Reducing Functional Dysconnectivity in Schizophrenia Spectrum Disorders

Reducing Functional Dysconnectivity 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 Reducing Functional Dysconnectivity in Schizophrenia Spectrum Disorders View ORCID Profile Stephan Wunderlich , View ORCID Profile Daniel Keeser , View ORCID Profile Johanna Spaeth , View ORCID Profile Isabel Maurus , View ORCID Profile Cagatay Alici , View ORCID Profile Andrea Schmitt , View ORCID Profile Peter Falkai , View ORCID Profile Sophia Stoecklein , View ORCID Profile Lukas Roell doi: https://doi.org/10.1101/2024.09.26.24314430 Stephan Wunderlich 1 Department of Radiology, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stephan Wunderlich For correspondence: Stephan.Wunderlich{at}med.uni-muenchen.de Daniel Keeser 2 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany 3 NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, Ludwig-Maximilians-University Munich , 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 Johanna Spaeth 2 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Johanna Spaeth Isabel Maurus 2 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , 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 Cagatay Alici 1 Department of Radiology, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cagatay Alici Andrea Schmitt 2 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany 4 Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of Sao Paulo , São Paulo, Brazil 5 DZPG (German Center for Mental Health), 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 2 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany 5 DZPG (German Center for Mental Health), partner site Munich/Augsburg , Munich, Germany 6 Max Planck Institute for Psychiatry , 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 Sophia Stoecklein 1 Department of Radiology, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sophia Stoecklein Lukas Roell 2 Department of Psychiatry and Psychotherapy, LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany 3 NeuroImaging Core Unit Munich (NICUM), LMU University Hospital, Ludwig-Maximilians-University Munich , Munich, Germany 6 Max Planck Institute for Psychiatry , 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 Background As a condition of dysconnectivity, schizophrenia spectrum disorders (SSD) are characterized by positive, negative, and cognitive symptoms. To improve these symptoms in SSD, physical exercise interventions show promise. We examined if reductions of functional dysconnectivity following exercise therapy are associated with clinical improvements in SSD and explored potential genetic underpinnings. Methods The study utilized data from the ESPRIT C3 trial, investigating the effects of aerobic exercise versus flexibility, strengthening, and balance training on different health outcomes in individuals with SSD. Functional dysconnectivity in 23 patients relative to a healthy reference sample, was assessed both pre- and post-intervention. Changes of functional dysconnectivity after exercise and their clinical relevance were evaluated. An imaging transcriptomics approach was used to study the link between changes in functional dysconnectivity and gene expression profiles. Results We observed substantial reductions of functional dysconnectivity on the whole-brain level linked to enhanced gene expression mainly in oligodendrocytes. With regard to the clinical implications, decreases of dysconnectivity in the default-mode network were associated with improvements in global functioning. Reductions of dysconnectivity within the salience network were linked to improvements in symptom severity. Lastly, reductions of functional dysconnectivity in language regions such as Broca’s area were related to cognitive benefits. Conclusions Our study supports a recent theory of oligodendrocyte pathology in SSD and suggests that reducing functional dysconnectivity in the default-mode, salience, and language network reflect a potential therapeutic target to improve global functioning, total symptom severity, and cognitive impairments in post-acute SSD. Trial name: ESPRIT C3 Registry: International Clinical Trials Database, ClinicalTrials.gov Registration number: NCT03466112 URL: https://clinicaltrials.gov/ct2/show/NCT03466112?term=NCT03466112&draw=2&rank=1 INTRODUCTION The dysconnection hypothesis of schizophrenia claims that psychosis is characterized by impaired synaptic efficacy throughout the brain, leading to widespread macroscale deteriorations in intrinsic and extrinsic connectivity related to schizophrenia-typical positive, negative, and cognitive symptoms ( 1 ). Accordingly, several large-scale neuroimaging studies have demonstrated multiple aberrant functional connectivity patterns in people with schizophrenia spectrum disorders (SSD) compared to healthy controls ( 2 – 10 ). In particular, functional dysconnectivity within and between