From default mode to action mode: biphasic network reconfiguration with meditation in schizophrenia

From default mode to action mode: biphasic network reconfiguration with meditation in schizophrenia | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search From default mode to action mode: biphasic network reconfiguration with meditation in schizophrenia View ORCID Profile Qing Wang , View ORCID Profile Lizhao Du , Jialing Sheng , View ORCID Profile Qiao Wang , Yuan Shi , View ORCID Profile Ting Xue , Zhong Sun , View ORCID Profile Yingying Tang , Donghong Cui doi: https://doi.org/10.1101/2025.11.20.25340575 Qing Wang a Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, 200030, China b NeuroImaging Core, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , 600 S. Wanping Rd., Shanghai, 200030, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qing Wang For correspondence: vincent.w.qing{at}gmail.com manyucc{at}126.com Lizhao Du a Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, 200030, China c Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University , Shanghai, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lizhao Du Jialing Sheng d The First Minzheng Mental Health Center , No. 9999 Zhongchun Rd., Minhang, Shanghai, 201105, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Qiao Wang e Department of Public Health, Brody School of Medicine, East Carolina University , 610 Moye Blvd, Mailstop 660, Greenville, NC 27834, U.S.A. Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qiao Wang Yuan Shi a Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, 200030, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ting Xue a Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, 200030, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ting Xue Zhong Sun d The First Minzheng Mental Health Center , No. 9999 Zhongchun Rd., Minhang, Shanghai, 201105, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yingying Tang a Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, 200030, China b NeuroImaging Core, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , 600 S. Wanping Rd., Shanghai, 200030, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yingying Tang Donghong Cui a Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, School of Medicine, Shanghai Jiao Tong University , Shanghai, 200030, China f Brain Science and Technology Research Center, Shanghai Jiao Tong University , Shanghai, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: vincent.w.qing{at}gmail.com manyucc{at}126.com Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Introduction Meditation is widely used to support mental well-being, and recent randomized trials suggest benefits for persistent psychotic symptoms in schizophrenia. However, the magnitude and timing of causal treatment effects, response heterogeneity, and underlying neurobiological mechanisms over clinically meaningful timescales remain unclear. Methods We analyzed data from an eight-month, parallel-group randomized clinical trial (ChiCTR1800014913) of 64 male inpatients with chronic schizophrenia, randomized to daily clinician-guided meditation plus rehabilitation or rehabilitation alone. Prespecified outcomes were PANSS percentage decrease rate and RBANS increase rate. Linear mixed-effects models estimated time-specific causal average treatment effects. Cross-lagged panel models examined temporal relations between symptom and cognitive benefits; latent-class mixed models characterized treatment-response heterogeneity. Resting-state fMRI at baseline, 3, and 8 months yielded functional components, their complexity indices, and functional-connectivity predictors of clinical benefit. Results Meditation produced progressive symptom improvement (average treatment effects on PANSS decrease rate: 11.8% after 3 months; 20.8% after 8 months) and an early cognitive gain (7.6% after 3 months) that plateaued. Early cognitive improvement predicted, but did not mediate, later symptom relief. Response trajectories were heterogeneous; marital status and lower antipsychotic burden characterized high responders. Neuroimaging revealed a biphasic pattern: higher baseline default-mode–cerebellar complexity predicted short-term benefit, whereas greater 3-month action-mode-sensorimotor–executive complexity predicted longer-term gains; functional-connectivity models converged on these findings. Conclusions Clinician-guided meditation, added to rehabilitation, yields robust causal treatment effects on symptoms in schizophrenia. A biphasic shift from default-mode–cerebellar involvement to action-mode engagement provides phase-specific, information-based indicators to guide personalized meditation in severe mental illness. Introduction Meditation, encompassing practices such as