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4 results on '"Kreilkamp BAK"'

1. Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy

Author

Park, B-Y, Lariviere, S, Rodriguez-Cruces, R, Royer, J, Tavakol, S, Wang, Y, Caciagli, L, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Alvim, MKM, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Kreilkamp, BAK, Domin, M, von Podewils, F, Langner, S, Rummel, C, Rebsamen, M, Wiest, R, Martin, P, Kotikalapudi, R, Bender, B, O'Brien, TJ, Law, M, Sinclair, B, Vivash, L, Kwan, P, Desmond, PM, Malpas, CB, Lui, E, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Lenge, M, Guerrini, R, Bartolini, E, Hamandi, K, Foley, S, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Parodi, C, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Galovic, M, Whelan, CD, Bargallo, N, Pariente, J, Conde-Blanco, E, Vaudano, AE, Tondelli, M, Meletti, S, Kong, X-Z, Francks, C, Fisher, SE, Caldairou, B, Ryten, M, Labate, A, Sisodiya, SM, Thompson, PM, McDonald, CR, Bernasconi, A, Bernasconi, N, Bernhardt, BC, Park, B-Y, Lariviere, S, Rodriguez-Cruces, R, Royer, J, Tavakol, S, Wang, Y, Caciagli, L, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Alvim, MKM, Yasuda, C, Bonilha, L, Gleichgerrcht, E, Focke, NK, Kreilkamp, BAK, Domin, M, von Podewils, F, Langner, S, Rummel, C, Rebsamen, M, Wiest, R, Martin, P, Kotikalapudi, R, Bender, B, O'Brien, TJ, Law, M, Sinclair, B, Vivash, L, Kwan, P, Desmond, PM, Malpas, CB, Lui, E, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Lenge, M, Guerrini, R, Bartolini, E, Hamandi, K, Foley, S, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Parodi, C, Tortora, D, Hatton, SN, Vos, SB, Duncan, JS, Galovic, M, Whelan, CD, Bargallo, N, Pariente, J, Conde-Blanco, E, Vaudano, AE, Tondelli, M, Meletti, S, Kong, X-Z, Francks, C, Fisher, SE, Caldairou, B, Ryten, M, Labate, A, Sisodiya, SM, Thompson, PM, McDonald, CR, Bernasconi, A, Bernasconi, N, and Bernhardt, BC

Abstract

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine ou

Published
2022

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

Author

Sisodiya, SM, Whelan, CD, Hatton, SN, Huynh, K, Altmann, A, Ryten, M, Vezzani, A, Caligiuri, ME, Labate, A, Gambardella, A, Ives-Deliperi, V, Meletti, S, Munsell, BC, Bonilha, L, Tondelli, M, Rebsamen, M, Rummel, C, Vaudano, AE, Wiest, R, Balachandra, AR, Bargallo, N, Bartolini, E, Bernasconi, A, Bernasconi, N, Bernhardt, B, Caldairou, B, Carr, SJA, Cavalleri, GL, Cendes, F, Concha, L, Desmond, PM, Domin, M, Duncan, JS, Focke, NK, Guerrini, R, Hamandi, K, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kowalczyk, MA, Kreilkamp, BAK, Kwan, P, Lariviere, S, Lenge, M, Lopez, SM, Martin, P, Mascalchi, M, Moreira, JCV, Morita-Sherman, ME, Pardoe, HR, Pariente, JC, Raviteja, K, Rocha, CS, Rodriguez-Cruces, R, Seeck, M, Semmelroch, MKHG, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Thomopoulos, SI, Velakoulis, D, Vivash, L, Weber, B, Yasuda, CL, Zhang, J, Thompson, PM, McDonald, CR, Sisodiya, SM, Whelan, CD, Hatton, SN, Huynh, K, Altmann, A, Ryten, M, Vezzani, A, Caligiuri, ME, Labate, A, Gambardella, A, Ives-Deliperi, V, Meletti, S, Munsell, BC, Bonilha, L, Tondelli, M, Rebsamen, M, Rummel, C, Vaudano, AE, Wiest, R, Balachandra, AR, Bargallo, N, Bartolini, E, Bernasconi, A, Bernasconi, N, Bernhardt, B, Caldairou, B, Carr, SJA, Cavalleri, GL, Cendes, F, Concha, L, Desmond, PM, Domin, M, Duncan, JS, Focke, NK, Guerrini, R, Hamandi, K, Jackson, GD, Jahanshad, N, Kalviainen, R, Keller, SS, Kochunov, P, Kowalczyk, MA, Kreilkamp, BAK, Kwan, P, Lariviere, S, Lenge, M, Lopez, SM, Martin, P, Mascalchi, M, Moreira, JCV, Morita-Sherman, ME, Pardoe, HR, Pariente, JC, Raviteja, K, Rocha, CS, Rodriguez-Cruces, R, Seeck, M, Semmelroch, MKHG, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Thomopoulos, SI, Velakoulis, D, Vivash, L, Weber, B, Yasuda, CL, Zhang, J, Thompson, PM, and McDonald, CR

Abstract

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.

Published
2022

3. Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression

Author

Lariviere, S, Royer, J, Rodriguez-Cruces, R, Paquola, C, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, CL, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Lui, E, Vaudano, AE, Meletti, S, Tondelli, M, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Winston, GP, Griffin, A, Singh, A, Tiwari, VK, Kreilkamp, BAK, Lenge, M, Guerrini, R, Hamandi, K, Foley, S, Ruber, T, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Kaestner, E, Hatton, SN, Vos, SB, Caciagli, L, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, Bernhardt, BC, Lariviere, S, Royer, J, Rodriguez-Cruces, R, Paquola, C, Caligiuri, ME, Gambardella, A, Concha, L, Keller, SS, Cendes, F, Yasuda, CL, Bonilha, L, Gleichgerrcht, E, Focke, NK, Domin, M, von Podewills, F, Langner, S, Rummel, C, Wiest, R, Martin, P, Kotikalapudi, R, O'Brien, TJ, Sinclair, B, Vivash, L, Desmond, PM, Lui, E, Vaudano, AE, Meletti, S, Tondelli, M, Alhusaini, S, Doherty, CP, Cavalleri, GL, Delanty, N, Kalviainen, R, Jackson, GD, Kowalczyk, M, Mascalchi, M, Semmelroch, M, Thomas, RH, Soltanian-Zadeh, H, Davoodi-Bojd, E, Zhang, J, Winston, GP, Griffin, A, Singh, A, Tiwari, VK, Kreilkamp, BAK, Lenge, M, Guerrini, R, Hamandi, K, Foley, S, Ruber, T, Weber, B, Depondt, C, Absil, J, Carr, SJA, Abela, E, Richardson, MP, Devinsky, O, Severino, M, Striano, P, Tortora, D, Kaestner, E, Hatton, SN, Vos, SB, Caciagli, L, Duncan, JS, Whelan, CD, Thompson, PM, Sisodiya, SM, Bernasconi, A, Labate, A, McDonald, CR, Bernasconi, N, and Bernhardt, BC

Abstract

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.

Published
2022

4. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Author

Gleichgerrcht, E, Munsell, BC, Alhusaini, S, Alvim, MKM, Bargallo, N, Bender, B, Bernasconi, A, Bernasconi, N, Bernhardt, B, Blackmon, K, Caligiuri, ME, Cendes, F, Concha, L, Desmond, PM, Devinsky, O, Doherty, CP, Domin, M, Duncan, JS, Focke, NK, Gambardella, A, Gong, B, Guerrini, R, Hatton, SN, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kreilkamp, BAK, Labate, A, Langner, S, Lariviere, S, Lenge, M, Lui, E, Martin, P, Mascalchi, M, Meletti, S, O'Brien, TJ, Pardoe, HR, Pariente, JC, Rao, JX, Richardson, MP, Rodriguez-Cruces, R, Ruber, T, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Vaudano, AE, Vivash, L, von Podewills, F, Vos, SB, Weber, B, Yao, Y, Yasuda, CL, Zhang, J, Thompson, PM, Sisodiya, SM, McDonald, CR, Bonilha, L, Gleichgerrcht, E, Munsell, BC, Alhusaini, S, Alvim, MKM, Bargallo, N, Bender, B, Bernasconi, A, Bernasconi, N, Bernhardt, B, Blackmon, K, Caligiuri, ME, Cendes, F, Concha, L, Desmond, PM, Devinsky, O, Doherty, CP, Domin, M, Duncan, JS, Focke, NK, Gambardella, A, Gong, B, Guerrini, R, Hatton, SN, Kalviainen, R, Keller, SS, Kochunov, P, Kotikalapudi, R, Kreilkamp, BAK, Labate, A, Langner, S, Lariviere, S, Lenge, M, Lui, E, Martin, P, Mascalchi, M, Meletti, S, O'Brien, TJ, Pardoe, HR, Pariente, JC, Rao, JX, Richardson, MP, Rodriguez-Cruces, R, Ruber, T, Sinclair, B, Soltanian-Zadeh, H, Stein, DJ, Striano, P, Taylor, PN, Thomas, RH, Vaudano, AE, Vivash, L, von Podewills, F, Vos, SB, Weber, B, Yao, Y, Yasuda, CL, Zhang, J, Thompson, PM, Sisodiya, SM, McDonald, CR, and Bonilha, L

Abstract

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Published
2021

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