Area:
Clinical Psychology
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High-probability grants
According to our matching algorithm, Antonia Kaczkurkin is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2018 — 2021 |
Kaczkurkin, Antonia |
K99Activity Code Description: To support the initial phase of a Career/Research Transition award program that provides 1-2 years of mentored support for highly motivated, advanced postdoctoral research scientists. R00Activity Code Description: To support the second phase of a Career/Research Transition award program that provides 1 -3 years of independent research support (R00) contingent on securing an independent research position. Award recipients will be expected to compete successfully for independent R01 support from the NIH during the R00 research transition award period. |
Delineating Neurobiological Heterogeneity in Internalizing Symptoms Using Machine Learning and Deep Phenotyping @ University of Pennsylvania
ABSTRACT Symptom-based classification approaches based on the DSM 5 are often not supported by epidemiological, genetic, and clinical neuroimaging research and may impede the advancement of interventions that target the pathophysiological mechanisms underlying mental health disorders. A novel alternative to classifying psychopathology based on presenting clinical symptoms is to identify neurobiologically-informed biotypes. Individuals are clustered according to shared patterns of brain dysfunction using data-driven machine learning techniques to reveal the heterogeneous biological mechanisms that underlie comorbid disorders. Internalizing symptoms often first begin during development, suggesting that this is a critical period of vulnerability. Additionally, strong sex differences are found in anxiety and depressive symptoms, starting in adolescence. Thus, studies are needed that examine sex differences in the neurobiological mechanisms associated with internalizing symptoms during development. The purpose of the current study is to uncover the neurobiological heterogeneity associated with internalizing symptoms in youth. During the K99 phase, Aim 1 will use machine-learning techniques to delineate patterns of neurobiological heterogeneity among youth with anxiety and depressive disorders using multimodal neuroimaging data from a large community-based sample of over 1,200 youth studied as part of the Philadelphia Neurodevelopmental Cohort (PNC; Training phase). We will test these heterogeneous patterns on a hold-out sample from the same cohort to examine the model?s validity (Validation phase). While the PNC provides an ideal dataset for developing a model, it does not have paradigms relevant to fear and anxiety that would allow us to identify important phenotypic differences between biotypes. Thus, Aim 2 will evaluate the generalizability of this model in an independent sample collected during the R00 phase, and further characterize these biotypes using pertinent measures related to error and reward processing. Finally, Aim 3 will investigate how sex differences in brain development associate with heterogeneous neural patterns in internalizing symptoms. Dr. Kaczkurkin?s long-term goal is to establish an independent research program where she will use advanced multi-modal neuroimaging techniques to study the mechanisms underlying internalizing disorders in youth. This study will provide a unique opportunity to capitalize on the PNC database at the University of Pennsylvania to develop a well-validated model while also collecting a refined independent dataset, which will provide Dr. Kaczkurkin with the training and experience needed to transition to an independent research career.
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