Age-related brain deviations and aggression

Abstract

Background: Disruptive behavior disorders (DBD) are heterogeneous at the clinical and the biological level. Therefore, the aims were to dissect the heterogeneous neurodevelopmental deviations of the affective brain circuitry and provide an integration of these differences across modalities.

Methods: We combined two novel approaches. First, normative modeling to map deviations from the typical age-related pattern at the level of the individual of (i) activity during emotion matching and (ii) of anatomical images derived from DBD cases (n = 77) and controls (n = 52) aged 8-18 years from the EU-funded Aggressotype and MATRICS consortia. Second, linked independent component analysis to integrate subject-specific deviations from both modalities.

Results: While cases exhibited on average a higher activity than would be expected for their age during face processing in regions such as the amygdala when compared to controls these positive deviations were widespread at the individual level. A multimodal integration of all functional and anatomical deviations explained 23% of the variance in the clinical DBD phenotype. Most notably, the top marker, encompassing the default mode network (DMN) and subcortical regions such as the amygdala and the striatum, was related to aggression across the whole sample.

Conclusions: Overall increased age-related deviations in the amygdala in DBD suggest a maturational delay, which has to be further validated in future studies. Further, the integration of individual deviation patterns from multiple imaging modalities allowed to dissect some of the heterogeneity of DBD and identified the DMN, the striatum and the amygdala as neural signatures that were associated with aggression.

Keywords: Aggression; disruptive behavior disorders; emotion processing; fMRI; normative modeling.

Fig. 1.

Methodological overview. (a). A normative model was estimated from brain structure and function across typically developing controls (TDs) and DBD cases, which allowed us to estimate normative probability maps, showing the regional deviations from the expected pattern in each subject. (b) These deviations were integrated using linked ICA, which yielded 20 components that were related to behavioral aggression measures.

Fig. 2.

(a) Spatial representation of the voxel-wise normative model for all modalities in males. The panels show the beta values depicting the change across 8 to 18 years of age for negative face processing, positive/neutral face processing, shape processing and anatomical images. Red and blue indicate an increase or decline, respectively, in activity or relative expansion or contraction to match the template for the Jacobian determinants. (b) Mean positive deviations from the model for controls and cases. Cases showed significantly more positive deviations, i.e. a higher age-related increase in prefrontal and limbic (i.e. cluster comprising the amygdala) activity presumably during negative and positive/neutral face matching. For visualization purposes only the left side is shown.

Fig. 3.

Cases showed increased deviations, i.e. higher age-related activity, from the normative model during negative face processing in the amygdala, the parahippocampal gyrus, the (inferior) temporal gyrus, the fusiform gyrus and the cuneus when compared to controls. This is suggestive of delayed maturational trajectories of brain activation in DBD cases.

Fig. 4.

Linked ICA. (a). Loading matrix showing the contribution of each modality to each of the 20 components (blue: negative faces, orange: positive/neutral faces, green: shapes, red: anatomy). The underlying brain pattern of component 8 (in the matrix number 8) per functional modality is depicted on the right side (thresholded at Z > 2). (b) Component 8 was negatively associated with the aggression factor, which was still significant when outliers were excluded (p = 0.003).