Suicidality, perceived chronic stress, and stress-induced cortisol changes of individuals with schizophrenia.

The associations of suicidality with stress are poorly studied in schizophrenia. The study aimed to determine whether suicidality was correlated with perceived chronic stress and the cortisol fluctuations under stress tasks in schizophrenia. High suicidality was defined as a lifetime history of suicide attempts or suicidal ideation in the past 2 weeks. Individuals with schizophrenia and high suicidality (SZ-HS, n = 59), with low suicidality (SZ-LS, n = 207), and healthy controls (HC, n = 196) finished the Perceived Stress Scale. Then, they participated in an experiment that induced stress using the Paced Auditory Serial Addition Task and the Mirror Tracing Persistence Task. Negative affect was measured at baseline and after finishing each task. The salivary cortisol was collected before-, after 20 min, and after 40 min of the tasks. SZ-HS had elevated perceived stress than SZ-LS and HC. Mixed effect models showed that stress tasks induced cortisol changes in all groups; cortisol of SZ-LS was reduced more than HC, but SZ-HS and SZ-LS did not differ in cortisol fluctuations. SZ-HS and SZ-LS experienced similar negative affect changes during tasks and the difference in withdrawal rates was nonsignificant. SZ-HS had an increased error rate than SZ-LS. In conclusion, suicidality was correlated with high-level perceived stress but did not result in differences in cortisol reactivities under stress tasks. It suggests the inconsistency between appraisal of stress and biological stress system disturbance among SZ-HS compared to SZ-LS.

Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia.

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.

Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers.

Apolipoprotein E ε4 (APOE4) is a strong genetic risk factor of Alzheimer's disease and metabolic dysfunction. However, whether APOE4 and markers of metabolic dysfunction synergistically impact the deterioration of white matter (WM) integrity in older adults remains unknown. In the UK Biobank data, we conducted a multivariate analysis to investigate the interactions between APOE4 and 249 plasma metabolites (measured using nuclear magnetic resonance spectroscopy) with whole-brain WM integrity (measured by diffusion-weighted magnetic resonance imaging) in a cohort of 1917 older adults (aged 65.0-81.0 years; 52.4 % female). Although no main association was observed between either APOE4 or metabolites with WM integrity (adjusted P > 0.05), significant interactions between APOE4 and metabolites with WM integrity were identified. Among the examined metabolites, higher concentrations of low-density lipoprotein and very low-density lipoprotein were associated with a lower level of WM integrity (b=-0.12, CI=-0.14,-0.10) among APOE4 carriers. Conversely, among non-carriers, they were associated with a higher level of WM integrity (b=0.05, CI=0.04,0.07), demonstrating a significant moderation role of APOE4 (b =-0.18, CI=-0.20,-0.15, P<0.00001).

White matter and latency of visual evoked potentials during maturation: A miniature pig model of adolescent development.

BACKGROUND: Continuous myelination of cerebral white matter (WM) during adolescence overlaps with the formation of higher cognitive skills and the onset of many neuropsychiatric disorders. We developed a miniature-pig model of adolescent brain development for neuroimaging and neurophysiological assessment during this critical period. Minipigs have gyroencephalic brains with a large cerebral WM compartment and a well-defined adolescence period. METHODS: Eight Sinclair™ minipigs (Sus scrofa domestica) were evaluated four times during weeks 14-28 (40, 28 and 28 days apart) of adolescence using monocular visual stimulation (1 Hz)-evoked potentials and diffusion MRI (dMRI) of WM. The latency for the pre-positive 30 ms (PP30), positive 30 ms (P30) and negative 50 ms (N50) components of the flash visual evoked potentials (fVEPs) and their interhemispheric latency (IL) were recorded in the frontal, central and occipital areas during ten 60-second stimulations for each eye. The dMRI imaging protocol consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32 directions/shell, providing measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axonal water fraction (AWF), and the permeability-diffusivity index (PDI). RESULTS: Significant reductions (p < 0.05) in the latency and IL of fVEP measurements paralleled significant rises in FA, KA, AWF and PDI over the same period. The longitudinal latency changes in fVEPs were primarily associated with whole-brain changes in diffusion parameters, while fVEP IL changes were related to maturation of the corpus callosum. CONCLUSIONS: Good agreement between reduction in the latency of fVEPs and maturation of cerebral WM was interpreted as evidence for ongoing myelination and confirmation of the minipig as a viable research platform. Adolescent development in minipigs can be studied using human neuroimaging and neurophysiological protocols and followed up with more invasive assays to investigate key neurodevelopmental hypotheses in psychiatry.

Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging.

People affected by psychotic, depressive and developmental disorders are at a higher risk for alcohol and tobacco use. However, the further associations between alcohol/tobacco use and symptoms/cognition in these disorders remain unexplored. We identified multimodal brain networks involving alcohol use (n = 707) and tobacco use (n = 281) via supervised multimodal fusion and evaluated if these networks affected symptoms and cognition in people with psychotic (schizophrenia/schizoaffective disorder/bipolar, n = 178/134/143), depressive (major depressive disorder, n = 260) and developmental (autism spectrum disorder/attention deficit hyperactivity disorder, n = 421/346) disorders. Alcohol and tobacco use scores were used as references to guide functional and structural imaging fusion to identify alcohol/tobacco use associated multimodal patterns. Correlation analyses between the extracted brain features and symptoms or cognition were performed to evaluate the relationships between alcohol/tobacco use with symptoms/cognition in 6 psychiatric disorders. Results showed that (1) the default mode network (DMN) and salience network (SN) were associated with alcohol use, whereas the DMN and fronto-limbic network (FLN) were associated with tobacco use; (2) the DMN and fronto-basal ganglia (FBG) related to alcohol/tobacco use were correlated with symptom and cognition in psychosis; (3) the middle temporal cortex related to alcohol/tobacco use was associated with cognition in depression; (4) the DMN related to alcohol/tobacco use was related to symptom, whereas the SN and limbic system (LB) were related to cognition in developmental disorders. In summary, alcohol and tobacco use were associated with structural and functional abnormalities in DMN, SN and FLN and had significant associations with cognition and symptoms in psychotic, depressive and developmental disorders likely via different brain networks. Further understanding of these relationships may assist clinicians in the development of future approaches to improve symptoms and cognition among psychotic, depressive and developmental disorders.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

A site-wise reliability analysis of the ABCD diffusion fractional anisotropy and cortical thickness: impact of scanner platforms.

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61±0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76±0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71±0.12 vs 0.46±0.17, p<0.001) and CT (AICC=0.80±0.10 vs 0.69±0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

A multivariate to multivariate approach for voxel-wise genome-wide association analysis.

The joint analysis of imaging-genetics data facilitates the systematic investigation of genetic effects on brain structures and functions with spatial specificity. We focus on voxel-wise genome-wide association analysis, which may involve trillions of single nucleotide polymorphism (SNP)-voxel pairs. We attempt to identify underlying organized association patterns of SNP-voxel pairs and understand the polygenic and pleiotropic networks on brain imaging traits. We propose a bi-clique graph structure (ie, a set of SNPs highly correlated with a cluster of voxels) for the systematic association pattern. Next, we develop computational strategies to detect latent SNP-voxel bi-cliques and an inference model for statistical testing. We further provide theoretical results to guarantee the accuracy of our computational algorithms and statistical inference. We validate our method by extensive simulation studies, and then apply it to the whole genome genetic and voxel-level white matter integrity data collected from 1052 participants of the human connectome project. The results demonstrate multiple genetic loci influencing white matter integrity measures on splenium and genu of the corpus callosum.

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults.

The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

Impact of lifetime stressor exposure on neuroenergetics in schizophrenia spectrum disorders.

