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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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Royer J, Larivière S, Rodriguez-Cruces R, Cabalo DG, Tavakol S, Auer H, Ngo A, Park BY, Paquola C, Smallwood J, Jefferies E, Caciagli L, Bernasconi A, Bernasconi N, Frauscher B, Bernhardt BC. Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy. Brain 2023; 146:3923-3937. [PMID: 37082950 PMCID: PMC10473569 DOI: 10.1093/brain/awad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/21/2023] [Accepted: 03/23/2023] [Indexed: 04/22/2023] Open
Abstract
Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.
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Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Bo-yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Data Science, Inha University, Incheon 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
| | - Casey Paquola
- Multiscale Neuroanatomy Lab, INM-1, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | | | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, MA 19104, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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Zhang S, She S, Qiu Y, Li Z, Wu X, Hu H, Zheng W, Huang R, Wu H. Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study. Neuroimage Clin 2023; 39:103468. [PMID: 37473494 PMCID: PMC10372163 DOI: 10.1016/j.nicl.2023.103468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.
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Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Shenglin She
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Yidan Qiu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zezhi Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaoyan Wu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Wei Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
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Ballerini A, Arienzo D, Stasenko A, Schadler A, Vaudano AE, Meletti S, Kaestner E, McDonald CR. Spatial patterns of gray and white matter compromise relate to age of seizure onset in temporal lobe epilepsy. Neuroimage Clin 2023; 39:103473. [PMID: 37531834 PMCID: PMC10415805 DOI: 10.1016/j.nicl.2023.103473] [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: 04/29/2023] [Revised: 06/29/2023] [Accepted: 07/06/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVE Temporal Lobe Epilepsy (TLE) is frequently a neurodevelopmental disorder, involving subcortical volume loss, cortical atrophy, and white matter (WM) disruption. However, few studies have addressed how these pathological changes in TLE relate to one another. In this study, we investigate spatial patterns of gray and white matter degeneration in TLE and evaluate the hypothesis that the relationship among these patterns varies as a function of the age at which seizures begin. METHODS Eighty-two patients with TLE and 59 healthy controls were enrolled. T1-weighted images were used to obtain hippocampal volumes and cortical thickness estimates. Diffusion-weighted imaging was used to obtain fractional anisotropy (FA) and mean diffusivity (MD) of the superficial WM (SWM) and deep WM tracts. Analysis of covariance was used to examine patterns of WM and gray matter alterations in TLE relative to controls, controlling for age and sex. Sliding window correlations were then performed to examine the relationships between SWM degeneration, cortical thinning, and hippocampal atrophy across ages of seizure onset. RESULTS Cortical thinning in TLE followed a widespread, bilateral pattern that was pronounced in posterior centroparietal regions, whereas SWM and deep WM loss occurred mostly in ipsilateral, temporolimbic regions compared to controls. Window correlations revealed a relationship between hippocampal volume loss and whole brain SWM disruption in patients who developed epilepsy during childhood. On the other hand, in patients with adult-onset TLE, co-occurring cortical and SWM alterations were observed in the medial temporal lobe ipsilateral to the seizure focus. SIGNIFICANCE Our results suggest that although cortical, hippocampal and WM alterations appear spatially discordant at the group level, the relationship among these features depends on the age at which seizures begin. Whereas neurodevelopmental aspects of TLE may result in co-occurring WM and hippocampal degeneration near the epileptogenic zone, the onset of seizures in adulthood may set off a cascade of SWM microstructural loss and cortical atrophy of a neurodegenerative nature.
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Affiliation(s)
- Alice Ballerini
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; Department of Psychiatry, University of California, San Diego, USA
| | - Donatello Arienzo
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Alena Stasenko
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Adam Schadler
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; Neurology Unit, OCB Hospital, AOU Modena, Italy
| | - Stefano Meletti
- Department of Biomedical, Metabolic and Neural Science, University of Modena and Reggio Emilia, Modena, Italy; Neurology Unit, OCB Hospital, AOU Modena, Italy
| | - Erik Kaestner
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA
| | - Carrie R McDonald
- Department of Psychiatry, University of California, San Diego, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego, USA; Department of Radiation Medicine & Applied Sciences, University of California, San Diego, USA.
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Yi Y, Zhong C, Wei-wei H. The long-term neurodevelopmental outcomes of febrile seizures and underlying mechanisms. Front Cell Dev Biol 2023; 11:1186050. [PMID: 37305674 PMCID: PMC10248510 DOI: 10.3389/fcell.2023.1186050] [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: 03/14/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Febrile seizures (FSs) are convulsions caused by a sudden increase in body temperature during a fever. FSs are one of the commonest presentations in young children, occurring in up to 4% of children between the ages of about 6 months and 5 years old. FSs not only endanger children's health, cause panic and anxiety to families, but also have many adverse consequences. Both clinical and animal studies show that FSs have detrimental effects on neurodevelopment, that cause attention deficit hyperactivity disorder (ADHD), increased susceptibility to epilepsy, hippocampal sclerosis and cognitive decline during adulthood. However, the mechanisms of FSs in developmental abnormalities and disease occurrence during adulthood have not been determined. This article provides an overview of the association of FSs with neurodevelopmental outcomes, outlining both the underlying mechanisms and the possible appropriate clinical biomarkers, from histological changes to cellular molecular mechanisms. The hippocampus is the brain region most significantly altered after FSs, but the motor cortex and subcortical white matter may also be involved in the development disorders induced by FSs. The occurrence of multiple diseases after FSs may share common mechanisms, and the long-term role of inflammation and γ-aminobutyric acid (GABA) system are currently well studied.
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Affiliation(s)
- You Yi
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang University School of Medicine, Hangzhou, China
| | - Chen Zhong
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, Zhejiang Chinese Medical University, Hangzhou, China
| | - Hu Wei-wei
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, Zhejiang University School of Medicine, Hangzhou, China
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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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7
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Wang X, Lin D, Zhao C, Li H, Fu L, Huang Z, Xu S. Abnormal metabolic connectivity in default mode network of right temporal lobe epilepsy. Front Neurosci 2023; 17:1011283. [PMID: 37034164 PMCID: PMC10076532 DOI: 10.3389/fnins.2023.1011283] [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: 08/04/2022] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Aims Temporal lobe epilepsy (TLE) is a common neurological disorder associated with the dysfunction of the default mode network (DMN). Metabolic connectivity measured by 18F-fluorodeoxyglucose Positron Emission Computed Tomography (18F-FDG PET) has been widely used to assess cumulative energy consumption and provide valuable insights into the pathophysiology of TLE. However, the metabolic connectivity mechanism of DMN in TLE is far from fully elucidated. The present study investigated the metabolic connectivity mechanism of DMN in TLE using 18F-FDG PET. Method Participants included 40 TLE patients and 41 health controls (HC) who were age- and gender-matched. A weighted undirected metabolic network of each group was constructed based on 14 primary volumes of interest (VOIs) in the DMN, in which Pearson's correlation coefficients between each pair-wise of the VOIs were calculated in an inter-subject manner. Graph theoretic analysis was then performed to analyze both global (global efficiency and the characteristic path length) and regional (nodal efficiency and degree centrality) network properties. Results Metabolic connectivity in DMN showed that regionally networks changed in the TLE group, including bilateral posterior cingulate gyrus, right inferior parietal gyrus, right angular gyrus, and left precuneus. Besides, significantly decreased (P < 0.05, FDR corrected) metabolic connections of DMN in the TLE group were revealed, containing bilateral hippocampus, bilateral posterior cingulate gyrus, bilateral angular gyrus, right medial of superior frontal gyrus, and left inferior parietal gyrus. Conclusion Taken together, the present study demonstrated the abnormal metabolic connectivity in DMN of TLE, which might provide further insights into the understanding the dysfunction mechanism and promote the treatment for TLE patients.
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Affiliation(s)
- Xiaoyang Wang
- Department of Medical Imaging, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, China
- Department of Medical Imaging, Affiliated Dongfang Hospital, Xiamen University, Fuzhou, Fujian, China
| | - Dandan Lin
- Department of Clinical Medicine, Fujian Health College, Fuzhou, Fujian, China
| | - Chunlei Zhao
- Department of Medical Imaging, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, China
- Department of Medical Imaging, Affiliated Dongfang Hospital, Xiamen University, Fuzhou, Fujian, China
| | - Hui Li
- Department of Medical Imaging, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, China
| | - Liyuan Fu
- Department of Medical Imaging, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, China
- Department of Medical Imaging, Affiliated Dongfang Hospital, Xiamen University, Fuzhou, Fujian, China
| | - Zhifeng Huang
- Department of Medical Imaging, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, China
- Department of Medical Imaging, Affiliated Dongfang Hospital, Xiamen University, Fuzhou, Fujian, China
| | - Shangwen Xu
- Department of Medical Imaging, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, China
- Department of Medical Imaging, Affiliated Dongfang Hospital, Xiamen University, Fuzhou, Fujian, China
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8
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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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9
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Li Z, Jiang C, Gao Q, Xiang W, Qi Z, Peng K, Lin J, Wang W, Deng B, Wang W. The relationship between the interictal epileptiform discharge source connectivity and cortical structural couplings in temporal lobe epilepsy. Front Neurol 2023; 14:1029732. [PMID: 36846133 PMCID: PMC9948620 DOI: 10.3389/fneur.2023.1029732] [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: 08/27/2022] [Accepted: 01/09/2023] [Indexed: 02/05/2023] Open
Abstract
Objective The objective of this study was to explore the relation between interictal epileptiform discharge (IED) source connectivity and cortical structural couplings (SCs) in temporal lobe epilepsy (TLE). Methods High-resolution 3D-MRI and 32-sensor EEG data from 59 patients with TLE were collected. Principal component analysis was performed on the morphological data on MRI to obtain the cortical SCs. IEDs were labeled from EEG data and averaged. The standard low-resolution electromagnetic tomography analysis was performed to locate the source of the average IEDs. Phase-locked value was used to evaluate the IED source connectivity. Finally, correlation analysis was used to compare the IED source connectivity and the cortical SCs. Results The features of the cortical morphology in left and right TLE were similar across four cortical SCs, which could be mainly described as the default mode network, limbic regions, connections bilateral medial temporal, and connections through the ipsilateral insula. The IED source connectivity at the regions of interest was negatively correlated with the corresponding cortical SCs. Significance The cortical SCs were confirmed to be negatively related to IED source connectivity in patients with TLE as detected with MRI and EEG coregistered data. These findings suggest the important role of intervening IEDs in treating TLE.
