Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders

Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders | 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 Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders View ORCID Profile Deniz Yilmaz , View ORCID Profile Sergi Papiol , View ORCID Profile Daniel Keeser , View ORCID Profile James H Cole , Berend Malchow , View ORCID Profile Andrea Schmitt , View ORCID Profile Peter Falkai , View ORCID Profile Isabel Maurus , View ORCID Profile Lukas Roell doi: https://doi.org/10.1101/2025.01.08.24319554 Deniz Yilmaz 1 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 2 NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 3 Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences , Stephanstraße 1A, 04103 Leipzig, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Deniz Yilmaz For correspondence: deniz.yilmaz{at}maxplanckschools.de Sergi Papiol 4 Max Planck Institute of Psychiatry , Kraepelinstrasse 2-10, 80804 Munich, Germany 5 Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital , LMU Munich, Nussbaumstraße 7, 80336 Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sergi Papiol Daniel Keeser 1 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 2 NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Keeser James H Cole 6 Hawkes Institute, Department of Computer Science, University College London , Gower St, London WC1E 6BT, UK 7 Dementia Research Centre, Queen Square Institute of Neurology, University College London , Queen Square, London WC1N 3BG, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for James H Cole Berend Malchow 8 Department of Psychiatry and Psychotherapy, University Medical Center Göttingen , Von-Siebold-Straße 5, 37075 Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrea Schmitt 1 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 4 Max Planck Institute of Psychiatry , Kraepelinstrasse 2-10, 80804 Munich, Germany 9 Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo , Rua Dr. Ovídio Pires de Campos, 785, São Paulo, SP 05403-903, Brazil 10 German Center for Mental Health (DZPG) , partner site Munich/Augsburg, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Schmitt Peter Falkai 1 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 4 Max Planck Institute of Psychiatry , Kraepelinstrasse 2-10, 80804 Munich, Germany 10 German Center for Mental Health (DZPG) , partner site Munich/Augsburg, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Peter Falkai Isabel Maurus 1 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabel Maurus Lukas Roell 1 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 2 NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, Ludwig-Maximilians-University Munich , Nussbaumstraße 7, 80336 Munich, Germany 4 Max Planck Institute of Psychiatry , Kraepelinstrasse 2-10, 80804 Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lukas Roell Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Schizophrenia spectrum disorders (SSD) are associated with accelerated brain aging, reflected in an increased brain age gap. This gap serves as a biomarker, indicating poorer brain health, cognitive deficits, and greater severity in specific symptom domains. Physical exercise holds promise as an adjunct therapy to mitigate these deficits by potentially promoting brain recovery. However, the extent of overall improvements in brain health following exercise, along with their predictors and relationships to symptom clusters, are yet to be determined. This study examined the brain age gap metric as a quantitative indicator of brain recovery in response to physical exercise. To achieve this, we aggregated data from two randomized controlled trials, analyzing baseline ( n = 134) and 3- or 6-month post-exercise ( n = 46) data from individuals with SSD. Our findings revealed that patients with a higher baseline BMI demonstrated greater brain recovery, as evidenced by a reduced brain age gap post-exercise. Furthermore, changes in the brain age gap were associated with improvements in negative symptoms and cognition, suggesting that reductions in brain-predicted age may reflect symptom relief, particularly in domains beyond positive symptoms. These results underscore the importance of BMI in brain health, support using the brain age gap as a surrogate marker for tracking clinically relevant brain recovery, and highlight the need for stratified interventions and combined lifestyle modifications to enhance outcomes in SSD. Glossary Schizophrenia spectrum disorders (SSD): Mental health conditions characterized by psychosis, an alteration of the perception of reality. Cardinal symptoms include hallucinations (sensory perception not mirroring reality) and delusions (persistent beliefs that are not rooted in reality). Positive symptoms: A symptom cluster of SSD including complaints that are distinctively present in the patiens: hallucinations, delusions, and thought disorder (disorganized thinking and speech). Negative symptoms: A symptom cluster of SSD including complaints that are distinctively absent in the patiens: loss of interest, motivation, enjoyment, and social interactions, flattened affect. Cognitive symptoms: Another cluster of symptoms in SSD including deficits in attention, executive function, and memory. Biomarker: Objective, quantifiable indicators of biological states or processes used to predict, diagnose, and treat illnesses. Brain age gap: A biomarker of brain health and aging. Brain-predicted age is the age predicted by a machine learning algorithm based on brain imaging data. Subtracting chronological age from the brain-predicted age results in the brain age gap, where positive values indicate an accelerated aging of the brain. Neuroplasticity: The brain’s ability reorganize itself through new synaptic connections following learning, treatment, or injury. Randomized Controlled Trials (RCTs): A study design that randomly assigns participants to an experimental group or a control group to test the efficacy of an intervention. 1. Introduction Ample evidence suggests that schizophrenia spectrum disorders (SSD) are associated with both brain volume loss ( Adriano et al., 2012 ; Hulshoff Pol et al., 2002 ; van Erp et al., 2016 ; van Haren et al., 2008 ) and faster brain aging compared to healthy controls ( Kaufmann et al., 2019 ). Consistent with this, machine learning algorithms based on structural brain imaging predict that SSD patients have a higher brain-predicted age than their chronological age ( Ballester et al., 2022 ; Constantinides et al., 2023 ; Hajek et al., 2019 ; McWhinney et al., 2021 , 2022 ; Nenadić et al., 2017 ; Shahab et al., 2019; Wrigglesworth et al., 2021 ). This difference, or brain age gap , constitutes a data-driven biomarker of brain health, with larger gaps indicating accelerated brain aging. Such biomarkers are promising as they can serve as a singular but global neurobiological outcome metric in targeted clinical interventions and aid in medical monitoring. Additionally, they can function as predictive identifiers of disease progression and treatment response ( Biondo et al., 2022a ; Cole et al., 2018 ; Falkai et al., 2018 ; Fan et al., 2024 ; Liew et al., 2023 ; Lu et al., 2024 ; Tseng et al., 2024 ; Van Gestel et al., 2019 ) The brain age gap correlates negatively with mindfulness and positively with rumination in schizophrenia but not in controls ( Abram et al., 2023 ) and is associated with reduced working memory and processing speed (Wang et al., 2021). Moreover, faster brain aging in first episode psychosis (FEP) positively correlates with worsening negative symptoms and functioning ( McWhinney et al., 2021 ), highlighting its clinical relevance. The neurodevelopmental hypothesis of schizophrenia provides a theoretical framework for these empirical findings ( Fatemi and Folsom, 2009 ; Marenco and Weinberger, 2000 ; Murray and Lewis, 1987). It posits that excessive synaptic pruning during development leads to accelerated synapse loss, contributing to the brain age gap ( Demro et al., 2022 ; Hua et al., 2024 ; Ling et al., 2024 ). This overpruning disrupts the excitation/inhibition balance, driving the negative and cognitive symptoms, while mesolimbic projections downstream from this imbalance are hypothesized to underlie dopaminergic alterations and positive symptoms ( Howes and Shatalina, 2022 ). This hypothesis aligns with the strong genetic basis of SSD pathogenesis ( Ling et al., 2024 ; Schmitt et al., 2023 ; Trubetskoy et al., 2022 ) and genetic modulation of treatment response ( Papiol et al., 2024 , 2019 , 2017 ). Along with the positive symptoms, which are often alleviated by antipsychotic medication, cognitive deficits, and negative symptoms are the cardinal features of SSD ( Thiebes et al., 2017a ). Despite the prevalence of the latter two, they remain difficult to treat, thereby encumbering the patients and contributing significantly to the global disease burden ( Mulert, 2012 ; Thiebes et al., 2017b ; Vos et al., 2020 ). As a promising add-on treatment, physical exercise has consistently shown beneficial effects in alleviating these cognitive and negative symptoms in addition to improving overall functioning ( Dauwan et al., 2021 , 2016 , 2016 ; Falkai et al., 2017 ; Fernández- Abascal et al., 2021; Firth et al., 2016 , 2015 , 2015 ; Kim et al., 2023 ; Maurus et al., 2021 , 2019a , 2019b ; Sabe et al., 2020 ; Schmitt et al., 2018 ; Shimada et al., 2022 ; Vancampfort et al., 2015 ; Vogel et al., 2019 ; Xu et al., 2022 ). However, the neural mechanisms driving improvements in cognitive impairment, negative symptoms, and overall functioning after physical exercise remain to be clarified. Current preliminary evidence in this field suggests that physical exercise as an add-on treatment for SSD may increase hippocampal volume ( Girdler et al., 2019 ; Pajonk et al., 2010 ; but see Malchow et al., 2016 ), anterior left temporal gyrus volume ( Malchow et al., 2016 ), white matter integrity in tracts related to motor functioning ( Svatkova et al., 2015 ), and serum BDNF levels —a mediator of structural and functional neuroplasticity ( Colucci-D’Amato et al., 2020 ; Knaepen et al., 2010 ). The treatment also counteracted the typically observed disturbances in the default-mode network, the cortico-striato-pallido-thalamo-cortical, and the cerebello-thalamo-cortical circuits in a group of SSD patients, who underwent a physical exercise intervention for 6 months ( Roell et al., 2022 ). Furthermore, the right posterior cingulate gyrus volume increase was related to clinical improvements. These findings show that physical exercise has the potential to affect multiple clinically relevant brain regions and networks in SSD. Nonetheless, possible global brain health improvements after physical exercise and their link to the precise symptomatic changes over time are yet to be determined. Addressing this question is particularly relevant, as in healthy older adults, a reduced brain age gap relates strongly to physical activity, including daily habits like stair climbing ( Bittner et al., 2021 ; Steffener et al., 2016 ), and correlates with current and retrospective physical fitness ( Ouyang et al., 2022 ; Wing et al., 2024 ). Here, we investigated the role of brain age as a singular measure of global brain health in exercise interventions for SSD, focusing on its sensitivity to capture clinical outcomes. First, we hypothesize that at baseline a larger positive brain age gap is associated with lower aerobic fitness, more severe clinical symptoms, and a higher genetic burden for SSD. Second, we assume that the brain age gap decreases following physical exercise treatment in SSD. Third, we expect that longitudinal decreases in the brain age gap are linked to improvements in clinical and cognitive symptoms. Fourth, we presume that the brain age gap at baseline predicts the amount of change in clinical and cognitive symptoms over time and vice versa. Lastly, we propose that a lower genetic burden for SSD predicts greater improvements in the brain age gap following the intervention. If our hypotheses are supported, the brain age gap variable could serve as a proxy for neurobiological improvements post-treatment and potentially aid in predicting treatment response, ultimately enhancing the prediction, monitoring, and quantification of outcomes. 2. Methods 2.1. Participants 2.1.1. Test Set—Patient Cohort We pooled the data of individuals with SSD (F20* diagnosis according to ICD-10, World Health Organization, 2004 ; confirmed by the Mini-International Neuropsychiatric Interview, Version 6.0.0, Sheehan et al., 1998) from two randomized controlled clinical trials with a similar design (Exercise2 and ESPRIT studies; for details see Malchow et al., 2016 and Maurus et al., 2021, registered at www.clinicaltrials.gov under NCT01776112 and NCT03466112 , respectively). Inclusion criteria required participants to be on a stable regimen of antipsychotic medication. Six subjects from the ESPRIT study were excluded due to the low quality of their T1-weighted scans. At baseline, T1-weighted MRI scans of n = 134 (47 females, M age = 36.44, SD age = 12.49, ranged from 18-61, n = 40 from Exercise2 and n = 94 from ESPRIT) participants were available. Post- exercise data obtained from the Exercise2 study were collected following 3 months of regular exercise, whereas the ESPRIT sample undertook 6 months of regular exercise. Both studies were in accord with the Declaration of Helsinki and were approved by the local ethics committees. All participants gave written consent. See Tables 1 , 2 , and 3 for sample characteristics of the pooled data, supplementary materials for the exclusion criteria, and https://osf.io/tr3nx/ for further materials of ESPRIT and the current paper. View this table: View inline View popup Download powerpoint Table 1 Descriptive Statistics for the Data Used in Baseline Analysis View this table: View inline View popup Download powerpoint Table 2 Descriptive Statistics and Bayes Factors for the Data Used in the Longitudinal Analysis View this table: View inline View popup Download powerpoint Table 3 Frequencies of the Categorical Data 2.1.2. Training Set—Healthy Cohort To calculate the brain-predicted ages of the patient cohort, we used the open-access validated brainageR model ( Biondo et al., 2022b ; Clausen et al., 2022 ; Dörfel et al., 2023 ; Hobday et al., 2022 ), trained on T1-weighted MRI scans of n = 3377 healthy individuals from seven publicly available datasets ( M age = 40.6 years, SD age = 21.4, ranged 18-92) and tested on a held-out set of n = 857 ( M age = 40.1 years, SD age = 21.8, ranged 18-90). See https://github.com/james-cole/brainageR for further information about the healthy cohort characteristics. 2.2. Materials & Measures 2.2.1. BrainAGE Prediction Methods The brainageR software (v2.1) segments and normalizes the raw T1-weighted scans with SPM12 ( Penny et al., 2011 ) and quality controls using the FSL slicesdir function. This ensures identical pre-processing of the healthy controls’ training data and the patients’ test dataset. It uses a Gaussian Processes Regression implemented in R to predict the brain age of individuals based on their combined vectorized and masked (0.3 probability threshold from the average image template derived from the brainageR model training set) grey matter, white matter, and cerebrospinal fluid images. We converted the dockerized version of brainageR ( Prados, 2024 ) into a singularity container (available at https://osf.io/9jmrt/ ) and executed it with the data collected from the patients, yielding the brain-predicted ages of each patient. The brain age gap was then calculated by subtracting chronological age from the brain-predicted age per participant (see Figure 1 ). A greater brain age gap, therefore, indicates that the algorithm predicted the person to be older than their chronological age based on their neuroimaging data, suggesting that they have previously experienced accelerated brain aging. BrainageR demonstrated very high test-retest reliability, with an intraclass correlation coefficient (ICC) of 0.98 (95% CI [0.98–0.99]) and an adjusted mean absolute difference (Adj MAD) of 1.27 years within a year ( Dörfel et al., 2023 ), making it an optimal choice for our longitudinal design. In the held-out test sample, Pearson’s correlation coefficient of r = 0.973 was found between chronological age and brain-predicted age (mean absolute error [MAE] = 3.933 years, range = 18-90 years, n = 857). In external validation, the model maintained high performance ( r = 0.947, MAE = 4.90 years, 18-90 years, n = 611). Further details of this sample and model have been explained elsewhere ( https://github.com/james-cole/brainageR ) ( Cole, 2019 ; Biondo et al., 2022; Clausen et al., 2022 ; Hobday et al., 2022 ). Download figure Open in new tab Figure 1 Study Protocols and Data Processing Workflow Note. The workflow schematic includes retrieving the Exercise2 and ESPRIT datasets, comprising baseline (pre-exercise) and 3- or 6-month post-exercise data collection time points. The participants were either in the Aerobic Exercise Training group or Flexibility Strength and Balance group, depicted with a bicycle and dumbbell, respectively. Other than PRS, which was calculated only from the baseline blood samples, and fitness, for which only baseline scores were utilized in this paper, all measures were assessed both pre- and post-exercise. Change values for these variables were calculated by subtracting the post- from the pre-exercise value. The brain age gap was calculated with the identical procedure using pre- and post-exercise T1-weighted MRI scans. This procedure involves processing raw MRI data using the brainageR software, which generates brain-predicted ages based solely on the MRI data. The brain age gap is then calculated by subtracting each participant’s chronological age from their brain-predicted age. BAG: brain age gap; Δ: change; PRS: schizophrenia polygenic risk score. 2.2.2. Fitness Measures The exercise intensity at which blood lactate concentration reaches 2 mmol/l denotes the aerobic threshold ( Faude et al., 2009 ). Based on this, both studies assessed aerobic fitness using the ergometer-based lactate threshold test, identifying the individual aerobic threshold at a lactate concentration of 2 mmol/l (as elaborated by Maurus et al., 2021 in their exercise protocol). The wattage reached at this threshold was standardized by dividing it by body weight, yielding the aerobic fitness score per participant, representing the capacity to perform at an aerobic exercise intensity. Increasing scores indicate better fitness. 