Intracranial and subcortical volumes in adolescents with early‐onset psychosis: A multisite mega‐analysis from the ENIGMA consortium

Early‐onset psychosis disorders are serious mental disorders arising before the age of 18 years. Here, we investigate the largest neuroimaging dataset, to date, of patients with early‐onset psychosis and healthy controls for differences in intracranial and subcortical brain volumes. The sample included 263 patients with early‐onset psychosis (mean age: 16.4 ± 1.4 years, mean illness duration: 1.5 ± 1.4 years, 39.2% female) and 359 healthy controls (mean age: 15.9 ± 1.7 years, 45.4% female) with magnetic resonance imaging data, pooled from 11 clinical cohorts. Patients were diagnosed with early‐onset schizophrenia (n = 183), affective psychosis (n = 39), or other psychotic disorders (n = 41). We used linear mixed‐effects models to investigate differences in intracranial and subcortical volumes across the patient sample, diagnostic subgroup and antipsychotic medication, relative to controls. We observed significantly lower intracranial (Cohen''s d = −0.39) and hippocampal (d = −0.25) volumes, and higher caudate (d = 0.25) and pallidum (d = 0.24) volumes in patients relative to controls. Intracranial volume was lower in both early‐onset schizophrenia (d = −0.34) and affective psychosis (d = −0.42), and early‐onset schizophrenia showed lower hippocampal (d = −0.24) and higher pallidum (d = 0.29) volumes. Patients who were currently treated with antipsychotic medication (n = 193) had significantly lower intracranial volume (d = −0.42). The findings demonstrate a similar pattern of brain alterations in early‐onset psychosis as previously reported in adult psychosis, but with notably low intracranial volume. The low intracranial volume suggests disrupted neurodevelopment in adolescent early‐onset psychosis.     

In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen''s d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.     

The additive impact of cardio‐metabolic disorders and psychiatric illnesses on accelerated brain aging

Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio‐metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning “BrainAge” index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD− (N = 964), SMI−/CMD+ (N = 3,765), SMI−/CMD− (N = 8,083). SMI (F = 40.47, p = 2.06 × 10⁻¹⁰) and CMD (F = 24.69, p = 6.82 × 10⁻⁷) significantly, independently impacted whole‐brain QRI in SMI+. SSD had the largest effect (Cohen’s d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI− (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole‐brain QRI was significantly (p < 10⁻¹⁶) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10⁻¹⁶). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio‐metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age‐related cognitive decline.     

Sex differences in acute and long‐term brain recovery after concussion

Concussion is associated with acute disturbances in brain function and behavior, with potential long‐term effects on brain health. However, it is presently unclear whether there are sex differences in acute and long‐term brain recovery. In this study, magnetic resonance imaging (MRI) was used to scan 61 participants with sport‐related concussion (30 male, 31 female) longitudinally at acute injury, medical clearance to return to play (RTP), and 1‐year post‐RTP. A large cohort of 167 controls (80 male, 87 female) was also imaged. Each MRI session assessed cerebral blood flow (CBF), along with white matter fractional anisotropy (FA) and mean diffusivity (MD). For concussed athletes, the parameters were converted to difference scores relative to matched control subgroups, and partial least squares modeled the main and sex‐specific effects of concussion. Although male and female athletes did not differ in acute symptoms or time to RTP , all MRI measures showed significant sex differences during recovery. Males had greater reductions in occipital‐parietal CBF (mean difference and 95%CI: 9.97 ml/100 g/min, [4.84, 15.12] ml/100 g/min, z = 3.73) and increases in callosal MD (9.07 × 10⁻⁵, [−14.14, −3.60] × 10⁻⁵, z = −3.46), with greatest effects at 1‐year post‐RTP. In contrast, females had greater reductions in FA of the corona radiata (16.50 × 10⁻³, [−22.38, −11.08] × 10⁻³, z = −5.60), with greatest effects at RTP. These findings provide new insights into how the brain recovers after a concussion, showing sex differences in both the acute and chronic phases of injury.     

