The Schizophrenia Imaging Laboratory (SIL) data are based on a collaboration of >10
years studying the schizophrenic brain using magnetic resonance imaging (MRI) in Xi'an,
China. Collection of SIL data (N = 665; 319 patients, 48 first-degree relatives, and
298 control participants) started in 2011, with the purpose of performing a trans-scale
study focusing on schizophrenia, and this has since diversified into three datasets:
pooling clinical assessment, neuroimaging and genetic data to answer clinical and
preclinical questions in psychiatry. Most of them come from Fourth Military Medical
University, and the rest of the data come from the Xi'an Mental Health Center. In
the SIL data, all the participants underwent clinical assessments (clinical characteristics,
e.g. Positive and Negative Syndrome Scale, and cognitive tests) and MRI scans, including
T2-weighted imaging, high-resolution T1-weighted imaging, functional imaging, diffusion
weighted imaging, and arterial spin labeling at baseline, and 103 participants had
transcriptome-wide data of whole blood (mRNA, small RNA, lncRNA, and circRNA). Scanning
machines included the GE Discovery MR750 3.0 T scanner and Siemens 3.0 T Magnetom
Trio Tim MR scanner. Clinical assessment at discharge from the hospital was available
for 188 patients whose episode resulted in hospitalization. Afterward, 148 participants
completed the follow-up assessments and scans. The whole study had no influence on
the therapy. It investigated different aspects of familial risk, neural mechanisms,
symptoms, diagnosis, treatment, and clinical translation.
Central to the success of these 10 years are the efforts of the dedicated staff at
SIL. Importantly, this work was supported by the National Key Basic Research and Development
Program (2011CB707805), National Natural Science Foundation (81571651, 81801675),
project funding by the China Postdoctoral Science Foundation (2019TQ0130, 2020M683739),
Fourth Military Medical University (2019CYJH, 2014D07), and the State Scholarship
Fund, China Scholarship Council (201603170143).
The data set has been a formidable force for innovation in neuroimaging of schizophrenia.
SIL data support >50 active studies. Summaries of these key studies are listed in
Table 1. SIL consistently contributes to the characterization of robust neuroimaging
phenotype, objective diagnosis, and therapeutic effects on brain and prediction of
treatment outcomes. First, targeting the biological phenotype, we discovered new evidence
of neurodevelopmental disorders associated with a disrupted brain connectome throughout
the course, and established a new phenotype of auditory verbal hallucinations. Second,
we explored new strategies in biological psychiatry for objective diagnosis, and creatively
applied radiomics to identify this disease without solid lesions. Furthermore, to
optimize treatment levels, we revealed the mechanism for predicting brain age by improved
brain structural networks after antipsychotic treatment, and constructed a new model
for efficacy prediction. SIL has worked with researchers to publish evidence that
advances psychiatry, neuroscience, and radiology, and improves schizophrenia patient
care. Requests to access SIL data and further inquiries can be directed to us.
Table 1:
A selection of key findings using SIL data.
Publications
Total N from SIL data
Main findings
Xi et al., 2022
100 controls and 100 patients
Early medication improves the brain aging of patients with schizophrenia.
Li et al., 2020
54 controls and 90 patients
A neuroimaging biomarker based on functional striatal abnormalities is developed for
schizophrenia identification, prognosis, and subtyping.
Cui et al., 2019
114 controls and 81 patients
Disrupted rich club organization and functional dynamics might be an early feature
in the pathophysiology of schizophrenia.
Rozycki et al., 2018
24 controls and 18 patients
Structural MRI provides a robust and reproducible imaging signature of schizophrenia.
Cui et al., 2017
19 controls and 32 patients
Dysfunction in brain regions in schizophrenia patients with auditory verbal hallucinations
are involved in auditory processing, language production and monitoring, and sensory
information filtering.
Conflict of Interest
The authors declare no conflict of interests.