Altered functional connectivity strength and its correlations with cognitive function in subjects with ultra-high risk for psychosis at rest

Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application of 18F-FDG-PET with antipsychotic dose correction

Few studies using Positron Emission Tomography with 18F-fluorodeoxyglucose (18F-FDG-PET) have examined the neurobiological basis of antipsychotic resistance in schizophrenia, primarily focusing on metabolic activity, with none investigating connectivity patterns. Here, we aimed to explore differential patterns of glucose metabolism between patients and controls (CTRL) through a graph theory-based approach and network comparison tests. PET scans with 18F-FDG were obtained by 70 subjects, 26 with treatment-resistant schizophrenia (TRS), 28 patients responsive to antipsychotics (nTRS), and 16 CTRL. Relative brain glucose metabolism maps were processed in the automated anatomical labeling (AAL)-Merged atlas template. Inter-subject connectivity matrices were derived using Gaussian Graphical Models and group networks were compared through permutation testing. A logistic model based on machine-learning was employed to estimate the association between the metabolic signals of brain regions and treatment resistance. To account for the potential influence of antipsychotic medication, we incorporated chlorpromazine equivalents as a covariate in the network analysis during partial correlation calculations. Additionally, the machine-learning analysis employed medication dose-stratified folds. Global reduced connectivity was detected in the nTRS (p-value = 0.008) and TRS groups (p-value = 0.001) compared to CTRL, with prominent alterations localized in the frontal lobe, Default Mode Network, and dorsal dopamine pathway. Disruptions in frontotemporal and striatal-cortical connectivity were detected in TRS but not nTRS patients. After adjusting for antipsychotic doses, alterations in the anterior cingulate, frontal and temporal gyri, hippocampus, and precuneus also emerged. The machine-learning approach demonstrated an accuracy ranging from 0.72 to 0.8 in detecting the TRS condition.

Abnormal global and local connectivity in patients with anti-N-methyl-D-aspartate receptor encephalitis: A resting-state functional MRI study

OBJECTIVE

We decided to investigate the changes of global and local connectivity in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis patients based on eigenvector centrality (EC) and regional homogeneity (ReHo). We sought new biomarkers to identify the patients based on multivariate pattern analysis (MVPA).

METHODS

Functional MRI (fMRI) was performed on all participants. EC, ReHo and MVPA were used to analyze the fMRI images. The correlation between the global or local connectivity and neuropsychology tests was detected.

RESULTS

The MoCA scores of the patients were lower than those of the healthy controls (HCs), while the HAMD24 and HAMA scores of the patients were higher than those of the HCs. Increased EC values in the right calcarine (CAL.R) and decreased EC values in the right putamen (PUT.R) distinguished these subjects with anti-NMDAR encephalitis from HCs. The higher ReHo values in the left postcentral gyrus (PoCG.L) were detected in the patients. The correlation analysis showed that the EC values in the PUT.R were negatively correlated with HAMD24 and HAMA scores, while the ReHo values in the PoCG.L were negatively correlated with MoCA scores. Better classification performance was reached in the EC-based classifier (AUC = 0.80), while weaker classification performance was achieved in the ReHo-based classifier (AUC = 0.74) or the classifier based on EC and ReHo (AUC = 0.74). The brain areas with large weights were located in the frontal lobe, parietal lobe, cerebellum and basal ganglia.

CONCLUSION

Our findings suggest that abnormal global and local connectivity may play an important part in the pathophysiological mechanism of neuropsychiatric symptoms in the anti-NMDAR encephalitis patients. The EC-based classifier may be better than the ReHo-based classifier in identifying anti-NMDAR encephalitis patients.