2.2.3. Clinical Measures The positive, negative, and total symptom severity was assessed with the Positive and Negative Syndrome Scale (PANSS; Kay et al., 1987 ), yielding the variables PANSS-positive, PANSS-negative, PANSS-general, and PANSS-total, the latter two indicating general non-SSD- specific psychopathology and the summarized symptom severity from the three subscales, respectively. Increasing PANSS scores denote more severe symptoms. The general functioning level was assessed using the Global Assessment of Functioning scale (GAF; Endicott et al., 1976 ). We calculated the chlorpromazine-equivalent medication dose (CPZ, for short) for each participant from their current antipsychotic medication ( Leucht et al., 2016 ). 2.2.4. Cognitive Measures Trail Making Tests A and B (TMT mean denoting their standardized average; Reitan, 1985 ) assessed global cognition encompassing visual search, cognitive flexibility, processing speed, scanning, and executive functions; the backward Digit Span Test (DST; Tewes, 1994 ) measured verbal working memory performance; and the short-term and long-term components of the Verbal Learning and Memory Test (VLMT; Helmstaedter et al., 2009 ) evaluated verbal declarative memory . The composite cognitive score denotes the normalized average of all the aforementioned constructs. Higher scores indicate greater performance for all variables (see supplementary materials for the score calculations). 2.2.5. Polygenic Risk Scores Genotyping, quality control, and imputation were conducted as described elsewhere ( Boudriot et al., 2024 ). Polygenic risk scores (PRS) were calculated using the PRS-CS tool, based on the most recent schizophrenia genome-wide association study of the Psychiatric Genomics Consortium ( Trubetskoy et al., 2022 ). A higher PRS indicates an increased genetic burden for schizophrenia. 2.3. Procedure Both studies had the following procedures: screening, baseline data collection, randomization into a treatment arm, three (Exercise2) or six (ESPRIT) months of physical exercise intervention, and post-exercise treatment data collection. Exercise2 included one exercise intervention arm of Aerobic Endurance Training with add-on computer-assisted cognitive remediation (AET and CACR; AET on bicycle ergometers, the full procedure explained by Malchow et al., 2016 ). Whereas, ESPRIT had two exercise arms: AET and Flexibility-Strength- Balance Training (FSBT; for further details see Maurus et al., 2021 ). Exercise2 and ESPRIT studies were overall very similar except for the double duration and the additional active arm of the ESPRIT protocol, and the addition of a 6-week CACR in Exercise2. We used all the acquired baseline data from SSD patients for the baseline analysis. For the longitudinal analysis, we only used the data from patients, who completed an exercise intervention including the pre- and post- exercise MRI measurements. Figure 1 depicts the study protocols and data processing workflow. 2.4. Statistical Analysis We based our inferences on Jeffreys’ Bayes factor (BF 10 ), a continuous quantification of the data-supported evidence for the alternative hypothesis (H 1 : r ≠ 0) relative to the null hypothesis (H 0 : r = 0). A BF 10 of 1.5, for instance, would indicate that the given data is 1.5 times more likely to be observed under the alternative hypothesis. We employed the conventional framework to infer different strengths of evidence (1<BF 10 <3: anecdotal evidence; 3<BF 10 <10: moderate evidence; 10<BF 10 30: very strong evidence for the alternative hypothesis) as well as examining the probability of direction (PD) and the region of practical equivalence (ROPE; for a more detailed explanation, see Makowski et al., 2019 ). For all coefficients in the regression models, we applied a conservative Gaussian prior ( M = 0, SD = 1), reducing the likelihood of extreme values. A medium-narrow prior was used for the correlation analysis, prioritizing smaller effects and rendering large effects less probable. Thereby, we aimed to allow for modest effects and avoid overfitting. We computed the brain age gap scores from brainageR as outlined above. Since the model does not automatically correct the predictions for the statistical dependency on chronological age, we included age as a covariate in all analyses. After calculating the brain age gaps, all analyses were conducted on R v4.4.1(2022). We opted for Pearson correlation for all correlation analyses except when assumptions were violated, in which case we ran Spearman correlations. For the categorical variables of sex (female vs. male), study (Exercise2 vs. ESPRIT), session (baseline vs. post-exercise), and group (AET vs. FSBT), we created dummy variables, with the first (leftmost) level in each comparison designated as the reference. We imputed missing values using k-nearest neighbors (kNN, k = 5) imputation if at least 80% of a given variable was available (see supplementary materials). Our analyses included outliers to account for the full spectrum of natural variability in behavior and physiology. To test our first hypothesis regarding the association between baseline brain age gap on the one hand and aerobic fitness levels and clinical parameters on the other hand, we ran Bayesian partial correlations adjusted for the baseline variables of age, sex, CPZ, education years, BMI, and study. To examine our second hypothesis that session impacts the brain age gap longitudinally in the context of physical exercise, we used a Bayesian linear mixed-effects model with the session as a within-subjects factor, covariables as fixed effects, and random intercepts and slopes for subjects: We calculated the change scores for PANSS, cognition, and brain age gap by subtracting baseline scores from those obtained after the exercise intervention (see Figure 1 ). Thus, a negative change score in PANSS or positive change score in cognition indicates symptom improvement, while a negative change in the brain age gap signifies a younger predicted brain age post-exercise interpreted as brain recovery. To test our third hypothesis regarding the relationship between changes in the brain age gap and improvements in clinical and cognitive parameters, we used Bayesian partial correlations, adjusting for age, sex, CPZ, education years, baseline BMI, number of training sessions, exercise group, and study. Additionally, our fourth hypothesis examined the potential predictive identifiers of treatment response. To this end, we ran Bayesian partial correlations between the baseline brain age gap and subsequent clinical changes, and vice versa— testing whether baseline clinical scores predict changes in the brain age gap. Similarly, we explored whether demographic variables and covariates (age, education years, baseline BMI, CPZ, and number of training sessions) were related to brain age gap change with Bayesian partial correlations. We performed an analogous analysis to evaluate our last hypothesis on the association between schizophrenia polygenic risk scores and changes in the brain age gap. 3. Results Descriptive statistics for the data used in the baseline and longitudinal analyses are reported in Tables 1 , 2 , and 3. 3.1. Association between Baseline Brain Age Gap, Aerobic Fitness, Clinical Measures, and PRS Baseline brain age gap was not associated with fitness, symptom severity, cognitive impairment, or PRS scores. 3.2. Longitudinal Changes Following Physical Exercise Intervention To examine the longitudinal changes in the brain age gap in the context of physical exercise, we ran a Bayesian linear mixed-effects model, which showed no evidence of an effect of the session, indicating no overall change in the brain age gap over time post-exercise (β = 0.13, 95% CI [-0.44, 0.71], BF 10 = 0.31; see Figure 2 ). However, a secondary finding from this model provided very strong evidence of the effect of BMI on the brain age gap (β = 0.26, 95% CI [0.12, 0.39], BF 10 = 134.81), demonstrating that a lower BMI was associated with a smaller brain age gap. Download figure Open in new tab Figure 2 Session Effects on Brain Age Gap Note . Data aggregated from the Exercise2 and ESPRIT datasets. Boxplots represent the brain age gap at baseline and post-exercise sessions. Individual participants are depicted as dots, with lines connecting them to illustrate change. Participants showing brain recovery (a decreased brain age gap) are connected to their post-exercise session in green, while those with an increased brain age gap are shown in red. A partial Spearman rank correlation revealed moderate evidence of a positive relationship between brain age gap change and PANSS negative change ( r = 0.27, 95% CI [0.02, 0.50], BF 10 = 3.22, PD = 0.98, ROPE = 0.10), indicating that post-exercise brain recovery is linked to negative symptom improvement. PANSS positive, general, and total change were not related to brain age gap change (see Figure 3 ). Download figure Open in new tab Figure 3 Partial Correlation Plots: Post-Exercise Brain Age Gap Change vs. PANSS Change Note. The graphs display the partial correlation between brain age gap change (post-pre) and PANSS total change, as well as its subscales: positive, negative, and general change. Change is operationalized as Post-Pre for all variables. Therefore, a higher positive brain age gap change indicates greater brain aging and a higher positive PANSS change reflects increased symptom severity. Conversely, negative changes indicate a reduction in both brain aging and symptom severity. All variables are adjusted for covariates, and thus, the values shown represent residuals. We found anecdotal evidence of a negative relationship between brain age gap change and a change in composite cognitive score (ρ = -0.26, 95% CI [-0.50, -0.01], BF 10 = 2.60, PD = 0.97, ROPE = 0.11) and VLMT long-term memory ( r = -0.25, 95% CI [-0.49, -0.01], BF 10 = 2.36, PD = 0.97, ROPE = 0.12). Hence, brain recovery post-exercise was related to improvements in general cognition and long-term memory. There was no evidence that brain age gap change is associated with a change in TMT mean, DST working memory, or VLMT short-term memory (see Figure 4 ). Download figure Open in new tab Figure 4 Partial Correlation Plots: Post-Exercise Brain Age Gap Change vs. Cognitive Change Note. The graphs display the partial correlation between brain age gap change (post-pre) and composite cognition change, as well as change in its subscales: TMT mean, DST working memory (DST WM), VLMT short-term memory (VLMT STM), and VLMT long-term memory (VLMT LTM). Change is operationalized as Post-Pre for all variables. Therefore, a higher positive brain age gap change indicates greater brain aging but a higher positive cognitive change reflects improved cognition. Conversely, negative changes indicate a reduction in brain aging but a worsening of cognition. All variables are adjusted for covariates, and thus, the values shown represent residuals. We inspected the predictors of treatment response and found that the baseline brain age gap was not associated with subsequent changes in the PANSS total, PANSS positive, PANSS negative, or PANSS general. However, examination of cognitive domain revealed anecdotal evidence suggesting that a lower brain age gap at baseline predicts greater subsequent improvement in composite cognitive score (ρ = -0.22, 95% CI [-0.47, 0.02], BF 10 = 1.71, PD = 0.95, ROPE = 0.17), TMT mean (ρ = -0.23, 95% CI [-0.47, 0.02], BF 10 = 1.80, PD = 0.96, ROPE = 0.15), and VLMT long-term memory ( r = -0.21, 95% CI [-0.44, 0.06], BF 10 = 1.39, PD = 0.94, ROPE = 0.20). No evidence was found for DST working memory change or VLMT short-term memory change (see Figure 5 ). Download figure Open in new tab Figure 5 Partial Correlations: Brain Age Gap at Baseline vs. Clinical Change (Adjusted Values) Note. The graphs display the partial correlations between the baseline brain age gap and change in all clinical and cognitive variables. Change is operationalized as post-pre for all variables. Therefore, higher PANSS change values reflect increased symptom severity after the intervention, whereas, for cognitive variables, higher change values indicate improved cognition. All variables are adjusted for covariates, and thus, the values shown represent residuals. STM: short-term memory; LTM: long-term memory. The relationship in the opposite direction, concerning the predictive value of baseline clinical characteristics on subsequent brain age change was observed anecdotally for baseline PANSS total (ρ = 0.17, 95% CI [-0.08, 0.42], BF 10 = 1.01, PD = 0.91, ROPE = 0.26), PANSS positive (ρ = 0.20, 95% CI [-0.07, 0.43], BF 10 = 1.27, PD = 0.94, ROPE = 0.22), and PANSS general (ρ = 0.23, 95% CI [-0.03, 0.47], BF 10 = 1.91, PD = 0.95, ROPE = 0.16), but not PANSS negative (ρ = -0.10, 95% CI [-0.35, 0.15], BF 10 = 0.58, PD = 0.78, ROPE = 0.43). These findings suggest that individuals with initially lower total, positive, and general symptom severity were more likely to exhibit brain recovery. We did not find evidence of a relationship between brain age gap reduction and the baseline cognitive impairments measured by the composite score, TMT mean, DST working memory, VLMT short-term memory, or VLMT long-term memory (see Figure 6 ). Download figure Open in new tab Figure 6 Partial Correlations: Clinical Values at Baseline vs. Brain Age Gap Change (Adjusted Values) Note. The graphs display the partial correlations between change in brain age gap (post-pre, larger values indicating accelerated brain aging) and baseline clinical (larger values indicating more severe symptoms) and cognitive variables (larger values reflecting better cognition). All variables are adjusted for covariates, and thus, the values shown represent residuals. STM: short-term memory; LTM: long-term memory. Our results showed no influence of the schizophrenia polygenic risk score on brain age change. 3.3. Exploratory Findings Exploring how the demographic variables relate to brain age gap change and brain age gap at baseline, we found strong evidence that patients with a higher BMI at baseline showed an increased brain recovery, denoted by a greater reduction in brain age gap (ρ = -0.36, 95% CI [- 0.57, -0.12], BF 10 = 21.18, PD = 0.85, ROPE = 0.36). The baseline brain age gap, on the other hand, showed very strong evidence of a positive correlation with CPZ (ρ = 0.39, 95% CI [0.16, 0.61], BF 10 = 34.21, PD = 1.00, ROPE = 0.01), whereby the brains of those patients who were receiving a higher medication dose were predicted to be older at baseline. For further exploratory results and the results on the Global Assessment of Functioning (GAF; Endicott et al., 1976 ), see supplementary materials. 4. Discussion This study aimed to validate the brain age gap as a neurobiological outcome measure, a metric for post-exercise treatment response, and a predictor of clinical and cognitive improvements following physical exercise interventions in individuals with SSD. We found no evidence for our first hypothesis, as the baseline brain age gap showed no association with aerobic fitness, clinical and cognitive parameters, or PRS. The inability to replicate previously reported relationships between fitness and the brain age gap likely stems from methodological heterogeneity in the literature. While some studies used BMI, waist circumference, heart rate, blood pressure, or cardiac stress test ( Dunås et al., 2021 ; Wing et al., 2022 ), we used a lactate threshold test, which has its own limitations. For instance, blood lactate is an indirect, albeit more ethically permissible, measure of lactate formation in muscle tissue, which may not linearly map onto muscle levels (Armstrong and Welsman, 2007). Furthermore, its quantitative value, definition, and underlying mechanisms remain debated in the literature, despite being commonly regarded as a reliable indicator of cardiovascular fitness ( Antonutto and Prampero, 1995 ; Boutcher, 1990 ; Faude et al., 2009 ). Nonetheless, we found a positive correlation between BMI and brain age gap at baseline. This echoes the recent findings by Wing and colleagues (2024) showing that body fat percentage and visceral adipose tissue, but not fitness, predicted brain age gap change following a 6-month exercise intervention. Obesity is also a risk factor for increased brain aging in people with SSD ( McWhinney et al., 2021 , 2022 ) and in the healthy population ( Beyer et al., 2019 ; Ronan et al., 2016 ) due to its disseminated impact on the brain ( Chen et al., 2018 ; Kullmann et al., 2015 ), possibly mediated by adipocytokine- or fibrinogen-driven inflammation ( Cazettes et al., 2011 ; Parimisetty et al., 2016 ; Wisse, 2004 ), cardiovascular strain ( Cox et al., 2019 ; Goldstein et al., 2020 ; Jagust et al., 2005 ; Van Gaal et al., 2006 ), or lifestyle-related behaviors such as poor dietary habits, physical inactivity, or substance use ( Kuo et al., 2013 ; Manu et al., 2015 ). In light of this, it is not surprising that patients with higher baseline BMI showed greater brain age improvement in our sample, likely due to their greater potential for exercise-induced enhancements. This observation parallels findings that structural and functional brain recovery follows interventions targeting obesity ( Li et al., 2023 ). Future interventions in SSD can leverage these results to optimally recruit patients with comorbid obesity, who are most likely to benefit in terms of brain health. Promising approaches to simultaneously address obesity include integrated lifestyle modifications—focusing on exercise, diet ( Agarwal et al., 2020 ; Aucoin et al., 2020 ), sleep ( Ferrarelli, 2020 ; Gica and Selvi, 2021 ; Waite et al., 2020 ), and microbiome health ( Dohnalová et al., 2022 ; Koblinsky et al., 2023 ), along with pharmacological options like semaglutide ( Agarwal and Hahn, 2024 ). By prioritizing these strategies, treatment outcomes may be significantly enhanced. The brain age gap at baseline did not relate to any clinical or cognitive parameters in our sample. Although a cross-sectional increase in brain age gap has been consistently reported in the literature, the exact link to symptom severity is equivocal ( Constantinides et al., 2023 ; Demro et al., 2022 ; Haas et al., 2022 ; Hua et al., 2024 ; Kaufmann et al., 2019 ; Koutsouleris et al., 2014 ; Nenadić et al., 2017 ; Schnack et al., 2016 ; Shahab et al., 2019b ; Tønnesen et al., 2020 ). Consistent with this, Haas et al. (2022) found no correlations between the brain age gap and symptom severity in early SSD. In contrast, McWhinney and colleagues (2021) reported a link between baseline brain age gap and negative symptom severity as well as functioning in FEP. One explanation could be the heterogeneity in the clinical presentation of SSD, such that this pattern is observed in subgroups but not in the overall SSD population. Indeed, Hua and colleagues (2024) found that experiential, but not expressive, negative symptom severity is associated with an increased brain age gap. Another possibility is that increased brain age results from an early developmental trajectory of neurological