Meta-analysis of structural and functional brain abnormalities in schizophrenia with persistent negative symptoms using activation likelihood estimation

Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning

PURPOSE

The neurobiological heterogeneity present in schizophrenia remains poorly understood. This likely contributes to the limited success of existing treatments and the observed variability in treatment responses. Our objective was to employ magnetic resonance imaging (MRI) and machine learning (ML) algorithms to improve the classification of schizophrenia and its subtypes.

METHOD

We utilized a public dataset provided by the UCLA (University of California, Los Angeles) Consortium for Neuropsychiatric Research, containing structural MRI and resting-state fMRI (rsfMRI) data. We integrated all individuals within the dataset diagnosed with schizophrenia (N = 50), along with age- and gender-matched healthy individuals (N = 50). We extracted volumetrics of 66 subcortical and thickness of 72 cortical regions. Additionally, we obtained four graph-based measures for 116 intracranial regions from rsfMRI data, including degree, betweenness centrality, participation coefficient, and local efficiency. Employing conventional ML methods, we sought to distinguish the patients with schizophrenia from healthy individuals. Furthermore, we applied the methods for discriminating subtypes of schizophrenia. To streamline the feature set, various feature selection techniques were applied. Moreover, a validation phase involved employing the model on a dataset domestically acquired using the same imaging assessments (N = 13). Finally, we explored the correlation between neuroimaging features and behavioral assessments.

FINDING

The classification accuracy reached as high as 79% in distinguishing schizophrenia patients from healthy in the UCLA dataset. This result was achieved by the k-nearest neighbor algorithm, utilizing 12 brain neuroimaging features, selected by the feature selection method of minimum redundancy maximum relevance (MRMR). The model demonstrated effectiveness (72% accuracy) in estimating the patient's label for a new dataset acquired domestically. Using a linear support vector machine (SVM) on 62 features obtained from MRMR, patients with schizophrenic subtypes were classified with an accuracy of 64%. The highest Spearman correlation coefficient between the neuroimaging features and behavioral assessments was observed between the degree of the postcentral gyrus and mean reaction time in the verbal capacity task (r = 0.49, p = 0.001).

CONCLUSION

The findings of this study underscore the utility of MRI and ML algorithms in enhancing the diagnostic process for schizophrenia. Furthermore, these methods hold promise for detecting both brain-related abnormalities and cognitive impairments associated with this disorder.

Functional MRI-Specific Alternations in default mode network in obsessive-compulsive disorder: A voxel-based meta-analysis

BACKGROUND

Obsessive-compulsive disorder (OCD) is a common and debilitating mental disorder. Neuroimaging studies have highlighted that the dysfunctional default mode network (DMN) plays a key role in the pathophysiology mechanisms of OCD. However, the findings of impaired DMN regions have been inconsistent. We employed meta-analysis to identify the fMRI-specific abnormalities of the DMN in OCD.

METHODS

PubMed, Web of science and Embase were searched to screen resting-state functional magnetic resonance imaging (rs-fMRI) studies on the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF) and regional homogeneity (ReHo) of the DMN in OCD patients. Based on the activation likelihood estimation (ALE) algorithm, we compared all patients with OCD and control group in a primary meta-analysis, and analyzed the unmedicated OCD without comorbidities in secondary meta-analyses.

RESULTS

A total of 26 eligible studies with 1219 OCD patients (707men) and 1238 healthy controls (684 men) were included in the primary meta-analysis. We concluded specific changes in brain regions of DMN, mainly in the left medial frontal gurus (MFG), bilateral superior temporal gyrus (STG), bilateral inferior parietal lobule (IPL), bilateral precuneus (PCUN), bilateral posterior cingulate cortex (PCC), and right parahippocampal gyrus (PHG).

CONCLUSION

OCD patients showed dysfunction in the DMN, including impaired local important nodal brain regions. The PCC/PCUN appear to be the most affected regions within the DMN, providing valuable insights into understanding the potential pathophysiology of OCD and targets for clinical interventions.

