histopathologically derived ILAE-HS subtypes. In conclusion, manual and automated segmentation methods offer advantages and limitations in diagnosing ILAE-HS subtypes, with ongoing research crucial for refining hippocampal subfield segmentation techniques and enhancing clinical applicability.
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Affiliation(s)
- Andrea C Ellsay
- Centre for Neuroscience Studies, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Gavin P Winston
- Centre for Neuroscience Studies, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
- Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
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Baset A, Huang F. Shedding light on subiculum's role in human brain disorders. Brain Res Bull 2024; 214:110993. [PMID: 38825254 DOI: 10.1016/j.brainresbull.2024.110993] [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: 04/09/2024] [Revised: 05/17/2024] [Accepted: 05/30/2024] [Indexed: 06/04/2024]
Abstract
Subiculum is a pivotal output component of the hippocampal formation, a structure often overlooked in neuroscientific research. Here, this review aims to explore the role of the subiculum in various brain disorders, shedding light on its significance within the functional-neuroanatomical perspective on neurological diseases. The subiculum's involvement in multiple brain disorders was thoroughly examined. In Alzheimer's disease, subiculum alterations precede cognitive decline, while in epilepsy, the subiculum plays a critical role in seizure initiation. Stress involves the subiculum's impact on the hypothalamic-pituitary-adrenocortical axis. Moreover, the subiculum exhibits structural and functional changes in anxiety, schizophrenia, and Parkinson's disease, contributing to cognitive deficits. Bipolar disorder is linked to subiculum structural abnormalities, while autism spectrum disorder reveals an alteration of inward deformation in the subiculum. Lastly, frontotemporal dementia shows volumetric differences in the subiculum, emphasizing its contribution to the disorder's complexity. Taken together, this review consolidates existing knowledge on the subiculum's role in brain disorders, and may facilitate future research, diagnostic strategies, and therapeutic interventions for various neurological conditions.
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Affiliation(s)
- Abdul Baset
- Department of Neuroscience, City University of Hong Kong, Hong Kong Special Administrative Region of China; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong Special Administrative Region of China
| | - Fengwen Huang
- Department of Neuroscience, City University of Hong Kong, Hong Kong Special Administrative Region of China; Centre for Regenerative Medicine and Health, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong Special Administrative Region of China.
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Eichert N, DeKraker J, Howard AFD, Huszar IN, Zhu S, Sallet J, Miller KL, Mars RB, Jbabdi S, Bernhardt BC. Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain. Nat Commun 2024; 15:5963. [PMID: 39013855 PMCID: PMC11252401 DOI: 10.1038/s41467-024-49823-8] [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: 09/25/2023] [Accepted: 06/17/2024] [Indexed: 07/18/2024] Open
Abstract
While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks.
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Affiliation(s)
- Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
| | - Jordan DeKraker
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Silei Zhu
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- INSERM U1208 Stem Cell and Brain Research Institute, Univ Lyon, Bron, France
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Boris C Bernhardt
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Fırat Z, Er F, Noyan H, Ekinci G, Üçok A, Uluğ AM, Aktekin B. Discriminant analysis using MRI asymmetry indices and cognitive scores of women with temporal lobe epilepsy or schizophrenia. Neuroradiology 2024; 66:1083-1092. [PMID: 38416211 DOI: 10.1007/s00234-024-03317-y] [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: 08/16/2023] [Accepted: 02/20/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE This study aims to assess the diagnostic power of brain asymmetry indices and neuropsychological tests for differentiating mesial temporal lobe epilepsy (MTLE) and schizophrenia (SCZ). METHODS We studied a total of 39 women including 13 MTLE, 13 SCZ, and 13 healthy individuals (HC). A neuropsychological test battery (NPT) was administered and scored by an experienced neuropsychologist, and NeuroQuant (CorTechs Labs Inc., San Diego, California) software was used to calculate brain asymmetry indices (ASI) for 71 different anatomical regions of all participants based on their 3D T1 MR imaging scans. RESULTS Asymmetry indices measured from 10 regions showed statistically significant differences between the three groups. In this study, a multi-class linear discriminant analysis (LDA) model was built based on a total of fifteen variables composed of the most five significantly informative NPT scores and ten significant asymmetry indices, and the model achieved an accuracy of 87.2%. In pairwise classification, the accuracy for distinguishing MTLE from either SCZ or HC was 94.8%, while the accuracy for distinguishing SCZ from either MTLE or HC was 92.3%. CONCLUSION The ability to differentiate MTLE from SCZ using neuroradiological and neuropsychological biomarkers, even within a limited patient cohort, could make a substantial contribution to research in larger patient groups using different machine learning techniques.
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Affiliation(s)
- Zeynep Fırat
- Department of Radiology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey.
| | - Füsun Er
- Department of Information Systems Engineering, Piri Reis University, Istanbul, Turkey
| | - Handan Noyan
- Faculty of Social Sciences, Department of Psychology, Beykoz University, 34810, Istanbul, Turkey
| | - Gazanfer Ekinci
- Department of Radiology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey
| | - Alp Üçok
- Istanbul Faculty of Medicine, Department of Psychiatry, Istanbul University, 34134, Istanbul, Turkey
| | - Aziz M Uluğ
- Institute of Biomedical Engineering, Boğaziçi University, Istanbul, Turkey
- CorTechs Labs Inc, San Diego, CA, USA
| | - Berrin Aktekin
- Department of Neurology, Yeditepe University Hospitals, Kosuyolu, 34718, Istanbul, Turkey
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Tavakol S, Kebets V, Royer J, Li Q, Auer H, DeKraker J, Jefferies E, Bernasconi N, Bernasconi A, Helmstaedter C, Arafat T, Armony J, Nathan Spreng R, Caciagli L, Frauscher B, Smallwood J, Bernhardt B. Differential relational memory impairment in temporal lobe epilepsy. Epilepsy Behav 2024; 155:109722. [PMID: 38643660 DOI: 10.1016/j.yebeh.2024.109722] [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: 10/23/2023] [Revised: 02/22/2024] [Accepted: 02/25/2024] [Indexed: 04/23/2024]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is typically associated with pathology of the hippocampus, a key structure involved in relational memory, including episodic, semantic, and spatial memory processes. While it is widely accepted that TLE-associated hippocampal alterations underlie memory deficits, it remains unclear whether impairments relate to a specific cognitive domain or multiple ones. METHODS We administered a recently validated task paradigm to evaluate episodic, semantic, and spatial memory in 24 pharmacoresistant TLE patients and 50 age- and sex-matched healthy controls. We carried out two-way analyses of variance to identify memory deficits in individuals with TLE relative to controls across different relational memory domains, and used partial least squares correlation to identify factors contributing to variations in relational memory performance across both cohorts. RESULTS Compared to controls, TLE patients showed marked impairments in episodic and spatial memory, with mixed findings in semantic memory. Even when additionally controlling for age, sex, and overall cognitive function, between-group differences persisted along episodic and spatial domains. Moreover, age, diagnostic group, and hippocampal volume were all associated with relational memory behavioral phenotypes. SIGNIFICANCE Our behavioral findings show graded deficits across relational memory domains in people with TLE, which provides further insights into the complex pattern of cognitive impairment in the condition.
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Affiliation(s)
- Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Qiongling Li
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Hans Auer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | | | - Neda Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Andrea Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | | | - Thaera Arafat
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Jorge Armony
- Department of Psychiatry, McGill University, Montreal, Canada.
| | - R Nathan Spreng
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Birgit Frauscher
- ANPHY Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
| | | | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.
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7
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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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8
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Bernasconi A, Gill RS, Bernasconi N. The use of automated and AI-driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia. Epilepsia 2024. [PMID: 38642009 DOI: 10.1111/epi.17989] [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: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/08/2024] [Indexed: 04/22/2024]
Abstract
In drug-resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole-brain coverage. In addition, the last decade has witnessed continued developments in MRI-based computer-aided machine-learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging-derived prognostic markers, including response to anti-seizure medication, post-surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person-centered care.
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Affiliation(s)
- Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ravnoor S Gill
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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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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Kerestes R, Perry A, Vivash L, O’Brien TJ, Alvim MK, Arienzo D, Aventurato ÍK, Ballerini A, Baltazar GF, Bargalló N, Bender B, Brioschi R, Bürkle E, Caligiuri ME, Cendes F, de Tisi J, Duncan JS, Engel JP, Foley S, Fortunato F, Gambardella A, Giacomini T, Guerrini R, Hall G, Hamandi K, Ives-Deliperi V, João RB, Keller SS, Kleiser B, Labate A, Lenge M, Marotta C, Martin P, Mascalchi M, Meletti S, Owens-Walton C, Parodi CB, Pascual-Diaz S, Powell D, Rao J, Rebsamen M, Reiter J, Riva A, Rüber T, Rummel C, Scheffler F, Severino M, Silva LS, Staba RJ, Stein DJ, Striano P, Taylor PN, Thomopoulos SI, Thompson PM, Tortora D, Vaudano AE, Weber B, Wiest R, Winston GP, Yasuda CL, Zheng H, McDonald CR, Sisodiya SM, Harding IH. Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study. Epilepsia 2024; 65:1072-1091. [PMID: 38411286 PMCID: PMC11120093 DOI: 10.1111/epi.17881] [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: 06/20/2023] [Revised: 12/26/2023] [Accepted: 01/03/2024] [Indexed: 02/28/2024]
Abstract
OBJECTIVE The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. METHODS A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. RESULTS Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset (η ρ max 2 = .05) and longer epilepsy duration (η ρ max 2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. SIGNIFICANCE We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.
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Affiliation(s)
- Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Andrew Perry
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Terence J. O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Marina K.M. Alvim
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Donatello Arienzo
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Ítalo K. Aventurato
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Alice Ballerini
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Gabriel F. Baltazar
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Núria Bargalló
- Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Department of Radiology of Center of Image Diagnosis (CDIC), Hospital Clinic de Barcelona, Barcelona, Spain
- CIBERSAM, Madrid, Spain
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Ricardo Brioschi
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Eva Bürkle
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
| | - Fernando Cendes
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Jerome P. Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
- Institute of Neurology, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Catanzaro, Italy
| | - Thea Giacomini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Renzo Guerrini
- Meyer Children’s Hospital IRCCS, Florence, Italy
- University of Florence, Florence, Italy
| | - Gerard Hall
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
| | | | - Rafael B. João
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Benedict Kleiser
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Angelo Labate
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
- Regional Epilepsy Center, University of Messina, Messina, Italy
| | - Matteo Lenge
- Meyer Children’s Hospital IRCCS, Florence, Italy
| | | | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Mario Mascalchi
- ‘Mario Serio’ Department of Clinical and Experimental Medical Sciences, University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and network in Oncology of the Tuscany Region, Florence, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Conor Owens-Walton
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Saül Pascual-Diaz
- Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - David Powell
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Jun Rao
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes Reiter
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | | | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Freda Scheffler
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | | | - Lucas S. Silva
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Richard J. Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Dan J. Stein
- SMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
- IRCCS Istituto ‘Giannina Gaslini’, Genoa, Italy
| | - Peter N. Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
- Department of Medicine (Division of Neurology), Queen’s University Kingston, ON, Canada
| | - Clarissa L. Yasuda
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carrie R. McDonald
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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11
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Mito R, Pedersen M, Pardoe H, Parker D, Smith RE, Cameron J, Scheffer IE, Berkovic SF, Vaughan DN, Jackson GD. Exploring individual fixel-based white matter abnormalities in epilepsy. Brain Commun 2023; 6:fcad352. [PMID: 38187877 PMCID: PMC10768884 DOI: 10.1093/braincomms/fcad352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 11/02/2023] [Accepted: 12/21/2023] [Indexed: 01/09/2024] Open
Abstract
Diffusion MRI has provided insight into the widespread structural connectivity changes that characterize epilepsies. Although syndrome-specific white matter abnormalities have been demonstrated, studies to date have predominantly relied on statistical comparisons between patient and control groups. For diffusion MRI techniques to be of clinical value, they should be able to detect white matter microstructural changes in individual patients. In this study, we apply an individualized approach to a technique known as fixel-based analysis, to examine fibre-tract-specific abnormalities in individuals with epilepsy. We explore the potential clinical value of this individualized fixel-based approach in epilepsy patients with differing syndromic diagnoses. Diffusion MRI data from 90 neurologically healthy control participants and 10 patients with epilepsy (temporal lobe epilepsy, progressive myoclonus epilepsy, and Dravet Syndrome, malformations of cortical development) were included in this study. Measures of fibre density and cross-section were extracted for all participants across brain white matter fixels, and mean values were computed within select tracts-of-interest. Scanner harmonized and normalized data were then used to compute Z-scores for individual patients with epilepsy. White matter abnormalities were observed in distinct patterns in individual patients with epilepsy, both at the tract and fixel level. For patients with specific epilepsy syndromes, the detected white matter abnormalities were in line with expected syndrome-specific clinical phenotypes. In patients with lesional epilepsies (e.g. hippocampal sclerosis, periventricular nodular heterotopia, and bottom-of-sulcus dysplasia), white matter abnormalities were spatially concordant with lesion location. This proof-of-principle study demonstrates the clinical potential of translating advanced diffusion MRI methodology to individual-patient-level use in epilepsy. This technique could be useful both in aiding diagnosis of specific epilepsy syndromes, and in localizing structural abnormalities, and is readily amenable to other neurological disorders. We have included code and data for this study so that individualized white matter changes can be explored robustly in larger cohorts in future work.
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Affiliation(s)
- Remika Mito
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Mangor Pedersen
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
- Department of Psychology and Neuroscience, Auckland University of Technology (AUT), Auckland 1142, New Zealand
| | - Heath Pardoe
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
| | - Donna Parker
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Jillian Cameron
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria 3084, Australia
| | - Ingrid E Scheffer
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria 3084, Australia
| | - Samuel F Berkovic
- Epilepsy Research Centre, Department of Medicine, University of Melbourne, Austin Health, Heidelberg, Victoria 3084, Australia
| | - David N Vaughan
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
- Department of Neurology, Austin Health, Heidelberg, Victoria 3084, Australia
| | - Graeme D Jackson
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria 3084, Australia
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia
- Department of Neurology, Austin Health, Heidelberg, Victoria 3084, Australia
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12
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DeKraker J, Palomero-Gallagher N, Kedo O, Ladbon-Bernasconi N, Muenzing SEA, Axer M, Amunts K, Khan AR, Bernhardt BC, Evans AC. Evaluation of surface-based hippocampal registration using ground-truth subfield definitions. eLife 2023; 12:RP88404. [PMID: 37956092 PMCID: PMC10642966 DOI: 10.7554/elife.88404] [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: 11/15/2023] Open
Abstract
The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.
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Affiliation(s)
- Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Olga Kedo
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | | | - Sascha EA Muenzing
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Markus Axer
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Ali R Khan
- Robarts Research Institute, University of Western OntarioLondonCanada
| | - Boris C Bernhardt
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
| | - Alan C Evans
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealCanada
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13
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Kerestes R, Perry A, Vivash L, O'Brien TJ, Alvim MKM, Arienzo D, Aventurato ÍK, Ballerini A, Baltazar GF, Bargalló N, Bender B, Brioschi R, Bürkle E, Caligiuri ME, Cendes F, de Tisi J, Duncan JS, Engel JP, Foley S, Fortunato F, Gambardella A, Giacomini T, Guerrini R, Hall G, Hamandi K, Ives-Deliperi V, João RB, Keller SS, Kleiser B, Labate A, Lenge M, Marotta C, Martin P, Mascalchi M, Meletti S, Owens-Walton C, Parodi CB, Pascual-Diaz S, Powell D, Rao J, Rebsamen M, Reiter J, Riva A, Rüber T, Rummel C, Scheffler F, Severino M, Silva LS, Staba RJ, Stein DJ, Striano P, Taylor PN, Thomopoulos SI, Thompson PM, Tortora D, Vaudano AE, Weber B, Wiest R, Winston GP, Yasuda CL, Zheng H, McDonald CR, Sisodiya SM, Harding IH. Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.21.562994. [PMID: 37961570 PMCID: PMC10634708 DOI: 10.1101/2023.10.21.562994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Objective The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group. Methods A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in i) all epilepsies; ii) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS); iii) non-lesional temporal lobe epilepsy (TLE-NL); iv) genetic generalised epilepsy; and (v) extra-temporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results Across all epilepsies, reduced total cerebellar volume was observed (d=0.42). Maximum volume loss was observed in the corpus medullare (dmax=0.49) and posterior lobe grey matter regions, including bilateral lobules VIIB (dmax= 0.47), Crus I/II (dmax= 0.39), VIIIA (dmax=0.45) and VIIIB (dmax=0.40). Earlier age at seizure onset (ηρ2max=0.05) and longer epilepsy duration (ηρ2max=0.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance We provide robust evidence of deep cerebellar and posterior lobe subregional grey matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in non-motor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellum subregions into neurobiological models of epilepsy.
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Affiliation(s)
- Rebecca Kerestes
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Andrew Perry
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Marina K M Alvim
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Donatello Arienzo
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Ítalo K Aventurato
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Alice Ballerini
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Gabriel F Baltazar
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Núria Bargalló
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Department of Radiology of Center of Image Diagnosis (CDIC), Hospital Clinic de Barcelona, Barcelona, Spain
- CIBERSAM, Madrid, Spain
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Ricardo Brioschi
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Eva Bürkle
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
| | - Fernando Cendes
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Jane de Tisi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Jerome P Engel
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
| | - Thea Giacomini
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | - Renzo Guerrini
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | - Gerard Hall
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
| | | | - Rafael B João
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Simon S Keller
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Benedict Kleiser
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Angelo Labate
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
- Regional Epilepsy Center, University of Messina, Messina, Italy
| | - Matteo Lenge
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | | | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Mario Mascalchi
- 'Mario Serio' Department of Clinical and Experimental Medical Sciences, University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and network in Oncology of the Tuscany Region, Florence, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Conor Owens-Walton
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Saül Pascual-Diaz
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | - David Powell
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
| | - Jun Rao
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Johannes Reiter
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | | | - Theodor Rüber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Christian Rummel
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Freda Scheffler
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Lucas S Silva
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Dan J Stein
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Bioinformatics Platform, Monash University, Melbourne, VIC, Australia
- Department of Medicine and Neurology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Department of Radiology of Center of Image Diagnosis (CDIC), Hospital Clinic de Barcelona, Barcelona, Spain
- CIBERSAM, Madrid, Spain
- Department of Radiology, Diagnostic and Interventional Neuroradiology, University Hospital Tübingen, Tübingen, Germany
- Neuroscience Research Center, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, UK
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Græcia" of Catanzaro, Catanzaro, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Wales, Cardiff, UK
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Neurophysiopatology and Movement Disorders Clinic, University of Messina, Messina, Italy
- Regional Epilepsy Center, University of Messina, Messina, Italy
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- Department of Neurology, Alfred Health, Melbourne, VIC, Australia
- 'Mario Serio' Department of Clinical and Experimental Medical Sciences, University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and network in Oncology of the Tuscany Region, Florence, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
- IRCCS Istituto 'Giannina Gaslini', Genova, Italy
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
- Department of Medicine (Division of Neurology), Queen's University Kingston, ON, Canada
- Chalfont Centre for Epilepsy, Bucks, UK
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
- IRCCS Istituto 'Giannina Gaslini', Genova, Italy
| | - Peter N Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Neurology Unit, OCB Hospital, Azienda Ospedaliera-Universitaria Modena, Modena, Italy
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn, Germany
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
- Department of Medicine (Division of Neurology), Queen's University Kingston, ON, Canada
| | - Clarissa L Yasuda
- Department of Neurology, University of Campinas - UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Carrie R McDonald
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, 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, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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14
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Huang X, Du Y, Guo D, Xie F, Zhou C. Structural-functional coupling abnormalities in temporal lobe epilepsy. Front Neurosci 2023; 17:1272514. [PMID: 37928725 PMCID: PMC10620528 DOI: 10.3389/fnins.2023.1272514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/04/2023] [Indexed: 11/07/2023] Open
Abstract
Background Nowadays, researchers are using advanced multimodal neuroimaging techniques to construct the brain network connectome to elucidate the complex relationship among the networks of brain functions and structure. The objective of this study was to evaluate the coupling of structural connectivity (SC) and functional connectivity (FC) in the entire brain of healthy controls (HCs), and to investigate modifications in SC-FC coupling in individuals suffering from temporal lobe epilepsy (TLE). Methods We evaluated 65 patients with TLE matched for age and gender with 48 healthy controls. The SC-FC coupling between regions was determined, based on which whole-brain nodes were clustered. Differences in the coupling among the three groups of nodes were compared. To further validate the results obtained, the within-cluster coupling indices of the three groups were compared to determine the inter-group differences. Results Nodes were divided into five clusters. Cluster 1 was primarily located in the limbic system (n = 9/27), whereas cluster 5 was mainly within the visual network (n = 12/29). By comparing average cluster SC-FC coupling in each cluster of the three groups, we identified marked discrepancies within the three cohorts in Cluster 3 (p = 0.001), Cluster 4 (p < 0.001), and Cluster 5 (p < 0.001). Post-hoc analysis revealed that the SC-FC coupling strengths in LTLE and RTLE were significantly lower than that in HCs in Cluster 3 (PL = 0.001/PR = 0.003), Cluster 4 (PL = 0.001/PR < 0.001), and Cluster 5 (PL < 0.001/PR < 0.001). We also observed that the within-cluster SC-FC coupling in cluster 5 of left- and right TLE was significantly lower than in HCs (PL = 0.0001, PR = 0.0005). Conclusion The SC and FC are inconsistently coupled across the brain with spatial heterogeneity. In the fifth cluster with the highest degree of coupling in HCs, the average SC-FC coupling index of individuals with TLE was notably less than that of HCs, manifesting that brain regions with high coupling may be more delicate and prone to pathological disruption.