N-acetylasparate and lactate are two prominent brain metabolites closely related to mitochondrial functioning. Prior research revealing lower levels of NAA and higher levels of lactate in the cerebral cortex of patients with schizophrenia suggest possible abnormalities in the energy supply pathway necessary for brain function. Given that stress and adversity are a strong risk factor for a variety of mental health problems, including psychotic disorders, we investigated the hypothesis that stress contributes to abnormal neuroenergetics in patients with schizophrenia. To test this hypothesis, we used the Stress and Adversity Inventory (STRAIN) to comprehensively assess the lifetime stressor exposure profiles of 35 patients with schizophrenia spectrum disorders and 33 healthy controls who were also assessed with proton magnetic resonance spectroscopy at the anterior cingulate cortex using 3 Tesla scanner. Consistent with the hypothesis, greater lifetime stressor exposure was significantly associated with lower levels of N-acetylasparate (ß = -0.36, p = .005) and higher levels of lactate (ß = 0.43, p = .001). Moreover, these results were driven by patients, as these associations were significant for the patient but not control group. Though preliminary, these findings suggest a possible role for stress processes in the pathophysiology of abnormal neuroenergetics in schizophrenia.

Cross-cohort replicable resting-state functional connectivity in predicting symptoms and cognition of schizophrenia.

Schizophrenia (SZ) is a debilitating mental illness characterized by adolescence or early adulthood onset of psychosis, positive and negative symptoms, as well as cognitive impairments. Despite a plethora of studies leveraging functional connectivity (FC) from functional magnetic resonance imaging (fMRI) to predict symptoms and cognitive impairments of SZ, the findings have exhibited great heterogeneity. We aimed to identify congruous and replicable connectivity patterns capable of predicting positive and negative symptoms as well as cognitive impairments in SZ. Predictable functional connections (FCs) were identified by employing an individualized prediction model, whose replicability was further evaluated across three independent cohorts (BSNIP, SZ = 174; COBRE, SZ = 100; FBIRN, SZ = 161). Across cohorts, we observed that altered FCs in frontal-temporal-cingulate-thalamic network were replicable in prediction of positive symptoms, while sensorimotor network was predictive of negative symptoms. Temporal-parahippocampal network was consistently identified to be associated with reduced cognitive function. These replicable 23 FCs effectively distinguished SZ from healthy controls (HC) across three cohorts (82.7%, 90.2%, and 86.1%). Furthermore, models built using these replicable FCs showed comparable accuracies to those built using the whole-brain features in predicting symptoms/cognition of SZ across the three cohorts (r = .17-.33, p < .05). Overall, our findings provide new insights into the neural underpinnings of SZ symptoms/cognition and offer potential targets for further research and possible clinical interventions.

Combining neuroimaging and brain stimulation to test alternative causal pathways for nicotine addiction in schizophrenia.

The smoking rate is high in patients with schizophrenia. Brain stimulation targeting conventional brain circuits associated with nicotine addiction has also yielded mixed results. We aimed to identify alternative circuitries associated with nicotine addiction in both the general population and schizophrenia, and then test whether modulation of such circuitries may alter nicotine addiction behaviors in schizophrenia. In Study I of 40 schizophrenia smokers and 51 non-psychiatric smokers, cross-sectional neuroimaging analysis identified resting state functional connectivity (rsFC) between the dorsomedial prefrontal cortex (dmPFC) and multiple extended amygdala regions to be most robustly associated with nicotine addiction severity in healthy controls and schizophrenia patients (p = 0.006 to 0.07). In Study II with another 30 patient smokers, a proof-of-concept, patient- and rater-blind, randomized, sham-controlled rTMS design was used to test whether targeting the newly identified dmPFC location may causally enhance the rsFC and reduce nicotine addiction in schizophrenia. Although significant interactions were not observed, exploratory analyses showed that this dmPFC-extended amygdala rsFC was enhanced by 4-week active 10Hz rTMS (p = 0.05) compared to baseline; the severity of nicotine addiction showed trends of reduction after 3 and 4 weeks (p ≤ 0.05) of active rTMS compared to sham; Increased rsFC by active rTMS predicted reduction of cigarettes/day (R = -0.56, p = 0.025 uncorrected) and morning smoking severity (R = -0.59, p = 0.016 uncorrected). These results suggest that the dmPFC-extended amygdala circuit may be linked to nicotine addiction in schizophrenia and healthy individuals, and future efforts targeting its underlying pathophysiological mechanisms may yield more effective treatment for nicotine addiction.

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI.

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.

The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.

Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank.

