Authors :
First Author: Gaby Moscol, MD – Schulich School of Medicine and Dentistry, Western University
Presenting Author: Seyed Mirsattari, MD, PhD, FRCPC – Western University, London, Ontario
Maryam Mofrad, PD – Western Institute of Neuroscience, Western University; Loxlan Kasa, PHD – Western Institute of Neuroscience, Western University; Ali Khan, PDS – Western Institute of Neuroscience, Western University; Brittney Castrilli, MS – Western University; Lyle Muller, PDS – Western Institute of Neuroscience, Western University; Seyed Mirsattari, PHD – Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University
Rationale:
Psychogenic non-epileptic seizures (PNES) are frequently described as a potential alternative diagnosis for epilepsy. However, both have different underlying pathogenesis. Based on current etiologic models, the biological factors interact with other factors for causing PNES. Specifically, the biological factors would include abnormalities in the functional brain connectivity among others.
Methods:
A total of 11 patients with PNES and 11 healthy control (HC) subjects were included. Functional brain connectivity differences were investigated using resting-state and movie-driven 7-Tesla functional magnetic resonance imaging (fMRI). Preprocessing involved fmriprep and nilearn denoising techniques. Connectivity matrices were generated using Pearson correlations between the 300 regions in the Schaffer parcellation. Centrality measures including degree centrality, closeness centrality, betweenness centrality and eigenvector centrality were computed for each ROI in both groups. The sensitivity index was used to identify the largest differences in centrality measures between the groups. Logistic regression models were constructed for PNES classification, incorporating ROIs with the highest sensitivity index. The classification model using eigenvector centrality demonstrated the highest accuracy when assessed with five fold cross-validation.
Results:
In the resting state data, three specific ROIs demonstrated the most significant differences in eigenvector centrality, resulting in the highest average prediction accuracy of 96%. These regions were specifically associated with the limbic network and the salience network. Similarly, in the movie-driven data, fifteen ROIs exhibited the largest differences in eigenvector centrality between the two groups, achieving an average prediction accuracy of 93%. These regions were involved in multiple networks, including the limbic network, salience network, attention network, and self-agency network.
Conclusions:
PNES represent a multi-network disorder involving alterations within and across brain circuits implicated in self-agency, emotion processing, attention, homeostatic balance, interoception multimodal integration, and cognitive/motor control among other functions. This study applies a unique approach to functional connectivity analysis by utilizing resting-state and movie-driven fMRI for data acquisition and eigenvector centrality as a measure of graph theory. This novel methodology provides insights into the functional connectivity patterns underlying PNES and sheds light on the network-level alterations associated with this disorder.
Funding: None