Distinct Neurochemical Signature of Mindfulness and Progressive Muscle Relaxation in limbic regions: a randomized controlled MR spectroscopy study

Distinct Neurochemical Signature of Mindfulness and Progressive Muscle Relaxation in limbic regions: a randomized controlled MR spectroscopy study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Distinct Neurochemical Signature of Mindfulness and Progressive Muscle Relaxation in limbic regions: a randomized controlled MR spectroscopy study Quentin Beaufort, Lucie Angel, Laurent Barantin, Frederic Andersson, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7722934/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Mindfulness training (MT) and progressive muscle relaxation (PMR) are widely used stress-reduction practices, yet their neurochemical specificity remains almost unexplored. In a randomized, pre–post study, 30 healthy right-handed female speech-language therapy students were assigned to six weeks of MT or PMR. Short-echo time single-voxel H 1 -MRS (3T PRESS, TE ≈ 35 ms) and structural MRI were acquired at baseline and post-intervention. Four voxels were placed in the right amygdala, left hippocampus, right dorsal anterior cingulate cortex (dACC), and right posterior cingulate cortex (PCC); spectra were quantified with standard procedures (Osprey). Relative to baseline, MT produced increased N-acetyl-aspartyl-glutamate (NAAG) and total creatine (tCr) in the PCC, accompanied by reductions in glutamine (Gln) and glycine (Gly). In the amygdala, NAAG decreased following MT. No significant metabolite changes were observed in the PMR group, and no effects emerged in the hippocampal or dACC voxels in either group. Whole-brain morphometry showed no detectable structural change over the six-week interval. Overall, these findings indicate that mindfulness—but not PMR—was associated with a selective neurochemical rebalancing at the limbic–default mode network interface, consistent with phenomenological and network-level accounts of mindfulness. NAAG modulation, in particular, may represent a candidate biomarker of contemplative practice. Health sciences/Neurology Biological sciences/Neuroscience magnetic resonance spectroscopy mindfulness meditation progressive muscle relaxation glutamatergic system Figures Figure 1 Figure 2 INTRODUCTION In recent decades, stress reduction techniques and cognitive therapies rooted in mindfulness have gained significant attention in healthcare 1 – 4 . Programs such as Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT) have demonstrated remarkable efficacy in reducing stress, anxiety, and depression while concurrently enhancing overall quality of life 5 – 7 . These interventions have not only proven beneficial for individuals across various demographics but have also shown promise in alleviating workplace stress, particularly among healthcare professionals 8 – 10 . Within academic settings, mindfulness training has emerged as an interesting tool for fostering emotional resilience in the face of academic pressures and for combatting anxiety 11 , 12 . Moreover, research suggests that mindfulness practices could positively influence cognitive functions, particularly attentional capacities, executive processes and memory abilities 13 – 17 . Another technique, Jacobson's Progressive Muscle Relaxation (PMR) method, has long been recognized for its ability to induce deep relaxation and reduce muscle tension 18 . PMR involves systematically tensing and then relaxing different muscle groups throughout the body, leading to a state of profound relaxation with mental calmness 19 . Studies have highlighted its efficacy in ameliorating symptoms of anxiety and depression, paralleling the benefits observed with mindfulness-based approaches. Additionally, PMR has also shown promising effects on cognitive functions, including improvements in executive functions and attentional processes 20 – 22 . However, the mechanisms of action of these two practices on cognitive functions remain unclear. We currently do not know whether these programs are effective through achieving mental calmness that enhances attentional and/or metacognitive abilities, or whether their effectiveness stems from emotional regulation. The effects of mindfulness have been widely studied with neuroimaging tools. Studies in experienced meditators have suggested that mindfulness effects are accompanied by structural and functional modifications in certain brain regions involved in metacognition, memory, and emotional regulation, such as the prefrontal cortex, hippocampus, amygdaloid complex, cingulate cortex, or insula 23 – 26 . Other studies in meditation-naïve subjects have observed functional MRI changes after short training programs of a few weeks, akin to those seen in experienced meditators 27 , 28 . PMR, in contrast, has almost not been studied by neuroimaging methods 29 . The neuro-metabolic mechanisms underlying the effect of mindfulness and relaxation remain almost unknown despite being investigable in vivo through magnetic resonance spectroscopy (MRS), which allows quantification of neurometabolites 30 . Especially, in limbic regions, it could offer valuable insights into the mechanisms through which mindfulness and PMR may modulate cognitive processes and emotional responses 31 . In this context, our study aimed to explore and compare the effects of mindfulness and PMR programs on brain metabolism in limbic regions through MRS, as well as their respective impacts on brain structure, to approach the neurobiological mechanisms involved during these practices. RESULTS Baseline characteristics and adherence At baseline, the two groups did not differ in demographic characteristics. The PMR and MT groups were comparable in terms of age (median [IQR]: 21 [20, 21] vs. 22 [20, 22]) and cognitive baseline as assessed by the Mill Hill Vocabulary Scale (25.0 [24.0, 28.5] vs. 25.0 [24.0, 28.0]). Academic levels were similarly distributed across 1st, 2nd, and 3rd-year students in both groups. All participants completed the intervention and both MRI sessions. Median self-reported daily home practice was 30 minutes [25–33] in the PMR group and 30 minutes [20–35] in the mindfulness group. Spectroscopy Metabolite estimates and change scores are reported in Table 1 A and 1 B for each region of interest. After six weeks, the mindfulness group showed a significant decrease in NAAG (2.15 [1.62, 2.53], 1,83 [1.52, 1.91] ; p = 0.008) within the right amygdala. In the right posterior cingulate cortex (PCC), mindfulness was associated with significant increases in NAAG (2.29 [2.14, 2.25], 2.36 [2.26, 2.63] ; p = 0.013) and total creatine (tCr) (12.46 [12.15, 12.77], 12.87 [12.23, 13.32] ; p = 0.035), accompanied by decreases in glutamine (Gln) (4.17 [3.90, 4.57], 3.84 [3.57, 4.10] ; p = 0.013) and glycine (Gly) (0.70 [0.47, 0.99], 0.38 [0.25, 0.69] ; p = 0.027). No significant metabolite changes were detected in the left hippocampus or right dorsal anterior cingulate cortex (dACC) in either group. A graphical summary of the spectroscopic findings is provided in Fig. 1 . Structural MRI Morphometric analyses revealed stable cortical and subcortical volumes across visits, with no longitudinal systematic differences between groups. In particular, gray-matter volume in the ACC, PCC, amygdala, hippocampus, caudate, insula, and temporo-parietal junction remained stable over the training interval. Complete morphometric results are provided in Supplementary Tables. DISCUSSION The present findings reveal distinct neurochemical signatures of mindfulness compared to PMR within limbic regions. After six weeks of training, mindfulness was associated with decreased NAAG in the amygdala, together with increased NAAG and total creatine (tCr) in the PCC, alongside reductions in Gln and Gly. In contrast, PMR did not produce significant changes within these regions. Importantly, these neurochemical effects were obtained from spectra of overall good-to-excellent quality. Spectral quality was generally good in the deeper regions (amygdala and hippocampus) and excellent in the cingulate voxels. In addition, voxel tissue composition was stable across visits and comparable between groups, with GM, WM, and CSF distributions matching the expected anatomy of each region. These methodological checks support the robustness of the present findings and reduce the likelihood that the metabolite differences reflect acquisition artefacts or voxel placement biases. Our results can therefore be examined through several complementary lenses spanning neurochemical mechanisms, phenomenology, and large-scale network dynamics. From a neurochemical interpretation perspective, the observed modulation of NAAG is compatible with differential engagement of inhibitory glutamatergic control. NAAG is a neuronally synthesized dipeptide neurotransmitter acting primarily on presynaptic and glial metabotropic glutamate receptors type 3 (mGluR3), thereby constraining excessive glutamate release 32 . The reduction of NAAG in the amygdala may indicate a loosening of this inhibitory brake in a context where emotional reactivity is already down-regulated by mindfulness training, consistent with fMRI studies showing attenuated amygdala responses after meditation practice 2 . By contrast, the increase of NAAG in the PCC accords with strengthened mGluR3-mediated suppression of background excitatory drive in a central hub of the DMN, which is deeply implicated in self-referential cognition 33 . Concomitant tCr elevation suggests enhanced energetic buffering, while decreases in Gln and Gly point toward down-regulation of the astrocytic glutamate–glutamine cycle and reduced NMDA co-activation 34 . Together, these changes converge on a more metabolically efficient and less excitatory baseline in PCC circuits. The glial contribution further supports this account: NAAG catabolism by glial GCPII and astrocyte-mediated neurotransmitter cycling are central for shaping cortical excitability 35 . These biochemical adaptations parallel phenomenological reports of mindfulness. Practitioners commonly describe reduced cognitive reactivity and increased equanimity, which may be understood as experiential correlates of enhanced inhibitory balance within networks subserving self-awareness. Neuroimaging studies have consistently demonstrated altered DMN activity and connectivity following mindfulness, particularly within PCC and medial prefrontal regions, together with greater coupling to executive control areas such as the dorsal anterior cingulate and dorsolateral prefrontal cortex 2 , 33 . The metabolite pattern observed here is congruent with these network-level changes, suggesting that biochemical adaptations provide a substrate for the transformation of self-referential processing. By contrast, PMR primarily anchors attention to interoceptive and somatic sensations through cycles of muscular tension and release. While this reliably decreases autonomic arousal 29 , it does not typically alter the experiential stance toward cognitive events, which may explain the absence of detectable changes in limbic neurochemical changes. Another dimension concerns the temporal profile of these practices. Mindfulness is thought to induce both state-related and trait-like changes, with cumulative adaptations emerging after sustained training, whereas PMR is often characterized by short-lived, state-dependent effects that may dissipate before metabolite acquisition. The regional pattern of NAAG, Gln, and Gly observed here points to a recalibration of the excitation–inhibition (E/I) balance, a mechanism central to DMN regulation and its integration with limbic circuitry 36 . In this sense, mindfulness appears to stabilize long-term network excitability, whereas PMR may primarily exert transient physiological benefits. From a clinical perspective, these preliminary findings may have translational relevance. Dysregulation of glutamatergic tone within the PCC and amygdala has been implicated in affective disorders. Converging preclinical and translational evidence indicates that augmenting NAAG–mGluR3 signaling—e.g., via inhibition of glutamate carboxypeptidase II (GCPII)—can normalize excitatory drive and improve cognition 37 – 39 . Our observation of region-specific NAAG modulation with mindfulness therefore raises the possibility that contemplative practice could act as an endogenous modulator of this pathway, offering a non-pharmacological adjunct to restore glutamatergic balance and improve emotion regulation. To date, very few studies have used MRS to investigate the cerebral effects of mindfulness, and, to our knowledge, none has specifically examined progressive PMR. In a pre–post exploratory study after 10 hours of mindfulness training, Tang et al 40 reported increases in glutamate and the composite Glx within the rostral ACC (which would be active following the commission of an error). By contrast, we did not observe comparable changes in the dorsal ACC —our pre-specified ROI chosen for its evaluative/performance-monitoring role 41 —suggesting that ACC subregions may differentially respond to contemplative practice. In long-term Zen meditators, Fayed et al. 42 identified higher myo-inositol in the PCC, interpreted as a glial/microglial signature; we did not replicate this effect, plausibly because our intervention was short in duration relative to years of practice in expert cohorts. In parallel with these neurochemical changes, we found no evidence of concomitant macroscopic structural modifications. Although morphometric alterations—particularly in the ACC, PCC, amygdala, and hippocampus—have been reported previously, the most methodologically rigorous study to date did not confirm such effects 43 . Our findings are consistent with that observation and suggest that a few weeks of training may be insufficient to produce detectable morphological changes. Notably, proton magnetic resonance spectroscopy can reveal biochemical alterations that precede—or occur in the absence of—observable morphometric differences 30 . The study has limitations. The modest sample size constrains power and precision. To enhance internal validity and reduce within-group variance, we recruited a homogeneous cohort (healthy, right-handed female speech-language therapy students); while this likely improved measurement precision, it limits generalizability across sex, handedness, age, and educational background. In addition, the absence of a passive control group prevents us from fully excluding non-specific influences such as expectancy or scanner habituation. Nevertheless, directly comparing two structurally matched interventions (mindfulness vs PMR) helped dissociate practice-specific neurochemical adaptations from general relaxation effects; the lack of significant metabolite changes in PMR, together with the known test–retest stability of major metabolites in 3T short-TE PRESS 44 , 45 , supports the specificity of the mindfulness-related findings. At the methodological level, the use of short-TE PRESS at 3T enabled the detection of metabolites with relatively short T2 relaxation, such as myo-inositol, Glu, and NAAG, but these measurements remain dependent on macromolecule modeling, voxel placement, and spectral quality 44 . The absence of PMR-related changes should therefore be interpreted cautiously, as it does not exclude the possibility of transient or regionally restricted effects, for example in sensorimotor or insular cortices. Replication with larger cohorts, ultra-high-field MRS (7T), spectral editing methods such as MEGA-PRESS for GABA/GSH, and functional MRS, will be valuable in future research. Moreover, acquisition timing relative to the intervention and interindividual differences in phenomenology (e.g., baseline meta-awareness) should be considered as potential moderators of metabolic plasticity. Overall, the data suggest that mindfulness—but not PMR—may promote a selective neurochemical rebalancing within the limbic–DMN interface, consistent with both subjective phenomenology and network-level adaptations. The modulation of NAAG, in particular, may represent a promising biomarker of contemplative practice, bridging neurochemical regulation, large-scale network dynamics, and altered self-awareness. METHODS Participants and study design We enrolled 30 healthy, right-handed female speech-language therapy students (≥ 18 years) from the School of Speech-Language Pathology, University of Tours. Exclusion criteria were: inability to complete the study; prior participation in MBSR or MBCT; neurological or psychiatric disorder (including major depressive disorder, schizophrenia, suicide attempt within the past 3 months, current substance use disorder, or complicated grief); ongoing psychotherapy; current psychotropic medication; standard contraindications to brain MRI; and pregnancy. All participants received detailed information about study procedures and provided written informed consent. Participants were randomized to one of two arms (n = 15 per group) for a 6-week intervention: mindfulness training (MT) or progressive muscle relaxation (PMR) 46 . The programs were delivered in parallel by certified instructors. Each arm comprised weekly supervised group sessions (2 h 30 min) and daily home practice with a 30-min target; participants self-reported daily practice time. Two MRI visits (baseline and post-intervention) included structural imaging and spectroscopy. Participants were instructed to maintain their daily practice until completion of the second visit. Investigators were blinded to group allocation. All procedures followed the pre-registered protocol and adhered to relevant guidelines and regulations. The study protocol was approved by the regional ethics review board (Comité de Protection des Personnes Ouest-1, IORG0008143; OMB No. 0990 − 0279) and registered on ClinicalTrials.gov (date of registration: 02/02/2023 ; identifier: NCT05710250). MRI and MRS acquisitions The MRI scans before and after training were performed on a 3.0-T scanner (Siemens Magnetom Prisma, Erlangen, Germany) with a 64-channel head coil. High-resolution sagittal images were acquired with volumetric T1-weighted 3D MPRAGE with the following parameters: TR = 2300ms, TE = 2.98ms, flip angle = 9°, FOV = 256mm, matrix = 256x256, slice thickness = 1mm). The MPRAGE sequence was used to prescribe the MRS volumes of interest (VOI). Proton MR spectroscopy used a short-echo time PRESS (short-echo time Point RESolved Spectroscopy) sequence (TE = 35 ms, TR = 2000 ms, flip angle = 90°). Four single-voxel placements targeted the right amygdala (RA; 16×20×14 mm³), left hippocampus (LH; 35×20×10 mm³), right dorsal anterior cingulate cortex (dACC; 40×13×14 mm³), et right posterior cingulate cortex (PCC; 37×12×16 mm³). Prior to data acquisition, automatic B0 shimming based on a double-echo gradient-echo (GRE) field map was performed initially on the MRS voxel of interest. For each voxel, spectra were acquired with and without water suppression (averages = 128 for the water-suppressed scan). The unsuppressed water scan was used for eddy-current correction, frequency/phase referencing, and absolute quantification (water scaling). Per-voxel acquisition time was 4:26 (water-suppressed) plus 0:18 (unsuppressed water reference). Voxel lateralization was chosen based on prior MRI evidence of anatomical or functional changes in these regions following mindfulness programs. Short-TE PRESS allows robust quantification of major metabolites with high signal-to-noise ratio and minimal J-modulation losses 47 . These metabolites are validated markers of neuronal, glial, osmotic, and energetic processes relevant to stress regulation 48 . PRESS was particularly suited to our multi-voxel design (four limbic regions), offering optimal balance between acquisition time and spectral reliability. MEGA-PRESS, while optimal for γ-aminobutyric acid (GABA) detection, is less practical for repeated multi-site acquisitions due to prolonged scan times 49 . DANTE-PRESS, though promising for resolving spectral overlaps, remains experimental and not widely validated for small deep structures. PRESS thus provided a versatile, validated framework appropriate for an exploratory, pre–post intervention design. Single-voxel MR spectroscopy placement was performed by a trained neuroradiologist (QB, 6 years of experience) using precise anatomical localization from the 3D T1-weighted sequence and multiplanar reconstructions, with preservation of these landmarks enabling accurate repositioning of voxels at identical locations during the post-training MRI by the same neuroradiologist. The PRESS sequence allowed independent adjustment of the three voxel dimensions to match the morphology of target structures (e.g., the hippocampus, with its narrow profile and oblique orientation) and to minimize partial volume effects (e.g., in the amygdala, due to its small size) (Fig. 2 ). MRS data analysis Spectral data were processed and quantified using Osprey, an open-source software package for in vivo magnetic resonance spectroscopy analysis (version v2.4.0 ; https://github.com/schorschinho/osprey ) 50 , implementing an automated pipeline that includes raw data conversion, coil combination, eddy-current correction, frequency and phase alignment, residual water removal, baseline modeling, co-registration to 3D T1 MPRAGE images, segmentation and tissue fraction calculation and finally metabolites quantification steps. Metabolite concentrations were estimated by non-linear least-squares fitting of simulated basis sets, with quality control metrics assessing signal-to-noise ratio, linewidth, and fit residuals. Spectra were retained for analysis only if they met the following quality criteria: SNR(NAA) ≥ 20 (NAA peak height at 2.01 ppm divided by the standard deviation of noise in a signal-free region), FWHM ≤ 0.09–0.1 ppm. These methodological standards are consistent with recent consensus guidelines 44 , 45 and ensure robust metabolite quantification in short-TE PRESS data at 3T. Due to suboptimal spectra (e.g., low SNR, broad linewidth), two amygdala and two hippocampus spectra, as well as one spectrum each from the dACC and PCC, were excluded from analysis in the MT group. For the same reason, one right-amygdala and one left-hippocampus spectrum were excluded from the PMR group. Analysed spectra quality metrics are presented in Table 2 . Table 1 A : Metabolite concentrations in posterior and dorsal anterior cingulate cortex (median [Q1–Q3], mM), pre- and post-training in both groups. dACC : dorsal Anterior cingulaire cortex, PCC : Posterior cingulate cortex, NAA: N-Acetylaspartate, NAAG : N-Acetylaspartylglutamate, tNAA : total N-Acetylaspartate, Cr : Creatine, PCr : Phopho-Creatine, tCr: total creatine, Gln : Glutamine, Glu : Glutamate, Glx : Glutamine + Glutamate, Gly : glycine, mI : Myo-inositol, tCho : total Choline, Lac : Lactate. PCC Progressive muscle relaxation group \(\:{n}_{PMR}=15\) Mindulness group \(\:{n}_{M}=14\) Pre-training Post-training p-value Pre-training Post-training p-value NAA \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 15.92 [15.36, 16.29] 15.14 [14.69, 15.98] 0.055 15.49 [14.88, 16.59] 15.42 [15.08, 15.92] 0.808 NAAG \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 2.38 [2.23, 2.55] 2.39 [2.14, 2.66] 0.489 2.29 [2.14, 2.35] 2.36 [2.26, 2.63] 0.013 tNAA \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 18.38 [17.70, 18.75] 17.64 [17.06, 18.24] 0.208 17.58 [17.14, 19.02] 17.74 [17.39, 18.36] 0.761 Cr \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 7.53 [7.28, 8.45] 7.33 [6.79, 7.72] 0.055 7.60 [7.09, 8.03] 7.51 [7.17, 7.90] 1.00 PCr \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 5.54 [4.79, 5.79] 5.66 [5.30, 6.24] 0.107 4.96 [4.87, 5.45] 5.34 [4.66, 5.88] 0.104 tCr \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 13.08 [12.64, 13.84] 13.03 [12.23, 13.28] 0.421 12.46 [12.15, 12.77] 12.87 [12.23, 13.32] 0.035 Gln \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 4.02 [3.81, 4.42] 3.94 [3.66, 4.41] 0.421 4.17 [3.90, 4.57] 3.84 [3.57, 4.10] 0.013 Glu \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 16.08 [15.88, 16.58] 15.46 [15.17, 16.37] 0.229 15.50 [15.35, 16.32] 15.78 [15.10, 16.45] 0.268 Glx \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 20.10 [19.59, 20.87] 19.48 [18.72, 20.47] 0.229 20.20 [19.60, 20.84] 19.47 [18.97, 20.37] 0.626 Gly \(\:{,\:n}_{PMR}=14,{n}_{M}=\) 13 0.67 [0.53, 0.76] 0.51 [0.36, 0.82] 0.583 0.70 [0.47, 0.99] 0.38 [0.25, 0.69] 0.027 mI \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 9.25 [9.01, 9.66] 9.39 [8.39, 9.92] 0.890 9.37 [8.41, 9.67] 9.46 [8.92, 10.00] 0.135 tCho \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 2.24 [2.09, 2.35] 2.23 [2.09, 2.36] 0.303 2.15 [2.07, 2.32] 2.20 [2.12, 2.35] 0.715 Lac \(\:{,\:n}_{c}=15,{n}_{e}=14\) 0.42 [0.31, 0.46] 0.47 [0.26, 0.55] 0.679 0.41 [0.23, 0.53] 0.37 [0.29, 0.69] 0.173 dACC Progressive muscle relaxation group \(\:{n}_{PMR}=15\) Mindulness group \(\:{n}_{M}=14\) Pre-training Post-training p-value Pre-training Post-training p-value NAA \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 14.69 [14.09, 15.19] 14.86 [14.10, 15.37] 0.804 14.93 [13.78, 15.49] 14.55 [13.96, 15.22] 0.542 NAAG \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 2.38 [2.22, 2.51] 2.29 [2.16, 2.58] 0.890 2.48 [2.17, 2.79] 2.41 [2.27, 2.62] 0.670 tNAA \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 17.14 [16.30, 17.58] 17.12 [16.30, 17.72] 0.804 17.19 [15.88, 18.38] 16.86 [16.16, 17.99] 0.358 Cr \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 7.38 [6.74, 7.84] 7.28 [6.79, 7.92] 0.679 7.23 [6.63, 7.72] 7.00 [6.51, 7.32] 0.463 PCr \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 5.52 [5.31, 6.40] 5.45 [5.14, 6.24] 0.359 6.07 [5.37, 6.41] 5.90 [5.50, 6.77] 0.583 tCr \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 13.02 [12.67, 13.32] 12.97 [12.31, 13.62] 0.561 13.16 [12.43, 13.78] 13.07 [12.15, 13.70] 0.463 Gln \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 3.53 [3.22, 4.22] 3.90 [3.70, 4.31] 0.252 4.02 [3.56, 4.42] 4.12 [3.91, 4.58] 0.426 Glu \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 15.21 [14.73, 15.82] 15.49 [14.22, 16.09] 0.639 16.33 [14.51, 17.39] 15.29 [14.26, 16.45] 0.173 Glx \(\:{,\:n}_{PMR}=15,{n}_{M}=\) 14 18.89 [17.83, 20.06] 19.58 [18.22, 20.24] 0.978 20.18 [18.41, 21.64] 19.90 [18.16, 20.48] 0.502 Gly \(\:{,\:n}_{PMR}=7,{n}_{M}=9\) 0.44 [0.16, 0.69] 0.55 [0.34, 0.65] 0.53 [0.40, 0.75] 0.34 [0.08, 0.59] mI \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 10.49 [9.70, 11.33] 10.46 [9.91, 11.41] 0.679 10.82 [10.30, 11.44] 11.26 [10.07, 11.89] 0.855 tCho \(\:{,\:n}_{PMR}=15,{n}_{M}=14\) 3.14 [3.03, 3.29] 3.25 [3.09, 3.40] 0.489 3.16 [3.01, 3.34] 3.12 [2.99, 3.33] 0.855 Lac \(\:{,\:n}_{c}=15,{n}_{e}=14\) 0.59 [0.41, 0.70] 0.37 [0.34, 0.72] 0.359 0.39 [0.32, 0.61] 0.43 [0.26, 0.57] 0.542 Table 2 Quality control metrics for MRS acquisitions and tissue fraction (median [Q1-Q3]), for each voxel, per group and visit . LH: Left hippcampus; dACC: dorsal Anterior cingulaire cortex; PCC: Posterior cingulate cortex; RA: Right Amygdala; Cr_SNR: signal-to-noise ratio of the creatine peak (arbitrary unit); Cr_FWHM: full width at half maximum of the creatine peak (parts-per-million); water_FWHM: full width at half maximum of the unsuppressed water peak (parts-per-million); residual_water_ampl: amplitude of residual unsuppressed water (arbitrary unit); freqShift: frequency shift applied to align the spectrum to reference metabolites (Hertz); relResA: relative residual amplitude of the model fit (arbitrary unit), reflecting the quality of spectral fitting. Higher SNR and lower FWHM, residual water, frequency shift and relResA values indicate better spectral quality.GM : Gray matter ; WM : White matter ; CSF : Cerebrospinal fluid . Voxel & group Visit Cr_SNR (a.u.) Cr_FWHM (ppm) water_FWHM (ppm) residual_water_ampl (a.u.) freqShift (Hz) relResA (a.u.) GM (%) WM (%) CSF (%) PCC Mindfulness Pre-training 77.48 [63.71–86.17] 0.039 [0.037–0.040] 0.050 [0.048–0.051] 3015.64 [2232.78-4969.82] 1.62 [1.43–1.74] 2.27 [1.69–2.79] 61.5 [57.5–63.0] 35.0 [33.0–39.5] 3.0 [2.2–3.8] Post-training 79.57 [65.85–96.60] 0.038 [0.037–0.040] 0.050 [0.048–0.051] 2756.07 [2420.90-3689.09] 1.66 [1.11–1.76] 2.62 [2.08–3.75] 61.0 [59.0–63.0] 35.5 [33.0–39.0] 3.0 [3.0–4.0] PCC PMR Pre-training 86.86 [71.04-101.42] 0.037 [0.036–0.040] 0.049 [0.047–0.051] 3062.17 [2368.53-3552.16] 1.66 [0.69–1.73] 3.03 [2.48–4.35] 60.5 [58.8–64.2] 35.5 [32.8–40.2] 2.5 [1.0–4.0] Post-training 80.49 [70.17–95.54] 0.038 [0.036–0.039] 0.049 [0.048–0.050] 3215.26 [2167.26-3490.14] 1.74 [1.68–1.76] 2.32 [1.93–3.35] 58.5 [55.0–63.0] 39.5 [33.0–43.0] 2.0 [2.0–3.2] dACC Mindfulness Pre-training 97.54 [74.54-122.24] 0.036 [0.035–0.037] 0.048 [0.048–0.049] 3841.64 [3110.10-4302.82] 1.77 [1.75–2.04] 2.93 [1.95–4.27] 51.5 [47.8–56.5] 41.0 [38.2–47.2] 4.0 [3.0–7.0] Post-training 104.23 [92.16-120.45] 0.036 [0.035–0.038] 0.047 [0.046–0.049] 3498.48 [2828.21-4990.02] 2.27 [1.75–2.91] 3.42 [2.77–3.97] 50.5 [46.2–53.0] 43.0 [41.2–48.2] 4.0 [3.2–5.8] dACC PMR Pre-training 111.84 [90.84-123.81] 0.036 [0.034–0.037] 0.049 [0.047–0.051] 3240.64 [2839.41-4615.36] 1.77 [1.66–2.90] 3.62 [2.80–4.35] 50.0 [46.8–53.0] 45.0 [40.0–46.2] 4.0 [3.0–7.0] Post-training 89.74 [77.39-121.51] 0.037 [0.035–0.040] 0.049 [0.047–0.050] 3474.51 [3077.84-3792.63] 1.76 [1.64–2.25] 2.57 [1.93–4.82] 51.0 [48.5–54.2] 44.0 [40.0–47.0] 4.0 [3.0–6.2] LH Mindfulness Pre-training 37.61 [35.14–43.98] 0.071 [0.063–0.084] 0.092 [0.084–0.101] 4048.35 [1919.21-5201.09] 1.83 [0.82–2.66] 2.06 [1.78–3.17] 64.5 [61.2–67.0] 29.5 [27.2–32.8] 6.0 [5.0–6.8] Post-training 38.95 [30.85–50.65] 0.072 [0.064–0.080] 0.089 [0.081–0.096] 6748.69 [2354.44-9271.70] 1.64 [0.84–2.93] 1.89 [1.51–2.48] 63.0 [61.2–64.8] 31.0 [28.2–33.0] 6.0 [5.2–7.0] LH PMR Pre-training 42.66 [33.73–51.97] 0.065 [0.055–0.073] 0.088 [0.080–0.095] 3316.45 [2715.36-5941.13] 3.00 [1.64–3.20] 2.24 [2.04–3.71] 65.0 [62.5–66.2] 28.5 [27.8–32.0] 6.5 [5.0–7.3] Post-training 37.67 [32.74–43.74] 0.065 [0.054–0.072] 0.089 [0.081–0.102] 3101.13 [2594.54-5371.94] 2.90 [1.95–3.01] 2.03 [1.33–4.22] 63.0 [59.8–66.2] 30.0 [27.5–33.0] 6.0 [5.8–8.0] RA Mindfulness Pre-training 37.64 [34.73–41.40] 0.052 [0.048–0.057] 0.065 [0.063–0.073] 1621.06 [1287.65-3183.72] 2.80 [1.95–4.19] 1.71 [1.52–2.66] 73.5 [71.0–76.0] 19.0 [15.5–21.8] 7.5 [7.0–8.8] Post-training 35.80 [31.35–40.52] 0.050 [0.049–0.053] 0.066 [0.063–0.079] 1665.46 [1263.06-2215.23] 2.61 [1.73–3.16] 1.59 [1.34–2.38] 72.5 [71.0–76.0] 19.0 [17.5–22.0] 8.0 [5.2–8.8] RA PMR Pre-training 39.60 [35.79–45.75] 0.048 [0.043–0.051] 0.065 [0.059–0.069] 2682.73 [1422.73-3898.46] 2.91 [1.72–3.20] 2.01 [1.86–2.51] 74.0 [70.8–77.0] 19.5 [15.0–21.2] 8.0 [6.8–10.2] Post-training 41.80 [38.34–44.26] 0.051 [0.048–0.054] 0.061 [0.061–0.070] 2198.37 [1592.73-2844.41] 2.19 [1.78–3.75] 2.18 [1.58–2.52] 73.5 [70.8–75.2] 20.0 [17.8–22.0] 8.0 [6.8–9.0] Morphometric analysis Morphometric analysis was conducted using the FreeSurfer image analysis suite (version 7.4.1; https://surfer.nmr.mgh.harvard.edu/ ) 51 , which performs an automated processing pipeline for cortical and subcortical brain segmentation and surface reconstruction. The standard “recon-all” pipeline was applied to 3D T1-weighted anatomical MRI data, including motion correction, intensity normalization, skull stripping, Talairach transformation, segmentation of subcortical white matter and deep gray matter structures, tessellation of the gray/white matter boundary, and generation of pial and white matter surfaces 52 . All segmentation outputs were visually inspected and, if necessary, manually corrected according to FreeSurfer quality control guidelines. Regional volumetric data were extracted to investigate potential structural correlates of spectroscopically detected neurochemical changes, as well as to assess morphological brain changes induced by mindfulness and relaxation training. Volumes were normalized to the estimated intracranial volume and are reported as fractions of total intracranial volume, to account for inter-individual differences in head size. Longitudinal structural changes in cortical thickness and regional volumes were quantified using the symmetrized percent change (SPC) index, defined as the rate of change normalized to the average volume between two time points 53 . This robust metric, widely applied in studies of mental training, aging, mood and neuropsychiatric disorders 54 , 55 , was calculated across the whole brain with a specific focus on limbic regions to compare the Mindfulness and Relaxation groups after six weeks of training. Statistical analysis The analyses were conducted using SAS version 9.4 and R version 4.3.1 software. Qualitative variables were described in frequencies and percentages, while quantitative variables were described using median and interquartile range due to their distribution. Metabolites concentrations comparison before and after training within groups were performed using the paired non-parametric Wilcoxon test. Statistical significance threshold was set at 5%. Declarations DATA AVAILABILITY The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. ACKNOWLEDGMENTS Marine Naudin and Isabelle Perrin for respectively carrying out the mindfulness and relaxation programs ; Cyrille Kuntz for participating to the construction of the figure 1. FUNDING This work was funded by public grants from the Young Researchers Call for tenders of Tours university hospital (funding registration code AOI2021). CRediT authorship contribution statement Each author has made substantial contributions, has approved the submitted version and agreed to be personally accountable for its content. Q.B. and J-Ph.C.: project design, coordination, writing—review and editing L.A., L.B., F.A., V.G., E.S.: data collection and analysis, reviewing F.L.V.A: statistical data processing W.E-H.: project design, reviewing COMPETING INTERESTS All other authors declare they have no competing interests References Goyal, M. et al. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Intern. Med. 174 , 357–368 (2014). Tang, Y. Y., Hölzel, B. K. & Posner M. I. The neuroscience of mindfulness meditation. Nat. Rev. Neurosci. 16 , 213–225 (2015). Kabat-Zinn, J., Lipworth, L. & Burney, R. The clinical use of mindfulness meditation for the self-regulation of chronic pain. J. Behav. Med. 8 , 163–190 (1985). Davies, J. N., Faschinger, A., Galante, J. & Van Dam, N. T. Prevalence and 20-year trends in meditation, yoga, guided imagery and progressive relaxation use among US adults from 2002 to 2022. Sci. Rep. 14 , 14987 (2024). Teasdale, J. D. et al. Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. J. Consult Clin. Psychol. 68 , 615–623 (2000). Burgess, E. E., Selchen, S., Diplock, B. D. & Rector, N. A. A Brief Mindfulness-Based Cognitive Therapy (MBCT) Intervention as a Population-Level Strategy for Anxiety and Depression. Int. J. Cogn. Ther. 1–19 10.1007/s41811-021-00105-x (2021). Khoury, B. et al. Mindfulness-based therapy: A comprehensive meta-analysis. Clin. Psychol. Rev. 33 , 763–771 (2013). Kriakous, S. A., Elliott, K. A., Lamers, C. & Owen, R. The Effectiveness of Mindfulness-Based Stress Reduction on the Psychological Functioning of Healthcare Professionals: a Systematic Review. Mindfulness 12 , 1–28 (2021). Lamothe, M., Rondeau, É., Malboeuf-Hurtubise, C., Duval, M. & Sultan, S. Outcomes of MBSR or MBSR-based interventions in health care providers: A systematic review with a focus on empathy and emotional competencies. Complement. Ther. Med. 24 , 19–28 (2016). Lamothe, M., McDuff, P., Pastore, Y. D., Duval, M. & Sultan, S. Developing professional caregivers’ empathy and emotional competencies through mindfulness-based stress reduction (MBSR): results of two proof-of-concept studies. BMJ Open. 8 , e018421 (2018). Dean, S. et al. The effects of a structured mindfulness program on the development of empathy in healthcare students. NursingPlus Open. 3 , 1–5 (2017). Galante, J. et al. A mindfulness-based intervention to increase resilience to stress in university students (the Mindful Student Study): a pragmatic randomised controlled trial. Lancet Public. Health . 3 , e72–e81 (2018). Whitfield, T. et al. The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis. Neuropsychol. Rev. 10.1007/s11065-021-09519-y (2021). Sperduti, M., Makowski, D., Blondé, P. & Piolino, P. Meditation and successful aging: Can meditative practices counteract age-related cognitive decline? Geriatrie et psychologie neuropsychiatrie du vieillissement . 15 , 205–213 (2017). Mrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B. & Schooler, J. W. Mindfulness Training Improves Working Memory Capacity and GRE Performance While Reducing Mind Wandering. Psychol. Sci. 24 , 776–781 (2013). Levi, U. & Rosenstreich, E. Mindfulness and Memory: a Review of Findings and a Potential Model. J. Cogn. Enhanc . 3 , 302–314 (2019). Brown, K. W., Goodman, R. J., Ryan, R. M. & Anālayo, B. Mindfulness Enhances Episodic Memory Performance: Evidence from a Multimethod Investigation. PLoS ONE . 11 , e0153309 (2016). Muhammad Khir, S. et al. Efficacy of Progressive Muscle Relaxation in Adults for Stress, Anxiety, and Depression: A Systematic Review. PRBM Volume . 17 , 345–365 (2024). Bernstein, D. A. & Borkovec, T. D. Progressive Relaxation Training: A Manual for the Helping Professions . viii, 66Research Press, Champaign, IL, US, (1973). Allison, S., Irwin Hamilton, K., Yuan, Y. & Wallis Hague, G. Assessment of Progressive Muscle Relaxation (PMR) as a Stress-Reducing Technique for First-Year Veterinary Students. J. Vet. Med. Educ. 47 , 737–744 (2020). Hubbard, K. K. & Blyler, D. Improving Academic Performance and Working Memory in Health Science Graduate Students Using Progressive Muscle Relaxation Training. Am. J. Occup. Therapy . 70 , 7006230010p1–7006230010p8 (2016). Jarraya, S., Jarraya, M. & Engel, F. A. Kindergarten-Based Progressive Muscle Relaxation Training Enhances Attention and Executive Functioning: A Randomized Controlled Trial. Percept. Mot Skills . 129 , 644–669 (2022). Fox, K. C. R. et al. Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. Neurosci. Biobehav Rev. 43 , 48–73 (2014). Young, K. S. et al. The impact of mindfulness-based interventions on brain activity: A systematic review of functional magnetic resonance imaging studies. Neurosci. Biobehav Rev. 84 , 424–433 (2018). Fox, K. C. R. et al. Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations. Neurosci. Biobehav Rev. 65 , 208–228 (2016). Sim, S. et al. Neural correlates of mindfulness meditation and hypnosis on magnetic resonance imaging: similarities and differences. A scoping review. J. Neuroradiol. S015098612300264X 10.1016/j.neurad.2023.11.002 (2023). Gotink, R. A., Meijboom, R., Vernooij, M. W., Smits, M. & Hunink, M. G. M. 8-week Mindfulness Based Stress Reduction induces brain changes similar to traditional long-term meditation practice - A systematic review. Brain Cogn. 108 , 32–41 (2016). Moss, A. S. et al. Cerebral Blood Flow and Brain Functional Connectivity Changes in Older Adults Participating in a Mindfulness-Based Stress Reduction Program. Behav. Sci. (Basel) . 12 , 48 (2022). Kobayashi, S. & Koitabashi, K. Effects of progressive muscle relaxation on cerebral activity: An fMRI investigation. Complement. Ther. Med. 26 , 33–39 (2016). MRS in brain tumors. in. in Clinical MR Spectroscopy: Techniques and Applications . 61–90 (eds Bizzi, A., Lin, D. D. M., De Stefano, N., Barker, P. B. & Gullapalli, R.) (Cambridge University Press, 2009). 10.1017/CBO9780511770647.006 Allaïli, N. et al. Single-voxel 1 H spectroscopy in the human hippocampus at 3 T using the LASER sequence: characterization of neurochemical profile and reproducibility: MRS in the Human Hippocampus at 3 T. NMR Biomed. 28 , 1209–1217 (2015). Neale, J. H. et al. Advances in understanding the peptide neurotransmitter NAAG and appearance of a new member of the NAAG neuropeptide family. J. Neurochem. 118 , 490–498 (2011). Brewer, J. A. et al. Meditation experience is associated with differences in default mode network activity and connectivity. Proceedings of the National Academy of Sciences 108, 20254–20259 (2011). Yang, Y. et al. Presynaptic Regulation of Astroglial Excitatory Neurotransmitter Transporter GLT1. Neuron 61 , 880–894 (2009). N-Acetylaspartylglutamate - Neale. – 2000 - Journal of Neurochemistry - Wiley Online Library. https://onlinelibrary.wiley.com/doi/epdf/ 10.1046/j.1471-4159.2000.0750443.x Northoff, G. & Duncan, N. W. How do abnormalities in the brain’s spontaneous activity translate into symptoms in schizophrenia? From an overview of resting state activity findings to a proposed spatiotemporal psychopathology. Prog. Neurobiol. 145–146 , 26–45 (2016). Wiseman, R., Bigos, K. L., Arnsten, A. F. T. & Slusher, B. S. Chapter Two - Inhibition of brain glutamate carboxypeptidase II (GCPII) to enhance cognitive function. in Advances in Pharmacology (ed Tsukamoto, T.) vol. 102 27–63 (Academic, (2025). Morland, C. & Nordengen, K. N-Acetyl-Aspartyl-Glutamate in Brain Health and Disease. Int. J. Mol. Sci. 23 , 1268 (2022). Tyler, R. E., Besheer, J. & Joffe, M. E. Advances in translating mGlu2 and mGlu3 receptor selective allosteric modulators as breakthrough treatments for affective disorders and alcohol use disorder. Pharmacol. Biochem. Behav. 219 , 173450 (2022). Tang, Y. Y., Askari, P. & Choi, C. Brief mindfulness training increased glutamate metabolism in the anterior cingulate cortex. Neuroreport 31 , 1142–1145 (2020). Bush, G., Luu, P. & Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn. Sci. 4 , 215–222 (2000). Fayed, N. et al. Brain Changes in Long-Term Zen Meditators Using Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging: A Controlled Study. PLOS ONE . 8 , e58476 (2013). Kral, T. R. A. et al. Absence of structural brain changes from mindfulness-based stress reduction: Two combined randomized controlled trials. Sci. Adv. 8 , eabk3316 (2022). Wilson, M. et al. Methodological consensus on clinical proton MRS of the brain: Review and recommendations. Magn. Reson. Med. 82 , 527–550 (2019). Near, J. et al. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts’ consensus recommendations. NMR Biomed. 34 , e4257 (2021). Demarzo, M. et al. Efficacy of 8- and 4-Session Mindfulness-Based Interventions in a Non-clinical Population: A Controlled Study. Front. Psychol. 8 , 1343 (2017). Öz, G. et al. Clinical Proton MR Spectroscopy in Central Nervous System Disorders. Radiology 270 , 658–679 (2014). Maddock, R. J. & Buonocore, M. H. MR Spectroscopic Studies of the Brain in Psychiatric Disorders. in Brain Imaging in Behavioral Neuroscience (eds (eds Carter, C. S. & Dalley, J. W.) 199–251 (Springer, Berlin, Heidelberg, doi: 10.1007/7854_2011_197 . (2012). Mullins, P. G. et al. Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. NeuroImage 86, 43–52 (2014). Oeltzschner, G. et al. Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data. J. Neurosci. Methods . 343 , 108827 (2020). Osprey. Fischl, B. FreeSurfer Neuroimage 62 , 774–781 (2012). Dale, A., Fischl, B. & Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage 9, 179–194 (1999). Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis. NeuroImage 61, 1402–1418 (2012). Greenberg, J. et al. Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume. Brain Imaging Behav. 13 , 366–376 (2019). Tang, R., Friston, K. J. & Tang, Y. Y. Brief Mindfulness Meditation Induces Gray Matter Changes in a Brain Hub. Neural Plast 8830005 (2020). (2020). Additional Declarations No competing interests reported. 