Next Article in Journal
Adapting Minds: Exploring Cognition to Threatened Stimuli in the Post-COVID-19 Landscape Comparing Old and New Concerns about Pandemic
Previous Article in Journal
Role of Alternative and Augmentative Communication in Three Cases of Severe Acquired Brain Injury: A Neurorehabilitative Approach
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 14
Issue 7
10.3390/brainsci14070710
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Adapted Physical Activity on White Matter Integrity in Patients with Schizophrenia

by
Elise Leroux
1,*,
Laura Masson
1,
Maxime Tréhout
1,2 and
Sonia Dollfus
1,2,3,4
1
“Physiopathology and Imaging of Neurological Disorders” PhIND, UMR-S U1237, INSERM, GIP Cyceron, 14000 Caen, France
2
CHU de Caen Normandie, Centre Esquirol, Service de Psychiatrie Adulte, 14000 Caen, France
3
Normandie Univ, Université de Caen Normandie, UFR de Santé, 14000 Caen, France
4
Fédération Hospitalo-Universitaire “Améliorer le Pronostic des Troubles Addictifs et Mentaux par une Médecine Personnalisée (A2M2P)“, 14000 Caen, France
*
Author to whom correspondence should be addressed.
Brain Sci. 2024, 14(7), 710; https://doi.org/10.3390/brainsci14070710
Submission received: 30 April 2024 / Revised: 12 July 2024 / Accepted: 12 July 2024 / Published: 15 July 2024
(This article belongs to the Section Psychiatric Diseases)
Browse Figures
Versions Notes

Abstract

:
Schizophrenia is associated with changes in white matter (WM) integrity and with reduced life expectancy, in part because of the cardiometabolic side effects of antipsychotics. Physical activity (PA) has emerged as a candidate lifestyle intervention that is safe and effective. The study aimed to assess how an adapted PA program delivered remotely by web (e-APA) improved WM integrity in patients with schizophrenia (SZPs) and healthy controls (HCs) and to evaluate associations among WM integrity, cardiorespiratory fitness, and symptom severity. This longitudinal study was conducted over 16 weeks with 31 participants (18 SZPs and 13 HCs). Diffusion tensor imaging and tract-based spatial statistics were employed to assess WM integrity. Cardiorespiratory fitness was measured by maximal oxygen uptake (VO2max), and assessments for clinical symptoms included the Positive and Negative Syndrome Scale, Self-evaluation of Negative Symptoms and the Brief Negative Syndrome Scale (BNSS). Only the SZPs had significantly increased WM integrity after the e-APA program, with increased fractional anisotropy and decreased radial diffusivity in fasciculi involved in motor functions and language process. Furthermore, decreased negative symptoms assessed with BNSS were associated with greater WM integrity following the program. These findings suggest that e-APA may improve WM integrity abnormalities and support e-APA as a promising therapeutic strategy.

1. Introduction

Schizophrenia is associated with changes in white matter (WM) structure and integrity within a diffuse cortico-subcortical network mainly involving the frontal and temporal regions [1,2,3]. More specifically, several studies have shown abnormalities in WM integrity in patients with schizophrenia (SZPs) compared to healthy controls (HCs), particularly in the fronto–temporo-limbic pathways (e.g., bilateral superior longitudinal and inferior fronto-occipital fasciculi), interhemispheric connections (e.g., the Corpus callosum (CC) and the fornix), and cortico-cerebellar tracts (e.g., bilateral cortico-spinal tracts) [4,5,6], confirmed by several meta-analyses [7,8]. Furthermore, these WM alterations are associated with both positive (e.g., hallucinations, delusions) and negative symptoms (e.g., social withdrawal, avolition, anhedonia, alogia, blunted affect) and disorganized speech and behavior in SZPs [9,10,11]. Additionally, antipsychotics may have differential impacts on WM [12,13], with no or limited effects on negative symptoms and cognition [14,15,16]. Life expectancy in SZPs is reduced by 15–20 years because of high cardiovascular mortality related to a sedentary lifestyle, poor dietary habits, elevated substance use disorders and the cardiometabolic side effects of antipsychotics [17,18,19,20]. Adjuvant non-pharmacological interventions are needed for this population, and lifestyle interventions such as physical activity (PA) have emerged as an effective and safe strategy [21]. Indeed, studies have demonstrated that PA significantly improves cardiovascular capacities in SZPs by reducing weight, insulin resistance, lipidic dysregulation, type II diabetes and metabolic syndrome, leading to decreased mortality [22,23,24,25].
To our knowledge, few studies have investigated the effect of PA on WM integrity and connectivity. Svatkova et al. [26] demonstrated that PA significantly increased the integrity of tracts involved in motor functions, such as the body of the corpus callosum (BCC) and the left corticospinal tract (CST), suggesting an increase in structural brain connectivity. These authors also highlighted a reduction in positive symptom severity following a PA program that was significantly correlated with WM integrity improvement, based on fractional anisotropy (FA). Additionally, in their cross-sectional study, Maurus et al. [27] found trends toward positive associations between aerobic fitness and WM volumes in the left parahippocampus and several cerebellar regions in SZPs.
In the present study, we assessed changes in WM integrity in SZPs and HCs following an adapted PA program delivered remotely by web (e-APA). We also investigated associations among changes in WM integrity, cardiorespiratory fitness and symptom severity after e-APA.

2. Materials and Methods

2.1. Experimental Design and Participants

The present study came from longitudinal, interventional, open-label trial [28], registered at ClinicalTrial.gov (accessed on 16 August 2017, NCT03261817). All data were collected at baseline and after the 16-week intervention (post-intervention endpoint). SZPs were diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR, 4th edition, text revision) and the structured Mini International Neuropsychiatric Interview (MINI, French version 5.0.0). Eighteen eligible SZPs were recruited based on inclusion and exclusion criteria (Table 1), and 13 eligible HCs were then matched to SZPs based on PA level, using the Ricci and Gagnon (RG) self-questionnaire scores (very active: >35; active: 18–35; inactive: <18) and on age and gender.
All participants were free of auditory deficits, neurological disorders and cerebral abnormalities and gave their informed written consent in accordance with the Declaration of Helsinki. Patients were stabilized outpatients with no change in their antipsychotic treatment over the last two months and during the entirety of the protocol. The study protocol was approved by the French health authority, the Agence Nationale de Sécurité du Médicament et des produits de santé (ANSM) on 10 July 2016 (ID-RCB number 2016-A00930-51). Also, the study protocol was approved by the local ethics committee (Comité de Protection des Personnes; CPP Nord-Ouest IV, France; CPP 16/39) on 13 January 2017 in compliance with French regulations.

2.2. e-APA Intervention

All patients participated in the e-APA program via real-time videoconferencing using a secure web-based platform called VisioMoov, managed by Mooven®, as explained in our previous studies [28,29]. Each patient completed two 60-min sessions per week over a period of 16 weeks, totaling 32 sessions. Throughout the sessions, patients interacted with a qualified APA professional coach via videoconference.
The e-APA program was developed according to the guidelines from the World Health Organization (WHO) and the American College of Sports Medicine (ACSM), based on the recommendations from expert committees [21,30,31,32]. It included three types of exercises: predominantly aerobic (e.g., running/walking on the spot, step exercises, jumping jacks, dancing), resistance (e.g., planks, push-ups, burpees, squats) and balance exercises (e.g., standing crunch with under-the-leg clap, single-leg cross-body punches). Each session lasted 40 min, with additional warm-up and cool-down periods before and after each session, respectively (5–10 min each). The exercises were performed at a moderate-to-vigorous intensity, aiming to reach 60–75% of the maximal heart rate, which was determined by each patient’s baseline maximal exercise test and monitored through heart rate recording during the workouts.
Each participant’s exercise was personalized and adapted on their baseline fitness level, sporting history, existing pain, ongoing treatment and personal preferences or motivation. A motivational interview and verbal feedback were conducted at both the beginning and the end of each session, allowing for the adjustment of exercise intensity according to the participants’ fitness levels and any potential pain.

2.3. Cardiorespiratory Fitness

Cardiorespiratory fitness was assessed by measuring maximal oxygen uptake (VO2max, mL/min/kg) during a cardiac stress test under medical supervision in both sessions (baseline and post-intervention). Measurement was taken using Ergoline er900® equipment (ergoline moving to health, Bitz, Germany) and participants had to pedal as long as possible while the load increased by 20 watts every 2 min. O2 flow rate was recorded before the beginning of the test (resting values), at each level (relative values), until the end of the test (maximal values) and for the following 3 min of passive recovery (recovery values).

2.4. Clinical Symptoms

In SZPs, symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS) [33], which includes a total score and three subscores regarding positive symptoms, negative symptoms and general psychopathology. Each subscale consists of several items rated on a 7-point Likert scale. Negative symptoms were also evaluated using the Self-Evaluation of Negative Symptoms (SNS) [34] and the Brief Negative Syndrome Scale (BNSS) [35]. The SNS is a self-report measure that includes 20 items covering five domains such as social withdrawal, diminished emotional expression, anhedonia, alogia and avolition. Participants rate the severity of each symptom on a scale from 0 to 2. The BNSS is a hetero-evaluation consisting of 13 items that assess five domains: anhedonia, asociality, avolition, blunted affect and alogia. Each item is rated on a scale from 0 to 6. For all these scales, higher scores indicate a greater severity of symptoms.

2.5. Neuroimaging

2.5.1. Data Acquisition

Diffusion tensor imaging data were acquired using a 3-Tesla scanner (Intera Achieva 3T Quasar Dual, Philips Medical System, Andover, MA, USA). The two-session (baseline and post-intervention) imaging protocol included diffusion-weighted images (DWI) from 48 diffusion gradient directions, with two images without diffusion weighting and two images with either a postero-anterior or antero-posterior phase-encoding direction (EPI-SE diffusion sequence; factor b = 1000 s/mm2; TR/TE = 9937/62 ms; 90° FA; 128 × 128 × 60 matrix; slice thickness = 2 mm; 256 mm FOV; 2-mm isotropic voxel size; transverse slice orientation; SENSE factor = 2).

2.5.2. Data Processing

Diffusion data preprocessing involved using the standard pipeline from the FMRIB Software Library (FSL, Oxford, UK, http://www.fmrib.ox.ac.uk/fsl; v6.0.5, accessed on 16 August 2017) [36] and FMRIB’s Diffusion Toolbox (FDT). This process included: (1) correcting susceptibility-induced distortions using TOTUP [37]; (2) brain extraction with the brain extraction tool (BET); (3) correcting eddy current–induced distortions and motions using EDDY [38,39,40]; and (4) fitting the diffusion tensor model to preprocessed data using DTIFIT to generate diffusion maps of FA and radial diffusivity (RD in mm2/s) for each participant.
For data post-processing and to investigate the influence of e-APA on WM integrity, we conducted an analysis using tract-based spatial statistics (TBSS) from FSL [41]. The steps of TBSS included: (1) erosion of each participant’s FA maps; (2) nonlinear and linear registrations to align all FA maps into a common space (FMRIB58_FA and MNI152); (3) creation of a mean FA image from the previously transformed maps, which was then skeletonized to generate a skeleton of WM pathways (representing the central voxels of the pathways common to all participants), using a threshold value of FA 0.3 to exclude gray matter and cerebrospinal fluid-containing voxels; (4) creation of individual FA skeletons by projecting participants’ FA data onto the population-specific mean FA skeleton; and (5) mapping of RD values onto the population-specific mean FA skeleton using the projection vectors from each participant’s FA-to-skeleton transformation. These steps were necessary to conduct voxel-wise statistical analyses.

2.6. Statistical Analyses

All statistical analyses, except for the TBSS analyses, were performed using JMP v13.0 Software (SAS Institute, Inc., Cary, NC, USA) or JASP v0.18.3. The significance level was set at p < 0.05.

2.6.1. TBSS Analyses

To assess diffusion differences in skeleton voxels between groups (SZPs vs. HCs), we conducted statistical tests using the general linear model from FSL implemented with the randomize tool for nonparametric permutation inference [42]. A correction by threshold-free cluster enhancement (TFCE) [43] was applied and was, in conjunction, either corrected for multiple comparisons with family-wise error (pFWE < 0.05, TFCE) or not corrected (p < 0.05, TFCE) using 5000 permutations. The significant regions (p < 0.05) were identified using the Johns Hopkins University—International Consortium of Brain Mapping tract and label atlases [44]. Both groups were compared at baseline using a two-sample unpaired t-test adjusted for RG score, age and gender. Relative variation (RV) maps were generated to assess longitudinal changes. RV was taken as the difference between skeletonized diffusion maps in post-intervention and skeletonized diffusion maps at baseline, divided by skeletonized diffusion maps at baseline. This calculation was performed using the FSLmaths tool. A two-sample unpaired t-test, adjusted for RG score, age and gender, was used on the RV maps to assess for a diagnostic effect (SZPs vs. HCs) on WM integrity changes. Post-hoc analyses (paired t-tests) were carried out to assess these changes in each group (SZPs and HCs) between baseline and post-intervention.
To illustrate TBSS results, mean diffusion values (FA and RD) for each participant were extracted in the WM skeleton pre- and post-intervention for all significant regions and exported to JMP v13.0 or JASP v0.18.3 software. In the context of physiological interpretation, better integrity was characterized by higher FA (axonal development) and lower RD (better myelination) [45,46,47].

2.6.2. Correlation Analyses

Within-group correlation analyses (Spearman) between RV of clinical/VO2max and diffusion data were conducted to evaluate the effect of e-APA over time on the relationships between symptom severity/cardiorespiratory fitness and WM integrity. Correlations were considered statistically significant at p < 0.05 after Bonferroni correction for multiple comparisons (p = 0.0071 (0.05/7) for all clinical data (total, positive, negative and general-PANSS, SNS, BNSS) and VO2max). Mean diffusion values were extracted only in significant contrast maps from previous TBSS analyses.

3. Results

3.1. Per Protocol Population Characteristics

Among the 31 participants, following the e-APA program, four (2 SZPs and 2 HCs) did not complete the study and two (1 SZP and 1 HC) were excluded because of motion artefacts on imaging. Two HCs did not have a DWI acquisition because of a scanner problem. Therefore, the per protocol population consisted of 15 SZPs and 8 HCs. These two groups differed significantly in RG scores at baseline (Table 2). Post-intervention VO2max values were missing for two SZPs.

3.2. Diffusion Tensor Imaging

3.2.1. Baseline

TBSS analysis revealed that compared to SZPs, HCs had significantly increased FA (pFWE < 0.05, TFCE) in many WM fasciculi or regions (Figure 1A and Table 3) with a higher total mean FA value (HCs: median [quartile 1; quartile 3] = 0.61 [0.63; 0.59]; SZPs: 0.57 [0.58; 0.55]; Figure 1B). In addition, RD was significantly reduced in HCs compared to SZPs (pFWE < 0.05, TFCE; total mean RD value in HCs: 0.49 10−3 [0.52 10−3; 0.47 10−3]; SZPs: 0.54 10−3 [0.55 10−3; 0.52 10−3]). These RD decreases were highlighted in the same clusters as FA and in additional regions (Table 4).

