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Introduction This analysis aimed to investigate diabetes-specific psychological outcomes among adults with type 1 diabetes (T1D) using hybrid closed-loop (HCL) versus standard therapy.Research design and methods In this multicenter, open-label, randomized, controlled, parallel-group clinical trial, adults with T1D were allocated to 26 weeks of HCL (MiniMed™ 670G) or standard therapy (insulin pump or multiple daily injections without real-time continuous glucose monitoring). Psychological outcomes (awareness and fear of hypoglycemia; and diabetes-specific positive well-being, diabetes distress, diabetes treatment satisfaction, and diabetes-specific quality of life (QoL)) were measured at enrollment, mid-trial and end-trial. Linear mixed models were conducted, using restricted maximum likelihood estimation, unadjusted and adjusted (for covariates: age, sex, diabetes duration, glycated hemoglobin, recent severe hypoglycemia, pre-trial insulin delivery modality, enrollment and mid-study scores).Results 120 participants (mean age 44±12 years) were randomized to intervention (n=61) or standard therapy (n=59). At 13 weeks, the HCL group had better diabetes-specific positive well-being than the standard therapy group (unadjusted: Δ=1.0, p=0.025; adjusted: Δ=1.1, p=0.01), which was maintained at 26 weeks (unadjusted: Δ=0.9, p=0.042; adjusted: Δ=1.0, p=0.023). At 26 weeks, the HCL group also had less diabetes distress (adjusted: Δ=−6.4, p=0.039), fear of hypoglycemia (“maintain high”: adjusted: Δ=−0.8, p=0.034; and “worry”: adjusted: Δ=−1.8, p=0.048), and perceived “unacceptably high glucose levels” (unadjusted: Δ=−1.1, p

IntroductionPost-acute COVID syndrome (PACS) is a growing concern, given its impact on mental health and quality of life. However, its effects on cerebral white matter remain poorly understood, particularly in non-hospitalized cohorts. The goals of this cross-sectional, observational study were to examine (1) whether PACS was associated with distinct alterations in white matter microstructure, compared to symptom-matched non-COVID viral infection; and (2) whether microstructural alterations correlated with indices of post-COVID emotional health.MethodsData were collected for 54 symptomatic individuals who tested positive for COVID-19 (mean age 41 ± 12 yrs., 36 female) and 14 controls who tested negative for COVID-19 (mean age 41 ± 14 yrs., 8 female), with both groups assessed an average of 4–5 months after COVID testing. Diffusion magnetic resonance imaging data were collected, and emotional health was assessed via the NIH emotion toolbox, with summary scores indexing social satisfaction, well-being and negative affect.ResultsDespite similar symptoms, the COVID-19 group had reduced mean and axial diffusivity, along with increased mean kurtosis and neurite dispersion, in deep white matter. After adjusting for social satisfaction, higher levels of negative affect in the COVID-19 group were also correlated with increased mean kurtosis and reduced free water in white matter.DiscussionThese results provide preliminary evidence that indices of white matter microstructure distinguish PACS from symptomatic non-COVID infection. Moreover, white matter effects seen in PACS correlate with the severity of emotional sequelae, providing novel insights into this highly prevalent disorder.

Abstract Introduction Post‐acute coronavirus disease 2019 (COVID‐19) syndrome (PACS) is a growing concern, with headache being a particularly debilitating symptom with high prevalence. The long‐term effects of COVID‐19 and post‐COVID headache on brain function remain poorly understood, particularly among non‐hospitalized individuals. This study focused on the power‐law scaling behavior of functional brain dynamics, indexed by the Hurst exponent (H). This measure is suppressed during physiological and psychological distress and was thus hypothesized to be reduced in individuals with post‐COVID syndrome, with greatest reductions among those with persistent headache. Methods Resting‐state blood oxygenation level‐dependent (BOLD) functional magnetic resonance imaging data were collected for 57 individuals who had COVID‐19 (32 with no headache, 14 with ongoing headache, 11 recovered) and 17 controls who had cold and flu‐like symptoms but tested negative for COVID‐19. Individuals were assessed an average of 4–5 months after COVID testing, in a cross‐sectional, observational study design. Results No significant differences in H values were found between non‐headache COVID‐19 and control groups., while those with ongoing headache had significantly reduced H values, and those who had recovered from headache had elevated H values, relative to non‐headache groups. Effects were greatest in temporal, sensorimotor, and insular brain regions. Reduced H in these regions was also associated with decreased BOLD activity and local functional connectivity. Conclusions These findings provide new insights into the neurophysiological mechanisms that underlie persistent post‐COVID headache, with reduced BOLD scaling as a potential biomarker that is specific to this debilitating condition.