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Affiliation(s)
- Zhensheng Li
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Che Jiang
- Department of Neurosurgery, General Hospital of Southern Theater Command, Guangzhou, China
| | - Quwen Gao
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Wei Xiang
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Zijuan Qi
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Kairun Peng
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China
| | - Jian Lin
- Department of Neurosurgery, General Hospital of Southern Theater Command, Guangzhou, China
| | - Wei Wang
- Department of Neurosurgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bingmei Deng
- Department of Neurology, General Hospital of Southern Theater Command, Guangzhou, China,Bingmei Deng ✉
| | - Weimin Wang
- Department of Neurosurgery, General Hospital of Southern Theater Command, Guangzhou, China,*Correspondence: Weimin Wang ✉
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10
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Expression of fructose-1,6-bisphosphatase 1 is associated with [ 18F]FDG uptake and prognosis in patients with mesial temporal lobe epilepsy. Eur Radiol 2023; 33:3396-3406. [PMID: 36692596 DOI: 10.1007/s00330-023-09422-5] [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: 08/18/2022] [Revised: 12/09/2022] [Accepted: 01/02/2023] [Indexed: 01/25/2023]
Abstract
OBJECTIVES To determine whether fructose-1,6-bisphosphatase 1 (FBP1) expression is associated with [18F]FDG PET uptake and postsurgical outcomes in patients with mesial temporal lobe epilepsy (mTLE) and to investigate whether the molecular mechanism involving gamma-aminobutyric acid type A receptor (GABAAR), glucose transporter-3 (GLUT-3), and hexokinase-II (HK-II). METHODS Forty-three patients with mTLE underwent [18F]FDG PET/CT. Patients were divided into Ia (Engel class Ia) and non-Ia (Engel class Ib-IV) groups according to more than 1 year of follow-up after surgery. The maximum standard uptake value (SUVmax) and asymmetry index (AI) of hippocampus were measured. The relationship among the SUVmax, AI, prognosis, and FBP1 expression was analyzed. A lithium-pilocarpine acute mTLE rat model was subjected to [18F]FDG micro-PET/CT. Hippocampal SUVmax and FBP1, GABAAR, GLUT-3, and HK-II expression were analyzed. RESULTS SUVmax was higher in the Ia group than in the non-Ia group (7.31 ± 0.97 vs. 6.56 ± 0.96, p < 0.05) and FBP1 expression was lower in the Ia group (0.24 ± 0.03 vs. 0.27 ± 0.03, p < 0.01). FBP1 expression was negatively associated with SUVmax and AI (p < 0.01). In mTLE rats, the hippocampal FBP1 increased (0.26 ± 0.00 vs. 0.17 ± 0.00, p < 0.0001), and SUVmax, GLUT-3 and GABAAR levels decreased significantly (0.73 ± 0.12 vs. 1.46 ± 0.23, 0.20 ± 0.01 vs. 0.32 ± 0.05, 0.26 ± 0.02 vs. 0.35 ± 0.02, p < 0.05); no significant difference in HK-II levels was observed. In mTLE patients and rats, FBP1 negatively correlated with SUVmax and GLUT-3 and GABAAR levels (p < 0.05). CONCLUSION FBP1 expression was inversely associated with SUVmax in mTLE, which might inhibit [18F]FDG uptake by regulating GLUT-3 expression. High FBP1 expression was indicative of low GABAAR expression and poor prognosis. KEY POINTS • It is of paramount importance to explore the deep pathophysiological mechanisms underlying the pathogenesis of mesial temporal lobe epilepsy and find potential therapeutic targets. • [18F]FDG PET has demonstrated low metabolism in epileptic regions during the interictal period, and hypometabolism may be associated with prognosis, but the pathomechanism of this association remains uncertain. • Our results support the possibility that FBP1 might be simultaneously involved in the regulation of glucose metabolism levels and the excitability of neurons and suggest that targeting FBP1 may be a viable strategy in the diagnosis and treatment of mesial temporal lobe epilepsy.
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11
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Adan GH, de Bézenac C, Bonnett L, Pridgeon M, Biswas S, Das K, Richardson MP, Laiou P, Keller SS, Marson T. Protocol for an observational cohort study investigating biomarkers predicting seizure recurrence following a first unprovoked seizure in adults. BMJ Open 2022; 12:e065390. [PMID: 36576179 PMCID: PMC9723849 DOI: 10.1136/bmjopen-2022-065390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION A first unprovoked seizure is a common presentation, reliably identifying those that will have recurrent seizures is a challenge. This study will be the first to explore the combined utility of serum biomarkers, quantitative electroencephalogram (EEG) and quantitative MRI to predict seizure recurrence. This will inform patient stratification for counselling and the inclusion of high-risk patients in clinical trials of disease-modifying agents in early epilepsy. METHODS AND ANALYSIS 100 patients with first unprovoked seizure will be recruited from a tertiary neuroscience centre and baseline assessments will include structural MRI, EEG and a blood sample. As part of a nested pilot study, a subset of 40 patients will have advanced MRI sequences performed that are usually reserved for patients with refractory chronic epilepsy. The remaining 60 patients will have standard clinical MRI sequences. Patients will be followed up every 6 months for a 24-month period to assess seizure recurrence. Connectivity and network-based analyses of EEG and MRI data will be carried out and examined in relation to seizure recurrence. Patient outcomes will also be investigated with respect to analysis of high-mobility group box-1 from blood serum samples. ETHICS AND DISSEMINATION This study was approved by North East-Tyne & Wear South Research Ethics Committee (20/NE/0078) and funded by an Association of British Neurologists and Guarantors of Brain clinical research training fellowship. Findings will be presented at national and international meetings published in peer-reviewed journals. TRIAL REGISTRATION NUMBER NIHR Clinical Research Network's (CRN) Central Portfolio Management System (CPMS)-44976.
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Affiliation(s)
- Guleed H Adan
- Institute of Systems, Molecular, Integrated Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Christophe de Bézenac
- Institute of Systems, Molecular, Integrated Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Laura Bonnett
- University of Liverpool Department of Biostatistics, Liverpool, UK
| | | | | | - Kumar Das
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Mark P Richardson
- Department of Basic and Clinical Neuroscience, King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK
| | - Petroula Laiou
- Department of Basic and Clinical Neuroscience, King's College London Institute of Psychiatry Psychology and Neuroscience, London, UK
| | - Simon S Keller
- Institute of Systems, Molecular, Integrated Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Tony Marson
- Institute of Systems, Molecular, Integrated Biology, Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
- The Walton Centre NHS Foundation Trust, Liverpool, UK
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12
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Cruces RR, Royer J, Herholz P, Larivière S, Vos de Wael R, Paquola C, Benkarim O, Park BY, Degré-Pelletier J, Nelson MC, DeKraker J, Leppert IR, Tardif C, Poline JB, Concha L, Bernhardt BC. Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. Neuroimage 2022; 263:119612. [PMID: 36070839 PMCID: PMC10697132 DOI: 10.1016/j.neuroimage.2022.119612] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/20/2022] [Accepted: 09/03/2022] [Indexed: 11/25/2022] Open
Abstract
Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity.
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Affiliation(s)
- Raúl R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - Peer Herholz
- NeuroDataScience - ORIGAMI lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada; Department of Data Science, Inha University, Incheon, Republic of Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Janie Degré-Pelletier
- Labo IDEA, Département de Psychologie, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Mark C Nelson
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jordan DeKraker
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Christine Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Mexico
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
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13
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Shastin D, Genc S, Parker GD, Koller K, Tax CMW, Evans J, Hamandi K, Gray WP, Jones DK, Chamberland M. Surface-based tracking for short association fibre tractography. Neuroimage 2022; 260:119423. [PMID: 35809886 PMCID: PMC10009610 DOI: 10.1016/j.neuroimage.2022.119423] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.
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Affiliation(s)
- Dmitri Shastin
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom.
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Kristin Koller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom; Department of Neurology, University Hospital of Wales, Cardiff, United Kingdom
| | - William P Gray
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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Kasa LW, Peters T, Mirsattari SM, Jurkiewicz MT, Khan AR, A M Haast R. The role of the temporal pole in temporal lobe epilepsy: A diffusion kurtosis imaging study. Neuroimage Clin 2022; 36:103201. [PMID: 36126518 PMCID: PMC9486670 DOI: 10.1016/j.nicl.2022.103201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 09/13/2022] [Accepted: 09/14/2022] [Indexed: 12/14/2022]
Abstract
This study aimed to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) and its temporopolar cortex in temporal lobe epilepsy (TLE) patients. DKI quantitative maps were obtained from fourteen lesional TLE and ten non-lesional TLE patients, along with twenty-three healthy controls. Data collected included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with disease duration. To characterize temporopolar cortex microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Patients were separated according to the temporal lobe ipsilateral to seizure onset and their AFQ results were used as input for statistical analyses. Significant differences were observed between lesional TLE and controls, towards the most temporopolar segment of ILF and Unc proximal to the TP within the ipsilateral temporal lobe in left TLE patients for MK, RK, AWF and MD. No significant changes were observed with DKI maps in the non-lesional TLE group. DKI measurements correlated with disease duration, mostly towards the temporopolar segments of the WM bundles. Stronger differences in MK, RK and AWF within the temporopolar cortex were observed in the lesional TLE and noticeable differences (except for MD) in non-lesional TLE groups compared to controls. This study demonstrates that DKI has potential to detect subtle microstructural alterations within the temporopolar segments of the ILF and Unc and the connected temporopolar cortex in TLE patients including non-lesional TLE subjects. This could aid our understanding of the extrahippocampal areas, more specifically the temporal pole role in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes.
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Affiliation(s)
- Loxlan W Kasa
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada
| | - Terry Peters
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Medical Imaging, Western University, London, Ontario, Canada
| | - Seyed M Mirsattari
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Medical Imaging, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada; Department of Psychology, Western University, London, Ontario, Canada
| | - Michael T Jurkiewicz
- Department of Medical Biophysics, Western University, London, Ontario, Canada; Department of Medical Imaging, Western University, London, Ontario, Canada; Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada
| | - Ali R Khan
- Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada; School of Biomedical Engineering, Western University, London, Ontario, Canada; Department of Medical Biophysics, Western University, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.
| | - Roy A M Haast
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada
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15
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Petroff RL, Williams C, Li JL, MacDonald JW, Bammler TK, Richards T, English CN, Baldessari A, Shum S, Jing J, Isoherranen N, Crouthamel B, McKain N, Grant KS, Burbacher TM, Harry GJ. Prolonged, Low-Level Exposure to the Marine Toxin, Domoic Acid, and Measures of Neurotoxicity in Nonhuman Primates. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:97003. [PMID: 36102641 PMCID: PMC9472675 DOI: 10.1289/ehp10923] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The excitotoxic molecule, domoic acid (DA), is a marine algal toxin known to induce overt hippocampal neurotoxicity. Recent experimental and epidemiological studies suggest adverse neurological effects at exposure levels near the current regulatory limit (20 ppm, ∼0.075-0.1mg/kg). At these levels, cognitive effects occur in the absence of acute symptoms or evidence of neuronal death. OBJECTIVES This study aimed to identify adverse effects on the nervous system from prolonged, dietary DA exposure in adult, female Macaca fascicularis monkeys. METHODS Monkeys were orally exposed to 0, 0.075, and 0.15mg/kg per day for an average of 14 months. Clinical blood counts, chemistry, and cytokine levels were analyzed in the blood. In-life magnetic resonance (MR) imaging assessed volumetric and tractography differences in and between the hippocampus and thalamus. Histology of neurons and glia in the fornix, fimbria, internal capsule, thalamus, and hippocampus was evaluated. Hippocampal RNA sequencing was used to identify differentially expressed genes. Enrichment of gene networks for neuronal health, excitotoxicity, inflammation/glia, and myelin were assessed with Gene Set Enrichment Analysis. RESULTS Clinical blood counts, chemistry, and cytokine levels were not altered with DA exposure in nonhuman primates. Transcriptome analysis of the hippocampus yielded 748 differentially expressed genes (fold change≥1.5; p≤0.05), reflecting differences in a broad molecular profile of intermediate early genes (e.g., FOS, EGR) and genes related to myelin networks in DA animals. Between exposed and control animals, MR imaging showed comparable connectivity of the hippocampus and thalamus and histology showed no evidence of hypomyelination. Histological examination of the thalamus showed a larger microglia soma size and an extension of cell processes, but suggestions of a GFAP+astrocyte response showed no indication of astrocyte hypertrophy. DISCUSSION In the absence of overt hippocampal excitotoxicity, chronic exposure of Macaca fascicularis monkeys to environmentally relevant levels of DA suggested a subtle shift in the molecular profile of the hippocampus and the microglia phenotype in the thalamus that was possibly reflective of an adaptive response due to prolonged DA exposure. https://doi.org/10.1289/EHP10923.
