Preclinical Cerebral Network Connectivity Evidence of Deficits in Mild White Matter Lesions

White matter lesions (WMLs) are notable for their high prevalence and have been demonstrated to be a potential neuroimaging biomarker of early diagnosis of Alzheimer’s disease. This study aimed to identify the brain functional and structural mechanisms underlying cognitive decline observed in mild WMLs. Multi-domain cognitive tests, as well as resting-state, diffusion tensor and structural images were obtained on 42 mild WMLs and 42 age/sex-matched healthy controls. For each participant, we examined the functional connectivity (FC) of three resting-state networks (RSNs) related to the changed cognitive domains: the default mode network (DMN) and the bilateral fronto-parietal network (FPN). We also performed voxel-based morphometry analysis to compare whole-brain gray matter (GM) volume, atlas-based quantification of the white matter tracts interconnecting the RSNs, and the relationship between FC and structural connectivity. We observed FC alterations in the DMN and the right FPN combined with related white matter integrity disruption in mild WMLs. However, no significant GM atrophy difference was found. Furthermore, the right precuneus FC in the DMN exhibited a significantly negative correlation with the memory test scores. Our study suggests that in mild WMLs, dysfunction of RSNs might be a consequence of decreased white matter structural connectivity, which further affects cognitive performance.

[1]  Hiroko H. Dodge,et al.  Trajectory of white matter hyperintensity burden preceding mild cognitive impairment , 2011, Alzheimer's & Dementia.

[2]  Robert Leech,et al.  Default mode network functional and structural connectivity after traumatic brain injury. , 2011, Brain : a journal of neurology.

[3]  Kaia L. Vilberg,et al.  Memory retrieval and the parietal cortex: A review of evidence from a dual-process perspective , 2008, Neuropsychologia.

[4]  R. Palmer,et al.  The default mode network and related right hemisphere structures may be the key substrates of dementia. , 2012, Journal of Alzheimer's disease : JAD.

[5]  Zhao Hu Study of the Short Forms of Wechsler Adult Intelligence Scale-revised China in Patients with Traumatic Brain Injury , 2011 .

[6]  Guo Qi Boston Naming Test in Chinese Elderly, Patient with Mild Cognitive Impairment and Alzheimer's Dementia , 2006 .

[7]  Pei-Ning Wang,et al.  Confrontation Naming Errors in Alzheimer's Disease , 2013, Dementia and Geriatric Cognitive Disorders.

[8]  Lv Chuan-zhen,et al.  Norm of Auditory Verbal Learning Test in the Normal Aged in China Community , 2007 .

[9]  P. Scheltens,et al.  2001–2011: A Decade of the LADIS (Leukoaraiosis And DISability) Study: What Have We Learned about White Matter Changes and Small-Vessel Disease? , 2011, Cerebrovascular Diseases.

[10]  V. Menon Large-Scale Brain Networks in Cognition: Emerging Principles , 2010 .

[11]  Maximilian Reiser,et al.  White matter microstructure underlying default mode network connectivity in the human brain , 2010, NeuroImage.

[12]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[13]  H. Zhen Trail Making Test Used by Chinese Elderly Patients with Mild Cognitive Impairment and Mild Alzheimer' Dementia , 2006 .

[14]  G. Zhou,et al.  Cerebral White Matter Lesions and Cognitive Function in a Non-demented Chinese Veteran Cohort , 2008, The Journal of international medical research.

[15]  Yufeng Zang,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010 .

[16]  A. Brickman,et al.  Reconsidering harbingers of dementia: progression of parietal lobe white matter hyperintensities predicts Alzheimer's disease incidence , 2015, Neurobiology of Aging.

[17]  Joaquín Goñi,et al.  Changes in structural and functional connectivity among resting-state networks across the human lifespan , 2014, NeuroImage.

[18]  A. Hofman,et al.  Regional Variability in the Prevalence of Cerebral White Matter Lesions: An MRI Study in 9 European Countries (CASCADE) , 2005, Neuroepidemiology.

[19]  K. Jellinger,et al.  Heterogeneity in age-related white matter changes , 2011, Acta Neuropathologica.

[20]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[21]  A. Toga,et al.  Mapping brain asymmetry , 2003, Nature Reviews Neuroscience.

[22]  A. Fleisher,et al.  Altered default mode network connectivity in alzheimer's disease—A resting functional MRI and bayesian network study , 2011, Human brain mapping.

[23]  Klaus P. Ebmeier,et al.  A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease , 2011, Neurobiology of Aging.

[24]  P. Scheltens,et al.  A New Rating Scale for Age-Related White Matter Changes Applicable to MRI and CT , 2001, Stroke.

[25]  T. Klingberg,et al.  Combined analysis of DTI and fMRI data reveals a joint maturation of white and grey matter in a fronto-parietal network. , 2003, Brain research. Cognitive brain research.

[26]  周燕,et al.  Application of Stroop color-word test on Chinese elderly patients with mild cognitive impairment and mild Alzheimer's dementia , 2005 .

[27]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[28]  A Hofman,et al.  Hypertension and cerebral white matter lesions in a prospective cohort study. , 2002, Brain : a journal of neurology.

[29]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[30]  L. Squire,et al.  The medial temporal lobe memory system , 1991, Science.

[31]  J. Gore,et al.  The Relationship of Anatomical and Functional Connectivity to Resting-State Connectivity in Primate Somatosensory Cortex , 2013, Neuron.

[32]  V. Calhoun,et al.  Selective changes of resting-state networks in individuals at risk for Alzheimer's disease , 2007, Proceedings of the National Academy of Sciences.

[33]  K. Diba,et al.  Prefrontal Activity Links Nonoverlapping Events in Memory , 2013, The Journal of Neuroscience.

[34]  Chaogan Yan,et al.  DPARSF: A MATLAB Toolbox for “Pipeline” Data Analysis of Resting-State fMRI , 2010, Front. Syst. Neurosci..

[35]  P. Scheltens,et al.  White matter hyperintensities, cognitive impairment and dementia: an update , 2015, Nature Reviews Neurology.

[36]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[37]  G. Bartzokis Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease , 2004, Neurobiology of Aging.

[38]  R. Clark,et al.  The medial temporal lobe. , 2004, Annual review of neuroscience.

[39]  R. Kahn,et al.  Functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain , 2009, Human brain mapping.

[40]  Roland Bammer,et al.  Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies , 2008, NeuroImage.

[41]  Richard H. Swartz,et al.  A New Visual Rating Scale to Assess Strategic White Matter Hyperintensities Within Cholinergic Pathways in Dementia , 2005, Stroke.

[42]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[43]  L. Nyberg,et al.  Common fronto-parietal activity in attention, memory, and consciousness: Shared demands on integration? , 2005, Consciousness and Cognition.