State-dependent signatures of anti-N-methyl-d-aspartate receptor encephalitis

Traditional static functional connectivity analyses have shown distinct functional network alterations in patients with anti-N-methyl-d-aspartate receptor encephalitis. Here, we use a dynamic functional connectivity approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in anti-N-methyl-d-aspartate receptor encephalitis and assess the discriminatory power of functional brain states in a supervised classification approach. We included resting-state functional magnetic resonance imaging data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in functional connectivity, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. Compared to controls, patients exhibited diverging dynamic functional connectivity patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic functional connectivity measures were associated with disease severity and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic functional connectivity models and outperformed static analyses, reaching up to 78.6% classification accuracy. By applying time-resolved analyses, we disentangle state-specific functional connectivity impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on the functional reorganization underlying anti-N-methyl-d-aspartate receptor encephalitis. Finally, the correlation of dynamic functional connectivity measures with disease symptoms and severity demonstrates a clinical relevance of spatiotemporal connectivity dynamics in anti-N-methyl-d-aspartate receptor encephalitis. Von Schwanenflug et al. investigate whole-brain functional connectivity dynamics in anti-N-methyl-d-aspartate receptor encephalitis. Undetected in static analyses, patients exhibit state-specific alterations in connectivity, a shift in state preference and higher volatility of state transitions. A supervised machine learning algorithm showed higher predictive power in dynamic compared to static connectivity models.

Automated summary

The study used a dynamic functional connectivity approach to investigate the spatiotemporal variability of brain network activity in patients with anti-N-methyl-D-aspartate receptor encephalitis. The results showed distinct alterations in functional connectivity, dwell time patterns, and state transitions in patients compared to controls. The predictive power of dynamic functional connectivity models outperformed static analyses.