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Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.
Aims
To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.
Method
We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.
Results
We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.
Conclusions
These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.
Compulsive behaviors in obsessive-compulsive disorder (OCD) have been related to impairment within the associative cortical-striatal system connecting the caudate and prefrontal cortex that underlies consciously-controlled goal-directed learning and behavior. However, little is known whether this impairment may serve as a biomarker for vulnerability to OCD.
Methods
Using resting-state functional magnetic resonance imaging (fMRI), we employed Granger causality analysis (GCA) to measure effective connectivity (EC) in previously validated striatal sub-regions, including the caudate, putamen, and the nucleus accumbens, in 35 OCD patients, 35 unaffected first-degree relatives and 35 matched healthy controls.
Results
Both OCD patients and their first-degree relatives showed greater EC than controls between the left caudate and the orbital frontal cortex (OFC). Both OCD patients and their first-degree relatives showed lower EC than controls between the left caudate and lateral prefrontal cortex. These results are consistent with findings from task-related fMRI studies which found impairment in the goal-directed system in OCD patients.
Conclusions
The same changes in EC were present in both OCD patients and their unaffected first-degree relatives suggest that impairment in the goal-directed learning system may be a biomarker for OCD.
Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.
Aims
To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.
Method
Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.
Results
The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.
Conclusions
These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.
Cognitive impairment in late-life depression is common and associated with a higher risk of all-cause dementia. Late-life depression patients with comorbid cardiovascular diseases (CVDs) or related risk factors may experience higher risks of cognitive deterioration in the short term. We aim to investigate the effect of CVDs and their related risk factors on the cognitive function of patients with late-life depression.
Methods:
A total of 148 participants were recruited (67 individuals with late-life depression and 81 normal controls). The presence of hypertension, coronary heart disease, diabetes mellitus, or hyperlipidemia was defined as the presence of comorbid CVDs or related risk factors. Global cognitive functions were assessed at baseline and after a one-year follow-up by the Mini-Mental State Examination (MMSE). Global cognitive deterioration was defined by the reliable change index (RCI) of the MMSE.
Results:
Late-life depression patients with CVDs or related risk factors were associated with 6.8 times higher risk of global cognitive deterioration than those without any of these comorbidities at a one-year follow-up. This result remained robust after adjusting for age, gender, and changes in the Hamilton Depression Rating Scale (HAMD) scores.
Conclusions:
This study suggests that late-life depression patients with comorbid CVDs or their related risk factors showed a higher risk of cognitive deterioration in the short-term (one-year follow up). Given that CVDs and their related risk factors are currently modifiable, active treatment of these comorbidities may delay rapid cognitive deterioration in patients with late-life depression.
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