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.
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Data availability
See https://github.com/PennBBL/baller_heterogen_2019 for an overview and all data analysis code used in this manuscript. Data from the Philadelphia Neurodevelopmental Cohort can be accessed at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000607.v3.p2.
Code availability
All code for HYDRA can be found at https://github.com/evarol/HYDRA.
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EBB, M.D., M.S. was responsible for conceptual design, and did the primary data analysis, interpretation of results, manuscript preparation, and graphics. ANK, Ph.D. provided a template for data analysis, supervised data analysis, and verified the accuracy of the final results. AS provided support for HYDRA data analysis. AA, Ph.D. provided support for neuroimaging data analysis. DSB, Ph.D. aided in conceptual design. MEC, Ph.D. provided expert guidance in clinical phenotyping and clinical symptom factor analysis. GBC, Ph.D. performed the permutation analysis. ZC, Ph.D. provided expert guidance in data analysis, graphics support, and verified the accuracy of the final results. REG, M.D., Ph.D. leds the development of the PNC dataset. RCG, Ph.D. leads the development of the CNB. KAL, Ph.D. provided expert statistical guidance, specifically with the usage of the MatchIt R-package. TMM, Ph.D. performed the specialized clinical factor analysis. DRR, Ph.D. provided expert guidance on the interpretation of the CNB. EV, Ph.D. provided expert support in HYDRA usage. DHW, M.D., Ph.D. aided in conceptual design and supervised the processing of the neuroimaging data. CHX, Ph.D. provided data analysis and graphics support. CD, Ph.D. was involved in conceptual design, specifically with respect to HYDRA, which was developed in his lab. TDS, M.D., M.A. was the supervising mentor. He was involved in the conceptual design, data analysis, manuscript drafting, and review.
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Baller, E.B., Kaczkurkin, A.N., Sotiras, A. et al. Neurocognitive and functional heterogeneity in depressed youth. Neuropsychopharmacol. 46, 783–790 (2021). https://doi.org/10.1038/s41386-020-00871-w
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DOI: https://doi.org/10.1038/s41386-020-00871-w
