Epilepsy & behavior : E&B · 2026
Characterizing functional connectivity alterations in functional/ dissociative seizures using resting-state and naturalistic fMRI.
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Moscol Gaby, Castrilli Brittney, Bucha Jain Priya, Mofrad Maryam H, Jafari Anahita, Aur Dorian, Yartey Esther, Muller Lyle, Khan Ali R, Johnsrude Ingrid S, Kasa Loxlan W, Menon Ravi S, Mirsattari Seyed M
Аннотация
OBJECTIVE: This study investigates alterations in brain functional networks in patients with functional/dissociative seizures (FDS) using a novel functional connectivity framework, with the goal of showing network-level biomarkers that may differentiate FDS from healthy controls.
METHODS: We conducted a 7-Tesla fMRI study involving 11 patients with FDS and 11 healthy controls (HC) gotten during both resting-state (rs) and a naturalistic-stimulus (ns) movie paradigm. Functional connectivity) was computed using parcel-wise Pearson correlations, and centrality measures, including eigenvector centrality, were derived to assess network influence. Group differences were evaluated using motion-controlled general linear models A sensitivity index found key ROIs, which were used in cross-validated logistic regression models. The classification model uses eigenvector centrality with 5-fold cross-validation.
RESULTS: FDS patients showed consistent alterations in eigenvector centrality across both resting-state and naturalistic-stimulus fMRI, particularly within limbic, somatomotor, and ventral attention network. Three key ROIs during rest and fifteen during naturalistic stimulation yielded high classification accuracies (96% and 93%, respectively). Several hubs found in the movie condition remained altered at rest. Logistic regression models using these network features distinguished FDS from controls, though findings require cautious interpretation due to sample size limitations.
CONCLUSIONS: Using high-field fMRI and a novel connectivity analysis, this study found abnormal network hubs across multiple systems in FDS. These findings support a predictive processing model and offer preliminary biomarkers to improve FDS differentiation, pending validation in larger cohorts.
METHODS: We conducted a 7-Tesla fMRI study involving 11 patients with FDS and 11 healthy controls (HC) gotten during both resting-state (rs) and a naturalistic-stimulus (ns) movie paradigm. Functional connectivity) was computed using parcel-wise Pearson correlations, and centrality measures, including eigenvector centrality, were derived to assess network influence. Group differences were evaluated using motion-controlled general linear models A sensitivity index found key ROIs, which were used in cross-validated logistic regression models. The classification model uses eigenvector centrality with 5-fold cross-validation.
RESULTS: FDS patients showed consistent alterations in eigenvector centrality across both resting-state and naturalistic-stimulus fMRI, particularly within limbic, somatomotor, and ventral attention network. Three key ROIs during rest and fifteen during naturalistic stimulation yielded high classification accuracies (96% and 93%, respectively). Several hubs found in the movie condition remained altered at rest. Logistic regression models using these network features distinguished FDS from controls, though findings require cautious interpretation due to sample size limitations.
CONCLUSIONS: Using high-field fMRI and a novel connectivity analysis, this study found abnormal network hubs across multiple systems in FDS. These findings support a predictive processing model and offer preliminary biomarkers to improve FDS differentiation, pending validation in larger cohorts.