Neuronal dysfunction in individuals at early stage of schizophrenia, A resting-state fMRI study

Schizophrenia has been associated with abnormal intrinsic brain activity, involving various cognitive impairments. Qualitatively similar abnormalities are seen in individuals at ultra-high risk (UHR) for psychosis. In this study, resting-state fMRI (rs-fMRI) data were collected from 44 drug-naïve first-episode schizophrenia (Dn-FES) patients, 48 UHR individuals, and 40 healthy controls (HCs). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC), were performed to evaluate resting brain function. A support vector machine (SVM) was applied for classification analysis. Compared to HCs, both clinical groups showed increased fALFF in the central executive network (CEN), decreased ReHo in the ventral visual pathway (VVP) and decreased FC in temporal-sensorimotor regions. Excellent performance was achieved by using fALFF value in distinguishing both FES (sensitivity=83.21%, specificity=80.58%, accuracy=81.37%, p=0.009) and UHR (sensitivity=75.88%, specificity=85.72%, accuracy=80.72%, p<0.001) from HC group. Moreover, the study highlighted the importance of frontal and temporal alteration in the pathogenesis of schizophrenia. However, no fMRI features were observed that could well distinguish Dn-FES from UHR group. To conclude, fALFF in the CEN may provide potential power for identifying individuals at the early stage of schizophrenia and the alteration in the frontal and temporal lobe may be important to these individuals.

Identification of Homogeneous Subgroups from Resting-State fMRI Data

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.

Neurocognitive Deficits in First-Episode and Chronic Psychotic Disorders: A Systematic Review from 2009 to 2022

Cognitive impairment in patients suffering from schizophrenia spectrum disorders has been discussed as a strong predictor for multiple disease outcome variables, such as response to psychotherapy, stable relationships, employment, and longevity. However, the consistency and severity of cognitive deficits across multiple domains in individuals with first-episode and chronic psychotic disorders is still undetermined. We provide a comprehensive overview of primary research from the years 2009 to 2022. Based on a Cochrane risk assessment, a systematic synthesis of 51 out of 3669 original studies was performed. Impairment of cognitive functioning in patients diagnosed with first-episode psychotic disorders compared with healthy controls was predicted to occur in all assessed cognitive domains. Few overall changes were predicted for chronically affected patients relative to those in the first-episode stage, in line with previous longitudinal studies. Our research outcomes support the hypothesis of a global decrease in cognitive functioning in patients diagnosed with psychotic disorders, i.e., the occurrence of cognitive deficits in multiple cognitive domains including executive functioning, memory, working memory, psychomotor speed, and attention. Only mild increases in the frequency of cognitive impairment across studies were observed at the chronically affected stage relative to the first-episode stage. Our results confirm and extend the outcomes from prior reviews and meta-analyses. Recommendations for psychotherapeutic interventions are provided, considering the broad cognitive impairment already observed at the stage of the first episode. Based on the risk of bias assessment, we also make specific suggestions concerning the quality of future original studies.

A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Abnormal functional connectivity of the striatum in first-episode drug-naive early-onset Schizophrenia

Abnormal brain network connectivity is strongly implicated in the pathogenesis of schizophrenia. The striatum, consisting of the caudate and putamen, is the major treatment target for antipsychotics, the primary treatments for schizophrenia; however, there are few studies on the functional connectivity (FC) of striatum in drug-naive early-onset schizophrenia (EOS) patients. We examined the FC values of the caudate nucleus and putamen with whole brain by resting-state functional magnetic resonance imaging (RS-fMRI) and the associations with indices of clinical severity. Patients demonstrated abnormal FC between subregions of the putamen and both the visual network (left middle occipital gyrus) and default mode network (bilateral anterior cingulate, left superior frontal, and right middle frontal gyri). Furthermore, FC between dorsorostral putamen and left superior frontal gyrus correlated with both positive symptom subscore and total score on the Positive and Negative Syndrome Scale (PANSS). These findings demonstrate abnormal FC between the striatum and other brain areas even in the early stages of schizophrenia, supporting neurodevelopmental disruption in disease etiology and expression.

Altered Resting State Functional Connectivity and Its Correlation with Cognitive Functions at Ultra High Risk for Psychosis

The aim of this study is to identify robust resting state-functional connectivity (rs-FC) alterations and their correlations with the neuropsychological characteristics of Ultra-High Risk (UHR) for psychosis subjects compared to healthy controls (HCs). Twenty individuals with UHR and sixteen HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) and a cognitive battery evaluating attention, episodic memory and executive functions. Compared to HCs, UHR individuals showed working memory and set-shifting impairments. In functional connectivity (FC) analyses, the Default Mode Network (DMN) of the UHR subjects displayed increased FC with the visual areas and decreased FC with the Dorsal Attention Network (DAN). Additionally, the salience network (SN) of the UHR subjects displayed increased connectivity with wide posterior cortical areas in the temporal, parietal and occipital lobes, corresponding to posterior nodes of the SN itself, the Somato-Motor Network (SMN) and the DAN. The SN connectivity with the left SMN and DAN was positively correlated with the Trail Making Test - B scores of the UHR subjects. These findings show that the SN and DMN, which mostly show abnormal connectivity patterns in psychosis, are also affected in UHR subjects, while the SN plays a more central role with its hyperconnectivity to the DAN and SMN.