mindfulness, focused attention, open monitoring, and compassion, yields small to moderate improvements in stress, mood and wellbeing with good acceptability across diverse settings[ 1 , 2 ]. However, its role in severe psychiatric disorders, particularly treatment-resistant schizophrenia, is much less clear. The field is moving beyond the question of “does it work?” toward “for whom, when, and how does it work?”, with increasing emphasis on dose, specific components, and delivery format[ 3 ]. Across serious medical and psychiatric conditions, meditation and mindfulness-based interventions have shown condition-dependent benefits. Randomized trials report advantages for mood and quality of life in Parkinson’s disease[ 4 ], reductions in pain-related disability in chronic pain[ 5 ] and migraine[ 6 ], prevention of depressive relapse[ 7 ], and symptom reduction in post-traumatic stress disorder[ 8 ]. In schizophrenia, meta-analyses suggest small-to-moderate improvements in total and negative symptoms, functioning, and quality of life, especially with structured, clinician-guided programmes[ 9 , 10 ]. Our group recently reported an eight-month randomized trial of intensive clinician-guided meditation for male inpatients with chronic schizophrenia, showing meaningful improvements in refractory hallucinations, delusions, and quality of life compared with rehabilitation alone[ 11 ]. Yet that initial report focused only on primary clinical outcomes, without examining causal treatment effects, symptom–cognition interplay, treatment heterogeneity, and the potential underlying brain mechanisms. Neuroimaging work in meditation indicates that practice reshapes large-scale brain networks[ 3 , 12 ]. Training tends to reduce activity and dominance of self-referential systems such as the default-mode network (DMN)[ 13 , 14 ], while strengthening executive and frontoparietal control networks at rest[ 15 , 16 ]. Beyond static functional connectivity, meditation appears to increase the temporal complexity of BOLD signals, reflected in scale-free dynamics and entropy-based measures, which index neural flexibility and can distinguish clinical populations, including schizophrenia[ 17 , 18 ]. Recent experimental work in pain demonstrates that meditation engages neural signatures distinct from placebo, reinforcing the idea of mechanism-specific pathways rather than non-specific expectancy effects[ 19 ]. Despite this progress, schizophrenia remains under-studied in mechanistic meditation research. Most trials are short term, focus on single endpoints, and rarely model how benefits unfold over time or differ between individuals. Longitudinal neuroimaging within randomized designs is particularly scarce, and prior work seldom addresses: (i) how symptom and cognitive gains interplay over clinically meaningful timescales; (ii) treatment heterogeneity which can be captured by repose trajectories; and (iii) how large-scale brain networks reorganize from early to later phases of meditation-based treatment. In this study, we embedded longitudinal resting-state fMRI within the previously reported eight-month, parallel-group randomized clinical trial of clinician-guided meditation versus rehabilitation alone in male inpatients with chronic schizophrenia (ChiCTR1800014913)[ 11 ]. Building on the original clinical report, we re-estimated treatment effects using causal models, quantified the time-specific improvements in symptoms and cognition, examined whether early cognitive change predicts later symptom relief, and identified heterogeneous treatment-response trajectories and their pragmatic predictors. To generate mechanistic insight, we derived large-scale functional components from resting-state fMRI and characterized their temporal complexity and functional connectivity across baseline, 3-month, and 8-month sessions. Guided by contemporary accounts of the brain’s default-mode network[ 13 ] and the action-mode network[ 20 ], we hypothesized a stage-specific reconfiguration in which default-mode–cerebellar systems are prominent in early change, followed by later engagement of sensorimotor–executive, action-oriented networks as clinical gains consolidate. By linking these network-level markers to individual clinical trajectories, we aim to offer an integrated, clinically interpretable account of how meditation contributes to recovery in schizophrenia and how it might be personalized within psychotherapeutic and rehabilitative care. Methods Study Design and Participants We conducted an eight-month, single-center, parallel-group randomized clinical trial in male Han Chinese inpatients with chronic schizophrenia and persistent positive symptoms at the First Minzheng Mental Health Center, China (trial registration: ChiCTR1800014913). This study adhered to the Declaration of Helsinki and was approved by the institutional review board (YJZXLL2017005). Full details of this trial can be found in our previous report of the primary outcome [ 11 ] and the supplementary CONSORT checklist. Eligible participants met DSM-IV criteria for schizophrenia, had illness duration ≥ 5 years, and showed persistent positive symptoms (hallucinations: PANSS P3 ≥ 5 and/or ≥ 2 items among P1, P5, P6, G1, G3, G9 ≥ 5) despite ≥ 2 adequate antipsychotic trials. Key exclusion criteria included: any other major Axis I disorder; Mini-Mental State Examination (MMSE) < 24; language barriers precluding consent or assessment; known neurological disease; major medical illness; and current substance abuse or dependence. All participants provided written informed consent. Antipsychotic regimens were maintained under physician supervision and converted to chlorpromazine equivalents (CPZ). Of 64 randomized participants, all completed 4⍰months and 63 completed 811months (one death in the control arm, adjudicated unrelated to study procedures). Randomization and Masking Participants were randomized 1:1 to daily clinician-guided meditation plus rehabilitation or rehabilitation alone, using individual-level allocation. The randomization sequence was generated by an independent statistician and implemented by study staff not involved in outcome assessment. The outcome assessors were masked to group assignment; participants and instructors were unmasked due to the nature of the interventions. Procedures Rehabilitation Group (control arm) The rehabilitation-only arm received a 90-min general rehabilitation programme (GRP) Monday– Saturday, comprising manual work, gardening, painting, and reading, plus a 90-min Sunday group intervention session (GIS) focused on daily-living skills and psychoeducation about schizophrenia, including self-acceptance. Meditation Plus Rehabilitation Group (intervention arm) The meditation arm received 30 min of clinician-guided meditation followed by 60 min of GRP, Monday–Saturday. The meditation protocol included: (1) Seven-point posture of Buddha Vairocana; (2) Abdominal breathing; (3) Sustained awareness of and concentration on the breath; (4) Non-reactive attention to thoughts and mental events. The meditation-arm Sunday GIS comprised instruction in meditation methods and cultural context, psychoeducation about schizophrenia and self-acceptance, a 30-min group practice, and discussion of experiences and challenges. GISs were weekly during the first 8 weeks, then monthly thereafter. Clinical assessments and MRI were obtained at baseline, 3 months, and 8 months. Adverse events were monitored throughout; no adverse events were attributed to study procedures. Outcomes and Assessments Baseline demographics (age, education, family income, age at onset, illness duration, medications) were collected via structured questionnaire. The primary clinical endpoint was the Positive and Negative Syndrome Scale (PANSS) total decrease rate (PDR) with established rescaling[ 21 ]: Secondary endpoints were rescaled PANSS subscale decrease rates: PPDR, PNDR, and PGDR (rescaling factors 7, 7, and 16, respectively), and cognition measured as the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) increase rate (RIR; computed analogously without rescaling). Mindfulness (Five Facet Mindfulness Questionnaire, FFMQ) and CPZ were recorded as contextual measures. Imaging endpoints (temporal complexity and functional connectivity) were exploratory, designed to generate mechanistic hypotheses and predictive markers aligned with clinical timescales. Imaging Acquisition and Preprocessing Full brain MRI data were acquired on a Siemens 3⍰T Verio with a 32⍰channel head coil. T1⍰weighted MPRAGE: 1⍰× ⍰1⍰× ⍰1⍰mm 3 isotropic voxel, TR⍰= ⍰2530⍰ms, TE⍰= ⍰3.65⍰ms. Resting⍰state fMRI (rs⍰fMRI): gradient⍰echo EPI, 3⍰× ⍰3⍰× ⍰3⍰mm 3 isotropic voxel, TR⍰= ⍰1500⍰ms, TE⍰= ⍰30⍰ms, flip angle 77°, duration 6⍰min (240 volumes); first 10 volumes discarded for nonstationary. Data were organized in BIDS[ 22 ], and preprocessed using fMRIPrep v22.1.1[ 23 ]. Post⍰fMRIPrep rs⍰fMRI were detrended, smoothed with 611mm FWHM Gaussian kernel, and nuisance⍰regressed (six motion parameters and the global signal)[ 24 ]. Imaging quality control yielded 186 usable rs⍰fMRI datasets across sessions. Functional Components and Temporal Complexities Large⍰scale activity patterns were estimated with group canonical ICA[ 25 ] (canICA; k⍰= ⍰20 components) on all quality⍰controlled rs⍰fMRI datasets. Component maps were binarized; mean time series were extracted per participant and session. For each component we computed sample entropy (SampEn) and the Hurst exponent (HE) as complementary temporal complexity measures [ 17 ]. Components whose complexity predicted PDR were