Examining the association between exposome score for schizophrenia and functioning in schizophrenia,siblings, and healthy controls: Results from the EUGEI study

BackgroundA cumulative environmental exposure score for schizophrenia (exposome score for schizophrenia [ES-SCZ]) may provide potential utility for risk stratification and outcome prediction. Here, we investigated whether ES-SCZ was associated with functioning in patients with schizophrenia spectrum disorder, unaffected siblings, and healthy controls.MethodsThis cross-sectional sample consisted of 1,261 patients, 1,282 unaffected siblings, and 1,525 healthy controls. The Global Assessment of Functioning (GAF) scale was used to assess functioning. ES-SCZ was calculated based on our previously validated method. The association between ES-SCZ and the GAF dimensions (symptom and disability) was analyzed by applying regression models in each group (patients, siblings, and controls). Additional models included polygenic risk score for schizophrenia (PRS-SCZ) as a covariate.ResultsES-SCZ was associated with the GAF dimensions in patients (symptom: B = −1.53, p-value = 0.001; disability: B = −1.44, p-value = 0.001), siblings (symptom: B = −3.07, p-value < 0.001; disability: B = −2.52, p-value < 0.001), and healthy controls (symptom: B = −1.50, p-value < 0.001; disability: B = −1.31, p-value < 0.001). The results remained the same after adjusting for PRS-SCZ. The degree of associations of ES-SCZ with both symptom and disability dimensions were higher in unaffected siblings than in patients and controls. By analyzing an independent dataset (the Genetic Risk and Outcome of Psychosis study), we replicated the results observed in the patient group.ConclusionsOur findings suggest that ES-SCZ shows promise for enhancing risk prediction and stratification in research practice. From a clinical perspective, ES-SCZ may aid in efforts of clinical characterization, operationalizing transdiagnostic clinical staging models, and personalizing clinical management.     

Causal associations of intelligence with schizophrenia and bipolar disorder: A Mendelian randomization analysis

BackgroundIntelligence is inversely associated with schizophrenia (SCZ) and bipolar disorder (BD); it remains unclear whether low intelligence is a cause or consequence. We investigated causal associations of intelligence with SCZ or BD risk and a shared risk between SCZ and BD and SCZ-specific risk.MethodsTo estimate putative causal associations, we performed multi-single nucleotide polymorphism (SNP) Mendelian randomization (MR) using generalized summary-data-based MR (GSMR). Summary-level datasets from five GWASs (intelligence, SCZ vs. control [CON], BD vs. CON, SCZ + BD vs. CON, and SCZ vs. BD; sample sizes of up to 269,867) were utilized.ResultsA strong bidirectional association between risks for SCZ and BD was observed (odds ratio; OR_SCZ → BD = 1.47, p = 2.89 × 10⁻⁴¹, OR_BD → SCZ = 1.44, p = 1.85 × 10⁻⁵²). Low intelligence was bidirectionally associated with a high risk for SCZ, with a stronger effect of intelligence on SCZ risk (OR_{lower intelligence → SCZ} = 1.62, p = 3.23 × 10⁻¹⁴) than the reverse (OR_{SCZ → lower intelligence} = 1.06, p = 3.70 × 10⁻²³). Furthermore, low intelligence affected a shared risk between SCZ and BD (OR _{lower intelligence → SCZ + BD} = 1.23, p = 3.41 × 10⁻⁵) and SCZ-specific risk (OR_{lower intelligence → SCZvsBD} = 1.64, p = 9.72 × 10⁻¹⁰); the shared risk (OR_{SCZ + BD → lower intelligence} = 1.04, p = 3.09 × 10⁻¹⁴) but not SCZ-specific risk (OR_{SCZvsBD → lower intelligence} = 1.00, p = 0.88) weakly affected low intelligence. Conversely, there was no significant causal association between intelligence and BD risk (p > 0.05).ConclusionsThese findings support observational studies showing that patients with SCZ display impairment in premorbid intelligence and intelligence decline. Moreover, a shared factor between SCZ and BD might contribute to impairment in premorbid intelligence and intelligence decline but SCZ-specific factors might be affected by impairment in premorbid intelligence. We suggest that patients with these genetic factors should be categorized as having a cognitive disorder SCZ or BD subtype.     