Neurocognitive Functioning in Individuals at Clinical High Risk for Psychosis: A Systematic Review and Meta-analysis

IMPORTANCE

Neurocognitive functioning is a potential biomarker to advance detection, prognosis, and preventive care for individuals at clinical high risk for psychosis (CHR-P). The current consistency and magnitude of neurocognitive functioning in individuals at CHR-P are undetermined.

OBJECTIVE

To provide an updated synthesis of evidence on the consistency and magnitude of neurocognitive functioning in individuals at CHR-P.

DATA SOURCES

Web of Science database, Cochrane Central Register of Reviews, and Ovid/PsycINFO and trial registries up to July 1, 2020.

STUDY SELECTION

Multistep literature search compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology performed by independent researchers to identify original studies reporting on neurocognitive functioning in individuals at CHR-P.

DATA EXTRACTION AND SYNTHESIS

Independent researchers extracted the data, clustering the neurocognitive tasks according to 7 Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) domains and 8 CHR-P domains. Random-effect model meta-analyses, assessment of publication biases and study quality, and meta-regressions were conducted.

MAIN OUTCOMES AND MEASURES

The primary effect size measure was Hedges g of neurocognitive functioning in individuals at CHR-P (1) compared with healthy control (HC) individuals or (2) compared with individuals with first-episode psychosis (FEP) or (3) stratified for the longitudinal transition to psychosis.

RESULTS

A total of 78 independent studies were included, consisting of 5162 individuals at CHR-P (mean [SD; range] age, 20.2 [3.3; 12.0-29.0] years; 2529 [49.0%] were female), 2865 HC individuals (mean [SD; range] age, 21.1 [3.6; 12.6-29.2] years; 1490 [52.0%] were female), and 486 individuals with FEP (mean [SD; range] age, 23.0 [2.0; 19.1-26.4] years; 267 [55.9%] were female). Compared with HC individuals, individuals at CHR-P showed medium to large deficits on the Stroop color word reading task (g = -1.17; 95% CI, -1.86 to -0.48), Hopkins Verbal Learning Test-Revised (g = -0.86; 95% CI, -1.43 to -0.28), digit symbol coding test (g = -0.74; 95% CI, -1.19 to -0.29), Brief Assessment of Cognition Scale Symbol Coding (g = -0.67; 95% CI, -0.95 to -0.39), University of Pennsylvania Smell Identification Test (g = -0.55; 95% CI, -0.97 to -0.12), Hinting Task (g = -0.53; 95% CI, -0.77 to -0.28), Rey Auditory Verbal Learning Test (g = -0.50; 95% CI, -0.78 to -0.21), California Verbal Learning Test (CVLT) (g = -0.50; 95% CI, -0.64 to -0.36), and National Adult Reading Test (g = -0.52; 95% CI, -1.01 to -0.03). Individuals at CHR-P were less impaired than individuals with FEP. Longitudinal transition to psychosis from a CHR-P state was associated with medium to large deficits in the CVLT task (g = -0.58; 95% CI, -1.12 to -0.05). Meta-regressions found significant effects for age and education on processing speed.

CONCLUSIONS AND RELEVANCE

Findings from this meta-analysis support neurocognitive dysfunction as a potential detection and prognostic biomarker in individuals at CHR-P. These findings may advance clinical research and inform preventive approaches.