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Beaufort","email":"data:image/png;base64,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","orcid":"","institution":"Centre Hospitalier Universitaire","correspondingAuthor":true,"prefix":"","firstName":"Quentin","middleName":"","lastName":"Beaufort","suffix":""},{"id":524958273,"identity":"30981a24-e2b6-40b4-9712-5636c2279e83","order_by":1,"name":"Lucie Angel","email":"","orcid":"","institution":"UMR CNRS 7295, Université de Poitiers","correspondingAuthor":false,"prefix":"","firstName":"Lucie","middleName":"","lastName":"Angel","suffix":""},{"id":524958274,"identity":"bab7cefd-3fd7-4efe-a14c-ad210b34d8b2","order_by":2,"name":"Laurent Barantin","email":"","orcid":"","institution":"INSERM UMR 1253, iBrain, Université de Tours","correspondingAuthor":false,"prefix":"","firstName":"Laurent","middleName":"","lastName":"Barantin","suffix":""},{"id":524958275,"identity":"d6338288-7e79-4caa-afca-1e884a419c81","order_by":3,"name":"Frederic Andersson","email":"","orcid":"","institution":"INSERM UMR 1253, iBrain, Université de Tours","correspondingAuthor":false,"prefix":"","firstName":"Frederic","middleName":"","lastName":"Andersson","suffix":""},{"id":524958276,"identity":"3e6ce9ff-cc68-4ac8-9a7d-3d8b03cc629c","order_by":4,"name":"Valérie Gissot","email":"","orcid":"","institution":"Centre Hospitalier Universitaire","correspondingAuthor":false,"prefix":"","firstName":"Valérie","middleName":"","lastName":"Gissot","suffix":""},{"id":524958277,"identity":"92ce1b30-3685-4158-a7b5-eb5eb894b8ac","order_by":5,"name":"Eva Sizaret","email":"","orcid":"","institution":"INSERM UMR 1253, iBrain, Université de Tours","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Sizaret","suffix":""},{"id":524958278,"identity":"c98d33ed-1c7a-4701-b560-7546c03957f5","order_by":6,"name":"Floriane Le Vilain-Abraham","email":"","orcid":"","institution":"INSERM U 1246 - methodS in Patient-centered outcomes and HEalth ResEarch (SPHERE), Université de Tours, Université de Nantes","correspondingAuthor":false,"prefix":"","firstName":"Floriane","middleName":"Le","lastName":"Vilain-Abraham","suffix":""},{"id":524958279,"identity":"3186de42-4d58-4bea-989b-f2126b6782d2","order_by":7,"name":"Wissam El-Hage","email":"","orcid":"","institution":"Centre Hospitalier Universitaire","correspondingAuthor":false,"prefix":"","firstName":"Wissam","middleName":"","lastName":"El-Hage","suffix":""},{"id":524958280,"identity":"866e3085-5e11-44a9-8e81-bb55b7912e46","order_by":8,"name":"Jean-Philippe Cottier","email":"","orcid":"","institution":"Centre Hospitalier Universitaire","correspondingAuthor":false,"prefix":"","firstName":"Jean-Philippe","middleName":"","lastName":"Cottier","suffix":""}],"badges":[],"createdAt":"2025-09-26 14:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7722934/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7722934/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96239772,"identity":"823d15e3-611e-4d87-9da4-bef6f99dc74f","added_by":"auto","created_at":"2025-11-19 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07:08:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7740882,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/41e856e62207aa5d99714d25.docx"},{"id":95825938,"identity":"add89bb9-138c-4621-af3f-f8d2cd95827a","added_by":"auto","created_at":"2025-11-13 11:10:00","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177132,"visible":true,"origin":"","legend":"","description":"","filename":"d00665cd59c0479d9b58f6fd023bd7f11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/d5265837c06783b95de744c5.xml"},{"id":96239647,"identity":"715bc2d3-913a-447e-b6c0-565abce382d0","added_by":"auto","created_at":"2025-11-19 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11:10:00","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147453,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/531a1ac22f80c96ed5bdc5cc.png"},{"id":95825934,"identity":"41062d10-e1f0-4a0a-8078-aae06bcc048e","added_by":"auto","created_at":"2025-11-13 11:10:00","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":311651,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/429286705c66d58beeeeb8a0.png"},{"id":95825931,"identity":"7bb298b9-98d8-4e81-8c7d-4cd6d977dd93","added_by":"auto","created_at":"2025-11-13 11:10:00","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173964,"visible":true,"origin":"","legend":"","description":"","filename":"d00665cd59c0479d9b58f6fd023bd7f11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/c914ca507cf44e22614abdc9.xml"},{"id":95825935,"identity":"851ef603-4ed4-4b81-857f-afb44564bace","added_by":"auto","created_at":"2025-11-13 11:10:00","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":191938,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/4417e6bad0d0297ea0d84e44.html"},{"id":95825927,"identity":"eca7d3c9-3c8b-40f8-b246-e908a4f87eb9","added_by":"auto","created_at":"2025-11-13 11:10:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":265205,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolic variations in the limbic system following six weeks of mindfulness training (MT) or progressive muscle relaxation (PMR).\u003c/strong\u003e Boxes indicate the locations of spectroscopy voxels in a medial hemisphere view. Significant metabolite changes (Wilcoxon test, p \u0026lt; 0.05) are summarized on the left. Abbreviations: dACC, dorsal anterior cingulate cortex; PCC, posterior cingulate cortex; H, hippocampus; A, amygdala; NAAG, N-acetylaspartylglutamate; tCr, total creatine; Gln, glutamine; Gly, glycine.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/4aadb9b2cd0cd2e9d0822c35.jpeg"},{"id":95825928,"identity":"27ea0843-cd78-449a-89a5-ef82327470c7","added_by":"auto","created_at":"2025-11-13 11:10:00","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":826811,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpectroscopic study. Positioning of the monovoxel (white box) on 3D T1 MRI in axial, sagittal and coronal plans. Spectrum obtained in the monovoxel\u003c/strong\u003e\u003cem\u003e. \u003c/em\u003eLH: left hippocampus; ACC: right anterior cingular cortex, PCC Right posterior cingulate cortex, RA: right amygdala.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/8ca622dce2bb10b89ec261a7.jpeg"},{"id":96255010,"identity":"b19cbf28-4e54-418d-9397-3a187754cd75","added_by":"auto","created_at":"2025-11-19 07:47:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2396604,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/9444d5d9-23c1-445d-a36b-b5389e066743.pdf"},{"id":95825939,"identity":"cdd4ecb3-776c-451c-b3eb-691563cf9b08","added_by":"auto","created_at":"2025-11-13 11:10:01","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7740882,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7722934/v1/0c4b97b1b6dac7a90911f119.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinct Neurochemical Signature of Mindfulness and Progressive Muscle Relaxation in limbic regions: a randomized controlled MR spectroscopy study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn recent decades, stress reduction techniques and cognitive therapies rooted in mindfulness have gained significant attention in healthcare\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Programs such as Mindfulness-Based Stress Reduction (MBSR) and Mindfulness-Based Cognitive Therapy (MBCT) have demonstrated remarkable efficacy in reducing stress, anxiety, and depression while concurrently enhancing overall quality of life\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese interventions have not only proven beneficial for individuals across various demographics but have also shown promise in alleviating workplace stress, particularly among healthcare professionals \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Within academic settings, mindfulness training has emerged as an interesting tool for fostering emotional resilience in the face of academic pressures and for combatting anxiety\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, research suggests that mindfulness practices could positively influence cognitive functions, particularly attentional capacities, executive processes and memory abilities\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAnother technique, Jacobson's Progressive Muscle Relaxation (PMR) method, has long been recognized for its ability to induce deep relaxation and reduce muscle tension\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. PMR involves systematically tensing and then relaxing different muscle groups throughout the body, leading to a state of profound relaxation with mental calmness\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Studies have highlighted its efficacy in ameliorating symptoms of anxiety and depression, paralleling the benefits observed with mindfulness-based approaches. Additionally, PMR has also shown promising effects on cognitive functions, including improvements in executive functions and attentional processes\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, the mechanisms of action of these two practices on cognitive functions remain unclear. We currently do not know whether these programs are effective through achieving mental calmness that enhances attentional and/or metacognitive abilities, or whether their effectiveness stems from emotional regulation.\u003c/p\u003e\u003cp\u003eThe effects of mindfulness have been widely studied with neuroimaging tools. Studies in experienced meditators have suggested that mindfulness effects are accompanied by structural and functional modifications in certain brain regions involved in metacognition, memory, and emotional regulation, such as the prefrontal cortex, hippocampus, amygdaloid complex, cingulate cortex, or insula\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Other studies in meditation-na\u0026iuml;ve subjects have observed functional MRI changes after short training programs of a few weeks, akin to those seen in experienced meditators\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. PMR, in contrast, has almost not been studied by neuroimaging methods\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe neuro-metabolic mechanisms underlying the effect of mindfulness and relaxation remain almost unknown despite being investigable in vivo through magnetic resonance spectroscopy (MRS), which allows quantification of neurometabolites\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Especially, in limbic regions, it could offer valuable insights into the mechanisms through which mindfulness and PMR may modulate cognitive processes and emotional responses\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this context, our study aimed to explore and compare the effects of mindfulness and PMR programs on brain metabolism in limbic regions through MRS, as well as their respective impacts on brain structure, to approach the neurobiological mechanisms involved during these practices.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eBaseline characteristics and adherence\u003c/h2\u003e\u003cp\u003eAt baseline, the two groups did not differ in demographic characteristics. The PMR and MT groups were comparable in terms of age (median [IQR]: 21 [20, 21] vs. 22 [20, 22]) and cognitive baseline as assessed by the Mill Hill Vocabulary Scale (25.0 [24.0, 28.5] vs. 25.0 [24.0, 28.0]). Academic levels were similarly distributed across 1st, 2nd, and 3rd-year students in both groups.\u003c/p\u003e\u003cp\u003eAll participants completed the intervention and both MRI sessions. Median self-reported daily home practice was 30 minutes [25\u0026ndash;33] in the PMR group and 30 minutes [20\u0026ndash;35] in the mindfulness group.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSpectroscopy\u003c/h3\u003e\n\u003cp\u003eMetabolite estimates and change scores are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB for each region of interest. After six weeks, the mindfulness group showed a significant decrease in NAAG (2.15 [1.62, 2.53], 1,83 [1.52, 1.91] ; p\u0026thinsp;=\u0026thinsp;0.008) within the right amygdala. In the right posterior cingulate cortex (PCC), mindfulness was associated with significant increases in NAAG (2.29 [2.14, 2.25], 2.36 [2.26, 2.63] ; p\u0026thinsp;=\u0026thinsp;0.013) and total creatine (tCr) (12.46 [12.15, 12.77], 12.87 [12.23, 13.32] ; p\u0026thinsp;=\u0026thinsp;0.035), accompanied by decreases in glutamine (Gln) (4.17 [3.90, 4.57], 3.84 [3.57, 4.10] ; p\u0026thinsp;=\u0026thinsp;0.013) and glycine (Gly) (0.70 [0.47, 0.99], 0.38 [0.25, 0.69] ; p\u0026thinsp;=\u0026thinsp;0.027). No significant metabolite changes were detected in the left hippocampus or right dorsal anterior cingulate cortex (dACC) in either group. A graphical summary of the spectroscopic findings is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eStructural MRI\u003c/h3\u003e\n\u003cp\u003eMorphometric analyses revealed stable cortical and subcortical volumes across visits, with no longitudinal systematic differences between groups. In particular, gray-matter volume in the ACC, PCC, amygdala, hippocampus, caudate, insula, and temporo-parietal junction remained stable over the training interval. Complete morphometric results are provided in Supplementary Tables.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present findings reveal distinct neurochemical signatures of mindfulness compared to PMR within limbic regions. After six weeks of training, mindfulness was associated with decreased NAAG in the amygdala, together with increased NAAG and total creatine (tCr) in the PCC, alongside reductions in Gln and Gly. In contrast, PMR did not produce significant changes within these regions. Importantly, these neurochemical effects were obtained from spectra of overall good-to-excellent quality. Spectral quality was generally good in the deeper regions (amygdala and hippocampus) and excellent in the cingulate voxels. In addition, voxel tissue composition was stable across visits and comparable between groups, with GM, WM, and CSF distributions matching the expected anatomy of each region. These methodological checks support the robustness of the present findings and reduce the likelihood that the metabolite differences reflect acquisition artefacts or voxel placement biases. Our results can therefore be examined through several complementary lenses spanning neurochemical mechanisms, phenomenology, and large-scale network dynamics.\u003c/p\u003e\u003cp\u003eFrom a neurochemical interpretation perspective, the observed modulation of NAAG is compatible with differential engagement of inhibitory glutamatergic control. NAAG is a neuronally synthesized dipeptide neurotransmitter acting primarily on presynaptic and glial metabotropic glutamate receptors type 3 (mGluR3), thereby constraining excessive glutamate release\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The reduction of NAAG in the amygdala may indicate a loosening of this inhibitory brake in a context where emotional reactivity is already down-regulated by mindfulness training, consistent with fMRI studies showing attenuated amygdala responses after meditation practice \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. By contrast, the increase of NAAG in the PCC accords with strengthened mGluR3-mediated suppression of background excitatory drive in a central hub of the DMN, which is deeply implicated in self-referential cognition\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Concomitant tCr elevation suggests enhanced energetic buffering, while decreases in Gln and Gly point toward down-regulation of the astrocytic glutamate\u0026ndash;glutamine cycle and reduced NMDA co-activation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Together, these changes converge on a more metabolically efficient and less excitatory baseline in PCC circuits. The glial contribution further supports this account: NAAG catabolism by glial GCPII and astrocyte-mediated neurotransmitter cycling are central for shaping cortical excitability\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese biochemical adaptations parallel phenomenological reports of mindfulness. Practitioners commonly describe reduced cognitive reactivity and increased equanimity, which may be understood as experiential correlates of enhanced inhibitory balance within networks subserving self-awareness. Neuroimaging studies have consistently demonstrated altered DMN activity and connectivity following mindfulness, particularly within PCC and medial prefrontal regions, together with greater coupling to executive control areas such as the dorsal anterior cingulate and dorsolateral prefrontal cortex\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The metabolite pattern observed here is congruent with these network-level changes, suggesting that biochemical adaptations provide a substrate for the transformation of self-referential processing. By contrast, PMR primarily anchors attention to interoceptive and somatic sensations through cycles of muscular tension and release. While this reliably decreases autonomic arousal \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, it does not typically alter the experiential stance toward cognitive events, which may explain the absence of detectable changes in limbic neurochemical changes.