Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0036341 (schizophrenia)
 60,220 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Structural brain abnormalities, including decreased gray matter (GM) and white matter (WM) volume, have been observed in patients with schizophrenia. These decrements were found to be associated with positive and negative symptoms, but affective symptoms (depression and anxiety) were poorly explored. We hypothesized that abnormalities in GM and WM volume might also be related to affective symptoms. GM and WM volumes were calculated from high-resolution T1 structural images acquired from 24 patients with schizophrenia and 26 healthy controls, and the associations of positive, negative, and affective symptoms with the brain volumes that showed significant reduction in patients were investigated. Patients demonstrated GM volume reductions in the bilateral prefrontal cortex, and WM volume reductions in the right frontal and left corpus callosum. Prefrontal cortex volume was significantly and inversely associated with both auditory-verbal hallucinations and depression severity. WM volume alterations, in contrast, were related to alogia, anhedonia, and delusions. The combined impact of auditory-verbal hallucinations and depression on similar sub-regions of the prefrontal cortex suggests that depression is involved in hearing voices. Further, this adverse impact of depression on prefrontal GM volume may underlie the impairment demonstrated by these patients in cognitive tasks that rely on executive processes.
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PMID:Depression, auditory-verbal hallucinations, and delusions in patients with schizophrenia: Different patterns of association with prefrontal gray and white matter volume. 3054 51

Schizophrenia (Sz) is a chronic mental disorder characterized by disturbances in thought (such as delusions and confused thinking), perception (hearing voices), and behavior (lack of motivation). The lifetime prevalence of Sz is between 0.3% and 0.7%, with late adolescence and early adulthood, the peak period for the onset of psychotic symptoms. Causal factors in Sz include environmental and genetic factors and especially their interaction. About 50% of individuals with a diagnosis of Sz have lifelong impairment.
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PMID:Magnetoencephalography for Schizophrenia. 3233 7

Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-task learning to predict their ecological momentary assessment scores for 10 different symptom items. By taking into account both the similarities and differences between different participants and symptoms, our multi-task learning models perform statistically significantly better than the models trained with single-task learning for predicting patients' individual symptom trajectories, such as feeling depressed, social, and calm and hearing voices. We also found different subtypes for each of the symptoms by applying unsupervised clustering to the feature weights in the models. Taken together, compared to the features used in the previous studies, our rhythm features not only improved models' prediction accuracy but also provided better interpretability for how patients' behavioral rhythms and the rhythms of their environments influence their symptom conditions. This will enable both the patients and clinicians to monitor how these factors affect a patient's condition and how to mitigate the influence of these factors. As such, we envision that our solution allows early detection and early intervention before a patient's condition starts deteriorating without requiring extra effort from patients and clinicians.
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PMID:Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia. 3293 46


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