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Affiliation(s)
- Xiaoting Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Yangsa Du
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Fangfang Xie
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Chunyao Zhou
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
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15
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Li W, Jiang Y, Li X, Huang H, Lei D, Li J, Zhang H, Yao D, Luo C, Gong Q, Zhou D, An D. More extensive structural damage in temporal lobe epilepsy with hippocampal sclerosis type 1. Seizure 2023; 111:130-137. [PMID: 37633152 DOI: 10.1016/j.seizure.2023.08.003] [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: 03/23/2023] [Revised: 08/04/2023] [Accepted: 08/06/2023] [Indexed: 08/28/2023] Open
Abstract
OBJECTIVE To explore clinical and structural differences between mesial temporal lobe epilepsy (mTLE) patients with different hippocampal sclerosis (HS) subtypes. METHODS High-resolution T1-weighted MRI and diffusion tensor imaging data were obtained in 41 refractory mTLE patients and 52 age- and sex-matched healthy controls. Postoperative histopathological examination confirmed HS type 1 in 30 patients and HS type 2 in eleven patients. Clinical features, postoperative seizure outcomes, hippocampal subfields volumes, fractional anisotropy (FA) values of white matter regions and graph theory parameters were explored and compared between the HS type 1 and HS type 2 groups. RESULTS No significant differences in clinical features and postsurgical seizure outcomes were found between the HS type 1 and type 2 groups. However, the HS type 1 group showed extra atrophy in ipsilateral parasubiculum than healthy controls and more severe atrophy in contralateral hippocampal fissure than the HS type 2 group. More extensive FA decrease were also observed in the HS type 1 group, involving ipsilateral optic radiation, superior fronto-occipital fasciculus, contralateral uncinate fasciculus, tapetum, bilateral hippocampal cingulum, corona radiata, etc. Furthermore, in spite of similar impairments in characteristic path length, global efficiency and local efficiency in two HS groups, the HS type 1 group showed additional decrease of clustering coefficient than healthy controls. CONCLUSIONS HS type 1 and 2 groups had similar clinical characteristics and postoperative seizure outcomes. More widespread neuronal cell loss in the HS type 1 group contributed to more extensive structural damage and connectivity abnormality. These results shed new light on the imaging correlates of different HS pathology.
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Affiliation(s)
- Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; National Clinical Research Center for Geriatrics, Department of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiuli Li
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Du Lei
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jinmei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Heng Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, 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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16
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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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17
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Legouhy A, Allen LA, Vos SB, Oliveira JFA, Kassinopoulos M, Winston GP, Duncan JS, Ogren JA, Scott C, Kumar R, Lhatoo SD, Thom M, Lemieux L, Harper RM, Zhang H, Diehl B. Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk. Epilepsy Res 2023; 192:107139. [PMID: 37068421 DOI: 10.1016/j.eplepsyres.2023.107139] [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: 10/24/2022] [Revised: 02/24/2023] [Accepted: 04/06/2023] [Indexed: 04/19/2023]
Abstract
Although the mechanisms of sudden unexpected death in epilepsy (SUDEP) are not yet well understood, generalised- or focal-to-bilateral tonic-clonic seizures (TCS) are a major risk factor. Previous studies highlighted alterations in structures linked to cardio-respiratory regulation; one structure, the amygdala, was enlarged in people at high risk of SUDEP and those who subsequently died. We investigated volume changes and the microstructure of the amygdala in people with epilepsy at varied risk for SUDEP since that structure can play a key role in triggering apnea and mediating blood pressure. The study included 53 healthy subjects and 143 patients with epilepsy, the latter separated into two groups according to whether TCS occur in years before scan. We used amygdala volumetry, derived from structural MRI, and tissue microstructure, derived from diffusion MRI, to identify differences between the groups. The diffusion metrics were obtained by fitting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. The analyses were performed at the whole amygdala level and at the scale of amygdaloid nuclei. Patients with epilepsy showed larger amygdala volumes and lower neurite density indices (NDI) than healthy subjects; the left amygdala volumes were especially enhanced. Microstructural changes, reflected by NDI differences, were more prominent on the left side and localized in the lateral, basal, central, accessory basal and paralaminar amygdala nuclei; basolateral NDI lowering appeared bilaterally. No significant microstructural differences appeared between epilepsy patients with and without current TCS. The central amygdala nuclei, with prominent interactions from surrounding nuclei of that structure, project to cardiovascular regions and respiratory phase switching areas of the parabrachial pons, as well as to the periaqueductal gray. Consequently, they have the potential to modify blood pressure and heart rate, and induce sustained apnea or apneusis. The findings here suggest that lowered NDI, indicative of reduced dendritic density, could reflect an impaired structural organization influencing descending inputs that modulate vital respiratory timing and drive sites and areas critical for blood pressure control.
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Affiliation(s)
- Antoine Legouhy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Luke A Allen
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK; The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, UCL, London, UK; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Joana F A Oliveira
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Michalis Kassinopoulos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK; Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Jennifer A Ogren
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; Brain Research Institute, UCLA, Los Angeles, CA, USA
| | - Catherine Scott
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Rajesh Kumar
- Brain Research Institute, UCLA, Los Angeles, CA, USA; Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Sciences Center at Houston, Houston, TX, USA
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Ronald M Harper
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA; Brain Research Institute, UCLA, Los Angeles, CA, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK; The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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18
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Legouhy A, Allen LA, Vos SB, Oliveira JFA, Kassinopoulos M, Winston GP, Duncan JS, Ogren JA, Scott C, Kumar R, Lhatoo SD, Thom M, Lemieux L, Harper RM, Zhang H, Diehl B. Volumetric and microstructural abnormalities of the amygdala in focal epilepsy with varied levels of SUDEP risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.13.23287045. [PMID: 36993394 PMCID: PMC10055456 DOI: 10.1101/2023.03.13.23287045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Although the mechanisms of sudden unexpected death in epilepsy (SUDEP) are not yet well understood, generalised- or focal-to-bilateral tonic-clonic seizures (TCS) are a major risk factor. Previous studies highlighted alterations in structures linked to cardio-respiratory regulation; one structure, the amygdala, was enlarged in people at high risk of SUDEP and those who subsequently died. We investigated volume changes and the microstructure of the amygdala in people with epilepsy at varied risk for SUDEP since that structure can play a key role in triggering apnea and mediating blood pressure. The study included 53 healthy subjects and 143 patients with epilepsy, the latter separated into two groups according to whether TCS occur in years before scan. We used amygdala volumetry, derived from structural MRI, and tissue microstructure, derived from diffusion MRI, to identify differences between the groups. The diffusion metrics were obtained by fitting diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. The analyses were performed at the whole amygdala level and at the scale of amygdaloid nuclei. Patients with epilepsy showed larger amygdala volumes and lower neurite density indices (NDI) than healthy subjects; the left amygdala volumes were especially enhanced. Microstructural changes, reflected by NDI differences, were more prominent on the left side and localized in the lateral, basal, central, accessory basal and paralaminar amygdala nuclei; basolateral NDI lowering appeared bilaterally. No significant microstructural differences appeared between epilepsy patients with and without current TCS. The central amygdala nuclei, with prominent interactions from surrounding nuclei of that structure, project to cardiovascular regions and respiratory phase switching areas of the parabrachial pons, as well as to the periaqueductal gray. Consequently, they have the potential to modify blood pressure and heart rate, and induce sustained apnea or apneusis. The findings here suggest that lowered NDI, indicative of reduced dendritic density, could reflect an impaired structural organization influencing descending inputs that modulate vital respiratory timing and drive sites and areas critical for blood pressure control.
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Affiliation(s)
- Antoine Legouhy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Luke A Allen
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, UCL, London, UK
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Joana F A Oliveira
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Michalis Kassinopoulos
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
- Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Jennifer A Ogren
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
| | - Catherine Scott
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Rajesh Kumar
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Department of Anesthesiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Sciences Center at Houston, Houston, TX, USA
| | - Maria Thom
- Department of Neuropathology, Institute of Neurology, University College London, London, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
| | - Ronald M Harper
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Brain Research Institute, UCLA, Los Angeles, CA, USA
- Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, Buckinghamshire, UK
- The Center for SUDEP Research, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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19
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Zhao L, Mühleisen TW, Pelzer DI, Burger B, Beins EC, Forstner AJ, Herms S, Hoffmann P, Amunts K, Palomero-Gallagher N, Cichon S. Relationships between neurotransmitter receptor densities and expression levels of their corresponding genes in the human hippocampus. Neuroimage 2023; 273:120095. [PMID: 37030412 PMCID: PMC10167541 DOI: 10.1016/j.neuroimage.2023.120095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/02/2023] [Accepted: 04/05/2023] [Indexed: 04/08/2023] Open
Abstract
Neurotransmitter receptors are key molecules in signal transmission, their alterations are associated with brain dysfunction. Relationships between receptors and their corresponding genes are poorly understood, especially in humans. We combined in vitro receptor autoradiography and RNA sequencing to quantify, in the same tissue samples (7 subjects), the densities of 14 receptors and expression levels of their corresponding 43 genes in the Cornu Ammonis (CA) and dentate gyrus (DG) of human hippocampus. Significant differences in receptor densities between both structures were found only for metabotropic receptors, whereas significant differences in RNA expression levels mostly pertained ionotropic receptors. Receptor fingerprints of CA and DG differ in shapes but have similar sizes; the opposite holds true for their "RNA fingerprints", which represent the expression levels of multiple genes in a single area. In addition, the correlation coefficients between receptor densities and corresponding gene expression levels vary widely and the mean correlation strength was weak-to-moderate. Our results suggest that receptor densities are not only controlled by corresponding RNA expression levels, but also by multiple regionally specific post-translational factors.
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20
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Shahabi H, Nair DR, Leahy RM. Multilayer brain networks can identify the epileptogenic zone and seizure dynamics. eLife 2023; 12:e68531. [PMID: 36929752 PMCID: PMC10065796 DOI: 10.7554/elife.68531] [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: 03/18/2021] [Accepted: 03/16/2023] [Indexed: 03/18/2023] Open
Abstract
Seizure generation, propagation, and termination occur through spatiotemporal brain networks. In this paper, we demonstrate the significance of large-scale brain interactions in high-frequency (80-200Hz) for the identification of the epileptogenic zone (EZ) and seizure evolution. To incorporate the continuity of neural dynamics, here we have modeled brain connectivity constructed from stereoelectroencephalography (SEEG) data during seizures using multilayer networks. After introducing a new measure of brain connectivity for temporal networks, named multilayer eigenvector centrality (mlEVC), we applied a consensus hierarchical clustering on the developed model to identify the EZ as a cluster of nodes with distinctive brain connectivity in the ictal period. Our algorithm could successfully predict electrodes inside the resected volume as EZ for 88% of participants, who all were seizure-free for at least 12 months after surgery. Our findings illustrated significant and unique desynchronization between EZ and the rest of the brain in the early to mid-seizure. We showed that aging and the duration of epilepsy intensify this desynchronization, which can be the outcome of abnormal neuroplasticity. Additionally, we illustrated that seizures evolve with various network topologies, confirming the existence of different epileptogenic networks in each patient. Our findings suggest not only the importance of early intervention in epilepsy but possible factors that correlate with disease severity. Moreover, by analyzing the propagation patterns of different seizures, we demonstrate the necessity of collecting sufficient data for identifying epileptogenic networks.
