Disrupted working memory event-related network dynamics in multiple sclerosis

Abstract

In multiple sclerosis (MS), working memory (WM) impairment can occur soon after disease onset and significantly affects the patient’s quality of life. Functional imaging research in MS aims to investigate the neurophysiological underpinnings of WM impairment. In this context, we utilize a data-driven technique, the time delay embedded-hidden Markov model, to extract spectrally defined functional networks in magnetoencephalographic (MEG) data acquired during a WM visual-verbal n-back task. Here, we show that the activation of two networks is altered in relapsing remitting-MS patients. First, the activation of an early theta prefrontal network linked to stimulus encoding and attentional control significantly decreases in MS compared to HC. This diminished activation correlates with reduced accuracy and higher reaction time, suggesting that impaired attention control impacts task performance in MS patients. Secondly, a frontoparietal network characterized by beta coupling is activated between 300 and 600 ms post-stimulus, resembling the event-related P300, a cognitive marker extensively explored in EEG studies. The activation of this network is amplified in patients treated with benzodiazepine, in line with the well-known benzodiazepine-induced beta enhancement. Altogether, the TDE-HMM technique extracts task-relevant functional networks showing disease-specific and treatment-related alterations, revealing potential new markers to assess and track WM impairment in MS. Dynamic network analysis of MEG data reveals spatiospectral activation profiles underpinning working memory impairment in multiple sclerosis, distinguishing between the disease-induced and treatment-related neurophysiological alterations.