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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

GeroScience volume 43, pages 2015–2039 (2021)Cite this article

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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Authors and Affiliations

  1. Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA

    Kenneth A. Rostowsky & Andrei Irimia

  2. Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA

    Andrei Irimia

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  1. Kenneth A. Rostowsky
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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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Correspondence to Andrei Irimia.

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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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Keywords

  • Traumatic brain injury
  • Alzheimer’s disease
  • Mild cognitive impairment
  • Neuroimaging