Myo-Inositol Concentration in the Medial Prefrontal Cortex is Associated with Changes in Brain White Matter Microstructure in Early Psychosis

Myo-Inositol Concentration in the Medial Prefrontal Cortex is Associated with Changes in Brain White Matter Microstructure in Early Psychosis | 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 Myo-Inositol Concentration in the Medial Prefrontal Cortex is Associated with Changes in Brain White Matter Microstructure in Early Psychosis View ORCID Profile Tommaso Pavan , Qiaochu Wang , View ORCID Profile Yasser Alemán-Gómez , View ORCID Profile Raoul Jenni , Martine Cleusix , View ORCID Profile Luis Alameda , View ORCID Profile Kim Q. Do , View ORCID Profile Philippe Conus , View ORCID Profile Patric Hagmann , View ORCID Profile Pascal Steullet , View ORCID Profile Paul Klauser , View ORCID Profile Lijing Xin , View ORCID Profile Ileana Jelescu doi: https://doi.org/10.1101/2025.08.15.25333577 Tommaso Pavan 1 Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tommaso Pavan For correspondence: tommaso.pavan{at}chuv.ch Qiaochu Wang 2 Ecole Polytechnique Fédérale de Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yasser Alemán-Gómez 1 Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yasser Alemán-Gómez Raoul Jenni 3 Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Raoul Jenni Martine Cleusix 3 Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Luis Alameda 4 Service of General Psychiatry, Treatment and Early Intervention in Psychosis Program. Lausanne University Hospital (CHUV) , Lausanne, Switzerland 5 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience. King’s College of London , London, UK 6 Centro Investigacion Biomedica en Red de Salud Mental (CIBERSAM); Instituto de Biomedicina de Sevilla (IBIS), Hospital Universitario Virgen del Rocio, Departamento de Psiquiatria, Universidad de Sevilla , Sevilla, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Luis Alameda Kim Q. Do 3 Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kim Q. Do Philippe Conus 4 Service of General Psychiatry, Treatment and Early Intervention in Psychosis Program. Lausanne University Hospital (CHUV) , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philippe Conus Patric Hagmann 1 Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patric Hagmann Pascal Steullet 3 Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pascal Steullet Paul Klauser 3 Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne , Lausanne, Switzerland 7 Division of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and the University of Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul Klauser Lijing Xin 8 Center for Biomedical Imaging (CIBM), Institute of Physics (IPHYS), Ecole Polytechnique Fédérale de Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lijing Xin Ileana Jelescu 1 Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL) , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ileana Jelescu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Recent research highlights the critical role of white matter (WM) alterations in psychosis and schizophrenia (SZ), reporting volumetric and structural brain changes in affected individuals. In this study, we explored the role of astroglia in SZ, which is believed to play a role in white matter integrity. We investigated for the first time the associations between advanced diffusion Magnetic Resonance Imaging (dMRI) measures of WM microstructure, and Magnetic Resonance Spectroscopy (MRS)-derived glial markers in 30 subjects with early psychosis (EP, 24±6 y.o.) versus 41 healthy controls (HC, 25±6 y.o.). We focused on two metabolites involved in glia: myo-Inositol (myo-Ins) and total Choline (tCho), measured in the medial prefrontal cortex (mPFC), relating them to quantitative dMRI metrics derived from Diffusion Kurtosis Imaging (DKI) and WM Tract Integrity-Watson (WMTI-W) biophysical model including mean diffusivity and kurtosis, axonal water fraction and extra-axonal diffusivities in the whole white matter. Our findings reveal a difference between EP and HC in WM diffusivities, specifically in extra-axonal parallel direction, but not in MRS metabolites. However, we found that the mPFC myo-Ins concentrations in EP are exclusively and strongly associated with proximal WM microstructure features, in the form of a positive correlation with axonal water fraction, a proxy for axonal density, and negative correlation with extra-axonal parallel diffusivity, suggesting the white matter alteration could be linked to astrocytic changes in subset of early psychosis. Introduction Psychotic disorders, including schizophrenia (SZ), pose considerable challenges for the affected individuals, their families, and the broader community 1 . Recent research highlights the key role of white matter (WM) in this disease, reporting global structural changes across the brain of people suffering from psychosis 2 , 3 . Thanks to its ability to exploit the random motion of water molecules, diffusion MRI (dMRI) allows us to explore the cellular environment and infer the microstructural properties of the underlying biological tissue in vivo 4 . In participants with psychosis, Diffusion Tensor Imaging (DTI) studies of WM consistently reported patterns of reduced fractional anisotropy (FA) and increased mean diffusivity (MD) 3 , 5 , 6 . These WM alterations are considered global, affecting the entire WM to varying degrees depending on the region 7 , most notably in the frontal and prefrontal areas 2 , 3 . One of the largest meta-analysis of schizophrenia patients vs controls 3 reported the highest effect size in dMRI metrics for average FA across the whole brain ( Cohen’s d =0.42), followed by FA in the anterior corona radiata, and the genu and body of the corpus callosum. In our recent work 8 , we reproduced these high effect sizes in the same brain regions, in patients with early psychosis (EP) and SZ. Additionally, we reported a decrease in Diffusion Kurtosis Imaging metrics 9 (DKI, an extension of DTI which provides complementary information about tissue heterogeneity) in both clinical groups as compared to age range-matched controls, and identified specific microstructure patterns of alterations using the biophysical diffusion model, White Matter Tract Integrity - Watson 10 (WMTI-W). Specifically, WMTI-W enables the estimation of compartment-specific (intra- and extra-axonal) properties that are excellent proxies for intra-axonal injury, inflammation and abnormal myelin integrity 11 – 13 . Thanks to the advanced dMRI techniques, we found that WM alterations manifested predominantly in the extra-axonal space, as a significant increase in extra-axonal diffusivities, and that they were already present at the EP stage 8 . This latter finding is consistent with literature showing that WM changes have been observed before the onset of psychosis 2 , 14 . In ultra high-risk individuals, existing WM alterations do not appear to suddenly worsen with the onset of psychosis 14 . Instead, studies show a progressive reduction in WM FA in those who transitioned 15 , and a prematurely arrested FA increase during development in a longitudinal context 16 . Moreover, these alterations were not associated with symptomatology 3 , 8 . Various mechanisms have been proposed to explain WM alterations, ranging from early aberrant pruning 17 and neuroplasticity 18 , to redox dysregulation 19 , and glutamatergic disfunctions 20 , 21 . A growing number of studies included neuroinflammation 22 – 24 and changes in neuroglia 17 , 25 in the etiology of psychosis. Post-mortem studies reported a decrease in astrocytes 26 and oligodendrocytes 27 , which were estimated to be 20 to 27% lower than in controls 28 , 29 , in SZ specimens when compared to controls. On the other hand, in those specimens who had high expression of inflammatory markers, ~40-50% of the brains exhibit increased immune activation (as measured by interleukin-6) in the dorsolateral prefrontal (PFC) and orbitofrontal cortices 30 , increased astrogliosis 31 and reduced gray matter volume 32 . In-vivo dMRI studies have reported links between WM alterations (FW, free-water imaging 33 , i.e. reduced tissue compartment) and increased blood inflammatory cytokines 34 . Studies using Magnetic Resonance Spectroscopy (MRS), a quantitative MR technique that measures the brain tissue metabolic composition in vivo, have primarily focused on Myo-Inositol (myo-Ins) and total Choline (tCho) levels in patients 35 to investigate glial changes in SZ. Myo-Ins plays three main roles, as constituent of the lipids (phosphoglycerides) in biomembranes, as regulator of the cellular volume through osmolytic action, and as part of intracellular second messenger system 36 . Reports indicate elevated expression of myo-Ins in astrogliosis 37 , glioma 38 (independent of membrane turnover), and the myo-Ins diffusivity seems associated to astrocyte hypertrophy in mice 39 . Thus, myo-Ins has been proposed as a marker of astrocyte density 38 or integrity 40 , and it was associated with inflammatory processes when increased. In SZ, medial prefrontal myo-Ins is often found to be reduced with a small effect size (standardized mean difference = 0.19) 41 , and these reductions are evident already in the early, untreated stages of psychosis 40 . Decreased myo-Ins has been associated with higher depressive symptoms in SZ 42 and across various psychiatric diseases 43 , while an increase in myo-Ins to levels closer to HC improves general symptomatology and social-occupational functioning in first-episode SZ 40 . On the other hand, higher myo-Ins levels have been reported in