Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.

T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).

PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).

PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

Medicare claims are frequently used to study Clostridioides difficile infection (CDI) epidemiology. However, they lack specimen collection and diagnosis dates to assign location of onset. Algorithms to classify CDI onset location using claims data have been published, but the degree of misclassification is unknown.

We linked patients with laboratory-confirmed CDI reported to four Emerging Infections Program (EIP) sites from 2016–2021 to Medicare beneficiaries with fee-for-service Part A/B coverage. We calculated sensitivity of ICD-10-CM codes in claims within ±28 days of EIP specimen collection. CDI was categorized as hospital, long-term care facility, or community-onset using three different Medicare claims-based algorithms based on claim type, ICD-10-CM code position, duration of hospitalization, and ICD-10-CM diagnosis code presence-on-admission indicators. We assessed concordance of EIP case classifications, based on chart review and specimen collection date, with claims case classifications using Cohen’s kappa statistic.

Of 12,671 CDI cases eligible for linkage, 9,032 (71%) were linked to a single, unique Medicare beneficiary. Compared to EIP, sensitivity of CDI ICD-10-CM codes was 81%; codes were more likely to be present for hospitalized patients (93.0%) than those who were not (56.2%). Concordance between EIP and Medicare claims algorithms ranged from 68% to 75%, depending on the algorithm used (κ = 0.56–0.66).

ICD-10-CM codes in Medicare claims data had high sensitivity compared to laboratory-confirmed CDI reported to EIP. Claims-based epidemiologic classification algorithms had moderate concordance with EIP classification of onset location. Misclassification of CDI onset location using Medicare algorithms may bias findings of claims-based CDI studies.

Understand perceptions of COVID-19 messages and information sources among rural wastewater treatment plant operators to inform context-specific communication strategies for implementing wastewater surveillance methodologies locally.

Eight employees from 7 Eastern Kentucky facilities involved in SARS-CoV-2 wastewater surveillance participated in semi-structured interviews. Respondents shared perceptions of traditional and social media COVID-19 information channels in their communities, as well as factors influencing trustworthiness of sources. Using the U.S. Centers for Disease Control and Prevention’s Crisis and Emergency Risk Communication (CERC) framework, 3 investigators conducted iterative, thematic coding of interview transcripts.

Respondents’ statements most frequently related to “Be Credible,” “Be Right,” and “Promote Action” CERC constructs, while mixed messages, high volumes of information, and numerous sources undermined trust in COVID-19 information.

Understanding the relative importance of CERC constructs and their distractors may improve future risk communication to advance infectious disease surveillance strategies in rural contexts.

Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.