Association of Heritable Cognitive Ability and Psychopathology With White Matter Properties in Children and Adolescents

Abstract

Importance: Many mental disorders emerge during adolescence, which may reflect a cost of the potential for brain plasticity offered during this period. Brain dysconnectivity has been proposed as a common factor across diagnostic categories.

Objective: To investigate the hypothesis that brain dysconnectivity is a transdiagnostic phenotype in adolescence with increased susceptibility and symptoms of psychiatric disease.

Design, setting, and participants: We investigated clinical symptoms as well as cognitive function in 6487 individuals aged 8 to 21 years from November 1, 2009, to November 30, 2011, in the Philadelphia Neurodevelopmental Cohort and analyzed diffusion magnetic resonance imaging brain scans for 748 of the participants.

Main outcomes and measures: Independent component analysis was used to derive dimensional psychopathology scores, and genome-wide complex trait analysis was used to estimate its heritability. Multimodal fusion simultaneously modeled contributions of the diffusion magnetic resonance imaging metrics fractional anisotropy, mean diffusivity, radial diffusivity, L1 (the principal diffusion tensor imaging eigen value), mode of anisotropy, as well as dominant and secondary fiber orientations, and structural connectivity density, and their association with general psychopathology and cognition.

Results: Machine learning with 10-fold cross-validation and permutation testing in 729 individuals (aged 8 to 22 years; mean [SD] age, 15.1 [3.3] years; 343 females [46%]) revealed significant association with general psychopathology levels (r = 0.24, P < .001) and cognition (r = 0.39, P < .001). A brain white matter pattern reflecting frontotemporal connectivity and crossing fibers in the uncinate fasciculus was the most associated feature for both traits. Univariate analysis across a range of clinical domains and cognitive test scores confirmed its transdiagnostic importance. Both the general psychopathology (16%; SE, 0.095; P = .05) and cognitive (18%; SE, 0.09; P = .01) factor were heritable and showed a negative genetic correlation.

Conclusion and relevance: Dimensional and heritable general cognitive and psychopathology factors are associated with specific patterns of white matter properties, suggesting that dysconnectivity is a transdiagnostic brain-based phenotype in individuals with increased susceptibility and symptoms of psychiatric disorders.

Conflict of interest statement

Conflict of Interest Disclosures: None reported.

Figures

Figure 1.. Weights for 129 Clinical Questionnaire Items on the Estimated Components From the Clinical Independent Component (IC) Analysis

The color scale represents item loading on the independent components. ADHD indicates attention-deficit/hyperactivity disorder; OCD, obsessive-compulsive disorder; PTSD, posttraumatic stress disorder.

Figure 2.. Linked Independent Component Analysis (LICA) Brain Components Were Robustly Associated With Age, Fluid Intelligence, and Psychopathology

The bar graph shows the feature importance for each of the 20 components in cross-validated associations with age, fluid intelligence (Gf), and psychopathology (mean independent component analysis [ICA]) (A). Although LICA-01 is the most important feature for association with age, LICA-09 is most important for association with cognition and mental health. The scatterplots show the correlations between the true and predicted scores for age (B), Gf (C), and mean ICA (D) (P < .001 was considered significant; familywise error, 10 000 permutations).

Figure 3.. Linked Independent Component Analysis (LICA) Brain Components Were Robustly Associated With Cognition and Psychopathology

Spatial maps for LICA-09, with percentages representing the relative modality contributions (A). Warm and cool colors represent positive and negative LICA weights, respectively (pseudo z threshold of ±3), overlaid on the uncinate fasciculus (pink) and inferior fronto-occipital fasciculus (purple) from the Johns Hopkins University white matter tractography atlas. Weights are plotted as a function of age and stratified by fluid intelligence (Gf) (B) and clinical independent component analysis (ICA) scores (C). The lower participant weights for participants with higher symptom burden entails increased fractional anisotropy (FA), dominant fiber population (f1), L1 (the principal diffusion tensor imaging eigen-value), connectivity density (CD), and mode of anisotropy (MO), and decreased secondary fiber population (f2) and radial diffusivity (RD) in the uncinated in the insular region, consistent with decreased crossing fibers. Curves and shaded area represent the mean (SD) of smooth function fitting across 10 000 bootstraps.

Figure 4.. Univariate Results for the Participant Weights of the 20 Linked Independent Component Analysis (LICA) Components Support the Cognitive and Clinical Relevance of LICA-09

The brain pattern captured by LICA-09 shows robust and inverse associations with cognitive (ie, fluid intelligence [Gf]) and clinical component scores, as well as with individual cognitive and clinical domain scores. Associations adjusted for age, sex, and temporal-signal-to-noise ratio, and corrected for multiple comparisons using false discovery rate (FDR) of q = 0.05. pF indicates first principal component for clinical items.