Dissociable Default Mode Network Connectivity Patterns Underlie Distinct Symptoms in Psychosis Risk

The Clinical High Risk (CHR) stage of psychosis is characterized by subthreshold symptoms of schizophrenia including negative symptoms, dysphoric mood, and functional deterioration. Hyperconnectivity of the default-mode network (DMN) has been observed in early schizophrenia, but the extent to which hyperconnectivity is present in CHR, and the extent to which such hyperconnectivity may underlie transdiagnostic symptoms, is not clear. As part of the Shanghai At-Risk for Psychosis (SHARP) program, resting-state fMRI data were collected from 251 young adults (158 CHR and 93 controls, M = 18.72, SD = 4.68, 129 male). We examined functional connectivity of the DMN by performing a whole-brain seed-to-voxel analysis with the MPFC as the seed. Symptom severity across a number of dimensions, including negative symptoms, positive symptoms, and affective symptoms were assessed. Compared to controls, CHRs exhibited significantly greater functional connectivity (p < 0.001 uncorrected) between the MPFC and 1) other DMN nodes including the posterior cingulate cortex (PCC), and 2) auditory cortices (superior and middle temporal gyri, STG/MTG). Furthermore, these two patterns of hyperconnectivity were differentially associated with distinct symptom clusters. Within CHR, MPFC-PCC connectivity was significantly correlated with anxiety (r= 0.23, p=0.006), while MPFC-STG/MTG connectivity was significantly correlated with negative symptom severity (r=0.26, p=0.001). Secondary analyses using item-level symptom scores confirmed a similar dissociation. These results demonstrate that two dissociable patterns of DMN hyperconnectivity found in the CHR stage may underlie distinct dimensions of symptomatology.

Pharmacological Treatments of Negative Symptoms in Schizophrenia-An Update

Schizophrenia is a chronic psychotic disorder comprising positive symptoms, negative symptoms, and cognitive deficits. Negative symptoms are associated with stigma, worse functional outcomes, and a significant deterioration in quality of life. Clinical diagnosis is challenging despite its significance, and current treatments offer little improvement in the burden of negative symptoms. This article reviews current pharmacological strategies for treating negative symptoms. Dopaminergic, glutamatergic, serotonergic, noradrenergic, cholinergic, anti-inflammatory compounds, hormones, and psychostimulants are explored. Finally, we review pharmacological global treatment guidelines for negative symptoms. In general, switching to a second-generation antipsychotic seems to be most often recommended for patients with schizophrenia on first-generation antipsychotics, and an add-on antidepressant is considered when depression is also present. However, the treatment of negative symptoms remains an unmet need. Future, larger clinical studies and meta-analyses are needed to establish effective pharmacological agents for the effective treatment of negative symptoms.

Functional brain networks associated with the urge for action: Implications for pathological urge

Tics in Tourette syndrome (TS) are often preceded by sensory urges that drive the motor and vocal symptoms. Many everyday physiological behaviors are associated with sensory phenomena experienced as an urge for action, which may provide insight into the neural correlates of this pathological urge to tic that remains elusive. This study aimed to identify a brain network common to distinct physiological behaviors in healthy individuals, and in turn, examine whether this network converges with a network we previously localized in TS, using novel 'coordinate network mapping' methods. Systematic searches were conducted to identify functional neuroimaging studies reporting correlates of the urge to micturate, swallow, blink, or cough. Using activation likelihood estimation meta-analysis, we identified an 'urge network' common to these physiological behaviors, involving the bilateral insula/claustrum/inferior frontal gyrus/supplementary motor area, mid-/anterior- cingulate cortex (ACC), right postcentral gyrus, and left thalamus/precentral gyrus. Similarity between the urge and TS networks was identified in the bilateral insula, ACC, and left thalamus/claustrum. The potential role of the insula/ACC as nodes in the network for bodily representations of the urge to tic are discussed.

Convergent functional change of frontoparietal network in obsessive-compulsive disorder: a voxel-based meta-analysis

Background

Obsessive-compulsive disorder (OCD) is a chronic psychiatric illness with complex clinical manifestations. Cognitive dysfunction may underlie OC symptoms. The frontoparietal network (FPN) is a key region involved in cognitive control. However, the findings of impaired FPN regions have been inconsistent. We employed meta-analysis to identify the fMRI-specific abnormalities of the FPN in OCD.

Methods

PubMed, Web of Science, Scopus, and EBSCOhost were searched to screen resting-state functional magnetic resonance imaging (rs-fMRI) studies exploring dysfunction in the FPN of OCD patients using three indicators: the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo) and functional connectivity (FC). We compared all patients with OCD and control group in a primary analysis, and divided the studies by medication in secondary meta-analyses with the activation likelihood estimation (ALE) algorithm.

