Epigenetic changes are plausible molecular sources of clinical heterogeneity in schizophrenia. A subgroup of schizophrenia patients with elevated inflammatory or immune-dysregulation has been reported by previous studies. However, little is known about epigenetic changes in genes related to immune activation in never-treated first-episode patients with schizophrenia (FES) and its consistency with that in treated long-term ill (LTS) patients.

In this study, epigenome-wide profiling with a DNA methylation array was applied using blood samples of both FES and LTS patients, as well as their corresponding healthy controls. Non-negative matrix factorization (NMF) and k -means clustering were performed to parse heterogeneity of schizophrenia, and the consistency of subtyping results from two cohorts. was tested.

This study identified a subtype of patients in FES participants (47.5%) that exhibited widespread methylation level alterations of genes enriched in immune cell activity and a significantly higher proportion of neutrophils. This clustering of FES patients was validated in LTS patients, with high correspondence in epigenetic and clinical features across two cohorts

In summary, this study demonstrated a subtype of schizophrenia patients across both FES and LTS cohorts, defined by widespread alterations in methylation profile of genes related to immune function and distinguishing clinical features. This finding illustrates the promise of novel treatment strategies targeting immune dysregulation for a subpopulation of schizophrenia patients.

Enlarged pituitary gland volume could be a marker of psychotic disorders. However, previous studies report conflicting results. To better understand the role of the pituitary gland in psychosis, we examined a large transdiagnostic sample of individuals with psychotic disorders.

The study included 751 participants (174 with schizophrenia, 114 with schizoaffective disorder, 167 with psychotic bipolar disorder, and 296 healthy controls) across six sites in the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium. Structural magnetic resonance images were obtained, and pituitary gland volumes were measured using the MAGeT brain algorithm. Linear mixed models examined between-group differences with controls and among patient subgroups based on diagnosis, as well as how pituitary volumes were associated with symptom severity, cognitive function, antipsychotic dose, and illness duration.

Mean pituitary gland volume did not significantly differ between patients and controls. No significant effect of diagnosis was observed. Larger pituitary gland volume was associated with greater symptom severity (F = 13.61, p = 0.0002), lower cognitive function (F = 4.76, p = 0.03), and higher antipsychotic dose (F = 5.20, p = 0.02). Illness duration was not significantly associated with pituitary gland volume. When all variables were considered, only symptom severity significantly predicted pituitary gland volume (F = 7.54, p = 0.006).

Although pituitary volumes were not increased in psychotic disorders, larger size may be a marker associated with more severe symptoms in the progression of psychosis. This finding helps clarify previous inconsistent reports and highlights the need for further research into pituitary gland-related factors in individuals with psychosis.

Attention-deficit/hyperactivity disorder (ADHD) is a clinically heterogeneous neurodevelopmental disorder defined by characteristic behavioral and cognitive features. Abnormal brain dynamic functional connectivity (dFC) has been associated with the disorder. The full spectrum of ADHD-related variation of brain dynamics and its association with behavioral and cognitive features remain to be established.

We sought to identify patterns of brain dynamics linked to specific behavioral and cognitive dimensions using sparse canonical correlation analysis across a cohort of children with and without ADHD (122 children in total, 63 with ADHD). Then, using mediation analysis, we tested the hypothesis that cognitive deficits mediate the relationship between brain dynamics and ADHD-associated behaviors.

We identified four distinct patterns of dFC, each corresponding to a specific dimension of behavioral or cognitive function (r = 0.811–0.879). Specifically, the inattention/hyperactivity dimension was positively associated with dFC within the default mode network (DMN) and negatively associated with dFC between DMN and the sensorimotor network (SMN); the somatization dimension was positively associated with dFC within DMN and SMN; the inhibition and flexibility dimension and fluency and memory dimensions were both positively associated with dFC within DMN and between DMN and SMN, and negatively associated with dFC between DMN and the fronto-parietal network. Furthermore, we observed that cognitive functions of inhibition and flexibility mediated the relationship between brain dynamics and behavioral manifestations of inattention and hyperactivity.

These findings document the importance of distinct patterns of dynamic functional brain activity for different cardinal behavioral and cognitive features related to ADHD.

Persistent psychological distress associated with the coronavirus disease 2019 (COVID-19) pandemic has been well documented. This study aimed to identify pre-COVID brain functional connectome that predicts pandemic-related distress symptoms among young adults.

Baseline neuroimaging studies and assessment of general distress using the Depression, Anxiety and Stress Scale were performed with 100 healthy individuals prior to wide recognition of the health risks associated with the emergence of COVID-19. They were recontacted for the Impact of Event Scale-Revised and the Posttraumatic Stress Disorder Checklist in the period of community-level outbreaks, and for follow-up distress evaluation again 1 year later. We employed the network-based statistic approach to identify connectome that predicted the increase of distress based on 136-region-parcellation with assigned network membership. Predictive performance of connectome features and causal relations were examined by cross-validation and mediation analyses.

The connectome features that predicted emergence of distress after COVID contained 70 neural connections. Most within-network connections were located in the default mode network (DMN), and affective network-DMN and dorsal attention network-DMN links largely constituted between-network pairs. The hippocampus emerged as the most critical hub region. Predictive models of the connectome remained robust in cross-validation. Mediation analyses demonstrated that COVID-related posttraumatic stress partially explained the correlation of connectome to the development of general distress.

Brain functional connectome may fingerprint individuals with vulnerability to psychological distress associated with the COVID pandemic. Individuals with brain neuromarkers may benefit from the corresponding interventions to reduce the risk or severity of distress related to fear of COVID-related challenges.

Identification of treatment-specific predictors of drug therapies for bipolar disorder (BD) is important because only about half of individuals respond to any specific medication. However, medication response in pediatric BD is variable and not well predicted by clinical characteristics.

A total of 121 youth with early course BD (acute manic/mixed episode) were prospectively recruited and randomized to 6 weeks of double-blind treatment with quetiapine (n = 71) or lithium (n = 50). Participants completed structural magnetic resonance imaging (MRI) at baseline before treatment and 1 week after treatment initiation, and brain morphometric features were extracted for each individual based on MRI scans. Positive antimanic treatment response at week 6 was defined as an over 50% reduction of Young Mania Rating Scale scores from baseline. Two-stage deep learning prediction model was established to distinguish responders and non-responders based on different feature sets.

Pre-treatment morphometry and morphometric changes occurring during the first week can both independently predict treatment outcome of quetiapine and lithium with balanced accuracy over 75% (all p < 0.05). Combining brain morphometry at baseline and week 1 allows prediction with the highest balanced accuracy (quetiapine: 83.2% and lithium: 83.5%). Predictions in the quetiapine and lithium group were found to be driven by different morphometric patterns.

These findings demonstrate that pre-treatment morphometric measures and acute brain morphometric changes can serve as medication response predictors in pediatric BD. Brain morphometric features may provide promising biomarkers for developing biologically-informed treatment outcome prediction and patient stratification tools for BD treatment development.

Major depressive disorder (MDD) is a clinically and biologically heterogeneous syndrome. Identifying discrete subtypes of illness with distinguishing neurobiological substrates and clinical features is a promising strategy for guiding personalised therapeutics.

This study aimed to identify depression subtypes with correlated patterns of functional network connectivity and clinical symptoms by clustering patients according to a weighted linear combination of both features in a relatively large, medication-naïve depression sample.

We recruited 115 medication-naïve adults with MDD and 129 matched healthy controls, and evaluated all participants with magnetic resonance imaging. We used regularised canonical correlation analysis to identify component mapping relationships between functional network connectivity and symptom profiles, and K-means clustering was used to define distinct subtypes of patients.

Two subtypes of MDD were identified: insomnia-dominated subtype 1 and anhedonia-dominated subtype 2. Subtype 1 was characterised by abnormal hyperconnectivity within the ventral attention network and sleep maintenance insomnia. Subtype 2 was characterised by abnormal hypoconnectivity in the subcortical and dorsal attention networks, and prominent anhedonia symptoms.

Our study identified two distinct subtypes of patients with specific neurobiological and clinical symptom profiles. These findings advance understanding of the biological and clinical heterogeneity of MDD, offering a pathway for defining categorical subtypes of illness via consideration of both biological and clinical features.

Antisaccade tasks can be used to index cognitive control processes, e.g. attention, behavioral inhibition, working memory, and goal maintenance in people with brain disorders. Though diagnoses of schizophrenia (SZ), schizoaffective (SAD), and bipolar I with psychosis (BDP) are typically considered to be distinct entities, previous work shows patterns of cognitive deficits differing in degree, rather than in kind, across these syndromes.

Large samples of individuals with psychotic disorders were recruited through the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (B-SNIP2) study. Anti- and pro-saccade task performances were evaluated in 189 people with SZ, 185 people with SAD, 96 people with BDP, and 279 healthy comparison participants. Logistic functions were fitted to each group's antisaccade speed-performance tradeoff patterns.

Psychosis groups had higher antisaccade error rates than the healthy group, with SZ and SAD participants committing 2 times as many errors, and BDP participants committing 1.5 times as many errors. Latencies on correctly performed antisaccade trials in SZ and SAD were longer than in healthy participants, although error trial latencies were preserved. Parameters of speed-performance tradeoff functions indicated that compared to the healthy group, SZ and SAD groups had optimal performance characterized by more errors, as well as less benefit from prolonged response latencies. Prosaccade metrics did not differ between groups.

With basic prosaccade mechanisms intact, the higher speed-performance tradeoff cost for antisaccade performance in psychosis cases indicates a deficit that is specific to the higher-order cognitive aspects of saccade generation.

There is increasing evidence that blood oxygenation level-dependent signaling in white matter (WM) reflects WM functional activity. Whether this activity is altered in schizophrenia remains uncertain, as does whether it is related to established alterations of gray matter (GM) or the microstructure of WM tracts.

A total of 153 antipsychotic-naïve schizophrenia patients and 153 healthy comparison subjects were assessed by resting-state functional magnetic resonance imaging, diffusion tensor imaging, and high-resolution T1-weighted imaging. We tested for case–control differences in the functional activity of WM, and examined their relation to the functional activity of GM and WM microstructure. The relations between fractional anisotropy (FA) in WM and GM–WM functional synchrony were investigated as well. Then, we examined the associations of identified abnormalities to age, duration of untreated psychosis (DUP), and symptom severity.

Schizophrenia patients displayed reductions of the amplitude of low-frequency fluctuations (ALFF), GM–WM functional synchrony, and FA in widespread regions. Specifically, the genu of corpus callosum not only had weakening in the synchrony of functional activity but also had reduced ALFF and FA. Positive associations were found between FA and functional synchrony in the genu of corpus callosum as well. No significant association was found between identified abnormalities and DUP, and symptom severity.

The widespread weakening in the synchrony of functional activity of GM and WM provided novel evidence for functional alterations in schizophrenia. Regarding the WM function as a component of brain systems and investigating its alternation represent a promising direction for future research.

To evaluate whether incorporating mandatory prior authorization for Clostridioides difficile testing into antimicrobial stewardship pharmacist workflow could reduce testing in patients with alternative etiologies for diarrhea.

Single center, quasi-experimental before-and-after study.

Tertiary-care, academic medical center in Ann Arbor, Michigan.

Adult and pediatric patients admitted between September 11, 2019 and December 10, 2019 were included if they had an order placed for 1 of the following: (1) C. difficile enzyme immunoassay (EIA) in patients hospitalized >72 hours and received laxatives, oral contrast, or initiated tube feeds within the prior 48 hours, (2) repeat molecular multiplex gastrointestinal pathogen panel (GIPAN) testing, or (3) GIPAN testing in patients hospitalized >72 hours.

A best-practice alert prompting prior authorization by the antimicrobial stewardship program (ASP) for EIA or GIPAN testing was implemented. Approval required the provider to page the ASP pharmacist and discuss rationale for testing. The provider could not proceed with the order if ASP approval was not obtained.

An average of 2.5 requests per day were received over the 3-month intervention period. The weekly rate of EIA and GIPAN orders per 1,000 patient days decreased significantly from 6.05 ± 0.94 to 4.87 ± 0.78 (IRR, 0.72; 95% CI, 0.56–0.93; P = .010) and from 1.72 ± 0.37 to 0.89 ± 0.29 (IRR, 0.53; 95% CI, 0.37–0.77; P = .001), respectively.

We identified an efficient, effective C. difficile and GIPAN diagnostic stewardship approval model.