3.2.2. Longitudinal

SZPs had clusters with significantly increased FA-RV (p < 0.05, TFCE; Figure 2A) in comparison to HCs (total mean FA-RV value in SZPs: 0.018 [0.043; 0.0077]; HCs: −0.017 [−0.0035; −0.036]; Figure 2B and Table 3). SZPs also had decreased RD-RV compared to HCs (p < 0.05, TFCE; total mean RD-RV value in SZPs: −0.014 [0.0050; −0.081]; HCs: 0.072 [0.091; 0.047]; see Table 4).
Regarding pre- and post-intervention analyses, we demonstrated an FA increase after program only in SZPs and not found in HCs (p < 0.05, TFCE; Figure 3A and Table 3) with a total mean FA value higher in post-intervention than in baseline (baseline: 0.44 [0.43; 0.45], post-intervention: 0.46 [0.45; 0.47]; Figure 3B). SZPs also displayed a decreased RD after program e-APA, not found in HCs (p < 0.05, TFCE; baseline: 0.54 10−3 [0.52 10−3; 0.54 10−3], post-intervention: 0.52 10−3 [0.50 10−3; 0.52 10−3]; Table 4).

3.3. Relationships between White Matter Integrity and Cardiorespiratory Fitness/Clinical Data

We found a negative correlation between BNSS RV and r-inferior longitudinal fasciculus (ILF) FA-RV (p = 0.023, rho = −0.58). However, this correlation was not significant after Bonferroni correction (p < 0.0071). No other significant relationship was observed.

4. Discussion

Our findings highlight a positive effect of e-APA on WM integrity in SPZs, suggesting improved brain connectivity.
At baseline, SZPs had lower FA and higher RD compared to HCs, consistent with existing literature indicating WM integrity abnormalities in schizophrenia in a diffuse cortico-subcortical network [1,2,3,7]. Moreover, following the e-APA program, only SZPs had a significant FA increase and/or RD decrease, suggesting an axon myelination process in various WM fasciculi and notably in those altered at baseline. These fasciculi included the CST, BCC and middle cerebellar peduncle (CP), which are involved in motor functions that are engaged during PA practice. Indeed, the CST links the motor cortex, encompassing the supplementary motor area and the primary motor and premotor cortices, which are responsible for movement planning, coordination and voluntary movement control. It extends from these regions to the spinal cord, traversing through the internal capsule [48]. The regions of the motor cortex are interconnected in a homologous manner between the two hemispheres through the BCC, which plays a role in motor integration. The middle CP is involved in fine motor coordination, movement error correction and maintaining balance [49]. Thus, the CST, BCC and middle CP together may play a role in controlling motricity and mobilization during e-APA sessions, improving WM integrity in SZPs.
Previous studies have demonstrated a positive effect of physical exercise on these brain structures [50,51,52]. For example, Ikuta and Loprinzi [50] found a positive association in the CST between FA and PA, assessed using the International Physical Activity Questionnaire and a negative association with RD, suggesting that PA improved WM integrity in this fasciculus. The results of the present study indicated greater WM integrity improvement in the superior longitudinal fasciculus (SLF), which is involved in regulating motor behavior through its part I [53,54]. In addition, WM integrity improvement after the e-APA program in SZPs included other fiber bundles usually altered in schizophrenia such as the uncinate, inferior fronto-occipital fasciculi, ILF, SLF (especially its segment III, known as the arcuate fasciculus), the splenium of the CC and the cingulum bundle, which are involved in language and/or cognitive processes known to be altered in SZPs [55,56,57,58,59].
Taken together, these results suggest that brain plasticity mechanisms in SZPs are preserved and that e-APA may contribute to improved brain connectivity, in accordance with Svatkova et al. [26]. They reported a positive effect of PA regardless of diagnosis; however, we identified this effect only in patients, likely because of the smaller number of HCs. Several mechanisms may underlie the WM integrity improvement identified here, including decreased inflammation, increased blood flow and increases in growth factors such as brain-derived neurotrophic factor, which in turn can boost neurogenesis and gliogenesis [51,60]. Additionally, these different mechanisms could promote proliferation and differentiation of oligodendrocyte precursor cells, ultimately leading to enhanced bundle myelination [61].
We also highlighted a negative relationship between BNSS score and the right ILF FA over time, suggesting that WM integrity improvement in the right ILF might induce a reduction in negative symptom severity in SZPs who practice e-APA. This implication is supported by the fact that the ILF plays a role in socio-emotional behavior, which is related to negative symptoms [62].
Ultimately, our findings may have potential clinical implications for the management of schizophrenia. The observed negative relationship between BNSS score and the right ILF FA suggests that e-APA intervention aimed at improving WM integrity may contribute to an improvement of negative symptoms in SZPs. Indeed, negative symptoms, such as blunted affect, avolition, anhedonia and social withdrawal, are related to socio-emotional behavior that involve the ILF. Increasing the WM integrity in this brain region might lead to improve the negative symptoms. However, future research should investigate the specific mechanisms underlying these relationships and explore the potential of APA as an adjunctive treatment for the negative symptoms of schizophrenia.
Although the current study offers promising findings, it has limitations. Given the small sample size, it is important to consider this study exploratory. Nevertheless, this study is necessary as it addresses the nearly non-existent literature on the effects of APA on WM integrity. Among the limitations is a relatively small sample size, so that most of the results were uncorrected by FWE. Thus, further researches with a larger number of participants will be needed to validate and generalize our findings. It should be noted that the small size of the sample of HCs primarily resulted from logistical constraints, particularly associated with the employment status of the participants which prevented their participation for a long period (5 months) required by the protocol. Additionally, the COVID-19 pandemic (the period when HCs could be recruited), the participant withdrawals and the loss of imaging data also contributed to the reduction in sample size. Also, the lack of significant improvement in WM integrity in HCs following the e-APA program may be attributed to several factors. First, the relatively small size of the sample of HCs in the study may have limited the statistical power to detect significant effects. While previous studies have indicated a positive impact of PA on WM integrity in HCs [63,64], the extent of this enhancement may be lower compared to SZPs due to inherent differences. Specifically, HCs generally have higher baseline levels of physical activity than patients, which may result in a ceiling effect, making it more difficult to achieve further improvements. Additionally, the concept of dose response in PA interventions [65,66] suggests that HCs may need more intense and prolonged exercise to achieve measurable changes in WM integrity. In our present study, exercises were tailored to the patients’ physiological fitness level (“adapted”), which may not have been intense enough to induce changes in HCs. However, although the lack of significant findings in HCs warrants further investigation, it is plausible that the effects of APA on WM integrity differ between SZPs and HCs. Additionally, we did not test axial diffusivity (AD), also known as parallel diffusivity, which is defined as the diffusion of water along the direction of the principal diffusion. This decision was made due to its lack of sensitivity in regions with branching and crossing WM fibers as found in the intra-hemispheric tracts included in the present study [45]. A NODDI (Neurite Orientation Dispersion and Density Imaging) approach could have provided a more detailed assessment of WM integrity, particularly in regions with complex fiber arrangements. However, we did not initially acquire multi-shell DTI sequences in our study, given the prolonged acquisition time required for our patient population. We also did not test mean diffusivity (MD) that represents the average diffusion of water molecules along the fiber because of its less specific sensitivity to myelin alterations than RD (perpendicular diffusion). Ultimately, we did not compare the e-APA effects with another intervention in SZPs that did not allow us to conclude a more specific effect of e-APA on the WM integrity in patients. Therefore, future studies incorporating a standard control condition could elucidate the specific effects of e-APA in schizophrenia.

5. Conclusions

These findings contribute to the growing body of literature on how PA improve brain connectivity in SZPs. Our results indicate that e-APA improved WM integrity abnormalities observed in SZPs, suggesting preserved brain plasticity mechanisms in this population. Remote delivery by web of an APA program offers a convenient and accessible approach that could enhance treatment adherence in SZPs, making it a promising therapeutic strategy. Nevertheless, further investigations into the specific mechanisms underlying these WM integrity changes, as well as their functional and/or clinical implications, are warranted to advance understanding of how PA is beneficial in schizophrenia.

Author Contributions

Conceptualization, S.D.; methodology, E.L. and L.M.; software, E.L. and L.M.; formal analysis, E.L. and L.M.; investigation, E.L. and M.T.; data curation, E.L.; writing—original draft preparation, E.L.; writing—review and editing, E.L., L.M., M.T. and S.D.; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Normandy Country Council, European Union, in the framework of the European Regional Development Fund-European Social Funds (ERDF-ESF) operational program 2014–2020 (15P04195) and the Pierre Deniker Foundation as funding sources.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of the Comité de Protection des Personnes (CPP Nord-Ouest IV, France; CPP 16/39 on 13 January 2017) and the Agence Nationale de Sécurité du Médicament et des produits de santé (ANSM; ID-RCB number 2016-A00930-51 on 10 July 2016).

Informed Consent Statement

Informed consent was obtained from all participants involved in the study. Written informed consent has been obtained from the patients to publish this paper.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to legal reasons.

Acknowledgments

We thank Emmanuel Reboursiere for the management of the cardiac stress tests, Mooven company for delivering the remote adapted physical activity, Gaël Quarck for her expertise in adapted physical activity and the patients and volunteers for their participations.

Conflicts of Interest

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. M.T. participated in educational conferences for the following industrial laboratories: Otsuka, Lundbeck and Janssen. S.D. has been an expert and consultant or has participated in educational conferences for the following industrial laboratories or companies: Gedeon Richter, Lundbeck Otsuka, Roche, Takeda, Fabre, Janssen, Ono Pharmaceutical and Verasci. The other authors have no competing interests.