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Affiliation(s)
- Hossein Shahabi
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
| | - Dileep R Nair
- Epilepsy Center, Cleveland Clinic Neurological InstituteClevelandUnited States
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern CaliforniaLos AngelesUnited States
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21
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Proteomic and Bioinformatic Tools to Identify Potential Hub Proteins in the Audiogenic Seizure-Prone Hamster GASH/Sal. Diagnostics (Basel) 2023; 13:diagnostics13061048. [PMID: 36980356 PMCID: PMC10047193 DOI: 10.3390/diagnostics13061048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023] Open
Abstract
The GASH/Sal (Genetic Audiogenic Seizure Hamster, Salamanca) is a model of audiogenic seizures with the epileptogenic focus localized in the inferior colliculus (IC). The sound-induced seizures exhibit a short latency (7–9 s), which implies innate protein disturbances in the IC as a basis for seizure susceptibility and generation. Here, we aim to study the protein profile in the GASH/Sal IC in comparison to controls. Protein samples from the IC were processed for enzymatic digestion and then analyzed by mass spectrometry in Data-Independent Acquisition mode. After identifying the proteins using the UniProt database, we selected those with differential expression and performed ontological analyses, as well as gene-protein interaction studies using bioinformatics tools. We identified 5254 proteins; among them, 184 were differentially expressed proteins (DEPs), with 126 upregulated and 58 downregulated proteins, and 10 of the DEPs directly related to epilepsy. Moreover, 12 and 7 proteins were uniquely found in the GASH/Sal or the control. The results indicated a protein profile alteration in the epileptogenic nucleus that might underlie the inborn occurring audiogenic seizures in the GASH/Sal model. In summary, this study supports the use of bioinformatics methods in proteomics to delve into the relationship between molecular-level protein mechanisms and the pathobiology of rodent models of audiogenic seizures.
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Zhang Y, Liu Z, Dou W, Wei J, Lv Y, Hou B, You H, Feng F. Study of the microstructure of brain white matter in medial temporal lobe epilepsy based on diffusion tensor imaging. Brain Behav 2023; 13:e2919. [PMID: 36880299 PMCID: PMC10097073 DOI: 10.1002/brb3.2919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 01/02/2023] [Accepted: 01/07/2023] [Indexed: 03/08/2023] Open
Abstract
OBJECTIVES To compare the white matter (WM) asymmetry in left and right medial temporal lobe epilepsy (mTLE) with and without hippocampal sclerosis (HS+, HS-) and assess the correlation of preoperative asymmetry and the dynamics of WM fibers with surgical outcomes. MATERIALS AND METHODS Preoperative MRI scans were collected from 58 mTLE patients (40 HS+, 18 HS-); 15 (11 HS+, 4 HS-) then underwent postoperative MRI scans. DTI parameters, including the fractional anisotropy (FA), mean diffusion coefficient (MD), axial diffusion coefficient (AD), and radial diffusion coefficient (RD), were extracted from 20 paired WM tracts by PANDA based on the JHU WM tractography atlas. The bilateral cerebral parameters and the pre- to postoperative changes in the DTI parameters of specific fiber tracts were compared. The asymmetry indexes (AIs) of paired fibers were also analyzed. RESULTS There were fewer asymmetrical WM fibers in HS- patients than in HS+ patients. The pattern of WM asymmetry differed between left and right mTLE patients. Differences in the FA AI of the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus (ILF) were found in left HS+ patients with different surgical outcomes. All mTLE patients exhibited decreases in FA and increases in MD and RD in specific ipsilateral WM fibers. In International League Against Epilepsy (ILAE) grade 1 patients, the MD values in the ipsilateral CGH increased over time, whereas the RD values in the ipsilateral ILF and the AD values in the ipsilateral ILF and UNC decreased. In ILAE grade 2-5 patients, the FA values in the ipsilateral cingulate gyrus part of the cingulum (CGC) increased over time. CONCLUSION The WM tract asymmetry was more extensive in HS+ patients than in HS- patients. The preoperative WM fiber AIs in left HS+ patients may be useful for surgical prognosis. Additionally, pre- to postoperative changes in WM fibers may help predict surgical outcomes.
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Affiliation(s)
- Yiwei Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China.,Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhaoxi Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wanchen Dou
- Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Juan Wei
- GE Healthcare, MR Research China, Beijing, China
| | - Yuelei Lv
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Radiology, Beijing CHAO-YANG Hospital, Capital Medical University, Beijing, China
| | - Bo Hou
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui You
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Feng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,State Key Laboratory of Difficult, Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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23
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Duma GM, Danieli A, Mento G, Vitale V, Opipari RS, Jirsa V, Bonanni P, Sorrentino P. Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting-state hdEEG study. Epilepsia 2023; 64:1278-1288. [PMID: 36799098 DOI: 10.1111/epi.17551] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 02/18/2023]
Abstract
OBJECTIVE Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large-scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls. METHOD We defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients. RESULTS We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey-Osterrieth Complex Figure Test). SIGNIFICANCE Our results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.
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Affiliation(s)
- Gian Marco Duma
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Treviso, Italy
| | - Alberto Danieli
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Treviso, Italy
| | - Giovanni Mento
- Department of General Psychology, University of Padova, Padova, Italy.,Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Valerio Vitale
- Department of Neuroscience, Neuroradiology Unit, San Bortolo Hospital, Vicenza, Italy
| | | | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Paolo Bonanni
- Epilepsy Unit, IRCCS E. Medea Scientific Institute, Treviso, Italy
| | - Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
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24
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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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25
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Passaro EA. Neuroimaging in Adults and Children With Epilepsy. Continuum (Minneap Minn) 2023; 29:104-155. [PMID: 36795875 DOI: 10.1212/con.0000000000001242] [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: 02/18/2023]
Abstract
OBJECTIVE This article discusses the fundamental importance of optimal epilepsy imaging using the International League Against Epilepsy-endorsed Harmonized Neuroimaging of Epilepsy Structural Sequences (HARNESS) protocol and the use of multimodality imaging in the evaluation of patients with drug-resistant epilepsy. It outlines a methodical approach to evaluating these images, particularly in the context of clinical information. LATEST DEVELOPMENTS Epilepsy imaging is rapidly evolving, and a high-resolution epilepsy protocol MRI is essential in evaluating newly diagnosed, chronic, and drug-resistant epilepsy. The article reviews the spectrum of relevant MRI findings in epilepsy and their clinical significance. Integrating multimodality imaging is a powerful tool in the presurgical evaluation of epilepsy, particularly in "MRI-negative" cases. For example, correlation of clinical phenomenology, video-EEG with positron emission tomography (PET), ictal subtraction single-photon emission computerized tomography (SPECT), magnetoencephalography (MEG), functional MRI, and advanced neuroimaging such as MRI texture analysis and voxel-based morphometry enhances the identification of subtle cortical lesions such as focal cortical dysplasias to optimize epilepsy localization and selection of optimal surgical candidates. ESSENTIAL POINTS The neurologist has a unique role in understanding the clinical history and seizure phenomenology, which are the cornerstones of neuroanatomic localization. When integrated with advanced neuroimaging, the clinical context has a profound impact on identifying subtle MRI lesions or finding the "epileptogenic" lesion when multiple lesions are present. Patients with an identified lesion on MRI have a 2.5-fold improved chance of achieving seizure freedom with epilepsy surgery compared with those without a lesion. This clinical-radiographic integration is essential to accurate classification, localization, determination of long-term prognosis for seizure control, and identification of candidates for epilepsy surgery to reduce seizure burden or attain seizure freedom.
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26
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Cai L, He Q, Luo H, Gui X, Wei L, Lu Y, Liu J, Sun A. Is depression in patients with temporal lobe epilepsy related to hippocampal sclerosis? A meta-analysis. Clin Neurol Neurosurg 2023; 225:107602. [PMID: 36689793 DOI: 10.1016/j.clineuro.2023.107602] [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: 10/21/2022] [Revised: 12/14/2022] [Accepted: 01/16/2023] [Indexed: 01/19/2023]
Abstract
OBJECTIVE To systematically evaluate the association between hippocampal sclerosis (HS) and depression in patients with temporal lobe epilepsy (TLE) through a meta-analysis. METHODS Chinese and English databases, such as the China National Knowledge Infrastructure (CNKI), Chinese Scientific Journals (VIP), WanFang, the Chinese Biomedical Literature Service System (SinoMed), PubMed and the Web of Science, were searched. RESULTS Two evaluators independently screened the literature, extracted data and evaluated the risk of bias in the included studies in accordance with the inclusion and exclusion criteria. RevMan 5.1 was used to analyze the data. A total of 786 patients with epilepsy were included in the study, including 82 depressive patients with HS and 64 depressive patients without HS. The results showed that the TLE patients with HS were more likely to develop depression than those without HS (odds ratio (OR)= 2.14, 95% confidence interval (CI) [1.45, 3.16], Z = 3.85, p = 0.0001). CONCLUSION HS can be considered a high-risk factor for depression in patients with TLE, and the correlation is significant. However, the sample size included in the study was small; additional high-quality studies are needed in the future.
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Affiliation(s)
- Lun Cai
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China
| | - Qianchao He
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China
| | - Huazheng Luo
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China
| | - Xiongbin Gui
- Department of Surgery, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China.
| | - Liping Wei
- Department of Surgery, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China
| | - Yongjing Lu
- Department of Nuclear Medicine, Guangxi Minzu Hospital, Nanning 530001, PR China
| | - Jie Liu
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China
| | - Anna Sun
- Department of Neurology, The First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi University of Chinese Medicine, Nanning 530023, PR China
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Polyanskaya MV, Demushkina AA, Vasilyev IG, Kostylev FA, Kurbanova FA, Zavadenko NN, Alikhanov AA. [Neuroradiological and pathohistological markers of the main epileptogenic substrates in children.Cortical malformations]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:7-13. [PMID: 37084359 DOI: 10.17116/jnevro20231230417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023]
Abstract
High-resolution MRI is an important tool in the diagnosis of structural epilepsy in determining the seizure initiation zones, identification of the mechanisms of epileptogenesis in predicting outcomes and preventing postoperative complications in patients. In this article we demonstrate the neuroradiological and pathohistological characteristics of the main epileptogenic substrates in children using modern classification. The first part of the article is devoted to cortical malformations as the most common epileptogenic cerebral disorders.
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Affiliation(s)
- M V Polyanskaya
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - A A Demushkina
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - I G Vasilyev
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - F A Kostylev
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - F A Kurbanova
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - N N Zavadenko
- Pirogov Russian National Research Medical University, Moscow, Russia
| | - A A Alikhanov
- Pirogov Russian National Research Medical University, Moscow, Russia
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28
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Koo GE, Jeong HT, Youn YC, Han SH. Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy? J Epilepsy Res 2022; 12:62-67. [PMID: 36685746 PMCID: PMC9830024 DOI: 10.14581/jer.22011] [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: 11/19/2022] [Revised: 12/06/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Purpose There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not. Methods We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain® (iMediSync Inc., Suwon, Korea; https://isyncbrain.com/). In the source-level analysis, the current distribution across the brain was assessed using the standardized low-resolution brain electromagnetic tomography technique, to compare relative power values in 68 regions of interest and connectivity (the imaginary part of coherency) between regions of interest. Results In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls. Conclusions Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.