Results

A total of 31 eligible studies with 1359 OCD patients (756 men) and 1360 healthy controls (733 men) were included in the primary meta-analysis. We concluded specific changes in brain regions of FPN, mainly in the left dorsolateral prefrontal cortex (DLPFC, BA9), left inferior frontal gyrus (IFG, BA47), left superior temporal gyrus (STG, BA38), right posterior cingulate cortex (PCC, BA29), right inferior parietal lobule (IPL, BA40) and bilateral caudate. Additionally, altered connectivity within- and between-FPN were observed in the bilateral DLPFC, right cingulate gyrus and right thalamus. The secondary analyses showed improved convergence relative to the primary analysis.

Conclusion

OCD patients showed dysfunction FPN, including impaired local important nodal brain regions and hypoconnectivity within the FPN (mainly in the bilateral DLPFC), during the resting state. Moreover, FPN appears to interact with the salience network (SN) and default mode network (DMN) through pivotal brain regions. Consistent with the hypothesis of fronto-striatal circuit dysfunction, especially in the dorsal cognitive circuit, these findings provide strong evidence for integrating two pathophysiological models of OCD.

A systematic review of structural and functional magnetic resonance imaging studies on the neurobiology of depressive symptoms in schizophrenia spectrum disorders

Depressive symptoms in Schizophrenia Spectrum Disorders (SSDs) negatively impact suicidality, prognosis, and quality of life. Despite this, efficacious treatments are limited, largely because the neural mechanisms underlying depressive symptoms in SSDs remain poorly understood. We conducted a systematic review to provide an overview of studies that investigated the neural correlates of depressive symptoms in SSDs using neuroimaging techniques. We searched MEDLINE, PsycINFO, EMBASE, Web of Science, and Cochrane Library databases from inception through June 19, 2023. Specifically, we focused on structural and functional magnetic resonance imaging (MRI), encompassing: (1) T1-weighted imaging measuring brain morphology; (2) diffusion-weighted imaging assessing white matter integrity; or (3) T2*-weighted imaging measures of brain function. Our search yielded 33 articles; 14 structural MRI studies, 18 functional (f)MRI studies, and 1 multimodal fMRI/MRI study. Reviewed studies indicate potential commonalities in the neurobiology of depressive symptoms between SSDs and major depressive disorders, particularly in subcortical and frontal brain regions, though confidence in this interpretation is limited. The review underscores a notable knowledge gap in our understanding of the neurobiology of depression in SSDs, marked by inconsistent approaches and few studies examining imaging metrics of depressive symptoms. Inconsistencies across studies' findings emphasize the necessity for more direct and comprehensive research focusing on the neurobiology of depression in SSDs. Future studies should go beyond "total score" depression metrics and adopt more nuanced assessment approaches considering distinct subdomains. This could reveal unique neurobiological profiles and inform investigations of targeted treatments for depression in SSDs.

Cognitive Impairments in Drug-Naive Patients With First-Episode Negative Symptom-Dominant Psychosis

Importance

Available antipsychotic medications are predominantly used to treat positive symptoms, such as hallucinations and delusions, in patients with first-episode psychosis (FEP). However, treating negative and cognitive symptoms, which are closely related to functional outcomes, remains a challenge.

Objective

To explore the cognitive characteristics of patients with negative symptom-dominant (NSD) psychosis.

Design, Setting, and Participants

This large-scale cross-sectional study of patients with FEP was led by the Shanghai Mental Health Center in China from 2016 to 2021, with participants recruited from 10 psychiatric tertiary hospitals. A comprehensive cognitive assessment was performed among 788 patients with FEP who were drug-naive. Symptom profiles were determined using the Positive and Negative Symptoms Scale (PANSS), and NSD was defined as a PANSS score for negative symptoms higher than that for positive and general symptoms. Positive symptom-dominant (PSD) and general symptom-dominant (GSD) psychosis were defined similarly. Data were analyzed in 2023.

Exposure

Psychotic symptoms were categorized into 3 groups: NSD, PSD, and GSD.

Main Outcomes and Measures

Neurocognitive performance, assessed using the Chinese version of the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery.

Results

This study included 788 individuals with FEP (median age, 22 [IQR, 17-28] years; 399 men [50.6%]). Patients with NSD exhibited more-pronounced cognitive impairment than did those with PSD or GSD. Specifically, cognitive differences between the NSD and PSD group, as well as between the NSD and GSD group, were most notable in the processing speed and attention domains (Trail Making [F = 4.410; P = .01], Symbol Coding [F = 4.957; P = .007], Verbal Learning [F = 3.198; P = .04], and Continuous Performance [F = 3.057; P = .05]). Patients with PSD and GSD showed no significant cognitive differences. Cognitive impairment was positively associated with the severity of negative symptoms. Most of the cognitive function tests used were able to differentiate patients with NSD from those with PSD and GSD, with significant differences observed across a range of tests, from Brief Visuospatial Memory Test-Revised (χ2 = 3.968; P = .05) to Brief Assessment of Cognition in Schizophrenia symbol coding (χ2 = 9.765; P = .002).