References

  1. Dietsche, B.; Kircher, T.; Falkenberg, I. Structural Brain Changes in Schizophrenia at Different Stages of the Illness: A Selective Review of Longitudinal Magnetic Resonance Imaging Studies. Aust. N. Z. J. Psychiatry 2017, 51, 500–508. [Google Scholar] [CrossRef] [PubMed]
  2. Olabi, B.; Ellison-Wright, I.; McIntosh, A.M.; Wood, S.J.; Bullmore, E.; Lawrie, S.M. Are There Progressive Brain Changes in Schizophrenia? A Meta-Analysis of Structural Magnetic Resonance Imaging Studies. Biol. Psychiatry 2011, 70, 88–96. [Google Scholar] [CrossRef] [PubMed]
  3. Vita, A.; De, P.L.; Deste, G.; Sacchetti, E. Progressive Loss of Cortical Gray Matter in Schizophrenia: A Meta-Analysis and Meta-Regression of Longitudinal MRI Studies. Transl. Psychiatry 2012, 2, e190. [Google Scholar] [CrossRef] [PubMed]
  4. Reid, M.A.; White, D.M.; Kraguljac, N.V.; Lahti, A.C. A Combined Diffusion Tensor Imaging and Magnetic Resonance Spectroscopy Study of Patients with Schizophrenia. Schizophr. Res. 2016, 170, 341–350. [Google Scholar] [CrossRef] [PubMed]
  5. Ellison-Wright, I.; Nathan, P.J.; Bullmore, E.T.; Zaman, R.; Dudas, R.B.; Agius, M.; Fernandez-Egea, E.; Müller, U.; Dodds, C.M.; Forde, N.J.; et al. Distribution of Tract Deficits in Schizophrenia. BMC Psychiatry 2014, 14, 99. [Google Scholar] [CrossRef] [PubMed]
  6. Liu, X.; Lai, Y.; Wang, X.; Hao, C.; Chen, L.; Zhou, Z.; Yu, X.; Hong, N. A Combined DTI and Structural MRI Study in Medicated-Naïve Chronic Schizophrenia. Magn. Reson. Imaging 2014, 32, 1–8. [Google Scholar] [CrossRef] [PubMed]
  7. Vitolo, E.; Tatu, M.K.; Pignolo, C.; Cauda, F.; Costa, T.; Ando’, A.; Zennaro, A. White Matter and Schizophrenia: A Meta-Analysis of Voxel-Based Morphometry and Diffusion Tensor Imaging Studies. Psychiatry Res. Neuroimaging 2017, 270, 8–21. [Google Scholar] [CrossRef]
  8. Bora, E.; Fornito, A.; Radua, J.; Walterfang, M.; Seal, M.; Wood, S.J.; Yucel, M.; Velakoulis, D.; Pantelis, C. Neuroanatomical Abnormalities in Schizophrenia: A Multimodal Voxelwise Meta-Analysis and Meta-Regression Analysis. Schizophr. Res. 2011, 127, 46–57. [Google Scholar] [CrossRef] [PubMed]
  9. Dabiri, M.; Dehghani Firouzabadi, F.; Yang, K.; Barker, P.B.; Lee, R.R.; Yousem, D.M. Neuroimaging in Schizophrenia: A Review Article. Front. Neurosci. 2022, 16, 1042814. [Google Scholar] [CrossRef]
  10. Heilbronner, U.; Samara, M.; Leucht, S.; Falkai, P.; Schulze, T.G. The Longitudinal Course of Schizophrenia Across the Lifespan: Clinical, Cognitive, and Neurobiological Aspects. Harv. Rev. Psychiatry 2016, 24, 118–128. [Google Scholar] [CrossRef]
  11. Kaur, A.; Basavanagowda, D.M.; Rathod, B.; Mishra, N.; Fuad, S.; Nosher, S.; Alrashid, Z.A.; Mohan, D.; Heindl, S.E. Structural and Functional Alterations of the Temporal Lobe in Schizophrenia: A Literature Review. Cureus 2020, 12, e11177. [Google Scholar] [CrossRef] [PubMed]
  12. Bartzokis, G.; Lu, P.H.; Nuechterlein, K.H.; Gitlin, M.; Doi, C.; Edwards, N.; Lieu, C.; Altshuler, L.L.; Mintz, J. Differential Effects of Typical and Atypical Antipsychotics on Brain Myelination in Schizophrenia. Schizophr. Res. 2007, 93, 13–22. [Google Scholar] [CrossRef] [PubMed]
  13. Leroux, E.; Vandevelde, A.; Trehout, M.; Dollfus, S. Abnormalities of Fronto-Subcortical Pathways in Schizophrenia and the Differential Impacts of Antipsychotic Treatment: A DTI-Based Tractography Study. Psychiatry Res. Neuroimaging 2018, 280, 22–29. [Google Scholar] [CrossRef] [PubMed]
  14. Correll, C.U.; Detraux, J.; De, L.J.; De, H.M. Effects of Antipsychotics, Antidepressants and Mood Stabilizers on Risk for Physical Diseases in People with Schizophrenia, Depression and Bipolar Disorder. World Psychiatry 2015, 14, 119–136. [Google Scholar] [CrossRef] [PubMed]
  15. Galderisi, S.; Kaiser, S.; Bitter, I.; Nordentoft, M.; Mucci, A.; Sabé, M.; Giordano, G.M.; Nielsen, M.Ø.; Glenthøj, L.B.; Pezzella, P.; et al. EPA Guidance on Treatment of Negative Symptoms in Schizophrenia. Eur. Psychiatry 2021, 64, e21. [Google Scholar] [CrossRef] [PubMed]
  16. Keefe, R.S.E.; Bilder, R.M.; Davis, S.M.; Harvey, P.D.; Palmer, B.W.; Gold, J.M.; Meltzer, H.Y.; Green, M.F.; Capuano, G.; Stroup, T.S.; et al. Neurocognitive Effects of Antipsychotic Medications in Patients with Chronic Schizophrenia in the CATIE Trial. Arch. Gen. Psychiatry 2007, 64, 633–647. [Google Scholar] [CrossRef]
  17. Carli, M.; Kolachalam, S.; Longoni, B.; Pintaudi, A.; Baldini, M.; Aringhieri, S.; Fasciani, I.; Annibale, P.; Maggio, R.; Scarselli, M. Atypical Antipsychotics and Metabolic Syndrome: From Molecular Mechanisms to Clinical Differences. Pharmaceuticals 2021, 14, 238. [Google Scholar] [CrossRef]
  18. Firth, J.; Solmi, M.; Wootton, R.E.; Vancampfort, D.; Schuch, F.B.; Hoare, E.; Gilbody, S.; Torous, J.; Teasdale, S.B.; Jackson, S.E.; et al. A Meta-Review of “Lifestyle Psychiatry”: The Role of Exercise, Smoking, Diet and Sleep in the Prevention and Treatment of Mental Disorders. World Psychiatry 2020, 19, 360–380. [Google Scholar] [CrossRef] [PubMed]
  19. Gurusamy, J.; Gandhi, S.; Damodharan, D.; Ganesan, V.; Palaniappan, M. Exercise, Diet and Educational Interventions for Metabolic Syndrome in Persons with Schizophrenia: A Systematic Review. Asian J. Psychiatry 2018, 36, 73–85. [Google Scholar] [CrossRef]
  20. Singh, R.; Bansal, Y.; Medhi, B.; Kuhad, A. Antipsychotics-Induced Metabolic Alterations: Recounting the Mechanistic Insights, Therapeutic Targets and Pharmacological Alternatives. Eur. J. Pharmacol. 2019, 844, 231–240. [Google Scholar] [CrossRef]
  21. Falkai, P.; Schmitt, A.; Rosenbeiger, C.P.; Maurus, I.; Hattenkofer, L.; Hasan, A.; Malchow, B.; Heim-Ohmayer, P.; Halle, M.; Heitkamp, M. Aerobic Exercise in Severe Mental Illness: Requirements from the Perspective of Sports Medicine. Eur. Arch. Psychiatry Clin. Neurosci. 2022, 272, 643–677. [Google Scholar] [CrossRef]
  22. Hjorth, P.; Davidsen, A.S.; Kilian, R.; Skrubbeltrang, C. A Systematic Review of Controlled Interventions to Reduce Overweight and Obesity in People with Schizophrenia. Acta Psychiatr. Scand. 2014, 130, 279–289. [Google Scholar] [CrossRef]
  23. Holt, R.I.G. The Prevention of Diabetes and Cardiovascular Disease in People with Schizophrenia. Acta Psychiatr. Scand. 2015, 132, 86–96. [Google Scholar] [CrossRef] [PubMed]
  24. Kim, D.D.; Lang, D.J.; Warburton, D.E.R.; Barr, A.M.; Smith, G.N.; Thornton, A.E.; White, R.F.; Honer, W.G.; Procyshyn, R.M. Effects of Exercise on Serum Triglycerides and Symptoms of Schizophrenia. J. Clin. Psychopharmacol. 2017, 37, 273–274. [Google Scholar] [CrossRef] [PubMed]
  25. Schmitt, A.; Maurus, I.; Rossner, M.J.; Röh, A.; Lembeck, M.; von Wilmsdorff, M.; Takahashi, S.; Rauchmann, B.; Keeser, D.; Hasan, A.; et al. Effects of Aerobic Exercise on Metabolic Syndrome, Cardiorespiratory Fitness, and Symptoms in Schizophrenia Include Decreased Mortality. Front. Psychiatry 2018, 9, 690. [Google Scholar] [CrossRef]
  26. Svatkova, A.; Mandl, R.C.W.; Scheewe, T.W.; Cahn, W.; Kahn, R.S.; Hulshoff Pol, H.E. Physical Exercise Keeps the Brain Connected: Biking Increases White Matter Integrity in Patients with Schizophrenia and Healthy Controls. Schizophr. Bull. 2015, 41, 869–878. [Google Scholar] [CrossRef]
  27. Maurus, I.; Röll, L.; Keeser, D.; Karali, T.; Papazov, B.; Hasan, A.; Schmitt, A.; Papazova, I.; Lembeck, M.; Hirjak, D.; et al. Associations between Aerobic Fitness, Negative Symptoms, Cognitive Deficits and Brain Structure in Schizophrenia—A Cross-Sectional Study. Schizophrenia 2022, 8, 63. [Google Scholar] [CrossRef] [PubMed]
