Publications by authors named "C S Heck"

The COVID-19 pandemic may have exacerbated mental health conditions by introducing and/or modifying stressors, particularly in university populations. We examined longitudinal patterns, time-varying predictors, and contemporaneous correlates of moderate-severe psychological distress (MS-PD) among college students. During 2020-2021, participants completed self-administered questionnaires quarterly (T1 = 562, T2 = 334, T3 = 221, and T4 = 169).

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The hippocampus is crucial for forming new episodic memories. While the encoding of spatial and temporal information (where and when) in the hippocampus is well understood, the encoding of objects (what) remains less clear due to the high dimensions of object space. Rather than encoding each individual object separately, the hippocampus may instead encode categories of objects to reduce this dimensionality.

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Mesial temporal lobe epilepsy (MTLE) is a syndromic disorder presenting with seizures and cognitive comorbidities. Although seizure etiology is increasingly understood, the pathophysiological mechanisms contributing to cognitive decline and epilepsy progression remain less recognized. We have previously shown that adult hippocampal neurogenesis dramatically declines in MTLE patients with increased disease duration.

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Background: Costing and financing systematic implementation are recognized barriers to human immunodeficiency virus (HIV) prevention. In the absence of empiric implementation and economic data, perspectives from international stakeholders involved in developing and supporting daily oral pre-exposure prophylaxis (PrEP) policy, and programs can provide critical insights for developing costed plans to support and accelerate the rollout of novel long-acting PrEP (LA-PrEP) methods, such as the monthly dapivirine vaginal ring (PrEP ring).

Methods: We interviewed stakeholders from purposively selected international organizations about anticipated PrEP-ring implementation costs, evidence gaps and key process steps for developing a costed rollout plan template (CRPT).

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Article Synopsis
  • Post-traumatic epilepsy (PTE) is a common and challenging condition that arises after traumatic brain injury (TBI), but predicting its occurrence is difficult with existing methods.
  • This study explored using machine learning to identify imaging features like lesion volumes and resting-state fMRI measures to improve predictions of PTE.
  • The kernel support vector machine (KSVM) model was found to be the most effective, achieving a prediction accuracy of 0.78 AUC, and highlighted significant differences in the brain's temporal lobes and cerebellum between PTE and non-PTE patients.
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