Multiscale neuro-inspired models for interpretation of EEG signals in patients with epilepsy.

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  • Additional Information
    • Source:
      Publisher: Elsevier Country of Publication: Netherlands NLM ID: 100883319 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-8952 (Electronic) Linking ISSN: 13882457 NLM ISO Abbreviation: Clin Neurophysiol Subsets: MEDLINE
    • Publication Information:
      Original Publication: Amsterdam : Elsevier, c1999-
    • Subject Terms:
      Electroencephalography*/methods ; Models, Neurological* ; Epilepsy*/physiopathology ; Epilepsy*/diagnosis; Humans ; Cerebral Cortex/physiopathology ; Drug Resistant Epilepsy/physiopathology ; Drug Resistant Epilepsy/diagnosis
    • Abstract:
      Objective: The aim is to gain insight into the pathophysiological mechanisms underlying interictal epileptiform discharges observed in electroencephalographic (EEG) and stereo-EEG (SEEG, depth electrodes) recordings performed during pre-surgical evaluation of patients with drug-resistant epilepsy.
      Methods: We developed novel neuro-inspired computational models of the human cerebral cortex at three different levels of description: i) microscale (detailed neuron models), ii) mesoscale (neuronal mass models) and iii) macroscale (whole brain models). Although conceptually different, micro- and mesoscale models share some similar features, such as the typology of neurons (pyramidal cells and three types of interneurons), their spatial arrangement in cortical layers, and their synaptic connectivity (excitatory and inhibitory). The whole brain model consists of a large-scale network of interconnected neuronal masses, with connectivity based on the human connectome.
      Results: For these three levels of description, the fine-tuning of free parameters and the quantitative comparison with real data allowed us to reproduce interictal epileptiform discharges with a high degree of fidelity and to formulate hypotheses about the cell- and network-related mechanisms underlying the generation of fast ripples and SEEG-recorded epileptic spikes and spike-waves.
      Conclusions: The proposed models provide valuable insights into the pathophysiological mechanisms underlying the generation of epileptic events. The knowledge gained from these models effectively complements the clinical analysis of SEEG data collected during the evaluation of patients with epilepsy.
      Significance: These models are likely to play a key role in the mechanistic interpretation of epileptiform activity.
      Competing Interests: Declarations of interest None.
      (Copyright © 2024 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)
    • Contributed Indexing:
      Keywords: Brain; Computational modeling; EEG; Epilepsy; Fast ripples; Interictal activity; Largescale; Mesoscale; Microscale; SEEG; Spike-waves; Spikes
    • Publication Date:
      Date Created: 20240323 Date Completed: 20240426 Latest Revision: 20240426
    • Publication Date:
      20240427
    • Accession Number:
      10.1016/j.clinph.2024.03.006
    • Accession Number:
      38520800