Abnormal functional connectivity based on nodes of the default mode network in first-episode drug-naive early-onset schizophrenia

Schizophrenia is considered a connectivity disorder. Further, the functional connectivity (FC) of the default-mode network (DMN) has gained the interest of researchers. However, few studies have been conducted on the abnormal connectivity of DMN in early-onset schizophrenia (EOS). In this study, the key brain regions of the DMN were used as seed regions to analyze the FC of the whole brain in EOS. When the seed was located in the medial prefrontal cortex (mPFC), patients with EOS exhibited decreased FC between mPFC and other brain regions compared with healthy controls (voxel P value < 0.001, cluster P value < 0.05, Gaussian random field corrected). When the seed was located in the posterior cingulate cortex (PCC), the FC between PCC and other brain regions was enhanced and weakened (voxel P value < 0.001, cluster P value < 0.05, Gaussian random field corrected), and PCC connectivity with the right parahippocampal gyrus was associated with Positive and Negative Syndrome Scale scores for the general score (r = -0.315, P = 0.02). The results showed that the FC within the DMN and that between DMN and visual networks were abnormal, suggesting that the DMN might be involved in the pathogenesis of EOS.

Memory Impairments and Psychosis Prediction: A Scoping Review and Theoretical Overview

Impairments in memory functions are among the most robust correlates of schizophrenia and of poor functional outcomes in individuals with psychotic disorders. Prospective, longitudinal studies are crucial to determining the meaning of these deficits in relation to mechanisms associated with the onset and course of these disorders.The objective of this review is to examine the literature concerning premorbid memory impairments during the prodromal phase of psychosis to address three primary questions 1) are memory impairments present among individuals with a clinical high risk syndrome? 2) are memory deficits in clinical high risk cases predictive of future conversion to psychosis? and 3) what are the underlying neural correlates of memory impairment in clinical high risk individuals and are they also predictive of future conversion?PubMed and Google Scholar databases were systematically searched. The primary inclusion criteria were to select studies that 1) were original research articles published in a peer-reviewed journal in the past 25 years, 2) studied subjects at clinical high risk for psychosis or in the prodromal phase of illness, and 3) included examinations into verbal memory performance in those at clinical high risk for psychosis.64 articles were identified and screened for eligibility. The review included 34 studies investigating verbal memory impairment in clinical high risk individuals compared to controls. The average effect size of verbal learning total recall was .58, indicating a moderate level of impairment in verbal learning among individuals at clinical high risk for psychosis as compared to healthy controls. Of studies that predicted time to conversion, indices of memory, particularly declarative and verbal working memory, were especially predictive of future conversion. Finally, when examining investigations of the neural correlates of memory dysfunction in the clinical high risk state, findings suggest altered activation and functional connectivity among medial temporal lobe regions may underlie differences in memory performance between clinical high risk individuals and healthy controls.Findings to date strongly indicate that memory impairments are present during the premorbid phase of psychosis and that verbal memory impairment in particular is predictive of future conversion to psychosis. Evidence from fMRI studies is fairly consistent in showing greater activation of memory-related regions during retrieval among clinical high risk cases who convert, with less consistent evidence of altered functional connectivity in the encoding phase. These findings support the use of verbal learning and memory measures in the psychosis prediction and prevention field.

Neuroprogression across the Early Course of Psychosis

Psychotic disorders are severe, debilitating, and even fatal. The development of targeted and effective interventions for psychosis depends upon on clear understanding of the timing and nature of disease progression to target processes amenable to intervention. Strong evidence suggests early and ongoing neuroprogressive changes, but timing and inflection points remain unclear and likely differ across cognitive, clinical, and brain measures. Additionally, granular evidence across modalities is particularly sparse in the "bridging years" between first episode and established illness-years that may be especially critical for improving outcomes and during which interventions may be maximally effective. Our objective is the systematic, multimodal characterization of neuroprogression through the early course of illness in a cross-diagnostic sample of patients with psychosis. We aim to (1) interrogate neurocognition, structural brain measures, and network connectivity at multiple assessments over the first eight years of illness to map neuroprogressive trajectories, and (2) examine trajectories as predictors of clinical and functional outcomes. We will recruit 192 patients with psychosis and 36 healthy controls. Assessments will occur at baseline and 8- and 16-month follow ups using clinical, cognitive, and imaging measures. We will employ an accelerated longitudinal design (ALD), which permits ascertainment of data across a longer timeframe and at more frequent intervals than would be possible in a single cohort longitudinal study. Results from this study are expected to hasten identification of actionable treatment targets that are closely associated with clinical outcomes, and identify subgroups who share common neuroprogressive trajectories toward the development of individualized treatments.

