Introduction: World Trade Center (WTC) responders have a high risk of early-onset cognitive impairment (CI), but little is known about the etiology including the extent to which CI in WTC responders is accompanied by cortical atrophy as is common in progressive diseases causing age-related CI such as Alzheimer's disease and related dementias. In the current study, we entrained an artificial neural network (ANN) to determine the accuracy of cortical thickness (CTX) on magnetic resonance imaging to identify World Trade Center responders at midlife (aged 44-65 years) with possible dementia.
Methods: A total of 119 WTC responders (57 with CI and 62 with intact cognition) underwent a structural MRI scanning protocol including T1-weighted MPRAGE as part of two imaging studies. The discovery study was divided into training and validation samples, while a second replication sample was used. An ANN was trained using regional CTX measured across 34 unilateral regions of interest (ROIs) using Freesurfer software and 'Desikan-Killiany' brain atlas. The discovery sample was used for model development, and the replication sample was used to evaluate predictive accuracy.
Results: In the WTC responder cohort, the ANN algorithm showed high discrimination performance for CI. The ANN model using regional CTX data from both hemispheres achieved an area under the receiver operating characteristic curve (AUC) of 0.96 95% C.I. = [0.91-1.00] (Accuracy = 96.0%, Precision = 97.8%, Recall = 95.8%, Sensitivity = 95.8%, Specificity = 98.0%, F1 = 96.8%) for the discovery sample and AUC = 0.90 [0.70-1.00] (Accuracy = 90.0%, Precision = 90.0%, Sensitivity = 90.0%, Specificity = 90.0%, F1 = 90.0%) in the replication sample.
Conclusion: Analysis of bilateral regional CTX data derived from T1-weighted MPRAGE images by ANN analysis demonstrated excellent accuracy in distinguishing WTC responders with early-onset CI.
Keywords: Cognitive impairment; Cortical thickness; World trade center responders.