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Acute Cognitive Deficits After Traumatic Brain Injury Predict 2 Alzheimer’S Disease-Like Degradation of the Human Default Mode Network

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Acute Cognitive Deficits After Traumatic Brain Injury Predict 2 Alzheimer’S Disease-Like Degradation of the Human Default Mode Network medRxiv preprint doi: https://doi.org/10.1101/2020.08.01.20166561; this version posted August 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . 1 Acute cognitive deficits after traumatic brain injury predict 2 Alzheimer’s disease-like degradation of the human default mode network 3 Andrei Irimia1,2|ǂ, Alexander S. Maher1ǂ, Nikhil N. Chaudhari1, Nahian F. Chowdhury1, Elliot B. Jacobs1 4 and the Alzheimer’s Disease Neuroimaging Initiative3 5 1 Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern 6 California, Los Angeles, CA USA 7 2 Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, 8 University of Southern California, Los Angeles, CA USA 9 3 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging 10 Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to 11 the design and implementation of ADNI and/or provided data but did not participate in analysis or writing 12 of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp- 13 content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf 14 | Corresponding author: [email protected] 15 ǂ These authors contributed equally to this work. 16 ORCID of Andrei Irimia: 0000-0002-9254-9388 17 Abbreviated title: TBI deficits predict AD-like DMN degradation 18 Number of figures: 4 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.08.01.20166561; this version posted August 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . 19 Abstract 20 Traumatic brain injury (TBI) and Alzheimer’s disease (AD) are prominent neurological conditions whose 21 neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological 22 abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase 23 vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional 24 relationship between acute post-traumatic sequelae and chronic AD-like degradation remains elusive. 25 Here, functional magnetic resonance imaging (fMRI), network theory and machine learning (ML) are 26 leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting- 27 state activity in the default mode network (DMN). This network is found to contain modules whose extent 28 of AD-like, post-traumatic degradation can be accurately prognosticated based on the acute cognitive 29 deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive 30 physiological association between geriatric mTBI, cognitive impairment and AD-like functional 31 degradation, these findings advance the goal of acutely forecasting mTBI patients’ chronic deviations from 32 normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in 33 functional brain connectivity as early as ~6 months post-injury carries substantial implications for public 34 health because TBI has relatively high prevalence in the elderly. 35 Keywords: Alzheimer’s disease, traumatic brain injury, default mode network, resting state, 36 geriatrics, functional connectome 37 Acknowledgments 38 The authors thank Maria Calvillo, Lei Cao, Yu Hu, Jun H. Kim, Sean O. Mahoney, Van Ngo, Kenneth A. 39 Rostowsky and Shania Wang for their assistance. 40 Sources of funding 41 This work was supported by NIH grant R01 NS 100973 to A.I., by DoD award W81-XWH-1810413 to A.I., 42 by a Hanson-Thorell Research Scholarship to A.I. and by the Undergraduate Research Associate Program 43 (URAP) at the University of Southern California. Data collection and sharing for this project was funded by 44 the Alzheimer's Disease Neuroimaging Initiative (ADNI, NIH Grant U01 AG024904) and DoD ADNI (DoD 45 award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National 46 Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the 47 following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; 48 BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan 49 Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated 50 company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy 51 Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; 52 Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack 53 Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda 54 Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is 55 providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the 56 Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern 57 California Institute for Research and Education, and the study is coordinated by the Alzheimer’s 58 Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by 59 the Laboratory for Neuro Imaging at the University of Southern California. 2 medRxiv preprint doi: https://doi.org/10.1101/2020.08.01.20166561; this version posted August 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . 60 Conflicts of interest/competing interests 61 The authors declare no actual or perceived competing interests. 62 Data availability 63 MRI data acquired from HC and AD participants are publicly available from the ADNI database 64 (http://adni.loni.usc.edu). For TBI participants, primary data generated during and/or analyzed during the 65 current study are available subject to a data transfer agreement. At the request of some participants, their 66 written permission is additionally required in a limited number of cases. 67 Computer code availability. The computer code used in this study is freely available. FreeSurfer and FS- 68 FAST are freely available (https://surfer.nmr.mgh.harvard.edu). Equivalence testing was implemented 69 using freely available MATLAB software 70 (https://www.mathworks.com/matlabcentral/fileexchange/63204). Network analysis was implemented 71 using the freely available Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/). Network 72 visualizations were generated using Gephi (http://gephi.org). Regression and SVM analyses were 73 implemented in MATLAB (http://mathworks.com) using the glmfit, fitcsvm and predict 74 functions. 75 Authors’ contributions 76 A.I. contributed to study design, data analysis, result interpretation and wrote the manuscript. A.S.M., 77 N.N.C., N.F.C. and E.B.J. and contributed to study design, data analysis and result interpretation. 3 medRxiv preprint doi: https://doi.org/10.1101/2020.08.01.20166561; this version posted August 4, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . 78 Introduction 79 Traumatic brain injury (TBI) can result in functional brain alterations causing neural and cognitive deficits 80 [1, 2]. Even after mild TBI (mTBI), any cognitive domain can be affected by such deficits [3], whose 81 manifestation may accelerate the onset of mild cognitive impairment (MCI) [4, 5], particularly in geriatric 82 patients [6]. Although neurotrauma increases the risk for Alzheimer’s disease (AD) [7], there is little 83 knowledge on how TBI affects functional trajectories and gives rise to neuropathophysiology. 84 Furthermore, the relationship between TBI severity and AD-like brain dysfunction is poorly understood, 85 and the mechanisms whereby TBI can elicit functional abnormalities which increase AD risk remains 86 unclear. One hypothesis is that post-traumatic functional changes exhibit patterns which progressively 87 resemble those of AD [8, 5, 9]; if this is the case, characterizing AD-like brain alterations after TBI could 88 assist in improving AD risk assessment and the early identification of TBI patients at higher risk for this 89 disease. 90 The default mode network (DMN) is a large-scale brain network which is most commonly active when a 91 person is at wakeful rest [10]. In this resting state (RS), individuals lie quietly awake without performing 92 tasks or being exposed to stimuli, and their DMN activity can be recorded using techniques like functional 93 magnetic resonance imaging (fMRI). RS fMRI recordings only require passive participation from study 94 participants, such
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