Schizophrenia Clinical Trial
Official title:
Prediction of Violent Behavior in Patients With Schizophrenia by Multimodal Machine Learning
Patients with schizophrenia have a higher risk of committing violent crimes than the general
population, and the relative risk of violence against others is four times higher than the
general population. Violence is a major public health problem because it often leads to poor
prognosis, readmission and stigma in patients with schizophrenia.
MRI studies on violent behavior in schizophrenia are relatively few. These studies have found
that violence is primarily associated with dysfunction in the ventral prefrontal and temporal
limbic systems. Structural MRI found that violent behavior in schizophrenia was associated
with increased volume of white matter in caudate nucleus, left orbitofrontal gyrus and right
orbitofrontal gyrus. However, the current research results in this field are uneven, the
methods are not consistent, and there is a lack of breakthrough progress, which needs to be
integrated and deepened urgently.
If the violent behavior of the patients with schizophrenia could be predicted by magnetic
resonance imaging, it would be a revolutionary try. By doing so, the investigators can
strengthen the treatment of these patients and reduce the occurrence of violence. Based on
previous studies, the investigators believe that violent schizophrenics exhibit recognizable
imaging characteristics under structural phase, resting state, negative emotional images and
natural stimuli models. Anomalies in a particular mode may be subtle and difficult to
identify, but when multiple different modes are integrated, a significant and characteristic
set of imaging markers will be present. This study will use the multivariate model of machine
learning method, detection brain activation patterns under different situations among
patients with violence. The investigators are going to study imaging biomarkers, and try to
predict the possibility of onset of violence among schizophrenia patients, thus reduce the
risks of violence.
Status | Not yet recruiting |
Enrollment | 200 |
Est. completion date | September 2022 |
Est. primary completion date | September 2022 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years to 60 Years |
Eligibility |
Inclusion Criteria: for violent group 1. Inpatient or out-patient treatment; 2. Junior high school education or above, eliminate mental retardation; 3. 18-60 years old; 4. Han, right-handed; 5. Meet the diagnostic criteria of ICD-10 schizophrenia; 6. Self-report and confirmed by family members that violence occurred at least 2 times in the recent six months; 7. In the recent half year, the revised Version of the weighted total score of The Explicit Attack Scale (MOAS) =4; 8. Exclude severe heart, liver, kidney, blood, digestive and nervous system diseases; 9. Willing to participate in this study and sign the informed consent. Inclusion criteria for non-violent patients 1) Schizophrenic patients treated in hospital or outpatient; 2) Junior high school education or above, eliminate mental retardation; 3) 18-60 years old; 4) Han, right-handed; 5) Meet the diagnostic criteria of ICD-10 schizophrenia; 6) Self-reported and confirmed by family members that no violence has occurred in the past six months; 7) within the past revisions explicit scale (MOAS) weighted total score ? 4 points; 8) Exclude severe heart, liver, kidney, blood, digestive and nervous system diseases; 9) Willing to participate in this study and sign the informed consent. - |
Country | Name | City | State |
---|---|---|---|
China | Shanghai Mental Health Center | Shanghai | Shanghai |
Lead Sponsor | Collaborator |
---|---|
Shanghai Mental Health Center |
China,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | MRI testing results | structural MRI data | Sep.2022 | |
Primary | resting state MRI data | functional connections between brain regions | Sep.2020 | |
Primary | functional MRI data | brain stimulation models while in a natural stimulus or watching affective pictures | Sep.2020 | |
Primary | Integrated data | integrated MRI data from machine learning method | Sep.2020 |
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