White matter (WM) brain age, a neuroimaging-derived biomarker indicating WM microstructural changes, helps predict dementia and neurodegenerative disorder risks. The cumulative effect of chronic stress on WM brain aging remains unknown. In this study, we assessed cumulative stress using a multi-system composite allostatic load (AL) index based on inflammatory, anthropometric, respiratory, lipidemia, and glucose metabolism measures, and investigated its association with WM brain age gap (BAG), computed from diffusion tensor imaging data using a machine learning model, among 22 951 European ancestries aged 40 to 69 (51.40% women) from UK Biobank. Linear regression, Mendelian randomization, along with inverse probability weighting and doubly robust methods, were used to evaluate the impact of AL on WM BAG adjusting for age, sex, socioeconomic, and lifestyle behaviors. We found increasing one AL score unit significantly increased WM BAG by 0.29 years in association analysis and by 0.33 years in Mendelian analysis. The age- and sex-stratified analysis showed consistent results among participants 45-54 and 55-64 years old, with no significant sex difference. This study demonstrated that higher chronic stress was significantly associated with accelerated brain aging, highlighting the importance of stress management in reducing dementia and neurodegenerative disease risks.

Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study.

Schizophrenia is a prototypical network disorder with widespread brain-morphological alterations, yet it remains unclear whether these distributed alterations robustly reflect the underlying network layout. We tested whether large-scale structural alterations in schizophrenia relate to normative structural and functional connectome architecture, and systematically evaluated robustness and generalizability of these network-level alterations. Leveraging anatomical MRI scans from 2439 adults with schizophrenia and 2867 healthy controls from 26 ENIGMA sites and normative data from the Human Connectome Project (n = 207), we evaluated structural alterations of schizophrenia against two network susceptibility models: (i) hub vulnerability, which examines associations between regional network centrality and magnitude of disease-related alterations; (ii) epicenter mapping, which identifies regions whose typical connectivity profile most closely resembles the disease-related morphological alterations. To assess generalizability and specificity, we contextualized the influence of site, disease stages, and individual clinical factors and compared network associations of schizophrenia with that found in affective disorders. Our findings show schizophrenia-related cortical thinning is spatially associated with functional and structural hubs, suggesting that highly interconnected regions are more vulnerable to morphological alterations. Predominantly temporo-paralimbic and frontal regions emerged as epicenters with connectivity profiles linked to schizophrenia's alteration patterns. Findings were robust across sites, disease stages, and related to individual symptoms. Moreover, transdiagnostic comparisons revealed overlapping epicenters in schizophrenia and bipolar, but not major depressive disorder, suggestive of a pathophysiological continuity within the schizophrenia-bipolar-spectrum. In sum, cortical alterations over the course of schizophrenia robustly follow brain network architecture, emphasizing marked hub susceptibility and temporo-frontal epicenters at both the level of the group and the individual. Subtle variations of epicenters across disease stages suggest interacting pathological processes, while associations with patient-specific symptoms support additional inter-individual variability of hub vulnerability and epicenters in schizophrenia. Our work outlines potential pathways to better understand macroscale structural alterations, and inter- individual variability in schizophrenia.

Structural brain abnormalities and aggressive behaviour in schizophrenia: Mega-analysis of data from 2095 patients and 2861 healthy controls via the ENIGMA consortium.

