A deep learning approach for monitoring parietal-dominant Alzheimer's disease in World Trade Center responders at midlife. Issue 3 (2nd July 2021)

• Record Type: Journal Article
• Authors: Chen, Allen P F; Clouston, Sean A P; Kritikos, Minos; Richmond, Lauren; Meliker, Jaymie; Mann, Frank; Santiago-Michels, Stephanie; Pellecchia, Alison C; Carr, Melissa A; Kuan, Pei-Fen; Bromet, Evelyn J; Luft, Benjamin J
• Is Part Of: Brain communications. Volume 3:Issue 3(2021)
• Publication Date: 2021-07-02
• Subjects: cognitive impairment -- World Trade Center -- artificial neural network -- parietal-dominant Alzheimer's disease -- Alzheimer's disease and related dementias; 616
• DOI: 10.1093/braincomms/fcab145
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Little is known about the characteristics and causes of early-onset cognitive impairment. Responders to the 2001 New York World Trade Center disaster represent an ageing population that was recently shown to have an excess prevalence of cognitive impairment. Neuroimaging and molecular data demonstrate that a subgroup of affected responders may have a unique form of parietal-dominant Alzheimer's Disease. Recent neuropsychological testing and artificial intelligence approaches have emerged as methods that can be used to identify and monitor subtypes of cognitive impairment. We utilized data from World Trade Center responders participating in a health monitoring program and applied a deep learning approach to evaluate neuropsychological and neuroimaging data to generate a cortical atrophy risk score. We examined risk factors associated with the prevalence and incidence of high risk for brain atrophy in responders who are now at midlife. Training was conducted in a randomly selected two-thirds sample ( N = 99) enrolled using of the results of a structural neuroimaging study. Testing accuracy was estimated for each training cycle in the remaining third subsample. After training was completed, the scoring methodology that was generated was applied to longitudinal data from 1441 World Trade Center responders. The artificial neural network provided accurate classifications of these responders in both the testing (Area Under the Receiver Operating Curve, 0.91) and validation samples (Area Under the Receiver Operating Curve, 0.87). At baseline and follow-up, responders identified as having a high risk of atrophy ( n = 378) showed poorer cognitive functioning, most notably in domains that included memory, throughput, and variability as compared to their counterparts at low risk for atrophy ( n = 1063). Factors associated with atrophy risk included older age [adjusted hazard ratio, 1.045 (95% confidence interval = 1.027–1.065)], increased duration of exposure at the WTC site [adjusted hazard ratio, 2.815 (1.781–4.449)], and a higher prevalence of post-traumatic stress disorder [aHR, 2.072 (1.408–3.050)]. High atrophy risk was associated with an increased risk of all-cause mortality [adjusted risk ratio, 3.19 (1.13–9.00)]. In sum, the high atrophy risk group displayed higher levels of previously identified risk factors and characteristics of cognitive impairment, including advanced age, symptoms of post-traumatic stress disorder, and prolonged duration of exposure to particulate matter. Thus, this study suggests that a high risk of brain atrophy may be accurately monitored using cognitive data. Abstract : Dementia has a rising major impact on the health of ageing populations. World Trade Center responders are at increased risk for dementia, as characterized particularly by parietal-dominant cortical atrophy on neuroimaging. Clouston et al. used deep learning to predict the risk of developing cortical atrophy based on neuropsychological test results. Graphical abstract: