Functional brain network as a trajectory biomark for meditation of Schizophrenia
Poster No:
469
Submission Type:
Abstract Submission
Authors:
Qing Wang1, Ting Xue2, Donghong Cui2
Institutions:
1SMHC, Shanghai, N/A, 2SMHC, Shanghai, China
First Author:
Co-Author(s):
Introduction:
Schizophrenia (SZ), characterized by complex cognitive and behavioral disturbances, represents a significant challenge in mental health [1]. Emerging research has highlighted the therapeutic potential of meditation, a practice rooted in ancient Eastern traditions, particularly in the context of mental disorders. This practice, involving heightened awareness and a focus on the present, has shown promise in the modulation of neurological functions. Recent research reported the benefits of meditation in managing conditions like anxiety and depression. However, its efficacy in SZ and its neuroimaging markers remains under-explored.
We designed a randomized controlled trial (RCT) to study the treatment effect of meditation and explore its potential neuroimaging markers. The design of this study has been illustrated in Fig. 1, 64 SZ in patients were recruited and randomly assigned to meditation and normal rehabilitation group, clinical assessments and MRI images were acquired at baseline, 3rd month and 8th month.
The results showed that, 3-month meditation can slightly improve the positive and negative symptoms, while 8-month meditation can significantly improve the symptoms with a PANSS score decrease of 10.2 (Conditional Average Treatment Effect, p=0.003). We estimated the trajectories of PANSS score reduction using latent class mixed models and discovered the underlying subgroups. We further decomposed the resting state functional MRI into independent functional networks and localized 2 networks that can reflect the treatment effect trajectories for these patients, which suggests the potential imaging markers.
This study has demonstrated the effectiveness of meditation for SZ and reported its average treatment effect. Functional neuroimaging-based markers can reflect its treatment effect trajectories.
We designed a randomized controlled trial (RCT) to study the treatment effect of meditation and explore its potential neuroimaging markers. The design of this study has been illustrated in Fig. 1, 64 SZ in patients were recruited and randomly assigned to meditation and normal rehabilitation group, clinical assessments and MRI images were acquired at baseline, 3rd month and 8th month.
The results showed that, 3-month meditation can slightly improve the positive and negative symptoms, while 8-month meditation can significantly improve the symptoms with a PANSS score decrease of 10.2 (Conditional Average Treatment Effect, p=0.003). We estimated the trajectories of PANSS score reduction using latent class mixed models and discovered the underlying subgroups. We further decomposed the resting state functional MRI into independent functional networks and localized 2 networks that can reflect the treatment effect trajectories for these patients, which suggests the potential imaging markers.
This study has demonstrated the effectiveness of meditation for SZ and reported its average treatment effect. Functional neuroimaging-based markers can reflect its treatment effect trajectories.
·The experimental settings.
Methods:
The study design has been illustrated in Fig. 1 and detailed in reference [1]. To estimate the average treatment effect (ATE), we include age, age of onset, duration, BMI, smoking, education, family income, baseline RBANS, baseline PANSS and baseline FFMQ in the model to estimate the propensity score (PS) and use overlap weighting to estimate ATE [2]. We utilize a Latent Class Linear Mixed Models (LCLMM) to identify the subgroups of participants with similar longitudinal treatment response to derive heterogeneous, empirically based trajectory subgroups [3]. We derive the functional brain networks using canonical independent component analysis (canICA) and report the networks that are most related with the clinical outcome.
Results:
The results are summarized in Fig. 2: (a) The average treatment effect of 8-month meditation is 10.21 (p=0.003) for PANSS sum, 5.03 (p=0.0002) for PANSS positive and 2.93 (p=0.232) for PANSS negative. (b) The results are stable no mater whether remove the missing data or impute the missing data with multiple imputation. (c) 2 latent classes were estimated utilizing LCLMM, and it further identified the respondent and non-respondent groups in the meditation group. (d) The proportion of respondent and non-respondent groups at 3rd and 8th sessions. (e) The functional networks (2 and 8) that are most corelated with PANSS decrease, they were derived from rs-fMRI data using canICA. (f) The entropy of these networks can reflect meditation effect for SZ patients.
·Figure 2. Main results. (a-b) Clinical improvement. (c-d) Trajectory analysis; (e-f) ) Imaging markers.
Conclusions:
In conclusion, we showed the effectiveness of meditation for Schizophrenia (SZ) patients and identified the corresponding trajectory imaging markers. Trajectory analysis further identified the responder and non-responder subgroups in the meditation group, which can also be reflected by rs-fMRI derived functional brain networks. This study demonstrates that 8 month meditation is useful for improving the symptoms for SZ patients and neuroimaging can serve as markers for the longitudinal recovery path.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Keywords:
Data analysis
FUNCTIONAL MRI
MRI
Schizophrenia
Statistical Methods
Therapy
Other - meditation
1|2Indicates the priority used for review
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2. Zhou, T., Tong, G., Li, F., Thomas, L. E., & Li, F. (2022). PSweight: An R Package for Propensity Score Weighting Analysis. The R Journal, 14(1), 282–300.
3. Proust-Lima, C., Philipps, V., & Liquet, B. (2017). Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm. Journal of Statistical Software, 78(2).