Abnormality of subcortical volume and resting functional connectivity in adolescents with early-onset and prodromal schizophrenia

Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia

Objective

Although extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia.

Methods

To address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV.

Results

Our findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure.

Conclusion

Our research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia.

Longitudinal reduction in brain volume in patients with schizophrenia and its association with cognitive function

Establishing a brain biomarker for schizophrenia is strongly desirable not only to support diagnosis by psychiatrists but also to help track the progressive changes in the brain over the course of the illness. A brain morphological signature of schizophrenia was reported in a recent study and is defined by clusters of brain regions with reduced volume in schizophrenia patients compared to healthy individuals. This signature was proven to be effective at differentiating patients with schizophrenia from healthy individuals, suggesting that it is a good candidate brain biomarker of schizophrenia. However, the longitudinal characteristics of this signature have remained unclear. In this study, we examined whether these changes occurred over time and whether they were associated with clinical outcomes. We found a significant change in the brain morphological signature in schizophrenia patients with more brain volume loss than the natural, age-related reduction in healthy individuals, suggesting that this change can capture a progressive morphological change in the brain. We further found a significant association between changes in the brain morphological signature and changes in the full-scale intelligence quotient (IQ). The patients with IQ improvement showed preserved brain morphological signatures, whereas the patients without IQ improvement showed progressive changes in the brain morphological signature, suggesting a link between potential recovery of intellectual abilities and the speed of brain pathology progression. We conclude that the brain morphological signature is a brain biomarker that can be used to evaluate progressive changes in the brain that are associated with cognitive impairment due to schizophrenia.

Impaired large-scale cortico-hippocampal network connectivity, including the anterior temporal and posterior medial systems, and its associations with cognition in patients with first-episode schizophrenia

Background and objective

The cortico-hippocampal network is an emerging neural framework with striking evidence that it supports cognition in humans, especially memory; this network includes the anterior temporal (AT) system, the posterior medial (PM) system, the anterior hippocampus (aHIPPO), and the posterior hippocampus (pHIPPO). This study aimed to detect aberrant patterns of functional connectivity within and between large-scale cortico-hippocampal networks in first-episode schizophrenia patients compared with a healthy control group via resting-state functional magnetic resonance imaging (rs-fMRI) and to explore the correlations of these aberrant patterns with cognition.

Methods

A total of 86 first-episode, drug-naïve schizophrenia patients and 102 healthy controls (HC) were recruited to undergo rs-fMRI examinations and clinical evaluations. We conducted large-scale edge-based network analysis to characterize the functional architecture of the cortico-hippocampus network and investigate between-group differences in within/between-network functional connectivity. Additionally, we explored the associations of functional connectivity (FC) abnormalities with clinical characteristics, including scores on the Positive and Negative Syndrome Scale (PANSS) and cognitive scores.

Results

Compared with the HC group, schizophrenia patients exhibited widespread alterations to within-network FC of the cortico-hippocampal network, with decreases in FC involving the precuneus (PREC), amygdala (AMYG), parahippocampal cortex (PHC), orbitofrontal cortex (OFC), perirhinal cortex (PRC), retrosplenial cortex (RSC), posterior cingulate cortex (PCC), angular gyrus (ANG), aHIPPO, and pHIPPO. Schizophrenia patients also showed abnormalities in large-scale between-network FC of the cortico-hippocampal network, in the form of significantly decreased FC between the AT and the PM, the AT and the aHIPPO, the PM and the aHIPPO, and the aHIPPO and the pHIPPO. A number of these signatures of aberrant FC were correlated with PANSS score (positive, negative, and total score) and with scores on cognitive test battery items, including attention/vigilance (AV), working memory (WM), verbal learning and memory (Verb_Lrng), visual learning and memory (Vis_Lrng), reasoning and problem-solving (RPS), and social cognition (SC).

Conclusion

Schizophrenia patients show distinct patterns of functional integration and separation both within and between large-scale cortico-hippocampal networks, reflecting a network imbalance of the hippocampal long axis with the AT and PM systems, which regulate cognitive domains (mainly Vis_Lrng, Verb_Lrng, WM, and RPS), and particularly involving alterations to FC of the AT system and the aHIPPO. These findings provide new insights into the neurofunctional markers of schizophrenia.

Identification of schizophrenia symptom-related gene modules by postmortem brain transcriptome analysis

Schizophrenia is a multifactorial disorder, the genetic architecture of which remains unclear. Although many studies have examined the etiology of schizophrenia, the gene sets that contribute to its symptoms have not been fully investigated. In this study, we aimed to identify each gene set associated with corresponding symptoms of schizophrenia using the postmortem brains of 26 patients with schizophrenia and 51 controls. We classified genes expressed in the prefrontal cortex (analyzed by RNA-seq) into several modules by weighted gene co-expression network analysis (WGCNA) and examined the correlation between module expression and clinical characteristics. In addition, we calculated the polygenic risk score (PRS) for schizophrenia from Japanese genome-wide association studies, and investigated the association between the identified gene modules and PRS to evaluate whether genetic background affected gene expression. Finally, we conducted pathway analysis and upstream analysis using Ingenuity Pathway Analysis to clarify the functions and upstream regulators of symptom-related gene modules. As a result, three gene modules generated by WGCNA were significantly correlated with clinical characteristics, and one of these showed a significant association with PRS. Genes belonging to the transcriptional module associated with PRS significantly overlapped with signaling pathways of multiple sclerosis, neuroinflammation, and opioid use, suggesting that these pathways may also be profoundly implicated in schizophrenia. Upstream analysis indicated that genes in the detected module were profoundly regulated by lipopolysaccharides and CREB. This study identified schizophrenia symptom-related gene sets and their upstream regulators, revealing aspects of the pathophysiology of schizophrenia and identifying potential therapeutic targets.

The genetic influence of the DRD3 rs6280 polymorphism (Ser9Gly) on functional connectivity and gray matter volume of the hippocampus in patients with first-episode, drug-naïve schizophrenia

The D3 dopamine receptor (DRD3) plays a major role in cognitive function and is a candidate gene for schizophrenia. DRD3 is widely distributed in the hippocampus, but whether there are potential associations between the rs6280 genotype, the hippocampus, and cognitive function in first-episode, drug-naïve (FES) patients and healthy controls (HCs) is still poorly understood. First, using functional and structural magnetic resonance imaging data, we calculated the gray matter volume (GMV) and functional connectivity (FC) of the hippocampus. Then, we examined the possible interaction effect of the DRD3 genotype and the disease on the FC and GMV of the hippocampus in 52 FES patients and 51 HCs. Finally, the correlation between the FC and GMV in the hippocampus, influenced by rs6280, and the cognitive performance of subjects was analyzed. A significant interaction effect of diagnostic group by genotype of rs6280 on the GMV of the left hippocampus was found, with lower GMV in FES patients that were C carriers compared with TT homozygotes; the opposite pattern was found in the genetic subgroups of HCs. In the FES group, C carriers performed significantly worse on reasoning and problem-solving tests than TT homozygotes. The left hippocampal GMV positively correlated with reasoning and problem-solving performance in TT homozygotes, but this correlation disappeared in FES patients that were C carriers and in genetic subgroups of HCs. Together, these results suggest that FES patients that are C carriers of rs6280 have lower GMV in the hippocampus, resulting in greater cognitive impairment.

Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis

Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.

Thalamo-hippocampal dysconnectivity is associated with serum cholesterol level in drug-naïve patients with first-episode schizophrenia

Hippocampal deficits and metabolic dysregulations such as dyslipidemia have been frequently reported in schizophrenia and are suggested to contribute to the pathophysiology of schizophrenia. Hippocampus is particularly susceptible to environmental challenges including metabolism and inflammation. However, evidence linking hippocampal alterations and metabolic dysregulations are quite sparse in drug-naïve schizophrenia. A total of 166 drug-naïve patients with first-episode schizophrenia (FES) and 78 healthy controls (HC) underwent measures for several serum metabolic markers, structural and resting-state functional magnetic resonance imaging (rs-fMRI), as well as diffusion tensor imaging (DTI). Seed-to-voxel functional connectivity (FC) and probabilistic tractography were performed to assess the functional and microstructural connectivity of the bilateral hippocampi. Clinical symptoms were evaluated with Positive and Negative Syndrome Scale (PANSS). Patients with FES showed significantly decreased total cholesterol (Chol) level. Patients showed elevated FC between the left hippocampus and bilateral thalami while showing decreased microstructural connectivity between the left hippocampus and bilateral thalami. Multiple regression analyses showed that FC from the left hippocampus to the right superior frontal gyrus (SFG), bilateral frontal pole (FP), and right thalamus were negatively associated with the Chol level, while no association was observed in the HC group. Our study validated alterations in both functional and microstructural thalamo-hippocampal connectivities, and abnormal cholesterol level in FES. Moreover, decreased cholesterol level is associated with elevated thalamo-hippocampal functional connectivity in patients with FES, suggesting that dyslipidemia may interact with the hippocampal dysfunction in FES.

A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines feature selection capabilities with a formulation in the dual space which, in turn, enables efficient work with neuroimaging data. Thus, we have tested the proposed algorithm with real MRI data from an animal model of schizophrenia. The results show that our proposal efficiently predicts the diagnosis and, at the same time, detects regions which clearly match brain areas well-known to be related to schizophrenia.

Gray Matter Alterations in Pediatric Schizophrenia and Obsessive-Compulsive Disorder: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

OBJECTIVE

The aim of this study is comparing gray matter alterations in SCZ pediatric patients with those suffering from obsessive-compulsive disorder (OCD) based on a systematic review and an activation likelihood estimation (ALE) meta-analysis.

METHODS

A systematic literature search was performed in PubMed, Elsevier, and China National Knowledge Infrastructure (CNKI). A systematic review and an ALE meta-analysis were performed to quantitatively examine brain gray matter alterations.

RESULTS

Children and adolescents with schizophrenia had decreased gray matter volume (GMV) mainly in the prefrontal cortex (PFC), temporal cortex (such as the middle temporal gyrus and transverse temporal gyrus), and insula, while children and adolescents with OCD mainly had increased GMV in the PFC and the striatum (including the lentiform nucleus and caudate nucleus), and decreased GMV in the parietal cortex.

CONCLUSIONS

Our results suggest that gray matter abnormalities in the PFC may indicate homogeneity between the two diseases. In children and adolescents, structural alterations in schizophrenia mainly involve the fronto-temporal and cortico-insula circuits, whereas those in OCD mainly involve the prefrontal-parietal and the prefrontal-striatal circuits.

Subcortical Brain Volumes Relate to Neurocognition in First-Episode Schizophrenia, Bipolar Disorder, Major Depression Disorder, and Healthy Controls

OBJECTIVE

To explore differences and similarities in relationships between subcortical structure volumes and neurocognition among the four subject groups, including first-episode schizophrenia (FES), bipolar disorder (BD), major depression disorder (MDD), and healthy controls (HCs).

METHODS

We presented findings from subcortical volumes and neurocognitive analyses of 244 subjects (109 patients with FES; 63 patients with BD, 30 patients with MDD, and 42 HCs). Using the FreeSurfer software, volumes of 16 selected subcortical structures were automatically segmented and analyzed for relationships with results from seven neurocognitive tests from the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Cognitive Consensus Battery (MCCB).

RESULTS

Larger left lateral ventricle volumes in FES and BD, reduced bilateral hippocampus and amygdala volumes in FES, and lower bilateral amygdala volumes in BD and MDD were presented compared with HCs, and both FES and BD had a lower bilateral amygdala volume than MDD; there were seven cognitive dimension, five cognitive dimension, and two cognitive dimension impairments in FES, BD, and MDD, respectively; significant relationships were found between subcortical volumes and neurocognition in FES and BD but not in MDD and HCs; besides age and years of education, some subcortical volumes can predict neurocognitive performances variance.

CONCLUSION

The different degrees of subcortical volume lessening may contribute to the differences in cognitive impairment among the three psychiatric disorders.