Tau and atrophy: domain-specific relationships with cognition

BackgroundLate-onset Alzheimer’s disease (AD) is characterized by primary memory impairment, which then progresses towards severe deficits across cognitive domains. Here, we report how performance in cognitive domains relates to patterns of tau deposition and cortical thickness.MethodsWe analyzed data from 131 amyloid-β positive participants (55 cognitively normal, 46 mild cognitive impairment, 30 AD) of the Alzheimer’s Disease Neuroimaging Initiative who underwent magnetic resonance imaging (MRI), flortaucipir (FTP) positron emission tomography, and neuropsychological testing. Surface-based vertex-wise and region-of-interest analyses were conducted between FTP and cognitive test scores, and between cortical thickness and cognitive test scores.ResultsFTP and thickness were differentially related to cognitive performance in several domains. FTP-cognition associations were more widespread than thickness-cognition associations. Further, FTP-cognition patterns reflected cortical systems that underlie different aspects of cognition.ConclusionsOur findings indicate that AD-related decline in domain-specific cognitive performance reflects underlying progression of tau and atrophy into associated brain circuits. They also suggest that tau-PET may have better sensitivity to this decline than MRI-derived measures of cortical thickness.

[1]  M M Mesulam,et al.  Large‐scale neurocognitive networks and distributed processing for attention, language, and memory , 1990, Annals of neurology.

[2]  Xiao-Li Meng,et al.  Comparing correlated correlation coefficients , 1992 .

[3]  A. Evans,et al.  Correction for partial volume effects in PET: principle and validation. , 1998, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[4]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[5]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

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

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

[8]  J. Aharon-Peretz,et al.  Posterior Cortical Atrophy Variants of Alzheimer’s Disease , 1999, Dementia and Geriatric Cognitive Disorders.

[9]  J. Kril,et al.  Specific temporoparietal gyral atrophy reflects the pattern of language dissolution in Alzheimer's disease. , 1999, Brain : a journal of neurology.

[10]  J. Grafman,et al.  The calculating brain: an fMRI study , 2000, Neuropsychologia.

[11]  Ivanei E. Bramati,et al.  The cerebral correlates of set-shifting: an fMRI study of the trail making test. , 2002, Arquivos de neuro-psiquiatria.

[12]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[13]  D. Stuss,et al.  California Verbal Learning Test: performance by patients with focal frontal and non-frontal lesions. , 2003, Brain : a journal of neurology.

[14]  M. Mesulam,et al.  Neurofibrillary tangles, amyloid, and memory in aging and mild cognitive impairment. , 2003, Archives of neurology.

[15]  J. Morrison,et al.  Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease , 2003, Neurology.

[16]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.

[17]  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.

[18]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[19]  Ayse Pinar Saygin,et al.  Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data , 2006, NeuroImage.

[20]  J B Poline,et al.  Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease. , 2007, Brain : a journal of neurology.

[21]  B. Miller,et al.  Neurodegenerative Diseases Target Large-Scale Human Brain Networks , 2009, Neuron.

[22]  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.

[23]  Jeffrey A. Fessler,et al.  Reducing between scanner differences in multi-center PET studies , 2009, NeuroImage.

[24]  J. Morris,et al.  Clinical core of the Alzheimer's disease neuroimaging initiative: Progress and plans , 2010, Alzheimer's & Dementia.

[25]  C. Jack,et al.  Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade , 2010, The Lancet Neurology.

[26]  Cindee M. Madison,et al.  Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI , 2011, Neurobiology of Aging.

[27]  L. McEvoy,et al.  Predicting MCI outcome with clinically available MRI and CSF biomarkers , 2011, Neurology.

[28]  A. Dale,et al.  Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis. , 2011, Radiology.

[29]  D. Salmon,et al.  The neuropsychological profile of Alzheimer disease. , 2012, Cold Spring Harbor perspectives in medicine.

[30]  W. Jagust,et al.  Physical Activity and AD-Related Pathology-Reply. , 2012, Archives of neurology.

[31]  K. Jellinger,et al.  Correlation of Alzheimer Disease Neuropathologic Changes With Cognitive Status: A Review of the Literature , 2012, Journal of neuropathology and experimental neurology.

[32]  W. Jagust,et al.  Association of lifetime cognitive engagement and low β-amyloid deposition. , 2012, Archives of neurology.

[33]  G. Chételat,et al.  Region-Specific Hierarchy between Atrophy, Hypometabolism, and β-Amyloid (Aβ) Load in Alzheimer's Disease Dementia , 2012, The Journal of Neuroscience.

[34]  C. Jack,et al.  Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers , 2013, The Lancet Neurology.

[35]  H. Kolb,et al.  [18F]T807, a novel tau positron emission tomography imaging agent for Alzheimer's disease , 2013, Alzheimer's & Dementia.

[36]  Cindee M. Madison,et al.  Diverging patterns of amyloid deposition and hypometabolism in clinical variants of probable Alzheimer's disease. , 2013, Brain : a journal of neurology.

[37]  T. Hedden,et al.  Meta-analysis of amyloid-cognition relations in cognitively normal older adults , 2013, Neurology.

