More highly myelinated white matter tracts are associated with faster processing speed in healthy adults

&NA; The objective of this study was to investigate whether the estimated myelin content of white matter tracts is predictive of cognitive processing speed and whether such associations are modulated by age. Associations between estimated myelin content and processing speed were assessed in 570 community‐living individuals (277 middle‐age, 293 older‐age). Myelin content was estimated in‐vivo using the mean T1w/T2w magnetic resonance ratio, in six white matter tracts (anterior corona radiata, superior corona radiata, pontine crossing tract, anterior limb of the internal capsule, genu of the corpus callosum, and splenium of the corpus callosum). Processing speed was estimated by extracting a principal component from 5 separate tests of processing speed. It was found that estimated myelin content of the bilateral anterior limb of the internal capsule and left splenium of the corpus callosum were significant predictors of processing speed, even after controlling for socio‐demographic, health and genetic variables and correcting for multiple comparisons. One SD higher in the estimated myelin content of the anterior limb of the internal capsule was associated with 2.53% faster processing speed and within the left splenium of the corpus callosum with 2.20% faster processing speed. In addition, significant differences in estimated myelin content between middle‐age and older participants were found in all six white matter tracts. The present results indicate that myelin content, estimated in vivo using a neuroimaging approach in healthy older adults, is sufficiently precise to predict variability in processing speed in behavioural measures. HighlightsAssociations between myelin content and processing speed examined in 570 adults.Higher myelin content of white matter tracts predicted faster processing speed.This finding persisted even after controlling for health and genetic variables.Older adults have significantly lower myelin content within WM tracts.

[1]  Douglas L. Rosene,et al.  Age-related changes in human and non-human primate white matter: from myelination disturbances to cognitive decline , 2011, AGE.

[2]  Lars-Göran Nilsson,et al.  Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition. , 2012, Biochimica et biophysica acta.

[3]  H. Christensen,et al.  Corpus callosum size, reaction time speed and variability in mild cognitive disorders and in a normative sample , 2007, Neuropsychologia.

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

[5]  Maristela Monteiro,et al.  AUDIT - The alcohol use disorders identification test: guidelines for use in primary care. , 2001 .

[6]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[7]  Sterling C. Johnson,et al.  Associations between white matter microstructure and amyloid burden in preclinical Alzheimer's disease: A multimodal imaging investigation , 2014, NeuroImage: Clinical.

[8]  A. Toga,et al.  Tracking Alzheimer's Disease , 2007, Annals of the New York Academy of Sciences.

[9]  Marco Ganzetti,et al.  Whole brain myelin mapping using T1- and T2-weighted MR imaging data , 2014, Front. Hum. Neurosci..

[10]  G. Giovannoni,et al.  Multiple sclerosis: risk factors, prodromes, and potential causal pathways , 2010, The Lancet Neurology.

[11]  Scott A. Huettel,et al.  Diffusion tensor imaging of adult age differences in cerebral white matter: relation to response time , 2004, NeuroImage.

[12]  Y. Hong,et al.  Comparison of diffusion tensor imaging and voxel-based morphometry to detect white matter damage in Alzheimer's disease , 2011, Journal of the Neurological Sciences.

[13]  Arthur W. Toga,et al.  Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants , 2009, NeuroImage.

[14]  H. Christensen,et al.  ' s response to reviews Title : The association of APOE genotype and cognitive decline in interaction with risk factors in a 65-69 year old community sample , 2008 .

[15]  M. Knyazeva,et al.  Splenium of Corpus Callosum: Patterns of Interhemispheric Interaction in Children and Adults , 2013, Neural plasticity.

[16]  V. Mok,et al.  Age-related white matter changes in Asia , 2011 .

[17]  Thomas F. Babor,et al.  Alcohol Use Disorders Identification Test , 1995 .

[18]  Y. Munakata,et al.  Speed isn't everything: complex processing speed measures mask individual differences and developmental changes in executive control. , 2013, Developmental science.

