Structural networks involved in attention and executive functions in multiple sclerosis

Attention and executive deficits are disabling symptoms in multiple sclerosis (MS) that have been related to disconnection mechanisms. We aimed to investigate changes in structural connectivity in MS and their association with attention and executive performance applying an improved framework that combines high order probabilistic tractography and anatomical exclusion criteria postprocessing. We compared graph theory metrics of structural networks and fractional anisotropy (FA) of white matter (WM) connections or edges between 72 MS subjects and 38 healthy volunteers (HV) and assessed their correlation with cognition. Patients displayed decreased network transitivity, global efficiency and increased path length compared with HV (p < 0.05, corrected). Also, nodal strength was decreased in 26 of 84 gray matter regions. The distribution of nodes with stronger connections or hubs of the network was similar among groups except for the right pallidum and left insula, which became hubs in patients. MS subjects presented reduced edge FA widespread in the network, while FA was increased in 24 connections (p < 0.05, corrected). Decreased integrity of frontoparietal networks, deep gray nuclei and insula correlated with worse attention and executive performance (r between 0.38 and 0.55, p < 0.05, corrected). Contrarily, higher strength in the right transverse temporal cortex and increased FA of several connections (mainly from cingulate, frontal and occipital cortices) were associated with worse functioning (r between − 0.40 and − 0.47, p < 0.05 corrected). In conclusion, structural brain connectivity is disturbed in MS due to widespread impairment of WM connections and gray matter structures. The increased edge connectivity suggests the presence of reorganization mechanisms at the structural level. Importantly, attention and executive performance relates to frontoparietal networks, deep gray nuclei and insula. These results support the relevance of network integrity to maintain optimal cognitive skills.

[1]  Marcel A de Reus,et al.  An edge-centric perspective on the human connectome: link communities in the brain , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[2]  F. Barkhof,et al.  Structural MRI correlates of cognitive impairment in patients with multiple sclerosis , 2014, Human brain mapping.

[3]  Karl J. Friston,et al.  Functional mapping of brain areas implicated in auditory--verbal memory function. , 1993, Brain : a journal of neurology.

[4]  J. Sepulcre,et al.  Abnormalities in brain synchronization are correlated with cognitive impairment in multiple sclerosis , 2009, Multiple sclerosis.

[5]  D. Gronwall Paced Auditory Serial-Addition Task: A Measure of Recovery from Concussion , 1977, Perceptual and motor skills.

[6]  D. Auer,et al.  Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. , 2009, Brain : a journal of neurology.

[7]  D. Langdon Cognitive rehabilitation in multiple sclerosis , 1998, The Italian Journal of Neurological Sciences.

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

[9]  Stephen M. Rao,et al.  Development of a multiple sclerosis functional composite as a clinical trial outcome measure. , 1999, Brain : a journal of neurology.

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

[11]  Alan Connelly,et al.  Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.

[12]  Yong He,et al.  Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. , 2011, Cerebral cortex.

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

[14]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[15]  O Garcea,et al.  Cognitive impairment in patients with multiple sclerosis using the Brief Repeatable Battery-Neuropsychology test , 2006, Multiple sclerosis.

[16]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[17]  L. Querol,et al.  A one-year follow-up study of the Symbol Digit Modalities Test (SDMT) and the Paced Auditory Serial Addition Test (PASAT) in relapsing-remitting multiple sclerosis: an appraisal of comparative longitudinal sensitivity , 2015, BMC Neurology.

[18]  C Caltagirone,et al.  Anatomical brain connectivity can assess cognitive dysfunction in multiple sclerosis , 2013, Multiple sclerosis.

[19]  Anatol C. Kreitzer,et al.  Plasticity in gray and white: neuroimaging changes in brain structure during learning , 2012, Nature Neuroscience.

[20]  K. Worsley,et al.  Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. , 2009, Brain : a journal of neurology.

[21]  Snehashis Roy,et al.  Association of Cortical Lesion Burden on 7-T Magnetic Resonance Imaging With Cognition and Disability in Multiple Sclerosis. , 2015, JAMA neurology.

[22]  Kristine B. Walhovd,et al.  Reduced White Matter Integrity Is Related to Cognitive Instability , 2011, The Journal of Neuroscience.

[23]  Dieter Vaitl,et al.  Distinct mechanisms of altered brain activation in patients with multiple sclerosis , 2007, NeuroImage.

[24]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[25]  M. Filippi,et al.  Impaired functional integration in multiple sclerosis: a graph theory study , 2014, Brain Structure and Function.

[26]  H. Genova,et al.  Cognitive Rehabilitation in Multiple Sclerosis: The Role of Plasticity , 2015, Front. Neurol..

[27]  J. Lagopoulos,et al.  Decoding Diffusivity in Multiple Sclerosis: Analysis of Optic Radiation Lesional and Non-Lesional White Matter , 2015, PloS one.

