Neural correlates of digital measures shown by structural MRI: a post-hoc analysis of a smartphone-based remote assessment feasibility study in multiple sclerosis

[1]  M. Filippi,et al.  The association between cognition and motor performance is beyond structural damage in relapsing–remitting multiple sclerosis , 2022, Journal of Neurology.

[2]  X. Montalban,et al.  A smartphone sensor-based digital outcome assessment of multiple sclerosis , 2021, Multiple sclerosis.

[3]  Saurabh Jain,et al.  icobrain ms 5.1: Combining unsupervised and supervised approaches for improving the detection of multiple sclerosis lesions , 2021, NeuroImage: Clinical.

[4]  D. Arnold,et al.  Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes , 2021, Journal of Neurology, Neurosurgery, and Psychiatry.

[5]  C. Perez,et al.  Nine-Hole Peg Test (9-HPT) , 2021 .

[6]  R. Benedict,et al.  Thalamic Nuclei Volumes and Their Relationships to Neuroperformance in Multiple Sclerosis: A Cross‐Sectional Structural MRI Study , 2020, Journal of magnetic resonance imaging : JMRI.

[7]  M. Filippi,et al.  MRI correlates of clinical disability and hand-motor performance in multiple sclerosis phenotypes , 2020, Multiple sclerosis.

[8]  Christian Gaser,et al.  Cognitive impairment in early MS: contribution of white matter lesions, deep grey matter atrophy, and cortical atrophy , 2020, Journal of Neurology.

[9]  Thanh Vân Phan,et al.  Automated MRI volumetry as a diagnostic tool for Alzheimer's disease: Validation of icobrain dm , 2020, NeuroImage: Clinical.

[10]  M. Battaglini,et al.  MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice , 2020, Nature Reviews Neurology.

[11]  Fernanda Costa-Moura Anterior , 2020, Encyclopedic Dictionary of Archaeology.

[12]  Massimo Filippi,et al.  Structural connectivity in multiple sclerosis and modeling of disconnection , 2019, Multiple sclerosis.

[13]  K. Anja European Committee for Treatment and Research in Multiple Sclerosis , 2019, Kinder- und Jugendmedizin.

[14]  X. Montalban,et al.  Adherence and Satisfaction of Smartphone- and Smartwatch-Based Remote Active Testing and Passive Monitoring in People With Multiple Sclerosis: Nonrandomized Interventional Feasibility Study , 2019, Journal of medical Internet research.

[15]  G. Albouy,et al.  Differences in brain processing of proprioception related to postural control in patients with recurrent non-specific low back pain and healthy controls , 2019, NeuroImage: Clinical.

[16]  R. Henry,et al.  Silent progression in disease activity–free relapsing multiple sclerosis , 2019, Annals of neurology.

[17]  M. Filippi,et al.  Imaging patterns of gray and white matter abnormalities associated with PASAT and SDMT performance in relapsing-remitting multiple sclerosis , 2019, Multiple sclerosis.

[18]  M. Sormani,et al.  Learning ability correlates with brain atrophy and disability progression in RRMS , 2018, Journal of Neurology, Neurosurgery, and Psychiatry.

[19]  R. Benedict,et al.  Walking disability measures in multiple sclerosis patients: Correlations with MRI-derived global and microstructural damage , 2018, Journal of the Neurological Sciences.

[20]  J. Arrazola,et al.  Identification of Cortical and Subcortical Correlates of Cognitive Performance in Multiple Sclerosis Using Voxel-Based Morphometry , 2018, Front. Neurol..

[21]  A. Artemiadis,et al.  Structural MRI correlates of cognitive function in multiple sclerosis. , 2018, Multiple sclerosis and related disorders.

[22]  F. Esposito,et al.  Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume , 2017, Brain Imaging and Behavior.

[23]  Nicholas C. Firth,et al.  Progression of regional grey matter atrophy in multiple sclerosis , 2017, bioRxiv.

[24]  M. Battaglini,et al.  Deep grey matter volume loss drives disability worsening in multiple sclerosis , 2017, bioRxiv.

[25]  Massimo Filippi,et al.  Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis , 2017, Multiple sclerosis.