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Affiliation(s)
- Rebekah L. Petroff
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Christopher Williams
- Mechanistic Toxicology Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Jian-Liang Li
- Epigenetics & Stem Cell Biology Laboratory, NIEHS, NIH, DHHS, Research Triangle Park, North Carolina, USA
| | - James W. MacDonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Theo K. Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Todd Richards
- Department of Radiology, University of Washington, Seattle, Washington, USA
| | | | - Audrey Baldessari
- Washington National Primate Research Center, Seattle, Washington, USA
| | - Sara Shum
- Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
| | - Jing Jing
- Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
| | - Nina Isoherranen
- Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
- Center on Human Development and Disability, University of Washington, Seattle, Washington, USA
| | - Brenda Crouthamel
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Noelle McKain
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Kimberly S. Grant
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
- Washington National Primate Research Center, Seattle, Washington, USA
| | - Thomas M. Burbacher
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
- Washington National Primate Research Center, Seattle, Washington, USA
- Center on Human Development and Disability, University of Washington, Seattle, Washington, USA
| | - G. Jean Harry
- Mechanistic Toxicology Branch, Division of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
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16
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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 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, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
- Department of Pharmacology & Toxicology, University of Utah, Salt Lake City, Utah, USA
| | - Kara A Johnson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
- Department of Neurology, University of Florida, Gainesville, Florida, USA
| | - Brian J Philip
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Blake J Newman
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Angela Y Peters
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | - Alan D Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - John D Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
- Scientific Computing & Imaging Institute, University of Utah, Salt Lake City, Utah, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah, USA
| | - Christopher R Butson
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
- Department of Neurology, University of Florida, Gainesville, Florida, USA
- Department of Neurosurgery, University of Florida, Gainesville, Florida, USA
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
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17
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Lopez SM, Aksman LM, Oxtoby NP, Vos SB, Rao J, Kaestner E, Alhusaini S, Alvim M, Bender B, Bernasconi A, Bernasconi N, Bernhardt B, Bonilha L, Caciagli L, Caldairou B, Caligiuri ME, Calvet A, Cendes F, Concha L, Conde‐Blanco E, Davoodi‐Bojd E, de Bézenac C, Delanty N, Desmond PM, Devinsky O, Domin M, Duncan JS, Focke NK, Foley S, Fortunato F, Galovic M, Gambardella A, Gleichgerrcht E, Guerrini R, Hamandi K, Ives‐Deliperi V, Jackson GD, Jahanshad N, Keller SS, Kochunov P, Kotikalapudi R, Kreilkamp BAK, Labate A, Larivière S, Lenge M, Lui E, Malpas C, Martin P, Mascalchi M, Medland SE, Meletti S, Morita‐Sherman ME, Owen TW, Richardson M, Riva A, Rüber T, Sinclair B, Soltanian‐Zadeh H, Stein DJ, Striano P, Taylor P, Thomopoulos SI, Thompson PM, Tondelli M, Vaudano AE, Vivash L, Wang Y, Weber B, Whelan CD, Wiest R, Winston GP, Yasuda CL, McDonald CR, Alexander D, Sisodiya SM, Altmann A. Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data. Epilepsia 2022; 63:2081-2095. [PMID: 35656586 PMCID: PMC9540015 DOI: 10.1111/epi.17316] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 06/01/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
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Affiliation(s)
- Seymour M. Lopez
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Leon M. Aksman
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Neil P. Oxtoby
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sjoerd B. Vos
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Neuroradiological Academic Unit, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Jun Rao
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Erik Kaestner
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Saud Alhusaini
- Department of NeurologyAlpert Medical School of Brown UniversityProvidenceRhode IslandUSA
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Marina Alvim
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Benjamin Bender
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | | | - Lorenzo Caciagli
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
| | - Benoit Caldairou
- Neuroimaging of Epilepsy LaboratoryMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Maria Eugenia Caligiuri
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Angels Calvet
- Magnetic Resonance Image Core FacilityAugust Pi i Sunyer Biomedical Research Institute, University of BarcelonaBarcelonaSpain
| | - Fernando Cendes
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Luis Concha
- Institute of NeurobiologyNational Autonomous University of MexicoQuerétaroMexico
| | - Estefania Conde‐Blanco
- Epilepsy Program, Neurology DepartmentHospital Clinic of BarcelonaBarcelonaSpain
- August Pi i Sunyer Biomedical Research InstituteBarcelonaSpain
| | | | - Christophe de Bézenac
- Department of Pharmacology and TherapeuticsInstitute of Systems, Molecular and Integrative Biology, University of LiverpoolLiverpoolUK
| | - Norman Delanty
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
- FutureNeuro SFI Research Centre for Rare and Chronic Neurological DiseasesDublinIreland
| | - Patricia M. Desmond
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Orrin Devinsky
- New York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Martin Domin
- Functional Imaging Unit, Department of Diagnostic Radiology and NeuroradiologyGreifswald University MedicineGreifswaldGermany
| | - John S. Duncan
- Department of NeurologyEmory UniversityAtlantaUSA
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Niels K. Focke
- Department of NeurologyUniversity Medical CenterGöttingenGermany
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Francesco Fortunato
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Marian Galovic
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Department of NeurologyUniversity Hospital ZurichZurichSwitzerland
| | - Antonio Gambardella
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | | | - Renzo Guerrini
- Neuroscience DepartmentUniversity of FlorenceFlorenceItaly
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- Wales Epilepsy Unit, Department of NeurologyUniversity Hospital of WalesCardiffUK
| | | | - Graeme D. Jackson
- Florey Institute of Neuroscience and Mental Health, Austin CampusHeidelbergVictoriaAustralia
- University of MelbourneParkvilleVictoriaAustralia
- Department of NeurologyAustin HealthHeidelbergVictoriaAustralia
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Simon S. Keller
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Raviteja Kotikalapudi
- Department of Radiology, Diagnostic and Interventional NeuroradiologyUniversity Hospital TübingenTübingenGermany
- Department of Clinical NeurophysiologyUniversity Hospital GöttingenGöttingenGermany
- Department of Neurology and EpileptologyHertie Institute for Clinical Brain Research, University of TübingenTübingenGermany
| | - Barbara A. K. Kreilkamp
- Institute of Systems, Molecular and Integrative BiologyUniversity of LiverpoolLiverpoolUK
- Clinical NeurophysiologyUniversity Medical Center GöttingenGöttingenGermany
| | - Angelo Labate
- Neuroscience Research Center, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
- Institute of Neurology, Department of Medical and Surgical SciencesMagna Græcia University of CatanzaroCatanzaroItaly
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealQuebecCanada
| | - Matteo Lenge
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and LaboratoriesA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
- Functional and Epilepsy Neurosurgery Unit, Neurosurgery DepartmentA. Meyer Children's Hospital, University of FlorenceFlorenceItaly
| | - Elaine Lui
- Department of Radiology, Royal Melbourne HospitalUniversity of MelbourneMelbourneVictoriaAustralia
| | - Charles Malpas
- Department of NeurologyRoyal Melbourne HospitalMelbourneVictoriaAustralia
- Department of Medicine, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Pascal Martin
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Mario Mascalchi
- Mario Serio Department of Clinical and Experimental Medical SciencesUniversity of FlorenceFlorenceItaly
| | - Sarah E. Medland
- Psychiatric GeneticsQIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Stefano Meletti
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Marcia E. Morita‐Sherman
- Department of NeurologyUniversity of CampinasCampinasBrazil
- Cleveland Clinic Neurological InstituteClevelandOhioUSA
| | - Thomas W. Owen
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | | | - Antonella Riva
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Theodor Rüber
- Department of EpileptologyUniversity Hospital BonnBonnGermany
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Hamid Soltanian‐Zadeh
- Radiology and Research AdministrationHenry Ford Health SystemDetroitMichiganUSA
- School of Electrical and Computer EngineeringCollege of Engineering, University of TehranTehranIran
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | - Pasquale Striano
- Giannina Gaslini Institute, Scientific Institute for Research and Health CareGenoaItaly
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child HealthUniversity of GenoaGenoaItaly
| | - Peter N. Taylor
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Manuela Tondelli
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Primary Care DepartmentLocal Health Authority of ModenaModenaItaly
| | - Anna Elisabetta Vaudano
- Department of Biomedical, Metabolic, and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
- Neurology Unit, OCB HospitalModena University HospitalModenaItaly
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Alfred HospitalMonash UniversityMelbourneVictoriaAustralia
- Departments of Medicine and Radiology, Royal Melbourne HospitalUniversity of MelbourneParkvilleVictoriaAustralia
| | - Yujiang Wang
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- School of ComputingNewcastle UniversityNewcastle Upon TyneUK
| | - Bernd Weber
- Institute of Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Christopher D. Whelan
- Department of Molecular and Cellular TherapeuticsRoyal College of Surgeons in IrelandDublinIreland
| | - Roland Wiest
- Support Center for Advanced NeuroimagingUniversity Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of BernBernSwitzerland
| | - Gavin P. Winston
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
- Department of Medicine, Division of NeurologyQueen's UniversityKingstonOntarioCanada
| | - Clarissa Lin Yasuda
- Department of Neurology and Neuroimaging LaboratoryUniversity of CampinasCampinasBrazil
| | - Carrie R. McDonald
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Sanjay M. Sisodiya
- Department of Clinical and Experimental EpilepsyUCL Queen Square Institute of Neurology, University College LondonLondonUK
- Chalfont Centre for EpilepsyChalfont St PeterUK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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18
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Guerrero-Gonzalez JM, Yeske B, Kirk GR, Bell MJ, Ferrazzano PA, Alexander AL. Mahalanobis distance tractometry (MaD-Tract) - a framework for personalized white matter anomaly detection applied to TBI. Neuroimage 2022; 260:119475. [PMID: 35840117 PMCID: PMC9531540 DOI: 10.1016/j.neuroimage.2022.119475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 06/27/2022] [Accepted: 07/11/2022] [Indexed: 12/04/2022] Open
Abstract
Imaging-based quantitative measures from diffusion-weighted MRI (dMRI) offer the ability to non-invasively extract microscopic information from human brain tissues. Group-level comparisons of such measures represent an important approach to investigate abnormal brain conditions. These types of analyses are especially useful when the regions of abnormality spatially coincide across subjects. When this is not true, approaches for individualized analyses are necessary. Here we present a framework for single-subject multidimensional analysis based on the Mahalanobis distance. This is conducted along specific white matter pathways represented by tractography-derived streamline bundles. A definition for abnormality was constructed from Wilk’s criterion, which accounts for normative sample size, number of features used in the Mahalanobis distance, and multiple comparisons. One example of a condition exhibiting high heterogeneity across subjects is traumatic brain injury (TBI). Using the Mahalanobis distance computed from the three eigenvalues of the diffusion tensor along the cingulum, uncinate, and parcellated corpus callosum tractograms, 8 severe TBI patients were individually compared to a normative sample of 49 healthy controls. For all TBI patients, the analyses showed statistically significant deviations from the normative data at one or multiple locations along the analyzed bundles. The detected anomalies were widespread across the analyzed tracts, consistent with the expected heterogeneity that is hallmark of TBI. Each of the controls subjects was also compared to the remaining 48 subjects in the control group in a leave-one-out fashion. Only two segments were identified as abnormal out of the entire analysis in the control group, thus the method also demonstrated good specificity.
