Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank.

Background: 1.The use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the ICD-10 from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies. Results: 2.We investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain MRI feature sets) or socio-demographic features. Random forest classification models were adopted using rigorous shuffle splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest versus youngest) from the same feature sets and against additional classifier types (K-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features, and depression based on socio-demographic and functional neuroimaging features). Conclusion: 3.These findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression.

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

Mental health in the UK Biobank: A roadmap to self-report measures and neuroimaging correlates.

The UK Biobank (UKB) is a highly promising dataset for brain biomarker research into population mental health due to its unprecedented sample size and extensive phenotypic, imaging, and biological measurements. In this study, we aimed to provide a shared foundation for UKB neuroimaging research into mental health with a focus on anxiety and depression. We compared UKB self-report measures and revealed important timing effects between scan acquisition and separate online acquisition of some mental health measures. To overcome these timing effects, we introduced and validated the Recent Depressive Symptoms (RDS-4) score which we recommend for state-dependent and longitudinal research in the UKB. We furthermore tested univariate and multivariate associations between brain imaging-derived phenotypes (IDPs) and mental health. Our results showed a significant multivariate relationship between IDPs and mental health, which was replicable. Conversely, effect sizes for individual IDPs were small. Test-retest reliability of IDPs was stronger for measures of brain structure than for measures of brain function. Taken together, these results provide benchmarks and guidelines for future UKB research into brain biomarkers of mental health.

Modeling the Effects of HIV and Aging on Resting-State Networks Using Machine Learning.

BACKGROUND: The relationship between HIV infection, the functional organization of the brain, cognitive impairment, and aging remains poorly understood. Understanding disease progression over the life span is vital for the care of people living with HIV (PLWH). SETTING: Virologically suppressed PLWH (n = 297) on combination antiretroviral therapy and 1509 HIV-uninfected healthy controls were evaluated. PLWH were further classified as cognitively normal (CN) or cognitively impaired (CI) based on neuropsychological testing. METHODS: Feature selection identified resting-state networks (RSNs) that predicted HIV status and cognitive status within specific age bins (younger than 35 years, 35-55 years, and older than 55 years). Deep learning models generated voxelwise maps of RSNs to identify regional differences. RESULTS: Salience (SAL) and parietal memory networks (PMNs) differentiated individuals by HIV status. When comparing controls with PLWH CN, the PMN and SAL had the strongest predictive strength across all ages. When comparing controls with PLWH CI, the SAL, PMN, and frontal parietal network (FPN) were the best predictors. When comparing PLWH CN with PLWH CI, the SAL, FPN, basal ganglia, and ventral attention were the strongest predictors. Only minor variability in predictive strength was observed with aging. Anatomically, differences in RSN topology occurred primarily in the dorsal and rostral lateral prefrontal cortex, cingulate, and caudate. CONCLUSION: Machine learning identified RSNs that classified individuals by HIV status and cognitive status. The PMN and SAL were sensitive for discriminating HIV status, with involvement of FPN occurring with cognitive impairment. Minor differences in RSN predictive strength were observed by age. These results suggest that specific RSNs are affected by HIV, aging, and HIV-associated cognitive impairment.