  28. Tréhout, M.; Leroux, E.; Bigot, L.; Jego, S.; Leconte, P.; Reboursière, E.; Morello, R.; Chapon, P.-A.; Herbinet, A.; Quarck, G.; et al. A Web-Based Adapted Physical Activity Program (e-APA) versus Health Education Program (e-HE) in Patients with Schizophrenia and Healthy Volunteers: Study Protocol for a Randomized Controlled Trial (PEPSY V@Si). Eur. Arch. Psychiatry Clin. Neurosci. 2021, 271, 325–337. [Google Scholar] [CrossRef] [PubMed]
  29. Leroux, E.; Tréhout, M.; Reboursiere, E.; de Flores, R.; Morello, R.; Guillin, O.; Quarck, G.; Dollfus, S. Effects of Web-Based Adapted Physical Activity on Hippocampal Plasticity, Cardiorespiratory Fitness, Symptoms, and Cardiometabolic Markers in Patients with Schizophrenia: A Randomized, Controlled Study. Eur. Arch. Psychiatry Clin. Neurosci. 2024. [Google Scholar] [CrossRef]
  30. Maurus, I.; Roell, L.; Lembeck, M.; Papazova, I.; Greska, D.; Muenz, S.; Wagner, E.; Campana, M.; Schwaiger, R.; Schneider-Axmann, T.; et al. Exercise as an Add-on Treatment in Individuals with Schizophrenia: Results from a Large Multicenter Randomized Controlled Trial. Psychiatry Res. 2023, 328, 115480. [Google Scholar] [CrossRef]
  31. Stubbs, B.; Vancampfort, D.; Hallgren, M.; Firth, J.; Veronese, N.; Solmi, M.; Brand, S.; Cordes, J.; Malchow, B.; Gerber, M.; et al. EPA Guidance on Physical Activity as a Treatment for Severe Mental Illness: A Meta-Review of the Evidence and Position Statement from the European Psychiatric Association (EPA), Supported by the International Organization of Physical Therapists in Mental Health (IOPTMH). Eur. Psychiatry 2018, 54, 124–144. [Google Scholar] [CrossRef] [PubMed]
  32. Garber, C.E.; Blissmer, B.; Deschenes, M.R.; Franklin, B.A.; Lamonte, M.J.; Lee, I.-M.; Nieman, D.C.; Swain, D.P.; American College of Sports Medicine American College of Sports Medicine Position Stand. Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. Med. Sci. Sports Exerc. 2011, 43, 1334–1359. [Google Scholar] [CrossRef] [PubMed]
  33. Kay, S.R.; Fiszbein, A.; Opler, L.A. The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Schizophr. Bull. 1987, 13, 261–276. [Google Scholar] [CrossRef] [PubMed]
  34. Dollfus, S.; Delouche, C.; Hervochon, C.; Mach, C.; Bourgeois, V.; Rotharmel, M.; Tréhout, M.; Vandevelde, A.; Guillin, O.; Morello, R. Specificity and Sensitivity of the Self-Assessment of Negative Symptoms (SNS) in Patients with Schizophrenia. Schizophr. Res. 2019, 211, 51–55. [Google Scholar] [CrossRef]
  35. Kirkpatrick, B.; Saoud, J.B.; Strauss, G.P.; Ahmed, A.O.; Tatsumi, K.; Opler, M.; Luthringer, R.; Davidson, M. The Brief Negative Symptom Scale (BNSS): Sensitivity to Treatment Effects. Schizophr. Res. 2018, 197, 269–273. [Google Scholar] [CrossRef] [PubMed]
  36. Jenkinson, M.; Beckmann, C.F.; Behrens, T.E.J.; Woolrich, M.W.; Smith, S.M. FSL. Neuroimage 2012, 62, 782–790. [Google Scholar] [CrossRef] [PubMed]
  37. Andersson, J.L.R.; Skare, S.; Ashburner, J. How to Correct Susceptibility Distortions in Spin-Echo Echo-Planar Images: Application to Diffusion Tensor Imaging. Neuroimage 2003, 20, 870–888. [Google Scholar] [CrossRef]
  38. Andersson, J.L.R.; Graham, M.S.; Drobnjak, I.; Zhang, H.; Filippini, N.; Bastiani, M. Towards a Comprehensive Framework for Movement and Distortion Correction of Diffusion MR Images: Within Volume Movement. Neuroimage 2017, 152, 450–466. [Google Scholar] [CrossRef]
  39. Andersson, J.L.R.; Graham, M.S.; Zsoldos, E.; Sotiropoulos, S.N. Incorporating Outlier Detection and Replacement into a Non-Parametric Framework for Movement and Distortion Correction of Diffusion MR Images. Neuroimage 2016, 141, 556–572. [Google Scholar] [CrossRef]
  40. Andersson, J.L.R.; Sotiropoulos, S.N. An Integrated Approach to Correction for Off-Resonance Effects and Subject Movement in Diffusion MR Imaging. Neuroimage 2016, 125, 1063–1078. [Google Scholar] [CrossRef]
  41. Smith, S.M.; Jenkinson, M.; Johansen-Berg, H.; Rueckert, D.; Nichols, T.E.; Mackay, C.E.; Watkins, K.E.; Ciccarelli, O.; Cader, M.Z.; Matthews, P.M.; et al. Tract-Based Spatial Statistics: Voxelwise Analysis of Multi-Subject Diffusion Data. Neuroimage 2006, 31, 1487–1505. [Google Scholar] [CrossRef]
  42. Winkler, A.M.; Ridgway, G.R.; Webster, M.A.; Smith, S.M.; Nichols, T.E. Permutation Inference for the General Linear Model. Neuroimage 2014, 92, 381–397. [Google Scholar] [CrossRef] [PubMed]
  43. Smith, S.M.; Nichols, T.E. Threshold-Free Cluster Enhancement: Addressing Problems of Smoothing, Threshold Dependence and Localisation in Cluster Inference. Neuroimage 2009, 44, 83–98. [Google Scholar] [CrossRef] [PubMed]
  44. Wakana, S.; Caprihan, A.; Panzenboeck, M.M.; Fallon, J.H.; Perry, M.; Gollub, R.L.; Hua, K.; Zhang, J.; Jiang, H.; Dubey, P.; et al. Reproducibility of Quantitative Tractography Methods Applied to Cerebral White Matter. Neuroimage. 2007, 36, 630–644. [Google Scholar] [CrossRef] [PubMed]
  45. Alexander, A.L.; Lee, J.E.; Lazar, M.; Field, A.S. Diffusion Tensor Imaging of the Brain. Neurotherapeutics 2007, 4, 316–329. [Google Scholar] [CrossRef] [PubMed]
  46. Beaulieu, C. The Basis of Anisotropic Water Diffusion in the Nervous System—A Technical Review. NMR Biomed. 2002, 15, 435–455. [Google Scholar] [CrossRef] [PubMed]
  47. Leroux, E.; Delcroix, N.; Alary, M.; Razafimandimby, A.; Brazo, P.; Delamillieure, P.; Dollfus, S. Functional and White Matter Abnormalities in the Language Network in Patients with Schizophrenia: A Combined Study with Diffusion Tensor Imaging and Functional Magnetic Resonance Imaging. Schizophr. Res. 2013, 150, 93–100. [Google Scholar] [CrossRef] [PubMed]
  48. Martin, J.H. The Corticospinal System: From Development to Motor Control. Neuroscientist 2005, 11, 161–173. [Google Scholar] [CrossRef]
  49. Morales, H.; Tomsick, T. Middle Cerebellar Peduncles: Magnetic Resonance Imaging and Pathophysiologic Correlate. World J. Radiol. 2015, 7, 438–447. [Google Scholar] [CrossRef]
  50. Ikuta, T.; Loprinzi, P.D. Integrity of the Cortico-Spinal Tract Is Associated with Physical Activity. Int. J. Neurosci. 2020, 130, 413–416. [Google Scholar] [CrossRef]
  51. Loprinzi, P.D.; Harper, J.; Ikuta, T. The Effects of Aerobic Exercise on Corpus Callosum Integrity: Systematic Review. Physician Sportsmed. 2020, 48, 400–406. [Google Scholar] [CrossRef] [PubMed]
  52. Maleki, S.; Hendrikse, J.; Chye, Y.; Caeyenberghs, K.; Coxon, J.P.; Oldham, S.; Suo, C.; Yücel, M. Associations of Cardiorespiratory Fitness and Exercise with Brain White Matter in Healthy Adults: A Systematic Review and Meta-Analysis. Brain Imaging Behav. 2022, 16, 2402–2425. [Google Scholar] [CrossRef] [PubMed]
  53. Makris, N.; Kennedy, D.N.; McInerney, S.; Sorensen, A.G.; Wang, R.; Caviness, V.S.; Pandya, D.N. Segmentation of Subcomponents within the Superior Longitudinal Fascicle in Humans: A Quantitative, In Vivo, DT-MRI Study. Cereb. Cortex 2005, 15, 854–869. [Google Scholar] [CrossRef]
  54. Wang, X.; Pathak, S.; Stefaneanu, L.; Yeh, F.-C.; Li, S.; Fernandez-Miranda, J.C. Subcomponents and Connectivity of the Superior Longitudinal Fasciculus in the Human Brain. Brain Struct. Funct. 2016, 221, 2075–2092. [Google Scholar] [CrossRef] [PubMed]
  55. Leroux, E.; Poirel, N.; Dollfus, S. Anatomical Connectivity of the Visuospatial Attentional Network in Schizophrenia: A Diffusion Tensor Imaging Tractography Study. J. Neuropsychiatry Clin. Neurosci. 2020, 32, 266–273. [Google Scholar] [CrossRef] [PubMed]