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Affiliation(s)
- Jose M Guerrero-Gonzalez
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
| | - Benjamin Yeske
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Gregory R Kirk
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Michael J Bell
- Critical Care Medicine, Children's National Medical Center, Washington, DC, USA
| | - Peter A Ferrazzano
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
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19
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Wang S, Zhang F, Huang P, Hong H, Jiaerken Y, Yu X, Zhang R, Zeng Q, Zhang Y, Kikinis R, Rathi Y, Makris N, Lou M, Pasternak O, Zhang M, O'Donnell LJ. Superficial white matter microstructure affects processing speed in cerebral small vessel disease. Hum Brain Mapp 2022; 43:5310-5325. [PMID: 35822593 PMCID: PMC9812245 DOI: 10.1002/hbm.26004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/10/2022] [Accepted: 06/15/2022] [Indexed: 01/15/2023] Open
Abstract
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD), which contributes to about 50% of dementias worldwide. Microstructural alterations in deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In 141 CSVD patients, processing speed was assessed using Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (volume on T2-FLAIR) and diffusion MRI measures. SWM imaging measures had a large contribution to processing speed, despite a relatively low SWM WMH burden. Across all imaging measures, SWM free water (FW) had the strongest association with processing speed, followed by SWM mean diffusivity (MD). SWM FW was the only marker to significantly increase between two subgroups with the lowest WMH burdens. When comparing two subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Mediation analysis revealed that SWM FW fully mediated the association between WMH volume and processing speed, while no mediation effect of MD or DWM FW was observed. Overall, results suggest that the SWM has an important contribution to processing speed, while SWM FW is a sensitive imaging marker associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes.
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Affiliation(s)
- Shuyue Wang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina,Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Peiyu Huang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Hui Hong
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yeerfan Jiaerken
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Xinfeng Yu
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ruiting Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Qingze Zeng
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Yao Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ron Kikinis
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA,Center for Morphometric AnalysisMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Min Lou
- Department of Neurologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Minming Zhang
- Department of Radiologythe Second Affiliated Hospital of Zhejiang University School of MedicineChina
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20
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Urquia-Osorio H, Pimentel-Silva LR, Rezende TJR, Almendares-Bonilla E, Yasuda CL, Concha L, Cendes F. Superficial and deep white matter diffusion abnormalities in focal epilepsies. Epilepsia 2022; 63:2312-2324. [PMID: 35707885 DOI: 10.1111/epi.17333] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/11/2022] [Accepted: 06/14/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE This study was undertaken to evaluate superficial-white matter (WM) and deep-WM magnetic resonance imaging diffusion tensor imaging (DTI) metrics and identify distinctive patterns of microstructural abnormalities in focal epilepsies of diverse etiology, localization, and response to antiseizure medication (ASM). METHODS We examined DTI data for 113 healthy controls and 113 patients with focal epilepsies: 51 patients with temporal lobe epilepsy (TLE) and hippocampal sclerosis (HS) refractory to ASM, 27 with pharmacoresponsive TLE-HS, 15 with temporal lobe focal cortical dysplasia (FCD), and 20 with frontal lobe FCD. To assess WM microstructure, we used a multicontrast multiatlas parcellation of DTI. We evaluated fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), and assessed within-group differences ipsilateral and contralateral to the epileptogenic lesion, as well as between-group differences, in regions of interest (ROIs). RESULTS The TLE-HS groups presented more widespread superficial- and deep-WM diffusion abnormalities than both FCD groups. Concerning superficial WM, TLE-HS groups showed multilobar ipsilateral and contralateral abnormalities, with less extensive distribution in pharmacoresponsive patients. Both the refractory TLE-HS and pharmacoresponsive TLE-HS groups also presented pronounced changes in ipsilateral frontotemporal ROIs (decreased FA and increased MD, RD, and AD). Conversely, FCD patients showed diffusion changes almost exclusively adjacent to epileptogenic areas. SIGNIFICANCE Our findings add further evidence of widespread abnormalities in WM diffusion metrics in patients with TLE-HS compared to other focal epilepsies. Notably, superficial-WM microstructural damage in patients with FCD is more restricted around the epileptogenic lesion, whereas TLE-HS groups showed diffuse WM damage with ipsilateral frontotemporal predominance. These findings suggest the potential of superficial-WM analysis for better understanding the biological mechanisms of focal epilepsies, and identifying dysfunctional networks and their relationship with the clinical-pathological phenotype. In addition, lobar superficial-WM abnormalities may aid in the diagnosis of subtle FCDs.
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Affiliation(s)
- Hebel Urquia-Osorio
- Department of Neurology, University of Campinas, São Paulo, Brazil.,Faculty of Medical Science, National Autonomous University of Honduras, Honduras
| | | | | | - Eimy Almendares-Bonilla
- Department of Neurology, University of Campinas, São Paulo, Brazil.,Faculty of Medical Science, National Autonomous University of Honduras, Honduras
| | | | - Luis Concha
- Institute of Neurobiology, National Autonomous University of Mexico, Queretaro, Mexico
| | - Fernando Cendes
- Department of Neurology, University of Campinas, São Paulo, Brazil
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21
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Zhang S, Wang Y, Zheng S, Seger C, Zhong S, Huang H, Hu H, Chen G, Chen L, Jia Y, Huang L, Huang R. Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach. Prog Neuropsychopharmacol Biol Psychiatry 2022; 116:110533. [PMID: 35151795 DOI: 10.1016/j.pnpbp.2022.110533] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 11/29/2021] [Accepted: 02/05/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD. METHODS We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas. RESULTS The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients. CONCLUSIONS We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.
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Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
| | - Senning Zheng
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Carol Seger
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China; Department of Psychology and Program in Molecular, Cellular, and Integrative Neuroscience, Colorado State University, Fort Collins, CO 80523, USA
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Huiyuan Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Lixiang Chen
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China.
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22
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Wang ZM, Wei PH, Zhang M, Wu C, Shan Y, Yeh FC, Shan Y, Lu J. Diffusion spectrum imaging predicts hippocampal sclerosis in mesial temporal lobe epilepsy patients. Ann Clin Transl Neurol 2022; 9:242-252. [PMID: 35166461 PMCID: PMC8935311 DOI: 10.1002/acn3.51503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/20/2021] [Accepted: 12/28/2021] [Indexed: 12/30/2022] Open
Abstract
Objectives Epileptic patients suffer from seizure recurrence after surgery due to the challenging localization. Improvement of the noninvasive imaging‐based approach for a better definition of the abnormalities would be helpful for a better outcome. Methods The quantitative anisotropy (QA) of diffusion spectrum imaging (DSI) is a quantitative scalar of evaluating the water diffusivity. Herein, we investigated the association between neuronal diameters or density acquired in literature and QA of DSI as well as the seizure localization in temporal lobe epilepsy. Thirty healthy controls (HCs) and 30 patients with hippocampal sclerosis (HS) were retrospectively analyzed. QA values were calculated and interactively compared between the areas with different neuronal diameter/density acquired from literature in the HCP‐1021 template. Diagnostic tests were performed on Z‐transformed asymmetry indices (AIs) of QA (which exclude physical asymmetry) among HS patients to evaluate its clinical value. Results The QA values in HCs conformed with different pyramidal cell distributions ranged from giant to small; corresponding groups were the motor‐sensory, associative, and limbic groups, respectively. Additionally, the QA value was correlated with the neuronal diameter/density in cortical layer IIIc (correlation coefficient with diameter: 0.529, p = 0.035; density: −0.678, p = 0.011). Decreases in cingulum hippocampal segments (Chs) were consistently observed on the sclerosed side in patients. The area under the curve of the Z‐transformed AI in Chs to the lateralization of HS was 0.957 (sensitivity: 0.909, specificity: 0.895). Interpretation QA based on DSI is likely to be useful to provide information to reflect the neuronal diameter/density and further facilitate localization of epileptic tissues.
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Affiliation(s)
- Zhen-Ming Wang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Miao Zhang
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Chunxue Wu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Yi Shan
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
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23
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Kaestner E, Stasenko A, Ben-Haim S, Shih J, Paul BM, McDonald CR. The importance of basal-temporal white matter to pre- and post-surgical naming ability in temporal lobe epilepsy. Neuroimage Clin 2022; 34:102963. [PMID: 35220106 PMCID: PMC8888987 DOI: 10.1016/j.nicl.2022.102963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Emerging research highlights the importance of basal-temporal cortex, centered on the fusiform gyrus, to both pre-surgical naming ability and post-surgical naming outcomes in temporal lobe epilepsy (TLE). In this study, we investigate whether integrity of the white matter network that interconnects this basal region to the distributed language network affects naming ability and risk for post-surgical naming decline. METHODS Patients with drug-resistant TLE were recruited from two epilepsy centers in a prospective longitudinal study. The pre-surgical dataset included 50 healthy controls, 47 left TLE (L-TLE), and 41 right TLE (R-TLE) patients. All participants completed pre-surgical T1- and diffusion-weighted MRI (dMRI), as well as neuropsychological tests of auditory and visual naming. Nineteen L-TLE and 18 R-TLE patients underwent anterior temporal lobectomy (ATL) and also completed post-surgical neuropsychological testing. Pre-surgical fractional anisotropy (FA) of the white matter directly beneath the fusiform neocortex (i.e., superficial white matter; SWM) and of deep white matter tracts with connections to the basal-temporal cortex [inferior longitudinal fasciculus (ILF) and inferior frontal occipital fasciculus (IFOF)] was calculated. Clinical variables, hippocampal volume, and FA of each white matter tract or region were examined in linear regressions with naming scores, or change in naming scores, as the primary outcomes. RESULTS Pre-surgically, higher FA in the bilateral ILF, bilateral IFOF, and left fusiform SWM was associated with better visual and auditory naming scores (all ps < 0.05 with FDR correction). In L-TLE, higher pre-surgical FA was also associated with less naming decline post-surgically, but results varied across tracts. When including only patients with typical language dominance, only integrity of the right fusiform SWM was associated with less visual naming decline (p = .0018). DISCUSSION Although a broad network of white matter network matter may contribute to naming ability pre-surgically, the reserve capacity of the contralateral (right) fusiform SWM may be important for mitigating visual naming decline following ATL in L-TLE. This shows that the study of the structural network interconnecting the basal-temporal region to the wider language network has implications for understanding both pre- and post-surgical naming in TLE.