The stage‐specifically accelerated brain aging in never‐treated first‐episode patients with depression

Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never‐treated first‐episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel‐based morphometry derived from T1‐weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never‐treated first‐episode patients with depression (N = 195). The brain‐predicted age difference (brain‐PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age‐, gender‐, educational level‐matched HCs in Dataset 1. Overall, patients presented higher brain‐PAD scores suggesting patients with depression having an “older” brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data‐driven method and significant correlation between brain‐PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage‐dependent phenomenon in depression.     

Pre‐ and post‐conditioning with poly I:C exerts neuroprotective effect against cerebral ischemia injury in animal models: A systematic review and meta‐analysis

BackgroundToll‐like receptor (TLR) agonist polyinosinic–polycytidylic acid (poly I:C) exerts neuroprotective effects against cerebral ischemia (CI), but concrete evidence supporting its exact mechanism of action is unclear.MethodsWe evaluated the neuroprotective role of poly I:C by assessing CI indicators such as brain infarct volume (BIV), neurological deficit score (N.S.), and signaling pathway proteins. Moreover, we performed a narrative review to illustrate the mechanism of action of TLRs and their role in CI. Our search identified 164 articles and 10 met the inclusion criterion.ResultsPoly I:C reduces BIV and N.S. (p = 0.00 and p = 0.03). Interestingly, both pre‐ and post‐conditioning decrease BIV (preC p = 0.04 and postC p = 0.00) and N.S. (preC p = 0.03 and postC p = 0.00). Furthermore, poly I:C upregulates TLR3 [SMD = 0.64; CIs (0.56, 0.72); p = 0.00], downregulates nuclear factor‐κB (NF‐κB) [SMD = −1.78; CIs (−2.67, −0.88); p = 0.0)], and tumor necrosis factor alpha (TNF‐α) [SMD = −16.83; CIs (−22.63, −11.02); p = 0.00].ConclusionWe showed that poly I:C is neuroprotective and acts via the TLR3/NF‐κB/TNF‐α pathway. Our review indicated that suppressing TLR 2/4 may illicit neuroprotection against CI. Further research on simultaneous activation of TLR3 with poly I:C and suppression of TLR 2/4 might open new vistas for the development of therapeutics against CI.     

The morphometry of left cuneus mediating the genetic regulation on working memory

Working memory is a basic human cognitive function. However, the genetic signatures and their biological pathway remain poorly understood. In the present study, we tried to clarify this issue by exploring the potential associations and pathways among genetic variants, brain morphometry and working memory performance. We first carried out association analyses between 2‐back accuracy and 212 image‐derived phenotypes from 1141 Human Connectome Project (HCP) subjects using a linear mixed model (LMM). We found a significantly positive correlation between the left cuneus volume and 2‐back accuracy (T = 3.615, p = 3.150e−4, Cohen''s d = 0.226, corrected using family‐wise error [FWE] method). Based on the LMM‐based genome‐wide association study (GWAS) on the HCP dataset and UK Biobank 33 k GWAS summary statistics, we identified eight independent single nucleotide polymorphisms (SNPs) that were reliably associated with left cuneus volume in both UKB and HCP dataset. Within the eight SNPs, we found a negative correlation between the rs76119478 polymorphism and 2‐back accuracy accuracy (T = −2.045, p = .041, Cohen''s d = −0.129). Finally, an LMM‐based mediation analysis elucidated a significant effect of left cuneus volume in mediating rs76119478 polymorphism on the 2‐back accuracy (indirect effect = −0.007, 95% BCa CI = [−0.045, −0.003]). These results were also replicated in a subgroup of Caucasians in the HCP population. Further fine mapping demonstrated that rs76119478 maps on intergene CTD‐2315A10.2 adjacent to protein‐encoding gene DAAM1, and is significantly associated with L3HYPDH mRNA expression. Our study suggested this new variant rs76119478 may regulate the working memory through exerting influence on the left cuneus volume.     

Mixed‐effects multilevel analysis followed by canonical correlation analysis is an effective fMRI tool for the investigation of idiosyncrasies

We report that regions‐of‐interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed‐effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real‐world dataset for the neuronal response to nicotine use was acquired using a custom‐made MRI‐compatible apparatus for the smoking of electronic cigarettes (e‐cigarettes). Nineteen participants smoked e‐cigarettes in an MRI scanner using the apparatus with two experimental conditions: e‐cigarettes with nicotine (ECIG) and sham e‐cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ ²‐test of the MEMA but not from the t‐test, and the corresponding activations were significantly associated with the similarity scores (r = −.52, p = .041, confidence interval [CI] = [−0.78, −0.17]) and the urge‐to‐smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ ²‐tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = −.58, p = .01, CI = [−0.84, −0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project.     

