Cardiovascular disease risk factors, tract-based structural connectomics, and cognition in older adults

Cardiovascular disease risk factors (CVD-RFs) are associated with decreased gray and white matter integrity and cognitive impairment in older adults. Less is known regarding the interplay between CVD-RFs, brain structural connectome integrity, and cognition. We examined whether CVD-RFs were associated with measures of tract-based structural connectivity in 94 non-demented/non-depressed older adults and if alterations in connectivity mediated associations between CVD-RFs and cognition. Participants (age = 68.2 years; 52.1% female; 46.8% Black) underwent CVD-RF assessment, MRI, and cognitive evaluation. Framingham 10-year stroke risk (FSRP-10) quantified CVD-RFs. Graph theory analysis integrated T1-derived gray matter regions of interest (ROIs; 23 a-priori ROIs associated with CVD-RFs and dementia), and diffusion MRI-derived white matter tractography into connectivity matrices analyzed for local efficiency and nodal strength. A principal component analysis resulted in three rotated factor scores reflecting executive function (EF; FAS, Trail Making Test (TMT) B-A, Letter-Number Sequencing, Matrix Reasoning); attention/information processing (AIP; TMT-A, TMT-Motor, Digit Symbol); and memory (CVLT-II Trials 1-5 Total, Delayed Free Recall, Recognition Discriminability). Linear regressions between FSRP-10 and connectome ROIs adjusting for word reading, intracranial volume, and white matter hyperintensities revealed negative associations with nodal strength in eight ROIs (p-values<.05) and negative associations with efficiency in two ROIs, and a positive association in one ROI (p-values<.05). There was mediation of bilateral hippocampal strength on FSRP-10 and AIP, and left rostral middle frontal gyrus strength on FSRP-10 and AIP and EF. Stroke risk plays differential roles in connectivity and cognition, suggesting the importance of multi-modal neuroimaging biomarkers in understanding age-related CVD-RF burden and brain-behavior.

[1]  R B D'Agostino,et al.  Probability of stroke: a risk profile from the Framingham Study. , 1991, Stroke.

[2]  Sudha Seshadri,et al.  Framingham Stroke Risk Profile and Lowered Cognitive Performance , 2004, Stroke.

[3]  Craig M. Hales,et al.  Hypertension Prevalence and Control Among Adults: United States, 2015-2016. , 2017, NCHS data brief.

[4]  D. Delis,et al.  The California verbal learning test , 2016 .

[5]  A. Jefferson,et al.  Blood pressure and cognition among older adults: a meta-analysis. , 2013, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[6]  Victoria J. Williams,et al.  Association between white matter microstructure, executive functions, and processing speed in older adults: The impact of vascular health , 2013, Human brain mapping.

[7]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[8]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[9]  N. Raz,et al.  Prefrontal cortex and executive functions in healthy adults: A meta-analysis of structural neuroimaging studies , 2014, Neuroscience & Biobehavioral Reviews.

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

[11]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[12]  E. Reiman,et al.  Disrupted Frontoparietal Network Mediates White Matter Structure Dysfunction Associated with Cognitive Decline in Hypertension Patients , 2015, The Journal of Neuroscience.

[13]  Y. Stern,et al.  Efficiency, capacity, compensation, maintenance, plasticity: emerging concepts in cognitive reserve , 2013, Trends in Cognitive Sciences.

[14]  Sudha Seshadri,et al.  Impact of Hypertension on Cognitive Function: A Scientific Statement From the American Heart Association , 2016, Hypertension.

[15]  Owen Carmichael,et al.  Associations Among Vascular Risk Factors, Carotid Atherosclerosis, and Cortical Volume and Thickness in Older Adults , 2012, Stroke.

[16]  Wilbert S Aronow,et al.  2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2018, Hypertension.

[17]  K J Rothman,et al.  No Adjustments Are Needed for Multiple Comparisons , 1990, Epidemiology.

[18]  R. S. Jorgensen,et al.  Composite Cardiovascular Risk Scores and Neuropsychological Functioning: A Meta-Analytic Review , 2015, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[19]  Sterling C. Johnson,et al.  Cardiorespiratory fitness is associated with brain structure, cognition, and mood in a middle-aged cohort at risk for Alzheimer’s disease , 2014, Brain Imaging and Behavior.

[20]  A. Hayes Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach , 2013 .

[21]  J. Gee,et al.  The Insight ToolKit image registration framework , 2014, Front. Neuroinform..

[22]  R. D'Agostino,et al.  Revised Framingham Stroke Risk Profile to Reflect Temporal Trends , 2017, Circulation.

[23]  C. Barnes,et al.  Impact of aging brain circuits on cognition , 2013, The European journal of neuroscience.

[24]  O. Ajilore,et al.  Divergent Influences of Cardiovascular Disease Risk Factor Domains on Cognition and Gray and White Matter Morphology , 2017, Psychosomatic medicine.

[25]  Wiro J. Niessen,et al.  Tract-specific white matter degeneration in aging: The Rotterdam Study , 2015, Alzheimer's & Dementia.

[26]  Christos Davatzikos,et al.  Measuring Brain Lesion Progression with a Supervised Tissue Classification System , 2008, MICCAI.