BackgroundSymptoms of depression are present in neurodegenerative disorders (ND). It is important that depression-related symptoms be adequately screened and monitored in persons living with ND. The Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) is a widely-used self-report measure to assess and monitor depressive severity across different patient populations. However, the measurement properties of the QIDS-SR have not been assessed in ND.AimTo use Rasch Measurement Theory to assess the measurement properties of the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) in ND and in comparison to major depressive disorder (MDD).MethodsDe-identified data from the Ontario Neurodegenerative Disease Research Initiative (NCT04104373) and Canadian Biomarker Integration Network in Depression (NCT01655706) were used in the analyses. Five hundred and twenty participants with ND (Alzheimer’s disease or mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia and Parkinson’s disease) and 117 participants with major depressive disorder (MDD) were administered the QIDS-SR. Rasch Measurement Theory was used to assess measurement properties of the QIDS-SR, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning.ResultsThe QIDS-SR fit well to the Rasch model in ND and MDD, including unidimensionality, satisfactory category ordering and goodness-of-fit. Item-person measures (Wright maps) showed gaps in item difficulties, suggesting poor precision for persons falling between those severity levels. Differences between mean person and item measures in the ND cohort logits suggest that QIDS-SR items target more severe depression than experienced by the ND cohort. Some items showed differential item functioning between cohorts.ConclusionThe present study supports the use of the QIDS-SR in MDD and suggest that the QIDS-SR can be also used to screen for depressive symptoms in persons with ND. However, gaps in item targeting were noted that suggests that the QIDS-SR cannot differentiate participants falling within certain severity levels. Future studies would benefit from examination in a more severely depressed ND cohort, including those with diagnosed clinical depression.

IntroductionThe long-term impact of COVID-19 on brain function remains poorly understood, despite growing concern surrounding post-acute COVID-19 syndrome (PACS). The goal of this cross-sectional, observational study was to determine whether there are significant alterations in resting brain function among non-hospitalized individuals with PACS, compared to symptomatic individuals with non-COVID infection.MethodsData were collected for 51 individuals who tested positive for COVID-19 (mean age 41±12 yrs., 34 female) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19 (mean age 41±14 yrs., 9 female), with both groups assessed an average of 4-5 months after COVID testing. None of the participants had prior neurologic, psychiatric, or cardiovascular illness. Resting brain function was assessed via functional magnetic resonance imaging (fMRI), and self-reported symptoms were recorded.ResultsIndividuals with COVID-19 had lower temporal and subcortical functional connectivity relative to controls. A greater number of ongoing post-COVID symptoms was also associated with altered functional connectivity between temporal, parietal, occipital and subcortical regions.DiscussionThese results provide preliminary evidence that patterns of functional connectivity distinguish PACS from non-COVID infection and correlate with the severity of clinical outcome, providing novel insights into this highly prevalent disorder.

Upon publication of this article [1], it was brought to our attention that one of the 303 participants in the normative study should have been deleted from the database.

Magnetic resonance-guided focused ultrasound with injected microbubbles has been used to temporarily open the blood–brain barrier (BBB) in animal models of Alzheimer's disease (AD). Here, the authors use this technology to non-invasively open the BBB in 5 patients with mild-to-moderate AD in a phase I trial, and show that the procedure is safe.

Abstract Background A need exists for easily administered assessment tools to detect mild cognitive changes that are more comprehensive than screening tests but shorter than a neuropsychological battery and that can be administered by physicians, as well as any health care professional or trained assistant in any medical setting. The Toronto Cognitive Assessment (TorCA) was developed to achieve these goals. Methods We obtained normative data on the TorCA (n = 303), determined test reliability, developed an iPad version, and validated the TorCA against neuropsychological assessment for detecting amnestic mild cognitive impairment (aMCI) (n = 50/57, aMCI/normal cognition). For the normative study, healthy volunteers were recruited from the Rotman Research Institute registry. For the validation study, the sample was comprised of participants with aMCI or normal cognition based on neuropsychological assessment. Cognitively normal participants were recruited from both healthy volunteers in the normative study sample and the community. Results The TorCA provides a stable assessment of multiple cognitive domains. The total score correctly classified 79% of participants (sensitivity 80%; specificity 79%). In an exploratory logistic regression analysis, indices of Immediate Verbal Recall, Delayed Verbal and Visual Recall, Visuospatial Function, and Working Memory/Attention/Executive Control, a subset of the domains assessed by the TorCA, correctly classified 92% of participants (sensitivity 92%; specificity 91%). Paper and iPad version scores were equivalent. Conclusions The TorCA can improve resource utilization by identifying patients with aMCI who may not require more resource-intensive neuropsychological assessment. Future studies will focus on cross-validating the TorCA for aMCI, and validation for disorders other than aMCI.

Accurate detection of Alzheimer's disease (AD) is of considerable clinical importance. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) is the current research standard for evaluating the quality of studies that validate diagnostic tests; however, its own construct validity has not yet been evaluated empirically. Our aim was to evaluate how well the proposed QUADAS-2 items and its domains converge to indicate the study quality criteria. This study applies confirmatory factor analysis to determine whether a measurement model would be consistent with meta-analytic data. Cochrane meta-analyses assessing the accuracy of AD diagnostic tests were identified. The seven ordinal QUADAS-2 items, intended to inform study quality based on risk of bias and applicability concerns, were extracted for each of the included studies. The QUADAS-2 pre-specified factor structure (i.e., four domains assessed in terms of risk of bias and applicability concerns) was not testable. An alternative model based on two correlated factors (i.e., risk of bias and applicability concerns) returned a poor fit model. Poor factor loadings were obtained, indicating that we cannot provide evidence that the indicators convergent validity markers in the context of AD diagnostic accuracy metanalyses, where normally the sample size is low (around 60 primary included studies). A Monte Carlo simulation suggested that such a model would require at least 90 primary studies to estimate these parameters with 80% power. The reliability of the QUADAS-2 items to inform a measurement model for study quality remains unconfirmed. Considerations for conceptualizing such a tool are discussed.