Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study

\ua9 2024 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results: Across all epilepsies, reduced total cerebellar volume was observed (d =.42). Maximum volume loss was observed in the corpus medullare (dmax =.49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax =.47), crus I/II (dmax =.39), VIIIA (dmax =.45), and VIIIB (dmax =.40). Earlier age at seizure onset ((Formula presented.) =.05) and longer epilepsy duration ((Formula presented.) =.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy

The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller‐scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well‐established by the ENIGMA Consortium, ENIGMA‐Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event‐based modeling analysis. We explore age of onset‐ and duration‐related features, as well as phenomena‐specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA‐Epilepsy

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from theENIGMABipolar Disorder Working Group

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness

The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy

The ENIGMA-Epilepsy working group: Mapping disease from large data sets

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy

The ENIGMA-Epilepsy working group: Mapping disease from large data sets

none72siEpilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.mixedSisodiya S.M.; Whelan C.D.; Hatton S.N.; Huynh K.; Altmann A.; Ryten M.; Vezzani A.; Caligiuri M.E.; Labate A.; Gambardella A.; Ives-Deliperi V.; Meletti S.; Munsell B.C.; Bonilha L.; Tondelli M.; Rebsamen M.; Rummel C.; Vaudano A.E.; Wiest R.; Balachandra A.R.; Bargallo N.; Bartolini E.; Bernasconi A.; Bernasconi N.; Bernhardt B.; Caldairou B.; Carr S.J.A.; Cavalleri G.L.; Cendes F.; Concha L.; Desmond P.M.; Domin M.; Duncan J.S.; Focke N.K.; Guerrini R.; Hamandi K.; Jackson G.D.; Jahanshad N.; Kalviainen R.; Keller S.S.; Kochunov P.; Kowalczyk M.A.; Kreilkamp B.A.K.; Kwan P.; Lariviere S.; Lenge M.; Lopez S.M.; Martin P.; Mascalchi M.; Moreira J.C.V.; Morita-Sherman M.E.; Pardoe H.R.; Pariente J.C.; Raviteja K.; Rocha C.S.; Rodriguez-Cruces R.; Seeck M.; Semmelroch M.K.H.G.; Sinclair B.; Soltanian-Zadeh H.; Stein D.J.; Striano P.; Taylor P.N.; Thomas R.H.; Thomopoulos S.I.; Velakoulis D.; Vivash L.; Weber B.; Yasuda C.L.; Zhang J.; Thompson P.M.; McDonald C.R.Sisodiya, S. M.; Whelan, C. D.; Hatton, S. N.; Huynh, K.; Altmann, A.; Ryten, M.; Vezzani, A.; Caligiuri, M. E.; Labate, A.; Gambardella, A.; Ives-Deliperi, V.; Meletti, S.; Munsell, B. C.; Bonilha, L.; Tondelli, M.; Rebsamen, M.; Rummel, C.; Vaudano, A. E.; Wiest, R.; Balachandra, A. R.; Bargallo, N.; Bartolini, E.; Bernasconi, A.; Bernasconi, N.; Bernhardt, B.; Caldairou, B.; Carr, S. J. A.; Cavalleri, G. L.; Cendes, F.; Concha, L.; Desmond, P. M.; Domin, M.; Duncan, J. S.; Focke, N. K.; Guerrini, R.; Hamandi, K.; Jackson, G. D.; Jahanshad, N.; Kalviainen, R.; Keller, S. S.; Kochunov, P.; Kowalczyk, M. A.; Kreilkamp, B. A. K.; Kwan, P.; Lariviere, S.; Lenge, M.; Lopez, S. M.; Martin, P.; Mascalchi, M.; Moreira, J. C. V.; Morita-Sherman, M. E.; Pardoe, H. R.; Pariente, J. C.; Raviteja, K.; Rocha, C. S.; Rodriguez-Cruces, R.; Seeck, M.; Semmelroch, M. K. H. G.; Sinclair, B.; Soltanian-Zadeh, H.; Stein, D. J.; Striano, P.; Taylor, P. N.; Thomas, R. H.; Thomopoulos, S. I.; Velakoulis, D.; Vivash, L.; Weber, B.; Yasuda, C. L.; Zhang, J.; Thompson, P. M.; Mcdonald, C. R

The ENIGMA-Epilepsy working group: Mapping disease from large data sets.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy