Abstract
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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Abbreviations
- ACR:
-
Anterior corona radiata
- AD:
-
Alzheimer’s disease
- ADNI:
-
Alzheimer’s Disease Neuroimaging Initiative
- AIC:
-
Anterior internal capsule
- ApoE:
-
Apolipoprotein E
- Aβ:
-
Amyloid beta
- BCC:
-
Body of the corpus callosum
- BCF:
-
Body and column of the fornix
- CAA:
-
Cerebral amyloid angiopathy
- CB:
-
Cingulum bundle
- CC:
-
Corpus callosum
- CDR:
-
Clinical dementia rating
- CDR-SB:
-
Clinical dementia rating sum of boxes
- CF:
-
Crus of the fornix
- CI:
-
Confidence interval
- CMB:
-
Cerebral microbleed
- CP:
-
Cerebral peduncle
- CST:
-
Corticospinal tract
- CT:
-
Computed tomography
- DAI:
-
Diffuse axonal injury
- DL:
-
Deep learning
- DMN:
-
Default mode network
- dMRI:
-
Diffusion magnetic resonance imaging
- DTI:
-
Diffusion tensor imaging
- DWI:
-
Diffusion weighted imaging
- EC:
-
External capsule
- FA:
-
Fractional anisotropy
- FLAIR:
-
Fluid-attenuated inversion recovery
- FN:
-
False negative
- FP:
-
False positive
- FSL:
-
FMRIB software library
- FWER:
-
Family-wise error rate
- GCC:
-
Genu of the corpus callosum
- GCS:
-
Glasgow Coma Scale
- GLM:
-
General linear model
- GM:
-
Gray matter
- GRE:
-
Gradient-recalled echo
- HC:
-
Healthy control
- ICbP:
-
Inferior cerebellar peduncle
- ICBM:
-
International Consortium of Brain Mapping
- IFG:
-
Inferior frontal gyrus
- IFOF:
-
Inferior fronto-occipital fasciculus
- ISDA:
-
Iterative single data algorithm
- JHU:
-
Johns Hopkins University
- LOC:
-
Loss of consciousness
- MCC:
-
Matthews’ correlation coefficient
- MCI:
-
Mild cognitive impairment
- ML:
-
Medial lemniscus
- MMSE:
-
Mini mental state examination
- MNI:
-
Montreal Neurological Institute
- MoCA:
-
Montreal cognitive assessment
- MP-RAGE:
-
Magnetization-prepared rapid acquisition gradient echo
- MRI:
-
Magnetic resonance imaging
- MRS:
-
Magnetic resonance spectroscopy
- mTBI:
-
Mild traumatic brain injury
- MTG:
-
Medial temporal gyrus
- NFT:
-
Neurofibrillary tangle
- OFC:
-
Orbitofrontal cortex
- PCR:
-
Posterior corona radiata
- PCT:
-
Pontine crossing tract
- PET:
-
Positron emission tomography
- PFC:
-
Prefrontal cortex
- PIC:
-
Posterior internal capsule
- PPV:
-
Positive prediction value
- PS:
-
Processing speed
- PTR:
-
Posterior thalamic radiation
- RIC:
-
Retrolenticular internal capsule
- ROI:
-
Region of interest
- SCC:
-
Splenium of the corpus callosum
- SCbP:
-
Superior cerebellar peduncle
- SCR:
-
Superior corona radiata
- SFOF:
-
Superior fronto-occipital fasciculus
- SLR:
-
Superior longitudinal fasciculus
- SS:
-
Sagittal stratum
- STG:
-
Superior temporal gyrus
- SVM:
-
Support vector machine
- SWI:
-
Susceptibility weighted imaging
- TBI:
-
Traumatic brain injury
- TBSS:
-
Tract-based spatial statistics
- TCC:
-
Tapetum of the corpus callosum
- TFCE:
-
Threshold-free cluster enhancement
- TN:
-
True negative
- TNR:
-
True negative rate
- TOST:
-
Two one-sided t test
- TP:
-
True positive
- TPR:
-
True positive rate
- UF:
-
Uncinate fasciculus
- VBM:
-
Voxel-based morphometry
- vmPFC:
-
Ventromedial prefrontal cortex
- WM:
-
White matter
- 3D:
-
Three-dimensional
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Acknowledgements
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 (adni.loni.usc.edu). 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: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledge-ment_List.pdf.
Availability of data and material
MRI data acquired from HC and AD participants are publicly available from the ADNI database (http://adni.loni.usc.edu). 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 (https://surfer.nmr.mgh.harvard.edu) and the FMRIB Software Library (https://fsl.fmrib.ox.ac.uk) are freely available. Equivalence testing was implemented using freely available MATLAB software (https://www.mathworks.com/matlabcentral/fileexchange/63204). Regression and SVM analyses were implemented in MATLAB (http://mathworks.com) using the glmfit, fitcsvm, and predict functions.
Funding
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 (www.fnih.org). 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.
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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). https://doi.org/10.1007/s11357-021-00355-9
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DOI: https://doi.org/10.1007/s11357-021-00355-9