core brain networks, such as the default-mode, salience, fronto-parietal, somatormotor, limbic, visual, subcortical or dorsal attention network, reflects a stable transdiagnostic neural marker in psychiatry as assessed by functional magnetic resonance imaging (fMRI) ( 3 , 4 , 8 , 9 ). Concerning SSD-specific alterations, several particular dysconnectivity patterns have been proposed: For instance, specific seeds within the default-mode, salience, fronto-parietal and limbic networks show functional dysconnectivity in SSD, but not in other psychiatric conditions ( 3 ). Moreover, certain functional disturbances within numerous essential circuits, such as the cortico-striato-pallido-thalamo-cortical circuit ( 3 , 11 – 19 ), have been frequently reported in SSD. Another central epicenter of continuous neural decline throughout the disease course of psychosis is the hippocampal formation ( 20 ), whose connections with the prefrontal cortex are reported to be disturbed in SSD ( 21 ). Recently, striatal dysconnectivity has become one of the most promising fMRI-based biomarkers of SSD ( 6 , 7 , 22 ) that even has the potential to predict improvements in positive symptom severity after antipsychotic treatment ( 6 ). Accordingly, as part of the cortico-striato-pallido-thalamo-cortical loop, functional hyperconnectivity between the thalamus/basal ganglia and the auditory-somatomotor network contributes to positive symptoms, whereas hypoconnectivity between the thalamus/basal ganglia and the middle frontal gyrus is linked to cognitive deficits ( 14 ). Furthermore, negative symptom severity is associated with disturbed functional connectivity within the default-mode network ( 10 ). At the same time, deficits in cognitive domains such as processing speed and working memory are linked to functional dysconnectivity within the salience, auditory, somatomotor and visual networks ( 2 ). Importantly, cognitive deterioration is strongly related to impairments in social and occupational functioning ( 23 , 24 ) and thus contributes to low recovery rates in SSD ( 25 ). Hence, grounded in the dysconnection hypothesis of schizophrenia, the current state of research demonstrates that SSD is characterized by multiple dysconnectivity patterns across the whole brain that are closely intertwined with the main clinical symptoms of psychotic disorders, namely positive and negative symptoms, as well as cognitive deficits accompanied by impairments in daily life functioning. To achieve further clinical improvements beyond treatment-as-usual, physical exercise interventions have been proposed as an add-on therapy: Large-scale evidence reveals beneficial effects of different types of physical exercise treatments in people with SSD on positive symptoms ( 26 – 35 ), negative symptoms ( 26 – 38 ), cognitive impairments ( 35 , 39 – 41 ) and daily life functioning ( 27 , 29 , 42 ). These effects are expected to be driven by multiple structural and functional adaptations of the brain ( 43 ), but the underlying neural mechanisms are not fully understood. Preliminary evidence indicates that higher aerobic fitness, as an indicator of regular involvement in physical exercise, is associated with increased functional connectivity within the default-mode network in SSD ( 44 ). Concurrently, aerobic exercise may induce functional adaptations in the cortico-striato-pallido-thalamo-cortical loop and the default-mode network ( 45 ). However, these studies examined functional connectivity patterns within specific networks relevant to SSD without putting these patterns in relation to a healthy norm. Hence, previous evidence does not allow conclusions to be drawn about potential normalization processes of functional connectivity following physical exercise treatment in SSD. To address this gap, techniques of normative modeling are required. Concerning functional brain connectivity assessed by resting-state fMRI, Stoecklein et al. ( 46 – 48 ) have proposed the dysconnectivity index (DCI) that quantifies the deviation of functional connectivity from a healthy reference norm for each voxel in each subject and that summarizes these deviation scores as a whole-brain measure of functional dysconnectivity. Importantly, the DCI can also be computed for specific brain regions and networks, referred to as the specific DCI. Aiming to provide further insights in the genetic underpinnings of macroscale neural alterations such as functional dysconnectivity, an imaging transcriptomics approach has been proposed recently ( 49 – 51 ). In this context, such technique enables to explore associations between functional dysconnectivity patterns in patients with SSD and general gene expression profiles in the human brain derived from the Allen Human Brain Atlas ( 52 ). Applying imaging transcriptomics has the potential to provide insights into the genetic origins of functional dysconnectivity in SSD, thereby offering a more sophisticated mechanistic understanding of the disorder ( 49 – 51 ). Combining the DCI with an imaging transcriptomics approach, we investigate connectome-based mechanisms that drive clinical improvements in schizophrenia and study the genetic basis of changes in functional dysconnectivity. A better understanding of such mechanisms may help to develop targeted therapies and guide future treatment decisions. METHODS AND MATERIALS The current work is based on data from the Enhancing Schizophrenia Prevention and Recovery through Innovative Treatments (ESPRIT) C3 study. The ESPRIT C3 study is an already completed multicenter randomized-controlled trial that explored the effects of aerobic exercise compared to flexibility, strengthening and balance training on several health outcomes in people with SSD. Details on the study and the main clinical results are published elsewhere ( 35 , 53 ). The study is in line with the Declaration of Helsinki and ethical approval was provided by the local ethics committee of the Ludwig-Maximilians-University Hospital. With respect to the project at hand, only data that were acquired at the Department of Psychiatry and Psychotherapy and the Department of Radiology of the Ludwig-Maximilians-University Hospital in Munich were utilized. Study Design and Sample For the main ESPRIT C3 study, the intention-to-treat sample consisted of 180 individuals with SSD. Participants were randomly assigned either to aerobic endurance training (AET) or to flexibility, strengthening and balance training (FSBT). Patients in both groups exercised up to three times per week, between 40 and 50 minutes, for six months. In the AET group, participants cycled on a stationary bicycle ergometer at an individualized moderate exercise intensity determined via a stepwise lactate threshold test. Patients in the FSBT group performed various stretching, mobility, stability, balance, and relaxation exercises. Details regarding the study design, randomization process, blinding procedures, organization of the exercise training, and other relevant methodological considerations are described elsewhere ( 35 , 53 ). Regarding the present work, only data from subjects who underwent structural and functional magnetic resonance imaging (MRI) before and after six months of the intervention were considered. After quality control, a total of 23 people with SSD were considered in the final statistical analyses. The healthy reference sample comprised 200 individuals (120 female, 80 male), with an average age of 30.20 years (± 3.19 years), using data from the Genomics Superstruct Project (GSP) ( 54 ). MRI Data Acquisition SSD patients were scanned with a 3T Siemens Magnetom Skyra MRI scanner (SIEMENS Healthineers AG, Erlangen, Germany) at the Department of Radiology of the Ludwig-Maximilians-University Hospital Munich. One 3D T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence with an isotropic spatial resolution of 0.8 x 0.8 x 0.8 mm 3 and two resting-state functional echo planar imaging (EPI) sequences with a total duration of twelve minutes were acquired. Supplementary Table S1 displays the scanning parameters. The scanning protocol utilized for the GSP data closely resembles that of the current scanning protocol. Quality Control and Preprocessing Structural and resting-state fMRI data were quality controlled based on both visual inspection of images before and after preprocessing and the automated quality control software MRIQC ( 55 ). All data achieved after preprocessing had a temporal signal-to-noise ratio (tSNR) above 100 and a mean framewise displacement (mFD) below 0.3 mm. Structural MRI data were processed using FreeSurfer version 6.0 ( http://surfer.nmr.mgh.harvard.edu ). Preprocessing of the resting-state fMRI was performed using fMRIPrep version 20.2.2 ( 56 ). The first five functional frames were removed, while the remaining frames were standardized and smoothed with a 6.0 mm Full Width at Half Maximum Gaussian kernel. MCFLIRT (FSL v5.0.9) was applied for the subsequent head motion correction using ICA-AROMA ( 57 ). Lastly, nuisance regression, filtering and detrending were performed. For the whole-brain DCI, blood-oxygenation level-dependent signals were extracted for each subject, and correlation matrices were calculated separately for the left hemisphere (3352 voxels) and right hemisphere (3316 voxels), followed by z-standardization. In the case of the calculation of the specific DCI, correlation matrices were computed based on blood-oxygenation level-dependent signals from specific seeds representing the respective brain regions and networks of interest and subsequently z-standardized. Further details on data preprocessing are described in the supplemental information. Calculation of the Whole-Brain DCI The DCI was computed similarly to approaches outlined in prior work by Stoecklein et al. ( 46 ), as detailed in the supplemental information. In