impairments, termed early hit non-progressive, paralleling the neurodevelopmental hypothesis ( Fatemi and Folsom, 2009 ; Marenco and Weinberger, 2000 ; Murray and Lewis, 1987b ), with subsequent aging comparable to the healthy population. This model suggests that accelerated brain aging may be independent of current symptom severity, especially in post-acute samples like ours ( Demro et al., 2022 ; Koutsouleris et al., 2014 ; Shahab et al., 2019c ). Empirical support for this comes from an observational study, where FEP was characterized by an increased brain age gap at baseline but with analogous yearly brain aging rates compared to healthy controls ( McWhinney et al., 2021 ). Contrary evidence also exists ( Wang et al., 2021b ), once again highlighting the heterogeneity, as many—but not all—patients fit into this model, underscoring the need for further research ( Mjellem and Kringlen, 2001 ). In addition, a protective effect of antipsychotic agents against accelerated aging has been proposed, which can further complicate the relationship between symptom severity and brain age gap ( Higgins-Chen et al., 2020 ; Schnack et al., 2016 ; Teeuw et al., 2021 ). For instance, severe symptoms treated with higher-dose medication may lead to a stabilization of brain aging. Alternatively, a history of severe symptoms could explain both the current larger brain age gap and higher medication dose, which is in turn effective in symptom reduction. Consistent with this, we observed a strong positive link between CPZ and the baseline brain age gap. Such a process may have obscured the relationship between the baseline brain age gap and clinical variables. A critical step forward in understanding these complex multivariate relationships and addressing the heterogeneity in the schizophrenia spectrum would be using stratification strategies, for instance, based on the progressiveness of the neural impairments, medical history, and medication dose. Contrary to our hypothesis, we did not find an association between schizophrenia PRS and the baseline brain age gap. Only a few studies to date have examined this relationship. While Teeuw et al. (2021) reported a positive relationship in a mixed sample of 394 patients and controls, this finding did not survive Bonferroni corrections. Similarly, Constantinides et al. (2024) found null results in a sample of 189 participants from the general population. In contrast, our study focused exclusively on patients and incorporated a longitudinal exercise intervention, but the sample size (particularly in the longitudinal analysis testing our fifth hypothesis, linking PRS to brain age change, n = 21) limited our ability to detect a potential effect. Despite the much larger sample sizes, however, the studies mentioned above reported null results, prompting Constantinides and colleagues (2024) to suggest that structural MRI-derived brain age gap estimates may not be sensitive to schizophrenia PRS. Another plausible explanation, however, could be the low statistical power of these studies. Notably, with a much larger sample size of 18,088, Zhu et al. (2021) revealed a relationship between schizophrenia PRS and temporal and frontal, but not global, cortical volume. Accordingly, the direct effect of PRS on brain structure may be modest, requiring either very large sample sizes or region-focused local algorithms, as opposed to global ones like in this paper. In accord with this, a previous investigation of the Exercise2 sample found that PRS predicted post-exercise volume recovery in left hippocampal subfields of cornu ammonis4 and dentate gyrus (CA4/DG) but not in total hippocampus ( Papiol et al., 2017 ). In a similar vein, the polygenic risk associated with specific cell types, oligodendrocyte precursor and radial glia cells, predicted less recovery in CA4/DG post-exercise in the same sample ( Papiol et al., 2019 ). These findings suggest that the influence of genetic burden on brain recovery may be cell-type- or region-dependent. Interestingly, Teeuw and colleagues (2021) reported an inverse relationship between schizophrenia PRS and epigenetic aging measured by DNA methylation, contrasting with their findings for the PRS and brain age gap. This suggests that different biological aging processes may diverge in their relationship with genetic risk factors. Taken together, these highlight the relevance of considering different algorithms as well as neuroimaging and biological modalities when assessing accelerated aging. Combining these approaches (e.g., epigenetic and brain aging, local and global brain aging) in future research may offer deeper insights into potentially independent mechanisms, enhancing both predictive accuracy and informativeness ( Cole et al., 2019 , 2018 ; Jansen et al., 2021 ; Rokicki et al., 2021 ; Teeuw et al., 2021 ). We did not observe an omnibus decrease in the brain age gap after the exercise intervention. This contrasts with our hypothesis but parallels the results of a study on healthy older adults ( Wing et al., 2024 ). This finding underscores the heterogeneity in individual treatment-response profiles, indicating that not everybody benefits from the same treatment. Future designs can incorporate comprehensive multi-domain interventions—addressing nutrition, microbiome, sleep, immunology, psychoeducation, social interactions, and mindfulness—to achieve higher response rates and synergistic effects on neuroplasticity ( Abram et al., 2023 ; Chang et al., 2022 ; de Jong et al., 2019 ; Díaz-Zuluaga et al., 2017 ; Dohnalová et al., 2022 ; Firth et al., 2020 , 2016 , 2015 ; Lyman et al., 2014 ; Mrazek et al., 2016 ; Salzman et al., 2022 ; Sethi et al., 2024 ; Sivera et al., 2020 ; Solomon et al., 2021 ). Our findings related to our third hypothesis showed that improvements, specifically in negative symptoms, were related to longitudinal reductions in brain age. This specificity may be attributed to several factors, including our sample characteristics and the distinct neurobiological and behavioral processes underlying different symptom clusters. First, negative and positive symptoms are distinct clusters and are hypothesized to have different etiologies. A developmental frontal overpruning is argued to underlie negative and cognitive symptoms, while dopaminergic disruptions underpin positive symptoms ( Howes and Shatalina, 2022 ). Conversely, the neurobiology underlying general psychopathology, as measured by the PANSS general, appears more diffuse, with changes in cortisol levels leading to broad metabolic effects, contributing to symptoms such as anxiety, somatic concern, and tension ( Andrade et al., 2016 ; Iob and Steptoe, 2019 ). Moreover, on the behavioral level, negative symptoms specifically reflect dysfunctional avoidance, characterized by a lack of interest, social withdrawal, anhedonia (inability to experience pleasure), and avolition (severe lack of motivation). Improvement in this domain is particular as it represents a global lifestyle shift, manifesting in increased motivation and self-efficacy. While a sedentary lifestyle with minimal social engagement contributes to neural decline ( Cai et al., 2023 ; Raichlen et al., 2023 ), exercise interventions promote an active, healthy, and social lifestyle, which can support brain recovery ( Bradley et al., 2022 ; Chia et al., 2023 ; Liu et al., 2024 ). Alternatively, this effect could work in the opposite direction—or even bidirectionally—where neural recovery drives changes in behavior and lifestyle, reducing negative symptoms. Paralleling this argument, readiness or capability for lifestyle change may explain our findings concerning our fourth hypothesis: that lower baseline symptom severity predicts greater brain recovery, or conversely, that a lower brain age gap predicts improvement in composite and global cognition and long-term memory. Further evidence is required to make definitive claims, however, as the last two findings were only anecdotal. Our findings are nevertheless in accord with those of ( Fan et al., 2024 ), which show that greater baseline symptom severity predicts an increase in the brain age gap, and that a higher baseline brain age gap predicts less functional recovery in response to risperidone treatment. Second, we included post-acute patients in this study with at least two weeks of stable treatment on one or two antipsychotic agents, which are known to effectively reduce rates of major positive symptoms and general psychopathology ( Haddad and Correll, 2018 ; Leucht et al., 2017 ). Consequently, patients tended to exhibit less severe positive symptoms compared to negative symptoms. The limited potential for further improvement in positive symptoms, largely addressed by antipsychotic treatment, may be less related to underlying brain structure due to the more episode-specific, dynamic, and transient nature of positive symptoms, often persisting for shorter durations than negative symptoms. In line with this argument, treatment response to antipsychotic medication correlates with advancements in the brain’s functional organization ( Hadley et al., 2016 ; Li et al., 2020). In contrast, increasing severity of negative symptoms is associated with structural alterations, for instance, a longitudinal rise in cerebrospinal fluid volume and a volume reduction in frontal regions, left cerebellum, left posterior cingulate, and occipitoparietal cortices ( Ho et al., 2003 ; McKechanie et al., 2016 ). Moreover, improvements in negative symptoms correlate not only with brain recovery assessed through functional imaging ( Li et al., 2018 ), but also with the reversal of volume reductions in the left hippocampus, parahippocampus, and precuneus following brain stimulation ( Hasan et al., 2017 ). Although longitudinal changes in positive symptoms have also been linked to structural changes in the brain, these often occur around disease onset and in concentrated areas like the perisylvian region or lateral ventricles. Conversely, negative-symptom-related morphological alterations are more widespread, spanning thalamocortical, temporal, and subcortical regions as well as the ventricles, occurring throughout the critical and chronic periods ( Ho et al., 2003 ; Koutsouleris et al., 2008 ; Kurachi et al., 2018 ). However, caution is warranted in generalizing these results due to heterogeneity in study designs, sample characteristics and sizes, and the lack of targeted interventions ( Dietsche et al., 2017 ; Mathalon et al., 2001 ). Our study uniquely contributes to the literature by drawing parallels between longitudinal structural neuroplasticity in response to a non-pharmaceutical intervention and distinctive symptom clusters. A fruitful realm of future investigation is to incorporate brain age algorithms utilizing functional MRI and EEG data, alongside structural MRI, to better capture dynamic treatment responses and connect functional neuroplasticity to improvements in specific symptom clusters. As with negative symptom improvements, gains in composite cognition and long-term memory were also captured anecdotally in the brain age changes post-exercise. In line with this, cognitive decline related to aging and neurodegenerative disorders is linked to longitudinal brain atrophy ( Carmichael et al., 2010 ; Jiang et al., 2016 ; Kramer et al., 2007 ; Mungas et al., 2005 ). In these studies, follow-up measurements were often taken at least 1 year after baseline, while we assessed changes after 3 to 6 months, which may explain the lower strength of evidence we observed. Indeed, in a short-duration cognitive training program lasting three to four weeks, stroke patients’ responses to the treatment did not reflect changes in the brain age gap ( Richard et al., 2020 ). A 2-year cognitive enhancement therapy, on the other hand, reversed cognitive decline in early schizophrenia patients, where preservation of the left parahippocampal and fusiform gyrus and growth of the left amygdala predicted cognitive recovery ( Eack et al., 2010 ). Shorter interventions also demonstrate this effect, with more pronounced changes observed in localized structural or functional domains rather than global structural ones. For instance, in a 3-month exercise intervention for healthy older adults, local grey matter plasticity in the middle frontal sulcus was related to enhanced cognitive functioning ( Soshi et al., 2021 ). Similarly, a 4-month computerized cognitive training for schizophrenia patients led to increased functional efficiency in the middle frontal gyri, which correlated with working memory improvements ( Subramaniam et al., 2014 ). Taken together, these results highlight several critical factors: type of neuroimaging methods, diagnostic sample, duration and nature of the intervention, statistical power, and the specificity of the effects. Shorter interventions, such as ours, might primarily induce localized or functional changes, which precede global morphological changes ( Isaac and Januel, 2016 ; Osmanlıoğlu et al., 2020 ). As our brain age predictions were based on global brain structure, we may have observed only low strength of evidence for these subtler local shifts. Despite our null findings for specific cognitive domains other than long-term memory, the negative correlation trends are descriptively in line with the previous findings. Evidence for the other cognitive domains could emerge with a larger sample size, a longer or multidomain intervention, or functional imaging techniques. Additionally, locally trained brain age models targeting specific regions of interest, such as the medial frontal cortex, may better capture subtle changes associated with cognitive improvements, specifically with working memory. Long-term memory processes are dissociable from working memory ( Grover et al., 2022 ; Simons and Spiers, 2003 ) and regulated by the medial temporal cortices and the hippocampus, regions known for their high plasticity ( Cutsuridis and Yoshida, 2017 ). Notably, the hippocampus is one of the few areas in the adult brain where extensive neurogenesis is observed (Bruel- Jungerman et al., 2007). Furthermore, neuroplasticity in the hippocampal formation has been demonstrated in response to physical exercise interventions in individuals with SSD ( Girdler et al., 2019 ; Maurus et al., 2019a ; Pajonk et al., 2010 ; Roell et al., 2024 ; Woodward et al., 2018 ). The malleability of these regions and their pivotal role in long-term memory may explain our finding that, aside from composite cognition, long-term memory change was the only cognitive domain to correlate with brain age gap change. Further investigations are needed to establish a more robust link between improvements in specific cognitive domains and brain recovery, as our findings were anecdotal and existing research remains limited. A limitation of our study is the inability to make causal claims about the effects of exercise due to the observational design of the study. Additionally, the add-on CACR in the Exercise2 sample hinders us from making any exercise-specific statements. Instead, we focused on examining the mechanistic processes underlying improvement in schizophrenia, using non-pharmaceutical interventional datasets—a context in which clinical recovery has been well- documented ( Dauwan et al., 2016 ; Falkai et al., 2017 ; Firth et al., 2020 , 2015 ; Haeger et al., 2019 ; Maurus et al., 2021 , 2019a , 2019c ; Roell et al., 2022 ; Schmitt et al., 2018 ). Future directions involve investigating the specificity of exercise, the interrelationship between positive health behaviors, and the directionality of the effects. On a related note, progressive brain aging in SSD could theoretically confound our longitudinal results—given the absence of a no-exercise control group. However, as previously mentioned, current evidence suggests that brain age in SSD is advanced but non-progressive ( Demro et al., 2022 ; McWhinney et al., 2021 ). Nonetheless, some argue that progressive brain aging may occur, particularly in FEP ( Dietsche et al., 2017 ; Lieberman et al., 2001 ). This emphasizes the need for further investigation, especially since our sample primarily did not include this population, making it less likely that our results are affected by such a process. Our claims are also limited by our method of choice for determining the biological age. The growing interest in bodily ages above and beyond the morphology and function of the brain calls for a more holistic approach, including blood indicators within the measures ( Cole et al., 2019 ; Han et al., 2019 ; Teeuw et al., 2021 ). We acknowledge that age, sex, medication, education, BMI, and smoking can influence biological age indicators, but we were unable to statistically account for smoking due to missing data for most of our sample, possibly introducing some unexplained variability. To our knowledge, this study is the first to show that the brain age gap, as a singular neurological parameter, is sensitive to aspects of treatment response in SSD. Our results suggest that brain age prediction could serve as a valuable tool for quantifying neuroplasticity in response to treatment in severe mental illness. Furthermore, our findings stress the prominence of the modifiable lifestyle factors in brain recovery. BMI and clinical variables, but not PRS, were related to brain age variables. This suggests that substantial opportunities for rehabilitation exist in severe mental illness, with low-cost, non-pharmaceutical, and add-on interventions holding significant potential to improve outcomes and enhance long-term recovery. We identified BMI as a key variable influencing brain health, underscoring its potential role in future treatment strategies. Our findings also emphasize the importance of brain health in circumventing negative symptoms in post-acute SSD. These insights pave the way for developing stratification strategies based on biomarkers underlying comorbid obesity or severe negative symptoms, which are essential as personalized medicine gains traction. Such strategies can optimize patient allocation and enhance treatment outcomes, addressing the multifaceted and heterogeneous nature of SSD. Furthermore, we highlight promising future directions, including combined lifestyle interventions—such as diet, exercise, social interactions—and targeted pharmacological options like semaglutide. Ultimately, we provide evidence supporting the use of brain age prediction as a surrogate outcome metric for tracking clinical progress and brain recovery in clinical trials. Moving forward, we advocate for the integration of multimodal brain imaging and assessments of other biological ages to enhance our understanding and treatment of severe mental illness. Data Availability Explicit permission for data sharing was not included in the informed consent obtained during the data collection phase. As a result, and due to the sensitive nature of the clinical data, we are unable to share the data publicly. However, the data can be made available to individual researchers upon request. Researchers interested in accessing the data may contact us directly to discuss potential arrangements. Author contributions Conceptualization: IM, LR, DY; Data curation: DY, LR, SP, JHC, BM; Formal analysis: DY; Funding acquisition: PF, AS, DY; Methodology: LR, DY, JHC, DK; Project administration: PF, AS, IM, DK, LR, BM; Resources: LR, JHC; Software: DY, LR, JHC, Supervision: PF, AS, IM, LR; Visualization: DY; Writing – original draft: DY; Writing – review and editing: DY, LR, IM, DK, JHC, SP, AS, PF. Funding The work was supported by the German Federal Ministry of Education and Research (BMBF) through the research network on psychiatric diseases ESPRIT (Enhancing Schizophrenia Prevention and Recovery through Innovative Treatments; coordinator: Andreas Meyer- Lindenberg; grant number, 01EE1407E) awarded to PF and AS. Furthermore, the study was supported by the Else Kröner-Fresenius Foundation with the Research College “Translational Psychiatry” for PF, AS, and IM (Residency/PhD track of the International Max Planck Research School for Translational Psychiatry [IMPRS-TP]), and Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, for DY. The study was endorsed by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung [BMBF]) within the initial phase of the German Center for Mental Health (DZPG) (grant: 01EE2303A, 01EE2303F to PF, AS). Disclosures PF is a co-editor of the German (DGPPN) schizophrenia treatment guidelines and a co-author of the WFSBP schizophrenia treatment guidelines; he is on the advisory boards and receives speaker fees from Boehringer-Ingelheim, Janssen, Lundbeck, Otsuka, Servier, and Richter. JHC is a scientific advisor to and shareholder in Brain Key and Claritas HealthTech PTE. DY, AS, DK, BM, SP, IM, LR declare no conflicts of interest or financial disclosures relevant to this research. There was no role of the sponsors in relation to the study design, collection, analysis and interpretation of data, writing of the report, and decision to submit the article for publication. Use of AI Tools: During the review of this work the author(s) used Grammarly and ChatGPT 3.5 in order to perform grammar checks and enhance conciseness. These tools were used solely to refine existing text, and no content was created from scratch by AI. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. Data Sharing Explicit permission for data sharing was not included in the informed consent obtained during the data collection phase. As a result, and due to the sensitive nature of the clinical data, we are unable to share the data publicly. However, the data can be made available to individual researchers upon request. Researchers interested in accessing the data may contact us directly to discuss potential arrangements. Acknowledgements We express our appreciation to the Clinical Open Research Engine (CORE) at the University Hospital LMU (Munich, Germany) for providing the computational infrastructure to run the CPU- intensive MRI analysis pipelines. References 1. ↵ Abram , S.V. , Roach , B.J. , Hua , J.P.Y. , Han , L.K.M. , Mathalon , D.H. , Ford , J.M. , Fryer , S.L ., 2023 . Advanced brain age correlates with greater rumination and less mindfulness in schizophrenia . NeuroImage Clin . 37 , 103301 . doi: 10.1016/j.nicl.2022.103301 OpenUrl CrossRef PubMed 2. ↵ Adriano , F. , Caltagirone , C. , Spalletta , G ., 2012 . Hippocampal Volume Reduction in First- Episode and Chronic Schizophrenia: A Review and Meta-Analysis . The Neuroscientist 18 , 180 – 200 . doi: 10.1177/1073858410395147 OpenUrl CrossRef PubMed 3. ↵ Agarwal , S.M. , Caravaggio , F. , Costa-Dookhan , K.A. , Castellani , L. , Kowalchuk , C. , Asgariroozbehani , R. , Graff-Guerrero , A. , Hahn , M ., 2020 . Brain insulin action in schizophrenia: Something borrowed and something new . Neuropharmacology 163 , 107633 . doi: 10.1016/j.neuropharm.2019.05.010 OpenUrl CrossRef PubMed 4. ↵ Agarwal , S.M. , Hahn , M ., 2024 . Semaglutide in Psychiatry—Opportunities and Challenges . JAMA Psychiatry 81 , 955 – 956 . doi: 10.1001/jamapsychiatry.2024.2412 OpenUrl CrossRef PubMed 5. ↵ Andrade , E.H. , Rizzo , L.B. , Noto , C. , Ota , V.K. , Gadelha , A. , Daruy-Filho , L. , Tasso , B. de C. , Mansur , R.B. , Cordeiro , Q. , Belangero , S.I. , Bressan , R.A. , Grassi-Oliveira , R. , Brietzke , E. , 2016 . Hair cortisol in drug-naïve first-episode individuals with psychosis. Braz . J. Psychiatry 38 , 11 – 16 . doi: 10.1590/1516-4446-2014-1634 OpenUrl CrossRef 6. ↵ Antonutto , G. , Prampero , P.D. di , 1995 . The concept of lactate threshold. A short review . J. Sports Med. Phys. Fitness . 7. Armstrong , N. , Welsman, 2007 . Aerobic fitness: what are we measuring? Med. Sport Sci . 50 . doi: 10.1159/000101073 OpenUrl CrossRef PubMed 8. ↵ Aucoin , M. , LaChance , L. , Clouthier , S.N. , Cooley , K ., 2020 . Dietary modification in the treatment of schizophrenia spectrum disorders: A systematic review . World J. Psychiatry 10 , 187 . doi: 10.5498/wjp.v10.i8.187 OpenUrl CrossRef PubMed 9. ↵ Ballester , P.L. , Romano , M.T. , de Azevedo Cardoso , T. , Hassel , S. , Strother , S.C. , Kennedy , S.H. , Frey , B.N. , 2022 . Brain age in mood and psychotic disorders: a systematic review and meta-analysis . Acta Psychiatr. Scand . 145 , 42 – 55 . doi: 10.1111/acps.13371 OpenUrl CrossRef PubMed 10. ↵ Beyer , F. , Kharabian Masouleh , S. , Kratzsch , J. , Schroeter , M.L. , Röhr , S. , Riedel-Heller , S.G. , Villringer , A. , Witte , A.V ., 2019 . A Metabolic Obesity Profile Is Associated With Decreased Gray Matter Volume in Cognitively Healthy Older Adults . Front. Aging Neurosci . 11 . 11. ↵ Biondo , F. , Jewell , A. , Pritchard , M. , Aarsland , D. , Steves , C.J. , Mueller , C. , Cole , J.H ., 2022a . Brain-age is associated with progression to dementia in memory clinic patients . NeuroImage Clin . 36 , 103175 . doi: 10.1016/j.nicl.2022.103175 OpenUrl CrossRef PubMed 12. ↵ Biondo , F. , Jewell , A. , Pritchard , M. , Aarsland , D. , Steves , C.J. , Mueller , C. , Cole , J.H ., 2022b . Brain-age is associated with progression to dementia in memory clinic patients . NeuroImage Clin . 36 , 103175 . doi: 10.1016/j.nicl.2022.103175 OpenUrl CrossRef PubMed 13. ↵ Bittner , N. , Jockwitz , C. , Franke , K. , Gaser , C. , Moebus , S. , Bayen , U.J. , Amunts , K. , Caspers , S ., 2021 . When your brain looks older than expected: combined lifestyle risk and BrainAGE . Brain Struct. Funct . 226 , 621 – 645 . doi: 10.1007/s00429-020-02184-6 OpenUrl CrossRef 14. ↵ Boudriot , E. , Gabriel , V. , Popovic , D. , Pingen , P. , Yakimov , V. , Papiol , S. , Roell , L. , Hasanaj , G. , Xu , S. , Moussiopoulou , J. , Priglinger , S. , Kern , C. , Schulte , E.C. , Hasan , A. , Pogarell , O. , Falkai , P. , Schmitt , A. , Schworm , B. , CDP Working Group , Wagner , E. , Keeser , D. , Raabe , F.J ., 2024 . Signature of Altered Retinal Microstructures and Electrophysiology in Schizophrenia Spectrum Disorders Is Associated With Disease Severity and Polygenic Risk . Biol. Psychiatry 96 , 792 – 803 . doi: 10.1016/j.biopsych.2024.04.014 OpenUrl CrossRef 15. ↵ Boutcher , S.H. , 1990 . Aerobic Fitness: Measurement and Issues . doi: 10.1123/jsep.12.3.235 OpenUrl CrossRef 16. ↵ Bradley , T. , Campbell , E. , Dray , J. , Bartlem , K. , Wye , P. , Hanly , G. , Gibson , L. , Fehily , C. , Bailey , J. , Wynne , O. , Colyvas , K. , Bowman , J ., 2022 . Systematic review of lifestyle interventions to improve weight, physical activity and diet among people with a mental health condition . Syst. Rev . 11 , 198 . doi: 10.1186/s13643-022-02067-3 OpenUrl CrossRef PubMed 17. Bruel-Jungerman , E. , Davis , S. , Laroche , S ., 2007 . Brain plasticity mechanisms and memory: a party of four . Neurosci. Rev. J. Bringing Neurobiol. Neurol. Psychiatry 13 , 492 – 505 . doi: 10.1177/1073858407302725 OpenUrl CrossRef PubMed Web of Science 18. ↵ Cai , X.-Y. , Qian , G.-P. , Wang , F. , Zhang , M.-Y. , Da , Y.-J. , Liang , J.-H ., 2023 . Association between sedentary behavior and risk of cognitive decline or mild cognitive impairment among the elderly: a systematic review and meta-analysis . Front. Neurosci . 17 , 1221990 . doi: 10.3389/fnins.2023.1221990 OpenUrl CrossRef PubMed 19. ↵ Carmichael , O. , Schwarz , C. , Drucker , D. , Fletcher , E. , Harvey , D. , Beckett , L. , Jack , C.R. , Jr . , Weiner , M. , DeCarli , C ., Alzheimer’s Disease Neuroimaging Initiative , 2010 . Longitudinal Changes in White Matter Disease and Cognition in the First Year of the Alzheimer Disease Neuroimaging Initiative . Arch. Neurol . 67 , 1370 – 1378 . doi: 10.1001/archneurol.2010.284 OpenUrl CrossRef PubMed 20. ↵ Cazettes , F. , Cohen , J.I. , Yau , P.L. , Talbot , H. , Convit , A ., 2011 . Obesity-mediated inflammation may damage the brain circuit that regulates food intake . Brain Res . 1373 , 101 – 109 . doi: 10.1016/j.brainres.2010.12.008 OpenUrl CrossRef PubMed 21. ↵ Chang , Y.-K. , Erickson , K.I. , Aghjayan , S.L. , Chen , F.-T. , Li , R.-H. , Shih , J.-R. , Chang , S.-H. , Huang , C.-M. , Chu , C.-H ., 2022 . The multi-domain exercise intervention for memory and brain function in late middle-aged and older adults at risk for Alzheimer’s disease: A protocol for Western–Eastern Brain Fitness Integration Training trial . Front. Aging Neurosci . 14 . doi: 10.3389/fnagi.2022.929789 OpenUrl CrossRef 22. ↵ Chen , V.C.-H. , Liu , Y.-C. , Chao , S.-H. , McIntyre , R.S. , Cha , D.S. , Lee , Y. , Weng , J.-C ., 2018 . Brain structural networks and connectomes: the brain–obesity interface and its impact on mental health . Neuropsychiatr. Dis. Treat . 14 , 3199 – 3208 . doi: 10.2147/NDT.S180569 OpenUrl CrossRef PubMed 23. ↵ Chia , F. , Huang , W.-Y. , Huang , H. , Wu , C.-E ., 2023 . Promoting Healthy Behaviors in Older Adults to Optimize Health-Promoting Lifestyle: An Intervention Study . Int. J. Environ. Res. Public. Health 20 , 1628 . doi: 10.3390/ijerph20021628 OpenUrl CrossRef PubMed 24. ↵ Clausen , A.N. , Fercho , K.A. , Monsour , M. , Disner , S. , Salminen , L. , Haswell , C.C. , Rubright , E.C. , Watts , A.A. , Buckley , M.N. , Maron-Katz , A. , Sierk , A. , Manthey , A. , Suarez-Jimenez , B. , Olatunji , B.O. , Averill , C.L. , Hofmann , D. , Veltman , D.J. , Olson , E.A. , Li , G. , Forster , G.L. , Walter , H. , Fitzgerald , J. , Théberge , J. , Simons , J.S. , Bomyea , J.A. , Frijling , J.L. , Krystal , J.H. , Baker , J.T. , Phan , K.L. , Ressler , K. , Han , L.K.M. , Nawijn , L. , Lebois , L.A.M. , Schmaal , L. , Densmore , M. , Shenton , M.E. , van Zuiden , M. , Stein , M. , Fani , N. , Simons , R.M. , Neufeld , R.W.J. , Lanius , R. , van Rooij , S. , Koch , S.B.J. , Bonomo , S. , Jovanovic , T. , deRoon-Cassini , T ., Ely , T.D. , Magnotta , V.A. , He , X. , Abdallah , C.G. , Etkin , A. , Schmahl , C. , Larson , C. , Rosso , I.M. , Blackford , J.U. , Stevens , J.S. , Daniels , J.K. , Herzog , J. , Kaufman , M.L. , Olff , M. , Davidson , R.J. , Sponheim , S.R. , Mueller , S.C. , Straube , T. , Zhu , X. , Neria , Y. , Baugh , L.A. , Cole , J.H. , Thompson , P.M. , Morey , R.A. , 2022 . Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups . Brain Behav . 12 , e2413 . doi: 10.1002/brb3.2413 OpenUrl CrossRef PubMed 25. ↵ Cole , J ., 2019 . james-cole/brainageR: brainageR v2.0 . doi: 10.5281/ZENODO.3463212 OpenUrl CrossRef 26. ↵ Cole , J.H. , Marioni , R.E. , Harris , S.E. , Deary , I.J ., 2019 . Brain age and other bodily ‘ages’: implications for neuropsychiatry . Mol. Psychiatry 24 , 266 – 281 . doi: 10.1038/s41380-018-0098-1 OpenUrl CrossRef 27. ↵ Cole , J.H. , Ritchie , S.J. , Bastin , M.E. , Valdés Hernández , M.C. , Muñoz Maniega , S. , Royle , N. , Corley , J. , Pattie , A. , Harris , S.E. , Zhang , Q. , Wray , N.R. , Redmond , P. , Marioni , R.E. , Starr , J.M. , Cox , S.R. , Wardlaw , J.M. , Sharp , D.J. , Deary , I.J ., 2018 . Brain age predicts mortality . Mol. Psychiatry 23 , 1385 – 1392 . doi: 10.1038/mp.2017.62 OpenUrl CrossRef PubMed 28. ↵ Colucci-D’Amato , L. , Speranza , L. , Volpicelli , F ., 2020 . Neurotrophic Factor BDNF, Physiological Functions and Therapeutic Potential in Depression, Neurodegeneration and Brain Cancer . Int. J. Mol. Sci . 21 , 7777 . doi: 10.3390/ijms21207777 OpenUrl CrossRef PubMed 29. ↵ Constantinides , C. , Baltramonaityte , V. , Caramaschi , D. , Han , L.K.M. , Lancaster , T.M. , Zammit , S. , Freeman , T.P. , Walton , E ., 2024 . Assessing the association between global structural brain age and polygenic risk for schizophrenia in early adulthood: A recall-by-genotype study . Cortex 172 , 1 – 13 . doi: 10.1016/j.cortex.2023.11.015 OpenUrl CrossRef PubMed 30. ↵ Constantinides , C. , Han , L.K.M. , Alloza , C. , Antonucci , L.A. , Arango , C. , Ayesa-Arriola , R. , Banaj , N. , Bertolino , A. , Borgwardt , S. , Bruggemann , J. , Bustillo , J. , Bykhovski , O. , Calhoun , V. , Carr , V. , Catts , S. , Chung , Y.-C. , Crespo-Facorro , B. , Díaz-Caneja , C.M. , Donohoe , G. , Plessis , S.D. , Edmond , J. , Ehrlich , S. , Emsley , R. , Eyler , L.T. , Fuentes- Claramonte , P. , Georgiadis , F. , Green , M. , Guerrero-Pedraza , A. , Ha , M. , Hahn , T. , Henskens , F.A. , Holleran , L. , Homan , S. , Homan , P. , Jahanshad , N. , Janssen , J. , Ji , E. , Kaiser , S. , Kaleda , V. , Kim , M. , Kim , W.-S. , Kirschner , M. , Kochunov , P. , Kwak , Y.B. , Kwon , J.S. , Lebedeva , I. , Liu , J. , Mitchie , P. , Michielse , S. , Mothersill , D. , Mowry , B. , de la Foz , V.O.-G. , Pantelis , C. , Pergola , G. , Piras , F. , Pomarol-Clotet , E. , Preda , A. , Quidé , Y. , Rasser , P.E. , Rootes-Murdy , K. , Salvador , R. , Sangiuliano , M. , Sarró , S. , Schall , U. , Schmidt , A. , Scott , R.J. , Selvaggi , P. , Sim , K. , Skoch , A. , Spalletta , G. , Spaniel , F. , Thomopoulos , S.I. , Tomecek , D. , Tomyshev , A.S. , Tordesillas-Gutiérrez , D. , van Amelsvoort , T. , Vázquez-Bourgon , J. , Vecchio , D. , Voineskos , A. , Weickert , C.S. , Weickert , T. , Thompson , P.M. , Schmaal , L. , van Erp , T.G.M. , Turner , J. , Cole , J.H. , Dima , D. , Walton , E ., 2023 . Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium . Mol. Psychiatry 28 , 1201 – 1209 . doi: 10.1038/s41380-022-01897-w OpenUrl CrossRef PubMed 31. ↵ Cox , S.R. , Lyall , D.M. , Ritchie , S.J. , Bastin , M.E. , Harris , M.A. , Buchanan , C.R. , Fawns-Ritchie , C. , Barbu , M.C. , de Nooij , L. , Reus , L.M. , Alloza , C. , Shen , X. , Neilson , E. , Alderson , H.L. , Hunter , S. , Liewald , D.C. , Whalley , H.C. , McIntosh , A.M. , Lawrie , S.M. , Pell , J.P. , Tucker-Drob , E.M. , Wardlaw , J.M. , Gale , C.R. , Deary , I.J. , 2019 . Associations between vascular risk factors and brain MRI indices in UK Biobank . Eur. Heart J . 40 , 2290 – 2300 . doi: 10.1093/eurheartj/ehz100 OpenUrl CrossRef 32. ↵ Cutsuridis , V. , Yoshida , M. , 2017 . Editorial: Memory Processes in Medial Temporal Lobe: Experimental, Theoretical and Computational Approaches . Front. Syst. Neurosci . 11 . doi: 10.3389/fnsys.2017.00019 OpenUrl CrossRef 33. ↵ Dauwan , M. , Begemann , M.J.H. , Heringa , S.M. , Sommer , I.E ., 2016 . Exercise Improves Clinical Symptoms, Quality of Life, Global Functioning, and Depression in Schizophrenia: A Systematic Review and Meta-analysis . Schizophr. Bull . 42 , 588 – 599 . doi: 10.1093/schbul/sbv164 OpenUrl CrossRef PubMed 34. ↵ Dauwan , M. , Begemann , M.J.H. , Slot , M.I.E. , Lee , E.H.M. , Scheltens , P. , Sommer , I.E.C ., 2021 . Physical exercise improves quality of life, depressive symptoms, and cognition across chronic brain disorders: a transdiagnostic systematic review and meta-analysis of randomized controlled trials . J. Neurol . 268 , 1222 – 1246 . doi: 10.1007/s00415-019-09493-9 OpenUrl CrossRef PubMed 35. ↵ de Jong , M.H. , Wierdsma , A.I. , Van Gool , A.R. , Mulder , C.L. , 2019 . An intensive multimodal group programme for patients with psychotic disorders at risk of rehospitalization: a controlled intervention study . BMC Psychiatry 19 , 241 . doi: 10.1186/s12888-019-2229-x OpenUrl CrossRef PubMed 36. ↵ Demro , C. , Shen , C. , Hendrickson , T.J. , Arend , J.L. , Disner , S.G. , Sponheim , S.R ., 2022 . Advanced Brain-Age in Psychotic Psychopathology: Evidence for Transdiagnostic Neurodevelopmental Origins . Front. Aging Neurosci . 14 . doi: 10.3389/fnagi.2022.872867 OpenUrl CrossRef PubMed 37. ↵ Díaz-Zuluaga , A.M. , Vargas , C. , Duica , K. , Richard , S. , Palacio , J.D. , Agudelo Berruecos , Y. , Ospina , S. , López-Jaramillo , C. , Díaz-Zuluaga , A.M. , Vargas , C. , Duica , K. , Richard , S. , Palacio , J.D. , Agudelo Berruecos , Y. , Ospina , S. , López-Jaramillo , C ., 2017 . Impact of a multimodal intervention on the psychological profile of schizophrenic and bipolar I patients: A study of PRISMA programme . Rev. Colomb. Psiquiatr . 46 , 56 – 64 . doi: 10.1016/j.rcpeng.2017.05.008 OpenUrl CrossRef PubMed 38. ↵ Dietsche , B. , Kircher , T. , Falkenberg , I ., 2017 . Structural brain changes in schizophrenia at different stages of the illness: A selective review of longitudinal magnetic resonance imaging studies . Aust. N. Z. J. Psychiatry 51 , 500 – 508 . doi: 10.1177/0004867417699473 OpenUrl CrossRef PubMed 39. ↵ Dohnalová , L. , Lundgren , P. , Carty , J.R.E. , Goldstein , N. , Wenski , S.L. , Nanudorn , P. , Thiengmag , S. , Huang , K.-P. , Litichevskiy , L. , Descamps , H.C. , Chellappa , K. , Glassman , A. , Kessler , S. , Kim , J. , Cox , T.O. , Dmitrieva-Posocco , O. , Wong , A.C. , Allman , E.L. , Ghosh , S. , Sharma , N. , Sengupta , K. , Cornes , B. , Dean , N. , Churchill , G.A. , Khurana , T.S. , Sellmyer , M.A. , FitzGerald , G.A. , Patterson , A.D. , Baur , J.A. , Alhadeff , A.L. , Helfrich , E.J.N. , Levy , M. , Betley , J.N. , Thaiss , C.A ., 2022 . A microbiome-dependent gut-brain pathway regulates motivation for exercise . Nature 612 , 739 – 747 . doi: 10.1038/s41586-022-05525-z OpenUrl CrossRef PubMed 40. ↵ Dörfel , R.P. , Arenas-Gomez , J.M. , Fisher , P.M. , Ganz , M. , Knudsen , G.M. , Svensson , J.E. , Plavén-Sigray , P ., 2023 . Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test–retest reliability of publicly available software packages . Hum. Brain Mapp . 44 , 6139 . doi: 10.1002/hbm.26502 OpenUrl CrossRef 41. ↵ Dunås , T. , Wåhlin , A. , Nyberg , L. , Boraxbekk , C.-J ., 2021 . Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance . Cereb. Cortex 31 , 3393 – 3407 . doi: 10.1093/cercor/bhab019 OpenUrl CrossRef PubMed 42. ↵ Eack , S. , Ge , H. , Ry , C. , Km , P. , Dp , G. , Ss , H. , Ms , K ., 2010 . Neuroprotective effects of cognitive enhancement therapy against gray matter loss in early schizophrenia: results from a 2-year randomized controlled trial . Arch. Gen. Psychiatry 67 . doi: 10.1001/archgenpsychiatry.2010.63 OpenUrl CrossRef PubMed Web of Science 43. ↵ Endicott , J. , Spitzer , R.L. , Fleiss , J.L. , Cohen , J ., 1976 . The global assessment scale. A procedure for measuring overall severity of psychiatric disturbance . Arch. Gen. Psychiatry 33 , 766 – 771 . doi: 10.1001/archpsyc.1976.01770060086012 OpenUrl CrossRef PubMed Web of Science 44. ↵ Falkai , P. , Malchow , B. , Schmitt , A ., 2017 . Aerobic exercise and its effects on cognition in schizophrenia . Curr. Opin. Psychiatry 30 , 171 – 175 . doi: 10.1097/YCO.0000000000000326 OpenUrl CrossRef PubMed 45. ↵ Falkai , P. , Schmitt , A. , Andreasen , N ., 2018 . Forty years of structural brain imaging in mental disorders: is it clinically useful or not? Dialogues Clin. Neurosci . 20 , 179 – 186 . OpenUrl PubMed 46. ↵ Fan , L. , Zhang , Z. , Ma , X. , Liang , L. , Yuan , L. , Ouyang , L. , Wang , Y. , Li , Z. , Chen , X. , He , Y. , Palaniyappan , L ., 2024 . Brain Age Gap as a Predictor of Early Treatment Response and Functional Outcomes in First-Episode Schizophrenia: A Longitudinal Study: L’écart d’âge cérébral comme prédicteur de la réponse en début de traitement et des résultats fonctionnels dans un premier épisode de schizophrénie : une étude longitudinale . Can. J. Psychiatry 07067437241293981 . doi: 10.1177/07067437241293981 OpenUrl CrossRef 47. ↵ Fatemi , S.H. , Folsom , T.D ., 2009 . The Neurodevelopmental Hypothesis of Schizophrenia, Revisited . Schizophr. Bull . 35 , 528 – 548 . doi: 10.1093/schbul/sbn187 OpenUrl CrossRef PubMed Web of Science 48. ↵ Faude , O. , Kindermann , W. , Meyer , T ., 2009 . Lactate Threshold Concepts . Sports Med . 39 , 469 – 490 . doi: 10.2165/00007256-200939060-00003 OpenUrl CrossRef PubMed Web of Science 49. Fernández-Abascal , B. , Suárez-Pinilla , P. , Cobo-Corrales , C. , Crespo-Facorro , B. , Suárez- Pinilla , M ., 2021 . In- and outpatient lifestyle interventions on diet and exercise and their effect on physical and psychological health: a systematic review and meta-analysis of randomised controlled trials in patients with schizophrenia spectrum disorders and first episode of psychosis . Neurosci. Biobehav. Rev . 125 , 535 – 568 . doi: 10.1016/j.neubiorev.2021.01.005 OpenUrl CrossRef PubMed 50. ↵ Ferrarelli , F ., 2020 . Sleep disturbances in Schizophrenia and Psychosis . Schizophr. Res . 221 , 1 . doi: 10.1016/j.schres.2020.05.022 OpenUrl CrossRef PubMed 51. ↵ Firth , J. , Cotter , J. , Elliott , R. , French , P. , Yung , A.R ., 2015 . A systematic review and meta- analysis of exercise interventions in schizophrenia patients . Psychol. Med . 45 , 1343 – 1361 . doi: 10.1017/S0033291714003110 OpenUrl CrossRef PubMed 52. ↵ Firth , J. , Rosenbaum , S. , Stubbs , B. , Gorczynski , P. , Yung , A.R. , Vancampfort , D ., 2016 . Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis . Psychol. Med . 46 , 2869 – 2881 . doi: 10.1017/S0033291716001732 OpenUrl CrossRef PubMed 53. ↵ Firth , J. , Solmi , M. , Wootton , R.E. , Vancampfort , D. , Schuch , F.B. , Hoare , E. , Gilbody , S. , Torous , J. , Teasdale , S.B. , Jackson , S.E. , Smith , L. , Eaton , M. , Jacka , F.N. , Veronese , N. , Marx , W. , Ashdown-Franks , G. , Siskind , D. , Sarris , J. , Rosenbaum , S. , Carvalho , A.F. , Stubbs , B ., 2020 . A meta-review of “lifestyle psychiatry”: the role of exercise, smoking, diet and sleep in the prevention and treatment of mental disorders . World Psychiatry 19 , 360 – 380 . doi: 10.1002/wps.20773 OpenUrl CrossRef PubMed 54. ↵ Gica , Ş. , Selvi , Y. , 2021 . Sleep Interventions in the Treatment of Schizophrenia and Bipolar Disorder. Arch . Neuropsychiatry 58 , S53 . doi: 10.29399/npa.27467 OpenUrl CrossRef 55. ↵ Girdler , S.J. , Confino , J.E. , Woesner , M.E ., 2019 . Exercise as a Treatment for Schizophrenia: A Review . Psychopharmacol. Bull . 49 , 56 – 69 . OpenUrl PubMed 56. ↵ Goldstein , B.I. , Baune , B.T. , Bond , D.J. , Chen , P.-H. , Eyler , L. , Fagiolini , A. , Gomes , F. , Hajek , T. , Hatch , J. , McElroy , S.L. , McIntyre , R.S. , Prieto , M. , Sylvia , L.G. , Tsai , S.-Y. , Kcomt , A. , Fiedorowicz , J.G ., 2020 . Call to action regarding the vascular-bipolar link: A report from the Vascular Task Force of the International Society for Bipolar Disorders . Bipolar Disord . 22 , 440 . doi: 10.1111/bdi.12921 OpenUrl CrossRef 57. ↵ Grover , S. , Wen , W. , Viswanathan , V. , Gill , C.T. , Reinhart , R.M.G ., 2022 . Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation . Nat. Neurosci . 25 , 1237 – 1246 . doi: 10.1038/s41593-022-01132-3 OpenUrl CrossRef PubMed 58. ↵ Haas , S.S. , Ge , R. , Sanford , N. , Modabbernia , A. , Reichenberg , A. , Whalley , H.C. , Kahn , R.S. , Frangou , S ., 2022 . Accelerated Global and Local Brain Aging Differentiate Cognitively Impaired From Cognitively Spared Patients With Schizophrenia . Front. Psychiatry 13 . 59. ↵ Haddad , P.M. , Correll , C.U ., 2018 . The acute efficacy of antipsychotics in schizophrenia: a review of recent meta-analyses . Ther. Adv. Psychopharmacol . 8 , 303 – 318 . doi: 10.1177/2045125318781475 OpenUrl CrossRef PubMed 60. ↵ Hadley , J.A. , Kraguljac , N.V. , White , D.M. , Ver Hoef , L. , Tabora , J. , Lahti , A.C ., 2016 . Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory . Npj Schizophr . 2 , 1 – 7 . doi: 10.1038/npjschz.2016.14 OpenUrl CrossRef PubMed 61. ↵ Haeger , A. , Costa , A.S. , Schulz , J.B. , Reetz , K ., 2019 . Cerebral changes improved by physical activity during cognitive decline: A systematic review on MRI studies . NeuroImage Clin . 23 , 101933 . doi: 10.1016/j.nicl.2019.101933 OpenUrl CrossRef PubMed 62. ↵ Hajek , T. , Franke , K. , Kolenic , M. , Capkova , J. , Matejka , M. , Propper , L. , Uher , R. , Stopkova , P. , Novak , T. , Paus , T ., 2019 . Brain age in early stages of bipolar disorders or schizophrenia . Schizophr. Bull . 45 , 190 – 198 . OpenUrl CrossRef PubMed 63. ↵ Han , L.K.M. , Verhoeven , J.E. , Tyrka , A.R. , Penninx , B.W.J.H. , Wolkowitz , O.M. , Månsson , K.N.T. , Lindqvist , D. , Boks , M.P. , Révész , D. , Mellon , S.H. , Picard , M ., 2019 . Accelerating research on biological aging and mental health: Current challenges and future directions . Psychoneuroendocrinology 106 , 293 – 311 . doi: 10.1016/j.psyneuen.2019.04.004 OpenUrl CrossRef PubMed 64. ↵ Hasan , A. , Wobrock , T. , Guse , B. , Langguth , B. , Landgrebe , M. , Eichhammer , P. , Frank , E. , Cordes , J. , Wölwer , W. , Musso , F. , Winterer , G. , Gaebel , W. , Hajak , G. , Ohmann , C. , Verde , P.E. , Rietschel , M. , Ahmed , R. , Honer , W.G. , Dechent , P. , Malchow , B. , Castro , M.F.U. , Dwyer , D. , Cabral , C. , Kreuzer , P.M. , Poeppl , T.B. , Schneider-Axmann , T. , Falkai , P. , Koutsouleris , N ., 2017 . Structural brain changes are associated with response of negative symptoms to prefrontal repetitive transcranial magnetic stimulation in patients with schizophrenia . Mol. Psychiatry 22 , 857 – 864 . doi: 10.1038/mp.2016.161 OpenUrl CrossRef PubMed 65. ↵ Helmstaedter , C. , Wietzke , J. , Lutz , M.T ., 2009 . Unique and shared validity of the “Wechsler logical memory test”, the “California verbal learning test”, and the “verbal learning and memory test” in patients with epilepsy . Epilepsy Res . 87 , 203 – 212 . doi: 10.1016/j.eplepsyres.2009.09.002 OpenUrl CrossRef PubMed Web of Science 66. ↵ Higgins-Chen , A.T. , Boks , M.P. , Vinkers , C.H. , Kahn , R.S. , Levine , M.E ., 2020 . Schizophrenia and Epigenetic Aging Biomarkers: Increased Mortality, Reduced Cancer Risk, and Unique Clozapine Effects . Biol. Psychiatry 88 , 224 – 235 . doi: 10.1016/j.biopsych.2020.01.025 OpenUrl CrossRef PubMed 67. ↵ Ho , B.-C. , Andreasen , N.C. , Nopoulos , P. , Arndt , S. , Magnotta , V. , Flaum , M ., 2003 . Progressive Structural Brain Abnormalities and Their Relationship to Clinical Outcome: A Longitudinal Magnetic Resonance Imaging Study Early in Schizophrenia . Arch. Gen. Psychiatry 60 , 585 – 594 . doi: 10.1001/archpsyc.60.6.585 OpenUrl CrossRef PubMed Web of Science 68. ↵ Hobday , H. , Cole , J.H. , Stanyard , R.A. , Daws , R.E. , Giampietro , V. , O’Daly , O. , Leech , R. , Váša , F ., 2022 . Tissue volume estimation and age prediction using rapid structural brain scans . Sci. Rep . 12 , 12005 . doi: 10.1038/s41598-022-14904-5 OpenUrl CrossRef PubMed 69. ↵ Howes , O.D. , Shatalina , E ., 2022 . Integrating the Neurodevelopmental and Dopamine Hypotheses of Schizophrenia and the Role of Cortical Excitation-Inhibition Balance. Biol. Psychiatry , Synaptic Mechanisms of Pefrontal Circuit Failure in Schizophrenia: Crossing a Developmental Threshold 92 , 501 – 513 . doi: 10.1016/j.biopsych.2022.06.017 OpenUrl CrossRef PubMed 70. ↵ Hua , J.P.Y. , Abram , S.V. , Loewy , R.L. , Stuart , B. , Fryer , S.L. , Vinogradov , S. , Mathalon , D.H ., 2024 . Brain Age Gap in Early Illness Schizophrenia and the Clinical High-Risk Syndrome: Associations With Experiential Negative Symptoms and Conversion to Psychosis . Schizophr. Bull . 50 , 1159 – 1170 . doi: 10.1093/schbul/sbae074 OpenUrl CrossRef PubMed 71. ↵ Hulshoff Pol , H.E. , Schnack , H.G. , Bertens , M.G.B.C. , van Haren , N.E.M. , van der Tweel , I. , Staal , W.G. , Baaré , W.F.C. , Kahn , R.S. , 2002 . Volume Changes in Gray Matter in Patients With Schizophrenia . Am. J. Psychiatry 159 , 244 – 250 . doi: 10.1176/appi.ajp.159.2.244 OpenUrl CrossRef PubMed Web of Science 72. ↵ Iob , E. , Steptoe , A ., 2019 . Cardiovascular Disease and Hair Cortisol: a Novel Biomarker of Chronic Stress . Curr. Cardiol. Rep . 21 , 116 . doi: 10.1007/s11886-019-1208-7 OpenUrl CrossRef 73. ↵ Isaac , C. , Januel , D ., 2016 . Neural correlates of cognitive improvements following cognitive remediation in schizophrenia: a systematic review of randomized trials . Socioaffective Neurosci. Psychol . 6 , doi: 10.3402/snp.v6.30054 . https://doi.org/10.3402/snp.v6.30054 OpenUrl CrossRef 74. ↵ Jagust , W. , Harvey , D. , Mungas , D. , Haan , M ., 2005 . Central Obesity and the Aging Brain . Arch. Neurol . 62 , 1545 – 1548 . doi: 10.1001/archneur.62.10.1545 OpenUrl CrossRef PubMed Web of Science 75. ↵ Jansen , R. , Han , L.K. , Verhoeven , J.E. , Aberg , K.A. , van den Oord , E.C. , Milaneschi , Y. , Penninx , B.W. , 2021 . An integrative study of five biological clocks in somatic and mental health . eLife 10 , e59479 . doi: 10.7554/eLife.59479 OpenUrl CrossRef PubMed 76. ↵ Jiang , L. , Cao , X. , Li , T. , Tang , Y. , Li , W. , Wang , J. , Chan , R.C. , Li , C ., 2016 . Cortical Thickness Changes Correlate with Cognition Changes after Cognitive Training: Evidence from a Chinese Community Study . Front. Aging Neurosci . 8 . doi: 10.3389/fnagi.2016.00118 OpenUrl CrossRef 77. ↵ Kaufmann , T. , van der Meer , D. , Doan , N.T. , Schwarz , E. , Lund , M.J. , Agartz , I. , Alnæs , D. , Barch , D.M. , Baur-Streubel , R. , Bertolino , A. , Bettella , F. , Beyer , M.K. , Bøen , E. , Borgwardt , S. , Brandt , C.L. , Buitelaar , J. , Celius , E.G. , Cervenka , S. , Conzelmann , A. , Córdova-Palomera , A. , Dale , A.M. , de Quervain , D.J.F. , Di Carlo , P. , Djurovic , S. , Dørum , E.S. , Eisenacher , S. , Elvsåshagen , T. , Espeseth , T. , Fatouros-Bergman , H. , Flyckt , L. , Franke , B. , Frei , O. , Haatveit , B. , Håberg , A.K. , Harbo , H.F. , Hartman , C.A. , Heslenfeld , D. , Hoekstra , P.J. , Høgestøl , E.A. , Jernigan , T.L. , Jonassen , R. , Jönsson , E.G. , Kirsch , P. , Kłoszewska , I. , Kolskår , K.K. , Landrø , N.I. , Le Hellard , S. , Lesch , K.- P. , Lovestone , S. , Lundervold , A. , Lundervold , A.J. , Maglanoc , L.A. , Malt , U.F. , Mecocci , P. , Melle , I. , Meyer-Lindenberg , A. , Moberget , T. , Norbom , L.B. , Nordvik , J.E. , Nyberg , L. , Oosterlaan , J. , Papalino , M. , Papassotiropoulos , A. , Pauli , P. , Pergola , G. , Persson , K. , Richard , G. , Rokicki , J. , Sanders , A.-M. , Selbæk , G. , Shadrin , A.A. , Smeland , O.B. , Soininen , H. , Sowa , P. , Steen , V.M. , Tsolaki , M. , Ulrichsen , K.M. , Vellas , B. , Wang , L. , Westman , E. , Ziegler , G.C. , Zink , M. , Andreassen , O.A. , Westlye , L.T. , 2019 . Common brain disorders are associated with heritable patterns of apparent aging of the brain . Nat. Neurosci . 22 , 1617 – 1623 . doi: 10.1038/s41593-019-0471-7 OpenUrl CrossRef PubMed 78. ↵ Kay , S.R. , Fiszbein , A. , Opler , L.A ., 1987 . The positive and negative syndrome scale (PANSS) for schizophrenia . Schizophr. Bull . 13 , 261 – 276 . doi: 10.1093/schbul/13.2.261 OpenUrl CrossRef PubMed Web of Science 79. Kim , H. , Song , B. , So , B. , Lee , O. , Song , W. , Kim , Y ., 2014 . Increase of circulating BDNF levels and its relation to improvement of physical fitness following 12 weeks of combined exercise in chronic patients with schizophrenia: A pilot study . Psychiatry Res . 220 , 792 – 796 . doi: 10.1016/j.psychres.2014.09.020 OpenUrl CrossRef PubMed Web of Science 80. ↵ Kim , M. , Lee , Y. , Kang , H ., 2023 . Effects of Exercise on Positive Symptoms, Negative Symptoms, and Depression in Patients with Schizophrenia: A Systematic Review and Meta-Analysis . Int. J. Environ. Res. Public. Health 20 , 3719 . doi: 10.3390/ijerph20043719 OpenUrl CrossRef 81. ↵ Knaepen , K. , Goekint , M. , Heyman , E.M. , Meeusen , R ., 2010 . Neuroplasticity — Exercise- Induced Response of Peripheral Brain-Derived Neurotrophic Factor . Sports Med . 40 , 765 – 801 . doi: 10.2165/11534530-000000000-00000 OpenUrl CrossRef PubMed Web of Science 82. ↵ Koblinsky , N.D. , Power , K.A. , Middleton , L. , Ferland , G. , Anderson , N.D ., 2023 . The Role of the Gut Microbiome in Diet and Exercise Effects on Cognition: A Review of the Intervention Literature . J. Gerontol. Ser. A 78 , 195 – 205 . doi: 10.1093/gerona/glac166 OpenUrl CrossRef 83. ↵ Koutsouleris , N. , Davatzikos , C. , Borgwardt , S. , Gaser , C. , Bottlender , R. , Frodl , T. , Falkai , P. , Riecher-Rössler , A. , Möller , H.-J. , Reiser , M. , Pantelis , C. , Meisenzahl , E ., 2014 . Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders . Schizophr. Bull . 40 , 1140 – 1153 . doi: 10.1093/schbul/sbt142 OpenUrl CrossRef PubMed 84. ↵ Koutsouleris , N. , Gaser , C. , Jäger , M. , Bottlender , R. , Frodl , T. , Holzinger , S. , Schmitt , G.J.E. , Zetzsche , T. , Burgermeister , B. , Scheuerecker , J. , Born , C. , Reiser , M. , Möller , H.-J. , Meisenzahl , E.M ., 2008 . Structural correlates of psychopathological symptom dimensions in schizophrenia: A voxel-based morphometric study . NeuroImage 39 , 1600 – 1612 . doi: 10.1016/j.neuroimage.2007.10.029 OpenUrl CrossRef PubMed Web of Science 85. ↵ Kramer , J.H. , Mungas , D. , Reed , B.R. , Wetzel , M.E. , Burnett , M.M. , Miller , B.L. , Weiner , M.W. , Chui , H.C ., 2007 . Longitudinal MRI and cognitive change in healthy elderly . Neuropsychology 21 , 412 – 418 . doi: 10.1037/0894-4105.21.4.412 OpenUrl CrossRef PubMed 86. ↵ Kullmann , S. , Schweizer , F. , Veit , R. , Fritsche , A. , Preissl , H ., 2015 . Compromised white matter integrity in obesity . Obes. Rev . 16 , 273 – 281 . doi: 10.1111/obr.12248 OpenUrl CrossRef PubMed 87. ↵ Kuo , F.-C. , Lee , C.-H. , Hsieh , C.-H. , Kuo , P. , Chen , Y.-C. , Hung , Y.-J ., 2013 . Lifestyle modification and behavior therapy effectively reduce body weight and increase serum level of brain-derived neurotrophic factor in obese non-diabetic patients with schizophrenia . Psychiatry Res . 209 , 150 – 154 . doi: 10.1016/j.psychres.2012.11.020 OpenUrl CrossRef PubMed 88. ↵ Kurachi , M. , Takahashi , T. , Sumiyoshi , T. , Uehara , T. , Suzuki , M ., 2018 . Early Intervention and a Direction of Novel Therapeutics for the Improvement of Functional Outcomes in Schizophrenia: A Selective Review . Front. Psychiatry 9 . doi: 10.3389/fpsyt.2018.00039 OpenUrl CrossRef 89. ↵ Leucht , S. , Leucht , C. , Huhn , M. , Chaimani , A. , Mavridis , D. , Helfer , B. , Samara , M. , Rabaioli , M. , Bächer , S. , Cipriani , A. , Geddes , J.R. , Salanti , G. , Davis , J.M ., 2017 . Sixty Years of Placebo-Controlled Antipsychotic Drug Trials in Acute Schizophrenia: Systematic Review, Bayesian Meta-Analysis, and Meta-Regression of Efficacy Predictors . Am. J. Psychiatry 174 , 927 – 942 . doi: 10.1176/appi.ajp.2017.16121358 OpenUrl CrossRef PubMed 90. ↵ Leucht , S. , Samara , M. , Heres , S. , Davis , J.M ., 2016 . Dose Equivalents for Antipsychotic Drugs: The DDD Method . Schizophr. Bull . 42 , S90 – S94 . doi: 10.1093/schbul/sbv167 OpenUrl CrossRef PubMed 91. Li, A., A, Z., W, Y., O, H., H, Y., Y, L., L, F., Kj, W., K, X., G, R., J, L., S, L., M, W., Y, Sun, M, S., P, L., J, C., Y, Chen, H, Wang, W, L., Z, L., Y, Y., H, G., P, W., L, Lv, L, Lu, J, Y., Y, Song, H, Wang, H, Z., H, Wu, Y, N., Y, D., Y, Cheng, J, X., X, X., D, Z., X, W., T, J., B, L. , 2020 . A neuroimaging biomarker for striatal dysfunction in schizophrenia . Nat. Med . 26 . doi: 10.1038/s41591-020-0793-8 OpenUrl CrossRef PubMed 92. ↵ Li , G. , Hu , Y. , Zhang , W. , Wang , J. , Ji , W. , Manza , P. , Volkow , N.D. , Zhang , Y. , Wang , G.-J ., 2023 . Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions . Mol. Psychiatry 28 , 1466 – 1479 . doi: 10.1038/s41380-023-02025-y OpenUrl CrossRef PubMed 93. ↵ Li , M. , Deng , W. , Das , T. , Li , Y. , Zhao , L. , Ma , X. , Wang , Y. , Yu , H. , Li , X. , Meng , Y. , Wang , Q. , Palaniyappan , L. , Li , T ., 2018 . Neural substrate of unrelenting negative symptoms in schizophrenia: a longitudinal resting-state fMRI study . Eur. Arch. Psychiatry Clin. Neurosci . 268 , 641 – 651 . doi: 10.1007/s00406-017-0851-5 OpenUrl CrossRef PubMed 94. ↵ Lieberman , J. , Chakos , M. , Wu , H. , Alvir , J. , Hoffman , E. , Robinson , D. , Bilder , R ., 2001 . Longitudinal study of brain morphology in first episode schizophrenia . Biol. Psychiatry 49 , 487 – 499 . doi: 10.1016/S0006-3223(01)01067-8 OpenUrl CrossRef PubMed Web of Science 95. ↵ Liew , S.-L. , Schweighofer , N. , Cole , J.H. , Zavaliangos-Petropulu , A. , Tavenner , B.P. , Han , L.K.M. , Hahn , T. , Schmaal , L. , Donnelly , M.R. , Jeong , J.N. , Wang , Z. , Abdullah , A. , Kim , J.H. , Hutton , A. , Barisano , G. , Borich , M.R. , Boyd , L.A. , Brodtmann , A. , Buetefisch , C.M. , Byblow , W.D. , Cassidy , J.M. , Charalambous , C.C. , Ciullo , V. , Conforto , A.B. , Dacosta-Aguayo , R. , DiCarlo , J.A. , Domin , M. , Dula , A.N. , Egorova- Brumley , N. , Feng , W. , Geranmayeh , F. , Gregory , C.M. , Hanlon , C.A. , Hayward , K. , Holguin , J.A. , Hordacre , B. , Jahanshad , N. , Kautz , S.A. , Khlif , M.S. , Kim , H. , Kuceyeski , A. , Lin , D.J. , Liu , J. , Lotze , M. , MacIntosh , B.J. , Margetis , J.L. , Mataro , M. , Mohamed , F.B. , Olafson , E.R. , Park , G. , Piras , F. , Revill , K.P. , Roberts , P. , Robertson , A.D. , Sanossian , N. , Schambra , H.M. , Seo , N.J. , Soekadar , S.R. , Spalletta , G. , Stinear , C.M. , Taga , M. , Tang , W.K. , Thielman , G.T. , Vecchio , D. , Ward , N.S. , Westlye , L.T. , Winstein , C.J. , Wittenberg , G.F. , Wolf , S.L. , Wong , K.A. , Yu , C. , Cramer , S.C. , Thompson , P.M ., 2023 . Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke . Neurology 100 , e2103 – e2113 . doi: 10.1212/WNL.0000000000207219 OpenUrl CrossRef 96. ↵ Ling , E. , Nemesh , J. , Goldman , M. , Kamitaki , N. , Reed , N. , Handsaker , R.E. , Genovese , G. , Vogelgsang , J.S. , Gerges , S. , Kashin , S. , Ghosh , S. , Esposito , J.M. , Morris , K. , Meyer , D. , Lutservitz , A. , Mullally , C.D. , Wysoker , A. , Spina , L. , Neumann , A. , Hogan , M. , Ichihara , K. , Berretta , S. , McCarroll , S.A ., 2024 . A concerted neuron–astrocyte program declines in ageing and schizophrenia . Nature 627 , 604 – 611 . doi: 10.1038/s41586-024-07109-5 OpenUrl CrossRef PubMed 97. ↵ Liu , M. , Mei , D. , Zhang , Y. , Kang , N. , Wang , D. , Chen , G ., 2024 . Meta-analysis of exercise intervention on health behaviors in middle-aged and older adults . Front. Psychol . 14 . doi: 10.3389/fpsyg.2023.1308602 OpenUrl CrossRef 98. ↵ Lu , H. , Li , J. , Chan , S.S.M. , Ma , S.L. , Mok , V.C.T. , Shi , L. , Mak , A.D.-P. , Lam , L.C.W ., 2024 . Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial . Dialogues Clin. Neurosci . 26 , 38 – 52 . doi: 10.1080/19585969.2024.2373075 OpenUrl CrossRef PubMed 99. ↵ Lyman , M. , Lloyd , D.G. , Ji , X. , Vizcaychipi , M.P. , Ma , D ., 2014 . Neuroinflammation: The role and consequences . Neurosci. Res . 79 , 1 – 12 . doi: 10.1016/j.neures.2013.10.004 OpenUrl CrossRef 100. ↵ Makowski , D. , Ben-Shachar , M.S. , Lüdecke , D ., 2019 . bayestestR: Describing Effects and their Uncertainty , Existence and Significance within the Bayesian Framework. J. Open Source Softw . 4 , 1541 . doi: 10.21105/joss.01541 OpenUrl CrossRef 101. ↵ Malchow , B. , Keeser , D. , Keller , K. , Hasan , A. , Rauchmann , B.-S. , Kimura , H. , Schneider- Axmann , T. , Dechent , P. , Gruber , O. , Ertl-Wagner , B. , Honer , W.G. , Hillmer-Vogel , U. , Schmitt , A. , Wobrock , T. , Niklas , A. , Falkai , P ., 2016 . Effects of endurance training on brain structures in chronic schizophrenia patients and healthy controls . Schizophr. Res., Progressive Brain Tissue Loss in Schizophrenia 173 , 182 – 191 . doi: 10.1016/j.schres.2015.01.005 OpenUrl CrossRef 102. ↵ Manu , P. , Dima , L. , Shulman , M. , Vancampfort , D. , De Hert , M. , Correll , C.U. , 2015 . Weight gain and obesity in schizophrenia: epidemiology, pathobiology, and management . Acta Psychiatr. Scand . 132 , 97 – 108 . doi: 10.1111/acps.12445 OpenUrl CrossRef PubMed 103. ↵ Marenco , S. , Weinberger , D.R ., 2000 . The neurodevelopmental hypothesis of schizophrenia: following a trail of evidence from cradle to grave . Dev. Psychopathol . 12 , 501 – 527 . doi: 10.1017/s0954579400003138 OpenUrl CrossRef PubMed Web of Science 104. ↵ Mathalon , D.H. , Sullivan , E.V. , Lim , K.O. , Pfefferbaum , A ., 2001 . Progressive Brain Volume Changes and the Clinical Course of Schizophrenia in Men: A Longitudinal Magnetic Resonance Imaging Study . Arch. Gen. Psychiatry 58 , 148 – 157 . doi: 10.1001/archpsyc.58.2.148 OpenUrl CrossRef PubMed Web of Science 105. ↵ Maurus , I. , Hasan , A. , Röh , A. , Takahashi , S. , Rauchmann , B. , Keeser , D. , Malchow , B. , Schmitt , A. , Falkai , P ., 2019a . Neurobiological effects of aerobic exercise, with a focus on patients with schizophrenia . Eur. Arch. Psychiatry Clin. Neurosci . 269 , 499 – 515 . doi: 10.1007/s00406-019-01025-w OpenUrl CrossRef PubMed 106. ↵ Maurus , I. , Hasan , A. , Röh , A. , Takahashi , S. , Rauchmann , B. , Keeser , D. , Malchow , B. , Schmitt , A. , Falkai , P ., 2019b . Neurobiological effects of aerobic exercise, with a focus on patients with schizophrenia . Eur. Arch. Psychiatry Clin. Neurosci . 269 , 499 – 515 . doi: 10.1007/s00406-019-01025-w OpenUrl CrossRef PubMed 107. ↵ Maurus , I. , Hasan , A. , Röh , A. , Takahashi , S. , Rauchmann , B. , Keeser , D. , Malchow , B. , Schmitt , A. , Falkai , P ., 2019c . Neurobiological effects of aerobic exercise, with a focus on patients with schizophrenia . Eur. Arch. Psychiatry Clin. Neurosci . 269 , 499 – 515 . doi: 10.1007/s00406-019-01025-w OpenUrl CrossRef PubMed 108. ↵ Maurus , I. , Hasan , A. , Schmitt , A. , Roeh , A. , Keeser , D. , Malchow , B. , Schneider-Axmann , T. , Hellmich , M. , Schmied , S. , Lembeck , M. , Keller-Varady , K. , Papazova , I. , Hirjak , D. , Topor , C.E. , Walter , H. , Mohnke , S. , Vogel , B.O. , Wölwer , W. , Schneider , F. , Henkel , K. , Meyer-Lindenberg , A. , Falkai , P ., 2021 . Aerobic endurance training to improve cognition and enhance recovery in schizophrenia: design and methodology of a multicenter randomized controlled trial . Eur. Arch. Psychiatry Clin. Neurosci . 271 , 315 – 324 . doi: 10.1007/s00406-020-01175-2 OpenUrl CrossRef PubMed 109. ↵ McKechanie , A.G. , Moorhead , T.W.J. , Stanfield , A.C. , Whalley , H.C. , Johnstone , E.C. , Lawrie , S.M. , Owens , D.G.C ., 2016 . Negative symptoms and longitudinal grey matter tissue loss in adolescents at risk of psychosis: Preliminary findings from a 6-year follow-up study . Br. J. Psychiatry 208 , 565 – 570 . doi: 10.1192/bjp.bp.114.154526 OpenUrl Abstract / FREE Full Text 110. ↵ McWhinney , S. , Kolenic , M. , Franke , K. , Fialova , M. , Knytl , P. , Matejka , M. , Spaniel , F. , Hajek , T ., 2021 . Obesity as a Risk Factor for Accelerated Brain Ageing in First-Episode Psychosis-A Longitudinal Study . Schizophr. Bull . 47 , 1772 – 1781 . doi: 10.1093/schbul/sbab064 OpenUrl CrossRef PubMed 111. ↵ McWhinney , S.R. , Brosch , K. , Calhoun , V.D. , Crespo-Facorro , B. , Crossley , N.A. , Dannlowski , U. , Dickie , E. , Dietze , L.M.F. , Donohoe , G. , Du Plessis , S. , Ehrlich , S. , Emsley , R. , Furstova , P. , Glahn , D.C. , Gonzalez- Valderrama , A. , Grotegerd , D. , Holleran , L. , Kircher , T.T.J. , Knytl , P. , Kolenic , M. , Lencer , R. , Nenadić , I. , Opel , N. , Pfarr , J.-K. , Rodrigue , A.L. , Rootes-Murdy , K. , Ross , A.J. , Sim , K. , Škoch , A. , Spaniel , F. , Stein , F. , Švancer , P. , Tordesillas-Gutiérrez , D. , Undurraga , J. , Vázquez-Bourgon , J. , Voineskos , A. , Walton , E. , Weickert , T.W. , Weickert , C.S. , Thompson , P.M. , van Erp , T.G.M. , Turner , J.A. , Hajek , T. , 2022 . Obesity and brain structure in schizophrenia – ENIGMA study in 3021 individuals . Mol. Psychiatry 27 , 3731 – 3737 . doi: 10.1038/s41380-022-01616-5 OpenUrl CrossRef PubMed 112. ↵ Mjellem , N. , Kringlen , E ., 2001 . Schizophrenia: a review, with emphasis on the neurodevelopmental hypothesis . Nord. J. Psychiatry 55 , 301 – 309 . doi: 10.1080/080394801317080792 OpenUrl CrossRef PubMed Web of Science 113. ↵ Mrazek , M.D. , Mooneyham , B.W. , Mrazek , K.L. , Schooler , J.W ., 2016 . Pushing the Limits: Cognitive, Affective, and Neural Plasticity Revealed by an Intensive Multifaceted Intervention . Front. Hum. Neurosci . 10 . doi: 10.3389/fnhum.2016.00117 OpenUrl CrossRef 114. ↵ Mulert , C ., 2012 . Editorial [Hot Topic: New Treatment Strategies in Schizophrenia Beyond Dopamine: Glutamatergic Neurotransmission and More (Guest Editor: Christoph Mulert & Elizabeth Scarr)] . Curr. Pharm. Biotechnol . 13 , 1474 – 1475 . doi: 10.2174/138920112800784871 OpenUrl CrossRef PubMed 115. ↵ Mungas , D. , Harvey , D. , Reed , B.R. , Jagust , W.J. , DeCarli , C. , Beckett , L. , Mack , W.J. , Kramer , J.H. , Weiner , M.W. , Schuff , N. , Chui , H.C ., 2005 . Longitudinal volumetric MRI change and rate of cognitive decline . Neurology 65 , 565 – 571 . doi: 10.1212/01.wnl.0000172913.88973.0d OpenUrl Abstract / FREE Full Text 116. Murray , R.M. , Lewis , S.W ., 1987a . Is schizophrenia a neurodevelopmental disorder? Br. Med. J. Clin. Res. Ed 295 , 681 . doi: 10.1136/bmj.295.6600.681 OpenUrl FREE Full Text 117. ↵ Murray , R.M. , Lewis , S.W ., 1987b . Is schizophrenia a neurodevelopmental disorder? Br Med J Clin Res Ed 295 , 681 – 682 . doi: 10.1136/bmj.295.6600.681 OpenUrl FREE Full Text 118. ↵ Nenadić , I. , Dietzek , M. , Langbein , K. , Sauer , H. , Gaser , C ., 2017 . BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder . Psychiatry Res. Neuroimaging 266 , 86 – 89 . doi: 10.1016/j.pscychresns.2017.05.006 OpenUrl CrossRef 119. ↵ Organization , W.H ., 2004 . ICD-10 : international statistical classification of diseases and related health problems : tenth revision . World Health Organization . 120. ↵ Osmanlıoğlu , Y. , Alappatt , J.A. , Parker , D. , Verma , R ., 2020 . Connectomic consistency: a systematic stability analysis of structural and functional connectivity . J. Neural Eng . 17 , 045004 . doi: 10.1088/1741-2552/ab947b OpenUrl CrossRef PubMed 121. ↵ Ouyang , A. , Zhang , C. , Adra , N. , Tesh , R.A. , Sun , H. , Lei , D. , Jing , J. , Fan , P. , Paixao , L. , Ganglberger , W. , Briggs , L. , Salinas , J. , Bevers , M. , Wrann , C. , Chemali , Z. , Fricchione , G. , Thomas , R.J. , Rosand , J. , Tanzi , R.E. , Westover , M.B ., 2022 . Effects of Aerobic Exercise on Brain Age and Health in Older Adults: A Single-Arm Clinical Trial . doi: 10.1101/2022.06.13.22276337 OpenUrl Abstract / FREE Full Text 122. ↵ Pajonk , F.-G. , Wobrock , T. , Gruber , O. , Scherk , H. , Berner , D. , Kaizl , I. , Kierer , A. , Müller , S. , Oest , M. , Meyer , T. , Backens , M. , Schneider-Axmann , T. , Thornton , A.E. , Honer , W.G. , Falkai , P ., 2010 . Hippocampal plasticity in response to exercise in schizophrenia . Arch. Gen. Psychiatry 67 , 133 – 143 . doi: 10.1001/archgenpsychiatry.2009.193 OpenUrl CrossRef PubMed Web of Science 123. ↵ Papiol , S. , Keeser , D. , Hasan , A. , Schneider-Axmann , T. , Raabe , F. , Degenhardt , F. , Rossner , M.J. , Bickeböller , H. , Cantuti-Castelvetri , L. , Simons , M. , Wobrock , T. , Schmitt , A. , Malchow , B. , Falkai , P ., 2019 . Polygenic burden associated to oligodendrocyte precursor cells and radial glia influences the hippocampal volume changes induced by aerobic exercise in schizophrenia patients . Transl. Psychiatry 9 , 1 – 8 . doi: 10.1038/s41398-019-0618-z OpenUrl CrossRef PubMed 124. ↵ Papiol , S. , Popovic , D. , Keeser , D. , Hasan , A. , Schneider-Axmann , T. , Degenhardt , F. , Rossner , M.J. , Bickeböller , H. , Schmitt , A. , Falkai , P. , Malchow , B ., 2017 . Polygenic risk has an impact on the structural plasticity of hippocampal subfields during aerobic exercise combined with cognitive remediation in multi-episode schizophrenia . Transl. Psychiatry 7 , e1159 – e1159 . doi: 10.1038/tp.2017.131 OpenUrl CrossRef PubMed 125. ↵ Papiol , S. , Roell , L. , Maurus , I. , Hirjak , D. , Keeser , D. , Schmitt , A. , Meyer-Lindenberg , A. , Falkai , P ., 2024 . Cell type-specific polygenic burden modulates exercise effects in schizophrenia patients: further evidence on volumes of hippocampal subfields . Eur. Arch. Psychiatry Clin. Neurosci . 274 , 1241 – 1244 . doi: 10.1007/s00406-024-01841-9 OpenUrl CrossRef PubMed 126. ↵ Parimisetty , A. , Dorsemans , A.-C. , Awada , R. , Ravanan , P. , Diotel , N. , Lefebvre d’Hellencourt , C ., 2016 . Secret talk between adipose tissue and central nervous system via secreted factors-an emerging frontier in the neurodegenerative research . J. Neuroinflammation 13 , 67 . doi: 10.1186/s12974-016-0530-x OpenUrl CrossRef PubMed 127. ↵ Penny , W.D. , Friston , K.J. , Ashburner , J.T. , Kiebel , S.J. , Nichols , T.E ., 2011 . Statistical Parametric Mapping: The Analysis of Functional Brain Images . Elsevier . 128. ↵ Prados , F. , 2024 . fprados/brainageR_dockerfile . 129. ↵ Raichlen , D.A. , Aslan , D.H. , Sayre , M.K. , Bharadwaj , P.K. , Ally , M. , Maltagliati , S. , Lai , M.H.C. , Wilcox , R.R. , Klimentidis , Y.C. , Alexander , G.E ., 2023 . Sedentary Behavior and Incident Dementia Among Older Adults . JAMA 330 , 934 – 940 . doi: 10.1001/jama.2023.15231 OpenUrl CrossRef PubMed 130. ↵ Reitan , R.M ., 1985 . The Halstead-Reitan Neuropsychological Test Battery: Therapy and clinical interpretation . No Title. 131. ↵ Richard , G. , Kolskår , K. , Ulrichsen , K.M. , Kaufmann , T. , Alnæs , D. , Sanders , A.-M. , Dørum , E.S. , Monereo Sánchez , J. , Petersen , A. , Ihle-Hansen , H. , Nordvik , J.E. , Westlye , L.T ., 2020 . Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training . NeuroImage Clin . 25 , 102159 . doi: 10.1016/j.nicl.2019.102159 OpenUrl CrossRef PubMed 132. ↵ Roell , L. , Fischer , T. , Keeser , D. , Papazov , B. , Lembeck , M. , Papazova , I. , Greska , D. , Muenz , S. , Schneider-Axmann , T. , Sykorova , E. , Thieme , C.E. , Vogel , B.O. , Mohnke , S. , Huppertz , C. , Roeh , A. , Keller-Varady , K. , Malchow , B. , Stoecklein , S. , Ertl-Wagner , B. , Henkel , K. , Wolfarth , B. , Tantchik , W. , Walter , H. , Hirjak , D. , Schmitt , A. , Hasan , A. , Meyer-Lindenberg , A. , Falkai , P. , Maurus , I ., 2024 . Effects of aerobic exercise on hippocampal formation volume in people with schizophrenia – a systematic review and meta-analysis with original data from a randomized-controlled trial . Psychol. Med . 54 , 4009 – 4020 . doi: 10.1017/S0033291724001867 OpenUrl CrossRef 133. ↵ Roell , L. , Maurus , I. , Keeser , D. , Karali , T. , Papazov , B. , Hasan , A. , Schmitt , A. , Papazova , I. , Lembeck , M. , Hirjak , D. , Sykorova , E. , Thieme , C.E. , Muenz , S. , Seitz , V. , Greska , D. , Campana , M. , Wagner , E. , Loehrs , L. , Stoecklein , S. , Ertl-Wagner , B. , Poemsl , J. , Roeh , A. , Malchow , B. , Keller-Varady , K. , Meyer-Lindenberg , A. , Falkai , P ., 2022 . Association between aerobic fitness and the functional connectome in patients with schizophrenia . Eur. Arch. Psychiatry Clin. Neurosci . 272 , 1253 – 1272 . doi: 10.1007/s00406-022-01411-x OpenUrl CrossRef PubMed 134. ↵ Rokicki , J. , Wolfers , T. , Nordhøy , W. , Tesli , N. , Quintana , D.S. , Alnæs , D. , Richard , G. , de Lange , A.-M.G. , Lund , M.J. , Norbom , L. , Agartz , I. , Melle , I. , Nærland , T. , Selbæk , G. , Persson , K. , Nordvik , J.E. , Schwarz , E. , Andreassen , O.A. , Kaufmann , T. , Westlye , L.T. , 2021 . Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders . Hum. Brain Mapp . 42 , 1714 – 1726 . doi: 10.1002/hbm.25323 OpenUrl CrossRef PubMed 135. ↵ Ronan , L. , Alexander-Bloch , A.F. , Wagstyl , K. , Farooqi , S. , Brayne , C. , Tyler , L.K. , Fletcher , P.C ., 2016 . Obesity associated with increased brain age from midlife . Neurobiol. Aging 47 , 63 – 70 . doi: 10.1016/j.neurobiolaging.2016.07.010 OpenUrl CrossRef PubMed 136. ↵ Sabe , M. , Kaiser , S. , Sentissi , O ., 2020 . Physical exercise for negative symptoms of schizophrenia: Systematic review of randomized controlled trials and meta-analysis . Gen. Hosp. Psychiatry 62 , 13 – 20 . doi: 10.1016/j.genhosppsych.2019.11.002 OpenUrl CrossRef PubMed 137. ↵ Salzman , T. , Sarquis-Adamson , Y. , Son , S. , Montero-Odasso , M. , Fraser , S ., 2022 . Associations of Multidomain Interventions With Improvements in Cognition in Mild Cognitive Impairment: A Systematic Review and Meta-analysis. JAMA Netw . Open 5 , e226744 . doi: 10.1001/jamanetworkopen.2022.6744 OpenUrl CrossRef 138. ↵ Schmitt , A. , Falkai , P. , Papiol , S ., 2023 . Neurodevelopmental disturbances in schizophrenia: evidence from genetic and environmental factors . J. Neural Transm . 130 , 195 – 205 . doi: 10.1007/s00702-022-02567-5 OpenUrl CrossRef PubMed 139. ↵ Schmitt , A. , Maurus , I. , Rossner , M.J. , Röh , A. , Lembeck , M. , von Wilmsdorff , M. , Takahashi , S. , Rauchmann , B. , Keeser , D. , Hasan , A. , Malchow , B. , Falkai , P. , 2018 . Effects of Aerobic Exercise on Metabolic Syndrome, Cardiorespiratory Fitness, and Symptoms in Schizophrenia Include Decreased Mortality . Front. Psychiatry 9 . doi: 10.3389/fpsyt.2018.00690 OpenUrl CrossRef 140. ↵ Schnack , H.G. , Van Haren , N.E.M. , Nieuwenhuis , M. , Pol , H.E.H. , Cahn , W. , Kahn , R.S. , 2016 . Accelerated brain aging in schizophrenia: A longitudinal pattern recognition study . Am. J. Psychiatry 173 , 607 – 616 . doi: 10.1176/APPI.AJP.2015.15070922/ASSET/IMAGES/LARGE/APPI.AJP.2015.15070922F5.JPEG OpenUrl CrossRef PubMed 141. ↵ Sethi , S. , Wakeham , D. , Ketter , T. , Hooshmand , F. , Bjornstad , J. , Richards , B. , Westman , E. , Krauss , R.M. , Saslow , L ., 2024 . Ketogenic Diet Intervention on Metabolic and Psychiatric Health in Bipolar and Schizophrenia: A Pilot Trial . Psychiatry Res . 335 , 115866 . doi: 10.1016/j.psychres.2024.115866 OpenUrl CrossRef PubMed 142. Shahab , S. , Mulsant , B.H. , Levesque , M.L. , Calarco , N. , Nazeri , A. , Wheeler , A.L. , Foussias , G. , Rajji , T.K. , Voineskos , A.N ., 2019a . Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls . Neuropsychopharmacology 44 , 898 – 906 . doi: 10.1038/s41386-018-0298-z OpenUrl CrossRef PubMed 143. ↵ Shahab , S. , Mulsant , B.H. , Levesque , M.L. , Calarco , N. , Nazeri , A. , Wheeler , A.L. , Foussias , G. , Rajji , T.K. , Voineskos , A.N ., 2019b . Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls . Neuropsychopharmacology 44 , 898 – 906 . doi: 10.1038/s41386-018-0298-z OpenUrl CrossRef PubMed 144. ↵ Shahab , S. , Mulsant , B.H. , Levesque , M.L. , Calarco , N. , Nazeri , A. , Wheeler , A.L. , Foussias , G. , Rajji , T.K. , Voineskos , A.N ., 2019c . Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls . Neuropsychopharmacology 44 , 898 – 906 . doi: 10.1038/s41386-018-0298-z OpenUrl CrossRef PubMed 145. Sheehan , D.V. , n.d. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The Development and Validation of a Structured Diagnostic Psychiatric Interview for DSM- IV and ICD-10 . J Clin Psychiatry . 146. ↵ Shimada , T. , Ito , S. , Makabe , A. , Yamanushi , A. , Takenaka , A. , Kawano , K. , Kobayashi , M ., 2022 . Aerobic exercise and cognitive functioning in schizophrenia: An updated systematic review and meta-analysis . Psychiatry Res . 314 , 114656 . doi: 10.1016/j.psychres.2022.114656 OpenUrl CrossRef PubMed 147. ↵ Simons , J.S. , Spiers , H.J ., 2003 . Prefrontal and medial temporal lobe interactions in long-term memory . Nat. Rev. Neurosci . 4 , 637 – 648 . doi: 10.1038/nrn1178 OpenUrl CrossRef PubMed Web of Science 148. ↵ Sivera , R. , Capet , N. , Manera , V. , Fabre , R. , Lorenzi , M. , Delingette , H. , Pennec , X. , Ayache , N. , Robert , P ., 2020 . Voxel-based assessments of treatment effects on longitudinal brain changes in the Multidomain Alzheimer Preventive Trial cohort . Neurobiol. Aging 94 , 50 – 59 . doi: 10.1016/j.neurobiolaging.2019.11.020 OpenUrl CrossRef PubMed 149. ↵ Solomon , A. , Stephen , R. , Altomare , D. , Carrera , E. , Frisoni , G.B. , Kulmala , J. , Molinuevo , J.L. , Nilsson , P. , Ngandu , T. , Ribaldi , F. , Vellas , B. , Scheltens , P. , Kivipelto , M. , Abramowicz , M. , Altomare , D. , Barkhof , F. , Berthier , M. , Bieler , M. , Blennow , K. , Brayne , C. , Brioschi , A. , Carrera , E. , Chételat , G. , Csajka , C. , Demonet , J.-F. , Dodich , A. , Dubois , B. , Frisoni , G.B. , Garibotto , V. , Georges , J. , Hurst , S. , Jessen , F. , Kivipelto , M. , Llewellyn , D. , Mcwhirter , L. , Milne , R. , Minguillón , C. , Miniussi , C. , Molinuevo , J.L. , Nilsson , P.M. , Ranson , J. , Ribaldi , F. , Ritchie , C. , Scheltens , P. , Solomon , A. , van Duijn , C. , van der Flier , W. , Vellas , B. , Visser, L., on behalf of the European Task Force for Brain Health Services , 2021 . Multidomain interventions: state-of-the-art and future directions for protocols to implement precision dementia risk reduction. A user manual for Brain Health Services—part 4 of 6 . Alzheimers Res. Ther . 13 , 171 . doi: 10.1186/s13195-021-00875-8 OpenUrl CrossRef PubMed 150. ↵ Soshi , T. , Andersson , M. , Kawagoe , T. , Nishiguchi , S. , Yamada , M. , Otsuka , Y. , Nakai , R. , Abe , N. , Aslah , A. , Igasaki , T. , Sekiyama , K ., 2021 . Prefrontal Plasticity after a 3-Month Exercise Intervention in Older Adults Relates to Enhanced Cognitive Performance . Cereb. Cortex N. Y. N 1991 31 , 4501 – 4517 . doi: 10.1093/cercor/bhab102 OpenUrl CrossRef 151. ↵ Steffener , J. , Habeck , C. , O’Shea , D. , Razlighi , Q. , Bherer , L. , Stern , Y ., 2016 . Differences between chronological and brain age are related to education and self-reported physical activity . Neurobiol. Aging 40 , 138 – 144 . doi: 10.1016/j.neurobiolaging.2016.01.014 OpenUrl CrossRef PubMed 152. ↵ Subramaniam , K. , Luks , T.L. , Garrett , C. , Chung , C. , Fisher , M. , Nagarajan , S. , Vinogradov , S ., 2014 . Intensive cognitive training in schizophrenia enhances working memory and associated prefrontal cortical efficiency in a manner that drives long-term functional gains . NeuroImage 99 , 281 – 292 . doi: 10.1016/j.neuroimage.2014.05.057 OpenUrl CrossRef PubMed Web of Science 153. ↵ Svatkova , A. , Mandl , R.C.W. , Scheewe , T.W. , Cahn , W. , Kahn , R.S. , Hulshoff Pol , H.E ., 2015 . Physical Exercise Keeps the Brain Connected: Biking Increases White Matter Integrity in Patients With Schizophrenia and Healthy Controls . Schizophr. Bull . 41 , 869 – 878 . doi: 10.1093/schbul/sbv033 OpenUrl CrossRef PubMed 154. ↵ Teeuw , J. , Aps , O. , Rm , B. , Smc, de Z., Hg, S., He, H.P., Ra, O., 2021 . Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia . Schizophr. Res . 231 . doi: 10.1016/j.schres.2021.04.005 OpenUrl CrossRef PubMed 155. ↵ Tewes , U ., 1994 . HAWIE-R: Hamburg-Wechsler-Intelligenztest für Erwachsene, Revision 1991 ; Handbuch und Testanweisung. Huber . 156. ↵ Thiebes , S. , Leicht , G. , Curic , S. , Steinmann , S. , Polomac , N. , Andreou , C. , Eichler , I. , Eichler , L. , Zöllner , C. , Gallinat , J. , Hanganu-Opatz , I. , Mulert , C ., 2017a . Glutamatergic deficit and schizophrenia-like negative symptoms: new evidence from ketamine-induced mismatch negativity alterations in healthy male humans . J. Psychiatry Neurosci . 42 , 273 – 283 . doi: 10.1503/jpn.160187 OpenUrl Abstract / FREE Full Text 157. ↵ Thiebes , S. , Leicht , G. , Curic , S. , Steinmann , S. , Polomac , N. , Andreou , C. , Eichler , I. , Eichler , L. , Zöllner , C. , Gallinat , J. , Hanganu-Opatz , I. , Mulert , C ., 2017b . Glutamatergic deficit and schizophrenia-like negative symptoms: new evidence from ketamine-induced mismatch negativity alterations in healthy male humans . J. Psychiatry Neurosci. JPN 42 , 273 – 283 . doi: 10.1503/jpn.160187 OpenUrl Abstract / FREE Full Text 158. ↵ Tønnesen , S. , Kaufmann , T. , de Lange , A.-M.G. , Richard , G. , Doan , N.T. , Alnæs , D. , van der Meer , D. , Rokicki , J. , Moberget , T. , Maximov , I.I. , Agartz , Ingrid , Aminoff , S.R. , Beck , D. , Barch , D.M. , Beresniewicz , J. , Cervenka , Simon , Fatouros-Bergman , Helena , Craven , A.R. , Flyckt , Lena , Gurholt , T.P. , Haukvik , U.K. , Hugdahl , K. , Johnsen , E. , Jönsson , E.G. , Farde , L. , Flyckt , L. , Engberg , G. , Erhardt , S. , Fatouros-Bergman , H. , Cervenka , S. , Schwieler , L. , Piehl , F. , Agartz , I. , Collste , K. , Victorsson , P. , Malmqvist , A. , Hedberg , M. , Orhan , F. , Sellgren , C. , Kolskår , K.K. , Kroken , R.A. , Lagerberg , T.V. , Løberg , E.- M. , Nordvik , J.E. , Sanders , A.-M. , Ulrichsen , K. , Andreassen , O.A. , Westlye , L.T. , 2020 . Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study . Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5 , 1095 – 1103 . doi: 10.1016/j.bpsc.2020.06.014 OpenUrl CrossRef PubMed 159. ↵ Trubetskoy , V. , Pardiñas , A.F. , Qi , T. , Panagiotaropoulou , G. , Awasthi , S. , Bigdeli , T.B. , Bryois , J. , Chen , C.-Y. , Dennison , C.A. , Hall , L.S. , Lam , M. , Watanabe , K. , Frei , O. , Ge , T. , Harwood , J.C. , Koopmans , F. , Magnusson , S. , Richards , A.L. , Sidorenko , J. , Wu , Y. , Zeng , J. , Grove , J. , Kim , M. , Li , Z. , Voloudakis , G. , Zhang , W. , Adams , M. , Agartz , I. , Atkinson , E.G. , Agerbo , E. , Al Eissa , M. , Albus , M. , Alexander , M. , Alizadeh , B.Z. , Alptekin , K. , Als , T.D. , Amin , F. , Arolt , V. , Arrojo , M. , Athanasiu , L. , Azevedo , M.H. , Bacanu , S.A. , Bass , N.J. , Begemann , M. , Belliveau , R.A. , Bene , J. , Benyamin , B. , Bergen , S.E. , Blasi , G. , Bobes , J. , Bonassi , S. , Braun , A. , Bressan , R.A. , Bromet , E.J. , Bruggeman , R. , Buckley , P.F. , Buckner , R.L. , Bybjerg-Grauholm , J. , Cahn , W. , Cairns , M.J. , Calkins , M.E. , Carr , V.J. , Castle , D. , Catts , S.V. , Chambert , K.D. , Chan , R.C.K. , Chaumette , B. , Cheng , W. , Cheung , E.F.C. , Chong , S.A. , Cohen , D. , Consoli , A. , Cordeiro , Q. , Costas , J. , Curtis , C. , Davidson , M. , Davis , K.L. , De Haan , L. , Degenhardt , F. , DeLisi , L.E. , Demontis , D. , Dickerson , F. , Dikeos , D. , Dinan , T. , Djurovic , S. , Duan , J. , Ducci , G. , Dudbridge , F. , Eriksson , J.G. , Fañanás , L. , Faraone , S.V. , Fiorentino , A. , Forstner , A. , Frank , J. , Freimer , N.B. , Fromer , M. , Frustaci , A. , Gadelha , A. , Genovese , G. , Gershon , E.S. , Giannitelli , M. , Giegling , I. , Giusti-Rodríguez , P. , Godard , S. , Goldstein , J.I. , González Peñas , J. , González-Pinto , A. , Gopal , S. , Gratten , J. , Green , M.F. , Greenwood , T.A. , Guillin , O. , Gülöksüz , S. , Gur , R.E. , Gur , R.C. , Gutiérrez , B. , Hahn , E. , Hakonarson , H. , Haroutunian , V. , Hartmann , A.M. , Harvey , C. , Hayward , C. , Henskens , F.A. , Herms , S. , Hoffmann , P. , Howrigan , D.P. , Ikeda , M. , Iyegbe , C. , Joa , I. , Julià , A. , Kähler , A.K. , Kam-Thong , T. , Kamatani , Y. , Karachanak-Yankova , S. , Kebir , O. , Keller , M.C. , Kelly , B.J. , Khrunin , A. , Kim , S.-W. , Klovins , J. , Kondratiev , N. , Konte , B. , Kraft , J. , Kubo , M. , Kučinskas , V. , Kučinskiene , Z.A. , Kusumawardhani , A. , Kuzelova-Ptackova , H. , Landi , S. , Lazzeroni , L.C. , Lee , P.H. , Legge , S.E. , Lehrer , D.S. , Lencer , R. , Lerer , B. , Li , Miaoxin , Lieberman , J. , Light , G.A. , Limborska , S. , Liu , C.-M. , Lönnqvist , J. , Loughland , C.M. , Lubinski , J. , Luykx , J.J. , Lynham , A. , Macek , M. , Mackinnon , A. , Magnusson , P.K.E. , Maher , B.S. , Maier , W. , Malaspina , D. , Mallet , J. , Marder , S.R. , Marsal , S. , Martin , A.R. , Martorell , L. , Mattheisen , M. , McCarley , R.W. , McDonald , C. , McGrath , J.J. , Medeiros , H. , Meier , S. , Melegh , B. , Melle , I. , Mesholam- Gately , R.I. , Metspalu , A. , Michie , P.T. , Milani , L. , Milanova , V. , Mitjans , M. , Molden , E. , Molina , E. , Molto , M.D. , Mondelli , V. , Moreno , C. , Morley , C.P. , Muntané , G. , Murphy , K.C. , Myin-Germeys , I. , Nenadić , I. , Nestadt , G. , Nikitina-Zake , L. , Noto , C. , Nuechterlein , K.H. , O’Brien , N.L. , O’Neill , F.A. , Oh , S.-Y. , Olincy , A. , Ota , V.K. , Pantelis , C. , Papadimitriou , G.N. , Parellada , M. , Paunio , T. , Pellegrino , R. , Periyasamy , S. , Perkins , D.O. , Pfuhlmann , B. , Pietiläinen , O. , Pimm , J. , Porteous , D. , Powell , J. , Quattrone , D. , Quested , D. , Radant , A.D. , Rampino , A. , Rapaport , M.H. , Rautanen , A. , Reichenberg , A. , Roe , C. , Roffman , J.L. , Roth , J. , Rothermundt , M. , Rutten , B.P.F. , Saker-Delye , S. , Salomaa , V. , Sanjuan , J. , Santoro , M.L. , Savitz , A. , Schall , U. , Scott , R.J. , Seidman , L.J. , Sharp , S.I. , Shi , J. , Siever , L.J. , Sigurdsson , E. , Sim , K. , Skarabis , N. , Slominsky , P. , So , H.-C. , Sobell , J.L. , Söderman , E. , Stain , H.J. , Steen , N.E. , Steixner- Kumar , A.A. , Stögmann , E. , Stone , W.S. , Straub , R.E. , Streit , F. , Strengman , E. , Stroup , T.S. , Subramaniam , M. , Sugar , C.A. , Suvisaari , J. , Svrakic , D.M. , Swerdlow , N.R. , Szatkiewicz , J.P. , Ta , T.M.T. , Takahashi , A. , Terao , C. , Thibaut , F. , Toncheva , D. , Tooney , P.A. , Torretta , S. , Tosato , S. , Tura , G.B. , Turetsky , B.I. , Üçok , A. , Vaaler , A. , Van Amelsvoort , T. , Van Winkel , R. , Veijola , J. , Waddington , J. , Walter , H. , Waterreus , A. , Webb , B.T. , Weiser , M. , Williams , N.M. , Witt , S.H. , Wormley , B.K. , Wu , J.Q. , Xu , Z. , Yolken , R. , Zai , C.C. , Zhou , W. , Zhu , F. , Zimprich , F. , Atbaşoğlu , E.C. , Ayub , M. , Benner , C. , Bertolino , A. , Black , D.W. , Bray , N.J. , Breen , G. , Buccola , N.G. , Byerley , W.F. , Chen , W.J. , Cloninger , C.R. , Crespo-Facorro , B. , Donohoe , G. , Freedman , R. , Galletly , C. , Gandal , M.J. , Gennarelli , M. , Hougaard , D.M. , Hwu , H.-G. , Jablensky , A.V. , McCarroll , S.A. , Moran , J.L. , Mors , O. , Mortensen , P.B. , Müller-Myhsok , B. , Neil , A.L. , Nordentoft , M. , Pato , M.T. , Petryshen , T.L. , Pirinen , M. , Pulver , A.E. , Schulze , T.G. , Silverman , J.M. , Smoller , J.W. , Stahl , E.A. , Tsuang , D.W. , Vilella , E. , Wang , S.- H. , Xu , S. , Indonesia Schizophrenia Consortium , Dai , N. , Wenwen , Q. , Wildenauer , D. B. , Agiananda , F. , Amir , N. , Antoni , R. , Arsianti , T. , Asmarahadi , A. , Diatri , H. , Djatmiko , P. , Irmansyah , I. , Khalimah , S. , Kusumadewi , I. , Kusumaningrum , P. , Lukman , P.R. , Nasrun , M.W. , Safyuni , N.S. , Prasetyawan , P. , Semen , G. , Siste , K. , Tobing , H. , Widiasih , N. , Wiguna , T. , Wulandari , D. , Evalina , N. , Hananto , A.J. , Ismoyo , J.H. , Marini , T.M. , Henuhili , S. , Reza , M. , Yusnadewi , S. , PsychENCODE , Abyzov , A. , Akbarian , S. , Ashley-Koch , A. , Van Bakel , H. , Breen , M. , Brown , M. , Bryois , J. , Carlyle , B. , Charney , A. , Coetzee , G. , Crawford , G. , Dracheva , S. , Emani , P. , Farnham , P. , Fromer , M. , Galeev , T. , Gandal , M. , Gerstein , M. , Giase , G. , Girdhar , K. , Goes , F. , Grennan , K. , Gu , M. , Guerra , B. , Gursoy , G. , Hoffman , G. , Hyde , T. , Jaffe , A. , Jiang , S. , Jiang , Y. , Kefi , A. , Kim , Y. , Kitchen , R. , Knowles , J.A. , Lay , F. , Lee , D. , Li , Mingfeng , Liu , C. , Liu , S. , Mattei , E. , Navarro , F. , Pan , X. , Peters , M.A. , Pinto , D. , Pochareddy , S. , Polioudakis , D. , Purcaro , M. , Purcell , S. , Pratt , H. , Reddy , T. , Rhie , S. , Roussos , Panagiotis , Rozowsky , J. , Sanders , S. , Sestan , N. , Sethi , A. , Shi , X. , Shieh , A. , Swarup , V. , Szekely , A. , Wang , D. , Warrell , J. , Weissman , S. , Weng , Z. , White , K. , Wiseman , J. , Witt , H. , Won , H. , Wood , S. , Wu , F. , Xu , X. , Yao , L. , Zandi , P., Psychosis Endophenotypes International Consortium , Arranz, M.J. , Bakker , S. , Bender , S. , Bramon , E. , Collier , D.A. , Crepo-Facorro , B. , Hall , J. , Iyegbe , C. , Kahn , R. , Lawrie , S. , Lewis , C. , Lin , K. , Linszen , D.H. , Mata , I. , McIntosh , A. , Murray , R.M. , Ophoff , R.A. , Van Os , J. , Powell , J. , Rujescu , D. , Walshe , M. , Weisbrod , M., The SynGO Consortium, Achsel , T. , Andres-Alonso , M. , Bagni , C. , Bayés , À. , Biederer , T. , Brose , N. , Brown , T.C. , Chua , J.J.E. , Coba , M.P. , Cornelisse , L.N. , De Jong , A.P.H. , De Juan-Sanz , J. , Dieterich , D.C. , Feng , G. , Goldschmidt , H.L. , Gundelfinger , E.D. , Hoogenraad , C. , Huganir , R.L. , Hyman , S.E. , Imig , C. , Jahn , R. , Jung , H. , Kaeser , P.S. , Kim , E. , Koopmans , F. , Kreutz , M.R. , Lipstein , N. , MacGillavry , H.D. , Malenka , R. , McPherson , P.S. , O’Connor , V. , Pielot , R. , Ryan , T.A. , Sahasrabudhe , D. , Sala , C. , Sheng , M. , Smalla , K.-H. , Smit , A.B. , Südhof , T.C. , Thomas , P.D. , Toonen , R.F. , Van Weering , J.R.T. , Verhage , M. , Verpelli , C. , Adolfsson , R. , Arango , C. , Baune , B.T. , Belangero , S.I. , Børglum , A.D. , Braff , D. , Bramon , E. , Buxbaum , J.D. , Campion , D. , Cervilla , J.A. , Cichon , S. , Collier , D.A. , Corvin , A. , Curtis , D. , Forti , M.D. , Domenici , E. , Ehrenreich , H. , Escott-Price , V. , Esko , T. , Fanous , A.H. , Gareeva , A. , Gawlik , M. , Gejman , P.V. , Gill , M. , Glatt , S.J. , Golimbet , V. , Hong , K.S. , Hultman , C.M. , Hyman , S.E. , Iwata , N. , Jönsson , E.G. , Kahn , R.S. , Kennedy , J.L. , Khusnutdinova , E. , Kirov , G. , Knowles , J.A. , Krebs , M.-O. , Laurent-Levinson , C. , Lee , J. , Lencz , T. , Levinson , D.F. , Li , Q.S. , Liu , J. , Malhotra , A.K. , Malhotra , D. , McIntosh , A. , McQuillin , A. , Menezes , P.R. , Morgan , V.A. , Morris , D.W. , Mowry , B.J. , Murray , R.M. , Nimgaonkar , V. , Nöthen , M.M. , Ophoff , R.A. , Paciga , S.A. , Palotie , A. , Pato , C.N. , Qin , S. , Rietschel , M. , Riley , B.P. , Rivera , M. , Rujescu , D. , Saka , M.C. , Sanders , A.R. , Schwab , S.G. , Serretti , A. , Sham , P.C. , Shi , Y. , St Clair , D. , Stefánsson , H. , Stefansson , K. , Tsuang , M.T. , Van Os , J. , Vawter , M.P. , Weinberger , D.R. , Werge , T. , Wildenauer , Dieter B. , Yu , X. , Yue , W. , Holmans , P.A. , Pocklington , A.J. , Roussos , Panos , Vassos , E. , Verhage , M. , Visscher , P.M. , Yang , J. , Posthuma , D. , Andreassen , O.A. , Kendler , K.S. , Owen , M.J. , Wray , N.R. , Daly , M.J. , Huang , H. , Neale , B.M. , Sullivan , P.F. , Ripke , S. , Walters , J.T.R. , O’Donovan , M.C. , Schizophrenia Working Group of the Psychiatric Genomics Consortium , De Haan , L. , Van Amelsvoort , T. , Van Winkel , R. , Gareeva , A. , Sham , P.C. , Shi , Y. , St Clair , D. , Van Os , J. , 2022 . Mapping genomic loci implicates genes and synaptic biology in schizophrenia . Nature 604 , 502 – 508 . doi: 10.1038/s41586-022-04434-5 OpenUrl CrossRef PubMed 160. ↵ Tseng , W.-Y.I. , Hsu , Y.-C. , Huang , L.-K. , Hong , C.-T. , Lu , Y.-H. , Chen , J.-H. , Fu , C.-K. , Chan , L ., 2024 . Brain Age Is Associated with Cognitive Outcomes of Cholinesterase Inhibitor Treatment in Patients with Mild Cognitive Impairment . J. Alzheimers Dis. JAD 98 , 1095 – 1106 . doi: 10.3233/JAD-231109 OpenUrl CrossRef PubMed 161. ↵ van Erp , T.G.M. , Hibar , D.P. , Rasmussen , J.M. , Glahn , D.C. , Pearlson , G.D. , Andreassen , O.A. , Agartz , I. , Westlye , L.T. , Haukvik , U.K. , Dale , A.M. , Melle , I. , Hartberg , C.B. , Gruber , O. , Kraemer , B. , Zilles , D. , Donohoe , G. , Kelly , S. , McDonald , C. , Morris , D.W. , Cannon , D.M. , Corvin , A. , Machielsen , M.W.J. , Koenders , L. , de Haan , L. , Veltman , D.J. , Satterthwaite , T.D. , Wolf , D.H. , Gur , R.C. , Gur , R.E. , Potkin , S.G. , Mathalon , D.H. , Mueller , B.A. , Preda , A. , Macciardi , F. , Ehrlich , S. , Walton , E. , Hass , J. , Calhoun , V.D. , Bockholt , H.J. , Sponheim , S.R. , Shoemaker , J.M. , van Haren , N.E.M. , Hulshoff Pol , H.E. , Ophoff , R.A. , Kahn , R.S. , Roiz-Santiañez , R. , Crespo-Facorro , B. , Wang , L. , Alpert , K.I. , Jönsson , E.G. , Dimitrova , R. , Bois , C. , Whalley , H.C. , McIntosh , A.M. , Lawrie , S.M. , Hashimoto , R. , Thompson , P.M. , Turner , J.A. , 2016 . Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium . Mol. Psychiatry 21 , 547 – 553 . doi: 10.1038/mp.2015.63 OpenUrl CrossRef PubMed 162. ↵ Van Gaal , L.F. , Mertens , I.L. , De Block , C.E. , 2006 . Mechanisms linking obesity with cardiovascular disease . Nature 444 , 875 – 880 . doi: 10.1038/nature05487 OpenUrl CrossRef PubMed Web of Science 163. ↵ Van Gestel , H. , Franke , K. , Petite , J. , Slaney , C. , Garnham , J. , Helmick , C. , Johnson , K. , Uher , R. , Alda , M. , Hajek , T. , 2019 . Brain age in bipolar disorders: Effects of lithium treatment . Aust. N. Z. J. Psychiatry 53 , 1179 – 1188 . doi: 10.1177/0004867419857814 OpenUrl CrossRef PubMed 164. ↵ van Haren , N.E.M. , Pol , H.E.H. , Schnack , H.G. , Cahn , W. , Brans , R. , Carati , I. , Rais , M. , Kahn , R.S. , 2008 . Progressive Brain Volume Loss in Schizophrenia Over the Course of the Illness: Evidence of Maturational Abnormalities in Early Adulthood. Biol. Psychiatry , Schizophrenia: From Genetics to Treatment 63 , 106 – 113 . doi: 10.1016/j.biopsych.2007.01.004 OpenUrl CrossRef PubMed Web of Science 165. ↵ Vancampfort , D. , Rosenbaum , S. , Probst , M. , Soundy , A. , Mitchell , A.J. , De Hert , M. , Stubbs , B. , 2015 . Promotion of cardiorespiratory fitness in schizophrenia: a clinical overview and meta-analysis . Acta Psychiatr. Scand . 132 , 131 – 143 . doi: 10.1111/acps.12407 OpenUrl CrossRef PubMed 166. ↵ Vogel , J.S. , van der Gaag , M. , Slofstra , C. , Knegtering , H. , Bruins , J. , Castelein , S. , 2019 . The effect of mind-body and aerobic exercise on negative symptoms in schizophrenia: A meta-analysis . Psychiatry Res . 279 , 295 – 305 . doi: 10.1016/j.psychres.2019.03.012 OpenUrl CrossRef PubMed 167. ↵ Vos , T. , Lim , S.S. , Abbafati , C. , Abbas , K.M. , Abbasi , M. , Abbasifard , M. , Abbasi-Kangevari , M. , Abbastabar , H. , Abd-Allah , F. , Abdelalim , A. , Abdollahi , M. , Abdollahpour , I. , Abolhassani , H. , Aboyans , V. , Abrams , E.M. , Abreu , L.G. , Abrigo , M.R.M. , Abu- Raddad , L.J. , Abushouk , A.I. , Acebedo , A. , Ackerman , I.N. , Adabi , M. , Adamu , A.A. , Adebayo , O.M. , Adekanmbi , V. , Adelson , J.D. , Adetokunboh , O.O. , Adham , D. , Afshari , M. , Afshin , A. , Agardh , E.E. , Agarwal , G. , Agesa , K.M. , Aghaali , M. , Aghamir , S.M.K. , Agrawal , A. , Ahmad , T. , Ahmadi , A. , Ahmadi , M. , Ahmadieh , H. , Ahmadpour , E. , Akalu , T.Y. , Akinyemi , R.O. , Akinyemiju , T. , Akombi , B. , Al-Aly , Z. , Alam , K. , Alam , N. , Alam , S. , Alam , T. , Alanzi , T.M. , Albertson , S.B. , Alcalde-Rabanal , J.E. , Alema , N.M. , Ali , M. , Ali , S. , Alicandro , G. , Alijanzadeh , M. , Alinia , C. , Alipour , V. , Aljunid , S.M. , Alla , F. , Allebeck , P. , Almasi-Hashiani , A. , Alonso , J. , Al-Raddadi , R.M. , Altirkawi , K.A. , Alvis-Guzman , N. , Alvis-Zakzuk , N.J. , Amini , S. , Amini-Rarani , M. , Aminorroaya , A. , Amiri , F. , Amit , A.M.L. , Amugsi , D.A. , Amul , G.G.H. , Anderlini , D. , Andrei , C.L. , Andrei , T. , Anjomshoa , M. , Ansari , F. , Ansari , I. , Ansari-Moghaddam , A. , Antonio , C.A.T. , Antony , C.M. , Antriyandarti , E. , Anvari , D. , Anwer , R. , Arabloo , J. , Arab-Zozani , M. , Aravkin , A.Y. , Ariani , F. , Ärnlöv , J. , Aryal , K.K. , Arzani , A. , Asadi- Aliabadi , M. , Asadi-Pooya , A.A. , Asghari , B. , Ashbaugh , C. , Atnafu , D.D. , Atre , S.R. , Ausloos , F. , Ausloos , M. , Quintanilla , B.P.A. , Ayano , G. , Ayanore , M.A. , Aynalem , Y.A. , Azari , S. , Azarian , G. , Azene , Z.N. , Babaee , E. , Badawi , A. , Bagherzadeh , M. , Bakhshaei , M.H. , Bakhtiari , A. , Balakrishnan , S. , Balalla , S. , Balassyano , S. , Banach , M. , Banik , P.C. , Bannick , M.S. , Bante , A.B. , Baraki , A.G. , Barboza , M.A. , Barker-Collo , S.L. , Barthelemy , C.M. , Barua , L. , Barzegar , A. , Basu , S. , Baune , B.T. , Bayati , M. , Bazmandegan , G. , Bedi , N. , Beghi , E. , Béjot , Y. , Bello , A.K. , Bender , R.G. , Bennett , D.A. , Bennitt , F.B. , Bensenor , I.M. , Benziger , C.P. , Berhe , K. , Bernabe , E. , Bertolacci , G.J. , Bhageerathy , R. , Bhala , N. , Bhandari , D. , Bhardwaj , P. , Bhattacharyya , K. , Bhutta , Z.A. , Bibi , S. , Biehl , M.H. , Bikbov , B. , Sayeed , M.S.B. , Biondi , A. , Birihane , B.M. , Bisanzio , D. , Bisignano , C. , Biswas , R.K. , Bohlouli , S. , Bohluli , M. , Bolla , S.R.R. , Boloor , A. , Boon-Dooley , A.S. , Borges , G. , Borzì , A.M. , Bourne , R. , Brady , O.J. , Brauer , M. , Brayne , C. , Breitborde , N.J.K. , Brenner , H. , Briant , P.S. , Briggs , A.M. , Briko , N.I. , Britton , G.B. , Bryazka , D. , Buchbinder , R. , Bumgarner , B.R. , Busse , R. , Butt , Z.A. , Santos , F.L.C. dos , Cámera , L.L.A. , Campos-Nonato , I.R. , Car , J. , Cárdenas , R. , Carreras , G. , Carrero , J.J. , Carvalho , F. , Castaldelli-Maia , J.M. , Castañeda-Orjuela , C.A. , Castelpietra , G. , Castle , C.D. , Castro , F. , Catalá-López , F. , Causey , K. , Cederroth , C.R. , Cercy , K.M. , Cerin , E. , Chandan , J.S. , Chang , A.R. , Charlson , F.J. , Chattu , V.K. , Chaturvedi , S. , Chimed-Ochir , O. , Chin , K.L. , Cho , D.Y. , Christensen , H. , Chu , D.-T. , Chung , M.T. , Cicuttini , F.M. , Ciobanu , L.G. , Cirillo , M. , Collins , E.L. , Compton , K. , Conti , S. , Cortesi , P.A. , Costa , V.M. , Cousin , E. , Cowden , R.G. , Cowie , B.C. , Cromwell , E.A. , Cross , D.H. , Crowe , C.S. , Cruz , J.A. , Cunningham , M. , Dahlawi , S.M.A. , Damiani , G. , Dandona , L. , Dandona , R. , Darwesh , A.M. , Daryani , A. , Das , J.K. , Gupta , R.D. , Neves , J. das , Dávila-Cervantes , C.A. , Davletov , K. , Leo , D.D. , Dean , F.E. , DeCleene , N.K. , Deen , A. , Degenhardt , L. , Dellavalle , R.P. , Demeke , F.M. , Demsie , D.G. , Denova- Gutiérrez , E. , Dereje , N.D. , Dervenis , N. , Desai , R. , Desalew , A. , Dessie , G.A. , Dharmaratne , S.D. , Dhungana , G.P. , Dianatinasab , M. , Diaz , D. , Forooshani , Z.S.D. , Dingels , Z.V. , Dirac , M.A. , Djalalinia , S. , Do , H.T. , Dokova , K. , Dorostkar , F. , Doshi , C.P. , Doshmangir , L. , Douiri , A. , Doxey , M.C. , Driscoll , T.R. , Dunachie , S.J. , Duncan , B.B. , Duraes , A.R. , Eagan , A.W. , Kalan , M.E. , Edvardsson , D. , Ehrlich , J.R. , Nahas , N.E. , Sayed , I.E. , Tantawi , M.E. , Elbarazi , I. , Elgendy , I.Y. , Elhabashy , H.R. , El-Jaafary , S.I. , Elyazar , I.R. , Emamian , M.H. , Emmons-Bell , S. , Erskine , H.E. , Eshrati , B. , Eskandarieh , S. , Esmaeilnejad , S. , Esmaeilzadeh , F. , Esteghamati , A. , Estep , K. , Etemadi , A. , Etisso , A.E. , Farahmand , M. , Faraj , A. , Fareed , M. , Faridnia , R. , Farinha , C.S. e S. , Farioli , A. , Faro , A. , Faruque , M. , Farzadfar , F. , Fattahi , N. , Fazlzadeh , M. , Feigin , V.L. , Feldman , R. , Fereshtehnejad , S.-M. , Fernandes , E. , Ferrari , A.J. , Ferreira , M.L. , Filip , I. , Fischer , F. , Fisher , J.L. , Fitzgerald , R. , Flohr , C. , Flor , L.S. , Foigt , N.A. , Folayan , M.O. , Force , L.M. , Fornari , C. , Foroutan , M. , Fox , J.T. , Freitas , M. , Fu , W. , Fukumoto , T. , Furtado , J.M. , Gad , M.M. , Gakidou , E. , Galles , N.C. , Gallus , S. , Gamkrelidze , A. , Garcia-Basteiro , A.L. , Gardner , W.M. , Geberemariyam , B.S. , Gebrehiwot , A.M. , Gebremedhin , K.B. , Gebreslassie , A.A.A.A. , Hayoon , A.G. , Gething , P.W. , Ghadimi , M. , Ghadiri , K. , Ghafourifard , M. , Ghajar , A. , Ghamari , F. , Ghashghaee , A. , Ghiasvand , H. , Ghith , N. , Gholamian , A. , Gilani , S.A. , Gill , P.S. , Gitimoghaddam , M. , Giussani , G. , Goli , S. , Gomez , R.S. , Gopalani , S.V. , Gorini , G. , Gorman , T.M. , Gottlich , H.C. , Goudarzi , H. , Goulart , A.C. , Goulart , B.N.G. , Grada , A. , Grivna , M. , Grosso , G. , Gubari , M.I.M. , Gugnani , H.C. , Guimaraes , A.L.S. , Guimarães , R.A. , Guled , R.A. , Guo , G. , Guo , Y. , Gupta , R. , Haagsma , J.A. , Haddock , B. , Hafezi-Nejad , N. , Hafiz , A. , Hagins , H. , Haile , L.M. , Hall , B.J. , Halvaei , I. , Hamadeh , R.R. , Abdullah , K.H. , Hamilton , E.B. , Han , C. , Han , H. , Hankey , G.J. , Haro , J.M. , Harvey , J.D. , Hasaballah , A.I. , Hasanzadeh , A. , Hashemian , M. , Hassanipour , S. , Hassankhani , H. , Havmoeller , R.J. , Hay , R.J. , Hay , S.I. , Hayat , K. , Heidari , B. , Heidari , G. , Heidari-Soureshjani , R. , Hendrie , D. , Henrikson , H.J. , Henry , N.J. , Herteliu , C. , Heydarpour , F. , Hird , T.R. , Hoek , H.W. , Hole , M.K. , Holla , R. , Hoogar , P. , Hosgood , H.D. , Hosseinzadeh , M. , Hostiuc , M. , Hostiuc , S. , Househ , M. , Hoy , D.G. , Hsairi , M. , Hsieh , V.C. , Hu , G. , Huda , T.M. , Hugo , F.N. , Huynh , C.K. , Hwang , B.-F. , Iannucci , V.C. , Ibitoye , S.E. , Ikuta , K.S. , Ilesanmi , O.S. , Ilic , I.M. , Ilic , M.D. , Inbaraj , L.R. , Ippolito , H. , Irvani , S.S.N. , Islam , M.M. , Islam , M. , Islam , S.M.S. , Islami , F. , Iso , H. , Ivers , R.Q. , Iwu , C.C.D. , Iyamu , I.O. , Jaafari , J. , Jacobsen , K.H. , Jadidi-Niaragh , F. , Jafari , H. , Jafarinia , M. , Jahagirdar , D. , Jahani , M.A. , Jahanmehr , N. , Jakovljevic , M. , Jalali , A. , Jalilian , F. , James , S.L. , Janjani , H. , Janodia , M.D. , Jayatilleke , A.U. , Jeemon , P. , Jenabi , E. , Jha , R.P. , Jha , V. , Ji , J.S. , Jia , P. , John , O. , John-Akinola , Y.O. , Johnson , C.O. , Johnson , S.C. , Jonas , J.B. , Joo , T. , Joshi , A. , Jozwiak , J.J. , Jürisson , M. , Kabir , A. , Kabir , Z. , Kalani , H. , Kalani , R. , Kalankesh , L.R. , Kalhor , R. , Kamiab , Z. , Kanchan , T. , Matin , B.K. , Karch , A. , Karim , M.A. , Karimi , S.E. , Kassa , G.M. , Kassebaum , N.J. , Katikireddi , S.V. , Kawakami , N. , Kayode , G.A. , Keddie , S.H. , Keller , C. , Kereselidze , M. , Khafaie , M.A. , Khalid , N. , Khan , M. , Khatab , K. , Khater , M.M. , Khatib , M.N. , Khayamzadeh , M. , Khodayari , M.T. , Khundkar , R. , Kianipour , N. , Kieling , C. , Kim , D. , Kim , Y.-E. , Kim , Y.J. , Kimokoti , R.W. , Kisa , A. , Kisa , S. , Kissimova-Skarbek , K. , Kivimäki , M. , Kneib , C.J. , Knudsen , A.K.S. , Kocarnik , J.M. , Kolola , T. , Kopec , J.A. , Kosen , S. , Koul , P.A. , Koyanagi , A. , Kravchenko , M.A. , Krishan , K. , Krohn , K.J. , Defo , B.K. , Bicer , B.K. , Kumar , G.A. , Kumar , M. , Kumar , P. , Kumar , V. , Kumaresh , G. , Kurmi , O.P. , Kusuma , D. , Kyu , H.H. , Vecchia , C.L. , Lacey , B. , Lal , D.K. , Lalloo , R. , Lam , J.O. , Lami , F.H. , Landires , I. , Lang , J.J. , Lansingh , V.C. , Larson , S.L. , Larsson , A.O. , Lasrado , S. , Lassi , Z.S. , Lau , K.M.-M. , Lavados , P.M. , Lazarus , J.V. , Ledesma , J.R. , Lee , P.H. , Lee , S.W.H. , LeGrand , K.E. , Leigh , J. , Leonardi , M. , Lescinsky , H. , Leung , J. , Levi , M. , Lewington , S. , Li , S. , Lim , L.-L. , Lin , C. , Lin , R.-T. , Linehan , C. , Linn , S. , Liu , H.-C. , Liu , S. , Liu , Z. , Looker , K.J. , Lopez , A.D. , Lopukhov , P.D. , Lorkowski , S. , Lotufo , P.A. , Lucas , T.C.D. , Lugo , A. , Lunevicius , R. , Lyons , R.A. , Ma , J. , MacLachlan , J.H. , Maddison , E.R. , Maddison , R. , Madotto , F. , Mahasha , P.W. , Mai , H.T. , Majeed , A. , Maled , V. , Maleki , S. , Malekzadeh , R. , Malta , D.C. , Mamun , A.A. , Manafi , A. , Manafi , N. , Manguerra , H. , Mansouri , B. , Mansournia , M.A. , Herrera , A.M.M. , Maravilla , J.C. , Marks , A. , Martins-Melo , F.R. , Martopullo , I. , Masoumi , S.Z. , Massano , J. , Massenburg , B.B. , Mathur , M.R. , Maulik , P.K. , McAlinden , C. , McGrath , J.J. , McKee , M. , Mehndiratta , M.M. , Mehri , F. , Mehta , K.M. , Meitei , W.B. , Memiah , P.T.N. , Mendoza , W. , Menezes , R.G. , Mengesha , E.W. , Mengesha , M.B. , Mereke , A. , Meretoja , A. , Meretoja , T.J. , Mestrovic , T. , Miazgowski , B. , Miazgowski , T. , Michalek , I.M. , Mihretie , K.M. , Miller , T.R. , Mills , E.J. , Mirica , A. , Mirrakhimov , E.M. , Mirzaei , H. , Mirzaei , M. , Mirzaei-Alavijeh , M. , Misganaw , A.T. , Mithra , P. , Moazen , B. , Moghadaszadeh , M. , Mohamadi , E. , Mohammad , D.K. , Mohammad , Y. , Mezerji , N.M.G. , Mohammadian-Hafshejani , A. , Mohammadifard , N. , Mohammadpourhodki , R. , Mohammed , S. , Mokdad , A.H. , Molokhia , M. , Momen , N.C. , Monasta , L. , Mondello , S. , Mooney , M.D. , Moosazadeh , M. , Moradi , G. , Moradi , M. , Moradi-Lakeh , M. , Moradzadeh , R. , Moraga , P. , Morales , L. , Morawska , L. , Velásquez , I.M. , Morgado-da-Costa , J. , Morrison , S.D. , Mosser , J.F. , Mouodi , S. , Mousavi , S.M. , Khaneghah , A.M. , Mueller , U.O. , Munro , S.B. , Muriithi , M.K. , Musa , K.I. , Muthupandian , S. , Naderi , M. , Nagarajan , A.J. , Nagel , G. , Naghshtabrizi , B. , Nair , S. , Nandi , A.K. , Nangia , V. , Nansseu , J.R. , Nayak , V.C. , Nazari , J. , Negoi , I. , Negoi , R.I. , Netsere , H.B.N. , Ngunjiri , J.W. , Nguyen , C.T. , Nguyen , J. , Nguyen , Michele , Nguyen , Minh , Nichols , E. , Nigatu , D. , Nigatu , Y.T. , Nikbakhsh , R. , Nixon , M.R. , Nnaji , C.A. , Nomura , S. , Norrving , B. , Noubiap , J.J. , Nowak , C. , Nunez-Samudio , V. , Oţoiu , A. , Oancea , B. , Odell , C.M. , Ogbo , F.A. , Oh , I.-H. , Okunga , E.W. , Oladnabi , M. , Olagunju , A.T. , Olusanya , B.O. , Olusanya , J.O. , Oluwasanu , M.M. , Bali , A.O. , Omer , M.O. , Ong , K.L. , Onwujekwe , O.E. , Orji , A.U. , Orpana , H.M. , Ortiz , A. , Ostroff , S.M. , Otstavnov , N. , Otstavnov , S.S. , Øverland , S. , Owolabi , M.O. , A, M.P., Padubidri , J.R. , Pakhare , A.P. , Palladino , R. , Pana , A. , Panda-Jonas , S. , Pandey , A. , Park , E.-K. , Parmar , P.G.K. , Pasupula , D.K. , Patel , S.K. , Paternina-Caicedo , A.J. , Pathak , A. , Pathak , M. , Patten , S.B. , Patton , G.C. , Paudel , D. , Toroudi , H.P. , Peden , A.E. , Pennini , A. , Pepito , V.C.F. , Peprah , E.K. , Pereira , A. , Pereira , D.M. , Perico , N. , Pham , H.Q. , Phillips , M.R. , Pigott , D.M. , Pilgrim , T. , Pilz , T.M. , Pirsaheb , M. , Plana-Ripoll , O. , Plass , D. , Pokhrel , K.N. , Polibin , R.V. , Polinder , S. , Polkinghorne , K.R. , Postma , M.J. , Pourjafar , H. , Pourmalek , F. , Kalhori , R.P. , Pourshams , A. , Poznańska , A. , Prada , S.I. , Prakash , V. , Pribadi , D.R.A. , Pupillo , E. , Syed , Z.Q. , Rabiee , M. , Rabiee , N. , Radfar , A. , Rafiee , A. , Rafiei , A. , Raggi , A. , Rahimi-Movaghar , A. , Rahman , M.A. , Rajabpour-Sanati , A. , Rajati , F. , Ramezanzadeh , K. , Ranabhat , C.L. , Rao , P.C. , Rao , S.J. , Rasella , D. , Rastogi , P. , Rathi , P. , Rawaf , D.L. , Rawaf , S. , Rawal , L. , Razo , C. , Redford , S.B. , Reiner , R.C. , Reinig , N. , Reitsma , M.B. , Remuzzi , G. , Renjith , V. , Renzaho , A.M.N. , Resnikoff , S. , Rezaei , N. , Rezai , M. sadegh , Rezapour , A. , Rhinehart , P.-A. , Riahi , S.M. , Ribeiro , A.L.P. , Ribeiro , D.C. , Ribeiro , D. , Rickard , J. , Roberts , N.L.S. , Roberts , S. , Robinson , S.R. , Roever , L. , Rolfe , S. , Ronfani , L. , Roshandel , G. , Roth , G.A. , Rubagotti , E. , Rumisha , S.F. , Sabour , S. , Sachdev , P.S. , Saddik , B. , Sadeghi , E. , Sadeghi , M. , Saeidi , S. , Safi , S. , Safiri , S. , Sagar , R. , Sahebkar , A. , Sahraian , M.A. , Sajadi , S.M. , Salahshoor , M.R. , Salamati , P. , Zahabi , S.S. , Salem , H. , Salem , M.R.R. , Salimzadeh , H. , Salomon , J.A. , Salz , I. , Samad , Z. , Samy , A.M. , Sanabria , J. , Santomauro , D.F. , Santos , I.S. , Santos , J.V. , Santric- Milicevic , M.M. , Saraswathy , S.Y.I. , Sarmiento-Suárez , R. , Sarrafzadegan , N. , Sartorius , B. , Sarveazad , A. , Sathian , B. , Sathish , T. , Sattin , D. , Sbarra , A.N. , Schaeffer , L.E. , Schiavolin , S. , Schmidt , M.I. , Schutte , A.E. , Schwebel , D.C. , Schwendicke , F. , Senbeta , A.M. , Senthilkumaran , S. , Sepanlou , S.G. , Shackelford , K.A. , Shadid , J. , Shahabi , S. , Shaheen , A.A. , Shaikh , M.A. , Shalash , A.S. , Shams-Beyranvand , M. , Shamsizadeh , M. , Shannawaz , M. , Sharafi , K. , Sharara , F. , Sheena , B.S. , Sheikhtaheri , A. , Shetty , R.S. , Shibuya , K. , Shiferaw , W.S. , Shigematsu , M. , Shin , J.I. , Shiri , R. , Shirkoohi , R. , Shrime , M.G. , Shuval , K. , Siabani , S. , Sigfusdottir , I.D. , Sigurvinsdottir , R. , Silva , J.P. , Simpson , K.E. , Singh , A. , Singh , J.A. , Skiadaresi , E. , Skou , S.T. , Skryabin , V.Y. , Sobngwi , E. , Sokhan , A. , Soltani , S. , Sorensen , R.J.D. , Soriano , J.B. , Sorrie , M.B. , Soyiri , I.N. , Sreeramareddy , C.T. , Stanaway , J.D. , Stark , B.A. , Ştefan , S.C. , Stein , C. , Steiner , C. , Steiner , T.J. , Stokes , M.A. , Stovner , L.J. , Stubbs , J.L. , Sudaryanto , A. , Sufiyan , M.B. , Sulo , G. , Sultan , I. , Sykes , B.L. , Sylte , D.O. , Szócska , M. , Tabarés-Seisdedos , R. , Tabb , K.M. , Tadakamadla , S.K. , Taherkhani , A. , Tajdini , M. , Takahashi , K. , Taveira , N. , Teagle , W.L. , Teame , H. , Tehrani-Banihashemi , A. , Teklehaimanot , B.F. , Terrason , S. , Tessema , Z.T. , Thankappan , K.R. , Thomson , A.M. , Tohidinik , H.R. , Tonelli , M. , Topor- Madry , R. , Torre , A.E. , Touvier , M. , Tovani-Palone , M.R.R. , Tran , B.X. , Travillian , R. , Troeger , C.E. , Truelsen , T.C. , Tsai , A.C. , Tsatsakis , A. , Car , L.T. , Tyrovolas , S. , Uddin , R. , Ullah , S. , Undurraga , E.A. , Unnikrishnan , B. , Vacante , M. , Vakilian , A. , Valdez , P.R. , Varughese , S. , Vasankari , T.J. , Vasseghian , Y. , Venketasubramanian , N. , Violante , F.S. , Vlassov , V. , Vollset , S.E. , Vongpradith , A. , Vukovic , A. , Vukovic , R. , Waheed , Y. , Walters , M.K. , Wang , J. , Wang , Y. , Wang , Y.-P. , Ward , J.L. , Watson , A. , Wei , J. , Weintraub , R.G. , Weiss , D.J. , Weiss , J. , Westerman , R. , Whisnant , J.L. , Whiteford , H.A. , Wiangkham , T. , Wiens , K.E. , Wijeratne , T. , Wilner , L.B. , Wilson , S. , Wojtyniak , B. , Wolfe , C.D.A. , Wool , E.E. , Wu , A.-M. , Hanson , S.W. , Wunrow , H.Y. , Xu , G. , Xu , R. , Yadgir , S. , Jabbari , S.H.Y. , Yamagishi , K. , Yaminfirooz , M. , Yano , Y. , Yaya , S. , Yazdi-Feyzabadi , V. , Yearwood , J.A. , Yeheyis , T.Y. , Yeshitila , Y.G. , Yip , P. , Yonemoto , N. , Yoon , S.-J. , Lebni , J.Y. , Younis , M.Z. , Younker , T.P. , Yousefi , Z. , Yousefifard , M. , Yousefinezhadi , T. , Yousuf , A.Y. , Yu , C. , Yusefzadeh , H. , Moghadam , T.Z. , Zaki , L. , Zaman , S.B. , Zamani , M. , Zamanian , M. , Zandian , H. , Zangeneh , A. , Zastrozhin , M.S. , Zewdie , K.A. , Zhang , Y. , Zhang , Z.-J. , Zhao , J.T. , Zhao , Y. , Zheng , P. , Zhou , M. , Ziapour , A. , Zimsen , S.R.M. , Naghavi , M. , Murray , C.J.L. , 2020 . Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 . The Lancet 396 , 1204 – 1222 . doi: 10.1016/S0140-6736(20)30925-9 OpenUrl CrossRef PubMed 168. ↵ Waite , F. , Sheaves , B. , Isham , L. , Reeve , S. , Freeman , D ., 2020 . Sleep and schizophrenia: From epiphenomenon to treatable causal target . Schizophr. Res., Sleep Pathology in Schizophrenia and the Psychosis Spectrum 221 , 44 – 56 . doi: 10.1016/j.schres.2019.11.014 OpenUrl CrossRef PubMed 169. Wang , J. , Kochunov , P. , Sampath , H. , Hatch , K.S. , Ryan , M.C. , Xue , F. , Neda , J. , Paul , T. , Hahn , B. , Gold , J. , Waltz , J. , Hong , L.E. , Chen , S ., 2021a . White matter brain aging in relationship to schizophrenia and its cognitive deficit . Schizophr. Res . 230 , 9 – 16 . doi: 10.1016/j.schres.2021.02.003 OpenUrl CrossRef PubMed 170. ↵ Wang , J. , Kochunov , P. , Sampath , H. , Hatch , K.S. , Ryan , M.C. , Xue , F. , Neda , J. , Paul , T. , Hahn , B. , Gold , J. , Waltz , J. , Hong , L.E. , Chen , S ., 2021b . White matter brain aging in relationship to schizophrenia and its cognitive deficit . Schizophr. Res . 230 , 9 – 16 . doi: 10.1016/j.schres.2021.02.003 OpenUrl CrossRef PubMed 171. ↵ Wing , D. , Eyler , L.T. , Lenze , E.J. , Wetherell , J.L. , Nichols , J.F. , Meeusen , R. , Godino , J.G. , Shimony , J.S. , Snyder , A.Z. , Nishino , T. , Nicol , G.E. , Nagels , G. , Roelands , B ., 2024 . Fatness but Not Fitness Linked to BrainAge: Longitudinal Changes in Brain Aging during an Exercise Intervention . Med. Sci. Sports Exerc . 56 , 655 . doi: 10.1249/MSS.0000000000003337 OpenUrl CrossRef 172. ↵ Wing , D. , Eyler , L.T. , Lenze , E.J. , Wetherell , J.L. , Nichols , J.F. , Meeusen , R. , Godino , J.G. , Shimony , J.S. , Snyder , A.Z. , Nishino , T. , Nicol , G.E. , Nagels , G. , Roelands , B ., 2022 . Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body composition . Neuroimage Rep . 2 , 100146 . doi: 10.1016/j.ynirp.2022.100146 OpenUrl CrossRef PubMed 173. ↵ Wisse , B.E ., 2004 . The inflammatory syndrome: the role of adipose tissue cytokines in metabolic disorders linked to obesity . J. Am. Soc. Nephrol. JASN 15 , 2792 – 2800 . doi: 10.1097/01.ASN.0000141966.69934.21 OpenUrl Abstract / FREE Full Text 174. ↵ Woodward , M.L. , Gicas , K.M. , Warburton , D.E. , White , R.F. , Rauscher , A. , Leonova , O. , Su , W. , Smith , G.N. , Thornton , A.E. , Vertinsky , A.T. , Phillips , A.A. , Goghari , V.M. , Honer , W.G. , Lang , D.J ., 2018 . Hippocampal volume and vasculature before and after exercise in treatment-resistant schizophrenia . Schizophr. Res . 202 , 158 – 165 . doi: 10.1016/j.schres.2018.06.054 OpenUrl CrossRef PubMed 175. ↵ Wrigglesworth , J. , Ward , P. , Harding , I.H. , Nilaweera , D. , Wu , Z. , Woods , R.L. , Ryan , J ., 2021 . Factors associated with brain ageing - a systematic review . BMC Neurol . 21 , 312 . doi: 10.1186/s12883-021-02331-4 OpenUrl CrossRef PubMed 176. ↵ Xu , Y. , Cai , Z. , Fang , C. , Zheng , J. , Shan , J. , Yang , Y ., 2022 . Impact of aerobic exercise on cognitive function in patients with schizophrenia during daily care: A meta-analysis . Psychiatry Res . 312 , 114560 . doi: 10.1016/j.psychres.2022.114560 OpenUrl CrossRef PubMed 177. ↵ Zhu , X. , Ward , J. , Cullen , B. , Lyall , D.M. , Strawbridge , R.J. , Smith , D.J. , Lyall , L.M ., 2021 . Polygenic Risk for Schizophrenia , Brain Structure, and Environmental Risk in UK Biobank. Schizophr. Bull. Open 2 , sgab042 . doi: 10.1093/schizbullopen/sgab042 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted January 08, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders Deniz Yilmaz , Sergi Papiol , Daniel Keeser , James H Cole , Berend Malchow , Andrea Schmitt , Peter Falkai , Isabel Maurus , Lukas Roell medRxiv 2025.01.08.24319554; doi: https://doi.org/10.1101/2025.01.08.24319554 Share This Article: Copy Citation Tools Brain Age Gap Reduction Following Physical Exercise Mirrors Negative Symptom Improvement in Schizophrenia Spectrum Disorders Deniz Yilmaz , Sergi Papiol , Daniel Keeser , James H Cole , Berend Malchow , Andrea Schmitt , Peter Falkai , Isabel Maurus , Lukas Roell medRxiv 2025.01.08.24319554; doi: https://doi.org/10.1101/2025.01.08.24319554 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 (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) 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:'9ff56619decb593a',t:'MTc3OTM4NTU2OA=='};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())}}}})();