Evaluating computational assays of chronic fatigue using UK Biobank data

Chronic fatigue, characterized by persistent physical and/or mental exhaustion, is a frequent and debilitating symptom in medicine. Despite its impact, clinical management remains a challenge. A key problem is the absence of any biomarkers; as a consequence, diagnosis rests entirely on patients' self-report. This contributes to patient stigmatization and highlights the need for objective diagnostic tools. In this study, we explored the feasibility of constructing computational assays of chronic fatigue, using clinical and functional neuroimaging data from over 2,200 participants in the UK Biobank. Whole-brain analyses of functional and effective connectivity were followed by machine learning, based on a preregistered analysis plan and a strict separation of training data and held-out test data. We found that clinical data, including prior medical diagnoses, cancer history, sleep-related information, and alcohol consumption, enabled a statistically significant prediction of chronic fatigue (61% balanced accuracy, p=0.001). Combining clinical information with brain connectivity data again enabled statistically significant predictions (up to 64% balanced accuracy) but did not consistently outperform the model trained on clinical data only. Across all models, sleep-related information, especially insomnia symptoms, emerged as a particularly important feature for prediction. Our results suggest a high degree of heterogeneity amongst individuals with chronic fatigue. While the predictive performance achieved in this study is not yet sufficient for clinical application, our findings provide a foundation for future developments of objective assays of fatigue. In particular, our results highlight the importance of sleep-related information and suggest new avenues for harnessing neuroimaging information for the prediction of fatigue.