Information processing speed (IPS) is a core cognitive deficit in people with multiple sclerosis (PwMS). Previous efforts have associated IPS performance to frontal regions, but were constrained by limited temporal resolution. In this work, we employed a data-driven method, the time delay embedded-hidden Markov model (TDE-HMM), to identify task-specific states that are spectrally defined with distinct temporal and spatial profiles. We used magnetoencephalographic (MEG) data recorded while healthy controls and PwMS performed a cognitive task designed to capture IPS, the Symbol Digit Modalities Test (SDMT). The TDE-HMM identified five task-relevant states, supporting a tri-factor contribution to IPS: sensory speed (occipital visual detection and processing), cognitive speed (prefrontal executive and frontoparietal attention shift), and motor speed (sensorimotor). We observed reduced prefrontal activation in PwMS, while peak features across prefrontal, frontoparietal, and occipital networks were associated with task reaction time and clinical SDMT performance. This work can drive future research for MS treatments targeting IPS improvements.
Burta, OD, Akbarian, F, Rossi, C, Vidaurre, D, D'Hooghe, MB, D'Haeseleer, M, Nagels, G & Van Schependom, J 2026, 'Temporally defined brain network activation associated with slowed information processing speed in multiple sclerosis', Human Brain Mapping, vol. 47, no. 3, e70478. https://doi.org/10.1002/hbm.70478
Burta, O. D., Akbarian, F., Rossi, C., Vidaurre, D., D'Hooghe, M. B., D'Haeseleer, M., Nagels, G., & Van Schependom, J. (2026). Temporally defined brain network activation associated with slowed information processing speed in multiple sclerosis. Human Brain Mapping, 47(3), Article e70478. https://doi.org/10.1002/hbm.70478
@article{d6c3ec8220444eacb6f7cd8973addb5c,
title = "Temporally defined brain network activation associated with slowed information processing speed in multiple sclerosis",
abstract = "Information processing speed (IPS) is a core cognitive deficit in people with multiple sclerosis (PwMS). Previous efforts have associated IPS performance to frontal regions, but were constrained by limited temporal resolution. In this work, we employed a data-driven method, the time delay embedded-hidden Markov model (TDE-HMM), to identify task-specific states that are spectrally defined with distinct temporal and spatial profiles. We used magnetoencephalographic (MEG) data recorded while healthy controls and PwMS performed a cognitive task designed to capture IPS, the Symbol Digit Modalities Test (SDMT). The TDE-HMM identified five task-relevant states, supporting a tri-factor contribution to IPS: sensory speed (occipital visual detection and processing), cognitive speed (prefrontal executive and frontoparietal attention shift), and motor speed (sensorimotor). We observed reduced prefrontal activation in PwMS, while peak features across prefrontal, frontoparietal, and occipital networks were associated with task reaction time and clinical SDMT performance. This work can drive future research for MS treatments targeting IPS improvements.",
keywords = "Multiple Sclerosis, Information Processing Speed, symbol Digit Modalities Test, MEG task, TDE-HMM, dynamic functional networks",
author = "Burta, {Olivier Daniel} and Fahimeh Akbarian and Chiara Rossi and Diego Vidaurre and D'Hooghe, {Marie Beatrice} and Miguel D'Haeseleer and Guy Nagels and {Van Schependom}, Jeroen",
note = "Publisher Copyright: {\textcopyright} 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.",
year = "2026",
month = feb,
day = "18",
doi = "10.1002/hbm.70478",
language = "English",
volume = "47",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley-Liss Inc.",
number = "3",
}