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.

Treatment guidelines recommend evidence-based psychological therapies for adults with intellectual disabilities with co-occurring anxiety or depression. No previous research has explored the effectiveness of these therapies in mainstream psychological therapy settings or outside specialist settings.

To evaluate the effectiveness of psychological therapies delivered in routine primary care settings for people with intellectual disability who are experiencing co-occurring depression or anxiety.

This study used linked electronic healthcare records of 2 048 542 adults who received a course of NHS Talking Therapies for anxiety and depression in England between 2012 and 2019 to build a retrospective, observational cohort of individuals with intellectual disability, matched 1:2 with individuals without intellectual disability. Logistic regressions were used to compare metrics of symptom improvement and deterioration used in the national programme, on the basis of depression and anxiety measures collected before and at the last attended therapy session.

The study included 6870 adults with intellectual disability and 2 041 672 adults without intellectual disability. In unadjusted analyses, symptoms improved on average for people with intellectual disability after a course of therapy, but these individuals experienced poorer outcomes compared with those without intellectual disability (reliable improvement 60.2% for people with intellectual disability v. 69.2% for people without intellectual disability, odds ratio 0.66, 95% CI 0.63–0.70; reliable deterioration 10.3% for people with intellectual disability v. 5.7% for those without intellectual disability, odds ratio 1.89, 95% CI 1.75–2.04). After propensity score matching, some differences were attenuated (reliable improvement, adjusted odds ratio 0.97, 95% CI 1.91–1.04), but some outcomes remained poorer for people with intellectual disability (reliable deterioration, adjusted odds ratio 1.28, 95% CI 1.16–1.42).

Evidence-based psychological therapies may be effective for adults with intellectual disability, but their outcomes may be similar to (for improvement and recovery) or poorer than (for deterioration) those for adults without intellectual disability. Future work should investigate the impact of adaptations of therapies for those with intellectual disability to make such interventions more effective and accessible for this population.

During the COVID-19 pandemic, virtual physician visits rapidly increased among community-dwelling older persons living with dementia (PLWD) in Ontario. Rural residents often have less access to medical care compared to their urban counterparts, and it is unclear whether access to virtual care was equitable between PLWD in urban versus rural locations.

Using population-based health administrative data and a repeated cross-sectional study design, we identified and described community-dwelling PLWD between March 2020 and August 2022 in Ontario, Canada. Poisson regression was used to calculate rate ratios (RR) and 95% confidence intervals comparing rates of virtual visits between rural and urban PLWD by key physician specialties: family physicians, neurologists and psychiatrists/geriatricians.

Of 122,751 PLWD in our cohort, 9.2% (n = 11,304) resided in rural areas. Rural PLWD were slightly younger compared to their urban counterparts (mean age = 81 vs. 82 years; standardized difference = 0.16). There were no differences across areas by sex or income quintile. In adjusted models, rates of virtual visits were significantly lower for rural compared to urban PLWD across all specialties: family physicians (RR = 0.71 [0.69–0.73]), neurologists (RR = 0.79 [0.75–0.83]) and psychiatrists/geriatricians (RR = 0.72 [0.68–0.76]).

PLWD in rural areas had significantly lower rates of virtual family physician, neurologist and psychiatrist/geriatrician visits compared to urban dwellers during the study period. This finding raises important issues regarding access to primary and specialist healthcare services for rural PLWD. Future work should explore barriers to care to improve health care access among PLWD in rural communities.

The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5–18 years). Potential predictors included demographic, clinical, parental, and treatment variables with data obtained pre-treatment, post-treatment, and at least one follow-up.

All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. There was significant alignment between the factors that contributed to the models predicting two target outcomes: remission of all anxiety disorders and the primary anxiety disorder. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. In both models, the absence of a social anxiety disorder and being treated by a therapist with less experience contributed to the model predicting a higher likelihood of remission.

These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.

The depression, obstructive sleep apnea and cognitive impairment (DOC) screen assesses three post-stroke comorbidities, but additional information may be gained from the time to complete the screen. Cognitive screening completion time is rarely used as an outcome measure.

To assess DOC screen completion time as a predictor of cognitive impairment in stroke/transient ischemic attack clinics.

Consecutive English-speaking stroke prevention clinic patients consented to undergo screening and neuropsychological testing (n = 437). DOC screen scores and times were compared to scores on the NINDS-CSC battery using multiple linear regression (controlling for age, sex, education and stroke severity) and receiver operating characteristic (ROC) curve analysis.

Completion time for the DOC screen was 3.8 ± 1.3 minutes. After accounting for covariates, the completion time was a significant predictor of the speed of processing (p = 0.002, 95% CI: −0.016 to −0.004), verbal fluency (p < 0.001, CI: −0.012 to −0.006) and executive function (p = 0.004, CI: −0.006 to −0.001), but not memory. Completion time above 5.5 minutes was associated with a high likelihood of impairment on executive and speed of processing tasks (likelihood ratios 3.9–5.2).

DOC screen completion time is easy to collect in routine care. People needing over 5.5 minutes to be screened likely have deficits in executive functioning and speed of processing – areas commonly impaired, but challenging to screen for, after stroke. DOC screen time provides a simple, feasible approach to assess these under-identified cognitive impairments.

Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

We identified a patient cohort with diabetes from the institutional Diabetes Registry (N = 67,699) within the Duke University Health System. Patient-level information (demographics, comorbidities, service utilization, laboratory results, and medications) was extracted to Tableau. Neighborhood-level socioeconomic status was assessed via the Area Deprivation Index (ADI), and geospatial files incorporated additional data related to points of interest (i.e., parks/green space). Interactive Tableau dashboards were developed to understand risk and contextual factors affecting diabetes management at the individual, group, neighborhood, and population levels.

The Tableau-powered digital health tool offers dynamic visualizations, identifying T2DM-related disparities. The dashboard allows for the exploration of contextual factors affecting diabetes management (e.g., food insecurity, built environment) and possesses capabilities to generate targeted patient lists for personalized diabetes care planning.

As part of a broader health equity initiative, this application meets the needs of a diverse range of users. The interactive dashboard, incorporating clinical, sociodemographic, and environmental factors, enhances understanding at various levels and facilitates targeted interventions to address disparities in diabetes care and outcomes. Ultimately, this transformative approach aims to manage SDoH and improve patient care.

Symptom clustering research provides a unique opportunity for understanding complex medical conditions. The objective of this study was to apply a variable-centered analytic approach to understand how symptoms may cluster together, within and across domains of functioning in mild cognitive impairment (MCI) and dementia, to better understand these conditions and potential etiological, prevention, and intervention considerations.

Cognitive, motor, sensory, emotional, and social measures from the NIH Toolbox were analyzed using exploratory factor analysis (EFA) from a dataset of 165 individuals with a research diagnosis of either amnestic MCI or dementia of the Alzheimer’s type.

The six-factor EFA solution described here primarily replicated the intended structure of the NIH Toolbox with a few deviations, notably sensory and motor scores loading onto factors with measures of cognition, emotional, and social health. These findings suggest the presence of cross-domain symptom clusters in these populations. In particular, negative affect, stress, loneliness, and pain formed one unique symptom cluster that bridged the NIH Toolbox domains of physical, social, and emotional health. Olfaction and dexterity formed a second unique cluster with measures of executive functioning, working memory, episodic memory, and processing speed. A third novel cluster was detected for mobility, strength, and vision, which was considered to reflect a physical functioning factor. Somewhat unexpectedly, the hearing test included did not load strongly onto any factor.

This research presents a preliminary effort to detect symptom clusters in amnestic MCI and dementia using an existing dataset of outcome measures from the NIH Toolbox.

Psychological therapies can be effective in reducing symptoms of depression and anxiety in people living with dementia (PLWD). However, factors associated with better therapy outcomes in PLWD are currently unknown.

To investigate whether dementia-specific and non-dementia-specific factors are associated with therapy outcomes in PLWD.

National linked healthcare records were used to identify 1522 PLWD who attended psychological therapy services across England. Associations between various factors and therapy outcomes were explored.

People with frontotemporal dementia were more likely to experience reliable deterioration in depression/anxiety symptoms compared with people with vascular dementia (odds ratio 2.98, 95% CI 1.08–8.22; P = 0.03) or Alzheimer's disease (odds ratio 2.95, 95% CI 1.15–7.55; P = 0.03). Greater depression severity (reliable recovery: odds ratio 0.95, 95% CI 0.92–0.98, P < 0.001; reliable deterioration: odds ratio 1.73, 95% CI 1.04–2.90, P = 0.04), lower work and social functioning (recovery: odds ratio 0.98, 95% CI 0.96–0.99, P = 0.002), psychotropic medication use (recovery: odds ratio 0.67, 95% CI 0.51–0.90, P = 0.01), being of working age (recovery: odds ratio 2.03, 95% CI 1.10–3.73, P = 0.02) and fewer therapy sessions (recovery: odds ratio 1.12, 95% CI 1.09–1.16, P < 0.001) were associated with worse therapy outcomes in PLWD.

Dementia type was generally not associated with outcomes, whereas clinical factors were consistent with those identified for the general population. Additional support and adaptations may be required to improve therapy outcomes in PLWD, particularly in those who are younger and have more severe depression.