Basic Symptoms Are Associated With Age in Patients With a Clinical High-Risk State for Psychosis: Results From the PRONIA Study

In community studies, both attenuated psychotic symptoms (APS) and basic symptoms (BS) were more frequent but less clinically relevant in children and adolescents compared to adults. In doing so, they displayed differential age thresholds that were around age 16 for APS, around age 18 for perceptive BS, and within the early twenties for cognitive BS. Only the age effect has previously been studied and replicated in clinical samples for APS. Thus, we examined the reported age effect on and age thresholds of 14 criteria-relevant BS in a patient sample at clinical-high risk of psychosis (N = 261, age 15-40 yrs.), recruited within the European multicenter PRONIA-study. BS and the BS criteria, "Cognitive Disturbances" (COGDIS) and "Cognitive-perceptive BS" (COPER), were assessed with the "Schizophrenia Proneness Instrument, Adult version" (SPI-A). Using logistic regressions, prevalence rates of perceptive and cognitive BS, and of COGDIS and COPER, as well as the impact of social and role functioning on the association between age and BS were studied in three age groups (15-18 years, 19-23 years, 24-40 years). Most patients (91.2%) reported any BS, 55.9% any perceptive and 87.4% any cognitive BS. Furthermore, 56.3% met COGDIS and 80.5% COPER. Not exhibiting the reported differential age thresholds, both perceptive and cognitive BS, and, at trend level only, COPER were less prevalent in the oldest age group (24-40 years); COGDIS was most frequent in the youngest group (15-18 years). Functional deficits did not better explain the association with age, particularly in perceptive BS and cognitive BS meeting the frequency requirement of BS criteria. Our findings broadly confirmed an age threshold in BS and, thus, the earlier assumed link between presence of BS and brain maturation processes. Yet, age thresholds of perceptive and cognitive BS did not differ. This lack of differential age thresholds might be due to more pronounced the brain abnormalities in this clinical sample compared to earlier community samples. These might have also shown in more frequently occurring and persistent BS that, however, also resulted from a sampling toward these, i.e., toward COGDIS. Future studies should address the neurobiological basis of CHR criteria in relation to age.

Network functional connectivity analysis in individuals at ultrahigh risk for psychosis and patients with schizophrenia

Schizophrenia is a severe mental disorder, and the onset of which is preceded by a stage of ultrahigh risk (UHR) for developing psychosis. Therefore, analyzing individuals with UHR is essential for identifying predictive biomarkers for the onset of schizophrenia. The current study aimed to identify such biomarkers based on a voxelwise whole-brain functional degree centrality (FDC) analysis. Conjunction analysis showed that, compared with healthy controls, both UHR subjects and patients with schizophrenia showed significantly increased FDC at the medial prefrontal cortex (MPFC) and significantly decreased FDC at the right fusiform gyrus (FG). The subsequent partial correlation analysis showed significant correlations between the disorganization symptoms and FDCs at the MPFC and the right FG for both UHR subjects and patients with schizophrenia. These findings suggest that FDC within the MPFC and the right FG could be candidate biomarkers for the onset of schizophrenia.

Occipital Alpha Connectivity During Resting-State Electroencephalography in Patients With Ultra-High Risk for Psychosis and Schizophrenia

Schizophrenia patients always show cognitive impairment, which is proved to be related to hypo-connectivity or hyper-connectivity. Further, individuals with an ultra-high risk for psychosis also show abnormal functional connectivity-related cognitive impairment, especially in the alpha rhythm. Thus, the identification of functional networks is essential to our understanding of the disorder. We investigated the resting-state functional connectivity of the alpha rhythm measured by electroencephalography (EEG) to reveal the relation between functional network and clinical symptoms. The participants included 28 patients with first-episode schizophrenia (FES), 28 individuals with ultra-high risk for psychosis (UHR), and 28 healthy controls (HC). After the professional clinical symptoms evaluation, all the participants were instructed to keep eyes closed for 3-min resting-state EEG recording. The 3-min EEG data were segmented into artefact-free epochs (the length was 3 s), and the functional connectivity of the alpha phase was estimated using the phase lag index (PLI), which measures the phase differences of EEG signals. The FES and UHR groups displayed increased resting-state PLI connectivity compared with the HC group [F(2,74) = 10.804, p < 0.001]. Significant increases in the global efficiency, the local efficiency, and the path length were found in the FES and UHR groups compared with those of the HC group. FES and UHR showed an increased degree of connectivity compared with HC. The degree of the left occipital lobe area was higher in the UHR group than in the FES group. The hypothesis of disconnection is confirmed. Furthermore, differences between the UHR and FES group were found, which is valuable for producing clinical significance before the onset of schizophrenia.