screened for further analysis using elastic⍰net with 5⍰fold cross⍰validation and 10,000 permutations. To summarise component complexity with consistent directionality, we defined a composite complexity index (CCI) using weighted z-scores: where i indexes participant, t session, and c component; 1 { HE itc > 0.5} is an indicator equal to 1 if HE itc > 0.5 and 0 otherwise. Higher CCI denotes greater overall temporal complexity. To further test the phase-specific roles of these screened components, we utilized a 2-wave structural equation model (SEM) with PDR 3 and PDR 8 as outcomes. Functional connectivity (FC) was computed as the Pearson correlation between AAL2 atlas regions[ 26 , 27 ]. We evaluated three prediction tasks per arm: (1) 3-month PDR from baseline FC; (2) 8-month PDR from baseline FC; (3) 8-month PDR from FC changes in the first 3 months (Δ FC 3−0 ). Models used z-scored FC values, elastic net with 5-fold cross-validated hyperparameters, and 10,000 permutations to derive signed importance scores (magnitude=R 2 contributions; sign=direction of association with PDR)[ 28 ]. Edges with importance⍰>⍰0.05 are listed as candidate predictors; full edgewise results are provided in the supplementary materials. Statistical analysis All analyses were performed in the intention-to-treat population with available data at each timepoint. Average treatment effects (ATEs) for PDR and RIR were estimated using linear mixed-effects models (LMM) with fixed effects for group (meditation vs rehabilitation), time (baseline, 3 months, 8 months), and group × time interaction, and subject-level random effects to account for within-participant correlation. ATEs at 3 and 8 months are reported as model-based marginal differences with 95% confidence intervals. The moderator effects from baseline chlorpromazine equivalents (CPZ) dose, PANSS, RBANS, and FFMQ were also tested by adding the interaction term group × moderator into the original test. Temporal relations between symptom and cognitive changes were examined with a cross-lagged panel model (CLPM) including autoregressive paths, concurrent treatment effects, and cross-lagged paths between 3-month cognition ( RIR 3 ) and 8-month symptoms ( PDR 8 ) and vice versa. Bootstrap-based mediation analysis (10,000 resamples) tested the indirect effect of treatment on PDR 8 through RIR 3 . Treatment-response heterogeneity was assessed using latent-class linear mixed models (LCLMM)[ 29 ] applied to PDR trajectories (2–5 classes; Bayesian information criterion for model selection). Baseline predictors of high-responder membership were identified using elastic-net logistic regression with 5-fold cross-validation and 10,000 permutations. Phase-specific imaging contributions were tested using a two-wave structural equation model (SEM) with PDR at 3 and 8 months as outcomes. The model included: (i) autoregression for CCI; (ii) paths from baseline CCI to 3-month PDR, i.e., PDR i 3 ~ group i + CCI i 0( DMN − CIC ) + CCI i 0( AMN − SEC ) + age i + PANSS i 0 + RBANS i 0 + ε i 3 ; and (iii) paths from 3-month CCI to 8-month PDR, i.e., PDR i 8 ~ group i + CCI i 3( DMN − CIC ) + CCI i 3( AMN − SEC ) + age i + PANSS i 0 + RBANS i 3 + ε i 8 ; Full specifications, fit indices, and Wald contrasts are provided in the Supplementary Materials. Missing Data All models used all available observations under a missing-at-random assumption. LMMs naturally accommodate intermittently missing outcome data. SEMs used full-information maximum likelihood with robust (MLR) standard errors and Yuan–Bentler scaled χ 2 . Imaging sessions failing quality control were excluded from imaging analyses. Estimation and Software All analyses were conducted in a Python 3.9.12 and R 4.2.2 environment. Key packages included: statsmodels 0.14.4 for LMM, semopy 2.3.11 for CLPM, lcmm 2.1.0 for LCLMM, HeuDiConv 0.9.0 for BIDS conversion, fMRIPrep 22.1.1 for rs-fMRI preprocessing, nilearn 0.10.4 for imaging analysis, and lavaan 0.6-18 for 2-wave SEM. Patient and Public Involvement Patients or members of the public were not involved in the design, conduct, reporting, or dissemination plans of this study. Results Meditation produces progressive symptom improvement and plateauing cognitive gains As shown in Fig. 1 , linear mixed-effects models indicated a progressive clinical benefit of meditation on symptoms and a front-loaded cognitive gain. The average treatment effect (ATE) on PANSS decrease rate (PDR) was 11.8% (95% CI 9.3–14.3; p<0.001) after 3 months of meditation and increased to 20.8% (95% CI 16.0–25.6; p<0.001) after 8 months ( Fig. 1b ). Cognition (RBANS increase rate, RIR) improved 7.6% after 3 months (95% CI 2.4–12.8; p=0.004) but was not significant after 8 months (5.5%, 95% CI −2.1 to 13.1; p=0.153) ( Fig. 1c ). Baseline CPZ dose, PANSS, RBANS, and mindfulness did not show robust moderation effects (Tables S3–S12). Download figure Open in new tab Fig. 1. Trial design and time-specific clinical effects of clinician-guided meditation. (a) Overview of the parallel-group randomized clinical trial and assessment schedule (baseline, 3 months, 8 months). (b) Time-specific average treatment effects (ATEs) of meditation (meditation + rehabilitation vs rehabilitation alone) on PANSS total percentage decrease rate (PDR). (c) ATEs on RBANS percentage increase rate (RIR), showing early improvement with plateauing thereafter. (d) Cross-lagged/mediation model testing temporal coupling between cognition and symptoms: early cognitive improvement predicts later symptom improvement but does not mediate the treatment effect on 8-month PDR (standardized coefficients shown in the figure). Abbreviations: ATE, average treatment effect; PANSS, Positive and Negative Syndrome Scale; PDR, PANSS decrease rate; RBANS, Repeatable Battery for the Assessment of Neuropsychological Status; RIR, RBANS increase rate. Early cognitive gains forecast, but do not mediate later symptom relief Cross-lagged panel modeling supported temporal precedence of early cognition over later symptoms. Meditation exerted a direct effect on PDR at 3 months (β=0.118; p<0.001) and a smaller direct effect at 8 months (β=0.070; p=0.036), with additional indirect influence via earlier symptom change. Meditation produced 3-month RIR (β=0.076; p=0.011), but no direct treatment effect was observed for 8-month RIR. Critically, 3-month RIR predicted 8-month PDR (β=0.197; p=0.017), whereas 3-month PDR did not predict 8-month RIR (β=0.020; p=0.946). Bootstrap mediation provided no evidence that early cognition mediated the 8-month symptom benefit (indirect effect=0.020; 95% CI −0.0004 to 0.0479) (Table S14; Fig. S2). Together, these results suggest partly dissociable therapeutic pathways in which early cognitive improvement forecasts later symptom relief without statistically explaining it. Meditation elicits heterogeneous clinical trajectories Latent-class mixed modeling identified two response trajectories within the meditation arm: a high-responder subgroup (n=10) and a normal-responder subgroup (n=22) ( Fig. 2a ). Trajectories were similar through the first 3 months, after which high responders continued to accrue symptom gains while normal responders decelerated (both outperforming rehabilitation alone; n=31). Baseline profiles suggested higher responders were more often married, had lower antipsychotic burden, with elder age and age of onset etc. ( Fig. 2b ). In permutation-tested elastic-net classification, marital status (β=0.681; p=0.003) and lower CPZ dose (β=−0.566; p=0.017) remained robust correlates of high response ( Fig. 2c ), nominating pragmatic baseline markers for prospective testing. Download figure Open in new tab Fig. 2. Heterogeneity of clinical response to meditation and baseline predictors. (a) Latent-class trajectory model of PDR in the meditation arm identifying high- and normal-responder subgroups, shown alongside the rehabilitation-only group for context. (b) Standardized mean differences (SMDs) in baseline characteristics comparing high vs normal responders. (c) Permutation-based inference (10,000 permutations) for elastic-net logistic regression distinguishing high responders from normal responders; marital status and lower chlorpromazine-equivalent dose (CPZ) emerge as robust predictors (coefficients and p values shown in the figure). Abbreviations: CPZ, chlorpromazine equivalents; GRP, general rehabilitation programme; PDR, PANSS decrease rate; SMD, standardized mean difference. Biphasic network complexities align with symptom improvement Group canICA identified two components ( Fig. 3a–b ): an action-mode-sensorimotor–executive component (AMN–SEC) and a default-mode–cerebellar-integrative component (DMN–CIC). Permutation-validated screening linked symptom benefit to component temporal complexity, with convergent validation by SHAP analyses (Fig. S3). AMN–SEC comprised bilateral precentral gyri, superior, middle, and inferior frontal regions, Rolandic opercula, insulae, mid-cingulate, sensorimotor cortices, supramarginal gyri, precuneus, superior temporal gyri, and inferior motor cerebellar lobules (VIIb–VIII). It is involved in cognitive control, sensorimotor integration, and goal-directed behaviors[ 20 ]. DMN–CIC included posterior cingulate, calcarine and lingual regions, superior and middle occipital areas, superior parietal cortex, precunei, middle temporal gyrus, extensive posterolateral cerebellar hemispheres (Crus I/II, lobules VI–VIII), and vermal lobules IV–IX. DMN–CIC is involved in high-level integrative processes: self-referential ideation, sensory-cognitive binding, and internal modeling[ 13 ]. Download figure Open in new tab Fig. 3. Biphasic network–symptom associations across treatment and time. (a) Spatial map of the action-mode network–sensorimotor–executive component (AMN–SEC) derived from group ICA, with correlations between its composite complexity index (CCI) and clinical improvement (PDR and PANSS subscale decrease rates) by group and timepoint. (b) Spatial map of the default mode network–cerebellar-integrative component (DMN–CIC) with corresponding CCI– outcome correlations. (c) Two-wave structural equation model (SEM) testing phase-specific contributions: baseline DMN–CIC complexity predicts 3-month symptom improvement, whereas 3-month AMN–SEC complexity predicts additional 8-month gains (paths and standardized coefficients shown in the figure). Abbreviations: AMN–SEC, action-mode network–sensorimotor–executive component; CCI, composite complexity index; DMN–CIC, default mode network–cerebellar integrative component; PDR, PANSS decrease rate; PPDR/PNDR/PGDR, PANSS positive/negative/general subscale decrease rates. Cross-sectional associations between component complexity and concurrent clinical benefit were small after 3 months but strengthened after 8 months, particularly for AMN–SEC. To test time-specific contributions, a two-wave SEM focused on PDR after 3 and 8 months showed good fit ( χ 2 (37) = 39.321, p = 0.366) and high explained variance (71.5% and 71.6% for 3-month and 8-month respectively). Baseline DMN–CIC complexity predicted greater 3-month improvement (β=0.017; 95% CI 0.005–0.029; p=0.004), whereas 3-month AMN–SEC complexity predicted 8-month improvement after accounting for carry-over effect of earlier symptom improvement (β=0.032; 95% CI 0.007–0.057; p=0.013). Within-phase contrasts supported an early DMN–CIC contribution and a later AMN–SEC contribution (Wald tests: p=0.009 and p=0.047, respectively). The above patterns were not evident in the rehabilitation alone arm, supporting a biphasic model in which early benefit aligns with default-mode–cerebellar characteristics, while later gains align with action-mode-sensorimotor–executive recalibration. Functional connectivity predicts short- and long-term response under meditation Elastic-net models with permutation importance identified functional connectivity predictors of symptom improvement across three tasks ( Fig. 4 ). In the meditation arm, baseline connectivity contributed to short-term prediction, with stronger baseline DMN–CIC couplings associated with less improvement (e.g., Cerebellum_4–5_R–Vermis_1–2 etc .). For long-term response, prediction was strongest for action-mode-sensorimotor–executive couplings and early connectivity changes, with Precentral_L–SMA_L emerging as the dominant marker (baseline importance 0.154; early-change importance 0.371). In the rehabilitation-only arm, predictive utility was minimal (largest importance 0.023). Together with the complexity and SEM results, these FC findings converge on a biphasic account linking early default-mode–cerebellar features to short-term benefit and later precentral-SMA-centered action-mode engagement to sustained improvement. Download figure Open in new tab Fig. 4. Functional connectivity features predicting short- and long-term symptom improvement under meditation. Elastic-net models (permutation-validated) predicting PDR in the meditation arm using AAL2 atlas functional connectivity (FC). (a) Prediction of 3-month PDR from baseline FC; highlighted edges (importance threshold as indicated) primarily involve DMN–CIC–related couplings. (b) Prediction of 8-month PDR from baseline FC; highlighted edges emphasize AMN–SEC–related couplings. (c) Prediction of 8-month PDR from early FC change (ΔFC, baseline to 3 months); strongest predictive features include precentral–SMA coupling. Only edges meeting the prespecified importance thresholds are summarized and visualized (thresholds stated in the figure). Abbreviations: ΔFC, change in functional connectivity; FC, functional connectivity; PDR, PANSS decrease rate; SMA, supplementary motor area. Discussion In this randomized clinical trial, adding daily clinician-guided meditation to standard rehabilitation produced progressive symptom improvement across eight months, alongside an early, modest cognitive gain that plateaued. By estimating time-specific average treatment effects, we extend the prior clinical report of this trial by clarifying the magnitude and timing of benefit, and by linking clinical change to individual differences in response and phase-specific neuroimaging signatures. A key behavioral insight is that symptoms and cognition improved on partly separable tracks. Early cognitive gains predicted later symptom improvement but did not mediate it, suggesting that cognition may function as an early marker of capacity to benefit (e.g., learning, engagement, adherence, or strategy acquisition) rather than as the primary causal pathway by which meditation reduces symptoms. Clinically, this implies that measuring early cognitive change could help identify patients likely to accrue longer-term symptomatic benefit, while also cautioning against assuming that strengthening cognition alone will automatically drive subsequent symptom relief. We also observed meaningful treatment-response heterogeneity within the meditation arm. Although average effects were robust, a subgroup continued to improve after 3 months while others showed attenuated gains. Baseline correlates, including marital status and lower antipsychotic burden, are pragmatic and clinically interpretable, consistent with roles for social support and medication-related functional constraints in sustaining practice and recovery. Because subgroup sizes were modest, these predictors should be treated as candidate stratification features for prospective testing rather than definitive selection rules. The longitudinal neuroimaging results provide a coherent, time-resolved account of brain–behavior relationships during treatment. We found evidence for a biphasic pattern in which baseline default-mode–cerebellar temporal complexity related to earlier symptom improvement, whereas mid-treatment action-mode-sensorimotor–executive complexity, and especially precentral–SMA connectivity, tracked longer-term gains. Importantly, these imaging measures should be interpreted as predictive and associative markers of clinical trajectory rather than as direct proof that meditation induces a specific network change that causes improvement. Nevertheless, the convergence between complexity-based findings (lagged SEM results) and functional connectivity prediction models supports a staged account of recovery in which early benefit aligns with default-mode– cerebellar dynamics while later benefit aligns with increased engagement of systems supporting goal-directed action and executive control. These findings have practical implications for phase-aware personalization. Baseline default-mode– cerebellar features could help anticipate early response, while early shifts in action-mode-sensorimotor–executive coupling may indicate whether patients are likely to maintain improvement over longer periods. In principle, this could motivate adaptive strategies (e.g., reinforcing movement-linked or goal-directed practice elements for those showing early action-mode engagement, or revising treatment plan when early neural change is absent), ideally within preregistered adaptive trials that explicitly test such decision rules. The recurrent involvement of the posterior cerebellum across complexity and connectivity markers is notable. Cerebello–thalamo–cortical loops are increasingly implicated in predictive/feedforward control and in calibrating cortical dynamics during skill learning and switching[ 30 ]. In this context, baseline default-mode–cerebellar features may index capacity for early clinical change, whereas subsequent action-mode-sensorimotor–executive engagement may reflect consolidation of externally oriented control during sustained practice. A modest association between AMN–SEC features and symptom change in the rehabilitation arm is plausible given its emphasis on manual, goal-directed activities that recruit sensorimotor–executive systems. What appears distinctive in the meditation arm is the temporal ordering: early default-mode–cerebellar features forecasting short-term improvement, followed by mid-treatment action-mode-sensorimotor–executive features forecasting longer-term gains. This sequence was not evident with rehabilitation alone, supporting the phase-specific model rather than a nonspecific activity effect. Several limitations temper interpretation. The sample was single-center, Asian male-only, limiting generalizability across sex, culture, and clinical settings. Some analyses, particularly the imaging screens and prediction models, were exploratory and pipeline-sensitive[ 31 ], and require out-of-sample validation with harmonized preprocessing and prespecified analytic choices. Finally, while longitudinal models support temporal precedence, they do not establish mechanistic causality[ 32 ]; future work combining preregistered phase-specific hypotheses with causal perturbations (e.g., targeted behavioral manipulation or neuromodulation) would be needed to test whether modulating these circuits changes clinical outcome. Conclusion Daily clinician-guided meditation, delivered in a ward-compatible format as an adjunct to rehabilitation, was associated with robust, time-increasing symptom improvement and early cognitive gains in chronic schizophrenia. Clinical trajectories were heterogeneous, and longitudinal neuroimaging identified phase-specific predictive markers consistent with a biphasic shift from default-mode–cerebellar involvement to action-mode-sensorimotor–executive engagement. These results motivate personalized, stage-sensitive meditation delivery and call for preregistered multisite studies to validate the proposed markers and treatment-adaptation strategies. Data Availability All data produced in the present study are available upon reasonable request to the authors Statement of Ethics The trial protocol was approved by the Ethics Committee of Shanghai First Minzheng Mental Health Center (reference No. YJZXLL2017005). The study was registered at the WHO Primary Registry – Chinese Clinical Trials Registry: ChiCTR1800014913. All participants provided written informed consent at enrolment. Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding Sources This research was supported by the National Key R&D Program of China (No. 2017YFC0909200); the Shanghai Mental Health Center Key Project (No. 23zd01), Shanghai Jiao Tong University Joint Art and Science Funding (No. 14JCRZ05); Shanghai Jiao Tong University Medical and Engineering Joint Grant (No. YG2016ZD06); Shanghai Key Laboratory of Psychotic Disorders Open Grant (No. 17-K04), and the Integrated Innovation Research Team of Shanghai Mental Health Center. The funder had no role in the design, data collection, data analysis, and reporting of this study. All authors were independent from the funders. Author Contributions Dr. Qing Wang had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conceptualization and design: Qing Wang (lead), Lizhao Du (supporting), and Donghong Cui (supporting). Acquisition, analysis, or interpretation of data: Qing Wang (lead), Lizhao Du (supporting), Jialing Sheng (supporting), Qiao Wang (supporting), Yuan Shi (supporting), Ting Xue (supporting), Zhong Sun (supporting), Yingying Tang (supporting), and Donghong Cui (supporting). Drafting of the manuscript: Qing Wang (lead), Lizhao Du (supporting), Qiao Wang (supporting), and Donghong Cui (supporting). Critical revision of the manuscript for important intellectual content: Qing Wang (lead), Lizhao Du (supporting), Qiao Wang (supporting), and Donghong Cui (supporting). Statistical analysis: Qing Wang (lead), and Qiao Wang (supporting). Obtained funding: Donghong Cui (lead), Qing Wang (supporting), Jialing Sheng (supporting), and Zhong Sun (supporting). Administrative, technical, or material support: Qing Wang (lead), Jialing Sheng (supporting), Zhong Sun (supporting), Yingying Tang (supporting), and Donghong Cui (lead). Study supervision: Qing Wang (lead), Yingying Tang (supporting), and Donghong Cui (lead). Data Availability Statement The raw data of this study are not publicly available since they contain information that could compromise the privacy of study participants. The data for statistical analysis and figures in this paper are shared together with the codes via GitHub: https://github.com/Vincent-wq/repo_sz_meditation . Reasonable requests for patient level data should be made to the corresponding author and will be considered by the trial management group. Acknowledgement The authors would like to thank all the participants, doctors, nurses and students who were involved in the implementation of this clinical trial. The authors also would like to thank Dr. Pedro Antonio Valdés-Sosa and Dr. María Luisa Bringas-Vega for their feedbacks and insights in improving this manuscript. The authors thank Dr. Hu Chuan-Peng for his suggestions during the 2025 COSN hackathon in Shenzhen, China. 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Share From default mode to action mode: biphasic network reconfiguration with meditation in schizophrenia Qing Wang , Lizhao Du , Jialing Sheng , Qiao Wang , Yuan Shi , Ting Xue , Zhong Sun , Yingying Tang , Donghong Cui medRxiv 2025.11.20.25340575; doi: https://doi.org/10.1101/2025.11.20.25340575 Share This Article: Copy Citation Tools From default mode to action mode: biphasic network reconfiguration with meditation in schizophrenia Qing Wang , Lizhao Du , Jialing Sheng , Qiao Wang , Yuan Shi , Ting Xue , Zhong Sun , Yingying Tang , Donghong Cui medRxiv 2025.11.20.25340575; doi: https://doi.org/10.1101/2025.11.20.25340575 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1510) Epidemiology (15230) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6609) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3337) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (664) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9237) Radiology and Imaging (2202) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a01982754bd2bd1a',t:'MTc3OTc2NDIxNw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();