\u003c/p\u003e\u003cp\u003eAnother dimension concerns the temporal profile of these practices. Mindfulness is thought to induce both state-related and trait-like changes, with cumulative adaptations emerging after sustained training, whereas PMR is often characterized by short-lived, state-dependent effects that may dissipate before metabolite acquisition. The regional pattern of NAAG, Gln, and Gly observed here points to a recalibration of the excitation\u0026ndash;inhibition (E/I) balance, a mechanism central to DMN regulation and its integration with limbic circuitry\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In this sense, mindfulness appears to stabilize long-term network excitability, whereas PMR may primarily exert transient physiological benefits.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, these preliminary findings may have translational relevance. Dysregulation of glutamatergic tone within the PCC and amygdala has been implicated in affective disorders. Converging preclinical and translational evidence indicates that augmenting NAAG\u0026ndash;mGluR3 signaling\u0026mdash;e.g., via inhibition of glutamate carboxypeptidase II (GCPII)\u0026mdash;can normalize excitatory drive and improve cognition\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Our observation of region-specific NAAG modulation with mindfulness therefore raises the possibility that contemplative practice could act as an endogenous modulator of this pathway, offering a non-pharmacological adjunct to restore glutamatergic balance and improve emotion regulation.\u003c/p\u003e\u003cp\u003eTo date, very few studies have used MRS to investigate the cerebral effects of mindfulness, and, to our knowledge, none has specifically examined progressive PMR. In a pre\u0026ndash;post exploratory study after 10 hours of mindfulness training, Tang et al\u003csup\u003e40\u003c/sup\u003e reported increases in glutamate and the composite Glx within the rostral ACC (which would be active following the commission of an error). By contrast, we did not observe comparable changes in the dorsal ACC \u0026mdash;our pre-specified ROI chosen for its evaluative/performance-monitoring role\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e \u0026mdash;suggesting that ACC subregions may differentially respond to contemplative practice. In long-term Zen meditators, Fayed et al.\u003csup\u003e42\u003c/sup\u003e identified higher myo-inositol in the PCC, interpreted as a glial/microglial signature; we did not replicate this effect, plausibly because our intervention was short in duration relative to years of practice in expert cohorts.\u003c/p\u003e\u003cp\u003eIn parallel with these neurochemical changes, we found no evidence of concomitant macroscopic structural modifications. Although morphometric alterations\u0026mdash;particularly in the ACC, PCC, amygdala, and hippocampus\u0026mdash;have been reported previously, the most methodologically rigorous study to date did not confirm such effects\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our findings are consistent with that observation and suggest that a few weeks of training may be insufficient to produce detectable morphological changes. Notably, proton magnetic resonance spectroscopy can reveal biochemical alterations that precede\u0026mdash;or occur in the absence of\u0026mdash;observable morphometric differences\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe study has limitations. The modest sample size constrains power and precision. To enhance internal validity and reduce within-group variance, we recruited a homogeneous cohort (healthy, right-handed female speech-language therapy students); while this likely improved measurement precision, it limits generalizability across sex, handedness, age, and educational background. In addition, the absence of a passive control group prevents us from fully excluding non-specific influences such as expectancy or scanner habituation. Nevertheless, directly comparing two structurally matched interventions (mindfulness vs PMR) helped dissociate practice-specific neurochemical adaptations from general relaxation effects; the lack of significant metabolite changes in PMR, together with the known test\u0026ndash;retest stability of major metabolites in 3T short-TE PRESS\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, supports the specificity of the mindfulness-related findings.\u003c/p\u003e\u003cp\u003eAt the methodological level, the use of short-TE PRESS at 3T enabled the detection of metabolites with relatively short T2 relaxation, such as myo-inositol, Glu, and NAAG, but these measurements remain dependent on macromolecule modeling, voxel placement, and spectral quality\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The absence of PMR-related changes should therefore be interpreted cautiously, as it does not exclude the possibility of transient or regionally restricted effects, for example in sensorimotor or insular cortices. Replication with larger cohorts, ultra-high-field MRS (7T), spectral editing methods such as MEGA-PRESS for GABA/GSH, and functional MRS, will be valuable in future research. Moreover, acquisition timing relative to the intervention and interindividual differences in phenomenology (e.g., baseline meta-awareness) should be considered as potential moderators of metabolic plasticity.\u003c/p\u003e\u003cp\u003eOverall, the data suggest that mindfulness\u0026mdash;but not PMR\u0026mdash;may promote a selective neurochemical rebalancing within the limbic\u0026ndash;DMN interface, consistent with both subjective phenomenology and network-level adaptations. The modulation of NAAG, in particular, may represent a promising biomarker of contemplative practice, bridging neurochemical regulation, large-scale network dynamics, and altered self-awareness.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and study design\u003c/h2\u003e\u003cp\u003eWe enrolled 30 healthy, right-handed female speech-language therapy students (\u0026ge;\u0026thinsp;18 years) from the School of Speech-Language Pathology, University of Tours. Exclusion criteria were: inability to complete the study; prior participation in MBSR or MBCT; neurological or psychiatric disorder (including major depressive disorder, schizophrenia, suicide attempt within the past 3 months, current substance use disorder, or complicated grief); ongoing psychotherapy; current psychotropic medication; standard contraindications to brain MRI; and pregnancy. All participants received detailed information about study procedures and provided written informed consent.\u003c/p\u003e\u003cp\u003eParticipants were randomized to one of two arms (n\u0026thinsp;=\u0026thinsp;15 per group) for a 6-week intervention: mindfulness training (MT) or progressive muscle relaxation (PMR)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The programs were delivered in parallel by certified instructors. Each arm comprised weekly supervised group sessions (2 h 30 min) and daily home practice with a 30-min target; participants self-reported daily practice time. Two MRI visits (baseline and post-intervention) included structural imaging and spectroscopy. Participants were instructed to maintain their daily practice until completion of the second visit. Investigators were blinded to group allocation. All procedures followed the pre-registered protocol and adhered to relevant guidelines and regulations.\u003c/p\u003e\u003cp\u003e The study protocol was approved by the regional ethics review board (Comit\u0026eacute; de Protection des Personnes Ouest-1, IORG0008143; OMB No. 0990\u0026thinsp;\u0026minus;\u0026thinsp;0279) and registered on ClinicalTrials.gov (date of registration: 02/02/2023 ; identifier: NCT05710250).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMRI and MRS acquisitions\u003c/h3\u003e\n\u003cp\u003eThe MRI scans before and after training were performed on a 3.0-T scanner (Siemens Magnetom Prisma, Erlangen, Germany) with a 64-channel head coil. High-resolution sagittal images were acquired with volumetric T1-weighted 3D MPRAGE with the following parameters: TR\u0026thinsp;=\u0026thinsp;2300ms, TE\u0026thinsp;=\u0026thinsp;2.98ms, flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;256mm, matrix\u0026thinsp;=\u0026thinsp;256x256, slice thickness\u0026thinsp;=\u0026thinsp;1mm). The MPRAGE sequence was used to prescribe the MRS volumes of interest (VOI).\u003c/p\u003e\u003cp\u003eProton MR spectroscopy used a short-echo time PRESS (short-echo time Point RESolved Spectroscopy) sequence (TE\u0026thinsp;=\u0026thinsp;35 ms, TR\u0026thinsp;=\u0026thinsp;2000 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;). Four single-voxel placements targeted the right amygdala (RA; 16\u0026times;20\u0026times;14 mm\u0026sup3;), left hippocampus (LH; 35\u0026times;20\u0026times;10 mm\u0026sup3;), right dorsal anterior cingulate cortex (dACC; 40\u0026times;13\u0026times;14 mm\u0026sup3;), et right posterior cingulate cortex (PCC; 37\u0026times;12\u0026times;16 mm\u0026sup3;). Prior to data acquisition, automatic B0 shimming based on a double-echo gradient-echo (GRE) field map was performed initially on the MRS voxel of interest. For each voxel, spectra were acquired with and without water suppression (averages\u0026thinsp;=\u0026thinsp;128 for the water-suppressed scan). The unsuppressed water scan was used for eddy-current correction, frequency/phase referencing, and absolute quantification (water scaling). Per-voxel acquisition time was 4:26 (water-suppressed) plus 0:18 (unsuppressed water reference). Voxel lateralization was chosen based on prior MRI evidence of anatomical or functional changes in these regions following mindfulness programs.\u003c/p\u003e\u003cp\u003eShort-TE PRESS allows robust quantification of major metabolites with high signal-to-noise ratio and minimal J-modulation losses\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. These metabolites are validated markers of neuronal, glial, osmotic, and energetic processes relevant to stress regulation\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. PRESS was particularly suited to our multi-voxel design (four limbic regions), offering optimal balance between acquisition time and spectral reliability. MEGA-PRESS, while optimal for γ-aminobutyric acid (GABA) detection, is less practical for repeated multi-site acquisitions due to prolonged scan times\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. DANTE-PRESS, though promising for resolving spectral overlaps, remains experimental and not widely validated for small deep structures. PRESS thus provided a versatile, validated framework appropriate for an exploratory, pre\u0026ndash;post intervention design.\u003c/p\u003e\u003cp\u003eSingle-voxel MR spectroscopy placement was performed by a trained neuroradiologist (QB, 6 years of experience) using precise anatomical localization from the 3D T1-weighted sequence and multiplanar reconstructions, with preservation of these landmarks enabling accurate repositioning of voxels at identical locations during the post-training MRI by the same neuroradiologist. The PRESS sequence allowed independent adjustment of the three voxel dimensions to match the morphology of target structures (e.g., the hippocampus, with its narrow profile and oblique orientation) and to minimize partial volume effects (e.g., in the amygdala, due to its small size) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMRS data analysis\u003c/h3\u003e\n\u003cp\u003eSpectral data were processed and quantified using Osprey, an open-source software package for in vivo magnetic resonance spectroscopy analysis (version v2.4.0 ; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/schorschinho/osprey\u003c/span\u003e\u003cspan address=\"https://github.com/schorschinho/osprey\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e50\u003c/sup\u003e, implementing an automated pipeline that includes raw data conversion, coil combination, eddy-current correction, frequency and phase alignment, residual water removal, baseline modeling, co-registration to 3D T1 MPRAGE images, segmentation and tissue fraction calculation and finally metabolites quantification steps. Metabolite concentrations were estimated by non-linear least-squares fitting of simulated basis sets, with quality control metrics assessing signal-to-noise ratio, linewidth, and fit residuals. Spectra were retained for analysis only if they met the following quality criteria: SNR(NAA)\u0026thinsp;\u0026ge;\u0026thinsp;20 (NAA peak height at 2.01 ppm divided by the standard deviation of noise in a signal-free region), FWHM\u0026thinsp;\u0026le;\u0026thinsp;0.09\u0026ndash;0.1 ppm. These methodological standards are consistent with recent consensus guidelines\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and ensure robust metabolite quantification in short-TE PRESS data at 3T. Due to suboptimal spectra (e.g., low SNR, broad linewidth), two amygdala and two hippocampus spectra, as well as one spectrum each from the dACC and PCC, were excluded from analysis in the MT group. For the same reason, one right-amygdala and one left-hippocampus spectrum were excluded from the PMR group. Analysed spectra quality metrics are presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eA : Metabolite concentrations in posterior and dorsal anterior cingulate cortex (median [Q1\u0026ndash;Q3], mM), pre- and post-training in both groups.\u003c/b\u003e \u003cem\u003edACC : dorsal Anterior cingulaire cortex, PCC : Posterior cingulate cortex, NAA: N-Acetylaspartate, NAAG : N-Acetylaspartylglutamate, tNAA : total N-Acetylaspartate, Cr : Creatine, PCr : Phopho-Creatine, tCr: total creatine, Gln : Glutamine, Glu : Glutamate, Glx : Glutamine\u0026thinsp;+\u0026thinsp;Glutamate, Gly : glycine, mI : Myo-inositol, tCho : total Choline, Lac : Lactate.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePCC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eProgressive muscle relaxation group\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{PMR}=15\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMindulness group\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNAA\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.92 [15.36, 16.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.14 [14.69, 15.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.49 [14.88, 16.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.42 [15.08, 15.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNAAG\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.38 [2.23, 2.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.39 [2.14, 2.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.29 [2.14, 2.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.36 [2.26, 2.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003etNAA\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.38 [17.70, 18.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.64 [17.06, 18.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.58 [17.14, 19.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.74 [17.39, 18.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCr\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.53 [7.28, 8.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.33 [6.79, 7.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.60 [7.09, 8.03]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.51 [7.17, 7.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCr\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.54 [4.79, 5.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.66 [5.30, 6.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.96 [4.87, 5.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.34 [4.66, 5.88]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003etCr\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.08 [12.64, 13.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.03 [12.23, 13.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.46 [12.15, 12.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.87 [12.23, 13.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGln\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.02 [3.81, 4.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.94 [3.66, 4.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.17 [3.90, 4.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.84 [3.57, 4.10]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlu\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.08 [15.88, 16.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.46 [15.17, 16.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.50 [15.35, 16.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.78 [15.10, 16.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlx\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.10 [19.59, 20.87]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.48 [18.72, 20.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.20 [19.60, 20.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.47 [18.97, 20.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGly\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=14,{n}_{M}=\\)\u003c/span\u003e\u003c/span\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 [0.53, 0.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51 [0.36, 0.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70 [0.47, 0.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38 [0.25, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emI\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.25 [9.01, 9.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.39 [8.39, 9.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.37 [8.41, 9.67]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.46 [8.92, 10.00]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003etCho\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.24 [2.09, 2.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23 [2.09, 2.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.15 [2.07, 2.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.20 [2.12, 2.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLac\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{c}=15,{n}_{e}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42 [0.31, 0.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47 [0.26, 0.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41 [0.23, 0.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.37 [0.29, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003edACC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003eProgressive muscle relaxation group\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{PMR}=15\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eMindulness group\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePre-training\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ePost-training\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ePre-training\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ePost-training\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNAA\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.69 [14.09, 15.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.86 [14.10, 15.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.93 [13.78, 15.49]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.55 [13.96, 15.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNAAG\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.38 [2.22, 2.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.29 [2.16, 2.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.48 [2.17, 2.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.41 [2.27, 2.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003etNAA\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.14 [16.30, 17.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.12 [16.30, 17.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.19 [15.88, 18.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.86 [16.16, 17.99]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.358\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCr\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.38 [6.74, 7.84]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.28 [6.79, 7.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.23 [6.63, 7.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.00 [6.51, 7.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCr\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.52 [5.31, 6.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.45 [5.14, 6.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.07 [5.37, 6.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.90 [5.50, 6.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.583\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003etCr\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.02 [12.67, 13.32]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.97 [12.31, 13.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.16 [12.43, 13.78]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.07 [12.15, 13.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGln\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.53 [3.22, 4.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.90 [3.70, 4.31]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.02 [3.56, 4.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.12 [3.91, 4.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlu\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.21 [14.73, 15.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.49 [14.22, 16.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.33 [14.51, 17.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.29 [14.26, 16.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlx\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=\\)\u003c/span\u003e\u003c/span\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.89 [17.83, 20.06]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.58 [18.22, 20.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.18 [18.41, 21.64]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.90 [18.16, 20.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGly\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=7,{n}_{M}=9\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44 [0.16, 0.69]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55 [0.34, 0.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53 [0.40, 0.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.34 [0.08, 0.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emI\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.49 [9.70, 11.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.46 [9.91, 11.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.82 [10.30, 11.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.26 [10.07, 11.89]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003etCho\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{PMR}=15,{n}_{M}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.14 [3.03, 3.29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.25 [3.09, 3.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.16 [3.01, 3.34]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.12 [2.99, 3.33]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLac\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{,\\:n}_{c}=15,{n}_{e}=14\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59 [0.41, 0.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.37 [0.34, 0.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39 [0.32, 0.61]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43 [0.26, 0.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eQuality control metrics for MRS acquisitions and tissue fraction (median [Q1-Q3]), for each voxel, per group and visit\u003c/b\u003e. \u003cem\u003eLH: Left hippcampus; dACC: dorsal Anterior cingulaire cortex; PCC: Posterior cingulate cortex; RA: Right Amygdala; Cr_SNR: signal-to-noise ratio of the creatine peak (arbitrary unit); Cr_FWHM: full width at half maximum of the creatine peak (parts-per-million); water_FWHM: full width at half maximum of the unsuppressed water peak (parts-per-million); residual_water_ampl: amplitude of residual unsuppressed water (arbitrary unit); freqShift: frequency shift applied to align the spectrum to reference metabolites (Hertz); relResA: relative residual amplitude of the model fit (arbitrary unit), reflecting the quality of spectral fitting. Higher SNR and lower FWHM, residual water, frequency shift and relResA values indicate better spectral quality.GM : Gray matter ; WM : White matter ; CSF : Cerebrospinal fluid\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVoxel \u0026amp; \u003cem\u003egroup\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCr_SNR (a.u.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCr_FWHM (ppm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ewater_FWHM (ppm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eresidual_water_ampl (a.u.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003efreqShift (Hz)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003erelResA (a.u.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGM (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eWM (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eCSF (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePCC\u003c/b\u003e \u003cem\u003eMindfulness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.48 [63.71\u0026ndash;86.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039 [0.037\u0026ndash;0.040]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050 [0.048\u0026ndash;0.051]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3015.64 [2232.78-4969.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.62 [1.43\u0026ndash;1.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.27 [1.69\u0026ndash;2.79]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e61.5 [57.5\u0026ndash;63.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e35.0 [33.0\u0026ndash;39.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3.0 [2.2\u0026ndash;3.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.57 [65.85\u0026ndash;96.60]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038 [0.037\u0026ndash;0.040]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050 [0.048\u0026ndash;0.051]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e2756.07 [2420.90-3689.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.66 [1.11\u0026ndash;1.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.62 [2.08\u0026ndash;3.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e61.0 [59.0\u0026ndash;63.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e35.5 [33.0\u0026ndash;39.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3.0 [3.0\u0026ndash;4.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePCC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePMR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.86 [71.04-101.42]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037 [0.036\u0026ndash;0.040]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049 [0.047\u0026ndash;0.051]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3062.17 [2368.53-3552.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.66 [0.69\u0026ndash;1.73]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.03 [2.48\u0026ndash;4.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e60.5 [58.8\u0026ndash;64.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e35.5 [32.8\u0026ndash;40.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.5 [1.0\u0026ndash;4.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.49 [70.17\u0026ndash;95.54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038 [0.036\u0026ndash;0.039]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049 [0.048\u0026ndash;0.050]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3215.26 [2167.26-3490.14]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.74 [1.68\u0026ndash;1.76]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.32 [1.93\u0026ndash;3.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e58.5 [55.0\u0026ndash;63.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e39.5 [33.0\u0026ndash;43.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2.0 [2.0\u0026ndash;3.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003edACC\u003c/b\u003e \u003cem\u003eMindfulness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.54 [74.54-122.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.036 [0.035\u0026ndash;0.037]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.048 [0.048\u0026ndash;0.049]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3841.64 [3110.10-4302.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.77 [1.75\u0026ndash;2.04]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.93 [1.95\u0026ndash;4.27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e51.5 [47.8\u0026ndash;56.5]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e41.0 [38.2\u0026ndash;47.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.0 [3.0\u0026ndash;7.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104.23 [92.16-120.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.036 [0.035\u0026ndash;0.038]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.047 [0.046\u0026ndash;0.049]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3498.48 [2828.21-4990.02]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.27 [1.75\u0026ndash;2.91]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.42 [2.77\u0026ndash;3.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e50.5 [46.2\u0026ndash;53.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e43.0 [41.2\u0026ndash;48.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.0 [3.2\u0026ndash;5.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003edACC\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePMR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111.84 [90.84-123.81]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.036 [0.034\u0026ndash;0.037]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049 [0.047\u0026ndash;0.051]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3240.64 [2839.41-4615.