Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
Edited by Brian Wandell, Stanford University, Stanford, CA; received August 26, 2022; accepted November 10, 2022.
Significance
The phenotypic age of the human brain, as revealed via deep learning of anatomic magnetic resonance images, reflects patterns of structural change related to cognitive decline. Our interpretable deep learning estimates that the brain ages more accurately than any other approaches to date. Furthermore, compared to chronological age, our inferred brain ages are significantly more strongly associated with early signs of Alzheimer’s disease. Maps conveying the importance of each brain region for estimating brain age reveal differences in patterns of neurological aging between males and females and between persons with and without cognitive impairment. These findings provide insight into early identification of persons at high risk of Alzheimer’s disease.
Abstract
The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.
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Data, Materials, and Software Availability
3D-CNN software is available from https://github.com/irimia-laboratory/USC_BA_estimator MRI data are publicly available from ADNI (https://adni.loni.usc.edu/), UKBB (https://www.ukbiobank.ac.uk/), CamCAN (https://www.cam-can.org/), and HCP (https://www.humanconnectome.org/). There are no relevant accession codes required to access these data, and the authors had no special access privileges that others would not have to the data obtained from any of these databases. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD).
Acknowledgments
A.I. is grateful to Kenneth H. Buetow, Caleb E. Finch, Margaret Gatz, and Mara Mather for useful discussions. The authors acknowledge Alexander S. Maher for assistance with data retrieval. P.E., A.A., N.F.C., R.J.M., N.N.C., and A.I. gratefully acknowledge the support from the (NIH) under grant R01 NS 100973, the US Department of Defense (DoD) under award W81-XWH-1810413, the James J. & Sue Femino Foundation, a Hanson-Thorell Research Scholarship, the Undergraduate Research Associate Program (URAP), and the Center for Undergraduate Research in Viterbi Engineering (CURVE) at the University of Southern California. C.Y., M.C., X.C., and P.B. gratefully acknowledge the support from the NSF under CAREER award CPS/CNS-1453860 and grants CCF-1837131, MCB-1936775, CNS-1932620, and CMMI-1936624, from the Okawa Foundation, from the Defense Advanced Research Projects Agency (DARPA) under a Young Faculty Award and a Director Award under grant N66001-17-1-4044, from a 2021 USC Stevens Center Technology Advancement Grant (TAG) award, from an Intel Faculty Award and from a Northrop Grumman grant. P.M.T. gratefully acknowledges the support from the NIH under grants R01 AG 058854, U01 AG 068057, R01 AG 060610, and P41 EB 015922. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) through NIH grant U01 AG 024904 and DoD ADNI through DoD award number W81-XWH-1220012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development, LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH (http://www.fnih.org). The ADNI grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data for this study were provided, in part, by the Human Connectome Project, WUMinn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1 U54 MH 091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. Research reported in this publication was also supported by the National Institute on Aging of the NIH under award U01 AG 052564. CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and the University of Cambridge, UK. This research has been conducted using the UK Biobank Resource, under application numbers 11559 and 47656. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied by the NIH, DARPA, DoD, NSF, or any other entity acknowledged here. The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Author contributions
P.B. and A.I. designed research; C.Y. and A.A. performed research; C.Y., M.C., and A.A. contributed new reagents/analytic tools; C.Y., P.I., M.C., A.A., N.F.C., R.J.M., N.N.C., and X.C. analyzed data; A.N. data used in research was sourced from the ADNI repository; and C.Y., P.I., A.A., N.F.C., P.M.T., P.B., and A.I. wrote the paper.
Competing interest
The authors have research support to disclose, P.M.T. discloses research grant support from Biogen, Inc. for work unrelated to this study.
Supporting Information
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Copyright
Copyright © 2023 the Author(s). Published by PNAS. This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Data, Materials, and Software Availability
3D-CNN software is available from https://github.com/irimia-laboratory/USC_BA_estimator MRI data are publicly available from ADNI (https://adni.loni.usc.edu/), UKBB (https://www.ukbiobank.ac.uk/), CamCAN (https://www.cam-can.org/), and HCP (https://www.humanconnectome.org/). There are no relevant accession codes required to access these data, and the authors had no special access privileges that others would not have to the data obtained from any of these databases. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). The ADNI was launched in 2003 as a public–private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD).
Submission history
Received: August 26, 2022
Accepted: November 10, 2022
Published online: January 3, 2023
Published in issue: January 10, 2023
Keywords
Acknowledgments
A.I. is grateful to Kenneth H. Buetow, Caleb E. Finch, Margaret Gatz, and Mara Mather for useful discussions. The authors acknowledge Alexander S. Maher for assistance with data retrieval. P.E., A.A., N.F.C., R.J.M., N.N.C., and A.I. gratefully acknowledge the support from the (NIH) under grant R01 NS 100973, the US Department of Defense (DoD) under award W81-XWH-1810413, the James J. & Sue Femino Foundation, a Hanson-Thorell Research Scholarship, the Undergraduate Research Associate Program (URAP), and the Center for Undergraduate Research in Viterbi Engineering (CURVE) at the University of Southern California. C.Y., M.C., X.C., and P.B. gratefully acknowledge the support from the NSF under CAREER award CPS/CNS-1453860 and grants CCF-1837131, MCB-1936775, CNS-1932620, and CMMI-1936624, from the Okawa Foundation, from the Defense Advanced Research Projects Agency (DARPA) under a Young Faculty Award and a Director Award under grant N66001-17-1-4044, from a 2021 USC Stevens Center Technology Advancement Grant (TAG) award, from an Intel Faculty Award and from a Northrop Grumman grant. P.M.T. gratefully acknowledges the support from the NIH under grants R01 AG 058854, U01 AG 068057, R01 AG 060610, and P41 EB 015922. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) through NIH grant U01 AG 024904 and DoD ADNI through DoD award number W81-XWH-1220012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development, LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH (http://www.fnih.org). The ADNI grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data for this study were provided, in part, by the Human Connectome Project, WUMinn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1 U54 MH 091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. Research reported in this publication was also supported by the National Institute on Aging of the NIH under award U01 AG 052564. CamCAN funding was provided by the UK Biotechnology and Biological Sciences Research Council (grant number BB/H008217/1), together with support from the UK Medical Research Council and the University of Cambridge, UK. This research has been conducted using the UK Biobank Resource, under application numbers 11559 and 47656. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied by the NIH, DARPA, DoD, NSF, or any other entity acknowledged here. The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Author Contributions
P.B. and A.I. designed research; C.Y. and A.A. performed research; C.Y., M.C., and A.A. contributed new reagents/analytic tools; C.Y., P.I., M.C., A.A., N.F.C., R.J.M., N.N.C., and X.C. analyzed data; A.N. data used in research was sourced from the ADNI repository; and C.Y., P.I., A.A., N.F.C., P.M.T., P.B., and A.I. wrote the paper.
Competing Interest
The authors have research support to disclose, P.M.T. discloses research grant support from Biogen, Inc. for work unrelated to this study.
Notes
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