Delineating pharmacological treatment effect on brain state in schizophrenia using normative models
Poster No:
538
Submission Type:
Abstract Submission
Authors:
Rixing Jing1, Xiao Lin2, Yanxi Huo1, Peng Li2
Institutions:
1Beijing Information Science and Technology University, Beijing, Beijing, 2Peking University Sixth Hospital, Beijing, Beijing
First Author:
Co-Author(s):
Introduction:
Schizophrenia is diagnosed exclusively based on symptoms, and the clinical and biological heterogeneity makes it difficult to fully and accurately assess pharmacological treatment effects on the brain state (Lin et al., 2021; Mehta et al., 2021). As a promising approach, normative modeling highlights the value of understanding individual variation relative to group means (Marquand, Rezek, Buitelaar, & Beckmann, 2016; Rutherford et al., 2022). In this study, we aimed to map deviations using normative modeling technique on dynamic functional connectome for individuals to investigate the pharmacological treatment effect in schizophrenia.
Methods:
In this study, resting state fMRI scans were acquired in 689 individuals from two datasets. Dataset 1 (the Cambridge Centre for Ageing and Neuroscience, Cam-CAN) included 652 healthy individuals. Dataset 2 included 37 patients with schizophrenia from the Peking University Sixth Hospital. All patients underwent two experimental sessions. The first session began before beginning antipsychotic treatment. The second session occurred on the last day of 6 weeks of antipsychotic treatment. The fMRI preprocessing protocol was common and similar to our previous study (Lin et al., 2021; Taylor et al., 2017).
For each individual, we computed dynamic functional connectivity (FC) matrices between all pairs of regions of the Human Brainnetome Atlas (Fan et al., 2016) using a sliding time window method (window length=60s) . Then, each FC matrix was decomposed into 17 networks based on Yeo-17 network template (Yeo et al., 2011). The average FC connection strength (aFCS, mean of FC strength from all window-wise FC matrices) and fluctuation characteristic of connection strength (fFCS, average change of the window-wise FC strength) for each Yeo-17 network was yielded from time-varying dynamic FC matrices. Finally, each individual's dynamic FC pattern was represented by 34 features.
Based on Cam-CAN dataset, we built normative models of 34 features using quantile regression to estimate the normative range, and mapped the deviations of the brain characteristics of each patient before treatment (bSCZ) or after treatment(aSCZ) (Jing et al., 2023). Then, we tested whether deviations from these models were related to psychiatric symptoms in patients with schizophrenia to investigated the pharmacological treatment effect on deviation distributions.
For each individual, we computed dynamic functional connectivity (FC) matrices between all pairs of regions of the Human Brainnetome Atlas (Fan et al., 2016) using a sliding time window method (window length=60s) . Then, each FC matrix was decomposed into 17 networks based on Yeo-17 network template (Yeo et al., 2011). The average FC connection strength (aFCS, mean of FC strength from all window-wise FC matrices) and fluctuation characteristic of connection strength (fFCS, average change of the window-wise FC strength) for each Yeo-17 network was yielded from time-varying dynamic FC matrices. Finally, each individual's dynamic FC pattern was represented by 34 features.
Based on Cam-CAN dataset, we built normative models of 34 features using quantile regression to estimate the normative range, and mapped the deviations of the brain characteristics of each patient before treatment (bSCZ) or after treatment(aSCZ) (Jing et al., 2023). Then, we tested whether deviations from these models were related to psychiatric symptoms in patients with schizophrenia to investigated the pharmacological treatment effect on deviation distributions.
Results:
Fig1A showed the overall deviation scores for 37 patients across 17 brain networks at the individual level before and after treatment. Fig1B shows the coefficients of variation (the ratio of the standard deviation to the absolute mean value at group level) at network-level for bSCZ and aSCZ, and the brain networks of the patients converged (the drug made them develop in one direction) in aFCS variability after treatment.
In Fig2, we analyzed the predictive effects of baseline clinical symptoms for the change of deviations in patients (p<0.05). For aFCS in Fig2A, the severity of depressive symptoms at baseline could negatively predict the deviation changes. These results suggested that, the brain networks of those patients with milder depressive symptoms were more likely to be placed back to the normal range in clinical therapy. As for fFCS in Fig2B, the severity of overall symptoms and the positive symptoms at baseline could negatively predict the dynamic fluctuations deviations of dorsal and ventral attention, salience, control, and default mode networks.
In Fig2, we analyzed the predictive effects of baseline clinical symptoms for the change of deviations in patients (p<0.05). For aFCS in Fig2A, the severity of depressive symptoms at baseline could negatively predict the deviation changes. These results suggested that, the brain networks of those patients with milder depressive symptoms were more likely to be placed back to the normal range in clinical therapy. As for fFCS in Fig2B, the severity of overall symptoms and the positive symptoms at baseline could negatively predict the dynamic fluctuations deviations of dorsal and ventral attention, salience, control, and default mode networks.
·Fig1
·Fig2
Conclusions:
This study adopted a normative model to assess brain changes affected by pharmacological treatment in patients with schizophrenia. We found that the baseline severity of symptoms, especially the depressive and the overall symptoms, could predict the deviation of the brain networks after treatment. Our study suggested that modeling brain states as deviations from the normative range may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Keywords:
FUNCTIONAL MRI
Pharmacotherapy
Schizophrenia
1|2Indicates the priority used for review
Provide references using author date format
Jing, R., Lin, X., Ding, Z., Chang, S., Shi, L., Liu, L., . . . Lu, L. (2023). Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder. Human Brain Mapping, 44(8), 3112-3122.
Lin, X., Deng, J., Dong, G., Li, S., Wu, P., Sun, H., . . . Li, P. (2021). Effects of Chronic Pharmacological Treatment on Functional Brain Network Connectivity in Patients with Schizophrenia. Psychiatry Research, 295, 113338.
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016). Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Biological Psychiatry, 80(7), 552-561.
Mehta, U. M., Ibrahim, F. A., Sharma, M. S., Venkatasubramanian, G., Thirthalli, J., Bharath, R. D., . . . Keshavan, M. S. (2021). Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis. Schizophrenia Research, 237, 153-165.
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