[27]  Guillaume Marrelec,et al.  Increase of posterior connectivity in aging within the Ventral Attention Network: A functional connectivity analysis using independent component analysis , 2017, Brain Research.

[28]  Naftali Raz,et al.  Pattern of normal age-related regional differences in white matter microstructure is modified by vascular risk , 2009, Brain Research.

[29]  O. Ajilore,et al.  What Metabolic Syndrome Contributes to Brain Outcomes in African American & Caucasian Cohorts. , 2015, Current Alzheimer research.

[30]  Henrik Zetterberg,et al.  Alzheimer's disease markers, hypertension, and gray matter damage in normal elderly , 2012, Neurobiology of Aging.

[31]  J. Morris,et al.  The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals , 2008, Cerebral cortex.

[32]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[33]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[34]  Christine Fennema-Notestine,et al.  Hypertension-Related Alterations in White Matter Microstructure Detectable in Middle Age , 2015, Hypertension.

[35]  Gary A. Ford,et al.  Brain atrophy and white matter hyperintensity change in older adults and relationship to blood pressure , 2007, Journal of Neurology.

[36]  Bruce Fischl,et al.  Thickness of the human cerebral cortex is associated with metrics of cerebrovascular health in a normative sample of community dwelling older adults , 2011, NeuroImage.

[37]  Adam J. Woods,et al.  Cognitive Aging and the Hippocampus in Older Adults , 2016, Front. Aging Neurosci..

[38]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[39]  B. MacIntosh,et al.  A systematic review of type 2 diabetes mellitus and hypertension in imaging studies of cognitive aging: time to establish new norms , 2014, Front. Aging Neurosci..

[40]  Paul M. Thompson,et al.  Statistical Properties of Jacobian Maps and the Realization of Unbiased Large-Deformation Nonlinear Image Registration , 2007, IEEE Transactions on Medical Imaging.

[41]  J T O'Brien,et al.  Hippocampal atrophy, whole brain volume, and white matter lesions in older hypertensive subjects , 2004, Neurology.

[42]  Anders M. Dale,et al.  Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.

[43]  J. J. Ryan,et al.  Wechsler Adult Intelligence Scale-III , 2001 .

[44]  Anand R. Kumar,et al.  Differential associations between types of verbal memory and prefrontal brain structure in healthy aging and late life depression , 2012, Neuropsychologia.

[45]  Y. Stern,et al.  Reading level attenuates differences in neuropsychological test performance between African American and White elders , 2002, Journal of the International Neuropsychological Society.

[46]  Cassandra D. Leonardo,et al.  Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease , 2015, Front. Aging Neurosci..

[47]  N. Turk-Browne,et al.  How Hippocampal Memory Shapes, and Is Shaped by, Attention , 2017 .

[48]  Cheuk Y. Tang,et al.  Brain imaging changes associated with risk factors for cardiovascular and cerebrovascular disease in asymptomatic patients. , 2014, JACC. Cardiovascular imaging.

[49]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[50]  M. Hamilton A RATING SCALE FOR DEPRESSION , 1960, Journal of neurology, neurosurgery, and psychiatry.

[51]  Marek Kubicki,et al.  Cerebral White Matter Integrity and Resting-State Functional Connectivity in Middle-aged Patients With Type 2 Diabetes , 2014, Diabetes.

[52]  Clifford R. Jack,et al.  Association of type 2 diabetes with brain atrophy and cognitive impairment , 2014, Neurology.

[53]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[54]  C. Iadecola,et al.  The Pathobiology of Vascular Dementia , 2013, Neuron.

[55]  Ikuko Mukai,et al.  A role of right middle frontal gyrus in reorienting of attention: a case study , 2015, Front. Syst. Neurosci..

[56]  C. Annweiler,et al.  Blood pressure levels and brain volume reduction: a systematic review and meta-analysis , 2013, Journal of hypertension.

[57]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[58]  Yong He,et al.  Changing topological patterns in normal aging using large-scale structural networks , 2012, Neurobiology of Aging.

[59]  P. Bosco,et al.  Brain atrophy in Alzheimer’s Disease and aging , 2016, Ageing Research Reviews.

[60]  L. Fratiglioni,et al.  Effects of vascular risk factors and APOE ε4 on white matter integrity and cognitive decline , 2015, Neurology.

[61]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[62]  Jing Xie,et al.  Framingham Stroke Risk Profile and poor cognitive function: a population-based study , 2008, BMC neurology.

[63]  C. DeCarli,et al.  Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline , 2011, Alzheimer's & Dementia.

[64]  David A. Bennett,et al.  White matter hyperintensities, incident mild cognitive impairment, and cognitive decline in old age , 2016, Annals of clinical and translational neurology.

[65]  L. Wolfson,et al.  Processing speed in normal aging: Effects of white matter hyperintensities and hippocampal volume loss , 2014, Neuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition.

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

[67]  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.

[68]  Wiro J. Niessen,et al.  Disconnection due to white matter hyperintensities is associated with lower cognitive scores , 2018, NeuroImage.

[69]  Paul Horton,et al.  Meta-analyses of structural regional cerebral effects in type 1 and type 2 diabetes , 2015, Brain Imaging and Behavior.

[70]  Paul M. Thompson,et al.  Brain network efficiency and topology depend on the fiber tracking method: 11 tractography algorithms compared in 536 subjects , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.