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.77 [1.66\u0026ndash;2.90]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.62 [2.80\u0026ndash;4.35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e50.0 [46.8\u0026ndash;53.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e45.0 [40.0\u0026ndash;46.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.0 [3.0\u0026ndash;7.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.74 [77.39-121.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037 [0.035\u0026ndash;0.040]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.049 [0.047\u0026ndash;0.050]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3474.51 [3077.84-3792.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.76 [1.64\u0026ndash;2.25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.57 [1.93\u0026ndash;4.82]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e51.0 [48.5\u0026ndash;54.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e44.0 [40.0\u0026ndash;47.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.0 [3.0\u0026ndash;6.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLH\u003c/b\u003e \u003cem\u003eMindfulness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.61 [35.14\u0026ndash;43.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.071 [0.063\u0026ndash;0.084]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.092 [0.084\u0026ndash;0.101]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e4048.35 [1919.21-5201.09]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.83 [0.82\u0026ndash;2.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.06 [1.78\u0026ndash;3.17]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e64.5 [61.2\u0026ndash;67.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e29.5 [27.2\u0026ndash;32.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.0 [5.0\u0026ndash;6.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.95 [30.85\u0026ndash;50.65]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072 [0.064\u0026ndash;0.080]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.089 [0.081\u0026ndash;0.096]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e6748.69 [2354.44-9271.70]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.64 [0.84\u0026ndash;2.93]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.89 [1.51\u0026ndash;2.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e63.0 [61.2\u0026ndash;64.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e31.0 [28.2\u0026ndash;33.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.0 [5.2\u0026ndash;7.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLH\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePMR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.66 [33.73\u0026ndash;51.97]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065 [0.055\u0026ndash;0.073]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.088 [0.080\u0026ndash;0.095]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3316.45 [2715.36-5941.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.00 [1.64\u0026ndash;3.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.24 [2.04\u0026ndash;3.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e65.0 [62.5\u0026ndash;66.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e28.5 [27.8\u0026ndash;32.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.5 [5.0\u0026ndash;7.3]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.67 [32.74\u0026ndash;43.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.065 [0.054\u0026ndash;0.072]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.089 [0.081\u0026ndash;0.102]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e3101.13 [2594.54-5371.94]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.90 [1.95\u0026ndash;3.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.03 [1.33\u0026ndash;4.22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e63.0 [59.8\u0026ndash;66.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e30.0 [27.5\u0026ndash;33.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6.0 [5.8\u0026ndash;8.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eRA\u003c/b\u003e \u003cem\u003eMindfulness\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37.64 [34.73\u0026ndash;41.40]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052 [0.048\u0026ndash;0.057]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065 [0.063\u0026ndash;0.073]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e1621.06 [1287.65-3183.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.80 [1.95\u0026ndash;4.19]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.71 [1.52\u0026ndash;2.66]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e73.5 [71.0\u0026ndash;76.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e19.0 [15.5\u0026ndash;21.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e7.5 [7.0\u0026ndash;8.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.80 [31.35\u0026ndash;40.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.050 [0.049\u0026ndash;0.053]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.066 [0.063\u0026ndash;0.079]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e1665.46 [1263.06-2215.23]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.61 [1.73\u0026ndash;3.16]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.59 [1.34\u0026ndash;2.38]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e72.5 [71.0\u0026ndash;76.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e19.0 [17.5\u0026ndash;22.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8.0 [5.2\u0026ndash;8.8]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eRA\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePMR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePre-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.60 [35.79\u0026ndash;45.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.048 [0.043\u0026ndash;0.051]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.065 [0.059\u0026ndash;0.069]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e2682.73 [1422.73-3898.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.91 [1.72\u0026ndash;3.20]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.01 [1.86\u0026ndash;2.51]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e74.0 [70.8\u0026ndash;77.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e19.5 [15.0\u0026ndash;21.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8.0 [6.8\u0026ndash;10.2]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.80 [38.34\u0026ndash;44.26]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051 [0.048\u0026ndash;0.054]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061 [0.061\u0026ndash;0.070]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e\u003cp\u003e2198.37 [1592.73-2844.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.19 [1.78\u0026ndash;3.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.18 [1.58\u0026ndash;2.52]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e73.5 [70.8\u0026ndash;75.2]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e20.0 [17.8\u0026ndash;22.0]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8.0 [6.8\u0026ndash;9.0]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMorphometric analysis\u003c/h2\u003e\u003cp\u003eMorphometric analysis was conducted using the FreeSurfer image analysis suite (version 7.4.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e51\u003c/sup\u003e, which performs an automated processing pipeline for cortical and subcortical brain segmentation and surface reconstruction. The standard \u0026ldquo;recon-all\u0026rdquo; pipeline was applied to 3D T1-weighted anatomical MRI data, including motion correction, intensity normalization, skull stripping, Talairach transformation, segmentation of subcortical white matter and deep gray matter structures, tessellation of the gray/white matter boundary, and generation of pial and white matter surfaces \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. All segmentation outputs were visually inspected and, if necessary, manually corrected according to FreeSurfer quality control guidelines. Regional volumetric data were extracted to investigate potential structural correlates of spectroscopically detected neurochemical changes, as well as to assess morphological brain changes induced by mindfulness and relaxation training. Volumes were normalized to the estimated intracranial volume and are reported as fractions of total intracranial volume, to account for inter-individual differences in head size.\u003c/p\u003e\u003cp\u003eLongitudinal structural changes in cortical thickness and regional volumes were quantified using the symmetrized percent change (SPC) index, defined as the rate of change normalized to the average volume between two time points\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. This robust metric, widely applied in studies of mental training, aging, mood and neuropsychiatric disorders\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, was calculated across the whole brain with a specific focus on limbic regions to compare the Mindfulness and Relaxation groups after six weeks of training.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe analyses were conducted using SAS version 9.4 and R version 4.3.1 software. Qualitative variables were described in frequencies and percentages, while quantitative variables were described using median and interquartile range due to their distribution. Metabolites concentrations comparison before and after training within groups were performed using the paired non-parametric Wilcoxon test. Statistical significance threshold was set at 5%.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarine Naudin and Isabelle Perrin for respectively carrying out the mindfulness and relaxation programs ; Cyrille Kuntz for participating to the construction of the figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by public grants from the Young Researchers Call for tenders of Tours university hospital (funding registration code AOI2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eCRediT authorship contribution statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach author has made substantial contributions, has approved the submitted version and agreed to be personally accountable for its content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQ.B. and J-Ph.C.: project design, coordination, writing\u0026mdash;review and editing\u003cbr\u003e\u0026nbsp;L.A., L.B., F.A., V.G., E.S.: data collection and analysis, reviewing\u003c/p\u003e\n\u003cp\u003eF.L.V.A: statistical data processing\u003cbr\u003e\u0026nbsp;W.E-H.: project design, reviewing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All other authors declare they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoyal, M. et al. Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. \u003cem\u003eJAMA Intern. Med.\u003c/em\u003e \u003cb\u003e174\u003c/b\u003e, 357\u0026ndash;368 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, Y. Y., H\u0026ouml;lzel, B. K. \u0026amp; Posner M. I. The neuroscience of mindfulness meditation. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 213\u0026ndash;225 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKabat-Zinn, J., Lipworth, L. \u0026amp; Burney, R. The clinical use of mindfulness meditation for the self-regulation of chronic pain. \u003cem\u003eJ. Behav. Med.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 163\u0026ndash;190 (1985).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavies, J. N., Faschinger, A., Galante, J. \u0026amp; Van Dam, N. T. Prevalence and 20-year trends in meditation, yoga, guided imagery and progressive relaxation use among US adults from 2002 to 2022. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 14987 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeasdale, J. D. et al. Prevention of relapse/recurrence in major depression by mindfulness-based cognitive therapy. \u003cem\u003eJ. Consult Clin. Psychol.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 615\u0026ndash;623 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurgess, E. E., Selchen, S., Diplock, B. D. \u0026amp; Rector, N. A. A Brief Mindfulness-Based Cognitive Therapy (MBCT) Intervention as a Population-Level Strategy for Anxiety and Depression. \u003cem\u003eInt. J. Cogn. Ther.\u003c/em\u003e \u003cb\u003e1\u0026ndash;19\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s41811-021-00105-x\u003c/span\u003e\u003cspan address=\"10.1007/s41811-021-00105-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhoury, B. et al. Mindfulness-based therapy: A comprehensive meta-analysis. \u003cem\u003eClin. Psychol. Rev.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 763\u0026ndash;771 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKriakous, S. A., Elliott, K. A., Lamers, C. \u0026amp; Owen, R. The Effectiveness of Mindfulness-Based Stress Reduction on the Psychological Functioning of Healthcare Professionals: a Systematic Review. \u003cem\u003eMindfulness\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 1\u0026ndash;28 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamothe, M., Rondeau, \u0026Eacute;., Malboeuf-Hurtubise, C., Duval, M. \u0026amp; Sultan, S. Outcomes of MBSR or MBSR-based interventions in health care providers: A systematic review with a focus on empathy and emotional competencies. \u003cem\u003eComplement. Ther. Med.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 19\u0026ndash;28 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamothe, M., McDuff, P., Pastore, Y. D., Duval, M. \u0026amp; Sultan, S. Developing professional caregivers\u0026rsquo; empathy and emotional competencies through mindfulness-based stress reduction (MBSR): results of two proof-of-concept studies. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, e018421 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDean, S. et al. The effects of a structured mindfulness program on the development of empathy in healthcare students. \u003cem\u003eNursingPlus Open.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 1\u0026ndash;5 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalante, J. et al. A mindfulness-based intervention to increase resilience to stress in university students (the Mindful Student Study): a pragmatic randomised controlled trial. \u003cem\u003eLancet Public. Health\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, e72\u0026ndash;e81 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhitfield, T. et al. The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis. \u003cem\u003eNeuropsychol. Rev.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11065-021-09519-y\u003c/span\u003e\u003cspan address=\"10.1007/s11065-021-09519-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSperduti, M., Makowski, D., Blond\u0026eacute;, P. \u0026amp; Piolino, P. Meditation and successful aging: Can meditative practices counteract age-related cognitive decline? \u003cem\u003eGeriatrie et psychologie neuropsychiatrie du vieillissement\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 205\u0026ndash;213 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B. \u0026amp; Schooler, J. W. Mindfulness Training Improves Working Memory Capacity and GRE Performance While Reducing Mind Wandering. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 776\u0026ndash;781 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevi, U. \u0026amp; Rosenstreich, E. Mindfulness and Memory: a Review of Findings and a Potential Model. \u003cem\u003eJ. Cogn. Enhanc\u003c/em\u003e. \u003cb\u003e3\u003c/b\u003e, 302\u0026ndash;314 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown, K. W., Goodman, R. J., Ryan, R. M. \u0026amp; Anālayo, B. Mindfulness Enhances Episodic Memory Performance: Evidence from a Multimethod Investigation. \u003cem\u003ePLoS ONE\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, e0153309 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMuhammad Khir, S. et al. Efficacy of Progressive Muscle Relaxation in Adults for Stress, Anxiety, and Depression: A Systematic Review. \u003cem\u003ePRBM Volume\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, 345\u0026ndash;365 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernstein, D. A. \u0026amp; Borkovec, T. D. \u003cem\u003eProgressive Relaxation Training: A Manual for the Helping Professions\u003c/em\u003e. viii, 66Research Press, Champaign, IL, US, (1973).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllison, S., Irwin Hamilton, K., Yuan, Y. \u0026amp; Wallis Hague, G. Assessment of Progressive Muscle Relaxation (PMR) as a Stress-Reducing Technique for First-Year Veterinary Students. \u003cem\u003eJ. Vet. Med. Educ.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 737\u0026ndash;744 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHubbard, K. K. \u0026amp; Blyler, D. Improving Academic Performance and Working Memory in Health Science Graduate Students Using Progressive Muscle Relaxation Training. \u003cem\u003eAm. J. Occup. Therapy\u003c/em\u003e. \u003cb\u003e70\u003c/b\u003e, 7006230010p1\u0026ndash;7006230010p8 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJarraya, S., Jarraya, M. \u0026amp; Engel, F. A. Kindergarten-Based Progressive Muscle Relaxation Training Enhances Attention and Executive Functioning: A Randomized Controlled Trial. \u003cem\u003ePercept. Mot Skills\u003c/em\u003e. \u003cb\u003e129\u003c/b\u003e, 644\u0026ndash;669 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox, K. C. R. et al. Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners. \u003cem\u003eNeurosci. Biobehav Rev.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 48\u0026ndash;73 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoung, K. S. et al. The impact of mindfulness-based interventions on brain activity: A systematic review of functional magnetic resonance imaging studies. \u003cem\u003eNeurosci. Biobehav Rev.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, 424\u0026ndash;433 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFox, K. C. R. et al. Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations. \u003cem\u003eNeurosci. Biobehav Rev.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 208\u0026ndash;228 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSim, S. et al. Neural correlates of mindfulness meditation and hypnosis on magnetic resonance imaging: similarities and differences. A scoping review. \u003cem\u003eJ. Neuroradiol.\u003c/em\u003e \u003cb\u003eS015098612300264X\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neurad.2023.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neurad.2023.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGotink, R. A., Meijboom, R., Vernooij, M. W., Smits, M. \u0026amp; Hunink, M. G. M. 8-week Mindfulness Based Stress Reduction induces brain changes similar to traditional long-term meditation practice - A systematic review. \u003cem\u003eBrain Cogn.\u003c/em\u003e \u003cb\u003e108\u003c/b\u003e, 32\u0026ndash;41 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoss, A. S. et al. Cerebral Blood Flow and Brain Functional Connectivity Changes in Older Adults Participating in a Mindfulness-Based Stress Reduction Program. \u003cem\u003eBehav. Sci. (Basel)\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 48 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKobayashi, S. \u0026amp; Koitabashi, K. Effects of progressive muscle relaxation on cerebral activity: An fMRI investigation. \u003cem\u003eComplement. Ther. Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 33\u0026ndash;39 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMRS in brain tumors. in. in \u003cem\u003eClinical MR Spectroscopy: Techniques and Applications\u003c/em\u003e. 61\u0026ndash;90 (eds Bizzi, A., Lin, D. D. M., De Stefano, N., Barker, P. B. \u0026amp; Gullapalli, R.) (Cambridge University Press, 2009). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/CBO9780511770647.006\u003c/span\u003e\u003cspan address=\"10.1017/CBO9780511770647.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlla\u0026iuml;li, N. et al. Single-voxel \u003csup\u003e1\u003c/sup\u003e H spectroscopy in the human hippocampus at 3 T using the LASER sequence: characterization of neurochemical profile and reproducibility: MRS in the Human Hippocampus at 3 T. \u003cem\u003eNMR Biomed.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e, 1209\u0026ndash;1217 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeale, J. H. et al. Advances in understanding the peptide neurotransmitter NAAG and appearance of a new member of the NAAG neuropeptide family. \u003cem\u003eJ. Neurochem.\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e, 490\u0026ndash;498 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrewer, J. A. et al. Meditation experience is associated with differences in default mode network activity and connectivity. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 108, 20254\u0026ndash;20259 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang, Y. et al. Presynaptic Regulation of Astroglial Excitatory Neurotransmitter Transporter GLT1. \u003cem\u003eNeuron\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 880\u0026ndash;894 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eN-Acetylaspartylglutamate - Neale. \u0026ndash;\u0026thinsp;2000 - Journal of Neurochemistry - Wiley Online Library. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinelibrary.wiley.com/doi/epdf/\u003c/span\u003e\u003cspan address=\"https://onlinelibrary.wiley.com/doi/epdf/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1046/j.1471-4159.2000.0750443.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1471-4159.2000.0750443.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorthoff, G. \u0026amp; Duncan, N. W. How do abnormalities in the brain\u0026rsquo;s spontaneous activity translate into symptoms in schizophrenia? From an overview of resting state activity findings to a proposed spatiotemporal psychopathology. \u003cem\u003eProg. Neurobiol.\u003c/em\u003e \u003cb\u003e145\u0026ndash;146\u003c/b\u003e, 26\u0026ndash;45 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiseman, R., Bigos, K. L., Arnsten, A. F. T. \u0026amp; Slusher, B. S. Chapter Two - Inhibition of brain glutamate carboxypeptidase II (GCPII) to enhance cognitive function. in Advances in Pharmacology (ed Tsukamoto, T.) vol. 102 27\u0026ndash;63 (Academic, (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorland, C. \u0026amp; Nordengen, K. N-Acetyl-Aspartyl-Glutamate in Brain Health and Disease. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 1268 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTyler, R. E., Besheer, J. \u0026amp; Joffe, M. E. Advances in translating mGlu2 and mGlu3 receptor selective allosteric modulators as breakthrough treatments for affective disorders and alcohol use disorder. \u003cem\u003ePharmacol. Biochem. Behav.\u003c/em\u003e \u003cb\u003e219\u003c/b\u003e, 173450 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, Y. Y., Askari, P. \u0026amp; Choi, C. Brief mindfulness training increased glutamate metabolism in the anterior cingulate cortex. \u003cem\u003eNeuroreport\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 1142\u0026ndash;1145 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBush, G., Luu, P. \u0026amp; Posner, M. I. Cognitive and emotional influences in anterior cingulate cortex. \u003cem\u003eTrends Cogn. Sci.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 215\u0026ndash;222 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFayed, N. et al. Brain Changes in Long-Term Zen Meditators Using Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging: A Controlled Study. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e58476 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKral, T. R. A. et al. Absence of structural brain changes from mindfulness-based stress reduction: Two combined randomized controlled trials. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, eabk3316 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilson, M. et al. Methodological consensus on clinical proton MRS of the brain: Review and recommendations. \u003cem\u003eMagn. Reson. Med.\u003c/em\u003e \u003cb\u003e82\u003c/b\u003e, 527\u0026ndash;550 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNear, J. et al. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts\u0026rsquo; consensus recommendations. \u003cem\u003eNMR Biomed.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, e4257 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDemarzo, M. et al. Efficacy of 8- and 4-Session Mindfulness-Based Interventions in a Non-clinical Population: A Controlled Study. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 1343 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026Ouml;z, G. et al. Clinical Proton MR Spectroscopy in Central Nervous System Disorders. \u003cem\u003eRadiology\u003c/em\u003e \u003cb\u003e270\u003c/b\u003e, 658\u0026ndash;679 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaddock, R. J. \u0026amp; Buonocore, M. H. MR Spectroscopic Studies of the Brain in Psychiatric Disorders. in Brain Imaging in Behavioral Neuroscience (eds (eds Carter, C. S. \u0026amp; Dalley, J. W.) 199\u0026ndash;251 (Springer, Berlin, Heidelberg, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/7854_2011_197\u003c/span\u003e\u003cspan address=\"10.1007/7854_2011_197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMullins, P. G. et al. Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. \u003cem\u003eNeuroImage\u003c/em\u003e 86, 43\u0026ndash;52 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOeltzschner, G. et al. Open-source processing, reconstruction \u0026amp; estimation of magnetic resonance spectroscopy data. \u003cem\u003eJ. Neurosci. Methods\u003c/em\u003e. \u003cb\u003e343\u003c/b\u003e, 108827 (2020). Osprey.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischl, B. \u003cem\u003eFreeSurfer Neuroimage\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 774\u0026ndash;781 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDale, A., Fischl, B. \u0026amp; Sereno, M. I. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. \u003cem\u003eNeuroImage\u003c/em\u003e 9, 179\u0026ndash;194 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReuter, M., Schmansky, N. J., Rosas, H. D. \u0026amp; Fischl, B. Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis. \u003cem\u003eNeuroImage\u003c/em\u003e 61, 1402\u0026ndash;1418 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreenberg, J. et al. Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume. \u003cem\u003eBrain Imaging Behav.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 366\u0026ndash;376 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang, R., Friston, K. J. \u0026amp; Tang, Y. Y. Brief Mindfulness Meditation Induces Gray Matter Changes in a Brain Hub. \u003cem\u003eNeural Plast\u003c/em\u003e 8830005 (2020). (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"magnetic resonance spectroscopy, mindfulness meditation, progressive muscle relaxation, glutamatergic system","lastPublishedDoi":"10.21203/rs.3.rs-7722934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7722934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMindfulness training (MT) and progressive muscle relaxation (PMR) are widely used stress-reduction practices, yet their neurochemical specificity remains almost unexplored. In a randomized, pre\u0026ndash;post study, 30 healthy right-handed female speech-language therapy students were assigned to six weeks of MT or PMR. Short-echo time single-voxel H\u003csup\u003e1\u003c/sup\u003e-MRS (3T PRESS, TE\u0026thinsp;\u0026asymp;\u0026thinsp;35 ms) and structural MRI were acquired at baseline and post-intervention. Four voxels were placed in the right amygdala, left hippocampus, right dorsal anterior cingulate cortex (dACC), and right posterior cingulate cortex (PCC); spectra were quantified with standard procedures (Osprey).\u003c/p\u003e\u003cp\u003eRelative to baseline, MT produced increased N-acetyl-aspartyl-glutamate (NAAG) and total creatine (tCr) in the PCC, accompanied by reductions in glutamine (Gln) and glycine (Gly). In the amygdala, NAAG decreased following MT. No significant metabolite changes were observed in the PMR group, and no effects emerged in the hippocampal or dACC voxels in either group. Whole-brain morphometry showed no detectable structural change over the six-week interval.\u003c/p\u003e\u003cp\u003eOverall, these findings indicate that mindfulness\u0026mdash;but not PMR\u0026mdash;was associated with a selective neurochemical rebalancing at the limbic\u0026ndash;default mode network interface, consistent with phenomenological and network-level accounts of mindfulness. NAAG modulation, in particular, may represent a candidate biomarker of contemplative practice.\u003c/p\u003e","manuscriptTitle":"Distinct Neurochemical Signature of Mindfulness and Progressive Muscle Relaxation in limbic regions: a randomized controlled MR spectroscopy study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 11:09:55","doi":"10.21203/rs.3.rs-7722934/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"320757650976260132493437666018089328701","date":"2026-03-23T15:25:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T07:50:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337079435620466895426679802187365898050","date":"2026-01-24T03:56:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T16:28:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T11:46:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-05T16:57:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-05T16:54:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ab984cc-33f4-41e3-af60-ce22dfdf8ce6","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55792553,"name":"Health sciences/Neurology"},{"id":55792554,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-23T16:38:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 11:09:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7722934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7722934","identity":"rs-7722934","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}