A deep learning approach for monitoring parietal-dominant Alzheimer's disease in World Trade Center responders at midlife

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.

Keywords: Alzheimer’s disease and related dementias; World Trade Center; artificial neural network; cognitive impairment; parietal-dominant Alzheimer’s disease.

Graphical Abstract

Figure 1

Study flow diagram. This flowchart diagram depicts the major steps in artificial neural network training and development of the atrophy risk score based on neuroimaging and cognitive data from World Trade Center responders as inputs. Three major steps were used to train an artificial neural network to assess atrophy risk based on the results of neuropsychological testing. Step 1: sample n₁ included World Trade Center responders (participants; pts.) with neuroimaging data (±cortical atrophy) that was linked to results of neuropsychological testing. The artificial neural network underwent training to classify atrophy risk based on the associated cognitive tests. Step 2: n₂ subsample with similar neuroimaging data was used to validate the artificial neural network trained in Step 1. Cutoffs were determined for high atrophy risk and low atrophy risk classifications. Step 3: We used the validated artificial neural network to study, classify and characterize the longitudinal dataset, n₃.

Figure 2

Cortical atrophy risk score distribution in World Trade Center responders. The distribution of Cortical Atrophy Risk scores indicating increasing risk of cortical atrophy as estimated in the longitudinal follow-up study of World Trade Center responders (n₃). A bar histogram of the distribution of atrophy risk scores and the percentage of each score identified in the longitudinal sample (n₃). The vertical red line shows the cutoff for high atrophy risk.