Lithium attenuates blood–brain barrier damage and brain edema following intracerebral hemorrhage via an endothelial Wnt/β‐catenin signaling‐dependent mechanism in mice

BackgroundVasogenic cerebral edema resulting from blood–brain barrier (BBB) damage aggravates the devastating consequences of intracerebral hemorrhage (ICH). Although augmentation of endothelial Wnt/β‐catenin signaling substantially alleviates BBB breakdown in animals, no agents based on this mechanism are clinically available. Lithium is a medication used to treat bipolar mood disorders and can upregulate Wnt/β‐catenin signaling.MethodsWe evaluated the protective effect of lithium on the BBB in a mouse model of collagenase IV‐induced ICH. Furthermore, we assessed the effect and dependency of lithium on Wnt/β‐catenin signaling in mice with endothelial deletion of the Wnt7 coactivator Gpr124.ResultsLithium treatment (3 mmol/kg) significantly decreased the hematoma volume (11.15 ± 3.89 mm³ vs. 19.97 ± 3.20 mm³ in vehicle controls, p = 0.0016) and improved the neurological outcomes of mice following ICH. Importantly, lithium significantly increased the BBB integrity, as evidenced by reductions in the levels of brain edema (p = 0.0312), Evans blue leakage (p = 0.0261), and blood IgG extravasation (p = 0.0009) into brain tissue around the hematoma. Mechanistically, lithium upregulated the activity of endothelial Wnt/β‐catenin signaling in mice and increased the levels of tight junction proteins (occludin, claudin‐5 and ZO‐1). Furthermore, the protective effect of lithium on cerebral damage and BBB integrity was abolished in endothelial Gpr124 knockout mice, suggesting that its protective effect on BBB function was mainly dependent on Gpr124‐mediated endothelial Wnt/β‐catenin signaling.ConclusionOur findings indicate that lithium may serve as a therapeutic candidate for treating BBB breakdown and brain edema following ICH.     

Improving the predictive potential of diffusion MRI in schizophrenia using normative models—Towards subject‐level classification

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group‐level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject‐level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject‐level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free‐water) dMRI measures, were calculated by means of age and sex‐adjusted z‐scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z‐scores than are found with raw values (p < .001), predictions based on summary z‐score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject‐level classification.     

Brain opioid segments and striatal patterns of dopamine release induced by naloxone and morphine

Opioid receptors are expressed throughout the brain and play a major role in regulating striatal dopamine (DA) release. Clinical studies have shown that naloxone (NAL, a nonspecific opioid antagonist) in individuals with opioid use disorder and morphine (MRP, a nonspecific opioid agonist) in healthy controls, resulted in DA release in the dorsal and ventral striatum, respectively. It is not known whether the underlying patterns of striatal DA release are associated with the striatal distribution of opioid receptors. We leveraged previously published PET datasets (collected in independent cohorts) to study the brain‐wide distribution of opioid receptors and to compare striatal opioid receptor availability with striatal DA release patterns. We identified three major gray matter segments based on availability maps of DA and opioid receptors: striatum, and primary and secondary opioid segments with high and intermediate opioid receptor availability, respectively. Patterns of DA release induced by NAL and MRP were inversely associated and correlated with kappa (NAL: r(68) = −0.81, MRP: r(68) = 0.54), and mu (NAL: r(68) = −0.62, MRP: r(68) = 0.46) opioid receptor availability. Kappa opioid receptor availability accounted for a unique part of variance in NAL‐ and MRP‐DA release patterns (ΔR ² >0.14, p <.0001). In sum, distributions of opioid receptors distinguished major cortical and subcortical regions. Patterns of NAL‐ and MRP‐induced DA release had inverse associations with striatal opioid receptor availability. Our approach provides a pattern‐based characterization of drug‐induced DA targets and is relevant for modeling the role of opioid receptors in modulating striatal DA release.     

