Abstract
BackgroundBiological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention?ResultsHere, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data.ConclusionPopulation modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.
Highlights
IntroductionIndividual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism
Biological aging is revealed by physical measures, e.g., DNA probes or brain scans
The relative importance of brain and sociodemographic data depends on the target In a second step, we investigated the relative performance of proxy measures built from brain signals and distinct sociodemographic factors for the 3 targets: age, fluid intelligence, and neuroticism
Summary
Individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). Conclusion: Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations. A full neuropsychological evaluation is not an automated process but relies on expert judgement to confront multiple answers and interpret them in the context of the broader picture, such as the cultural back-
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