Conclusions and Relevance

The findings of this cross-sectional study of patients with FEP suggest the presence of a clinical subtype characterized by a predominance of negative symptoms and cognitive impairment.

Gene expression imputation provides clinical and biological insights into treatment-resistant schizophrenia polygenic risk

Genome-wide association studies (GWAS) have revealed the polygenic nature of treatment-resistant schizophrenia TRS. Gene expression imputation allowed the translation of GWAS results into regulatory mechanisms and the construction of gene expression (GReX) risk scores (GReX-RS).  In the present study we computed GReX-RS from the largest GWAS of TRS to assess its association with clinical features. We perform transcriptome imputation in the largest GWAS of TRS to find GReX associated with TRS using brain tissues. Then, for each tissue, we constructed a GReX-RS of the identified genes in a sample of 254 genotyped first episode of psychosis (FEP) patients to test its association with clinical phenotypes, including clinical symptomatology, global functioning and cognitive performance. Our analysis provides evidence that the polygenic basis of TRS includes genetic variants that modulate the expression of certain genes in certain brain areas (substantia nigra, hippocampus, amygdala and frontal cortex), which at the same time are related to clinical features in FEP patients, mainly persistence of negative symptoms and cognitive alterations in sustained attention, which have also been suggested as clinical predictors of TRS. Our results provide a clinical explanation of the polygenic architecture of TRS and give more insight into the biological mechanisms underlying TRS.

Functional Connectivity Biomarkers in Schizophrenia

Schizophrenia is a debilitating neuropsychiatric disorder that affects approximately 1% of the population and poses a major public health problem. Despite over 100 years of study, the treatment for schizophrenia remains limited, partially due to the lack of knowledge about the neural mechanisms of the illness and how they relate to symptoms. The US Food and Drug Administration (FDA) and the National Institute of Health (NIH) have provided seven biomarker categories that indicate causes, risks, and treatment responses. However, no FDA-approved biomarkers exist for psychiatric conditions, including schizophrenia, highlighting the need for biomarker development. Over three decades, magnetic resonance imaging (MRI)-based studies have identified patterns of abnormal brain function in schizophrenia. By using functional connectivity (FC) data, which gauges how brain regions interact over time, these studies have differentiated patient subgroups, predicted responses to antipsychotic medication, and correlated neural changes with symptoms. This suggests FC metrics could serve as promising biomarkers. Here, we present a selective review of studies leveraging MRI-derived FC to study neural alterations in schizophrenia, discuss how they align with FDA-NIH biomarkers, and outline the challenges and goals for developing FC biomarkers in schizophrenia.

Meta-analysis of cortical thickness reduction in adult schizophrenia

BACKGROUND

Numerous neuroimaging studies using surface-based morphometry analyses have reported altered cortical thickness among patients with schizophrenia, but the results have been inconsistent. We sought to provide a whole-brain meta-analysis, which may help enhance the spatial accuracy of identification.

METHODS

We conducted a meta-analysis of whole-brain studies that explored cortical thickness alteration among adult patients with schizophrenia, including first-episode patients with schizophrenia, and patients with chronic schizophrenia, compared with healthy controls by using the seed-based d mapping with permutation of subject images (SDM-PSI) software.

RESULTS

A systematic literature search identified 25 studies (33 data sets) of cortical thickness, including 2008 patients with schizophrenia and 2004 healthy controls. Overall, patients with schizophrenia showed decreased cortical thickness in the right inferior frontal gyrus (IFG) and bilateral insula extending to the superior temporal gyrus (STG). Subgroup meta-analysis reported that patients with chronic schizophrenia showed decreased cortical thickness in the right insula extending to the right IFG. There was no significant cortical thickness difference between first-episode patients with schizophrenia and healthy controls.

LIMITATIONS

The results of meta-regression analyses should be viewed cautiously since they were driven by a small number of studies or did not overlap with the between-group differences found in the primary analyses.