[38]  Cindee M. Madison,et al.  Intrinsic connectivity networks in healthy subjects explain clinical variability in Alzheimer’s disease , 2013, Proceedings of the National Academy of Sciences.

[39]  D. Fair,et al.  Hemispheric lateralization of verbal and spatial working memory during adolescence , 2013, Brain and Cognition.

[40]  C. Jack,et al.  Update on hypothetical model of Alzheimer's disease biomarkers , 2013, Alzheimer's & Dementia.

[41]  M. Mintun,et al.  Amyloid-β Imaging with Pittsburgh Compound B and Florbetapir: Comparing Radiotracers and Quantification Methods , 2013, The Journal of Nuclear Medicine.

[42]  Bruce R. Rosen,et al.  Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data , 2014, NeuroImage.

[43]  Keith A. Johnson,et al.  Validating novel tau positron emission tomography tracer [F‐18]‐AV‐1451 (T807) on postmortem brain tissue , 2015, Annals of neurology.

[44]  R. Sperling,et al.  Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease , 2016, Proceedings of the National Academy of Sciences.

[45]  A. Joshi,et al.  Regional profiles of the candidate tau PET ligand 18F-AV-1451 recapitulate key features of Braak histopathological stages. , 2016, Brain : a journal of neurology.

[46]  D. Spencer,et al.  Imaging synaptic density in the living human brain , 2016, Science Translational Medicine.

[47]  Steen Moeller,et al.  The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.

[48]  Daniel R. Schonhaut,et al.  PET Imaging of Tau Deposition in the Aging Human Brain , 2016, Neuron.

[49]  Talakad G. Lohith,et al.  Preclinical Characterization of 18F-MK-6240, a Promising PET Tracer for In Vivo Quantification of Human Neurofibrillary Tangles , 2016, The Journal of Nuclear Medicine.

[50]  Keith A. Johnson,et al.  A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers , 2016, Neurology.

[51]  Hanna Cho,et al.  Tau PET in Alzheimer disease and mild cognitive impairment , 2016, Neurology.

[52]  Daniel R. Schonhaut,et al.  Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. , 2016, Brain : a journal of neurology.

[53]  W. Jagust,et al.  Considerations and code for partial volume correcting [18F]-AV-1451 tau PET data , 2017, Data in brief.

[54]  Daniel R. Schonhaut,et al.  Tau pathology and neurodegeneration contribute to cognitive impairment in Alzheimer’s disease , 2017, Brain : a journal of neurology.

[55]  M. Weiner,et al.  Association Between Elevated Brain Amyloid and Subsequent Cognitive Decline Among Cognitively Normal Persons , 2017, JAMA.

[56]  O. Hansson,et al.  Tau Pathology Distribution in Alzheimer's disease Corresponds Differentially to Cognition-Relevant Functional Brain Networks , 2017, Frontiers in Neuroscience.

[57]  J. Cummings,et al.  Alzheimer's disease drug development pipeline: 2017 , 2017, Alzheimer's & dementia.

[58]  Keith A. Johnson,et al.  Lessons learned about [F-18]-AV-1451 off-target binding from an autopsy-confirmed Parkinson’s case , 2017, Acta Neuropathologica Communications.

[59]  J. Morris,et al.  The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement , 2017, Alzheimer's & Dementia.

[60]  Alexander Kmentt 2017 , 2018, The Treaty Prohibiting Nuclear Weapons.

[61]  J. Phillips,et al.  Tau PET imaging predicts cognition in atypical variants of Alzheimer's disease , 2018, Human brain mapping.

[62]  M. Citron,et al.  The tau positron‐emission tomography tracer AV‐1451 binds with similar affinities to tau fibrils and monoamine oxidases , 2018, Movement disorders : official journal of the Movement Disorder Society.

[63]  O. Hansson,et al.  ASSOCIATIONS BETWEEN TAU, Aβ AND CORTICAL THICKNESS WITH COGNITION IN ALZHEIMER’S DISEASE , 2018, Alzheimer's & Dementia.

[64]  D. Y. Lee,et al.  Association of Cerebral Amyloid-&bgr; Aggregation With Cognitive Functioning in Persons Without Dementia , 2018, JAMA psychiatry.

[65]  C. Jack,et al.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease , 2018, Alzheimer's & Dementia.

[66]  J. Brewer,et al.  Combined Biomarker Prognosis of Mild Cognitive Impairment: An 11-Year Follow-Up Study in the Alzheimer's Disease Neuroimaging Initiative. , 2019, Journal of Alzheimer's disease : JAD.

[67]  Patrick J. Lao,et al.  Letter and Category Fluency Performance Correlates with Distinct Patterns of Cortical Thickness in Older Adults. , 2019, Cerebral cortex.

[68]  Philip S. Insel,et al.  Associations between tau, Aβ, and cortical thickness with cognition in Alzheimer disease , 2019, Neurology.

[69]  J. Cummings,et al.  Repurposed agents in the Alzheimer’s disease drug development pipeline , 2020, Alzheimer's Research & Therapy.