essence, connections deviating beyond a specific threshold from the distribution observed in the reference group were classified as “dysconnected”. The dysconnectivity count (DCC) for each patient was then aggregated within each hemisphere and normalized by the number of voxels in that hemisphere to yield the whole-brain DCI. Calculation of the DCI within Specific Brain Regions and Networks In order to probe subtle changes in brain connectivity, we extracted pertinent brain voxels from both the reference group and schizophrenia patients. We examined these voxels across multiple networks, encompassing the visual, somatomotor, limbic, fronto-parietal, default-mode, dorsal attention, salience, and subcortical networks. Additionally, we explored connectivity within specific brain regions of interest, such as the hippocampal formation and between the hippocampus and the prefrontal cortex, the thalamus and the middle frontal gyrus and the thalamus and the somatomotor network. Following that, we exclusively calculated the DCC for each of these designated brain regions and networks, yielding a specific DCI that encapsulates these specific functional dysconnectivity patterns. Assessment of Whole-Brain DCC Changes and Clinical Outcomes The Global Assessment of Functioning (GAF) scale was administered to assess the general status of psychiatric symptoms and functioning ( 58 ). To cover total schizophrenia symptom severity, consisting of positive and negative symptoms, as well as general psychopathology, the Positive and Negative Syndrome Scale (PANSS) ( 59 ) was utilized. Aiming to measure global cognitive performance, several cognitive tests that target different subdomains of cognition (e.g. short- and long-term memory, working memory, verbal fluency, sustained attention) were performed and summarized to a composite score (for details see supplemental information). All clinical measures were acquired at baseline before the start of the intervention and post intervention after six months of physical exercise. The change scores between baseline and post-intervention were calculated in such a way that positive values represent an improvement and negative values represent a deterioration on the respective scale. The whole-brain DCC of each voxel was normalized across all subjects and the change scores were computed by subtracting the DCC for each voxel at baseline from the respective DCC at follow-up. Hence, a positive DCC change score reflected a reduction of dysconnectivity in the corresponding voxel. Statistical Data Analysis Aiming to examine if whole-brain and specific functional dysconnectivity in people with SSD decreases after physical exercise treatment, we calculated 13 linear mixed effect models for repeated measures using the lme4 package ( 60 ) in R v4.2.2. Session (baseline, six months), age, sex, chlorpromazine equivalents, number of achieved exercise sessions, group (AET, FSBT) and hemisphere (left, right) were included as fixed effects, while the whole-brain and specific DCI served as dependent variables. A random intercept was included for each subject. The p-values of the predictor Session were extracted and corrected for multiple testing across the 13 linear mixed effect models using the False Discovery Rate (FDR) method. In the case of significance (q < 0.05) after FDR correction, Tukey post-hoc tests for the particular DCI outcome were calculated to identify the direction and size of the effect, expressed as Coheńs d and the respective 95% confidence interval. To study associations between clinical changes in the GAF, PANSS, and cognitive composite scores on the one hand and changes in the whole-brain DCC of each voxel on the other hand, we performed voxel-wise Pearson’s correlations. Following this, we projected the correlation values onto a normative brain. This approach enabled us to investigate how changes in clinical measures aligned with dysconnectivity patterns in the brain across the aforementioned assessment scales. Imaging Transcriptomics Approach We first created DCC brain maps in the MNI standard space at 2mm isotropic resolution, indicating the severity of dysconnectivity for each subject in each voxel at both timepoints. Using the glm module in Nilearn version 0.10.4, we computed a voxel-wise ANOVA for repeated measures including session (baseline, six months), age, sex, chlorpromazine equivalents, number of achieved exercise sessions and group (AET, FSBT) as factors. We derived continuous statistical brain maps indicating the voxel-wise change of dysconnectivity from pre- to post-intervention across the whole sample. These maps were parcellated according to the Brainnetome atlas ( 61 ) using the parcellate module from neuromaps ( 62 ). Analogously, we parcellated the Allen Human Brain Atlas based on the Brainnetome atlas in MNI space at an isotropic resolution of 2mm using the abagen toolbox ( 63 ). Based on the resampled