Neurocognitive and functional heterogeneity in depressed youth

Abstract

Depression is a common psychiatric illness that often begins in youth, and is sometimes associated with cognitive deficits. However, there is significant variability in cognitive dysfunction, likely reflecting biological heterogeneity. We sought to identify neurocognitive subtypes and their neurofunctional signatures in a large cross-sectional sample of depressed youth. Participants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (MDD n = 368, TD n = 200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A recently developed semi-supervised machine learning algorithm was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task. We identified three neurocognitive subtypes in the depressed group. Subtype 1 was high-performing (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), and Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition. Taken together, these data suggest disparate mechanisms of cognitive vulnerability and resilience in depressed youth, which may inform the identification of biomarkers for prognosis and treatment response.

Fig. 1. Heterogeneity through Discriminative Analysis (HYDRA) algorithm and subtype selection.

A HYDRA is a semi-supervised machine learning algorithm that reveals homogenous subtypes within a clinical group by maximizing subtype-specific margins between patient subtypes and controls, while adjusting for covariates. B The stability of the clustering solution after cross-validation was evaluated over a resolution range of 2–10 clusters (2–6 shown here), and was quantified by the adjusted rand index (ARI). The maximum ARI was seen with three subtypes.

Fig. 2. Subtypes revealed by HYDRA differ in their neurocognitive profiles.

A Three neurocognitive signatures emerged in depressed youth: High-performing Subtype 1 had preserved cognition, with high accuracy and speed; Impaired Subtype 2 had low accuracy and speed; Impulsive Subtype 3 had high speed but low accuracy. Patterns were largely consistent for all measures of accuracy (B) and speed (C). Horizontal dashed lines reflect the mean. Error bars reflect standard error of the mean. HYDRA Heterogeneity through Discriminative Analysis, ABF abstraction/mental flexibility, ATT attention, WM working memory, VMEM verbal memory, FMEM face memory, SMEM spatial memory, LAN language/verbal reasoning, NVR nonverbal reasoning, SPA spatial reasoning, EID emotion recognition, EDI emotion discrimination, ADI age discrimination, MOT motor, SM sensorimotor.

Fig. 3. Neurocognitive subtypes differ in activation of executive regions during an n-back working memory paradigm.

A Group differences (P_fdr < 0.05) in n-back activation between subtypes were present in six functionally defined regions of interest, which were defined a priori in prior published work [32]. See Supplementary Fig. 1 for all twenty-one regions of interest. B Group differences were driven by a consistent pattern across regions, with greater activation in High-performing Subtype 1 and TDs than in Impaired Subtype 2 or Impulsive Subtype 3. Error bars reflect standard error of the mean.