treatment-naїve first-episode psychosis compared to HC 44 , in treatment resistant compared to both non-treatment resistant SZ and HC, and associated with positive symptoms 44 . These findings may suggest that antipsychotic treatment may decrease the myo-Ins levels 45 , which seems to be higher only during an active/untreated phase. Choline is utilized for phospholipid synthesis and strongly correlates with cell density, making it a marker for membrane turnover 36 , 46 and myelin 36 . Similarly to myo-Ins, tCho concentration is higher in (astro and micro) glia than neurons 47 , in oligodendrocytes precursor cells 48 , 49 , and its diffusion has been proposed as marker of (micro) glia morphology 49 . tCho is reported to increase in dorsolateral and medial PFC in SZ compared to HC 50 , 51 , in treatment-naїve first-episode psychosis 44 , and in treatment resistant SZ compared to non-treatment resistant SZ and HC 45 , likewise myo-Ins. In this study, we aim to investigate the role of astroglia underlying WM alterations in psychosis by linking concentrations of metabolites associated with glial changes (myo-Ins and tCho) in the medial prefrontal cortex (mPFC), a key area for SZ 3 , to the WM microstructure changes observed in early psychosis. To our knowledge, only one other study 42 investigated a similar association in chronic SZ, and reported that, when correlating metabolites with dMRI measures of the anterior corona radiata, myo-Ins was negatively associated with FA in both EP and HC, whereas tCho was not, and that myo-Ins was the sole metabolite associated to WM diffusion changes when accounting for other major metabolites (NAA, Glu). Based on these findings and on previous literature, we anticipate finding lower myo-Ins levels in our treated EP cohort as compared to HC. Therefore, we expect a negative association of myo-Ins with DTI diffusivities and extra-axonal diffusivities, and a positive association with kurtosis and axonal water fraction (i.e. lower glial density leads to higher extra-axonal water mobility, lower tissue heterogeneity and lower density of axons and other cellular processes). In addition, to test the specificity of this association to myo-Ins and tCho, we examine the association with three other major metabolites in an exploratory and comparative manner: glutamate (Glu) and glutathione (GSH), which are respectively involved in glutamatergic dysfunction 20 , 21 and oxidative stress 19 , and N-acetylaspartate (NAA), a marker for neural metabolism and myelin formation 52 , 53 , that has been associated with WM alterations. Furthermore, we hypothesize that WM microstructure metrics will correlate with the glial marker rather than neuronal metabolites since most of our previous findings indicate a marked increase in extra-axonal diffusivities 8 , consistent with glial density changes that would reduce water hindrance outside the axons. In previous work 8 , we also tested the hypothesis that oxidative stress 19 estimated from blood markers could be partially responsible for the WM alteration in SZ. Here, we briefly tested again this hypothesis using direct brain MRS GSH. Methods Participants Data were collected from 79 individuals ( Table 1 ) divided into two groups: 49 healthy controls (HC) and 30 participants with a diagnosis of EP, i.e. individuals within 5 years of having met psychosis threshold according to the Comprehensive Assessment of At-Risk Mental States 57 , CAARMS. Subjects with psychosis related to intoxication or organic brain disease, IQ < 70, reporting alcoholism, drug abuse, major somatic disease, documented anamnestic or current organic brain damage were excluded. EP subjects were recruited from the Treatment and Early Intervention in Psychosis Program 58 (TIPP, University Hospital of Lausanne, Lausanne, Switzerland). HC participants were recruited from the same sociodemographic area as the patients. HC participants were excluded if they or a first-degree family member reported to have suffered from neurological, traumatic, major mood disorder, psychosis, prodromal symptoms, and current or past antipsychotic treatments. The study was approved by the local Ethics Committee (CER-VD 382/11 and 2018-01731). View this table: View inline View popup Download powerpoint Table 1. Cohort demographics. EP: early psychosis; HC: healthy controls; mPFC: medial prefrontal cortex; GSH: glutathione; Glu: Glutamate; Ins: Inositol; TCho: Total Choline; WM: white matter; GAF: global assessment of functioning; PANSS: positive and negative syndrome scale; CPZ: chlorpromazine. † : brain volumes t-tests statistics were computed on the volumes normalized by the estimated total intracranial volume (eTICV). MRI acquisition Data acquisition was performed as described in our previous works 8 , 59 . Briefly, MRI scanning sessions were performed on a 