and parcellated statistical DCC brain map, we generated respective null models according to Burt et al. (2020) ( 64 ) to preserve spatial autocorrelation and computed Spearman correlations between DCC changes and gene expression data utilizing the stats module from neuromaps ( 62 ). This resulted in a vector of correlation coefficients for each gene included in the Allen Human Brain Atlas, revealing the correlation between functional dysconnectivity changes in patients and the gene-specific expression in the human brain across all regions defined in the Brainnetome atlas. To gain an insight in the types of genes that reveal consistent associations with changes in functional dysconnectivity, we used this vector to calculate a gene set enrichment analysis based on the corresponding gene-to-cell-type annotations provided by Lake et al. (2018) ( 65 ) using the gseapy toolbox ( 66 ). RESULTS Patients Characteristics The majority of the final study sample was male, middle-aged, well-educated, and in the post-acute phase of the disease. Moreover, most of the participants considered here were randomized to the FSBT exercise group, having on average completed 1.3 exercise sessions per week. The detailed characteristics of the study sample are summarized in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Sample Characteristics Changes of Whole-Brain and Specific Functional Dysconnectivity after Physical Exercise The predictor Time was significant after FDR correction for the following whole-brain and specific DCI variants: whole brain (q < 0.001), visual network (q < 0.001), somatomotor network (q < 0.001), limbic network (q < 0.001), default-mode network (q < 0.001), salience network (q < 0.001), subcortical network (q < 0.001), thalamic-prefrontal connection (q < 0.001), thalamic-somatomotor connection (q < 0.001), hippocampal-prefrontal connection (q < 0.001), and connectivity within the hippocampal formation (q < 0.001). These effects indicated changes over time in the corresponding DCI values across both exercise groups. There was no significant change in the specific DCI for the fronto-parietal and dorsal attention network. Tukey post-hoc tests demonstrated significant reductions from baseline to post-intervention of the whole-brain DCI (d = −2.73, CI = [−3.15, −2.31], p tukey < 0.001) and the specific DCI of the somatomotor network (d = −2.52, CI = [−2.94, −2.12], p tukey < 0.001), limbic network (d = −1.87, CI = [−2.28, − 1.45], p tukey < 0.001), default-mode network (d = −2.19, CI = [−2.61, −1.78], p tukey < 0.001), salience network (d = −1.84, CI = [−2.25, −1.42], p tukey < 0.001), subcortical network (d = −1.18, CI = [−1.59, −0.76], p tukey < 0.001), thalamic-middle frontal connection (d = −1.47, CI = [−1.89, −1.05], p tukey < 0.001), thalamic-somatomotor connection (d = −2.25, CI = [−2.67, −1.84], p tukey < 0.001), hippocampal-prefrontal connection (d = −1.34, CI = [−1.76, −0.92], p tukey < 0.001), and connectivity within the hippocampal formation (d = −2.19, CI = [−2.61, −1.78], p tukey < 0.001). Increases in the DCI were observed for the visual network (d = 1.45, CI = [1.03, 1.86], p tukey < 0.001). Figure 1 illustrates the time effects on all computed DCI scores. The full test statistics of the other fixed effects are summarized in Table S2. Download figure Open in new tab Figure 1. Changes in Whole-Brain and Specific DCI Brain Region and Network Specific Correlations Between Reductions of Whole-Brain DCC and Clinical Improvements Figure 2A depicts the strongest correlation between improvement in the GAF and adaptations in the DCC in the default-mode network on a voxel-wise level. Dysconnectivity reduction in insular regions is associated with improvements in GAF and PANSS. Furthermore, dysconnectivity in somatomotor and frontoparietal networks predominantly correlates with improvements in the PANSS score (2B). Additionally, improved cognition predominantly manifests through a reduction in DCC, notably observed in left hemisphere language regions such as Broca’s areas (2C). Download figure Open in new tab Figure 2. Correlation Between DCC Change and Change in Clinical Measurements Association between Changes of Functional Dysconnectivity and Gene Expression Figure 3 summarizes the results from the gene set enrichment analysis. Across brain regions, we obtained that reductions of functional dysconnectivity from baseline to post-intervention were linked to higher expression of genes typically enriched in oligodendrocytes, oligodendrocyte precursor cells (OPCs), astrocytes, endothelial cells, pericytes, and microglia. Contrastingly, reductions of functional dysconnectivity from baseline to post-intervention were associated with lower expression of genes typically enriched in excitatory and inhibitory neurons. Download figure Open in new tab Figure 3. Gene set enrichment analysis DISCUSSION The current study explored the clinical relevance of