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Information processing speed in multiple sclerosis: Relevance of default mode network dynamics
Q van Geest1, L Douw2, S van 't Klooster1
1Department of Anatomy & Neurosciences, Neuroscience Amsterdam, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Neuroimage. Clinical
|July 10, 2018
Summary
Dynamic functional connectivity (dFC) of the default mode network (DMN) during rest and task states significantly predicts information processing speed (IPS) in people with multiple sclerosis (pwMS). This dFC adds crucial predictive value beyond traditional brain measures for understanding cognitive function in MS.
Area of Science:
- Neuroscience
- Cognitive Neurology
- Multiple Sclerosis Research
Background:
- Multiple sclerosis (MS) is a demyelinating disease affecting the central nervous system, often leading to cognitive impairments, particularly in information processing speed (IPS).
- Conventional neuroimaging measures in people with MS (pwMS) often fail to fully capture the neural underpinnings of cognitive deficits.
- The default mode network (DMN) plays a crucial role in intrinsic brain function, and its dynamic connectivity may be altered in neurological conditions.
Purpose of the Study:
- To investigate the added value of dynamic functional connectivity (dFC) of the DMN during resting-state (RS) and task-state, and their difference, in explaining IPS in pwMS.
- To compare the predictive power of dFC against conventional brain measures (e.g., white matter integrity, grey matter volume) for IPS.
- To assess the DMN's adaptability to cognitive demands in maintaining IPS in pwMS.
Main Methods:
- fMRI data were acquired during resting-state (RS) and an information processing speed (IPS) task in 29 pwMS and 18 healthy controls.
- Stationary functional connectivity (sFC) and dynamic functional connectivity (dFC) of the DMN were calculated, along with the difference between task and RS states (ΔdFC-DMN).
- Structural MRI (WM, GM volume, lesion load) and diffusion tensor imaging (WM integrity) were performed. Regression analyses identified predictors of IPS.
Main Results:
- PwMS exhibited worse IPS, lower grey matter volume, and reduced white matter integrity compared to controls.
- No group-level differences in sFC or dFC of the DMN were observed.
- In pwMS, cortical volume and ΔdFC-DMN significantly predicted IPS-composite, explaining 52% of the variance. Including ΔdFC-DMN increased explained variance by 26%.
Conclusions:
- Dynamic functional connectivity of the DMN during the transition from rest to task engagement provides significant added value in explaining IPS in pwMS.
- These findings underscore the importance of DMN's adaptive capacity in maintaining cognitive function, specifically IPS, in the context of multiple sclerosis.
- Dynamic connectivity measures offer a promising avenue for understanding and potentially targeting cognitive deficits in neurodegenerative diseases like MS.
Keywords:
CognitionDefault mode networkDynamic functional connectivityFunctional connectivityInformation processing speedMultiple sclerosisMore Related Videos
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