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Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer’s disease

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Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer’s disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.

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Anterior corona radiata


Alzheimer’s disease


Alzheimer’s Disease Neuroimaging Initiative


Anterior internal capsule


Apolipoprotein E


Amyloid beta


Body of the corpus callosum


Body and column of the fornix


Cerebral amyloid angiopathy


Cingulum bundle


Corpus callosum


Clinical dementia rating


Clinical dementia rating sum of boxes


Crus of the fornix


Confidence interval


Cerebral microbleed


Cerebral peduncle


Corticospinal tract


Computed tomography


Diffuse axonal injury


Deep learning


Default mode network


Diffusion magnetic resonance imaging


Diffusion tensor imaging


Diffusion weighted imaging


External capsule


Fractional anisotropy


Fluid-attenuated inversion recovery


False negative


False positive


FMRIB software library


Family-wise error rate


Genu of the corpus callosum


Glasgow Coma Scale


General linear model


Gray matter


Gradient-recalled echo


Healthy control


Inferior cerebellar peduncle


International Consortium of Brain Mapping


Inferior frontal gyrus


Inferior fronto-occipital fasciculus


Iterative single data algorithm


Johns Hopkins University


Loss of consciousness


Matthews’ correlation coefficient


Mild cognitive impairment


Medial lemniscus


Mini mental state examination


Montreal Neurological Institute


Montreal cognitive assessment


Magnetization-prepared rapid acquisition gradient echo


Magnetic resonance imaging


Magnetic resonance spectroscopy


Mild traumatic brain injury


Medial temporal gyrus


Neurofibrillary tangle


Orbitofrontal cortex


Posterior corona radiata


Pontine crossing tract


Positron emission tomography


Prefrontal cortex


Posterior internal capsule


Positive prediction value


Processing speed


Posterior thalamic radiation


Retrolenticular internal capsule


Region of interest


Splenium of the corpus callosum


Superior cerebellar peduncle


Superior corona radiata


Superior fronto-occipital fasciculus


Superior longitudinal fasciculus


Sagittal stratum


Superior temporal gyrus


Support vector machine


Susceptibility weighted imaging


Traumatic brain injury


Tract-based spatial statistics


Tapetum of the corpus callosum


Threshold-free cluster enhancement


True negative


True negative rate


Two one-sided t test


True positive


True positive rate


Uncinate fasciculus


Voxel-based morphometry


Ventromedial prefrontal cortex


White matter




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The authors thank Sean Mahoney, Van Ngo, and Di Fan for suggestions and comments on the manuscript, Nikhil N. Chaudhari for assistance with CMB identification, data archiving, and data retrieval, as well as Nahian F. Chowdhury, Gloria Chia-Yi Chiang, Ammar Dharani, Jun H. Kim, Hyung Jun Lee, David J. Robles, and Shania H. Wang for assistance with CMB identification. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at:

Availability of data and material

MRI data acquired from HC and AD participants are publicly available from the ADNI database ( For TBI participants, primary data generated during and/or analyzed during the current study are available subject to a data transfer agreement. At the request of some participants, their written permission is additionally required in some cases.

Code availability

The computer code used in this study is freely available. FreeSurfer ( and the FMRIB Software Library ( are freely available. Equivalence testing was implemented using freely available MATLAB software ( Regression and SVM analyses were implemented in MATLAB ( using the glmfit, fitcsvm, and predict functions.


This work was supported by NIH grant R01 NS 100973 to A.I., by DoD award W81-XWH-1810413 to A.I., by a Hanson-Thorell Research Scholarship to A.I., and by a grant from the Undergraduate Research Associate Program (URAP) at the University of Southern California to A.I. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI, NIH Grant U01 AG024904) and DoD ADNI (DoD award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through 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 National Institutes of Health ( The 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.

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K.A.R. and A.I. contributed to the study design, data analysis, result interpretation, and manuscript redaction.

Corresponding author

Correspondence to Andrei Irimia.

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This study was conducted with the approval of the Institutional Review Board at the University of Southern California and was carried out in accordance with the Declaration of Helsinki and with the U.S. Code of Federal Regulations (45 C.F.R. 46).

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All subjects provided written informed consent.

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Rostowsky, K.A., Irimia, A. & for the Alzheimer’s Disease Neuroimaging Initiative. Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer’s disease. GeroScience 43, 2015–2039 (2021).

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