Different sensorimotor mechanism in fast and slow progression amyotrophic lateral sclerosis

The huge heterogeneity of the disease progression rate may cause inconsistent findings between local activity and functional connectivity of the primary sensorimotor area (PSMA) in amyotrophic lateral sclerosis (ALS). For illustration of this hypothesis, resting‐state fMRI (RS‐fMRI) data were collected and analyzed on 38 “definite” or “probable” ALS patients (19 fast and 19 slow, cut off median = 0.41) and 37 matched healthy controls. Amplitude of low frequency fluctuations (ALFFs) and functional connectivity strength (FCS) were analyzed within the PSMA. There was a decreased ALFF (p _FDR <.05) and FCS (p = .022) in all ALS patients. The two metrics shared about 50% of variance (R = .7) and both showed significant positive correlation with ALS Functional Rating Scale‐Revised (ALSFRS‐R) in the fast (p values <.034) but not in the slow progression groups. Interestingly, when regressing out the ALFF, the PSMA network FCS, especially the inter‐hemisphere FCS, showed negative correlation with the ALSFRS‐R score in the slow (R = −.54, p = .026) but not the fast progression group. In summary, the current results suggest that RS‐fMRI local activity and network functional connectivity accounts for the severity differently in the slow and fast progression ALS patients.     

Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder

Emerging evidence suggests white matter network abnormalities in patients with schizophrenia (SZ) and bipolar disorder (BD), but the alterations in dynamics of the white matter network in patients with SZ and BD are largely unknown. The white matter network of patients with SZ (n = 45) and BD (n = 47) and that of healthy controls (HC, n = 105) were constructed. We used dynamics network control theory to quantify the dynamics metrics of the network, including controllability and synchronizability, to measure the ability to transfer between different states. Experiments show that the patients with SZ and BD showed decreasing modal controllability and synchronizability and increasing average controllability. The correlations between the average controllability and synchronizability of patients were broken, especially for those with SZ. The patients also showed alterations in brain regions with supercontroller roles and their distribution in the cognitive system. Finally, we were able to accurately discriminate and predict patients with SZ and BD. Our findings provide novel dynamic metrics evidence that patients with SZ and BD are characterized by a selective disruption of brain network controllability, potentially leading to reduced brain state transfer capacity, and offer new guidance for the clinical diagnosis of mental illness.     

Selective outcome reporting across psychopharmacotherapy randomized controlled trials

ObjectiveSelective reporting impairs the valid interpretation of trials and leads to bias with regards to the clinical evidence. We aimed to examine factors associated with selective reporting in psychopharmacotherapy trials and thus enable solutions to prevent such selective reporting in the future.MethodsWe retrieved all registry records of trials investigating medication for depressive, bipolar and psychotic disorders. Multivariate logistic regression was performed with selective reporting as outcome, and funding source, psychiatric disorder, year of study start date, participating centers, and anticipated sample size as explanatory variables, after testing for multicollinearity. Adjusted odds ratios (AOR) were calculated. Two‐sided Fisher exact test was used to compare the proportions of newly added positive primary outcomes with the proportions of positive results in the overall group of primary outcomes.ResultsOf 151 included trials (N = 94,303 participants), 21 (14%) showed irregularities between registered and published primary outcomes. Higher odds of such irregularities were associated with non‐industry‐funded RCTs (AOR 5.3; p = 0.014) and trials investigating major depressive disorder (AOR 12.7; p = 0.024) or schizophrenia (AOR 14.5; p = 0.016; Table 1).ConclusionWe demonstrate discrepancies between trial registrations and publications across RCTs investigating debilitating psychiatric disorders, especially in non‐industry funded RCTs.     

Mutation‐related magnetization‐transfer,not axon density,drives white matter differences in premanifest Huntington disease: Evidence from in vivo ultra‐strong gradient MRI

White matter (WM) alterations have been observed in Huntington disease (HD) but their role in the disease‐pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra‐strong‐gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion‐weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region‐specific alterations across callosal segments with well‐characterized early‐ and late‐myelinating axon populations, while brain‐wise differences were explored with tract‐based cluster analysis (TBCA). Behavioral measures were included to explore disease‐associated brain‐function relationships. We detected lower MTR in patients'' callosal rostrum (tractometry: p = .03; TBCA: p = .03), but higher MTR in their splenium (tractometry: p = .02). Importantly, patients'' mutation‐size and MTR were positively correlated (all p‐values < .01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = .003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the composite hindered and restricted model of diffusion (CHARMED) in the cortico‐spinal tract (p = .03), which correlated positively with MTR in the posterior callosum (p = .033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra‐strong gradient MRI study in HD provides novel evidence of mutation‐driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin, or a combination of these.     

Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting‐state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep‐deprived subjects showed increased regional‐level temporal variability in large‐scale brain regions, and decreased regional‐level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra‐network temporal variability in the default mode network (DMN) and increased inter‐network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter‐network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = −.42, p = 5.57 × 10⁻⁴) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep‐deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.     

Task‐related neural mechanisms of persecutory ideation in schizophrenia and community monozygotic twin‐pairs

Perceptions of spiteful behavior are common, distinct from rational fear, and may undergird persecutory ideation. To test this hypothesis and investigate neural mechanisms of persecutory ideation, we employed a novel economic social decision‐making task, the Minnesota Trust Game (MTG), during neuroimaging in patients with schizophrenia (n = 30) and community monozygotic (MZ) twins (n = 38; 19 pairs). We examined distinct forms of mistrust, task‐related brain activation and connectivity, and investigated relationships with persecutory ideation. We tested whether co‐twin discordance on these measurements was correlated to reflect a common source of underlying variance. Across samples persecutory ideation was associated with reduced trust only during the suspiciousness condition, which assessed spite sensitivity given partners had no monetary incentive to betray. Task‐based activation contrasts for specific forms of mistrust were limited and unrelated to persecutory ideation. However, task‐based connectivity contrasts revealed a dorsal cingulate anterior insula network sensitive to suspicious mistrust, a left frontal–parietal (lF‐P) network sensitive to rational mistrust, and a ventral medial/orbital prefrontal (vmPFC/OFC) network that was sensitive to the difference between these forms of mistrust (all p < .005). Higher persecutory ideation was predicted only by reduced connectivity between the vmPFC/OFC and lF‐P networks (p = .005), which was only observed when the intentions of the other player were relevant. Moreover, co‐twin differences in persecutory ideation predicted co‐twin differences in both spite sensitivity and in vmPFC/OFC–lF‐P connectivity. This work found that interconnectivity may be particularly important to the complex neurobiology underlying persecutory ideation, and that unique environmental variance causally linked persecutory ideation, decision‐making, and brain connectivity.     

Reading skills deficits in people with mental illness: A systematic review and meta-analysis

BackgroundGood reading skills are important for appropriate functioning in everyday life, scholastic performance, and acquiring a higher socioeconomic status. We conducted the first systematic review and meta-analysis to quantify possible deficits in specific reading skills in people with a variety of mental illnesses, including personality disorders (PDs).MethodsWe performed a systematic search of multiple databases from inception until February 2020 and conducted random-effects meta-analyses.ResultsThe search yielded 34 studies with standardized assessments of reading skills in people with one or more mental illnesses. Of these, 19 studies provided data for the meta-analysis. Most studies (k = 27; meta-analysis, k = 17) were in people with schizophrenia and revealed large deficits in phonological processing (Hedge’s g = −0.88, p < 0.00001), comprehension (Hedge’s g = −0.96, p < 0.00001) and reading rate (Hedge’s g = −1.22, p = 0.002), relative to healthy controls; the single-word reading was less affected (Hedge’s g = −0.70, p < 0.00001). A few studies in affective disorders and nonforensic PDs suggested weaker deficits (for all, Hedge’s g < −0.60). In forensic populations with PDs, there was evidence of marked phonological processing (Hedge’s g = −0.85, p < 0.0001) and comprehension deficits (Hedge’s g = −0.95, p = 0.0003).ConclusionsPeople with schizophrenia, and possibly forensic PD populations, demonstrate a range of reading skills deficits. Future studies are needed to establish how these deficits directly compare to those seen in developmental or acquired dyslexia and to explore the potential of dyslexia interventions to improve reading skills in these populations.