Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research
Much attention is currently devoted to developing diagnostic classifiers for mental disorders.
Complementing these efforts, we highlight the potential of machine learning to gain …
Complementing these efforts, we highlight the potential of machine learning to gain …
[HTML][HTML] Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Cognitive performance can be predicted from an individual's functional brain connectivity
with modest accuracy using machine learning approaches. As yet, however, predictive …
with modest accuracy using machine learning approaches. As yet, however, predictive …
[HTML][HTML] Neighborhood disadvantage and longitudinal brain-predicted-age trajectory during adolescence
Neighborhood disadvantage has consistently been linked to alterations in brain structure;
however, positive environmental (eg, positive parenting) and psychological factors (eg …
however, positive environmental (eg, positive parenting) and psychological factors (eg …
[HTML][HTML] Developmental brain changes during puberty and associations with mental health problems
Background Our understanding of the mechanisms relating pubertal timing to mental health
problems via brain development remains rudimentary. Method Longitudinal data was …
problems via brain development remains rudimentary. Method Longitudinal data was …
[HTML][HTML] Individual variation underlying brain age estimates in typical development
Typical brain development follows a protracted trajectory throughout childhood and
adolescence. Deviations from typical growth trajectories have been implicated in …
adolescence. Deviations from typical growth trajectories have been implicated in …
[HTML][HTML] Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort
The temporal characteristics of adolescent neurodevelopment are shaped by a complex
interplay of genetic, biological, and environmental factors. Using a large longitudinal dataset …
interplay of genetic, biological, and environmental factors. Using a large longitudinal dataset …
Deep relation learning for regression and its application to brain age estimation
Most deep learning models for temporal regression directly output the estimation based on
single input images, ignoring the relationships between different images. In this paper, we …
single input images, ignoring the relationships between different images. In this paper, we …
[HTML][HTML] Linking brain age gap to mental and physical health in the Berlin aging study II
From a biological perspective, humans differ in the speed they age, and this may manifest in
both mental and physical health disparities. The discrepancy between an individual's …
both mental and physical health disparities. The discrepancy between an individual's …
[HTML][HTML] Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study
There is significant interest in using neuroimaging data to predict behavior. The predictive
models are often interpreted by the computation of feature importance, which quantifies the …
models are often interpreted by the computation of feature importance, which quantifies the …
[HTML][HTML] Deviations from normative brain white and gray matter structure are associated with psychopathology in youth
Combining imaging modalities and metrics that are sensitive to various aspects of brain
structure and maturation may help identify individuals that show deviations in relation to …
structure and maturation may help identify individuals that show deviations in relation to …