Brain structural abnormalities as potential markers for detecting individuals with ultra-high risk for psychosis: A systematic review and meta-analysis

OBJECTIVE

This study aims to determine whether structural alterations can be used as neuroimaging markers to detect individuals with ultra-high risk (UHR) for psychosis for the diagnosis of schizophrenia and improvement of treatment outcomes.

METHODS

Embase and Pubmed databases were searched for related studies in July 2018. The search was performed without restriction on time and regions or languages. A total of 188 articles on voxel-based morphometry (VBM) and 96 articles on cortical thickness were obtained, and another 6 articles were included after the reference lists were checked. Our researchers assessed and extracted the data in accordance with the PRISMA guideline. The data were processed with a seed-based mapping method.

RESULTS

Fourteen VBM and nine cortical thickness studies were finally included in our study. In individuals with UHR, the gray matter volumes in the bilateral median cingulate (Z = 1.034), the right fusiform gyrus (Z = 1.051), the left superior temporal gyrus (Z = 1.048), and the right thalamus (Z = 1.039) increased relative to those of healthy controls. By contrast, the gray matter volumes in the right gyrus rectus (Z = -2.109), the right superior frontal gyrus (Z = -2.321), and the left superior frontal gyrus (Z = -2.228) decreased. The robustness of these findings was verified through Jackknife sensitivity analysis, and heterogeneity across studies was low. Typically, cortical thickness alterations were not detected in individuals with UHR.

CONCLUSIONS

Structural abnormalities of the thalamocortical circuit may underpin the neurophysiology of psychosis and mark the vulnerability of transition to psychosis in UHR subjects.

Increased frontal gray matter volume in individuals with prodromal psychosis

Background

Brain anatomical deficits associated with cognitive dysfunction have been reported in patients with schizophrenia. However, it remains unknown whether such anatomical deficits exist in individuals with prodromal psychosis. The present study is designed to investigate anatomical deficits in prodromal individuals and their associations with clinical/cognitive features.

Methods

Seventy‐four prodromal individuals and seventy‐six healthy controls were scanned using structural magnetic resonance imaging. Support vector machines were applied to test whether anatomical deficits might be used to discriminate prodromal individuals from healthy controls.

Results

Prodromal individuals showed significantly increased gray matter volume (GMV) in the right inferior frontal gyrus (IFG) and right rectus gyrus relative to healthy controls. No correlations were observed between increased GMV and clinical/cognitive characteristics. The combination of increased GMV in the right rectus gyrus and right IFG showed a sensitivity of 74.32%, a specificity of 67.11%, and an accuracy of 70.67% in differentiating prodromal individuals from healthy controls.

Conclusion

Our results provide evidence of increased frontal GMV in prodromal individuals. A combination of GMV values in the two frontal brain areas may serve as potential markers to discriminate prodromal individuals from healthy controls. The results thus highlight the importance of the frontal regions in the pathophysiology of psychosis.

Functional asymmetry of thalamocortical networks in subjects at ultra-high risk for psychosis and first-episode schizophrenia