CONCLUSION

The meta-analysis suggested robust cortical thickness reduction in the IFG, insula and STG among adult patients with schizophrenia, particularly in those with chronic schizophrenia. The results provide useful insights to understanding the underlying pathophysiology of schizophrenia.

The Neural Correlates of the Social Perception Dysfunction in Schizophrenia: An fMRI Study

Purpose

Patients with schizophrenia show deficits in facial emotion recognition and emotional intensity assessment, and also exhibit structural and functional irregularities in specific brain regions. In this study, we aimed to examine differences in active brain regions involved in processing the Emotion Intensity Recognition Task (EIRT), which can serve as an indicator of emotion recognition and ability to perceive intensity, between patients with schizophrenia and healthy controls (HCs). The purpose of this study was to investigate dysfunctional brain regions and investigate the role of the amygdala in social cognition deficits in patients with schizophrenia by focusing on alterations in amygdala activity linked to facial emotion recognition.

Participants and Methods

Twenty-two patients who met a diagnostic criteria for schizophrenia according to DSM-IV and 27 HCs participated in an MRI scan while completing the EIRT. Behavioral and MRI data were collected and analyzed.

Results

Behavioral results showed that patients with schizophrenia made significantly more errors in recognizing surprise, happiness, sadness, fear, and neutral expressions, and patients with schizophrenia exhibited significantly slower response times in recognizing happy facial expressions. Imaging results showed that schizophrenia patients found hypoactivation in several inferior parietal and temporal regions, in the cerebrum and anterior cingulate; and decreased amygdala activation in individuals with schizophrenia was associated with impaired recognition of fear in facial expressions.

Conclusion

Facial emotion processing deficits are emotion-specific (surprise, happiness, sadness, fear, and neutral expressions) in schizophrenia. Hypoactivation in several inferior parietal and temporal regions, in the cerebrum and anterior cingulate, was thought to contribute to symptom formation in schizophrenia. Reduction in amygdala activation in schizophrenia patients was associated with impairment of the fear-emotional process.

Clinical anatomy of the precuneus and pathogenesis of the schizophrenia

Recent evidence has shown that the precuneus plays a role in the pathogenesis of schizophrenia. The precuneus is a structure of the parietal lobe's medial and posterior cortex, representing a central hub involved in multimodal integration processes. Although neglected for several years, the precuneus is highly complex and crucial for multimodal integration. It has extensive connections with different cerebral areas and is an interface between external stimuli and internal representations. In human evolution, the precuneus has increased in size and complexity, allowing the development of higher cognitive functions, such as visual-spatial ability, mental imagery, episodic memory, and other tasks involved in emotional processing and mentalization. This paper reviews the functions of the precuneus and discusses them concerning the psychopathological aspects of schizophrenia. The different neuronal circuits, such as the default mode network (DMN), in which the precuneus is involved and its alterations in the structure (grey matter) and the disconnection of pathways (white matter) are described.

Efficacy of Serotonin and Dopamine Activity Modulators in the Treatment of Negative Symptoms in Schizophrenia: A Rapid Review

Schizophrenia is among the fifteen most disabling diseases worldwide. Negative symptoms (NS) are highly prevalent in schizophrenia, negatively affect the functional outcome of the disorder, and their treatment is difficult and rarely specifically investigated. Serotonin-dopamine activity modulators (SDAMs), of which aripiprazole, cariprazine, brexpiprazole, and lumateperone were approved for schizophrenia treatment, represent a possible therapy to reduce NS. The aim of this rapid review is to summarize the evidence on this topic to make it readily available for psychiatrists treating NS and for further research. We searched the PubMed database for original studies using SDAM, aripiprazole, cariprazine, brexpiprazole, lumateperone, schizophrenia, and NS as keywords. We included four mega-analyses, eight meta-analyses, two post hoc analyses, and 20 clinical trials. Aripiprazole, cariprazine, and brexpiprazole were more effective than placebo in reducing NS. Only six studies compared SDAMs with other classes of antipsychotics, demonstrating a superiority in the treatment of NS mainly for cariprazine. The lack of specific research and various methodological issues, related to the study population and the assessment of NS, may have led to these partial results. Here, we highlight the need to conduct new methodologically robust investigations with head-to-head treatment comparisons and long-term observational studies on homogeneous groups of patients evaluating persistent NS with first- and second-generation scales, namely the Brief Negative Symptom Scale and the Clinical Assessment Interview for Negative Symptoms. This rapid review can expand research on NS therapeutic strategies in schizophrenia, which is fundamental for the long-term improvement of patients’ quality of life.