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INTRODUCTION: The retina and brain exhibit similar pathologies in patients diagnosed with neurodegenerative diseases. The ability to access the retina through imaging techniques opens the possibility for non-invasive evaluation of Alzheimer's disease (AD) pathology. While retinal amyloid deposits are detected in individuals clinically diagnosed with AD, studies including preclinical individuals are lacking, limiting assessment of the feasibility of retinal imaging as a biomarker for early-stage AD risk detection. METHODS: In this small cross-sectional study we compare retinal and cerebral amyloid in clinically normal individuals who screened positive for high amyloid levels through positron emission tomography (PET) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) trial as well as a companion cohort of individuals who exhibited low levels of amyloid PET in the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) study. We quantified the number of curcumin-positive fluorescent retinal spots from a small subset of participants from both studies to determine retinal amyloid deposition at baseline. RESULTS: The four participants from the A4 trial showed a greater number of retinal spots compared to the four participants from the LEARN study. We observed a positive correlation between retinal spots and brain amyloid, as measured by the standardized uptake value ratio (SUVr). DISCUSSION: The results of this small pilot study support the use of retinal fundus imaging for detecting amyloid deposition that is correlated with brain amyloid PET SUVr. A larger sample size will be necessary to fully ascertain the relationship between amyloid PET and retinal amyloid both cross-sectionally and longitudinally.
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The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN) was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make "deadly force decisions" in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects' performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback), and applied across a variety of training environments.
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This article explores the psychophysiological metrics during expert and novice performances in marksmanship, combat deadly force judgment and decision making (DFJDM), and interactions of teams. Electroencephalography (EEG) and electrocardiography (ECG) are used to characterize the psychophysiological profiles within all categories. Closed-loop biofeedback was administered to accelerate learning during marksmanship training in which the results show a difference in groups that received feedback compared with the control. During known distance marksmanship and DFJDM scenarios, experts show superior ability to control physiology to meet the demands of the task. Expertise in teaming scenarios is characterized by higher levels of cohesiveness than those seen in novices.