potential decreases in functional dysconnectivity after physical exercise treatment in people with SSD and the respective genetic underpinnings, aiming to identify connectome-based mechanisms that drive clinical improvements. We observed substantial reductions in the whole-brain DCI after physical exercise therapy in SSD. To our knowledge, this is the first time such a finding has been reported. Previous results in manifested SSD and in individuals with clinical high-risk for psychosis suggest adaptations of single functional connections induced by aerobic exercise interventions within the default-mode network, the cortico-striato-pallido-thalamo-cortical loop or between hippocampal and occipital regions ( 45 , 67 , 68 ). These studies are in line with our current results, as they indicate that functional connectivity patterns in SSD can generally change in the context of a physical exercise intervention. However, direct comparisons to the current findings remain challenging due to substantial differences in methodology. Particularly, the distinctive approach of the DCI in contrast to conventional measures of functional connectivity provides unique insights into individual-specific deviations of functional connectivity. In our case, reductions of functional dysconnectivity after physical exercise can be interpreted as normalization processes of the individual brains of patients. Such inference is not possible when only focusing on functional connectivity changes, as performed in previous studies ( 45 , 67 , 68 ). From a clinical standpoint, compared to previous research, our objective was to investigate individual alterations in functional dysconnectivity over time and under two distinct exercise interventions. However, previous studies have primarily compared a moderate aerobic exercise intervention either against another form of exercise regimen ( 45 , 67 ) or against a waitlist control ( 68 ). Hence, these examinations aimed to investigate potential adaptations in functional connectivity particularly induced by aerobic exercise interventions compared to a control condition. In contrast, we were interested in potential changes in functional dysconnectivity after physical exercise treatment without claiming that the substantial reductions we obtained were specifically induced by exercise. Consequently, the large decrease in functional dysconnectivity observed in our data may result from a complex interaction of several factors the patients experienced in the course of their study participation, such as social, psychological, exercise or even placebo effects. Thus, based on our data, the current substantial reductions in whole-brain functional dysconnectivity are not attributable solely to the effects of physical exercise but reveal that such global interventions can be used to counteract functional brain dysconnectivity in SSD. This is of particular importance because SSD is seen as a disorder of dysconnectivity both on the theoretical ( 1 ) and on the empirical level ( 3 ). Beyond reductions in whole-brain functional dysconnectivity, we also found specific decreases in multiple networks and regions relevant to SSD, such as the default-mode network, salience network, somatomotor network, limbic network, subcortical network, prefrontal-thalamic-somatomotor pathway, hippocampal-prefrontal connections, and within the hippocampal formation. Aiming to cover the clinical implications of changes in functional dysconnectivity, our findings suggest that reductions of functional dysconnectivity in the default-mode network were linked to improvements in global functioning. The default-mode network is generally associated with self-referential and interoceptive behavior, theory of mind, and social cognition ( 69 , 70 ). In the psychiatric context, disorder-related alterations within the default-mode network are linked to negative symptom severity in people with SSD ( 10 ) and to impaired inhibition control in several psychiatric disorders ( 9 ). Our previous analysis indicated that aerobic exercise-induced volume increases in the posterior cingulate gyrus, a central node of the default-mode network, were linked to an improvement in general disorder severity ( 45 ). These results reveal the broad and diverse functionality of the default-mode network in both healthy individuals and psychiatric patients. Our findings on the associations between modifications of dysconnectivity in the default-mode network and changes in global levels of functioning correspond to this notion. In sum, we propose that functional dysconnectivity within the default-mode network may reflect a promising neural target for future therapeutic approaches to improve the global level of functioning in people with SSD. Targeted treatment candidates may be brain stimulation approaches such as focused ultrasound or transcranial magnetic stimulation that both have the potential to modulate functional connectivity ( 71 , 72 ). Our findings further indicate that a reduction in functional dysconnectivity in insular regions and the somatomotor network was associated with ameliorations in total symptom severity. Alterations of the insula in SSD have been associated with the impaired ability of patients to process representations of themselves, leading to a disturbed discrimination between self-generated and external information ( 73 ). As the latter is supposed to contribute to two main symptoms observed in people with SSD, namely hallucinations and delusions, our current findings on the relation between reductions in insular dysconnectivity and improvements in clinical symptoms appear plausible. With respect to the functional connectivity of the somatomotor network, previous evidence also suggests associations with different symptom domains of SSD ( 11 , 14 , 74 , 75 ). Hence, we conclude that reducing dysconnectivity in the insula and the somatomotor network through targeted therapeutic interventions such as aforementioned brain stimulation techniques ( 71 , 72 ) may represent a promising strategy to achieve ameliorations in the total symptom severity of patients with SSD. Lastly, a decrease in functional dysconnectivity in language regions on the left hemisphere such as Broca’s areas was related to cognitive improvements. To our knowledge, this is the first time such a finding has been described in people with SSD. In general, parts of Brocás area are part of a broader left-hemispheric language network involved in language processing, while other parts contribute to wider cognitive functions such as explicit and working memory ( 69 , 76 ). Given that the cognitive composite score used in the current study comprised several cognitive tests that required language and memory functions (see supplemental information), the observed link between dysconnectivity changes in Brocás area and cognitive adaptations seems reasonable. Consequently, achieving decreases in functional dysconnectivity in Brocás area using respective brain stimulation approaches ( 71 , 72 ) may help to increase cognitive benefits in future treatments for people with SSD. Delving into the genetic underpinnings of reductions of functional dysconnectivity after physical exercise in SSD, we observed that these reductions were most strongly associated with a higher expression of genes typically enriched in oligodendrocytes and OPCs. In other words: In brain regions that show stronger reductions of functional dysconnectivity, the expression of genes related to oligodendrocytes and OPCs is generally more pronounced. This is the first time such finding has been reported. However, a recent theory of the etiology of cognitive deficits in SSD claims that an erroneous maturation of OPCs into oligodendrocytes leads to impaired myelination and disturbed micro-und macroscale connectivity manifesting in global cognitive deteriorations ( 77 ). This theory is based on a wide range of different findings supporting the involvement of OPCs and oligodendrocytes in the pathophysiology of schizophrenia: For instance, among other cell types, OPCs and oligodendrocytes were partly enriched in genes linked to SSD ( 78 ), while several single genes that play a prominent role for the functioning of OPCs and oligodendrocytes have been related to reduced structural connectivity in SSD ( 77 ). In addition, several stereology studies reveal reductions of the number of oligodendrocytes in people with SSD compared to controls ( 79 – 81 ). Our current results can be embedded in this line of evidence, as they indicate that reductions of functional dysconnectivity in SSD mainly occur in brain regions in which the expression of genes related to OPCs and oligodendrocytes is higher. This may point toward a remyelination process that may have occurred in the context of the physical exercise intervention, although such inferences should be interpreted with caution given the limitations of an imaging transcriptomics approach ( 49 – 51 ). Despite these promising findings, we acknowledge that our study includes several important limitations that provide essential implications for future research. First, only 23 patients with SSD could be included in the final analysis. The study population, which the current analysis is based on, is at a rather stable phase of the disease, preventing the generalizability of our current findings to a broader spectrum of patients with SSD. Especially the current very large effect sizes regarding the dysconnectivity reductions may be rather sample-specific and will probably not be obtained in more representative patient samples. Therefore, we emphasize that our results require replication in an independent and larger sample of people with SSD assessed in a comparable interventional design. Second, due to a lack of a control group of healthy individuals we could not control for technical scanner