[19]  L. Westlye,et al.  Intracortical Myelin Links with Performance Variability across the Human Lifespan: Results from T1- and T2-Weighted MRI Myelin Mapping and Diffusion Tensor Imaging , 2013, The Journal of Neuroscience.

[20]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[21]  R. Reitan Validity of the Trail Making Test as an Indicator of Organic Brain Damage , 1958 .

[22]  G. Bartzokis,et al.  White matter structural integrity in healthy aging adults and patients with Alzheimer disease: a magnetic resonance imaging study. , 2003, Archives of neurology.

[23]  G. Winston The physical and biological basis of quantitative parameters derived from diffusion MRI. , 2012, Quantitative imaging in medicine and surgery.

[24]  G. Bartzokis,et al.  Multimodal Magnetic Resonance Imaging Assessment of White Matter Aging Trajectories Over the Lifespan of Healthy Individuals , 2012, Biological Psychiatry.

[25]  H. Christensen,et al.  Level of Cognitive Performance as a Correlate and Predictor of Health Behaviors that Protect against Cognitive Decline in Late Life: The Path through Life Study. , 2009 .

[26]  Roberto Cabeza,et al.  Assessing the effects of age on long white matter tracts using diffusion tensor tractography , 2009, NeuroImage.

[27]  Matthew P. G. Allin,et al.  Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography , 2011, NeuroImage.

[28]  J. Henry,et al.  Mediating effects of processing speed and executive functions in age-related differences in episodic memory performance: a cross-validation study. , 2012, Neuropsychology.

[29]  Armin H. Seidl,et al.  Regulation of conduction time along axons , 2014, Neuroscience.

[30]  Marco Ganzetti,et al.  Mapping pathological changes in brain structure by combining T1- and T2-weighted MR imaging data , 2015, Neuroradiology.

[31]  C. Zimmer,et al.  Tissue damage within normal appearing white matter in early multiple sclerosis: assessment by the ratio of T1- and T2-weighted MR image intensity , 2016, Journal of Neurology.

[32]  Jim Mintz,et al.  Apolipoprotein E Affects Both Myelin Breakdown and Cognition: Implications for Age-Related Trajectories of Decline Into Dementia , 2007, Biological Psychiatry.

[33]  Muzamil Arshad,et al.  Test–retest reliability and concurrent validity of in vivo myelin content indices: Myelin water fraction and calibrated T1w/T2w image ratio , 2017, Human brain mapping.

[34]  San Jung,et al.  Lesions in the splenium of the corpus callosum: Clinical and radiological implications , 2014 .

[35]  Kazuhiro Shinosaki,et al.  Use of T1‐weighted/T2‐weighted magnetic resonance ratio images to elucidate changes in the schizophrenic brain , 2015, Brain and behavior.

[36]  Carlo Pierpaoli,et al.  T2 relaxometry of normal pediatric brain development , 2009, Journal of magnetic resonance imaging : JMRI.

[37]  Paul M. Thompson,et al.  Lifespan trajectory of myelin integrity and maximum motor speed , 2010, Neurobiology of Aging.

[38]  G. Paxinos,et al.  Atlas of the Human Brain , 2000 .

[39]  P M Matthews,et al.  Axonal injury or loss in the internal capsule and motor impairment in multiple sclerosis. , 2000, Archives of neurology.

[40]  Rudolf Nieuwenhuys,et al.  A map of the human neocortex showing the estimated overall myelin content of the individual architectonic areas based on the studies of Adolf Hopf , 2016, Brain Structure and Function.

[41]  Paul M. Thompson,et al.  Myelin breakdown mediates age-related slowing in cognitive processing speed in healthy elderly men , 2013, Brain and Cognition.

[42]  Kirk M Welker,et al.  Assessment of Normal Myelination with Magnetic Resonance Imaging , 2012, Seminars in Neurology.

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

[44]  L. Roberts Tracking Alzheimer's. , 1999, U.S. news & world report.

[45]  B. Trapp,et al.  T1‐/T2‐weighted ratio differs in demyelinated cortex in multiple sclerosis , 2017, Annals of neurology.

[46]  Joanna M. Wardlaw,et al.  A General Factor of Brain White Matter Integrity Predicts Information Processing Speed in Healthy Older People , 2010, The Journal of Neuroscience.

[47]  N. Pedersen,et al.  Age changes in processing speed as a leading indicator of cognitive aging. , 2007, Psychology and aging.

[48]  G. Kerchner,et al.  Cognitive Processing Speed in Older Adults: Relationship with White Matter Integrity , 2012, PloS one.

[49]  P. Duncan-Jones,et al.  Detecting anxiety and depression in general medical settings. , 1988, BMJ.

[50]  J. O'Brien,et al.  Cognitive Associations of Subcortical White Matter Lesions in Older People , 2002, Annals of the New York Academy of Sciences.

[51]  Kaarin J Anstey,et al.  Cohort profile: the PATH through life project. , 2012, International journal of epidemiology.

[52]  O. Güntürkün,et al.  Intrahemispheric white matter asymmetries: the missing link between brain structure and functional lateralization? , 2016, Reviews in the neurosciences.

[53]  David Bunce,et al.  Processing Speed, Executive Function, and Age Differences in Remembering and Knowing , 2005, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[54]  S. Haber,et al.  The cortico-basal ganglia integrative network: The role of the thalamus , 2009, Brain Research Bulletin.

[55]  S. Holm A Simple Sequentially Rejective Multiple Test Procedure , 1979 .

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

[57]  J. DeLuca,et al.  Speed of information processing as a key deficit in multiple sclerosis: implications for rehabilitation , 1999, Journal of neurology, neurosurgery, and psychiatry.

[58]  S. Eickhoff,et al.  Gray matter asymmetries in aging and neurodegeneration: A review and meta‐analysis , 2017, Human brain mapping.

[59]  J. Cerella,et al.  The rise and fall in information-processing rates over the life span. , 1994, Acta psychologica.

[60]  J. Tiffin,et al.  The Purdue pegboard; norms and studies of reliability and validity. , 1948, The Journal of applied psychology.

[61]  O. Spreen,et al.  A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary , 1991 .

[62]  Matthew F. Glasser,et al.  Trends and Properties of Human Cerebral Cortex: Correlations with Cortical Myelin Content Introduction and Review , 2022 .

[63]  Srikantan S. Nagarajan,et al.  The Role of Corpus Callosum Development in Functional Connectivity and Cognitive Processing , 2012, PloS one.

[64]  A. Scheibel,et al.  Fiber composition of the human corpus callosum , 1992, Brain Research.

[65]  Erika P. Raven,et al.  Age-related slowing in cognitive processing speed is associated with myelin integrity in a very healthy elderly sample , 2011, Journal of clinical and experimental neuropsychology.

[66]  Martin Styner,et al.  Associations between white matter microstructure and infants' working memory , 2013, NeuroImage.

[67]  Adam M. Brickman,et al.  Testing the white matter retrogenesis hypothesis of cognitive aging , 2012, Neurobiology of Aging.

[68]  Timothy A. Salthouse,et al.  How localized are age-related effects on neuropsychological measures? , 1996 .

[69]  P. Sachdev,et al.  Neuropsychological predictors of transition from healthy cognitive aging to mild cognitive impairment: The PATH through life study. , 2010, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.

[70]  K. Shinosaki,et al.  Elucidating the aberrant brain regions in bipolar disorder using T1-weighted/T2-weighted magnetic resonance ratio images , 2017, Psychiatry Research: Neuroimaging.

[71]  A. Mackay,et al.  In vivo measurement of T2 distributions and water contents in normal human brain , 1997, Magnetic resonance in medicine.

[72]  K. Ohtomo,et al.  White matter asymmetry in healthy individuals: a diffusion tensor imaging study using tract-based spatial statistics , 2011, Neuroscience.

[73]  Nicolas Cherbuin,et al.  Using sulcal and gyral measures of brain structure to investigate benefits of an active lifestyle , 2014, NeuroImage.