[26]  F. Barkhof,et al.  Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy , 2017, NeuroImage: Clinical.

[27]  V. Fleischer,et al.  Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis , 2017, Multiple sclerosis.

[28]  Julien Cohen-Adad,et al.  SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data , 2017, NeuroImage.

[29]  F. Esposito,et al.  Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume , 2017, Brain Imaging and Behavior.

[30]  Lei Wu,et al.  Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis , 2016, International journal of molecular sciences.

[31]  Sara Llufriu,et al.  Structural networks involved in attention and executive functions in multiple sclerosis , 2016, NeuroImage: Clinical.

[32]  F. Barkhof,et al.  Multicenter Validation of Mean Upper Cervical Cord Area Measurements from Head 3D T1-Weighted MR Imaging in Patients with Multiple Sclerosis , 2016, American Journal of Neuroradiology.

[33]  S. Bressler,et al.  Dorsal anterior cingulate cortex modulates supplementary motor area in coordinated unimanual motor behavior , 2015, Front. Hum. Neurosci..

[34]  Saurabh Jain,et al.  Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images , 2015, NeuroImage: Clinical.

[35]  C. McCulloch,et al.  Magnetic resonance imaging correlates of clinical outcomes in early multiple sclerosis. , 2014, Multiple sclerosis and related disorders.

[36]  Kesshi M Jordan,et al.  Spinal cord gray matter atrophy correlates with multiple sclerosis disability , 2014, Annals of neurology.

[37]  C. Wheeler-Kingshott,et al.  Cervical cord area measurement using volumetric brain magnetic resonance imaging in multiple sclerosis. , 2015, Multiple sclerosis and related disorders.

[38]  F. Barkhof,et al.  Mean upper cervical cord area (MUCCA) measurement in long-standing multiple sclerosis: Relation to brain findings and clinical disability , 2014, Multiple sclerosis.

[39]  Hugo Merchant,et al.  Neural basis of the perception and estimation of time. , 2013, Annual review of neuroscience.

[40]  A. Achiron,et al.  Superior temporal gyrus thickness correlates with cognitive performance in multiple sclerosis , 2013, Brain Structure and Function.

[41]  M. Horsfield,et al.  A multicenter assessment of cervical cord atrophy among MS clinical phenotypes , 2011, Neurology.

[42]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[43]  Sharon E. Lee,et al.  Cerebral Ventricular Changes Associated With Transitions Between Normal Cognitive Function, Mild Cognitive Impairment, and Dementia , 2007, Alzheimer disease and associated disorders.

[44]  C. Mainero,et al.  Functional Brain Reorganization in Multiple Sclerosis: Evidence from fMRI Studies , 2006, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[45]  W. Helsen,et al.  Relationship between multiple sclerosis intention tremor severity and lesion load in the brainstem , 2005, Neuroreport.

[46]  M. Rovaris,et al.  Regional brain atrophy evolves differently in patients with multiple sclerosis according to clinical phenotype. , 2005, AJNR. American journal of neuroradiology.

[47]  P. J. Huber Robust Statistics: Huber/Robust Statistics , 2005 .

[48]  M. Filippi,et al.  Adaptive functional changes in the cerebral cortex of patients with nondisabling multiple sclerosis correlate with the extent of brain structural damage , 2002, Annals of neurology.

[49]  Alan C. Evans,et al.  Role of the human anterior cingulate cortex in the control of oculomotor, manual, and speech responses: a positron emission tomography study. , 1993, Journal of neurophysiology.

[50]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[51]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[52]  Gavin Giovannoni,et al.  Unmet needs, burden of treatment, and patient engagement in multiple sclerosis: A combined perspective from the MS in the 21st Century Steering Group. , 2018, Multiple sclerosis and related disorders.

[53]  Frederik Barkhof,et al.  Cortical atrophy patterns in multiple sclerosis are non-random and clinically relevant. , 2016, Brain : a journal of neurology.

[54]  Robert Zivadinov,et al.  Basal ganglia, thalamus and neocortical atrophy predicting slowed cognitive processing in multiple sclerosis , 2011, Journal of Neurology.

[55]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[56]  W. L. Benedict,et al.  Multiple Sclerosis , 2007, Journal - Michigan State Medical Society.