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Affiliation(s)
- Erik Kaestner
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA
| | - Alena Stasenko
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA
| | - Sharona Ben-Haim
- Department of Neurosurgery, University of California, San Diego, CA, USA
| | - Jerry Shih
- Department of Neurosurgery, University of California, San Diego, CA, USA
| | - Brianna M Paul
- Department of Neurology, University of California -San Francisco, San Francisco, CA, USA
| | - Carrie R McDonald
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, CA, USA; San Diego State University, University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
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24
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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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25
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Mutti C, Misirocchi F, Zilioli A, Rausa F, Pizzarotti S, Spallazzi M, Parrino L. Sleep and brain evolution across the human lifespan: A mutual embrace. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:938012. [PMID: 36926070 PMCID: PMC10013002 DOI: 10.3389/fnetp.2022.938012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Sleep can be considered a window to ascertain brain wellness: it dynamically changes with brain maturation and can even indicate the occurrence of concealed pathological processes. Starting from prenatal life, brain and sleep undergo an impressive developmental journey that accompanies human life throughout all its steps. A complex mutual influence rules this fascinating course and cannot be ignored while analysing its evolution. Basic knowledge on the significance and evolution of brain and sleep ontogenesis can improve the clinical understanding of patient's wellbeing in a more holistic perspective. In this review we summarized the main notions on the intermingled relationship between sleep and brain evolutionary processes across human lifespan, with a focus on sleep microstructure dynamics.
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Affiliation(s)
- Carlotta Mutti
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Francesco Misirocchi
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Alessandro Zilioli
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Francesco Rausa
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Silvia Pizzarotti
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Marco Spallazzi
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
| | - Liborio Parrino
- Department of General and Specialized Medicine, Parma University Hospital, Parma, Italy
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26
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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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27
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Bencurova P, Laakso H, Salo RA, Paasonen E, Manninen E, Paasonen J, Michaeli S, Mangia S, Bares M, Brazdil M, Kubova H, Gröhn O. Infantile status epilepticus disrupts myelin development. Neurobiol Dis 2021; 162:105566. [PMID: 34838665 PMCID: PMC8845085 DOI: 10.1016/j.nbd.2021.105566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/12/2021] [Accepted: 11/23/2021] [Indexed: 11/25/2022] Open
Abstract
Temporal lobe epilepsy (TLE) is the most prevalent type of epilepsy in adults; it often starts in infancy or early childhood. Although TLE is primarily considered to be a grey matter pathology, a growing body of evidence links this disease with white matter abnormalities. In this study, we explore the impact of TLE onset and progression in the immature brain on white matter integrity and development utilising the rat model of Li-pilocarpine-induced TLE at the 12th postnatal day (P). Diffusion tensor imaging (DTI) and Black-Gold II histology uncovered disruptions in major white matter tracks (corpus callosum, internal and external capsules, and deep cerebral white matter) spreading through the whole brain at P28. These abnormalities were mostly not present any longer at three months after TLE induction, with only limited abnormalities detectable in the external capsule and deep cerebral white matter. Relaxation Along a Fictitious Field in the rotating frame of rank 4 indicated that white matter changes observed at both timepoints, P28 and P72, are consistent with decreased myelin content. The animals affected by TLE-induced white matter abnormalities exhibited increased functional connectivity between the thalamus and medial prefrontal and somatosensory cortex in adulthood. Furthermore, histological analyses of additional animal groups at P15 and P18 showed only mild changes in white matter integrity, suggesting a gradual age-dependent impact of TLE progression. Taken together, TLE progression in the immature brain distorts white matter development with a peak around postnatal day 28, followed by substantial recovery in adulthood. This developmental delay might give rise to cognitive and behavioural comorbidities typical for early-onset TLE.
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Affiliation(s)
- Petra Bencurova
- CEITEC - Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic.
| | - Hanne Laakso
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Raimo A Salo
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Ekaterina Paasonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Eppu Manninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Jaakko Paasonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
| | - Shalom Michaeli
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Silvia Mangia
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States
| | - Martin Bares
- Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic; Department of Neurology, School of Medicine, University of Minnesota, Minneapolis, MN, United States
| | - Milan Brazdil
- CEITEC - Central European Institute of Technology, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Pekarska 53, 656 91 Brno, Czech Republic
| | - Hana Kubova
- Academy of Sciences Czech Republic, Institute of Physiology, Department of Developmental Epileptology, Videnska 1083, 14220 Prague, Czech Republic.
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, PO Box 1627, FI-70211 Kuopio, Finland
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28
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Vos de Wael R, Royer J, Tavakol S, Wang Y, Paquola C, Benkarim O, Eichert N, Larivière S, Xu T, Misic B, Smallwood J, Valk SL, Bernhardt BC. Structural Connectivity Gradients of the Temporal Lobe Serve as Multiscale Axes of Brain Organization and Cortical Evolution. Cereb Cortex 2021; 31:5151-5164. [PMID: 34148082 PMCID: PMC8491677 DOI: 10.1093/cercor/bhab149] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The temporal lobe is implicated in higher cognitive processes and is one of the regions that underwent substantial reorganization during primate evolution. Its functions are instantiated, in part, by the complex layout of its structural connections. Here, we identified low-dimensional representations of structural connectivity variations in human temporal cortex and explored their microstructural underpinnings and associations to macroscale function. We identified three eigenmodes which described gradients in structural connectivity. These gradients reflected inter-regional variations in cortical microstructure derived from quantitative magnetic resonance imaging and postmortem histology. Gradient-informed models accurately predicted macroscale measures of temporal lobe function. Furthermore, the identified gradients aligned closely with established measures of functional reconfiguration and areal expansion between macaques and humans, highlighting their potential role in shaping temporal lobe function throughout primate evolution. Findings were replicated in several datasets. Our results provide robust evidence for three axes of structural connectivity in human temporal cortex with consistent microstructural underpinnings and contributions to large-scale brain network function.
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Affiliation(s)
- Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Shahin Tavakol
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Yezhou Wang
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, NY 10022, USA
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 2B4, Canada
| | | | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany
| | - Boris C Bernhardt
- Address correspondence to Boris C. Bernhardt, McConnell Brain Imaging Centre, Montreal Neurological Institute (NW-256), McGill University, 3801 Rue University, Montréal, QC H3A2B4, Canada.
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Association of hypometabolic extension of 18F-FDG PET with diffusion tensor imaging indices in mesial temporal lobe epilepsy with hippocampal sclerosis. Seizure 2021; 88:130-137. [PMID: 33878604 DOI: 10.1016/j.seizure.2021.04.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To assess the association between hypometabolic extension of 18F-fluorodeoxyglucose positron emission tomography and diffusion tensor imaging indices, including mean diffusivity (MD) and fractional anisotropy (FA), in hippocampal sclerosis (HS). METHODS Thirty-six unilateral HS were retrospectively selected and stratified into two groups: broad and localized hypometabolic groups (hypometabolism beyond [n = 26] and within the temporal lobe [n = 10]). Forty-one pairs of gray matter (GM) regions of interest (ROIs) were segmented using FreeSurfer software. The GM ROIs were applied to MD maps, and median MD values within each ROI were compared between hemispheres ipsilateral and contralateral to HS using a mixed effect model. Tract-Based Spatial Statistics (TBSS) was used to evaluate FA of white matter (WM) tracts between hemispheres ipsilateral and contralateral to HS. Disease laterality was controlled for. RESULTS The MD values in the thalamus, caudate, hippocampus, amygdala, superior frontal gyrus, middle and inferior temporal gyrus, temporal pole, and isthmus cingulate gyrus were significantly higher in the HS side than the contralateral side for the broad hypometabolic group. Those in the amygdala and superior temporal sulcus were significantly higher in the HS side than the contralateral side for the localized group. The TBSS analyses showed significantly decreased FA in the WM tracts of the temporal and frontal lobes for the broad hypometabolic group, while no tracts showed significant differences for the localized group. CONCLUSION The hypometabolic extension for HS was associated with the abnormalities of MD and FA in GM and WM, respectively, with more widespread microstructural alterations for broad hypometabolic HS.
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30
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Girardi-Schappo M, Fadaie F, Lee HM, Caldairou B, Sziklas V, Crane J, Bernhardt BC, Bernasconi A, Bernasconi N. Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy. Epilepsia 2021; 62:1022-1033. [PMID: 33705572 DOI: 10.1111/epi.16864] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/15/2021] [Accepted: 02/15/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition. METHODS We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters. RESULTS We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities. SIGNIFICANCE Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE.
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Affiliation(s)
- Mauricio Girardi-Schappo
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, 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
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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31
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Diamond JM, Diamond BE, Trotta MS, Dembny K, Inati SK, Zaghloul KA. Travelling waves reveal a dynamic seizure source in human focal epilepsy. Brain 2021; 144:1751-1763. [PMID: 33693588 DOI: 10.1093/brain/awab089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/08/2020] [Accepted: 12/23/2020] [Indexed: 11/14/2022] Open
Abstract
Treatment of patients with drug-resistant focal epilepsy relies upon accurate seizure localization. Ictal activity captured by intracranial EEG has traditionally been interpreted to suggest that the underlying cortex is actively involved in seizures. Here, we hypothesize that such activity instead reflects propagated activity from a relatively focal seizure source, even during later time points when ictal activity is more widespread. We used the time differences observed between ictal discharges in adjacent electrodes to estimate the location of the hypothesized focal source and demonstrated that the seizure source, localized in this manner, closely matches the clinically and neurophysiologically determined brain region giving rise to seizures. Moreover, we determined this focal source to be a dynamic entity that moves and evolves over the time course of a seizure. Our results offer an interpretation of ictal activity observed by intracranial EEG that challenges the traditional conceptualization of the seizure source.
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Affiliation(s)
- Joshua M Diamond
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Benjamin E Diamond
- J.P. Morgan AI Research, Corporate and Investment Bank, JP Morgan Chase & Co., New York, NY 10017, USA
| | - Michael S Trotta
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kate Dembny
- Clinical Epilepsy Section, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sara K Inati
- Clinical Epilepsy Section, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD 20892, USA
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32
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Li R, Zhang L, Guo D, Zou T, Wang X, Wang H, Li J, Wang C, Liu D, Yang Z, Xiao B, Chen H, Feng L. Temporal Lobe Epilepsy Shows Distinct Functional Connectivity Patterns in Different Thalamic Nuclei. Brain Connect 2021; 11:119-131. [PMID: 33317410 DOI: 10.1089/brain.2020.0826] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background: The thalamus, as a key relay of neuronal information flow between subcortical structures and cortical networks, has been implicated in focal limbic seizures propagation, awareness maintenance, and seizure-related cognitive deficits. However, the specific functional alterations between different thalamic nuclei and subcortical-cortical systems in temporal lobe epilepsy (TLE) remain largely unknown. Methods: We examined thalamic functional connectivity (FC) in 26 TLE patients and 30 healthy controls matched for sex, age, and education. The anterior (ANT), ventral posterior medial, and central lateral nuclei of thalamus were employed to establish whole-brain seed-to-voxel thalamic FC maps. Secondary Pearson's correlation analysis was conducted to assess associations between the abnormal thalamic FC and the memory performance in TLE. Results: Seed-based FC analyses revealed typical distinct FC patterns within each thalamic nuclei in both controls and TLE patients. The TLE showed significantly decreased FC between different thalamic nuclei and subcortical-cortical networks, including the limbic structures, midbrain, sensorimotor network, medial prefrontal cortex, temporal-occipital fusiform gyrus, and cerebellum. Verification analyses yielded similar patterns of thalamic FC changes in TLE. Importantly, the decreased FC between the ANT and hippocampal pathway was correlated with the poorer memory performance of TLE. Conclusion: These findings suggest that the distinct thalamocortical FC patterns are damaged to some extent in TLE patients. Importantly, the specific pathology of the ANT-hippocampal pathway in TLE may be a potential factor that contributes to memory deficits. Our study may pave the way for improved treatments and cognitive function by directly targeting different thalamocortical circuits for TLE.
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Affiliation(s)
- Rong Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Leiyao Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Danni Guo
- Department of Neurology and Xiangya Hospital, Central South University, Changsha, P.R. China
| | - Ting Zou
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Xuyang Wang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Hongyu Wang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Jiyi Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Chong Wang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Dingyang Liu
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, P.R. China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, P.R. China
| | - Bo Xiao
- Department of Neurology and Xiangya Hospital, Central South University, Changsha, P.R. China
| | - Huafu Chen
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, P.R. China.,Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, P.R. China
| | - Li Feng
- Department of Neurology and Xiangya Hospital, Central South University, Changsha, P.R. China
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Larivière S, Bernasconi A, Bernasconi N, Bernhardt BC. Connectome biomarkers of drug-resistant epilepsy. Epilepsia 2020; 62:6-24. [PMID: 33236784 DOI: 10.1111/epi.16753] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/29/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
Drug-resistant epilepsy (DRE) considerably affects patient health, cognition, and well-being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long-term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.
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Affiliation(s)
- Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- 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
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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34
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Yin T, Sun G, Tian Z, Liu M, Gao Y, Dong M, Wu F, Li Z, Liang F, Zeng F, Lan L. The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura. Front Neurol 2020; 11:588207. [PMID: 33240209 PMCID: PMC7680874 DOI: 10.3389/fneur.2020.588207] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
The purpose of the present study was to explore whether and to what extent the neuroimaging markers could predict the relief of the symptoms of patients with migraine without aura (MWoA) following a 4-week acupuncture treatment period. In study 1, the advanced multivariate pattern analysis was applied to perform a classification analysis between 40 patients with MWoA and 40 healthy subjects (HS) based on the z-transformed amplitude of low-frequency fluctuation (zALFF) maps. In study 2, the meaningful classifying features were selected as predicting features and the support vector regression models were constructed to predict the clinical efficacy of acupuncture in reducing the frequency of migraine attacks and headache intensity in 40 patients with MWoA. In study 3, a region of interest-based comparison between the pre- and post-treatment zALFF maps was conducted in 33 patients with MwoA to assess the changes in predicting features after acupuncture intervention. The zALFF value of the foci in the bilateral middle occipital gyrus, right fusiform gyrus, left insula, and left superior cerebellum could discriminate patients with MWoA from HS with higher than 70% accuracy. The zALFF value of the clusters in the right and left middle occipital gyrus could effectively predict the relief of headache intensity (R 2 = 0.38 ± 0.059, mean squared error = 2.626 ± 0.325) and frequency of migraine attacks (R 2 = 0.284 ± 0.072, mean squared error = 20.535 ± 2.701) after the 4-week acupuncture treatment period. Moreover, the zALFF values of these two clusters were both significantly reduced after treatment. The present study demonstrated the feasibility and validity of applying machine learning technologies and individual cerebral spontaneous activity patterns to predict acupuncture treatment outcomes in patients with MWoA. The data provided a quantitative benchmark for selecting acupuncture for MWoA.
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Affiliation(s)
- Tao Yin
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guojuan Sun
- Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zilei Tian
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Mailan Liu
- College of Acupuncture and Moxibustion and Tui-na, Hunan University of Chinese Medicine, Changsha, China
| | - Yujie Gao
- Traditional Chinese Medicine School, Ningxia Medical University, Yinchuan, China
| | - Mingkai Dong
- Department of Acupuncture and Moxibustion, Xinjin Hospital of Traditional Chinese Medicine, Chengdu, China
| | - Feng Wu
- Department of Acupuncture and Moxibustion, Changsha Hospital of Traditional Chinese Medicine, Changsha, China
| | - Zhengjie Li
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Fanrong Liang
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, China
| | - Fang Zeng
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, China
| | - Lei Lan
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, China.,Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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35
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Hong SJ, Hyung B, Paquola C, Bernhardt BC. The Superficial White Matter in Autism and Its Role in Connectivity Anomalies and Symptom Severity. Cereb Cortex 2020; 29:4415-4425. [PMID: 30566613 DOI: 10.1093/cercor/bhy321] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/11/2018] [Accepted: 11/26/2018] [Indexed: 12/29/2022] Open
Abstract
In autism spectrum disorders (ASDs), the majority of neuroimaging studies have focused on the analysis of cortical morphology. White matter changes remain less understood, particularly their association to cortical structure and function. Here, we focused on region that has gained only little attention in ASD neuroimaging: the superficial white matter (SWM) immediately beneath the cortical interface, a compartment playing a prominent role in corticogenesis that incorporates long- and short-range fibers implicated in corticocortical connectivity. Studying a multicentric dataset of ASD and neurotypical controls, we harnessed surface-based techniques to aggregate microstructural SWM diffusion features. Multivariate analysis revealed SWM anomalies in ASD compared with controls in medial parietal and temporoparietal regions. Effects were similar in children and adolescents/adults and consistent across sites. Although SWM anomalies were more confined when correcting for cortical thickness and surface area, findings were overall robust. Diffusion anomalies modulated functional connectivity reductions in ASD and related to symptom severity. Furthermore, mediation models indicated a link between SWM changes, functional connectivity, and symptom load. Analyses targeting the SWM offer a novel perspective on the interplay between structural and functional network perturbations in ASD, highlighting a potentially important neurobiological substrate contributing to its diverse behavioral phenotype.
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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.,Center for the Developing Brain, Child Mind Institute, 101 E 56th St, New York, NY 10022, USA
| | - Brian Hyung
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - 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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36
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Kirilina E, Helbling S, Morawski M, Pine K, Reimann K, Jankuhn S, Dinse J, Deistung A, Reichenbach JR, Trampel R, Geyer S, Müller L, Jakubowski N, Arendt T, Bazin PL, Weiskopf N. Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping. SCIENCE ADVANCES 2020; 6:6/41/eaaz9281. [PMID: 33028535 PMCID: PMC7541072 DOI: 10.1126/sciadv.aaz9281] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 08/26/2020] [Indexed: 05/11/2023]
Abstract
Superficial white matter (SWM) contains the most cortico-cortical white matter connections in the human brain encompassing the short U-shaped association fibers. Despite its importance for brain connectivity, very little is known about SWM in humans, mainly due to the lack of noninvasive imaging methods. Here, we lay the groundwork for systematic in vivo SWM mapping using ultrahigh resolution 7 T magnetic resonance imaging. Using biophysical modeling informed by quantitative ion beam microscopy on postmortem brain tissue, we demonstrate that MR contrast in SWM is driven by iron and can be linked to the microscopic iron distribution. Higher SWM iron concentrations were observed in U-fiber-rich frontal, temporal, and parietal areas, potentially reflecting high fiber density or late myelination in these areas. Our SWM mapping approach provides the foundation for systematic studies of interindividual differences, plasticity, and pathologies of this crucial structure for cortico-cortical connectivity in humans.
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Affiliation(s)
- Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany.
- Center for Cognitive Neuroscience Berlin, Free University Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germany
| | - Saskia Helbling
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Markus Morawski
- Paul Flechsig Institute of Brain Research, Leipzig University, Liebigstr. 19, 04103 Leipzig, Germany
| | - Kerrin Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Katja Reimann
- Paul Flechsig Institute of Brain Research, Leipzig University, Liebigstr. 19, 04103 Leipzig, Germany
| | - Steffen Jankuhn
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany
| | - Juliane Dinse
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Andreas Deistung
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital-Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany
- Department of Radiology University Hospital Halle (Saale), Ernst-Grube-Str. 40, 06120 Halle, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital-Friedrich Schiller University Jena, Philosophenweg 3, 07743 Jena, Germany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Stefan Geyer
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Larissa Müller
- Federal Institute for Materials Research and Testing, Richard-Willstätter-Straße 11, 12489 Berlin, Germany
| | - Norbert Jakubowski
- Federal Institute for Materials Research and Testing, Richard-Willstätter-Straße 11, 12489 Berlin, Germany
- Spetec GmbH, Berghamer Str. 2, 85435 Erding, Germany
| | - Thomas Arendt
- Paul Flechsig Institute of Brain Research, Leipzig University, Liebigstr. 19, 04103 Leipzig, Germany
| | - Pierre-Louis Bazin
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, 1001 NK Amsterdam, The Netherlands
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3AR, UK
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37
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Lee HM, Gill RS, Fadaie F, Cho KH, Guiot MC, Hong SJ, Bernasconi N, Bernasconi A. Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale. NEUROIMAGE-CLINICAL 2020; 28:102438. [PMID: 32987299 PMCID: PMC7520429 DOI: 10.1016/j.nicl.2020.102438] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 09/10/2020] [Accepted: 09/14/2020] [Indexed: 02/03/2023]
Abstract
Consensus clustering of MRI contrasts maps focal cortical dysplasia lesional variability. Lesions were parcellated into four classes with distinct structural profiles. FCD classes reflected typical functional and histopathological characteristics. Class membership was replicated in two independent datasets. Class-informed detection algorithm outperformed a class-naïve paradigm.
