Abstract
Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14–24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression.
Highlights
Multivariate statistical methods[6], such as canonical correlation analysis (CCA)[7], allow an investigation of links between multiple sets of measures, such as brain imaging and behavioural data, collected from the same individuals
The first mode is associated with sex and has an interaction with depression, with healthy males clustering towards higher scores and depressed females clustering towards lower scores (Fig. 1a)
Depressed females seemed to cluster towards lower scores (Fig. 1b) again, both males and females were evenly distributed along this mode, and younger adolescents had higher scores whereas older ones were more distributed towards lower scores (Fig. 1d)
Summary
Multivariate statistical methods[6], such as canonical correlation analysis (CCA)[7], allow an investigation of links between multiple sets of measures, such as brain imaging and behavioural data, collected from the same individuals. All 306 subjects completed self-report questionnaires assessing well-being, affective symptoms, anxiety, impulsivity, compulsivity, self-esteem, self-harm, personality characteristics, psychotic spectrum symptoms, substance use, relations with peers and family and experience of trauma These item-level measures were supplemented with measures of subjects’ fluid and crystallized intelligence as well as additional demographic information (age, gender and socioeconomic deprivation index) amounting to a total of 364 behavioural (i.e. psychometrics/IQ/ demographics) measures for each subject. We performed a CCA resulting in pairs of canonical variates, which define modes of covariation between linear combinations of brain connectivity and behaviour profiles (in short ‘CCA modes’). We applied CCA embedded in a multiple hold-out framework[16] to investigate the generalizability of the model, i.e. to assess whether the CCA mode represent associations that can be found on new data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