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Affiliation(s)
- Ga Eun Koo
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Ho Tae Jeong
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Young Chul Youn
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
| | - Su-Hyun Han
- Department of Neurology, Chung-Ang University College of Medicine, Seoul, Korea
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29
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Duma GM, Danieli A, Mattar MG, Baggio M, Vettorel A, Bonanni P, Mento G. Resting state network dynamic reconfiguration and neuropsychological functioning in temporal lobe epilepsy: An HD-EEG investigation. Cortex 2022; 157:1-13. [PMID: 36257103 DOI: 10.1016/j.cortex.2022.08.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/07/2022] [Accepted: 08/24/2022] [Indexed: 12/15/2022]
Abstract
Temporal lobe epilepsy (TLE) is nowadays considered a network disorder impacting several cognitive domains. In this work we investigated dynamic network reconfiguration differences in patients with unilateral TLE compared to a healthy control group, focusing on two connectivity indices: flexibility and integration. We apply these indices for the first time to high-density EEG source-based functional connectivity. We observed that patients with TLE exhibited significantly lower flexibility than healthy controls in the Control, Default Mode and Attentive Dorsal networks, expressed in the delta, theta and alpha bands. In addition, patients with TLE displayed greater integration values across the majority of the resting state networks, especially in the delta, theta and gamma bands. Relevantly, a higher integration index in the Control, Attentive Dorsal and Visual networks in the delta band was correlated with lower performance in visual attention and executive functions. Moreover, a greater integration index in the gamma band of the Control, Somatomotor and Temporoparietal networks was related to lower long-term memory performance. These results suggest that patients with TLE display dysregulated network reconfiguration, with lower flexibility in the brain areas related to cognitive control and attention, together with excessive inter-network communication (integration index). Finally, the correlation between network integration and the reduced cognitive performance suggests a potential mechanism underlying specific alterations in neuropsychological profile of patients with TLE.
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Affiliation(s)
- Gian Marco Duma
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France; IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy.
| | - Alberto Danieli
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Marcelo G Mattar
- Department of Cognitive Science, University of California, San Diego, San Diego, CA, USA
| | - Martina Baggio
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Airis Vettorel
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Paolo Bonanni
- IRCCS E. Medea Scientific Institute, Epilepsy Unit, Conegliano, Treviso, Italy
| | - Giovanni Mento
- Department of General Psychology, University of Padova, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
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30
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Bayrak Ş, de Wael RV, Schaare HL, Hettwer MD, Caldairou B, Bernasconi A, Bernasconi N, Bernhardt BC, Valk SL. Heritability of hippocampal functional and microstructural organisation. Neuroimage 2022; 264:119656. [PMID: 36183945 DOI: 10.1016/j.neuroimage.2022.119656] [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: 05/19/2022] [Revised: 09/18/2022] [Accepted: 09/28/2022] [Indexed: 01/07/2023] Open
Abstract
The hippocampus is a uniquely infolded allocortical structure in the medial temporal lobe that consists of the microstructurally and functionally distinct subregions: subiculum, cornu ammonis, and dentate gyrus. The hippocampus is a remarkably plastic region that is implicated in learning and memory. At the same time it has been shown that hippocampal subregion volumes are heritable, and that genetic expression varies along a posterior to anterior axis. Here, we studied how a heritable, stable, hippocampal organisation may support its flexible function in healthy adults. Leveraging the twin set-up of the Human Connectome Project with multimodal neuroimaging, we observed that the functional connectivity between hippocampus and cortex was heritable and that microstructure of the hippocampus genetically correlated with cortical microstructure. Moreover, both functional and microstructural organisation could be consistently captured by anterior-to-posterior and medial-to-lateral axes across individuals. However, heritability of functional, relative to microstructural, organisation was found reduced, suggesting individual variation in functional organisation may be explained by experience-driven factors. Last, we demonstrate that structure and function couple along an inherited macroscale organisation, suggesting an interplay of stability and plasticity within the hippocampus. Our study provides new insights on the heritability of the hippocampal of the structure and function within the hippocampal organisation.
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Affiliation(s)
- Şeyma Bayrak
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Meike D Hettwer
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Max Planck School of Cognition, Max Planck Institute of Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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31
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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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32
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Říha P, Doležalová I, Mareček R, Lamoš M, Bartoňová M, Kojan M, Mikl M, Gajdoš M, Vojtíšek L, Bartoň M, Strýček O, Pail M, Brázdil M, Rektor I. Multimodal combination of neuroimaging methods for localizing the epileptogenic zone in MR-negative epilepsy. Sci Rep 2022; 12:15158. [PMID: 36071087 PMCID: PMC9452535 DOI: 10.1038/s41598-022-19121-8] [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: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
The objective was to determine the optimal combination of multimodal imaging methods (IMs) for localizing the epileptogenic zone (EZ) in patients with MR-negative drug-resistant epilepsy. Data from 25 patients with MR-negative focal epilepsy (age 30 ± 10 years, 16M/9F) who underwent surgical resection of the EZ and from 110 healthy controls (age 31 ± 9 years; 56M/54F) were used to evaluate IMs based on 3T MRI, FDG-PET, HD-EEG, and SPECT. Patients with successful outcomes and/or positive histological findings were evaluated. From 38 IMs calculated per patient, 13 methods were selected by evaluating the mutual similarity of the methods and the accuracy of the EZ localization. The best results in postsurgical patients for EZ localization were found for ictal/ interictal SPECT (SISCOM), FDG-PET, arterial spin labeling (ASL), functional regional homogeneity (ReHo), gray matter volume (GMV), cortical thickness, HD electrical source imaging (ESI-HD), amplitude of low-frequency fluctuation (ALFF), diffusion tensor imaging, and kurtosis imaging. Combining IMs provides the method with the most accurate EZ identification in MR-negative epilepsy. The PET, SISCOM, and selected MRI-post-processing techniques are useful for EZ localization for surgical tailoring.
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Affiliation(s)
- Pavel Říha
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Irena Doležalová
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Radek Mareček
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Lamoš
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Michaela Bartoňová
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Kojan
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Michal Mikl
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Gajdoš
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Lubomír Vojtíšek
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Marek Bartoň
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Ondřej Strýček
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Martin Pail
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Milan Brázdil
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Ivan Rektor
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic. .,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
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33
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Garcia-Ramos C, Nair V, Maganti R, Mathis J, Conant LL, Prabhakaran V, Binder JR, Meyerand B, Hermann B, Struck AF. Network phenotypes and their clinical significance in temporal lobe epilepsy using machine learning applications to morphological and functional graph theory metrics. Sci Rep 2022; 12:14407. [PMID: 36002603 PMCID: PMC9402557 DOI: 10.1038/s41598-022-18495-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 08/12/2022] [Indexed: 02/08/2023] Open
Abstract
Machine learning analyses were performed on graph theory (GT) metrics extracted from brain functional and morphological data from temporal lobe epilepsy (TLE) patients in order to identify intrinsic network phenotypes and characterize their clinical significance. Participants were 97 TLE and 36 healthy controls from the Epilepsy Connectome Project. Each imaging modality (i.e., Resting-state functional Magnetic Resonance Imaging (RS-fMRI), and structural MRI) rendered 2 clusters: one comparable to controls and one deviating from controls. Participants were minimally overlapping across the identified clusters, suggesting that an abnormal functional GT phenotype did not necessarily mean an abnormal morphological GT phenotype for the same subject. Morphological clusters were associated with a significant difference in the estimated lifetime number of generalized tonic-clonic seizures and functional cluster membership was associated with age. Furthermore, controls exhibited significant correlations between functional GT metrics and cognition, while for TLE participants morphological GT metrics were linked to cognition, suggesting a dissociation between higher cognitive abilities and GT-derived network measures. Overall, these findings demonstrate the existence of clinically meaningful minimally overlapping phenotypes of morphological and functional GT networks. Functional network properties may underlie variance in cognition in healthy brains, but in the pathological state of epilepsy the cognitive limits might be primarily related to structural cerebral network properties.
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Affiliation(s)
- Camille Garcia-Ramos
- grid.14003.360000 0001 2167 3675Department of Medical Physics, University of Wisconsin-Madison, Madison, USA ,grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Veena Nair
- grid.14003.360000 0001 2167 3675Department of Radiology, University of Wisconsin-Madison, Madison, USA
| | - Rama Maganti
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Jedidiah Mathis
- grid.30760.320000 0001 2111 8460Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
| | - Lisa L. Conant
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Vivek Prabhakaran
- grid.14003.360000 0001 2167 3675Department of Radiology, University of Wisconsin-Madison, Madison, USA
| | - Jeffrey R. Binder
- grid.30760.320000 0001 2111 8460Department of Neurology, Medical College of Wisconsin, Milwaukee, USA
| | - Beth Meyerand
- grid.14003.360000 0001 2167 3675Department of Medical Physics, University of Wisconsin-Madison, Madison, USA
| | - Bruce Hermann
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA
| | - Aaron F. Struck
- grid.14003.360000 0001 2167 3675Department of Neurology, University of Wisconsin-Madison, Madison, USA ,grid.417123.20000 0004 0420 6882William S Middleton VA Hospital, Madison, WI USA
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34
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Charlebois CM, Anderson DN, Johnson KA, Philip BJ, Davis TS, Newman BJ, Peters AY, Arain AM, Dorval AD, Rolston JD, Butson CR. Patient-specific structural connectivity informs outcomes of responsive neurostimulation for temporal lobe epilepsy. Epilepsia 2022; 63:2037-2055. [PMID: 35560062 PMCID: PMC11265293 DOI: 10.1111/epi.17298] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/09/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Responsive neurostimulation is an effective therapy for patients with refractory mesial temporal lobe epilepsy. However, clinical outcomes are variable, few patients become seizure-free, and the optimal stimulation location is currently undefined. The aim of this study was to quantify responsive neurostimulation in the mesial temporal lobe, identify stimulation-dependent networks associated with seizure reduction, and determine if stimulation location or stimulation-dependent networks inform outcomes. METHODS We modeled patient-specific volumes of tissue activated and created probabilistic stimulation maps of local regions of stimulation across a retrospective cohort of 22 patients with mesial temporal lobe epilepsy. We then mapped the network stimulation effects by seeding tractography from the volume of tissue activated with both patient-specific and normative diffusion-weighted imaging. We identified networks associated with seizure reduction across patients using the patient-specific tractography maps and then predicted seizure reduction across the cohort. RESULTS Patient-specific stimulation-dependent connectivity was correlated with responsive neurostimulation effectiveness after cross-validation (p = .03); however, normative connectivity derived from healthy subjects was not (p = .44). Increased connectivity from the volume of tissue activated to the medial prefrontal cortex, cingulate cortex, and precuneus was associated with greater seizure reduction. SIGNIFICANCE Overall, our results suggest that the therapeutic effect of responsive neurostimulation may be mediated by specific networks connected to the volume of tissue activated. In addition, patient-specific tractography was required to identify structural networks correlated with outcomes. It is therefore likely that altered connectivity in patients with epilepsy may be associated with the therapeutic effect and that utilizing patient-specific imaging could be important for future studies. The structural networks identified here may be utilized to target stimulation in the mesial temporal lobe and to improve seizure reduction for patients treated with responsive neurostimulation.
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Affiliation(s)
- Chantel M. Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
- Department of Pharmacology & Toxicology, University of Utah, Salt Lake City, UT, USA
| | - Kara A. Johnson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- Department of Neurology, University of Florida, Gainesville, FL, USA
| | - Brian J. Philip
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Tyler. S. Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Blake J. Newman
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Angela Y. Peters
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Amir M. Arain
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Alan D. Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - John D. Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Christopher R. Butson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- Department of Neurology, University of Florida, Gainesville, FL, USA
- Department of Neurosurgery, University of Florida, Gainesville, FL, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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35
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Larivière S, Royer J, Rodríguez-Cruces R, Paquola C, Caligiuri ME, Gambardella A, Concha L, Keller SS, Cendes F, Yasuda CL, Bonilha L, Gleichgerrcht E, Focke NK, Domin M, von Podewills F, Langner S, Rummel C, Wiest R, Martin P, Kotikalapudi R, O'Brien TJ, Sinclair B, Vivash L, Desmond PM, Lui E, 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, Winston GP, Griffin A, Singh A, Tiwari VK, Kreilkamp BAK, Lenge M, Guerrini R, Hamandi K, Foley S, Rüber T, Weber B, Depondt C, Absil J, Carr SJA, Abela E, Richardson MP, Devinsky O, Severino M, Striano P, Tortora D, Kaestner E, Hatton SN, Vos SB, Caciagli L, Duncan JS, Whelan CD, Thompson PM, Sisodiya SM, Bernasconi A, Labate A, McDonald CR, Bernasconi N, Bernhardt BC. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression. Nat Commun 2022; 13:4320. [PMID: 35896547 PMCID: PMC9329287 DOI: 10.1038/s41467-022-31730-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 06/30/2022] [Indexed: 12/12/2022] Open
Abstract
Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
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Affiliation(s)
- Sara Larivière
- 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
| | - Raúl Rodríguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Casey Paquola
- Institute for Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | | | - 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, UK
- Walton Centre NHS Foundation Trust, Liverpool, UK
| | - 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
| | | | | | - Niels K Focke
- 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 Podewills
- Department of Neurology, University Medicine Greifswald, 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 and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Benjamin Sinclair
- Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Alfred Hospital, Monash University, Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Patricia M Desmond
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - Elaine Lui
- Departments of Medicine and Radiology, The Royal Melbourne Hospital, The University of Melbourne, Parkville, VIC, Australia
| | - 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, Yale University School of Medicine, New Haven, CT, 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
| | - Gavin P Winston
- Division of Neurology, Department of Medicine, Queen's University, Kingston, ON, Canada
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Aoife Griffin
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University Belfast, Belfast, UK
| | - Aditi Singh
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University Belfast, Belfast, UK
| | - Vijay K Tiwari
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University Belfast, Belfast, UK
| | | | - Matteo Lenge
- Child Neurology Unit and Laboratories, Neuroscience Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | - Renzo Guerrini
- Child Neurology Unit and Laboratories, Neuroscience Department, Children's Hospital A. Meyer-University of Florence, Florence, Italy
| | - Khalid Hamandi
- The Welsh Epilepsy Unit, Department of Neurology, University Hospital of Whales, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), College of Biomedical Sciences, Cardiff University, Cardiff, UK
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), College of Biomedical Sciences, Cardiff University, Cardiff, UK
| | - Theodor Rüber
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe-University Frankfurt, Frankfurt am Main, Germany
- Center for Personalized Translational Epilepsy Research (CePTER), Goethe-University Frankfurt, Frankfurt am Main, Germany
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Chantal Depondt
- Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Julie Absil
- Department of Radiology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium
| | - Sarah J A Carr
- Division of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Eugenio Abela
- Division of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mark P Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Orrin Devinsky
- Department of Neurology, NYU Grossman School of Medicine, New York, NY, US
| | | | - 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 Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, US
| | - Sean N Hatton
- Department of Neurosciences, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, US
| | - Sjoerd B Vos
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL 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
| | - 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, US
| | - Sanjay M Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
- Chalfont Centre for Epilepsy, Bucks, UK
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Angelo Labate
- Neurology, BIOMORF Dipartment, University of Messina, Messina, Italy
| | - Carrie R McDonald
- Department of Psychiatry, Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, US
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, 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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Zhu Z, Zhang Z, Gao X, Feng L, Chen D, Yang Z, Hu S. Individual Brain Metabolic Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation Predicts Seizure Outcomes of Temporal Lobe Epilepsy. Front Cell Dev Biol 2022; 9:803800. [PMID: 35310541 PMCID: PMC8926031 DOI: 10.3389/fcell.2021.803800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/15/2021] [Indexed: 01/01/2023] Open
Abstract
Objective: We aimed to use an individual metabolic connectome method, the Jensen-Shannon Divergence Similarity Estimation (JSSE), to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and predict the long-term surgical outcomes in temporal lobe epilepsy (TLE). Methods: A total of 128 patients with TLE (63 females, 65 males; 25.07 ± 12.01 years) who underwent Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) imaging were enrolled. Patients were classified either as experiencing seizure recurrence (SZR) or seizure free (SZF) at least 1 year after surgery. Each individual’s metabolic brain network was ascertained using the proposed JSSE method. We compared the similarity and difference in the JSSE network and its topological measurements between the two groups. The two groups were then classified by combining the information from connection and topological metrics, which was conducted by the multiple kernel support vector machine. The validation was performed using the nested leave-one-out cross-validation strategy to confirm the performance of the methods. Results: With a median follow-up of 33 months, 50% of patients achieved SZF. No relevant differences in clinical features were found between the two groups except age at onset. The proposed JSSE method showed marked degree reductions in IFGoperc.R, ROL. R, IPL. R, and SMG. R; and betweenness reductions in ORBsup.R and IOG. R; meanwhile, it found increases in the degree analysis of CAL. L and PCL. L, and in the betweenness analysis of PreCG.R, IOG. R, PoCG.R, PCL. L and PCL.R. Exploring consensus significant metabolic connections, we observed that the most involved metabolic motor networks were the INS-TPOmid.L, MTG. R-SMG. R, and MTG. R-IPL.R pathways between the two groups, and yielded another detailed individual pathological connectivity in the PHG. R-CAU.L, PHG. R-HIP.L, TPOmid.L-LING.R, TPOmid.L-DCG.R, MOG. R-MTG.R, MOG. R-ANG.R, and IPL. R-IFGoperc.L pathways. These aberrant functional network measures exhibited ideal classification performance in predicting SZF individuals from SZR ones at a sensitivity of 75.00%, a specificity of 92.79%, and an accuracy of 83.59%. Conclusion: The JSSE method indicator can identify abnormal brain networks in predicting an individual’s long-term surgical outcome of TLE, thus potentially constituting a clinically applicable imaging biomarker. The results highlight the biological meaning of the estimated individual brain metabolic connectome.
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Affiliation(s)
- Zehua Zhu
- Department of Nuclear Medicine, XiangYa Hospital, Changsha, China
| | - Zhimin Zhang
- Department of Blood Transfusion, XiangYa Hospital, Changsha, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Dengming Chen
- Department of Nuclear Medicine, XiangYa Hospital, Changsha, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Shuo Hu
- Department of Nuclear Medicine, XiangYa Hospital, Changsha, China.,Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, China
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Rodriguez-Cruces R, Royer J, Larivière S, Bassett DS, Caciagli L, Bernhardt BC. Multimodal connectome biomarkers of cognitive and affective dysfunction in the common epilepsies. Netw Neurosci 2022; 6:320-338. [PMID: 35733426 PMCID: PMC9208009 DOI: 10.1162/netn_a_00237] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/02/2022] [Indexed: 11/05/2022] Open
Abstract
Epilepsy is one of the most common chronic neurological conditions, traditionally defined as a disorder of recurrent seizures. Cognitive and affective dysfunction are increasingly recognized as core disease dimensions and can affect patient well-being, sometimes more than the seizures themselves. Connectome-based approaches hold immense promise for revealing mechanisms that contribute to dysfunction and to identify biomarkers. Our review discusses emerging multimodal neuroimaging and connectomics studies that highlight network substrates of cognitive/affective dysfunction in the common epilepsies. We first discuss work in drug-resistant epilepsy syndromes, that is, temporal lobe epilepsy, related to mesiotemporal sclerosis (TLE), and extratemporal epilepsy (ETE), related to malformations of cortical development. While these are traditionally conceptualized as ‘focal’ epilepsies, many patients present with broad structural and functional anomalies. Moreover, the extent of distributed changes contributes to difficulties in multiple cognitive domains as well as affective-behavioral challenges. We also review work in idiopathic generalized epilepsy (IGE), a subset of generalized epilepsy syndromes that involve subcortico-cortical circuits. Overall, neuroimaging and network neuroscience studies point to both shared and syndrome-specific connectome signatures of dysfunction across TLE, ETE, and IGE. Lastly, we point to current gaps in the literature and formulate recommendations for future research. Epilepsy is increasingly recognized as a network disorder characterized by recurrent seizures as well as broad-ranging cognitive difficulties and affective dysfunction. Our manuscript reviews recent literature highlighting brain network substrates of cognitive and affective dysfunction in common epilepsy syndromes, namely temporal lobe epilepsy secondary to mesiotemporal sclerosis, extratemporal epilepsy secondary to malformations of cortical development, and idiopathic generalized epilepsy syndromes arising from subcortico-cortical pathophysiology. We discuss prior work that has indicated both shared and distinct brain network signatures of cognitive and affective dysfunction across the epilepsy spectrum, improves our knowledge of structure-function links and interindividual heterogeneity, and ultimately aids screening and monitoring of therapeutic strategies.
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Affiliation(s)
- Raul Rodriguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Dani S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania 19104 USA
| | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London WC1N 3BG, United Kingdom
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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38
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Fan JM, Lee AT, Kudo K, Ranasinghe KG, Morise H, Findlay AM, Kirsch HE, Chang EF, Nagarajan SS, Rao VR. Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy. Brain Commun 2022; 4:fcac104. [PMID: 35611310 PMCID: PMC9123848 DOI: 10.1093/braincomms/fcac104] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/23/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Responsive neurostimulation is a promising treatment for drug-resistant focal epilepsy; however, clinical outcomes are highly variable across individuals. The therapeutic mechanism of responsive neurostimulation likely involves modulatory effects on brain networks; however, with no known biomarkers that predict clinical response, patient selection remains empiric. This study aimed to determine whether functional brain connectivity measured non-invasively prior to device implantation predicts clinical response to responsive neurostimulation therapy. Resting-state magnetoencephalography was obtained in 31 participants with subsequent responsive neurostimulation device implantation between 15 August 2014 and 1 October 2020. Functional connectivity was computed across multiple spatial scales (global, hemispheric, and lobar) using pre-implantation magnetoencephalography and normalized to maps of healthy controls. Normalized functional connectivity was investigated as a predictor of clinical response, defined as percent change in self-reported seizure frequency in the most recent year of clinic visits relative to pre-responsive neurostimulation baseline. Area under the receiver operating characteristic curve quantified the performance of functional connectivity in predicting responders (≥50% reduction in seizure frequency) and non-responders (<50%). Leave-one-out cross-validation was furthermore performed to characterize model performance. The relationship between seizure frequency reduction and frequency-specific functional connectivity was further assessed as a continuous measure. Across participants, stimulation was enabled for a median duration of 52.2 (interquartile range, 27.0-62.3) months. Demographics, seizure characteristics, and responsive neurostimulation lead configurations were matched across 22 responders and 9 non-responders. Global functional connectivity in the alpha and beta bands were lower in non-responders as compared with responders (alpha, pfdr < 0.001; beta, pfdr < 0.001). The classification of responsive neurostimulation outcome was improved by combining feature inputs; the best model incorporated four features (i.e. mean and dispersion of alpha and beta bands) and yielded an area under the receiver operating characteristic curve of 0.970 (0.919-1.00). The leave-one-out cross-validation analysis of this four-feature model yielded a sensitivity of 86.3%, specificity of 77.8%, positive predictive value of 90.5%, and negative predictive value of 70%. Global functional connectivity in alpha band correlated with seizure frequency reduction (alpha, P = 0.010). Global functional connectivity predicted responder status more strongly, as compared with hemispheric predictors. Lobar functional connectivity was not a predictor. These findings suggest that non-invasive functional connectivity may be a candidate personalized biomarker that has the potential to predict responsive neurostimulation effectiveness and to identify patients most likely to benefit from responsive neurostimulation therapy. Follow-up large-cohort, prospective studies are required to validate this biomarker. These findings furthermore support an emerging view that the therapeutic mechanism of responsive neurostimulation involves network-level effects in the brain.
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Affiliation(s)
- Joline M Fan
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Anthony T Lee
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Kiwamu Kudo
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Kamalini G Ranasinghe
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Hirofumi Morise
- Medical Imaging Center, Ricoh Company, Ltd., Kanazawa, Japan.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Anne M Findlay
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Heidi E Kirsch
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Edward F Chang
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
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39
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Lee HM, Fadaie F, Gill R, Caldairou B, Sziklas V, Crane J, Hong SJ, Bernhardt BC, Bernasconi A, Bernasconi N. Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification. Brain 2021; 145:897-908. [PMID: 34849619 DOI: 10.1093/brain/awab425] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/28/2021] [Accepted: 10/28/2021] [Indexed: 11/14/2022] Open
Abstract
In drug-resistant temporal lobe epilepsy (TLE), precise predictions of drug response, surgical outcome, and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant "one-size-fits-all" group-level analytical approaches that does not allow parsing inter-individual variations along the disease spectrum. Conversely, analyzing inter-patient heterogeneity is increasingly recognized as a step towards person-centered care. Here, we utilized unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features (cortical thickness, hippocampal volume, FLAIR, T1/FLAIR, diffusion parameters) representing whole-brain patterns of structural pathology in 82 TLE patients. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically-verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor-1), bilateral paralimbic and hippocampal gliosis (Factor-2), bilateral neocortical atrophy (Factor-3), bilateral white matter microstructural alterations (Factor-4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug-response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is likely determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.
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Affiliation(s)
- Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research Institute for Basic Science, Department of Biomedical Engineering, Sungkyunkwan University Suwon South Korea
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, 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
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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40
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Genon S, Bernhardt BC, La Joie R, Amunts K, Eickhoff SB. The many dimensions of human hippocampal organization and (dys)function. Trends Neurosci 2021; 44:977-989. [PMID: 34756460 DOI: 10.1016/j.tins.2021.10.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/06/2021] [Accepted: 10/05/2021] [Indexed: 11/19/2022]
Abstract
The internal organization of hippocampal formation has been studied for more than a century. Although early accounts emphasized its subfields along the medial-lateral axis, findings in recent decades have highlighted also the anterior-to-posterior (i.e., longitudinal) axis as a key contributor to this brain region's functional organization. Hence, understanding of hippocampal function likely demands characterizing both medial-to-lateral and anterior-to-posterior axes, an approach that has been concretized by recent advances in in vivo parcellation and gradient mapping techniques. Following a short historical overview, we review the evidence provided by these approaches in brain-mapping studies, as well as the perspectives they open for addressing the behavioral relevance of the interacting organizational axes in healthy and clinical populations.
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Affiliation(s)
- Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
| | | | - Renaud La Joie
- Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, Structural and Functional Organisation of the Brain (INM-1), Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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41
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Caldairou B, Foit NA, Mutti C, Fadaie F, Gill R, Lee HM, Demerath T, Urbach H, Schulze-Bonhage A, Bernasconi A, Bernasconi N. MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy. Neurology 2021; 97:e1583-e1593. [PMID: 34475125 DOI: 10.1212/wnl.0000000000012699] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 07/30/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND AND OBJECTIVES MRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE. METHODS We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted and fluid-attenuated inversion recovery (FLAIR)/T1 (intensity) features. The classifier was trained on 60 patients with TLE (mean age 35.6 years, 58% female) with histologically verified hippocampal sclerosis (HS). Images were deemed to be MRI negative in 42% of cases on the basis of neuroradiologic reading (40% based on hippocampal volumetry). The predictive model automatically labeled patients as having left or right TLE. Lateralization accuracy was compared to electroclinical data, including side of surgery. Accuracy of the classifier was further assessed in 2 independent TLE cohorts with similar demographics and electroclinical characteristics (n = 57, 58% MRI negative). RESULTS The overall lateralization accuracy was 93% (95% confidence interval 92%-94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy in both the training (84%, area under the curve [AUC] 0.95 ± 0.02) and validation (cohort 1 90%, AUC 0.99; cohort 2 76%, AUC 0.94) cohorts. DISCUSSION This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiologic assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that in people with TLE and MRI-negative HS, an automated MRI-based classifier accurately determines the side of pathology.
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Affiliation(s)
- Benoit Caldairou
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Niels A Foit
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Carlotta Mutti
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Fatemeh Fadaie
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Ravnoor Gill
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Hyo Min Lee
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Theo Demerath
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Horst Urbach
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Andreas Schulze-Bonhage
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C., N.A.F., C.M., F.F., R.G., H.M.L., A.B., N.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Quebec, Canada; and Departments of Neurosurgery (N.A.F.) and Neuroradiology (T.D., H.U.), Freiburg Medical Center, and Department of Neurology (A.S.-B.), University of Freiburg, Germany.
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Li X, Jiang Y, Li W, Qin Y, Li Z, Chen Y, Tong X, Xiao F, Zuo X, Gong Q, Zhou D, Yao D, An D, Luo C. Disrupted functional connectivity in white matter resting-state networks in unilateral temporal lobe epilepsy. Brain Imaging Behav 2021; 16:324-335. [PMID: 34478055 DOI: 10.1007/s11682-021-00506-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/14/2021] [Indexed: 02/08/2023]
Abstract
Unilateral temporal lobe epilepsy (TLE) is the most common type of focal epilepsy characterized by foci in the unilateral temporal lobe grey matters of regions such as the hippocampus. However, it remains unclear how the functional features of white matter are altered in TLE. In the current study, resting-state functional magnetic resonance imaging (fMRI) was performed on 71 left TLE (LTLE) patients, 79 right TLE (RTLE) patients and 47 healthy controls (HC). Clustering analysis was used to identify fourteen white matter networks (WMN). The functional connectivity (FC) was calculated among WMNs and between WMNs and grey matter. Furthermore, the FC laterality of hemispheric WMNs was assessed. First, both patient groups showed decreased FCs among WMNs. Specifically, cerebellar white matter illustrated decreased FCs with the cerebral superficial WMNs, implying a dysfunctional interaction between the cerebellum and the cerebral cortex in TLE. Second, the FCs between WMNs and the ipsilateral hippocampus (grey matter foci) were also reduced in patient groups, which may suggest insufficient functional integration in unilateral TLE. Interestingly, RTLE showed more severe abnormalities of white matter FCs, including links to the bilateral hippocampi and temporal white matter, than LTLE. Taken together, these findings provide functional evidence of white matter abnormalities, extending the understanding of the pathological mechanism of white matter impairments in unilateral TLE.
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Affiliation(s)
- Xuan Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Wei Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Yingjie Qin
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Zhiliang Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Yan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Xin Tong
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Fenglai Xiao
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Xiaojun Zuo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Dong Zhou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China
| | - Dongmei An
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, 610054, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu, 610054, People's Republic of China.
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Schmidt MH, Crocker CE, Abdolell M, Ghuman MS, Pohlmann-Eden B. Toward individualized prediction of seizure recurrence: Hippocampal neuroimaging features in a cohort of patients from a first seizure clinic. Epilepsy Behav 2021; 122:108118. [PMID: 34144462 DOI: 10.1016/j.yebeh.2021.108118] [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: 03/11/2021] [Revised: 05/25/2021] [Accepted: 05/25/2021] [Indexed: 11/15/2022]
Abstract
PURPOSE We performed an exploratory analysis of electroencephalography (EEG) and neuroimaging data from a cohort of 51 patients with first seizure (FS) and new-onset epilepsy (NOE) to identify variables, or combinations of variables, that might discriminate between clinical trajectories over a one-year period and yield potential biomarkers of epileptogenesis. METHODS Patients underwent EEG, hippocampal and whole brain structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) within six weeks of the index seizure, and repeat neuroimaging one year later. We classified patients with FS as having had a single seizure (FS-SS) or having converted to epilepsy (FS-CON) after one year and performed logistic regression to identify combinations of variables that might discriminate between FS-SS and FS-CON, and between FS-SS and the combined group FS-CON + NOE. We performed paired t-tests to assess changes in quantitative variables over time. RESULTS Several combinations of variables derived from hippocampal structural MRI, DTI, and MRS provided excellent discrimination between FS-SS and FS-CON in our sample, with areas under the receiver operating curve (AUROC) ranging from 0.924 to 1. They also provided excellent discrimination between FS-SS and the combined group FS-CON + NOE in our sample, with AUROC ranging from 0.902 to 1. After one year, hippocampal fractional anisotropy (FA) increased bilaterally, hippocampal radial diffusivity (RD) decreased on the side with the larger initial measurement, and whole brain axial diffusivity (AD) increased in patients with FS-SS; hippocampal volume decreased on the side with the larger initial measurement, hippocampal FA increased bilaterally, hippocampal RD decreased bilaterally and whole brain AD, FA and mean diffusivity increased in the combined group FS-CON + NOE (corrected threshold for significance, q = 0.017). CONCLUSION We propose a prospective, multicenter study to develop and test models for the prediction of seizure recurrence in patients after a first seizure, based on hippocampal neuroimaging. Further longitudinal neuroimaging studies in patients with a first seizure and new-onset epilepsy may provide clues to the microstructural changes occurring at the earliest stages of epilepsy and yield biomarkers of epileptogenesis.
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Affiliation(s)
- Matthias H Schmidt
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada; Division of Neurosurgery, Dalhousie University, Halifax, Canada; Department of Medical Neuroscience, Dalhousie University, Halifax, Canada; Brain Repair Centre, Dalhousie University, Halifax, Canada.
| | - Candice E Crocker
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada; Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Mohamed Abdolell
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada; Department of Community Health and Epidemiology, Dalhousie University, Halifax, Canada
| | - Mandeep S Ghuman
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Canada
| | - Bernd Pohlmann-Eden
- Brain Repair Centre, Dalhousie University, Halifax, Canada; Division of Neurology, Dalhousie University, Halifax, Canada
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Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl K, DeKraker J, Toussaint PJ, Valk SL, Collins L, Khan AR, Amunts K, Evans AC, Dickscheid T, Bernhardt B. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 2021; 10:e70119. [PMID: 34431476 PMCID: PMC8445620 DOI: 10.7554/elife.70119] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Lindsay B Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontrealCanada
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College LondonLondonUnited Kingdom
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Brain and Mind Institute, University of Western OntarioOntarioCanada
| | - Paule-J Toussaint
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum JülichJülichGermany
| | - Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
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Puhlmann LMC, Linz R, Valk SL, Vrticka P, Vos de Wael R, Bernasconi A, Bernasconi N, Caldairou B, Papassotiriou I, Chrousos GP, Bernhardt BC, Singer T, Engert V. Association between hippocampal structure and serum Brain-Derived Neurotrophic Factor (BDNF) in healthy adults: A registered report. Neuroimage 2021; 236:118011. [PMID: 33852941 PMCID: PMC8280951 DOI: 10.1016/j.neuroimage.2021.118011] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/20/2021] [Indexed: 01/09/2023] Open
Abstract
The hippocampus is a highly plastic brain structure supporting functions central to human cognition. Morphological changes in the hippocampus have been implicated in development, aging, as well as in a broad range of neurological and psychiatric disorders. A growing body of research suggests that hippocampal plasticity is closely linked to the actions of brain-derived neurotrophic factor (BDNF). However, evidence on the relationship between hippocampal volume (HCV) and peripheral BDNF levels is scarce and limited to elderly and patient populations. Further, despite evidence that BDNF expression differs throughout the hippocampus and is implicated in adult neurogenesis specifically in the dentate gyrus, no study has so far related peripheral BDNF levels to the volumes of individual hippocampal subfields. Besides its clinical implications, BDNF-facilitated hippocampal plasticity plays an important role in regulating cognitive and affective processes. In the current registered report, we investigated how serum BDNF (sBDNF) levels relate to volumes of the hippocampal formation and its subfields in a large sample of healthy adults (N = 279, 160 f) with a broad age range (20-55 years, mean 40.5) recruited in the context of the ReSource Project. We related HCV to basal sBDNF and, in a subsample (n = 103, 57 f), to acute stress-reactive change in sBDNF. We further tested the role of age as a moderator of both associations. Contrary to our hypotheses, neither basal sBDNF levels nor stress-reactive sBDNF change were associated with total HCV or volume of the dentate gyrus/cornu ammonis 4 (DG/CA4) subfield. We also found no evidence for a moderating effect of age on any of these associations. Our null results provide a first point of reference on the relationship between sBDNF and HCV in healthy mid-age, in contrast to patient or aging populations. We suggest that sBDNF levels have limited predictive value for morphological differences of the hippocampal structure when notable challenge to its neuronal integrity or to neurotrophic capacity is absent.
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Affiliation(s)
- L M C Puhlmann
- Research Group "Social Stress and Family Health", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Leibniz Institute for Resilience Research, Mainz, Germany.
| | - R Linz
- Research Group "Social Stress and Family Health", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S L Valk
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Germany; Otto Hahn Research Group "Cognitive Neurogenetics", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - P Vrticka
- Research Group "Social Stress and Family Health", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Centre for Brain Science, Department of Psychology, University of Essex, Colchester, UK
| | - R Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - A Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - N Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - B Caldairou
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - I Papassotiriou
- Department of Clinical Biochemistry, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - G P Chrousos
- First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - B C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada
| | - T Singer
- Social Neuroscience Lab, Max Planck Society, Berlin, Germany
| | - V Engert
- Research Group "Social Stress and Family Health", Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Psychosocial Medicine, Psychotherapy and Psychooncology, Jena University Hospital, Friedrich Schiller University, Jena, Germany
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46
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Di Sapia R, Moro F, Montanarella M, Iori V, Micotti E, Tolomeo D, Wang KKW, Vezzani A, Ravizza T, Zanier ER. In-depth characterization of a mouse model of post-traumatic epilepsy for biomarker and drug discovery. Acta Neuropathol Commun 2021; 9:76. [PMID: 33902685 PMCID: PMC8073903 DOI: 10.1186/s40478-021-01165-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies and 10–20% of the acquired forms. The latency between traumatic brain injury (TBI) and epilepsy onset in high-risk patients offers a therapeutic window for intervention to prevent or improve the disease course. However, progress towards effective treatments has been hampered by the lack of sensitive prognostic biomarkers of PTE, and of therapeutic targets. There is therefore a pressing clinical need for preclinical PTE models suitable for biomarker discovery and drug testing. We characterized in-depth a model of severe TBI induced by controlled cortical impact evolving into PTE in CD1 adult male mice. To identify sensitive measures predictive of PTE development and severity, TBI mice were longitudinally monitored by video-electrocorticography (ECoG), examined by MRI, and tested for sensorimotor and cognitive deficits and locomotor activity. At the end of the video-ECoG recording mice were killed for brain histological analysis. PTE occurred in 58% of mice with frequent motor seizures (one seizure every other day), as determined up to 5 months post-TBI. The weight loss of PTE mice in 1 week after TBI correlated with the number of spontaneous seizures at 5 months. Moreover, the recovery rate of the sensorimotor deficit detected by the SNAP test before the predicted time of epilepsy onset was significantly lower in PTE mice than in those without epilepsy. Neuroscore, beam walk and cognitive deficit were similar in all TBI mice. The increase in the contusion volume, the volume of forebrain regions contralateral to the lesioned hemisphere and white matter changes over time assessed by MRI were similar in PTE and no-PTE mice. However, brain histology showed a more pronounced neuronal cell loss in the cortex and hippocampus contralateral to the injured hemisphere in PTE than in no-PTE mice. The extensive functional and neuropathological characterization of this TBI model, provides a tool to identify sensitive measures of epilepsy development and severity clinically useful for increasing PTE prediction in high-risk TBI patients. The high PTE incidence and spontaneous seizures frequency in mice provide an ideal model for biomarker discovery and for testing new drugs.
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47
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Li Q, Tavakol S, Royer J, Larivière S, Vos De Wael R, Park BY, Paquola C, Zeng D, Caldairou B, Bassett DS, Bernasconi A, Bernasconi N, Frauscher B, Smallwood J, Caciagli L, Li S, Bernhardt BC. Atypical neural topographies underpin dysfunctional pattern separation in temporal lobe epilepsy. Brain 2021; 144:2486-2498. [PMID: 33730163 DOI: 10.1093/brain/awab121] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 01/26/2021] [Accepted: 02/11/2021] [Indexed: 12/14/2022] Open
Abstract
Episodic memory is the ability to accurately remember events from our past. The process of pattern separation is hypothesized to underpin this ability and is defined as the ability to orthogonalize memory traces, to maximize the features that make them unique. Contemporary cognitive neuroscience suggests that pattern separation entails complex interactions between the hippocampus and the neocortex, where specific hippocampal subregions shape neural reinstatement in the neocortex. To test this hypothesis, the current work studied both healthy controls and patients with temporal lobe epilepsy (TLE) who present with hippocampal structural anomalies. In all participants, we measured neural activity using functional magnetic resonance imaging (fMRI) while they retrieved memorized items compared to lure items which share features with the target. Behaviorally, TLE patients were less able to exclude lures than controls, and showed a reduction in pattern separation. To assess the hypothesized relationship between neural patterns in the hippocampus and the neocortex, we identified topographic gradients of intrinsic connectivity along neocortical and hippocampal subfield surfaces and identified the topographic profile of the neural activity accompanying pattern separation. In healthy controls, pattern separation followed a graded pattern of neural activity, both along the hippocampal long axis (and peaked in anterior segments that are more heavily engaged in transmodal processing) and along the neocortical hierarchy running from unimodal to transmodal regions (peaking in transmodal default mode regions). In TLE patients, however, this concordance between task-based functional activations and topographic gradients was markedly reduced. Furthermore, person specific measures of concordance between task-related activity and connectivity gradients in patients and controls related to inter-individual differences in behavioral measures of pattern separation and episodic memory, highlighting the functional relevance of the observed topographic motifs. Our work is consistent with an emerging understanding that successful discrimination between memories with similar features entails a shift in the locus of neural activity away from sensory systems, a pattern that is mirrored along the hippocampal long axis and with respect to neocortical hierarchies. More broadly, our study establishes topographic profiling using intrinsic connectivity gradients captures the functional underpinnings of episodic memory processes in manner that is sensitive to their reorganization in pathology.
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Affiliation(s)
- Qiongling Li
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.,School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Reinder Vos De Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Debin Zeng
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA.,Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, USA.,Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, USA.,Santa Fe Institute, Santa Fe, New Mexico, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | | | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, USA
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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48
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Tavakol S, Li Q, Royer J, Vos de Wael R, Larivière S, Lowe A, Paquola C, Jefferies E, Hartley T, Bernasconi A, Bernasconi N, Smallwood J, Bohbot V, Caciagli L, Bernhardt B. A Structure-Function Substrate of Memory for Spatial Configurations in Medial and Lateral Temporal Cortices. Cereb Cortex 2021; 31:3213-3225. [PMID: 33667310 DOI: 10.1093/cercor/bhab001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 12/21/2020] [Accepted: 12/27/2020] [Indexed: 01/30/2023] Open
Abstract
Prior research has shown a role of the medial temporal lobe, particularly the hippocampal-parahippocampal complex, in spatial cognition. Here, we developed a new paradigm, the conformational shift spatial task (CSST), which examines the ability to encode and retrieve spatial relations between unrelated items. This task is short, uses symbolic cues, incorporates two difficulty levels, and can be administered inside the scanner. A cohort of 48 healthy young adults underwent the CSST, together with a set of behavioral measures and multimodal magnetic resonance imaging (MRI). Inter-individual differences in CSST performance correlated with scores on an established spatial memory paradigm, but neither with episodic memory nor mnemonic discrimination, supporting specificity. Analyzing high-resolution structural MRI data, individuals with better spatial memory showed thicker medial and lateral temporal cortices. Functional relevance of these findings was supported by task-based functional MRI analysis in the same participants and ad hoc meta-analysis. Exploratory resting-state functional MRI analyses centered on clusters of morphological effects revealed additional modulation of intrinsic network integration, particularly between lateral and medial temporal structures. Our work presents a novel spatial memory paradigm and supports an integrated structure-function substrate in the human temporal lobe. Task paradigms are programmed in python and made open access.
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Affiliation(s)
- Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Qiongling Li
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Alex Lowe
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | | | - Andrea Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | - Neda Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
| | | | - Veronique Bohbot
- Douglas Mental Health University Institute, McGill University, Montreal, Quebec H4H 1R3, Canada
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, WC1N 3BG London, United Kingdom.,Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada
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49
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Fei F, Wang X, Wang Y, Chen Z. Dissecting the role of subiculum in epilepsy: Research update and translational potential. Prog Neurobiol 2021; 201:102029. [PMID: 33636224 DOI: 10.1016/j.pneurobio.2021.102029] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 01/12/2021] [Accepted: 02/21/2021] [Indexed: 11/25/2022]
Abstract
The subiculum serves as the strategic core output of the hippocampus, through which neural activity exits the hippocampal proper and targets the entorhinal cortex and other more distant subcortical and cortical areas. The past decade has witnessed a growing interest in the subiculum, owing to discoveries revealing its critical role in regulating many physiological and pathophysiological processes. Notably, accumulating evidence from both clinical and experimental studies suggests that the subiculum plays a vital role in seizure initiation and propagation, in epilepsy. In this review, we briefly describe the structure and connectivity of the subiculum and then summarize the molecular and cellular mechanisms in the subiculum underlying the epileptic brain, in both epilepsy patients and animal models. Next, we review some translational approaches targeting the malfunctioned subiculum to treat epilepsy. Finally, we pose open questions for future research in the subiculum and their clinical translation challenges.
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Affiliation(s)
- Fan Fei
- Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xia Wang
- Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yi Wang
- Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhong Chen
- Institute of Pharmacology & Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, China; Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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50
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Abstract
Neuroimaging techniques, particularly magnetic resonance imaging, yield increasingly sophisticated markers of brain structure and function. Combined with ongoing developments in machine learning, these methods refine our abilities to detect subtle epileptogenic lesions and develop reliable prognostics.
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Affiliation(s)
- Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, 55981Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Irene Wang
- Epilepsy Center, Neurological Institute, 2569Cleveland Clinic, Cleveland, OH, USA
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