Neurostructural Heterogeneity in Youth with Internalizing Symptoms

Abstract

Internalizing disorders such as anxiety and depression are the most common psychiatric disorders, frequently begin in youth, and exhibit marked heterogeneity in treatment response and clinical course. It is increasingly recognized that symptom-based classification approaches to internalizing disorders do not align with underlying neurobiology. An alternative to classifying psychopathology based on clinical symptoms is to identify neurobiologically-informed subtypes based on brain imaging data. We used a recently developed semi-supervised machine learning method (HYDRA) to delineate patterns of neurobiological heterogeneity within youth with internalizing symptoms using structural imaging data collected at 3T from a large communitybased sample of 1,141 youth. Using volume and cortical thickness, cross-validation methods indicated a highly stable solution (ARI=.66; permutation-based pfdr < .001) and identified two subtypes of internalizing youth. Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both Subtype 2 and typically developing youth. Using resting-state fMRI and diffusion images not considered during clustering, we found that Subtype 1 also showed reduced amplitudes of low-frequency fluctuations in fronto-limbic regions at rest, as well as reduced fractional anisotropy in white matter tracts such as the parahippocampal cingulum bundle and the uncinate fasciculus. In contrast, Subtype 2 showed intact cognitive performance, greater volume, cortical thickness, and amplitudes during rest compared to Subtype 1 and typically developing youth, despite still showing clinically significant levels of psychopathology.

Systematic Review of Functional MRI Applications for Psychiatric Disease Subtyping

“…The last study in this section focused on finding structural subtypes in subjects with internalizing disorders, which are characterized by anxiety, depressive, and somatic symptoms. In this study, Kaczkurkin et al (63) took a different approach to disease subtyping. Instead of clustering diagnosed patients in a fully unsupervised way, they used a semi-supervised approach called HYDRA (64), which uses the binary case-control labels to find different disease subtypes regarding their difference to controls.…”

Neurocognitive Heterogeneity in Social Anxiety Disorder: The Role of Self-Referential Processing and Childhood Maltreatment

“…This has fueled interest in defining neurocognitive subtypes that capture some of this heterogeneity. Studies have increasingly turned to unsupervised (data-driven) clustering approaches to identify potential subgroups within particular disorders (Feczko et al, 2019;Kaczkurkin et al, 2020;Marquand et al, 2016).…”

Neurocognitive and functional heterogeneity in depressed youth

“…Given known developmental and sex differences in cognition, both age and sex were included as covariates in HYDRA. Consistent with prior studies using this technique, we derived multiple clustering solutions requesting 2 to 10 clusters in order to obtain a range of possible solutions (24,25). The adjusted Rand index (ARI) was calculated using 10-fold cross validation to evaluate the stability of each solution; the solution with the highest ARI value was selected for subsequent analyses.…”