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Abstract 

The brain activity supporting high-order cognitive functions, such as attention and working memory, has been extensively described in the spatial configuration (brain regions recruited) via fMRI studies, and in the spectral content and event-related temporal activation in M/EEG studies. However, different neuroimaging techniques and signal/image processing analyses provided scattered descriptions of the cognitive brain processes, which are difficult to integrate. We present an alternative experimental framework to simultaneously evaluate the three-dimensionalities (time, space, and frequency) of cognitive dynamics. First, we considered MEG data, which provide an optimal temporal resolution (milliseconds) to observe cognitive processes acquired during a working memory paradigm, the visual-verbal nback task. We conducted a dynamic functional network analysis of the data throughout the unsupervised time delay embedded-hidden Markov model (TDE-HMM). This technique identifies a predefined number of spectrally defined patterns that reoccur throughout the recordings, respecting the temporal resolution dictated by the MEG data. In the first stage, we conducted an exploratory analysis on 38 healthy subjects. This model inferred (working memory) task-specific networks with unique temporal (activation), spectral (phase-coupling connections), and spatial (power spectral density distribution) profiles. First, 200 ms after stimulus onset, a theta frontoparietal network exerts attentional control and encodes the stimulus. Then, an alpha temporo-occipital network rehearses the verbal information, and a broadband frontoparietal network with a P300-like temporal profile leads the retrieval process and motor response. Following this, we expanded the dataset to a cohort of 70 people with multiple sclerosis (pwMS). We observed that the activation of the early theta prefrontal network significantly decreased in pwMS, which correlated with reduced accuracy in task performance in the MS group, suggesting an impaired encoding and learning process. Secondly, the activation of the M300 frontoparietal network characterized by beta coupling increases in patients treated with benzodiazepine, in line with the well-known benzodiazepine-induced beta enhancement. Our work on the healthy cohort provides a unified and integrated description of the multidimensional working memory dynamics that can be interpreted within the neuropsychological multi-component model of WM, improving the overall neurophysiological and neuropsychological comprehension of WM functioning. Additionally, the TDE-HMM technique extracted task-relevant functional networks showing disease-specific and treatment-related alterations, revealing potential new markers to assess and track WM impairment in MS. To conclude, combining the MEG task data and a dynamic functional connectivity analysis via TDE-HMM represents the optimal condition to investigate the milliseconds network dynamics underlying any high-order cognitive task in healthy and pathological cases.

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