A Systematic Characterization of Structural Brain Changes in Schizophrenia.
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ABSTRACT:
SUBMITTER: Ediri Arachchi W
PROVIDER: S-EPMC7532248 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveBrain structural changes in schizoaffective disorder, and how far they resemble those seen in schizophrenia and bipolar disorder, have only been studied to a limited extent.MethodForty-five patients meeting DSM-IV and RDC criteria for schizoaffective disorder, groups of patients with 45 matched schizophrenia and bipolar disorder, and 45 matched healthy controls were examined using voxel-based morphometry (VBM).ResultsAnalyses comparing each patient group with the healthy control subjects found that the patients with schizoaffective disorder and the patients with schizophrenia showed widespread and overlapping areas of significant volume reduction, but the patients with bipolar disorder did not. A subsequent analysis compared the combined group of patients with the controls followed by extraction of clusters. In regions where the patients differed significantly from the controls, no significant differences in mean volume between patients with schizoaffective disorder and patients with schizophrenia in any of five regions of volume reduction were found, but mean volumes in the patients with bipolar disorder were significantly smaller in three of five.ConclusionThe findings provide evidence that, in terms of structural gray matter brain abnormality, schizoaffective disorder resembles schizophrenia more than bipolar disorder.
| S-EPMC5029760 | biostudies-literature
Project description:Cognitive impairment is highly prevalent in patients with major psychiatric disorders (MPDs), including schizophrenia (SCZ), bipolar disorder, major depressive disorder, in whom it can be highly disruptive to community functioning and worsen prognosis. Previously, genetic factors and cognitive impairments in MPD patients have been examined mostly in isolated circuits rather than in the whole brain. In the present study, genetic, neuroimaging, and psychometric approaches were combined to investigate the relationship among genetic factors, alterations throughout the brain, and cognitive impairments in a large cohort of patients diagnosed with SCZ, with a reference healthy control (HC) group. Single nucleotide polymorphisms (SNPs) in SCZ-risk genes were found to be strongly related to cognitive impairments as well as to gray matter volume (GMV) and functional connectivity (FC) alterations in the SCZ group. Annotating 136 high-ranking SNPs revealed 65 affected genes (including PPP1R16B, GBBR2, PDE4B, CANCNA1C, SLC12AB, SATB2, MAG12, and SATB2). Only one, a PDE4B SNP (rs1006737), correlated with GMV (r = 0:19 p = 0.015) and FC (r = 0.21, p = 0.0074) in SCZ patients. GMV and FC alterations correlated with one another broadly across brain regions. Moreover, the present data demonstrate three-way SNP-FC-GMV associations in patients with SCZ, thus providing clues regarding potential genetic bases of cognition impairments in SCZ. SNP-FC-GMV relationships correlated with visual learning and reasoning dimensions of cognition. These data provide evidence that SCZ-related cognitive impairments may reflect genetically underlain whole-brain structural and functional alterations.
| S-EPMC9437456 | biostudies-literature
Project description:BackgroundSchizophrenia is a multifaceted mental disorder characterized by cognitive, perceptual, and affective symptom dimensions. This heterogeneity at the phenomenological level may be subserved by complex and heterogeneous patterns of structural abnormalities. Thus, delineating such patterns may improve the insight into the variability of disease and facilitate future magnetic resonance imaging-based diagnosis.MethodsWe aimed to identify structurally complex signatures that directly differentiate patients with predominantly negative (pNEG), positive (pPOS), and disorganized (pDIS) symptoms using Optimally-Discriminative Voxel-Based Analysis (ODVBA). ODVBA is a new analytical framework for group analysis, which showed to have superior sensitivity and specificity over conventional voxel-based morphometric approaches, thus facilitating the identification of subtle neuroanatomical signatures delineating different subgroups.ResultspPOS were characterized by pronounced gray matter (GM) volume reductions in the ventromedial prefrontal cortex (vmPFC), which herein is defined to include the orbitofrontal cortex, and in occipitotemporal GM and parts of the lingual gyrus. pNEG was found to have vmPFC reduction but to a lesser degree than pPOS and with a relative sparing of the more medial vmPFC regions, compared to pDIS; it also had significantly less cerebellar GM. pDIS showed relatively highest GM volume preservation among three subtypes.ConclusionsAlthough a common prefronto-perisylvian GM reduction pattern was present at the whole-group level, marked morphometric differences emerged between the three subgroups, including reduced cerebellar GM in pNEG and reduced vmPFC and occipitotemporal GM in pPOS. Besides deepening our insight into the neurobiological underpinnings of clinical heterogeneity, these results also identify important imaging biomarkers that may aid patient stratification.