variance and natural fluctuations of functional dysconnectivity patterns potentially occurring between both measurement timepoints. However, we assume that potential natural fluctuations of functional dysconnectivity that may be present in healthy cohorts cannot explain the substantial and systematic effects obtained in this analysis. Nonetheless, future studies of this type should include a longitudinal cohort of healthy controls, aiming to systematically account for technical variability and natural changes in functional dysconnectivity. Third, the voxel-wise correlations between adaptations in functional dysconnectivity and clinical changes reflect rather descriptive associations without systematic significance testing and correction for multiple comparisons. We decided to follow a more liberal statistical approach as the first step, aiming to generate hypotheses for subsequent studies. Hence, future hypothesis-driven research approaches in this area should evaluate if the present associations between clinical changes and adaptations in functional dysconnectivity in the default-mode network, insula, somatomotor network and Brocás area are reproducible. To conclude, our study indicates that decreases of functional dysconnectivity within the default-mode network is closely tied to ameliorations in global functioning. Reductions in functional dysconnectivity within the salience and somatomotor network correlate with improvements in total symptom severity. Similarly, a decrease in functional dysconnectivity within Broca’s area corresponds with improved cognitive functioning. Observed reductions of functional dysconnectivity are related to an increased expression of genes enriched in oligodendrocytes and oligodendrocyte precursor cells. Our results provide first insights into potential connectome-based therapeutic targets in post-acute patients with SSD. In particular, future therapeutic approaches could aim to reduce functional dysconnectivity in the default-mode network, insula, somatomotor network, and Brocás area to achieve multifaceted improvements on the clinical level. Data Availability All data produced in the present study are available upon reasonable request to the authors. 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 Janssen, Lundbeck, Otsuka, Servier, and Richter. SW, DK, JS, IM, CA, AS, SS and LR declare no conflicts of interest or financial disclosures relevant to this research. ACKNOWLEDGMENTS 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) to PF and AS. Furthermore, the study was supported by the Else Kröner-Fresenius Foundation with the Research College “Translational Psychiatry” to PF, AS, and IM (Residency/PhD track of the International Max Planck Research School for Translational Psychiatry [IMPRS-TP]). 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). The authors express their 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. ↵ Friston K , Brown HR , Siemerkus J , Stephan KE (2016): The dysconnection hypothesis ( 2016 ). Schizophrenia Research 176 : 83 – 94 . OpenUrl CrossRef PubMed 2. ↵ Adhikari BM , Hong LE , Sampath H , Chiappelli J , Jahanshad N , Thompson PM , et al. 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Share Reducing Functional Dysconnectivity in Schizophrenia Spectrum Disorders Stephan Wunderlich , Daniel Keeser , Johanna Spaeth , Isabel Maurus , Cagatay Alici , Andrea Schmitt , Peter Falkai , Sophia Stoecklein , Lukas Roell medRxiv 2024.09.26.24314430; doi: https://doi.org/10.1101/2024.09.26.24314430 Share This Article: Copy Citation Tools Reducing Functional Dysconnectivity in Schizophrenia Spectrum Disorders Stephan Wunderlich , Daniel Keeser , Johanna Spaeth , Isabel Maurus , Cagatay Alici , Andrea Schmitt , Peter Falkai , Sophia Stoecklein , Lukas Roell medRxiv 2024.09.26.24314430; doi: https://doi.org/10.1101/2024.09.26.24314430 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 (573) Allergy and Immunology (865) Anesthesia (302) Cardiovascular Medicine (4453) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1515) Epidemiology (15242) Forensic Medicine (30) Gastroenterology (1131) Genetic and Genomic Medicine (6615) Geriatric Medicine (669) Health Economics (1001) Health Informatics (4552) Health Policy (1372) Health Systems and Quality Improvement (1614) Hematology (543) HIV/AIDS (1270) Infectious Diseases (except HIV/AIDS) (15929) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6625) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3344) Ophthalmology (979) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5461) Public and Global Health (9252) Radiology and Imaging (2207) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1197) Rheumatology (597) Sexual and Reproductive Health (715) Sports Medicine (530) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a02d3fa8de7f4807',t:'MTc3OTk3MTE5Ng=='};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())}}}})();