Disrupted functional asymmetry has been implicated in schizophrenia. However, it remains unknown whether disrupted functional asymmetry originates from intra-hemispheric and/or inter-hemispheric functional connectivity (FC) in the patients, and whether it starts at very early stage of psychosis. Seventy-six patients with first-episode, drug-naive schizophrenia, 74 subjects at ultra-high risk for psychosis (UHR), and 71 healthy controls underwent resting-state functional magnetic resonance imaging. The 'Parameter of asymmetry' (PAS) metric was calculated and support vector machine (SVM) classification analysis was applied to analyze the data. Compared with healthy controls, patients exhibited decreased PAS in the left thalamus/pallidum, right hippocampus/parahippocampus, right inferior frontal gyrus/insula, right thalamus, and left inferior parietal lobule, and increased PAS in the left calcarine, right superior occipital gyrus/middle occipital gyrus, and right precentral gyrus/postcentral gyrus. By contrast, UHR subjects showed decreased PAS in the left thalamus relative to healthy controls. A negative correlation was observed between decreased PAS in the right hippocampus/parahippocampus and Brief Visuospatial Memory Test-Revised (BVMT-R) scores in the patients (r = -0.364, p = 0.002). Moreover, the PAS values in the left thalamus could discriminate the patients/UHR subjects from the controls with acceptable sensitivities (68.42%/81.08%). First-episode patients and UHR subjects shared decreased PAS in the left thalamus. This observed pattern of functional asymmetry highlights the involvement of the thalamus in the pathophysiology of psychosis and may also be applied as a very early marker for psychosis.

Voxel-Mirrored Homotopic Connectivity of Resting-State Functional Magnetic Resonance Imaging in Blepharospasm

Objective: Several networks in human brain are involved in the development of blepharospasm. However, the underlying mechanisms for this disease are poorly understood. A voxel-mirrored homotopic connectivity (VMHC) method was used to quantify the changes in functional connectivity between two hemispheres of the brain in patients with blepharospasm.

Methods: Twenty-four patients with blepharospasm and 24 healthy controls matched by age, sex, and education were recruited. The VMHC method was employed to analyze the fMRI data. The support vector machine (SVM) method was utilized to examine whether these abnormalities could be applied to distinguish the patients from the controls.

Results: Compared with healthy controls, patients with blepharospasm showed significantly high VMHC in the inferior temporal gyrus, interior frontal gyrus, posterior cingulate cortex, and postcentral gyrus. No significant correlation was found between abnormal VMHC values and clinical variables. SVM analysis showed a combination of increased VMHC values in two brain areas with high sensitivities and specificities (83.33 and 91.67% in the combined inferior frontal gyrus and posterior cingulate cortex; and 83.33 and 87.50% in the combined inferior temporal gyrus and postcentral gyrus).

Conclusion: Enhanced homotopic coordination in the brain regions associated with sensory integration networks and default-mode network may be underlying the pathophysiology of blepharospasm. This phenomenon may serve as potential image markers to distinguish patients with blepharospasm from healthy controls.

Altered functional connectivity strength and its correlations with cognitive function in subjects with ultra-high risk for psychosis at rest

AIMS

Evidence of altered structural and functional connectivity in the frontal-occipital network is associated with cognitive deficits in patients with schizophrenia. However, the altered patterns of functional connectivity strength (FCS) in individuals with ultra-high risk (UHR) for psychosis remain unknown. In this study, whole-brain FCS was assessed to examine the altered patterns of FCS in UHR subjects.

METHODS

A total of 34 UHR subjects and 37 age- and sex-matched healthy controls were enrolled to undergo resting-state functional magnetic resonance imaging. The imaging data were analyzed using the graph theory method.

RESULTS

Compared with healthy controls, UHR subjects showed significantly decreased FCS in the left middle frontal gyrus and significantly increased FCS in the left calcarine cortex. The FCS values in the left middle frontal gyrus were positively correlated to the scores of the Brief Assessments of Cognitionin Schizophrenia Symbol Coding Test (r = 0.366, P = 0.033) in the UHR subjects. A negative correlation was found between the FCS values in the left calcarine cortex and the scores of the Stroop color-naming test (r = -0.475, P = 0.016) in the UHR subjects. A combination of the FCS values in the 2 brain areas showed an accuracy of 87.32%, a sensitivity of 73.53%, and a specificity of 100% for distinguishing UHR subjects from healthy controls.

CONCLUSIONS

Significantly altered FCS in the frontal-occipital network is observed in the UHR subjects. Furthermore, decreased FCS in the left middle frontal gyrus and increased FCS in the left calcarine have significant correlations with the cognitive measures of the UHR subjects and thus improve our understanding of the underlying pathophysiological mechanisms of schizophrenia. Moreover, a combination of the FCS values in the 2 brain areas can serve as a potential image marker to distinguish UHR subjects from healthy controls.