A novel patch-based procedure for estimating brain age across adulthood

Aging is associated with structural alterations in many regions of the brain. Monitoring these changes contributes to increasing our understanding of the brain's morphological alterations across its lifespan, and could allow the identification of departures from canonical trajectories. Here, we introduce a novel and unique patch-based grading procedure for estimating a synthetic estimate of cortical aging in cognitively intact individuals. The cortical age metric is computed based on image similarity between an unknown (test) cortical label and known (training) cortical labels using machine learning algorithms. The proposed method was trained on a dataset of 100 cognitively intact individuals aged 19-61 years, within the 31 bilateral cortical labels of the Desikan-Killiany-Tourville parcellation, then tested on an independent test set of 78 cognitively intact individuals spanning a similar age range. The proposed patch-based framework yielded a R2 = 0.94, as well as a mean absolute error of 1.66 years, which compared favorably to the literature. These experimental results demonstrate that the proposed patch-based grading framework is a reliable and robust method to estimate brain age from image data, even with a limited training size.

[1]  Khader M. Hasan,et al.  Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach , 2013, NeuroImage.

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

[3]  Tuan D. Pham,et al.  MRI-based age prediction using hidden Markov models , 2011, Journal of Neuroscience Methods.

[4]  Wiepke Cahn,et al.  Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study. , 2016, The American journal of psychiatry.

[5]  D. Hu,et al.  Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI , 2012, PloS one.

[6]  Charles Duyckaerts,et al.  Disentangling Alzheimer's disease , 2011, The Lancet Neurology.

[7]  A. Piquero,et al.  USING THE CORRECT STATISTICAL TEST FOR THE EQUALITY OF REGRESSION COEFFICIENTS , 1998 .

[8]  J. Schneider,et al.  National Institute on Aging–Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease , 2012, Alzheimer's & Dementia.

[9]  Michèle Allard,et al.  Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis , 2015, Human brain mapping.

[10]  Christian Gaser,et al.  The Effect of the APOE Genotype on Individual BrainAGE in Normal Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2016, PloS one.

[11]  Geraldo F. Busatto,et al.  Age-related gray matter volume changes in the brain during non-elderly adulthood , 2011, Neurobiology of Aging.

[12]  M Scazufca,et al.  Brain Structural Variability due to Aging and Gender in Cognitively Healthy Elders: Results from the São Paulo Ageing and Health Study , 2009, American Journal of Neuroradiology.

[13]  Chunlan Yang,et al.  Age-Related Gray and White Matter Changes in Normal Adult Brains , 2017, Aging and disease.

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[15]  Heath R. Pardoe,et al.  NAPR: a Cloud-Based Framework for Neuroanatomical Age Prediction , 2017, bioRxiv.

[16]  Christian Gaser,et al.  BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder , 2017, Psychiatry Research: Neuroimaging.

[17]  Olivier Potvin,et al.  Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain , 2017, NeuroImage.

[18]  Jean-Marc Constans,et al.  Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging , 2009, Neurobiology of Aging.

[19]  Cong Jin,et al.  Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks , 2016, Comput. Methods Programs Biomed..

[20]  Leanne M Williams,et al.  Preservation of limbic and paralimbic structures in aging , 2005, Human brain mapping.

[21]  D. Louis Collins,et al.  Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.

[22]  Pierrick Coupé,et al.  An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images , 2008, IEEE Transactions on Medical Imaging.

[23]  Christian Gaser,et al.  Advanced BrainAGE in older adults with type 2 diabetes mellitus , 2013, Front. Aging Neurosci..

[24]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.

[25]  Jenessa Lancaster,et al.  Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction , 2018, Front. Aging Neurosci..

[26]  Christian Gaser,et al.  Changes of individual BrainAGE during the course of the menstrual cycle , 2015, NeuroImage.

[27]  Christian Gaser,et al.  Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2012 .

[28]  Nick C Fox,et al.  Imaging cerebral atrophy: normal ageing to Alzheimer's disease , 2004, The Lancet.

[29]  Jean-Claude Baron,et al.  Early diagnosis of alzheimer’s disease: contribution of structural neuroimaging , 2003, NeuroImage.

[30]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[31]  Lei Wang,et al.  Correlations Between Antemortem Hippocampal Volume and Postmortem Neuropathology in AD Subjects , 2004, Alzheimer disease and associated disorders.

[32]  Daniel S. Margulies,et al.  Predicting brain-age from multimodal imaging data captures cognitive impairment , 2016, NeuroImage.

[33]  Eileen Luders,et al.  Estimating brain age using high-resolution pattern recognition: Younger brains in long-term meditation practitioners , 2016, NeuroImage.

[34]  C. Jack,et al.  Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia , 2002, Neurology.

[35]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[36]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[37]  Jing Hua,et al.  Age estimation using cortical surface pattern combining thickness with curvatures , 2013, Medical & Biological Engineering & Computing.

[38]  Kyung-Ah Sohn,et al.  Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study , 2018, Front. Aging Neurosci..

[39]  Min Chen,et al.  Multi-parametric neuroimaging reproducibility: A 3-T resource study , 2011, NeuroImage.

[40]  Nick C Fox,et al.  Clinical and biomarker changes in dominantly inherited Alzheimer's disease. , 2012, The New England journal of medicine.

[41]  D. Louis Collins,et al.  Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease , 2012, NeuroImage.

[42]  Seyed Abolfazl Valizadeh,et al.  Age prediction on the basis of brain anatomical measures , 2017, Human brain mapping.

[43]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[44]  Alan C. Evans,et al.  A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. , 2004, Cerebral cortex.

[45]  Eileen Luders,et al.  Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI , 2012, NeuroImage.

[46]  C. Igel,et al.  Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry* , 2016, NeuroImage: Clinical.