Background: Schizophrenia is associated with an increased risk of aggressive behaviour, which may partly be explained by illness-related changes in brain structure. However, previous studies have been limited by group-level analyses, small and selective samples of inpatients and long time lags between exposure and outcome. Methods: This cross-sectional study pooled data from 20 sites participating in the international ENIGMA-Schizophrenia Working Group. Sites acquired T1-weighted and diffusion-weighted magnetic resonance imaging scans in a total of 2095 patients with schizophrenia and 2861 healthy controls. Measures of grey matter volume and white matter microstructural integrity were extracted from the scans using harmonised protocols. For each measure, normative modelling was used to calculate how much patients deviated (in z-scores) from healthy controls at the individual level. Ordinal regression models were used to estimate the associations of these deviations with concurrent aggressive behaviour (as odds ratios [ORs] with 99% confidence intervals [CIs]). Mediation analyses were performed for positive symptoms (i.e., delusions, hallucinations and disorganised thinking), impulse control and illness insight. Aggression and potential mediators were assessed with the Positive and Negative Syndrome Scale, Scale for the Assessment of Positive Symptoms or Brief Psychiatric Rating Scale. Results: Aggressive behaviour was significantly associated with reductions in total cortical volume (OR [99% CI] = 0.88 [0.78, 0.98], p = .003) and global white matter integrity (OR [99% CI] = 0.72 [0.59, 0.88], p = 3.50 × 10-5) and additional reductions in dorsolateral prefrontal cortex volume (OR [99% CI] = 0.85 [0.74, 0.97], p =.002), inferior parietal lobule volume (OR [99% CI] = 0.76 [0.66, 0.87], p = 2.20 × 10-7) and internal capsule integrity (OR [99% CI] = 0.76 [0.63, 0.92], p = 2.90 × 10-4). Except for inferior parietal lobule volume, these associations were largely mediated by increased severity of positive symptoms and reduced impulse control. Conclusions: This study provides evidence that the co-occurrence of positive symptoms, poor impulse control and aggressive behaviour in schizophrenia has a neurobiological basis, which may inform the development of therapeutic interventions.

Age-Related Decline in Blood-Brain Barrier Function is More Pronounced in Males than Females in Parietal and Temporal Regions.

The blood-brain barrier (BBB) plays a pivotal role in protecting the central nervous system (CNS), shielding it from potential harmful entities. A natural decline of BBB function with aging has been reported in both animal and human studies, which may contribute to cognitive decline and neurodegenerative disorders. Limited data also suggest that being female may be associated with protective effects on BBB function. Here we investigated age and sex-dependent trajectories of perfusion and BBB water exchange rate (kw) across the lifespan in 186 cognitively normal participants spanning the ages of 8 to 92 years old, using a non-invasive diffusion prepared pseudo-continuous arterial spin labeling (DP-pCASL) MRI technique. We found that the pattern of BBB kw decline with aging varies across brain regions. Moreover, results from our DP-pCASL technique revealed a remarkable decline in BBB kw beginning in the early 60s, which was more pronounced in males. In addition, we observed sex differences in parietal and temporal regions. Our findings provide in vivo results demonstrating sex differences in the decline of BBB function with aging, which may serve as a foundation for future investigations into perfusion and BBB function in neurodegenerative and other brain disorders.

Revisiting delusion subtypes in schizophrenia based on their underlying structures.

A clear understanding of the pathophysiology of schizophrenia and related spectrum disorders has been limited by clinical heterogeneity. We investigated whether relative severity and predominance of one or more delusion subtypes might yield clinically differentiable patient profiles. Patients (N = 286) with schizophrenia spectrum disorders (SSD) completed the 21-item Peters et al. Delusions Inventory (PDI-21). We performed factor analysis followed by k-means clustering to identify delusion factors and patient subtypes. Patients were further assessed via the Brief Psychiatric Rating Scale, Brief Negative Symptom Scale, Digit Symbol and Digit Substitution tasks, use of cannabis and tobacco, and stressful life events. The overall patient sample clustered into subtypes corresponding to Low-Delusion, Grandiose-Predominant, Paranoid-Predominant, and Pan-Delusion patients. Paranoid-Predominant and Pan-Delusion patients showed significantly higher burden of positive symptoms, while Low-Delusion patients showed the highest burden of negative symptoms. The Paranoia delusion factor score showed a positive association with Digit Symbol and Digit Substitution tasks in the overall sample, and the Paranoid-Predominant subtype exhibited the best performance on both tasks. Grandiose-Predominant patients showed significantly higher tobacco smoking severity than other subtypes, while Paranoid-Predominant patients were significantly more likely to have a lifetime diagnosis of Cannabis Use Disorder. We suggest that delusion self-report inventories such as the PDI-21 may be of utility in identifying sub-syndromes in SSD. From the current study, a Paranoid-Predominant form may be most distinctive, with features including less cognitive impairment and a stronger association with cannabis use.