Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.

ABSTRACT:

Background

It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern

SUBMITTER: Portugal LCL 

PROVIDER: S-EPMC6517640 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature