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Predicting Antipsychotic Efficacy for Schizophrenia Using Resting-State Functional Connectome

Poster No:

375

Submission Type:

Abstract Submission

Authors:

Anran Chen¹^,2^,3, song liu¹^,2^,3, Kang Liu¹^,2^,3, Xue Li¹^,2^,3, Xuzhen Liu¹^,2^,3, Yushu Zhou¹^,2^,3, Weiyi Han¹^,2^,3, Xiaoyue Duan¹^,2^,3, Luxian Lv¹^,2^,3, Meng Wang⁴, Wenqiang Li¹^,2^,3, Yongfeng Yang¹^,2^,3

Institutions:

¹Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical, Xinxiang, China, ²Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang, China, ³Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang, China, ⁴State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

First Author:

Anran Chen
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Co-Author(s):

song liu
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Kang Liu
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Xue Li
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Xuzhen Liu
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Yushu Zhou
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Weiyi Han
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Xiaoyue Duan
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Luxian Lv
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Meng Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Wenqiang Li
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Yongfeng Yang
Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical|Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan|Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder
Xinxiang, China|Xinxiang, China|Xinxiang, China

Introduction:

Neuroimaging studies have identified a large number of biomarkers associated with schizophrenia (SZ), but there is still a lack of biomarkers that can predict the efficacy of antipsychotic medication in SZ patients. The aim of this study was to identify neuroimaging biomarkers of antipsychotic drug response among features of the resting-state connectome.

Methods:

Resting-state functional magnetic resonance scans were acquired from a discovery cohort of 105 patients with SZ at baseline and after 8 weeks of antipsychotic medication treatment. Baseline clinical status and post-treatment outcome were assessed using the Positive and Negative Symptom Scale (PANSS), and clinical improvement was rated by the total score reduction. Based on acquired imaging data, a resting-state functional connectivity matrix was constructed for each patient, and a connectome-based predictive model was subsequently established and trained to predict individual PANSS total score reduction. The model associates each FC matrix connection with the patient's PANSS total score reduction via Pearson correlation, selecting significantly correlated (p<0.001) connections as features. Model performance was assessed by calculating Pearson correlation coefficients between predicted and true score reduction with leave-one-out cross-validation. Finally, the generalizability of the model was tested using an independent validation cohort of 52 SZ patients.

Results:

The model incorporating resting-state connectome characteristics predicted individual treatment outcomes in both the discovery cohort (prediction vs. truth r = 0.59, mean squared error (MSE) = 0.021) and validation cohort (r = 0.41, MSE = 0.036). The model identified four positive features and eight negative features, which were respectively correlated positively and negatively with PANSS total score reduction. Among these positive features, the specific connections within the parietal lobe played a crucial role in the model's predictive performance. As for the negative features, they included the frontoparietal control network and the cerebello-thalamo-cortical connections.

Conclusions:

This study discovered and validated a set of functional features based on resting-state connectome, where higher connectivity of positive features and lower connectivity of negative features at baseline were associated with a higher reduction rate of PANSS total score in patients and a better therapeutic effect. These functional features can be used to predict the PANSS total score reduction rate of SZ patients through a model. Clinical doctors can potentially infer the effectiveness of antipsychotic medication treatment for patients based on the predicted results.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Modeling and Analysis Methods:

fMRI Connectivity and Network Modeling ²

Physiology, Metabolism and Neurotransmission:

Neurophysiology of Imaging Signals

Keywords:

Schizophrenia

Other - resting-state functional magnetic resonance imaging,functional connectome, neural features, prediction of efficacy

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Yes

Please indicate which methods were used in your research:

Functional MRI

Other, Please specify - functional connectome

For human MRI, what field strength scanner do you use?

3.0T

Provide references using APA citation style.

not applicable

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