Functional Connectivity and Structural Disruption in the Default‐Mode Network Predicts Cognitive Rehabilitation Outcomes in Multiple Sclerosis

Efficacy of restorative cognitive rehabilitation can be predicted from baseline patient factors. In addition, patient profiles of functional connectivity are associated with cognitive reserve and moderate the structure‐cognition relationship in people with multiple sclerosis (PwMS). Such interactions may help predict which PwMS will benefit most from cognitive rehabilitation. Our objective was to determine whether patient response to restorative cognitive rehabilitation is predictable from baseline structural network disruption and whether this relationship is moderated by functional connectivity.

[1]  R. Benedict,et al.  Response heterogeneity to home-based restorative cognitive rehabilitation in multiple sclerosis: An exploratory study. , 2019, Multiple sclerosis and related disorders.

[2]  D. Ramasamy,et al.  Preserved network functional connectivity underlies cognitive reserve in multiple sclerosis , 2019, Human brain mapping.

[3]  E. D’Angelo,et al.  Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis , 2019, Front. Cell. Neurosci..

[4]  Jeffrey A. Cohen,et al.  Symbol Digit Modalities Test: A valid clinical trial endpoint for measuring cognition in multiple sclerosis , 2018, Multiple sclerosis.

[5]  M. Schoonheim,et al.  Is impaired information processing speed a matter of structural or functional damage in MS? , 2018, NeuroImage: Clinical.

[6]  D. Ramasamy,et al.  Hypertension and heart disease are associated with development of brain atrophy in multiple sclerosis: a 5‐year longitudinal study , 2018, European journal of neurology.

[7]  D. Ramasamy,et al.  White matter tract network disruption explains reduced conscientiousness in multiple sclerosis , 2018, Human brain mapping.

[8]  G. Wylie,et al.  Information processing speed in multiple sclerosis: Relevance of default mode network dynamics , 2018, NeuroImage: Clinical.

[9]  J. DeLuca,et al.  Evidenced-Based Cognitive Rehabilitation for Persons With Multiple Sclerosis: An Updated Review of the Literature From 2007 to 2016. , 2017, Archives of physical medicine and rehabilitation.

[10]  Ludovica Griffanti,et al.  Hand classification of fMRI ICA noise components , 2017, NeuroImage.

[11]  Jianjin Xu,et al.  Cognitive function in multiple sclerosis improves with telerehabilitation: Results from a randomized controlled trial , 2017, PloS one.

[12]  C. Beckmann,et al.  Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses , 2017, Front. Neurosci..

[13]  Frederik Barkhof,et al.  Increased default-mode network centrality in cognitively impaired multiple sclerosis patients , 2017, Neurology.

[14]  Lynn D. Hudson,et al.  Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis , 2017, Multiple sclerosis.

[15]  M. Schoonheim Functional reorganization is a maladaptive response to injury – Commentary , 2017, Multiple sclerosis.

[16]  M. Filippi,et al.  Functional reorganization is a maladaptive response to injury – YES , 2017, Multiple sclerosis.

[17]  Menno M. Schoonheim,et al.  Network Collapse and Cognitive Impairment in Multiple Sclerosis , 2015, Front. Neurol..

[18]  M. Ramanathan,et al.  Cardiovascular risk factors are associated with increased lesion burden and brain atrophy in multiple sclerosis , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[19]  M. Breakspear,et al.  The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.

[20]  Daniel S. Margulies,et al.  Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping , 2014, Front. Neurosci..

[21]  Michael Eickenberg,et al.  Machine learning for neuroimaging with scikit-learn , 2014, Front. Neuroinform..

[22]  J. DeLuca,et al.  An RCT to treat learning impairment in multiple sclerosis , 2013, Neurology.

[23]  Ashish Raj,et al.  The Network Modification (NeMo) Tool: Elucidating the Effect of White Matter Integrity Changes on Cortical and Subcortical Structural Connectivity , 2013, Brain Connect..

[24]  Robert Zivadinov,et al.  Abnormal subcortical deep-gray matter susceptibility-weighted imaging filtered phase measurements in patients with multiple sclerosis A case-control study , 2012, NeuroImage.

[25]  M. Corbetta,et al.  Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis , 2011, Proceedings of the National Academy of Sciences.

[26]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[27]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[28]  Frederik Barkhof,et al.  The limits of functional reorganization in multiple sclerosis , 2010, Neurology.

[29]  M. Filippi,et al.  Default-mode network dysfunction and cognitive impairment in progressive MS , 2010, Neurology.

[30]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[31]  B. Weinstock-Guttman,et al.  Repeated assessment of neuropsychological deficits in multiple sclerosis using the Symbol Digit Modalities Test and the MS Neuropsychological Screening Questionnaire , 2008, Multiple sclerosis.

[32]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[33]  R. Bakshi,et al.  Frontal cortex atrophy predicts cognitive impairment in multiple sclerosis. , 2002, The Journal of neuropsychiatry and clinical neurosciences.

[34]  J. Duncan,et al.  Common regions of the human frontal lobe recruited by diverse cognitive demands , 2000, Trends in Neurosciences.

[35]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.