[28]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[29]  J. V. Schependoma,et al.  The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis , 2014 .

[30]  A. Minagar Cognitive compensation failure in multiple sclerosis , 2011 .

[31]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[32]  E. Bullmore,et al.  Hierarchical Organization of Human Cortical Networks in Health and Schizophrenia , 2008, The Journal of Neuroscience.

[33]  Matthew E. Wolak,et al.  Guidelines for estimating repeatability , 2012 .

[34]  R. Luján Fiber Pathways of the Brain, J.D. Schmahmann, D.N. Pandya (Eds.). Oxford University Press (2006), ISBN: 0-19-510423-4 , 2008 .

[35]  Derek K. Jones Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI , 2010 .

[36]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[37]  A. Connelly,et al.  Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions , 2009 .

[38]  D. Ibarrola,et al.  Functional MRI study of PASAT in normal subjects , 2005, Magnetic Resonance Materials in Physics, Biology and Medicine.

[39]  D. Ducreux,et al.  Short-term evolution of spinal cord damage in multiple sclerosis: a diffusion tensor MRI study , 2012, Neuroradiology.

[40]  C H Polman,et al.  The Brief Repeatable Battery of Neuropsychological Tests: normative values allow application in multiple sclerosis clinical practice , 2001, Multiple sclerosis.

[41]  Massimo Filippi,et al.  Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis , 2015, The Lancet Neurology.

[42]  G. Nagels,et al.  The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis , 2014, European journal of neurology.

[43]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[44]  Massimo Filippi,et al.  Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. , 2009, Archives of neurology.

[45]  A. Connelly,et al.  White matter fiber tractography: why we need to move beyond DTI. , 2013, Journal of neurosurgery.

[46]  M. Amato,et al.  Cognitive impairment in early stages of multiple sclerosis , 2010, Neurological Sciences.

[47]  Martijn P. van den Heuvel,et al.  Rich Club Organization and Cognitive Performance in Healthy Older Participants , 2015, Journal of Cognitive Neuroscience.

[48]  Cedric E. Ginestet,et al.  Cognitive relevance of the community structure of the human brain functional coactivation network , 2013, Proceedings of the National Academy of Sciences.

[49]  Yasheng Chen,et al.  Diffusion tensor imaging based network analysis detects alterations of neuroconnectivity in patients with clinically early relapsing‐remitting multiple sclerosis , 2013, Human brain mapping.

[50]  S. Carletto,et al.  Decline of Neuropsychological Abilities in a Large Sample of Patients with Multiple Sclerosis: A Two-Year Longitudinal Study , 2016, Front. Hum. Neurosci..

[51]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[52]  Fabrice Bartolomei,et al.  Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks , 2010, Magnetic Resonance Materials in Physics, Biology and Medicine.

[53]  Lester Melie-García,et al.  Characterizing brain anatomical connections using diffusion weighted MRI and graph theory , 2007, NeuroImage.

[54]  Sara Llufriu,et al.  Improved Framework for Tractography Reconstruction of the Optic Radiation , 2015, PloS one.

[55]  J. Voogd,et al.  The Human Central Nervous System , 1978, Springer Berlin Heidelberg.

[56]  J. Voogd,et al.  The human central nervous system , 1978 .

[57]  P. Hluštík,et al.  Fractional anisotropy and mean diffusivity in the corpus callosum of patients with multiple sclerosis: the effect of physiotherapy , 2011, Neuroradiology.

[58]  A. Glabinski,et al.  Neural Plasticity in Multiple Sclerosis: The Functional and Molecular Background , 2015, Neural plasticity.

[59]  F. Tomasello,et al.  MRI Tractography of Corticospinal Tract and Arcuate Fasciculus in High-Grade Gliomas Performed by Constrained Spherical Deconvolution: Qualitative and Quantitative Analysis , 2015, American Journal of Neuroradiology.

[60]  M. Sdika,et al.  Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping , 2009, Human brain mapping.

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

[62]  Chris Rorden,et al.  Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging , 2015, PloS one.

[63]  Yong He,et al.  Altered Topological Organization of White Matter Structural Networks in Patients with Neuromyelitis Optica , 2012, PloS one.

[64]  M. Berger,et al.  Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods☆ , 2013, NeuroImage: Clinical.

[65]  Gregory McCarthy,et al.  Scan–rescan reliability of subcortical brain volumes derived from automated segmentation , 2010, Human brain mapping.

[66]  Li Wang,et al.  Alteration of Brain Functional Networks in Early-Stage Parkinson’s Disease: A Resting-State fMRI Study , 2015, PloS one.

[67]  Sara Llufriu,et al.  Cognitive functions in multiple sclerosis: impact of gray matter integrity , 2014, Multiple sclerosis.