| S-EPMC4266302 | biostudies-literature
Project description:BackgroundInflammation has been implicated in the pathology of schizophrenia and may cause neuronal cell death and dendrite loss. Neuroimaging studies have highlighted longitudinal brain structural changes in patients with schizophrenia, yet it is unclear whether this is related to inflammation. We aim to address this question, by relating brain structural changes with the transcriptional profile of inflammation markers in the early stage of schizophrenia.MethodsThirty-eight patients with first-episode schizophrenia and 51 healthy controls were included. High-resolution T1-weighted magnetic resonance imaging (MRI) and clinical assessments were performed at baseline and 2 ~ 6 months follow-up for all subjects. Changes in the brain structure were analyzed using surface-based morphological analysis and correlated with the expression of immune cells-related gene sets of interest reported by previous reviews. Transcriptional data were retrieved from the Allen Human Brain Atlas. Furthermore, we examined the brain structural changes and peripheral inflammation markers in association with behavioral symptoms and cognitive functioning in patients.ResultsPatients exhibited accelerated cortical thickness decrease in the left frontal cortices, less decrease or an increase in the superior parietal lobule and right lateral occipital lobe, and increased volume in the bilateral pallidum, compared with controls. Changes in cortical thickness correlated with the transcriptional level of monocyte across cortical regions in patients (r = 0.54, p < 0.01), but not in controls (r = - 0.05, p = 0.76). In addition, cortical thickness change in the left superior parietal lobule positively correlated with changes in digital span-backward test scores in patients.ConclusionsPatients with schizophrenia exhibit regional-specific cortical thickness changes in the prefrontal and parietooccipital cortices, which is related to their cognitive impairment. Inflammation may be an important factor contributing to cortical thinning in first-episode schizophrenia. Our findings suggest that the immunity-brain-behavior association may play a crucial role in the pathogenesis of schizophrenia.
| S-EPMC10332052 | biostudies-literature
Project description:BackgroundThe genetic architecture of psychotic disorders is complex, with hundreds of genetic risk loci contributing to a polygenic model of disease. Overlap in the genetics of psychotic disorders and brain measures has been found in European populations, but has not been explored in populations of African ancestry. The aim of this study was to determine whether a relationship exists between a schizophrenia-derived PRS and (i) methamphetamine associated psychosis (MAP), and (ii) brain structural measures, in a South African population.MethodsThe study sample consisted of three participant groups: 31 individuals with MAP, 48 with apsychotic methamphetamine dependence, and 49 healthy controls. Using PRSice, PRS was generated for each of the participants with GWAS summary statistics from the Psychiatric Genomics Consortium Schizophrenia working group (PGC-SCZ2) as the discovery dataset. Regression analyses were performed to determine associations of PRS, with diagnosis, whole brain, and regional gray and white matter measures.ResultsSchizophrenia-derived PRS did not significantly predict MAP diagnosis. After correction for multiple testing, no significant associations were found between PRS and brain measures across all groups.DiscussionThe lack of significant associations here may indicate that the study is underpowered, that brain volumes in MAP are due to factors other than polygenic risk for schizophrenia, or that PRS derived from a largely European discovery set has limited utility in individuals of African ancestry. Larger studies, that include diverse populations, and more nuanced brain measures, may help elucidate the relationship between schizophrenia-PRS, brain structural changes, and psychosis.ConclusionThis research presents the first PRS study to investigate shared genetic effects across psychotic disorders and brain structural measures in an African population. Ancestrally comparable discovery datasets may be useful for future African genetic research.
