Temporally Defined Brain Network Activation Associated With Slowed Information Processing Speed in Multiple...

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Information processing speed (IPS) is a key cognitive deficit in multiple sclerosis (MS).
• Previous research linked IPS to frontal brain regions but lacked temporal resolution.
• Understanding the neural basis of IPS is crucial for developing targeted MS therapies.

Purpose of the Study:

• To investigate the neural underpinnings of IPS deficits in MS using advanced neuroimaging analysis.
• To identify distinct brain states associated with different aspects of IPS.
• To explore the relationship between brain activity, task performance, and clinical outcomes in MS.

Main Methods:

• Utilized magnetoencephalography (MEG) data from healthy controls and people with MS (PwMS) performing the Symbol Digit Modalities Test (SDMT).
• Employed a data-driven time delay embedded-hidden Markov model (TDE-HMM) to analyze neural activity.
• Identified spectrally defined, task-specific brain states with distinct temporal and spatial characteristics.

Main Results:

• The TDE-HMM identified five task-relevant brain states contributing to IPS: sensory, cognitive, and motor speed.
• PwMS exhibited reduced prefrontal activation compared to controls.
• Peak neural activity in prefrontal, frontoparietal, and occipital networks correlated with reaction time and SDMT performance.

Conclusions:

• IPS in MS is supported by a network involving sensory (occipital), cognitive (prefrontal/frontoparietal), and motor (sensorimotor) speed.
• Neural alterations in PwMS, particularly in prefrontal regions, are associated with cognitive deficits.
• Findings provide a foundation for future therapeutic strategies aimed at improving IPS in MS.