  56. Leroux, E.; Delcroix, N.; Dollfus, S. Abnormalities of Language Pathways in Schizophrenia Patients with and without a Lifetime History of Auditory Verbal Hallucinations: A DTI-Based Tractography Study. World J. Biol. Psychiatry 2017, 18, 528–538. [Google Scholar] [CrossRef] [PubMed]
  57. Leroux, E.; Delcroix, N.; Dollfus, S. Left Fronto-Temporal Dysconnectivity within the Language Network in Schizophrenia: An fMRI and DTI Study. Psychiatry Res. 2014, 223, 261–267. [Google Scholar] [CrossRef] [PubMed]
  58. Singh, S.; Singh, K.; Trivedi, R.; Goyal, S.; Kaur, P.; Singh, N.; Bhatia, T.; Deshpande, S.N.; Khushu, S. Microstructural Abnormalities of Uncinate Fasciculus as a Function of Impaired Cognition in Schizophrenia: A DTI Study. J. Biosci. 2016, 41, 419–426. [Google Scholar] [CrossRef] [PubMed]
  59. Whitford, T.J.; Lee, S.W.; Oh, J.S.; de Luis-Garcia, R.; Savadjiev, P.; Alvarado, J.L.; Westin, C.-F.; Niznikiewicz, M.; Nestor, P.G.; McCarley, R.W.; et al. Localized Abnormalities in the Cingulum Bundle in Patients with Schizophrenia: A Diffusion Tensor Tractography Study. Neuroimage Clin. 2014, 5, 93–99. [Google Scholar] [CrossRef]
  60. Tréhout, M.; Dollfus, S. Physical activity in patients with schizophrenia: From neurobiology to clinical benefits. Encephale 2018, 44, 538–547. [Google Scholar] [CrossRef]
  61. Sampaio-Baptista, C.; Johansen-Berg, H. White Matter Plasticity in the Adult Brain. Neuron 2017, 96, 1239–1251. [Google Scholar] [CrossRef] [PubMed]
  62. Herbet, G.; Zemmoura, I.; Duffau, H. Functional Anatomy of the Inferior Longitudinal Fasciculus: From Historical Reports to Current Hypotheses. Front. Neuroanat. 2018, 12, 77. [Google Scholar] [CrossRef] [PubMed]
  63. Chen, F.-T.; Soya, H.; Yassa, M.A.; Li, R.-H.; Chu, C.-H.; Chen, A.-G.; Hung, C.-L.; Chang, Y.-K. Effects of Exercise Types on White Matter Microstructure in Late Midlife Adults: Preliminary Results from a Diffusion Tensor Imaging Study. Front. Aging Neurosci. 2022, 14, 943992. [Google Scholar] [CrossRef] [PubMed]
  64. Bashir, S.; Al-Sultan, F.; Jamea, A.A.; Almousa, A.; Alnafisah, M.; Alzahrani, M.; Abualait, T.; Yoo, W.-K. Physical Exercise Keeps the Brain Connected by Increasing White Matter Integrity in Healthy Controls. Medicine 2021, 100, e27015. [Google Scholar] [CrossRef] [PubMed]
  65. Frank, H.R.; Mulder, H.; Sriram, K.; Santanam, T.S.; Skinner, A.C.; Perrin, E.M.; Armstrong, S.C.; Peterson, E.D.; Pencina, M.; Wong, C.A. The Dose-Response Relationship between Physical Activity and Cardiometabolic Health in Young Adults. J. Adolesc. Health 2020, 67, 201–208. [Google Scholar] [CrossRef]
  66. Lee, I.M.; Skerrett, P.J. Physical Activity and All-Cause Mortality: What Is the Dose-Response Relation? Med. Sci. Sports Exerc. 2001, 33, S459–S471, discussion S493–S494. [Google Scholar] [CrossRef]
Brainsci 14 00710 g001
Figure 1. White matter (WM) integrity difference at baseline between patients with schizophrenia (SZPs) and healthy controls (HCs). (A) Fractional anisotropy (FA) was significantly increased in HCs compared to compared to SZPs following a tract-based spatial statistics analysis (in blue/light blue; p < 0.05, TFCE and adjusted for Ricci and Gagnon scores). Statistical analysis was conducted within the population-specific WM skeleton (in green), and Montreal Neurological Institute coordinates are displayed. (B) Total mean FA across all clusters in both groups. L: left; R: right.
Figure 1. White matter (WM) integrity difference at baseline between patients with schizophrenia (SZPs) and healthy controls (HCs). (A) Fractional anisotropy (FA) was significantly increased in HCs compared to compared to SZPs following a tract-based spatial statistics analysis (in blue/light blue; p < 0.05, TFCE and adjusted for Ricci and Gagnon scores). Statistical analysis was conducted within the population-specific WM skeleton (in green), and Montreal Neurological Institute coordinates are displayed. (B) Total mean FA across all clusters in both groups. L: left; R: right.
Brainsci 14 00710 g001
Brainsci 14 00710 g002
Figure 2. Effect of adapted physical activity (e-APA) on white matter (WM) integrity changes in patients with schizophrenia (SZPs) compared to healthy controls (HCs). (A) SZPs had significantly increased fractional anisotropy relative variation (FA-RV = changes between baseline and post-intervention) compared to HCs following e-APA program with a tract-based spatial statistics analysis (in red–yellow; p < 0.05, TFCE and adjusted for Ricci and Gagnon score). Statistical analysis was conducted within the population-specific WM skeleton (in green), and Montreal Neurological Institute coordinates are displayed. (B) Total mean FA-RV across all clusters in both groups. L: left; R: right.
Figure 2. Effect of adapted physical activity (e-APA) on white matter (WM) integrity changes in patients with schizophrenia (SZPs) compared to healthy controls (HCs). (A) SZPs had significantly increased fractional anisotropy relative variation (FA-RV = changes between baseline and post-intervention) compared to HCs following e-APA program with a tract-based spatial statistics analysis (in red–yellow; p < 0.05, TFCE and adjusted for Ricci and Gagnon score). Statistical analysis was conducted within the population-specific WM skeleton (in green), and Montreal Neurological Institute coordinates are displayed. (B) Total mean FA-RV across all clusters in both groups. L: left; R: right.
Brainsci 14 00710 g002
Brainsci 14 00710 g003
Figure 3. Effect of adapted physical activity (e-APA) on white matter (WM) integrity in patients with schizophrenia (SZPs) before and after program. (A) SZPs had significantly increased fractional anisotropy (FA) in post-intervention compared to FA value at baseline with a tract-based spatial statistics analysis (in red–yellow; p < 0.05, TFCE). Statistical analysis was conducted within the population-specific WM skeleton (in green), and Montreal Neurological Institute coordinates are displayed. (B) Total mean FA across all clusters in SZPs in baseline and post-intervention. L: left; R: right.
Figure 3. Effect of adapted physical activity (e-APA) on white matter (WM) integrity in patients with schizophrenia (SZPs) before and after program. (A) SZPs had significantly increased fractional anisotropy (FA) in post-intervention compared to FA value at baseline with a tract-based spatial statistics analysis (in red–yellow; p < 0.05, TFCE). Statistical analysis was conducted within the population-specific WM skeleton (in green), and Montreal Neurological Institute coordinates are displayed. (B) Total mean FA across all clusters in SZPs in baseline and post-intervention. L: left; R: right.
Brainsci 14 00710 g003
Table 1. Inclusion and exclusion criteria for participants from our previous study [28].
Table 1. Inclusion and exclusion criteria for participants from our previous study [28].
Inclusion Criteria 
For all participants (SZPs and HCs):
Age range: 18 to 60 years old
Signed informed consent
Ability to receive intervention: possession of a computer, internet access and a webcam
Medical welfare affiliation
For SZPs:
Meeting DSM-IV-TR criteria for schizophrenia or schizoaffective disorders
Stable psychotropic medication for at least 2 months prior to inclusion
Guardian or trustee consent for protected majors
Exclusion criteria
For all participants (SZPs and HCs):
Under 18 or over 60 years old
Participation in another biomedical research protocol during the present study
Pregnancy
MRI contraindications
Progressive neurological diseases
Physical restrictions on physical activity (moderate to severe heart failure, unstable coronary disease, severe valvular disease, recent pulmonary embolism or untreated deep venous thrombosis, uncontrolled hypertension, pulmonary arterial hypertension treated or not)
Neuromuscular disorders, severe sensory and/or motor neuropathy
Rheumatic and articular disorders; orthopedic/rheumatologic problems or bone lesions at risk of fracture that contraindicate physical activity
History of stroke or myocardial infarction within 6 months prior to the selection visit
For HCs:
Lifetime diagnosis of schizophrenia or schizoaffective disorder according to the DSM-IV-TR criteria
DSM-IV-TR: Diagnosis and Statistical Manual of Mental Disorders, 4th edition, text revision; HCs: healthy controls; MRI: magnetic resonance imaging; SZPs: patients with schizophrenia.
Table 2. Per protocol population characteristics at baseline.
Table 2. Per protocol population characteristics at baseline.
Median (Quartile 1; Quartile 3)
[Min–Max]		SZPsHCsp-Value
Number of participants, n158-
Gender (males, n (%))11
(73.3)		5
(62.5)		0.59
Handedness (right/left/both, n (%))13/1/1
(86.7/6.7/6.7)		6/1/1
(75/12.5/12.5)		0.78
Age (years)40.5 (32.8; 47.3)
[25.8–55.1]		39 (35.8; 46.6)
[34.0–50.6]		0.72
Education level (years)12 (11; 16)
[7–20]		15 (12; 16.8)
[11–20]		0.16
RG’s class (active/inactive, n (%))9/6
(60/40)		7/1
(87.5/12.5)		0.17
RG score20 (13; 25)
[9–30]		27.5 (22.5; 31.3)
[13–33]		0.045
VO2max (mL/min/kg)22.5 (19.1; 30)
[16.5–36.8]		32.1 (24.7; 47.7)
[16.4–49.7]		0.076
Age of disease onset (years)20 (19; 23)
[17–30]		--
Illness duration (years)17 (11; 24)
[4–35]		--
Chlorpromazine equivalent (mg/day)325 (140; 400)
[50–800]		--
Total PANSS59 (48; 71)
[44–96]		--
Positive PANSS 13 (8; 15)
[7–23]		--
Negative PANSS 17 (12; 23)
[10–32]		--
General PANSS 31 (24; 35)
[19–44]		--
SNS 14 (10; 15)
[3–23]		--
BNSS 22 (10; 41)
[5–53]		--
BNSS: Brief Negative Syndrome Scale; HCs: healthy controls; m: mean; n: number of participants; PANSS: Positive and Negative Syndrome Scale; RG: Ricci and Gagnon; SNS: Self-Evaluation of Negative Symptoms; SZPs: patients with schizophrenia. Significance level at p < 0.05. To examine differences between groups, the Mann–Whitney U test was used for noncategorical variables and the chi-square test for categorical variables.
Table 3. Significant white matter (WM) fasciculi and regions grouped into clusters from fractional anisotropy (FA) TBSS analyses.
Table 3. Significant white matter (WM) fasciculi and regions grouped into clusters from fractional anisotropy (FA) TBSS analyses.
WM Fasciculi or Regions from JHU-ICBM Tracts and Labels Atlases
Number of Voxels (>100) (Proportion in %)		FA—Baseline
HCs > SZPs
pFWE < 0.05, TFCE		FA-RV—Longitudinal
SZPs > HCs
p < 0.05, TFCE		SZPs
Post-Intervention > Baseline
p < 0.05, TFCE
JHU-tracts
L-anterior thalamic radiation353 (0.22)871 (0.89)140 (0.61)
R-anterior thalamic radiation213 (0.12)1263 (1.83)254 (1.00)
L-corticospinal tract102 (0.19)427 (0.59)168 (0.73)
R-corticospinal tract411 (0.88)709 (0.68)462 (1.21)
L-cingulum gyrus440 (0.36)286 (0.30)-
R-cingulum gyrus185 (0.19)174 (0.058)-
R-cingulum hippocampus-339 (0.32)198 (1.29)
Forceps major1091 (2.48)1286 (1.49)-
Forceps minor845 (4.06)525 (1.21)-
L-inferior fronto-occipital fasciculus-794 (1.42)-
R-inferior fronto-occipital fasciculus575 (1.10)1837 (2.61)130 (0.82)
L-inferior longitudinal fasciculus147 (0.17)763 (1.20)193 (1.06)
R-inferior longitudinal fasciculus109 (0.52)1385 (1.20)322 (1.82)
L-superior longitudinal fasciculus892 (2.25)1095 (0.90)-
R-superior longitudinal fasciculus1450 (3.32)2039 (1.94)241 (1.68)
R-superior longitudinal fasciculus- part temporal-110 (0.63)150 (0.83)
L-uncinate fasciculus-331 (0.54)-
R-uncinate fasciculus-332 (0.45)-
JHU-labels
Genu of the corpus callosum790 (8.00)--
Body of the corpus callosum1260 (12.78)132 (0.71)-
Splenium of the corpus callosum1125 (11.34)672 (3.65)-
L-corona radiata502 (5.05)250 (1.35)-
R-corona radiata143 (1.44)516 (2.79)-
L-sagittal stratum-127 (0.69)-
R-sagittal stratum-209 (1.13)-
L-external capsule-276 (1.06)-
R-external capsule-399 (1.01)-
R-anterior internal capsule-231 (1.25)-
R-posterior internal capsule-142 (0.77)-
L-retrolenticular part of internal capsule-223 (1.21)-
Middle cerebellar peduncle-174 (0.94)399 (10.07)
L-cerebellar peduncle-123 (0.66)-
R-cerebellar peduncle-169 (0.92)-
HCs: healthy controls; JHU-ICBM: Johns Hopkins University–International Consortium of Brain Mapping; L: left; R: right; RV: relative variation; SZPs: patients with schizophrenia.
Table 4. Significant white matter (WM) fasciculi and regions grouped into clusters from radial diffusivity (RD) TBSS analyses.
Table 4. Significant white matter (WM) fasciculi and regions grouped into clusters from radial diffusivity (RD) TBSS analyses.
WM Fasciculi or Regions from JHU-ICBM Tracts and Labels Atlases
Number of Voxels (>100) (Proportion in %)		RD—Baseline
HCs > SZPs
pFWE < 0.05, TFCE		RD-RV—Longitudinal
SZPs > HCs
p < 0.05, TFCE		SZPs
Post-Intervention > Baseline
p < 0.05, TFCE
JHU-tracts
L-anterior thalamic radiation1096 (0.49)741 (1.26)629 (1.44)
R-anterior thalamic radiation1555 (1.03)953 (1.38)554 (1.49)
L-corticospinal tract334 (0.15)423 (0.47)855 (1.85)
R-corticospinal tract1488 (0.86)662 (0.77)670 (1.30)
L-cingulum gyrus1034 (0.40)--
R-cingulum gyrus559 (0.14)--
R-cingulum hippocampus137 (0.033)351 (0.50)231 (0.81)
Forceps major2395 (1.38)1035 (1.92)221 (0.65)
Forceps minor3009 (3.53)--
L-inferior fronto-occipital fasciculus993 (0.76)340 (0.91)-
R-inferior fronto-occipital fasciculus2875 (1.93)1050 (2.41)185 (0.81)
L-inferior longitudinal fasciculus469 (0.31)418 (1.00)337 (1.50)
R-inferior longitudinal fasciculus2043 (1.25)1071 (1.87)504 (1.55)
L-superior longitudinal fasciculus3420 (1.82)310 (0.38)266 (0.56)
R-superior longitudinal fasciculus4161 (2.10)1191 (1.36)196 (0.66)
R-superior longitudinal fasciculus—part temporal122 (0.63)226 (0.62)120 (0.48)
L-uncinate fasciculus276 (0.28)--
R-uncinate fasciculus531 (0.37)239 (0.48)-
JHU-labels
Genu of the corpus callosum1176 (3.17)--
Body of the corpus callosum1899 (5.11)--
Splenium of the corpus callosum1491 (4.00)355 (3.04)-
L-corona radiata1504 (4.04)--
R-corona radiata1568 (4.21)237 (1.95)-
R-sagittal stratum184 (0.49)121 (0.99)-
L-external capsule300 (0.79)--
R-external capsule618 (1.17)438 (2.18)-
R-anterior internal capsule305 (0.80)116 (0.95)-
R-posterior internal capsule330 (0.88)117 (0.96)-
L-retrolenticular part of internal capsule-115 (0.94)-
R-retrolenticular part of internal capsule199 (0.53)--
Middle cerebellar peduncle-604 (4.95)1019 (15.68)
R-cerebellar peduncle224 (0.59)155 (1.27)-
HCs: healthy controls; JHU: Johns Hopkins University–International Consortium of Brain Mapping; L: left; R: right; RV: relative variation; SZPs: patients with schizophrenia.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Leroux, E.; Masson, L.; Tréhout, M.; Dollfus, S. Effects of Adapted Physical Activity on White Matter Integrity in Patients with Schizophrenia. Brain Sci. 2024, 14, 710. https://doi.org/10.3390/brainsci14070710

AMA Style

Leroux E, Masson L, Tréhout M, Dollfus S. Effects of Adapted Physical Activity on White Matter Integrity in Patients with Schizophrenia. Brain Sciences. 2024; 14(7):710. https://doi.org/10.3390/brainsci14070710

Chicago/Turabian Style

Leroux, Elise, Laura Masson, Maxime Tréhout, and Sonia Dollfus. 2024. "Effects of Adapted Physical Activity on White Matter Integrity in Patients with Schizophrenia" Brain Sciences 14, no. 7: 710. https://doi.org/10.3390/brainsci14070710

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop