Abnormal brain functional network dynamics in sleep‐related hypermotor epilepsy

Abstract

Aims This study aimed to use resting‐state functional magnetic resonance imaging (rs‐fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep‐related hypermotor epilepsy (SHE). Methods High‐resolution T1 and rs‐fMRI scanning were performed on all the subjects. We used a sliding‐window approach to construct a dynamic functional connectivity (dFC) network. The k‐means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network‐based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed. Results After k‐means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE. Conclusion The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.

Abnormal dynamic functional connectivity and topological properties of cerebellar network in male obstructive sleep apnea

“…Reduced connectivity between them could underpin cognitive deficits by disrupting visual processing. 37 …”

“…Furthermore, in brain regions exhibiting decreased FC, the VN and SMN are vital for motor control and learning. Reduced connectivity between them could underpin cognitive deficits by disrupting visual processing 37 …”

“…We thresholded edges at p = 0.0001 to construct the initial subnetwork. Permutation testing (5000 iterations) determined subnetwork significance against an empirical null distribution ( p < 0.005), with age, years of education, body mass index (BMI), and head motion as covariates 37 …”

“…Reduced connectivity between them could underpin cognitive deficits by disrupting visual processing. 37 The DMPFC constitutes a key node within the DMN involved in sleep regulation. 56 The cognitive cerebellum, which supports executive functions and attentional shifts, receives input from the sensory cortex and coordinates SMN activity.…”