Acute cognitive deficits after traumatic brain injury predict Alzheimer’s disease-like degradation of the human default mode network

Abstract

Traumatic brain injury (TBI) and Alzheimer’s disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute posttraumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory, and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, posttraumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment, and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients’ chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ~6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.

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Data availability

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 a limited number of cases.

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Acknowledgments

The authors thank Maria Calvillo, Lei Cao, Yu Hu, Jun H. Kim, Sean O. Mahoney, Van Ngo, Kenneth A. Rostowsky, and Shania Wang for their assistance.

Computer code availability

The computer code used in this study is freely available. FreeSurfer and FS-FAST are freely available (https://surfer.nmr.mgh.harvard.edu). Equivalence testing was implemented using freely available MATLAB software (https://www.mathworks.com/matlabcentral/fileexchange/63204). Network analysis was implemented using the freely available Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/). Network visualizations were generated using Gephi (http://gephi.org). 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 the Undergraduate Research Associate Program (URAP) at the University of Southern California. Data collection and sharing for this project was 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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A.I. contributed to the study design, data analysis, and result interpretation and wrote the manuscript. A.S.M., N.N.C., N.F.C., and E.B.J. contributed to the study design, data analysis, and result interpretation.

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Correspondence to Andrei Irimia.

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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 the 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_Acknowledgement_List.pdf

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Irimia, A., Maher, A.S., Chaudhari, N.N. et al. Acute cognitive deficits after traumatic brain injury predict Alzheimer’s disease-like degradation of the human default mode network. GeroScience (2020). https://doi.org/10.1007/s11357-020-00245-6

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Keywords

  • Alzheimer’s disease
  • Traumatic brain injury
  • Default mode network
  • Resting state
  • Geriatrics
  • Functional connectome