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Guangxiang Rao
Chinese Academy of Sciences
PathologicalCross-validationBipolar disorderAntipsychoticNeuroimagingSchizophreniaBiomarker (medicine)Convolutional neural network
2Publications
1H-index
4Citations
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#1Guangxiang Rao (CAS: Chinese Academy of Sciences)H-Index: 1
#2Ang Li (CAS: Chinese Academy of Sciences)H-Index: 4
Last. Bing LiuLingzhong FanYue (CAS: Chinese Academy of Sciences)H-Index: 26
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Predicting individual chronological age based on neuroimaging data is very promising and important for understanding the trajectory of normal brain development. In this work, we proposed a new model to predict brain age ranging from 12 to 30 years old, based on structural magnetic resonance imaging and a deep learning approach with reduced model complexity and computational cost. We found that this model can predict brain age accurately not only in the training set ( \mathrm{N}=1721, mean abs...
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#1Ang Li (CAS: Chinese Academy of Sciences)H-Index: 4
#2Andrew Zalesky (University of Melbourne)H-Index: 45
Last. Guangxiang Rao (CAS: Chinese Academy of Sciences)H-Index: 1
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Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia1–5. We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scan...
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