Affiliations: [a] Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| [b] Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| [c] Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Correspondence:
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Correspondence to: Guorong Wu, PhD, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. Tel.: +1 919 966 2216; E-mail: grwu@med.unc.edu.
Note: [1] These authors contributed equally to this work.
Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://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: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Background:Although the abnormal depositions of amyloid plaques and neurofibrillary tangles are the hallmark of Alzheimer’s disease (AD), converging evidence shows that the individual’s neurodegeneration trajectory is regulated by the brain’s capability to maintain normal cognition. Objective:The concept of cognitive reserve has been introduced into the field of neuroscience, acting as a moderating factor for explaining the paradoxical relationship between the burden of AD pathology and the clinical outcome. It is of high demand to quantify the degree of conceptual cognitive reserve on an individual basis. Methods:We propose a novel statistical model to quantify an individual’s cognitive reserve against neuropathological burdens, where the predictors include demographic data (such as age and gender), socioeconomic factors (such as education and occupation), cerebrospinal fluid biomarkers, and AD-related polygenetic risk score. We conceptualize cognitive reserve as a joint product of AD pathology and socioeconomic factors where their interaction manifests a significant role in counteracting the progression of AD in our statistical model. Results:We apply our statistical models to re-investigate the moderated neurodegeneration trajectory by considering cognitive reserve, where we have discovered that 1) high education individuals have significantly higher reserve against the neuropathology than the low education group; however, 2) the cognitive decline in the high education group is significantly faster than low education individuals after the level of pathological burden increases beyond the tipping point. Conclusion:We propose a computational proxy of cognitive reserve that can be used in clinical routine to assess the progression of AD.
