Dataset Information

Cortical gray and white matter volume in unmedicated schizotypal and schizophrenia patients.

ABSTRACT: Magnetic resonance imaging (MRI) studies have revealed fronto-temporal cortical gray matter volume reductions in schizophrenia. However, to date studies have not examined whether age- and sex-matched unmedicated schizotypal personality disorder (SPD) patients share some or all of the structural brain-imaging characteristics of schizophrenia patients. We examined cortical gray/white matter volumes in a large sample of unmedicated schizophrenia-spectrum patients (n=79 SPD, n=57 schizophrenia) and 148 healthy controls. MRI images were reoriented to standard position parallel to the anterior-posterior commissure line, segmented into gray and white matter tissue types, and assigned to Brodmann areas (BAs) using a postmortem-histological atlas. Group differences in regional volume of gray and white matter in the BAs were examined with MANOVA. Schizophrenia patients had significantly reduced gray matter volume widely across the cortex but more marked in frontal and temporal lobes. SPD patients had reductions in the same regions but only about half that observed in schizophrenia and sparing in key regions including BA10. In schizophrenia, greater fronto-temporal volume loss was associated with greater negative symptom severity and in SPD, greater interpersonal and cognitive impairment. Overall, our findings suggest that increased prefrontal volume in BA10 and sparing of volume loss in temporal cortex (BAs 22 and 20) may be a protective factor in SPD which reduces vulnerability to psychosis.

SUBMITTER: Hazlett EA

PROVIDER: S-EPMC2672563 | biostudies-literature | 2008 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cortical gray and white matter volume in unmedicated schizotypal and schizophrenia patients.

Hazlett Erin A EA Buchsbaum Monte S MS Haznedar M Mehmet MM Newmark Randall R Goldstein Kim E KE Zelmanova Yuliya Y Glanton Cathryn F CF Torosjan Yuliya Y New Antonia S AS Lo Jennifer N JN Mitropoulou Vivian V Siever Larry J LJ

Schizophrenia research 20080213 1-3

Magnetic resonance imaging (MRI) studies have revealed fronto-temporal cortical gray matter volume reductions in schizophrenia. However, to date studies have not examined whether age- and sex-matched unmedicated schizotypal personality disorder (SPD) patients share some or all of the structural brain-imaging characteristics of schizophrenia patients. We examined cortical gray/white matter volumes in a large sample of unmedicated schizophrenia-spectrum patients (n=79 SPD, n=57 schizophrenia) and ...[more]

PMID: 18272348

Similar Datasets

Project description:Widespread cortical gray matter alternations in people with schizophrenia are correlated with both psychotic symptoms and cognitive/behavioral abnormalities, including the impairments of exploratory eye movement (EEM). Particularly, the loss of gray matter density is specifically related to deficits of the responsive search score (RSS) of EEM in schizophrenia. It is unknown, however, whether the schizophrenia-related RSS deficits are associated with certain psychotic symptoms, such as hallucinations.In 33 participants with schizophrenia, the measurement of EEM, assessment of the hallucination severity using Positive and Negative Syndrome Scale (PANSS) and a voxel-based morphometric analysis of cortical gray matter volume (GMV) were conducted to investigate the relationships between the RSS of EEM, symptom severity, and GMV. In 29 matched healthy controls, the measurement of EEM and a voxel-based morphometric analysis of cortical GMV were also conducted to investigate the relationship between the RSS of EEM and GMV.In participants with schizophrenia, the hallucination severity was significantly negatively correlated with both the RSS and the GMV of a large number of brain regions in the frontal, temporal, parietal, orbitofrontal, calcarine, cingulate, and insular cortices, and rolandic operculum, hippocampus, parahippocampal gyrus, and thalamus. Also in participants with schizophrenia, the RSS was significantly positively correlated with the GMV in the left supplementary motor area (SMA), left superior frontal cortex (SFG), bilateral precentral gyri, bilateral postcentral gyri, and bilateral middle frontal cortices. More importantly, the GMV of the SMA, SFG, and precentral gyrus in the left hemisphere was not only significantly negatively correlated with the hallucination severity but also significantly positively correlated with the RSS. No significant correlation could be revealed between the RSS and the GMV of any brain regions in healthy controls.There was a significantly negative association between the hallucination severity and the RSS of EEM, suggesting that the RSS may be a potential biomarker for predicting the hallucination severity of schizophrenia. Also, the GMV of the left SMA, SFG, and precentral gyrus may be the common substrates underlying both hallucination induction and the RSS in people with schizophrenia.

Project description:BackgroundVascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer's disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).MethodsA total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.ResultsMulti-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.ConclusionsBased on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.

Dataset Information

Cortical gray and white matter volume in unmedicated schizotypal and schizophrenia patients.

Publications

Cortical gray and white matter volume in unmedicated schizotypal and schizophrenia patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets