Remodeling of brain morphology in temporal lobe epilepsy

Roggenhofer, Elisabeth; Muller, Sandrine; Santarnecchi, Emiliano; Melie‐Garcia, Lester; Wiest, Roland; Kherif, Ferath; Draganski, Bogdan

doi:10.1002/brb3.1825

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Remodeling of brain morphology in temporal lobe epilepsy

Roggenhofer, E., Muller, S., Santarnecchi, E., Melie‐Garcia, L., Wiest, R., Kherif, F., et al. (2020). Remodeling of brain morphology in temporal lobe epilepsy. Brain and Behavior, e01825. doi:10.1002/brb3.1825.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0007-17F9-7 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0007-17FA-6

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Urheber:
Roggenhofer, Elisabeth^{1, 2}, Autor
Muller, Sandrine², Autor
Santarnecchi, Emiliano^{3, 4}, Autor
Melie‐Garcia, Lester^{2, 5}, Autor
Wiest, Roland⁶, Autor
Kherif, Ferath², Autor
Draganski, Bogdan^{2, 7}, Autor

Affiliations:
1Department of Neurology, University Hospital of Geneva, Switzerland, ou_persistent22
2Département des Neurosciences Cliniques, Laboratoire de Recherche en Neuroimagerie (LREN), Centre hospitalier universitaire vaudois, Lausanne, Switzerland, ou_persistent22
3Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA, ou_persistent22
4Siena Brain Investigation & Neuromodulation Lab, Department of Medicine, Surgery and Neuroscience, University of Siena, Italy, ou_persistent22
5Applied Signal Processing Group, Swiss Federal Institute of Technology, Zurich, Switzerland, ou_persistent22
6Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, University Hospital Bern, Switzerland, ou_persistent22
7Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549

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Schlagwörter: BMS; Bayesian model selection; MVB; Computational anatomy; Hippocampus; Magnetic resonance imaging; Multivariate Bayesian modeling; Temporal lobe epilepsy

Zusammenfassung:

Background: Mesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter-regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy-associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes?

Methods: We acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass-univariate analysis followed by multivariate Bayesian modeling.

Results: After obtaining TLE-associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para-/hippocampal regions contribute most to disease-related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left-sided TLE, whereas thalamus and temporal pole for right-sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis.

Conclusions: Our topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE-related spatial modulation of anatomical networks.

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Sprache(n): eng - English

Datum: Geändert: 2020-08-10Eingereicht: 2020-04-27Angenommen: 2019-08-14Online veröffentlicht: 2020-09-17

Publikationsstatus: Online veröffentlicht

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Identifikatoren: DOI: 10.1002/brb3.1825
Anderer: online ahead of print
PMID: 32945137

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Grant ID : 32003B_135679

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