Dynamic reconfiguration of aperiodic brain activity supports cognitive functioning in epilepsy
Poster No:
2121
Submission Type:
Abstract Submission
Authors:
Gian Marco Duma1, Marie-Constance Corsi2, Marianna Angiolelli3, Alberto Danieli4, Lisa Antoniazzi4, Paolo Bonanni1, Pierpaolo Sorrentino5, Emahnuel Troisi Lopez6
Institutions:
1IRCCS E. Medea Scientific Institute, Conegliano, Italy, 2Paris Brain Institute, Paris, France, 3Campus Bio-Medico University of Rome, Rome, Italy, 4Scientific Institute IRCCS E.Medea, Conegliano, Italy, 5INS - Aix Marseille University, Marseille, Italy, 6Department of Education and Sport Sciences, Pegaso Telematic University, Naples, Italy, Napoli, Italy
First Author:
Co-Author(s):
Emahnuel Troisi Lopez
Department of Education and Sport Sciences, Pegaso Telematic University, Naples, Italy
Napoli, Italy
Department of Education and Sport Sciences, Pegaso Telematic University, Naples, Italy
Napoli, Italy
Introduction:
Alterations of brain dynamics on the large-scale characterize epilepsy and they have been related both to clinical and neuropsychological outcomes (1,2). Temporal lobe epilepsy (TLE) is the most frequent drug-resistant focal epilepsy characterized by alterations in multiple cognitive domains (3). Converging evidence has shown a link between altered of large-scale brain dynamics in TLE and the reduced cognitive performance (4). Relevantly, the reconfiguration of functional dynamics over time contains enough information to unambiguously identify individuals, representing a neural fingerprint (6). Despite being a promising neural marker, the investigation of functional configuration can represent a clinically applicable only in the measure of its capability to account for the inter-individual variability across patients. We focused on the neuralfingerprint approach to account interindividuality of brain dynamics and its relation with cognitive outcome in patients with TLE.
Methods:
Neural fingerprint was extracted from EEG-derived connectome to measure the stability of brain and differentiability across groups and individuals. We recorded 10 min of resting state EEG activity (128 channels) from which we performed electrical source imaging. We excluded interictal epileptic discharges to investigate if the brain basal configuration, irrespective of epileptiform activities, could provide enough information to differentiate between epileptic conditions (UTLE vs. BTLE) and healthy controls. We then extracted neuronal avalanches (NAs), representing aperiodic bursts of brain activities spreading over the large scale. The propagation across brain regions of NAs were stored in the avalanche transition matrices (ATMs) (7). The ATMs are optimally suited to capture fingerprinting, as compared to classical functional connectivity measures (8). We exploited ATMs to capture the neural fingerprint of patients with TLE vs. a control group and the link with neuropsychological functioning.
Results:
We observed a larger individual self-similarity for patients as compared to controls (Iself TLE < Controls, p<.01). Healthy controls showed a larger similarity across group individuals (Iothers parameter) as compared to patients. We observe also a difference across patients groups with Iother in UTLE > BTLE (p < .01). Globally, TLE patients were more discriminable as compared to controls (Idiff controls < UTLE and BTLE (p <.01). BTLE patients resulted more discriminable than UTLE patients (Idiff pFDR = .048). We then investigated the stability of the functional links across regions using test-retest intraclass correlation coefficient. TLE patients showed larger stability (p < .001 TLE vs Controls), suggesting a reduced flexibility of functional organization in the neurological condition. Finally, we calculated the Iclinical score, representing ATM similarity between patients and controls. In UTLE pateints, the Iclinical was correlated with memory functioning (Rey figure test-recall) (r = .48, p = .032).
Conclusions:
The fingerprint analysis of the ATMs revealed more stereotyped patterns in patients with respect to controls, with the greatest stereotypy in bilateral TLE. Finally, indices extracted from individual patterns of brain dynamics correlated with the memory functioning in unilateral TLE. This study helped understand how dynamic brain activity in TLE is shaped and provided patient-specific indices useful for personalized medicine.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s)
Learning and Memory:
Long-Term Memory (Episodic and Semantic)
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
Data analysis
Electroencephaolography (EEG)
Epilepsy
Memory
Modeling
1|2Indicates the priority used for review
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2)Duma, G. M., Danieli, A., Vettorel, A., Antoniazzi, L., Mento, G., & Bonanni, P. (2021). Investigation of dynamic functional connectivity of the source reconstructed epileptiform discharges in focal epilepsy: A graph theory approach. Epilepsy Research, 176, 106745.
3)Ives-Deliperi, V., & Butler, J. T. (2021). Mechanisms of cognitive impairment in temporal lobe epilepsy: a systematic review of resting-state functional connectivity studies. Epilepsy & Behavior, 115, 107686.
4)Duma, G. M., Danieli, A., Mattar, M. G., Baggio, M., Vettorel, A., Bonanni, P., & Mento, G. (2022). Resting state network dynamic reconfiguration and neuropsychological functioning in temporal lobe epilepsy: An HD-EEG investigation. Cortex, 157, 1-13.
5) Girardi‐Schappo, M., Fadaie, F., Lee, H. M., Caldairou, B., Sziklas, V., Crane, J., ... & Bernasconi, N. (2021). Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy. Epilepsia, 62(4), 1022-1033.
6) Cipriano, L., Lopez, E. T., Liparoti, M., Minino, R., Romano, A., Polverino, A., ... & Sorrentino, P. (2023). Reduced clinical connectome fingerprinting in multiple sclerosis predicts fatigue severity. NeuroImage: Clinical, 39, 103464.
7) Sorrentino, P., Seguin, C., Rucco, R., Liparoti, M., Lopez, E. T., Bonavita, S., ... & Zalesky, A. (2021). The structural connectome constrains fast brain dynamics. Elife, 10, e67400.
8)Sorrentino, P., Lopez, E. T., Romano, A., Granata, C., Corsi, M. C., Sorrentino, G., & Jirsa, V. (2023). Brain fingerprint is based on the aperiodic, scale-free, neuronal activity. NeuroImage, 277, 120260.