Objective Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale. Methods We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls. Results Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naïve paradigm. Significance Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.
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Affiliation(s)
- Hyo M Lee
- 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
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Kyoo H Cho
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Marie C Guiot
- Department of Pathology, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Seok-Jun Hong
- 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
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
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Zhao B, Yang B, Tan Z, Hu W, Sang L, Zhang C, Wang X, Wang Y, Liu C, Mo J, Shao X, Zhang J, Zhang K. Intrinsic brain activity changes in temporal lobe epilepsy patients revealed by regional homogeneity analysis. Seizure 2020; 81:117-122. [PMID: 32781401 DOI: 10.1016/j.seizure.2020.07.030] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/26/2020] [Accepted: 07/28/2020] [Indexed: 01/19/2023] Open
Abstract
PURPOSE Temporal lobe epilepsy is increasingly being recognized as a disorder associated with brain networks extending outside the seizure onset zone. In the current study, we aim to clarify regional functional changes using a regional homogeneity method. METHODS We retrospectively included resting-state fMRI data from 14 left and 18 right temporal lobe epilepsy patients. Data from the control group were acquired from an open dataset. Regional homogeneity was calculated, and a two-sample t-test was performed to compare the left and right temporal lobe epilepsy groups with the control group. RESULTS Compared with the healthy control group, the left temporal lobe epilepsy group showed increased regional homogeneity in the left anterior and middle cingulate cortex, and putamen; right inferior frontal gyrus; bilateral temporal lobe and precentral gyrus and decreased regional homogeneity in the left superior parietal gyrus, cuneus and inferior occipital gyrus; right inferior parietal lobule and bilateral rectus. The right temporal lobe epilepsy group showed increased regional homogeneity in the left middle cingulate cortex, precuneus, precentral and postcentral gyrus; right insula and bilateral temporal lobe and decreased regional homogeneity in the left cuneus and superior occipital gyrus; right supramarginal gyrus, fusiform gyrus, lingual gyrus, inferior occipital gyrus and putamen; and the bilateral rectus. CONCLUSION Regional homogeneity measurements provide evidence supporting that temporal lobe epilepsy is a complex network disease. Functional disruption of temporal lobe epilepsy at the brain region level was revealed, which may provide novel insights for any potential diagnostic and therapeutic approaches.
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Affiliation(s)
- Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bowen Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhongjian Tan
- Department of Radiology, Dongzhimen Hospital, Beijing University of Chinese Medicine, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yao Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoqiu Shao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Stereotactic and Functional Neurosurgery Laboratory, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China; Beijing Key Laboratory of Neurostimulation, Beijing, China.
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Reyes A, Kaestner E, Ferguson L, Jones JE, Seidenberg M, Barr WB, Busch RM, Hermann BP, McDonald CR. Cognitive phenotypes in temporal lobe epilepsy utilizing data- and clinically driven approaches: Moving toward a new taxonomy. Epilepsia 2020; 61:1211-1220. [PMID: 32363598 PMCID: PMC7341371 DOI: 10.1111/epi.16528] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/13/2020] [Accepted: 04/14/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To identify cognitive phenotypes in temporal lobe epilepsy (TLE) and test their reproducibility in a large, multi-site cohort of patients using both data-driven and clinically driven approaches. METHOD Four-hundred seven patients with TLE who underwent a comprehensive neuropsychological evaluation at one of four epilepsy centers were included. Scores on tests of verbal memory, naming, fluency, executive function, and psychomotor speed were converted into z-scores based on 151 healthy controls (HCs). For the data-driven method, cluster analysis (k-means) was used to determine the optimal number of clusters. For the clinically driven method, impairment was defined as >1.5 standard deviations below the mean of the HC, and patients were classified into groups based on the pattern of impairment. RESULTS Cluster analysis revealed a three-cluster solution characterized by (a) generalized impairment (29%), (b) language and memory impairment (28%), and (c) no impairment (43%). Based on the clinical criteria, the same broad categories were identified, but with a different distribution: (a) generalized impairment (37%), (b) language and memory impairment (30%), and (c) no impairment (33%). There was a 82.6% concordance rate with good agreement (κ = .716) between the methods. Forty-eight patients classified as having a normal profile based on cluster analysis were classified as having generalized impairment (n = 16) or an isolated language/memory impairment (n = 32) based on the clinical criteria. Patients with generalized impairment had a longer disease duration and patients with no impairment had more years of education. However, patients demonstrating the classic TLE profile (ie, language and memory impairment) were not more likely to have an earlier age at onset or mesial temporal sclerosis. SIGNIFICANCE We validate previous findings from single-site studies that have identified three unique cognitive phenotypes in TLE and offer a means of translating the patterns into a clinical diagnostic criteria, representing a novel taxonomy of neuropsychological status in TLE.
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Affiliation(s)
- Anny Reyes
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Erik Kaestner
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Lisa Ferguson
- Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Jana E. Jones
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | | | - William B. Barr
- Departments of Neurology and Psychiatry, NYU-Langone Medical Center and NYU School of Medicine, New York, NY, USA
| | - Robyn M. Busch
- Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | - Bruce P. Hermann
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Carrie R. McDonald
- San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, CA, USA
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Larivière S, Weng Y, Vos de Wael R, Royer J, Frauscher B, Wang Z, Bernasconi A, Bernasconi N, Schrader DV, Zhang Z, Bernhardt BC. Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome. Epilepsia 2020; 61:1221-1233. [PMID: 32452574 DOI: 10.1111/epi.16540] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 04/27/2020] [Accepted: 04/27/2020] [Indexed: 01/02/2023]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood. METHODS We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques. RESULTS Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1 year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76 ± 4% accuracy, outperforming classifiers operating on clinical and structural imaging features. SIGNIFICANCE Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.
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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, Quebec, Canada
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Zhengge Wang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Dewi V Schrader
- Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Weng Y, Larivière S, Caciagli L, Vos de Wael R, Rodríguez-Cruces R, Royer J, Xu Q, Bernasconi N, Bernasconi A, Thomas Yeo BT, Lu G, Zhang Z, Bernhardt BC. Macroscale and microcircuit dissociation of focal and generalized human epilepsies. Commun Biol 2020; 3:244. [PMID: 32424317 PMCID: PMC7234993 DOI: 10.1038/s42003-020-0958-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 04/15/2020] [Indexed: 01/01/2023] Open
Abstract
Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulations, we examined thalamo-cortical and cortico-cortical signatures and underlying microcircuits in 96 genetic generalized (GE) and 107 temporal lobe epilepsy (TLE) patients, along with 65 healthy controls. Structural and functional network profiling highlighted extensive atrophy, microstructural disruptions and decreased thalamo-cortical connectivity in TLE, while GE showed only subtle structural anomalies paralleled by enhanced thalamo-cortical connectivity. Connectome-informed biophysical simulations indicated modest increases in subcortical drive contributing to cortical dynamics in GE, while TLE presented with reduced subcortical drive and imbalanced excitation-inhibition within limbic and somatomotor microcircuits. Multiple sensitivity analyses supported robustness. Our multiscale analyses differentiate human focal and generalized epilepsy at the systems-level, showing paradoxically more severe microcircuit and macroscale imbalances in the former.
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Affiliation(s)
- Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Sara Larivière
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Lorenzo Caciagli
- University College London Queen Square Institute of Neurology, London, United Kingdom
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Raúl Rodríguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Neda Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - Andrea Bernasconi
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Clinical Imaging Research Centre and N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada.
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Movahedian Attar F, Kirilina E, Haenelt D, Pine KJ, Trampel R, Edwards LJ, Weiskopf N. Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography. Cereb Cortex 2020; 30:4496-4514. [PMID: 32297628 PMCID: PMC7325803 DOI: 10.1093/cercor/bhaa049] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI—facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude—was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1–V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.
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Affiliation(s)
- Fakhereh Movahedian Attar
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Department of Education and Psychology, Center for Cognitive Neuroscience Berlin, Free University Berlin, 14195 Berlin, Germany
| | - Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, 04109 Leipzig, Germany
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Microstructural imaging in temporal lobe epilepsy: Diffusion imaging changes relate to reduced neurite density. NEUROIMAGE-CLINICAL 2020; 26:102231. [PMID: 32146320 PMCID: PMC7063236 DOI: 10.1016/j.nicl.2020.102231] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 01/06/2023]
Abstract
PURPOSE Previous imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics. METHODS 20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured. RESULTS In intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF. CONCLUSIONS Diffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.
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Zhang Z, Zhou X, Liu J, Qin L, Ye W, Zheng J. Aberrant functional connectivity of the cingulate subregions in right-sided temporal lobe epilepsy. Exp Ther Med 2020; 19:2901-2912. [PMID: 32256775 PMCID: PMC7086282 DOI: 10.3892/etm.2020.8551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 12/09/2019] [Indexed: 11/17/2022] Open
Abstract
Patients with temporal lobe epilepsy (TLE) have been indicated to exhibit abnormal resting-state functional connectivity (rsFC) of the cingulate cortex. However, it has remained elusive whether cingulate subregions show different connectivity patterns in TLE. The present study aimed to investigate the differences in rsFC of each cingulate subregion between patients with right-sided TLE (rTLE) and healthy controls (HCs), as well as their association with executive control performance in rTLE. A total of 27 patients with rTLE and 20 age-, sex- and education-matched healthy controls were recruited and all participants underwent resting-state functional MRI and an attention network test for the assessment executive control function. In each hemisphere, the cingulate gyrus (CG) was divided into CG-1 (dorsal area 23; A23d), CG-2 (rostroventral area 24; A24rv), CG-3 (pregenual area 32; A32p), CG-4 (ventral area 23; A23v), CG-5 (caudodorsal area 24; A24cd), CG-6 (caudal area 24; A23c) and CG-7 (subgenual area 32; A32sg). Pearson's correlation analysis was performed to assess the correlation between the altered FCs of the cingulate subregions and clinical variables. In patients with rTLE, the majority of the cingulate subregions exhibited decreased rsFC; this was primarily restricted to the right CG-2, the bilateral CG-6 and the bilateral CG-7. Increased rsFC was only detected in rTLE restricted to the left CG-1. Impairments in executive control efficiency were identified in patients with rTLE in comparison with the HCs. Significant alterations in rsFC between the cingulate subregion and the brain regions were mostly decreased (and some slightly increased), suggesting that FC may potentially have a left-side advantage in patients with rTLE. FC variations of the cingulate subregions were indicated to be specific to rTLE. In addition, increased connectivity in the left CG-1 and left superior frontal gyrus were negatively correlated with executive control performance, suggesting a compensatory mechanism on executive control deficits in pathological conditions. This information on differentially altered FC patterns of the cingulate subregions may provide a deeper understanding of the complex neurological mechanisms and executive control dysfunctions underlying rTLE.
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Affiliation(s)
- Zhao Zhang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Xia Zhou
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Jinping Liu
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Lu Qin
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Wei Ye
- Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
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45
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White Matter Topographic Anatomy Applied to Temporal Lobe Surgery. World Neurosurg 2019; 132:e670-e679. [DOI: 10.1016/j.wneu.2019.08.050] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 08/08/2019] [Accepted: 08/09/2019] [Indexed: 11/23/2022]
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46
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Wykes RC, Khoo HM, Caciagli L, Blumenfeld H, Golshani P, Kapur J, Stern JM, Bernasconi A, Dedeurwaerdere S, Bernasconi N. WONOEP appraisal: Network concept from an imaging perspective. Epilepsia 2019; 60:1293-1305. [PMID: 31179547 DOI: 10.1111/epi.16067] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 05/16/2019] [Accepted: 05/16/2019] [Indexed: 02/01/2023]
Abstract
Neuroimaging techniques applied to a variety of organisms-from zebrafish, to rodents to humans-can offer valuable insights into neuronal network properties and their dysfunction in epilepsy. A wide range of imaging methods used to monitor neuronal circuits and networks during evoked seizures in animal models and advances in functional magnetic resonance imaging (fMRI) applied to patients with epilepsy were discussed during the XIV Workshop on Neurobiology of Epilepsy (XIV WONOEP) organized in 2017 by the Neurobiology Commission of the International League Against Epilepsy (ILAE). We review the growing number of technological approaches developed, as well as the current state of knowledge gained from studies applying these advanced imaging approaches to epilepsy research.
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Affiliation(s)
- Robert C Wykes
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Hui Ming Khoo
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.,Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK.,Neuroimaging of Epilepsy Laboratory, Department of Neurosciences and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Hal Blumenfeld
- Department of Neurology, Neuroscience and Neurosurgery, Yale University School of Medicine, New Haven, Connecticut
| | - Peyman Golshani
- Department of Neurology, Geffen School of Medicine, UCLA, Los Angeles, California
| | - Jaideep Kapur
- School of Medicine, University of Virginia, Charlottesville, Virginia
| | - John M Stern
- Department of Neurology, Geffen School of Medicine, UCLA, Los Angeles, California
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Department of Neurosciences and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Department of Neurosciences and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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Chang YHA, Marshall A, Bahrami N, Mathur K, Javadi SS, Reyes A, Hegde M, Shih JJ, Paul BM, Hagler DJ, McDonald CR. Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy. Epilepsia 2019; 60:935-947. [PMID: 31020649 DOI: 10.1111/epi.14736] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 03/26/2019] [Accepted: 03/27/2019] [Indexed: 11/30/2022]
Abstract
OBJECTIVES Temporal lobe epilepsy (TLE) is known to affect large-scale gray and white matter networks, and these network changes likely contribute to the verbal memory impairments observed in many patients. In this study, we investigate multimodal imaging patterns of brain alterations in TLE and evaluate the sensitivity of different imaging measures to verbal memory impairment. METHODS Diffusion tensor imaging (DTI), volumetric magnetic resonance imaging (vMRI), and resting-state functional MRI (rs-fMRI) were evaluated in 46 patients with TLE and 33 healthy controls to measure patterns of microstructural, structural, and functional alterations, respectively. These measurements were obtained within the white matter directly beneath neocortex (ie, superficial white matter [SWM]) for DTI and across neocortex for vMRI and rs-fMRI. The degree to which imaging alterations within left medial temporal lobe/posterior cingulate (LMT/PC) and left lateral temporal regions were associated with verbal memory performance was evaluated. RESULTS Patients with left TLE and right TLE both demonstrated pronounced microstructural alterations (ie, decreased fractional anisotropy [FA] and increased mean diffusivity [MD]) spanning the entire frontal and temporolimbic SWM, which were highly lateralized to the ipsilateral hemisphere. Conversely, reductions in cortical thickness in vMRI and alterations in the magnitude of the rs-fMRI response were less pronounced and less lateralized than the microstructural changes. Both stepwise regression and mediation analyses further revealed that FA and MD within SWM in LMT/PC regions were the most robust predictors of verbal memory, and that these associations were independent of left hippocampal volume. SIGNIFICANCE These findings suggest that microstructural loss within the SWM is pronounced in patients with TLE, and injury to the SWM within the LMT/PC region plays a critical role in verbal memory impairment.
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Affiliation(s)
- Yu-Hsuan A Chang
- Department of Psychiatry, University of California, San Diego, California.,Center for Multimodal Imaging and Genetics, University of California, San Diego, California
| | - Anisa Marshall
- Center for Multimodal Imaging and Genetics, University of California, San Diego, California
| | - Naeim Bahrami
- Department of Psychiatry, University of California, San Diego, California.,Center for Multimodal Imaging and Genetics, University of California, San Diego, California
| | - Kushagra Mathur
- Center for Multimodal Imaging and Genetics, University of California, San Diego, California
| | - Sogol S Javadi
- Center for Multimodal Imaging and Genetics, University of California, San Diego, California
| | - Anny Reyes
- Center for Multimodal Imaging and Genetics, University of California, San Diego, California.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Manu Hegde
- Department of Neurology, University of California, San Francisco, California.,UCSF Comprehensive Epilepsy Center, San Francisco, California
| | - Jerry J Shih
- Department of Neurosciences, University of California, San Diego, California.,UCSD Comprehensive Epilepsy Center, San Diego, California
| | - Brianna M Paul
- Department of Neurology, University of California, San Francisco, California.,UCSF Comprehensive Epilepsy Center, San Francisco, California
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California, San Diego, California.,Department of Radiology, University of California, San Diego, California
| | - Carrie R McDonald
- Department of Psychiatry, University of California, San Diego, California.,Center for Multimodal Imaging and Genetics, University of California, San Diego, California.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California.,UCSD Comprehensive Epilepsy Center, San Diego, California
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48
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Zhao X, Zhou ZQ, Xiong Y, Chen X, Xu K, Li J, Hu Y, Peng XL, Zhu WZ. Reduced Interhemispheric White Matter Asymmetries in Medial Temporal Lobe Epilepsy With Hippocampal Sclerosis. Front Neurol 2019; 10:394. [PMID: 31068889 PMCID: PMC6491759 DOI: 10.3389/fneur.2019.00394] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 04/01/2019] [Indexed: 02/04/2023] Open
Abstract
Mesial temporal lobe epilepsy (MTLE), one of the most common types of refractory focal epilepsy, has shown white matter abnormalities both within and beyond the temporal lobe. In particular, the white matter abnormalities in the ipsilateral hemisphere are more obvious than those in the contralateral hemisphere in MTLE, that is, the abnormalities present asymmetrical characteristics. However, very few studies have characterized the white matter microstructure asymmetry in MTLE patients specifically. Thus, we performed diffusion tensor imaging (DTI) to investigate the white matter microstructure asymmetries of patients with MTLE with unilateral hippocampal sclerosis (MTLE-HS). We enrolled 25 MTLE-HS (left MTLE-HS group, n = 13; right MTLE-HS group, n = 12) and 26 healthy controls (HC). DTI data were analyzed by tract-based spatial statistics (TBSS) to test the hemispheric differences across the entire white matter skeleton. We also conducted a two-sample paired t-test for 21 paired region of interests (ROIs) parceled on the basis of the ICBM-DTI-81 white-matter label atlas of bilateral hemispheres to test the hemispheric differences. An asymmetry index (AI) was calculated to further quantify the differences between the left and right paired-ROIs. It was found that the asymmetries of white matter skeletons were significantly lower in the MTLE-HS groups than in the HC group. In particular, the asymmetry traits were moderately reduced in the RMTLE-HS group and obviously reduced in the LMTLE-HS group. In addition, AI was significantly different in the RMTLE-HS group from the LMTLE-HS or HC group in the limbic system and superior longitudinal fasciculus (SLF). The current study found that the interhemispheric white matter asymmetries were significantly reduced in the MTLE-HS groups than in the HC group. The interhemispheric white matter asymmetries are distinctly affected in left and right MTLE-HS groups. The differences in AI among RMTLE-HS, LMTLE-HS, and HC involved the limbic system and SLF, which may have some pragmatic implications for the diagnosis of MTLE and differentiating LMTLE-HS from RMTLE-HS.
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Affiliation(s)
- Xu Zhao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhi-Qiang Zhou
- Department of Anesthesiology and Pain Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xiong
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Chen
- Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ke Xu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juan Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Long Peng
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen-Zhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Bernhardt BC, Fadaie F, Liu M, Caldairou B, Gu S, Jefferies E, Smallwood J, Bassett DS, Bernasconi A, Bernasconi N. Temporal lobe epilepsy: Hippocampal pathology modulates connectome topology and controllability. Neurology 2019; 92:e2209-e2220. [PMID: 31004070 DOI: 10.1212/wnl.0000000000007447] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/08/2019] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVE To assess whether hippocampal sclerosis (HS) severity is mirrored at the level of large-scale networks. METHODS We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 temporal lobe epilepsy (TLE) patients with histopathologic diagnosis of HS (n = 25; TLE-HS) and isolated gliosis (n = 19; TLE-G) and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm that simulates functional dynamics from structural network data. RESULTS Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from gray matter atrophy. CONCLUSIONS Severe HS is associated with marked remodeling of connectome topology and structurally governed functional dynamics in TLE, as opposed to isolated gliosis, which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales.
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Affiliation(s)
- Boris C Bernhardt
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Fatemeh Fadaie
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Min Liu
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Benoit Caldairou
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Shi Gu
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Elizabeth Jefferies
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Jonathan Smallwood
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Danielle S Bassett
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK.
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50
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Tavakol S, Royer J, Lowe AJ, Bonilha L, Tracy JI, Jackson GD, Duncan JS, Bernasconi A, Bernasconi N, Bernhardt BC. Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks. Epilepsia 2019; 60:593-604. [PMID: 30889276 PMCID: PMC6447443 DOI: 10.1111/epi.14688] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/13/2019] [Accepted: 02/14/2019] [Indexed: 01/03/2023]
Abstract
Epilepsy is among the most common chronic neurologic disorders, with 30%-40% of patients having seizures despite antiepileptic drug treatment. The advent of brain imaging and network analyses has greatly improved the understanding of this condition. In particular, developments in magnetic resonance imaging (MRI) have provided measures for the noninvasive characterization and detection of lesions causing epilepsy. MRI techniques can probe structural and functional connectivity, and network analyses have shaped our understanding of whole-brain anomalies associated with focal epilepsies. This review considers the progress made by neuroimaging and connectomics in the study of drug-resistant epilepsies due to focal substrates, particularly temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal lobe epilepsies associated with malformations of cortical development. In these disorders, there is evidence of widespread disturbances of structural and functional connectivity that may contribute to the clinical and cognitive prognosis of individual patients. It is hoped that studying the interplay between macroscale network anomalies and lesional profiles will improve our understanding of focal epilepsies and assist treatment choices.
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Affiliation(s)
- Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Alexander J Lowe
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina
| | - Joseph I Tracy
- Cognitive Neuroscience and Brain Mapping Laboratory, Thomas Jefferson University Hospitals/Sidney Kimmel Medical College, Philadelphia, Pennsylvania
| | - Graeme D Jackson
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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