| S-EPMC7566162 | biostudies-literature
Project description:ObjectiveTo evaluate the association between migraine without aura (MO) and migraine with aura (MA) and 3 types of structural brain abnormalities detected by MRI: white matter abnormalities (WMAs), infarct-like lesions (ILLs), and volumetric changes in gray and white matter (GM, WM) regions.MethodsPubMed as well as the reference lists of identified studies and reviews were used to identify potentially eligible studies through January 2013. Candidate studies were reviewed and eligible studies were abstracted. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated for WMAs and ILLs.ResultsSix population-based and 13 clinic-based studies were identified. The studies suggested that structural brain changes, including WMAs, silent ILLs, and volumetric changes in GM and WM regions, were more common in migraineurs than in control groups. The results were strongest for MA. The meta-analysis of WMAs showed an association for MA (OR 1.68; 95% CI 1.07-2.65; p = 0.03) but not for MO (OR 1.34; 95% CI 0.96-1.87; p = 0.08). The association of ILLs was greater for MA (OR 1.44; 95% CI 1.02-2.03; p = 0.04) than for MO, but no association was found for MA (p = 0.52) and MO (p = 0.08) compared to controls.ConclusionThese data suggest that migraine may be a risk factor for structural changes in the brain. Additional longitudinal studies are needed to determine the differential influence of migraine without and with aura, to better characterize the effects of attack frequency, and to assess longitudinal changes in brain structure and function.
| S-EPMC3795609 | biostudies-literature
Project description:BackgroundDespite decades of research, the molecular changes responsible for the evolution of human cognitive abilities remain unknown. Comparative evolutionary studies provide detailed information about DNA sequence and mRNA expression differences between humans and other primates but, in the absence of other information, it has proved very difficult to identify molecular pathways relevant to human cognition.ResultsHere, we compare changes in gene expression and metabolite concentrations in the human brain and compare them to the changes seen in a disorder known to affect human cognitive abilities, schizophrenia. We find that both genes and metabolites relating to energy metabolism and energy-expensive brain functions are altered in schizophrenia and, at the same time, appear to have changed rapidly during recent human evolution, probably as a result of positive selection.ConclusionOur findings, along with several previous studies, suggest that the evolution of human cognitive abilities was accompanied by adaptive changes in brain metabolism, potentially pushing the human brain to the limit of its metabolic capabilities.
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Project description:Expression of polysialic acid (polySia) in the adult brain is enriched in areas of continuous neurogenesis and plasticity such as the hippocampus. Genome-wide association studies identified variants of glycosylation enzyme-encoding genes, required for the generation of polySia, to be associated with the development of schizophrenia and bipolar disorder. Here, we report that serum levels of polySia are increased in patients with schizophrenia spectrum disorder compared to patients with major depressive disorders or demographically matched healthy controls. Furthermore, elevated polySia serum levels are associated with structural hippocampal gray matter decline in schizophrenia spectrum and bipolar disorder. In patients with schizophrenia spectrum disorder, polySia serum levels correlate with the number, duration of disease-related hospitalizations, early retirement and medical leave as estimators of detrimental long-term disease trajectories. Our data show that polySia serum levels are linked to structural hippocampal brain changes in schizophrenia spectrum and bipolar disorders, and suggest a contribution of polySia to the pathophysiology of these diseases.
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Project description:Negative symptoms occur in several major mental health disorders with undetermined mechanisms and unsatisfactory treatments; identification of their neural correlates might unveil the underlying pathophysiological basis and pinpoint the therapeutic targets. In this study, participants with major depressive disorder (n?=?24), schizophrenia (n?=?22), and healthy controls (n?=?20) were assessed with 10 frequently used negative symptom scales followed by principal component analysis (PCA) of the scores. A linear model with the prominent components identified by PCA was then regressed on gray and white-matter volumes estimated from T1-weighted magnetic resonance imaging. In depressed patients, negative symptoms such as blunted affect, alogia, withdrawal, and cognitive impairment, assessed mostly via clinician-rated scales were inversely associated with gray matter volume in the bilateral cerebellum. In patients with schizophrenia, anhedonia, and avolition evaluated via self-rated scales inversely related to white-matter volume in the left anterior limb of internal capsule/anterior thalamic radiation and positively in the left superior longitudinal fasiculus. The pathophysiological mechanisms underlying negative symptoms might differ between depression and schizophrenia. These results also point to future negative symptom scale development primarily focused on detecting and monitoring the corresponding changes to brain structure or function.
| S-EPMC4145726 | biostudies-literature
Project description:Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.
| S-EPMC10475022 | biostudies-literature