3-Tesla system (Magnetom TrioTim, Siemens Healthineers, Erlangen, Germany), equipped with a 32-channel head coil. A 1-mm isotropic T 1 -weighted image was acquired. Whole-brain diffusion-weighted images (DWI) were acquired using diffusion spectrum imaging (DSI) scheme across 15 b-values, ranging from 0 to 8000 s/mm 2 , spatial resolution of 2.2 × 2.2 × 3 mm 3 . Additional acquisition details can be found in the Supplementary Material. Image preprocessing The T 1 -weighted images were bias field corrected 60 , volumes were estimated with FreeSurfer 61 and normalized by the estimated total intracranial volume (eTICV). The diffusion preprocessing pipeline included MP-PCA denoising and corrections for Gibbs ringing-, EPI distortions-, eddy currents and motion, following most recent guidelines 62 - see Supplementary Material for preprocessing details. Microstructure estimation For DKI and WMTI-W estimation, the diffusion dataset was truncated 63 at b≤2500 s/mm 2 . DKI was fit voxel-wise in the entire brain 64 using a weighted linear-least squares algorithm in Matlab 64 , from which seven scalar maps were derived – four from DTI: radial, mean, axial diffusivity (RD, MD, AD) and FA, and three from DKI: radial, mean, axial kurtosis (RK, MK, AK). WMTI-W parameters were estimated voxel-wise from the seven DTI/DKI scalars, using an in-house Python script ( github . com/Mic-map/WMTI-Watson_Python ), yielding five parameter maps: axonal water fraction f , intra-axonal diffusivity D a , extra-axonal parallel and perpendicular diffusivities D e ,|| , D e ,⊥ and axon orientation alignment c 2 . For each subject, voxels with unphysical values (RD, MD, AD > 4 µm 2 /ms; FA > 1, RK, MK, AK > 10) or negative values in any of the parameters were excluded from the analysis across all parametric maps, if present. 1 H Magnetic resonance spectroscopy All MRS measurements were performed on a 3T Trio MR scanner (Siemens Medical Solutions, Erlangen, Germany) with a TEM volume coil. B 0 field inhomogeneity was optimized using 1 st and 2 nd order shimming with FAST(EST)MAP 65 . 1H MR spectra were obtained in the voxel located in the mPFC using the SPECIAL 66 localization sequence (TE/TR=6/4000ms, VOI=20×20×25mm 3 , 148 averages). Water unsuppressed spectra were acquired with the same parameters (2 averages) as an internal reference. Spectral quantification Metabolite concentrations were quantified with LCModel 67 using unsuppressed water MR spectra as an internal reference. Tissue composition within the MRS voxel was evaluated based on the segmentation of 3D MPRAGE images. Fractions of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) were used to correct for water content in the measurement voxel. The GSH and Glu levels were reported previously 68 . Statistical analysis Tract-based spatial statistic Associations between MRS metabolite concentrations and dMRI microstructure metrics were tested at voxel level using FSL’s Tract-Based Spatial Statistics 69 (TBSS). First, individual FA maps were used to build a study-specific FA template using ANTs 70 and the estimated warps were applied to each of the 12 dMRI parametric maps for spatial normalization. From the average FA, the WM skeleton mask (FA>0.25) was estimated and used for permutation testing (FSL randomise 71 , 5000 permutations) on all dMRI metrics. The resulting statistical maps were corrected using False-Discovery Rate (FDR) and Threshold-Free Cluster Enhancement (TFCE). Significant voxels were averaged and plotted alongside the cluster maps for each significant contrast. Additionally, we report only the associations with average cluster p-value<0.0083 (0.05/6 contrasts). The relationship between metabolite concentration and each microstructure metric was modeled as where E ( V ) is the expected value of a voxel, and M is one of the metabolites of interest. A correction for age and sex was included systematically. Post hoc analyses To verify if the other metabolites could contribute or suppress the associations within the myo-Ins clusters, we re-computed the association between myo-Ins and each microstructure metric in the significant clusters combined, identified with TBSS at the previous step, by regressing myo-Ins together with the concentrations of the other metabolites: Finally, we investigated whether myo-Ins is influenced by treatment, illness duration, or substance use (e.g., cannabis) which could influence metabolite levels or their association with WM microstructure, given their known impact on brain metabolism 72 , 73 . For details see Post Hoc Analyses Methodology in the Supplementary Material. Results Demographics Summary demographics of the cohort can be found in Table 1 . EP (24±6 y/o) and HC (25±6 y/o) did not differ in age (p=0.40). EP participants showed lower normalized total brain (p=0.0068) volume and lower normalized white matter (p=0.020) volume than HC. Functioning levels as measured by the GAF were lower in EP than HC (p<0.0001). Noteworthy is an imbalance between males (~66%) and females (~34%) among both EP and HC participants. Group differences in metabolites and WM microstructure We did not find any difference between EP and HC in any metabolite concentrations (p>0.17). Total Choline (p<0.0001) and NAA (p=0.0024) were positively associated with age but only tCho showed differences between EP and HC in the age effect (age × diagnosis interaction, p=0.017). Metabolite concentrations were not associated with the current dose of antipsychotic medication (chlorpromazine equivalent, p>0.12). Differences between EP and HC in the WM microstructure were reported in our previous work in a larger cohort of which the current one is a subset (allowing for availability of both dMRI and MRS data in the same participant) 8 . Briefly, we found increased DTI diffusivities and reduced kurtosis in EP vs HC (mean | Cohen’s d |=0.46). WMTI-W attributed the findings primarily to reduced axonal water fraction, f , and changes in the extra-axonal compartment, evident from the higher extra-axonal parallel and perpendicular diffusivities D e ,|| , D e ,⊥ in EP compared to HC. In the current smaller cohort subset we only found significantly higher MD, AD, and D e ,|| (p<0.035). Associations between myo-inositol and total choline concentrations and WM microstructure Several clusters of associations were found in WM. Overall, we found that myo-Ins showed the largest clusters of association with the WMTI-W metrics, but not with DTI or DKI metrics (except for axial diffusivity, AD). No associations with tCho were found. Myo-Inositol Myo-Ins showed the widest and strongest association with dMRI metrics in EP. WMTI-W revealed a positive association of myo-Ins with the axonal water fraction, f (p<0.0001, Fig. 1A ), and a negative association with the extra-axonal parallel diffusivity, D e ,|| (p<0.0001, Fig. 1C ) in EP, with significantly different slopes from HC ( f, D e ,|| : p<0.0001, Fig. 1B, D ). Clusters were located primarily in the forceps minor ( Fig. 1C ), genu and body of the corpus callosum ( Fig. 1 ; Hofer and Frahm 74 : region I -prefrontal, II -premotor and supplementary motor, and III -motor), and to a lesser extent the anterior corona radiata ( Fig. 1A ). Myo-Ins was also negatively associated with AD (p<0.0001, Fig. S1 ), but only in the left internal capsule. Download figure Open in new tab Figure 1. TBSS clusters of associations between f and D e ,|| and myo-inositol concentration in the mPFC. Centered around the genu, body of the corpus callosum (CC) and some parts of the anterior corona radiata (ACR) and the forceps minor, WMTI-W identifies lower axonal water fraction, f (A), and higher extra-axonal parallel diffusivity, D e ,|| (C) with lower myo-Ins concentration. In HC, no relationship between myo-Ins and dMRI metrics in the WM was found, while the slopes of EP significantly differed from HC for all the metrics in the same areas (B, D). Note, removing the datapoints with more extreme values (i.e. f 1.6) does not change the results. βEP>0 : contrast testing the slope of EP is significantly positive or negative. βEP> βHC : contrast testing the difference in slopes between EP and HC is significant, L: left hemisphere. Download figure Open in new tab Figure 2. Association of EP’s WM microstructure values in the post hoc cluster with all the metabolite concentrations (myo-Ins, Glu, GSH, tCho, and NAA, see Eq. 2 ). Myo-Inositol drives most of the associations with the WM metrics and is not suppressed by including the other metabolites. Adjusted-R 2 : f =0.23 and D e ,|| =0.36. Post Hoc Analysis When investigating the association of the other metabolites (NAA, GSH, Glu), only one small cluster of WM microstructure association with NAA concentration was found. Higher NAA levels were associated with lower D a (p<0.0001) in parts of the left posterior and superior corona radiata ( Fig. S2 ). We did not find associations with GSH, or Glu. The frequency mask of the combined WM clusters associated with myo-Ins concentration ( Fig. S3 ) showed spatial proximity to the MRS voxel in the mPFC, indicating regional specificity of WM microstructure changes to myo-Ins levels. When the EP’s average cluster mask values were regressed with all metabolites together, the myo-Ins association was not suppressed, confirming the specificity of myo-Ins concentration correlating with WM microstructure in EP: in the f regression model ( Fig. 3A ), myo-Ins was the sole significant metabolite (p<0.014, f adjusted-R 2 =0.23). In the D e ,|| model ( Fig. 3B ), myo-Ins always drove the association (p<0.0035) but tCho showed a trend (p=0.079, D e ,|| adjusted-R 2 =0.36). Furthermore, to ensure robustness of our findings, we repeated the post hoc analyses excluding the EP participant with the most extreme value ( f 1.6 µm 2 /ms), but it did not change the results (p<0.028 for f and D e ,|| slope and group interactions). Excluding female EP participants (who are a minority) also did not change the myo-Ins association with dMRI metrics (p<0.039 for f and D e ,|| slope and group interactions). The results for the analyses including medication, drugs or cannabis exposure are reported in the Post Hoc Analyses Sections in the Supplementary Material. In short, none of the confounding variables influenced the relationship of WM microstructure ( f, D e ,|| ) with myo-Ins. Discussion In this work, we linked the mPFC myo-Ins and tCho concentrations, which are glial markers 35 , to the WM microstructure features in EP. Myo-Ins concentration in the mPFC showed the strongest and largest association clusters to the WM microstructure among all investigated metabolites. In EP, myo-Ins correlated positively with axonal water fraction, f , while large clusters of negative associations were also found with D e ,|| . However, we did not find such associations in HC nor with tCho. In an exploratory fashion, we also investigated associations with three major metabolites known to be involved in schizophrenia, Glu 20 , GSH 19 and NAA 55 , but we found limited associations with the microstructure, except for a negative association between NAA concentration and D a in EP, confirming myo-Ins as the most prominent metabolite associated with WM alterations in EP. Finally, we verified that our findings were not influenced by indirect effects of treatment, substance exposure 72 , 73 or other demographic characteristics. Microstructure metrics are considered sensitive to myelination but also to glial cell density 11 as increases or decreases in tissue cellularity could hinder or facilitate water movement, respectively, altering the diffusivity properties of the WM 11 , 75 . In cuprizone-intoxicated mice 75 , the initial acute inflammation sharply lowers extra-axonal diffusivities (↓ D e ,|| , ↓ D e ,⊥ ) due to cellular crowding of the extra-axonal space as a result of gliosis. Once acute inflammation subsides, demyelination dominates, reducing the axonal water fraction (↓ f ) while raising diffusivities (↑ D e ,|| , ↑ D e ,⊥ ), relative to controls. Furthermore, longitudinal validation studies related reduced parallel diffusivity to astrogliosis 75 – 77 , and a diffusion-weighted MRS study using the same model of cuprizone intoxication showed that myo-Ins diffusivity rises with histologically-confirmed astrocyte hypertrophy 78 . Together, these findings suggest that astrocytic changes directly influence diffusivity metrics. Contrary to our expectations, the average myo-Ins concentrations in the EP group did not differ from HC in our cohort. In their review, Das et al. reported that in studies with higher number of female patients, the myo-inositol reduction is much more pronounced 41 . Thus, the absence of such group differences in metabolite concentrations in our study may be due to the sex imbalance in our EP cohort (20 male vs 10 female), but our power to test sex × diagnosis interactions was limited. However, the TBSS analysis (corrected for age and sex) showed that participants with the most altered WM (estimates further away from HC, Fig. 1A,C ) had the lowest mPFC myo-Ins levels. Low myo-Ins may indicate a reduction in astrocyte density, integrity (e.g. atrophy 26 , 79 ) or deficits in astrocyte activation and recruitment 41 in the mPFC, which may extend beyond that area to neighboring WM tracts. The main clusters of associations included consistently the anterior corona radiata, forceps minor, and the genu and body of the corpus callosum, all white matter regions and fiber bundles adjacent or with projection to and from the mPFC, the target area of the MRS voxel. This suggests that mPFC myo-Ins concentrations are related to the WM microstructure both locally (within the MRS voxel: forceps minor) and proximally (adjacent or connected to the MRS voxel: corpus callosum body and corona radiata). Notably, the anterior corona radiata and corpus callosum have consistently been identified as among the most affected regions in SZ 3 , 8 and early-onset SZ 80 . Relative to distribution values in HC, only a subset of EP patients displays lower myo-Ins, lower f and higher D e ,|| which may constitute a patient subgroup. In a meta-analysis of Das et al. 41 , the authors reported a small but statistically significant reduction in medial prefrontal myo-inositol levels (effect size = 0.2) in individuals with SZ. In the light of the small effect size, the authors proposed that the biological pathways affecting the myo-inositol and astroglia were likely to operate only in a subset of patients with schizophrenia 41 . Remarkably, our findings strongly support this interpretation, suggesting that a combination of MRS mPFC myo-Ins and WMTI-W metrics could serve as a potential imaging biomarker for stratifying patients into subgroups. Similarly, glial changes are a core feature of SZ 26 . Electron microscopy studies showed dystrophic and swollen astrocytes in SZ 81 . However, GFAP-based and Nissl staining studies reported more controversial findings, indicating that the density reduction may not be extensive 26 , or possibly characterize only a subset of patients 41 which seems to be the case in our cohort. For example, in their meta-analysis Trépanier et al. 82 found no consistent alterations in glial fibrillary acidic protein (GFAP) expression, a result potentially explained by the heterogeneity of the SZ population, which may comprise distinct subgroups with and without astrocytic abnormalities. 3 , 8 ,80 The causal relation between mPFC myo-Ins and WMTI-W f and D e ,|| is complex and merits dedicated investigation beyond the current work. Here, we limit ourselves to some possible explanations: an astrocyte dysfunction more broadly affecting and propagating to the frontal WM in a subset of EP may be an interpretation for the observed association patterns. A reduction in the water hindrance due to the less dense astrocyte crowding of the WM extra-axonal space would be captured as a decrease in (↓ f ), and an increase of extra-axonal parallel diffusivity (↑ D e ,|| ) 11 , 75 – 77 . This would also suggest that the WMTI-W parameters f and D e ,|| may have higher specificity to WM alterations caused by glial changes than DKI and DTI scalars. In parallel, myo-Ins has an osmolytic 36 ,83 effect (i.e. regulates cellular osmotic pressure), thus low myo-Ins may also be related to astrocyte and glia volume reduction or atrophy 26 in the WM, which has been found in the prefrontal WM of SZ speciments 26 , 79 , and could show similar WM patterns (↓ f , ↑ D e ,|| ) at low myo-Ins concentrations. Alternatively, reduced oligodendrocyte density of up to 30% is a common replicated finding in SZ 26 ,84,85 . These cells provide metabolic 86 and myelination support 87 to the neurons. A reduced number of oligodendrocytes would yield a similar pattern to astrocytic deficits, via affected myelination and increased extra-axonal mobility (↓ f , ↑ D e ,|| , ↑ D e,⊥ ,), which is consistent with our findings in the current work and in our previous work 8 . Myo-Ins levels correlating primarily with D e ,|| rather than intra-axonal diffusivity ( D a ) further supports the extra-axonal nature of these changes, corroborating our previous reports of altered extra-axonal compartment in EP and SZ 88 . Notably, the lack of correlations in the same regions for Glu and NAA, intraneural metabolites 48 , 56 , reinforces this interpretation. We also reported a negative association between AD and myo-Ins in the internal capsule. Such association may reflect the indirect effect of myo-Ins changes to the mPFC axons projecting to and from the internal capsule. To our knowledge, this is the second work investigating the relationship of WM microstructure with myo-Ins and tCho in schizophrenia, and the first one reporting on EP together with specific WM microstructure metrics that aid the biological interpretation. In the work of Chiappelli and colleagues 89 , the authors reported a negative association between FA and myo-Ins in both SZ and HC, and concluded that, among other metabolites such as NAA, Glu and tCho, myo-Ins was the only one driving the FA changes. The negative association with FA was interpreted as evidence of an effect of inflammation on WM microstructure. Here, we did not find any association with FA and the significant associations were only in the EP group, but not in the HC. Several factors may explain this discrepancy. First, we measured the myo-Ins concentration in the frontal GM dominated area and not purely in the WM. Second, our cohort exclusively included EP instead of SZ. Third, our participants were younger (average age of ~25 vs ~39 years old) with a narrower age distribution (18-36 vs 20-58 years old), although Chiappelli et al. reported no age effect on such association. Aside from myo-Ins, the sole other metabolite we found associated with WM microstructure was NAA. In EP, NAA was found to be negatively associated with D a in parts of the left superior and posterior corona radiata, so in different regions than for myo-Ins, possibly indicating an indirect relation between mPFC NAA and less complex and ramified intra-axonal space, axonal metabolism 90 , or NAA diffusion 91 in the corona radiata of EP. The lack of association between WM and tCho, Glu, or GSH does not exclude their role in SZ but suggests that they may not directly contribute to WM alteration in EP, or that our study lacks sufficient power to detect their effect. This view is supported by the age-tCho relationship and its trend difference between EP and HC, which could act as a confounder, misattributing covariance between metabolites and WM to age or vice versa. The absence of associations with GSH is in line with our previous work on the peripheral GSH-redox system 92 . However, we did not replicate the previously reported association between GSH levels and the average generalized FA of the cingulum WM bundle 93 , possibly due to cohort differences (EP vs SZ). Taken together, this suggests that the effect of GSH on white matter may be subtle, potentially requiring larger regions of interest to detect it. As such, the TBSS method may lack the sensitivity to capture this effect. As last set of analyses, we also tested the effects of treatment, illness duration, and substance exposure 72 , 73 on our results, to verify our findings were not influenced or caused by unaccounted confounding effects. Previous works showed that lower myo-Ins was related to the severity of drug and cannabis abuse 72 , while myo-Ins levels were higher in treatment-naїve first-episode psychosis than controls 44 , and higher in treatment resistant than non-treatment resistant SZ or HC 45 . However, we found no association with antipsychotic medication, illness duration or substance exposure, indicating that the links between myo-Ins concentrations and WM microstructure in neighboring tracts are independent of these factors. Limitations The first limitation of our work is the different spatial localization of the MRS and dMRI signals. Whereas the dMRI metrics were computed and tested in the WM, our single voxel MRS acquisition was centered on the mPFC and contained primarily GM together with some WM and CSF fractions. However, the significant clusters were spatially coherent with the location of the MRS voxel, supporting a plausible microstructural–metabolic WM-GM coupling. Additionally, by employing a spatially unbiased approach such as TBSS rather than a region-of-interest method, we identified anatomically meaningful clusters aligned with the MRS voxel placement in a data-driven way, which strengthens the interpretability and validity of our findings. Furthermore, the sample size of our EP cohort is relatively small (n=30) and featured more males (n=20) than females (n=10). However, excluding the female group did not change the substance of our findings. Despite a systematic correction for age and sex in our tests, and the narrow age span of our participants, we cannot fully exclude the influence of maturation and aging on the WM microstructure and metabolite concentrations (myo-Ins and tCho increase, Glu and NAA decrease with aging 94 ). Finally, despite our best efforts, the measures used for medication (chlorpromazine-equivalent dose at the time of the scan) and substance use are imperfect and may not capture the patients’ cumulative drugs histories and effects. Conclusions In this work we showed for the first time the comprehensive associations between mPFC concentrations of glial metabolites and advanced dMRI WM microstructure metrics in EP. We demonstrated that lower levels of mPFC myo-inositol in patients are strongly associated with proximal changes in WM tissue diffusivities that corresponded to lower axonal water fraction and less hindered extra-axonal parallel diffusivity (higher D e ,|| , lower f ), suggesting white matter alteration could be linked to astrocytic changes in a subgroup of early psychosis patients. Furthermore, joint assessment of myo-inositol levels and white matter microstructure metrics may serve as a potential imaging biomarker for stratifying patients into subgroups, possibly characterized by glia-driven pathophysiology. Data Availability All data produced in the present study are available upon reasonable request to the authors Conflict of Interest The authors report no conflict of interest. Acknowledgments This work was supported by the Swiss National Science Foundation (PCEFP2_194260, to I.J.; 320030_197787 to PH and YA), the National Center of Competence in Research (NCCR) “SYNAPSY - The Synaptic Bases of Mental Diseases” from the Swiss National Science Foundation (n° 51NF40 – 185897 to KQD & PC) and the Foundation Alamaya. Dr. Alameda is supported by Carigest fellowship and by Frutiger Adrian et Simone fellowship. Dr. Dwir and P. Klauser are supported by Frutiger Adrian & Simone fellowship. Dr. L. Xin is supported by SNSF Consolidator grant n° 213769. We acknowledge the resources and expertise provided by the CIBM Center for Biomedical Imaging. References 1. ↵ Wittchen HU , Jacobi F , Rehm J , Gustavsson A , Svensson M , Jönsson B , et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010 . 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