Cognitive deficits linked to intrinsic timescales and gray matter volume abnormalities in children with Duchenne muscular dystrophy

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This study integrated intrinsic neural timescale (INT) and cerebral structural properties to examine relationships among cognitive function, functional hierarchy, and structural integrity in children with DMD. Methods: Thirty-six children with DMD and 30 healthy control children underwent magnetic resonance imaging (MRI), including T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI).INT and voxel-based morphometry (VBM) analyses were conducted to assess intrinsic timescales and gray matter volume (GMV). The statistical parametric mapping toolkit was utilized for two-sample t-tests with Gaussian random field correction. Statistical significance thresholds were voxel-wise P < 0.001 and cluster-wise P < 0.05. Spearman correlation analysis was further performed to identify associations between cognitive scores and neural abnormalities. Results: Children with DMD exhibited impaired cognitive function, and distinct neurodevelopmental trajectories, characterized by synchronized shorter INT and lower GMV in limbic-sensorimotor networks; widespread GMV atrophy in the visual, default mode, and dorsal attention networks. GMV in multiple regions was positively correlated with working memory and perceptual reasoning scores. Conclusion: These findings suggest that dystrophin deficiency induces synchronized functional-structural deficits and aberrant neurodevelopmental trajectories, offering insights into neurodevelopmental abnormalities in children with DMD. The integration of INT and GMV provides a novel framework for decoding hierarchical network dysfunction and morphological plasticity changes in DMD, identifying potential targets for cognitive intervention. Duchenne muscular dystrophy Intrinsic neural timescale Gray matter volume Cognitive deficits Network dysfunction Figures Figure 1 Figure 2 Figure 3 1. Introduction Duchenne muscular dystrophy (DMD), an X-linked recessive disorder caused by mutations in the dystrophin gene, affects approximately 1 in 3,500-5,000 male births and results in a lack of functional dystrophin protein( 1 , 2 ). Dystrophin isoforms are expressed in muscle, along with cortical neurons and cerebellar Purkinje cells( 3 ), suggesting involvement in neurocognitive functions( 4 , 5 ). Individuals with DMD typically exhibit a full-scale intelligence quotient (FSIQ) approximately one standard deviation below the population mean( 6 ). Cognitive deficits in attention, executive function and memory are frequently observed, in conjunction with increased prevalence of psychiatric disorders( 4 ). These findings indicate widespread neural dysfunction; however, the neurobiological mechanisms linking dystrophin deficiency to cognitive impairment remain unclear, impeding development of targeted interventions. In children with DMD, functional magnetic resonance imaging (MRI) studies have demonstrated abnormal hyperactivation in the default mode network (DMN) and executive control network (ECN), along with suppressed somatosensory and cerebellar-visual circuit activities. These patterns are correlated with cognitive impairments( 7 , 8 ). Gray matter volume (GMV) reductions have been identified in the insula, occipital lobes, and cerebellum, highlighting regional atrophy( 9 ). Nevertheless, previous evidences predominantly describe localized functional changes without examining disruptions in the brain’s global functional hierarchy. Specialization and hierarchical organization are fundamental aspects of primate cortical architecture( 10 ). Neuroimaging and electrophysiological studies have demonstrated that higher-order cortical areas (e.g., prefrontal cortex) exhibit longer intrinsic timescales for sustained cognitive processing; primary sensory cortices display shorter timescales for rapid stimulus detection( 10 – 12 ). The intrinsic neural timescale (INT), a recently proposed metric quantifying the temporal window over which a region integrates information, captures this hierarchical organization; it reflects functional specialization across cortical networks and may depend on structural integrity( 13 – 15 ). No published studies have investigated INT in DMD or its relationship with GMV alterations. This research gap remains critical to understanding the structure-function dynamics that underlie cognitive decline. Theoretical models suggest that intrinsic timescale gradients arise from structural properties such as synaptic density( 16 , 17 ), which are frequently disrupted in neurodevelopmental disorders. Dystrophin deficiency compromises neuronal cytoskeletal stability and synaptic plasticity( 2 , 18 ), potentially affecting both GMV and INT. For instance, cerebellar Purkinje cells, which strongly express dystrophin isoforms, exhibit GABAergic dysfunction and atrophy in DMD( 19 , 20 ); these phenomena may disrupt sensorimotor and cognitive processing. Similarly, prefrontal and limbic regions—critical for executive function and emotional regulation—might exhibit shortened intrinsic timescales due to dystrophin-related synaptic instability. Despite these plausible links, neuroimaging studies have separately analyzed structure and function in DMD without considering synergistic effects. This limitation hampers a comprehensive understanding of how dystrophin deficiency leads to network-level dysfunction, underscoring the need for multimodal approaches that integrate INT and GMV analyses. To address these gaps, the current study evaluated INT gradients, which reflect the brain’s functional hierarchy, and GMV alterations in children with DMD, while assessing their correlations with cognitive phenotypes. Based on prior research( 8 , 9 , 14 ), we hypothesized that children with DMD would exhibit differences in INT and GMV compared with healthy controls (HCs), particularly within brain regions linked to cognitive functions. Additionally, we anticipated overlap between regions exhibiting abnormalities in INT and GMV. 2. Materials and methods 2.1. Participants This study enrolled 42 male children with genetically or pathologically confirmed DMD from the Neurorehabilitation Department of West China Second University Hospital, along with 30 age- and sex-matched HCs. Both groups met the following inclusion criteria: ( 1 ) male sex and age < 18 years; ( 2 ) absence of neuropsychiatric disorders, neurosensory deficits, physical illnesses, or substance dependence. The exclusion criteria were: ( 1 ) recent use of antipsychotic or sedative medications and ( 2 ) contraindications for MRI. This study adhered to the principles of the Declaration of Helsinki and received ethical approval from the Medical Research Ethics Committee of West China Second University Hospital, Sichuan University (approval number 2024392). Written informed consent was obtained from all participants or their legal guardians. 2.2. Data acquisition 2.2.1. Neurodevelopmental assessment Cognitive function was assessed in all participants using the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV), which evaluated FSIQ, as well as the verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI)( 8 , 21 ). 2.2.2. MRI scanning MRI data were acquired using a 3T Siemens MAGNETOM Skyra scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 32-channel head coil. Participants remained supine, awake, and with eyes closed during scanning, while foam padding and earplugs minimized motion and noise. Structural images were obtained using a three-dimensional magnetization-prepared rapid gradient echo (3D-MPRAGE) sequence with the following parameters: repetition time/echo time = 2000/2.19 ms, field of view = 256 × 224 mm 2 , sagittal slices = 176, slice thickness = 1.0 mm, skip = 0 mm, and flip angle = 9°. Functional images were acquired using a gradient echo-planar imaging sequence with the following parameters: repetition time/echo time = 2000/30 ms, flip angle = 90°, field of view = 240 × 240 mm 2 , voxel size = 3.75 × 3.75 × 5 mm 3 , slices = 28, slice thickness = 5.0 mm, and a total of 200 volumes. 2.3. Data analysis 2.3.1. Functional data preprocessing and intrinsic timescale map Functional images were preprocessed using the Data Processing Assistant for Resting-State fMRI (DPARSF), built upon the framework of Statistical Parametric Mapping 12 (SPM 12) and MATLAB 2018a. The key steps included( 14 , 15 , 22 ): (i) DICOM-to-NIFTI conversion, removal of the first 10 volumes for signal equilibrium, slice-timing correction, and realignment (excluding participants with translation > 2.5 mm or rotation > 2.5°; six DMD patients were excluded during this step); (ii) spatial normalization to Montreal Neurological Institute (MNI) space (resampled to 3 × 3 × 3 mm 3 ); (iii) linear detrending and bandpass filtering (0.01–0.08 Hz) to reduce noise; (iv) removal of nuisance covariates (24 head motion parameters, global signal, white matter, and cerebrospinal fluid signals); and (v) scrubbing and despiking (via 3dDespike) to replace outliers with spline-interpolated values( 15 ). The intrinsic timescale was estimated from preprocessed resting state functional MRI data by calculating voxel-wise autocorrelation( 23 ). The intrinsic timescale for each voxel was regarded as the sum of positive values from the autocorrelation function, computed from the first lag until the first non-positive coefficient( 13 , 23 ), then multiplied by repetition time to standardize temporal resolution. This process was implemented using MATLAB scripts from the RaichleLab GitHub repository ( https://github.com/RaichleLab ), which generated whole-brain INT maps for each participant. 2.3.2. Structural data preprocessing and GMV map GMV was calculated from structural MRI data using the Computational Anatomy Toolbox 12 (CAT 12) ( http://www.neuro.uni-jena.de/cat/ ), an SPM12 extension based on MATLAB 2018a ( http://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ). The main steps were( 14 ): (i) segmentation of structural images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF); (ii) spatial normalization to the MNI template (resampled to 2 × 2 × 2 mm 3 ) and modulation; and (iii) smoothing of modulated GMV maps with a 6-mm full-width at half-maximum Gaussian kernel. 2.3.3. Statistical analysis Demographic and clinical data, along with whole-brain volumes—including total intracranial volume (TIV), GM, WM, and CSF—were compared between the two groups using two-sample t -tests in IBM SPSS Statistics (v26.0), with the significance threshold set at P < 0.05. Whole-brain volumes were modeled via quadratic regression to establish age-related trajectories. Group differences in intrinsic timescale and GMV were assessed using two-sample t -tests, with adjustments for age, years of education, duration of corticosteroid treatment, mean framewise displacement, and TIV. Statistical thresholds were set at voxel-wise P < 0.001, cluster-wise P < 0.05 (two-tailed), with a minimum cluster size of 30 voxels after Gaussian random field (GRF) correction. 2.3.4. Correlation analysis Spearman rank correlation analysis was conducted to evaluate relationships between WISC-IV scores (FSIQ, VCI, PRI, WMI, PSI) and intrinsic timescale or GMV values in brain regions exhibiting significant group differences. 3. Results 3.1. Demographic and clinical characteristics This study included 36 children with DMD and 30 HCs; there were no significant group differences in age or years of education. Compared with HCs, the DMD group exhibited significantly lower scores in FSIQ, VCI, PRI, and WMI, as well as higher whole-brain structural volumes in TIV, WM, and CSF. Group-specific age trajectories diverged across compartments: TIV declined with age in the DMD group but increased in HCs. Both groups exhibited quadratic GM trajectories, and the slope was steeper in the DMD group. WM peaked and then declined in the DMD group, whereas it steadily increased in HCs. CSF exhibited inverse quadratic patterns, rising and then falling in the DMD group, while it decreased before increasing in HCs. Further details are provided in Table 1 and Figure 1. Table 1 Participant demographic and clinical characteristics DMD HC t P- value Sex, M/F 36/0 30/0 - - Age, years 9.50±2.42 9.23±2.08 0.475 0.637 Education, years 3.42±2.22 2.90±1.97 0.990 0.326 Full-scale IQ 91.97±16.30 107.70±9.71 -4.484 < 0.001* Duration of corticosteroid treatment, months 19.14±15.20 - - - Verbal Comprehension Index 92.31±17.71 100.77±11.19 -2.185 0.034* Perceptual Reasoning Index 95.28±16.47 108.63±13.28 -3.435 0.001* Working Memory Index 88.62±15.19 93.93±12.83 -4.797 < 0.001* Processing Speed Index 97.10±17.39 107.13±14.45 0.799 0.428 Total intracranial volume 1580.12±112.04 1468.19±110.20 4.071 < 0.001* Gray matter 804.78±68.39 786.23±49.14 1.241 0.219 White matter 508.33±45.22 468.83±46.35 3.494 0.001* Cerebrospinal fluid 270.53±68.73 213.13±35.55 4.360 < 0.001* Abbreviations: DMD, Duchenne muscular dystrophy; HC, healthy control; M, male; F, female; IQ, intelligence quotient. Values are presented as mean ± standard deviation. * indicates statistically significant difference ( P < 0.05). 3.2. Intrinsic timescale abnormalities in DMD versus HC Figure 2A presents the mean intrinsic timescale maps for the DMD and HC groups. Both groups exhibited comparable whole-brain patterns, such that timescales were longer in the prefrontal lobe and shorter in the occipital lobe. However, the DMD group demonstrated significantly shorter intrinsic timescales in the bilateral cerebellum posterior lobe (CPL), right orbitofrontal cortex (OFC), insula, and temporal pole gyrus (TPO), extending to the hippocampus (Table 2, Figure 2B). Table 2 Voxel-wise comparisons of intrinsic timescale and GMV between DMD and HC groups Brain region Hemisphere Peak MNI coordinate Cluster size (voxels) Peak T value (X, Y, Z) Intrinsic timescale DMD HC None GMV DMD < HC CPL Right 21, -60, -65 1504 -6.34 CPL Left -39, -54, -62 2018 -6.29 OFC+TPO Right 35, 21, -21 994 -6.04 Insula Right 57, 11, -2 868 -5.23 Insula Left -42, 12, -2 617 -5.55 MTG Left -65, -30, -3 1101 -6.22 Thalamus Bilateral 9, -15, 20 1669 -5.88 Precuneus Right 12, -75, 41 785 -4.68 Precuneus Left -20, -72, 54 1331 -5.65 Occipital cortex Left -27, -86, 31 239 -4.32 DMD > HC DPFC Right 14, 2, 69 2275 5.96 DPFC Left -17, 5, 69 969 4.88 Abbreviations: GMV, gray matter volume; DMD, Duchenne muscular dystrophy; HC, healthy control; CPL, cerebellum posterior lobe; TPO, temporal pole gyrus; OFC, orbitofrontal cortex; MTG, middle temporal gyrus; DPFC, dorsal prefrontal cortex. 3.3. GMV abnormalities in DMD versus HC Compared with HCs, the DMD group exhibited lower GMV in the bilateral CPL, right OFC, right TPO, bilateral insula, left MTG, bilateral thalamus, and precuneus; it displayed higher GMV in the dorsal prefrontal cortex (DPFC) (GRF-corrected, voxel-wise P < 0.001, cluster-wise P < 0.05; Table 2, Figure 3A, B). 3.4 Correlations of GMV and intrinsic timescale with WISC-IV scores in the DMD group Correlation analyses indicated that GMV in the bilateral CPL, thalamus, and left MTG was positively correlated with WMI scores, whereas GMV in the right precuneus was positively correlated with PRI scores (Figure 3C). No significant correlations between intrinsic timescale and WISC-IV scores were observed in the DMD group. 4. Discussion This study integrated advanced methodologies of INT and voxel-based morphometry to comprehensively evaluate the relationship between functional hierarchy disruptions and structural degeneration in children with DMD. The results demonstrated (i) neurocognitive deficits and divergent whole-brain structural aging trajectories, (ii) synchronized abnormalities in intrinsic timescale and GMV in the limbic system and sensorimotor network, (iii) widespread GMV-specific alterations in the visual network, DMN, and dorsal attention network, and (iv) cognitive-specific structural correlations. Relative to HCs, children with DMD exhibited distinctive neurodevelopmental trajectories, which were characterized by global cognitive deficits (e.g., lower FSIQ, VCI, PRI, and WMI), as well as abnormalities in whole-brain volume (TIV, WM, and CSF). The distinct age-related patterns in the DMD and HC groups suggest disrupted neurodevelopmental programming and accelerated neurodegeneration. These findings confirmed that multidimensional cognitive deficits occur in DMD, including reduced FSIQ and domain-specific deficits in memory, language processing, and perceptual reasoning(6, 24). The observed increase in TIV was primarily attributed to WM expansion and CSF enlargement. Although increased TIV might indicate preserved brain growth, CSF enlargement likely reflects compensatory ventriculomegaly resulting from cortical thinning—a pattern that matches neurodegenerative mechanisms observed in Huntington’s disease(25). WM hypertrophy in DMD may result from dystrophin deficiency-related abnormal myelination or reactive gliosis(26). Notably, the absence of observed GM differences may obscure regionally heterogeneous changes, whereby localized GM atrophy coexists with compensatory increases in other regions. Furthermore, age-specific volumetric trajectories underscore critical neurodevelopmental periods, revealing distinctive patterns of whole-brain volume alterations in DMD that contrast with those in HCs. These findings suggest that dystrophin plays a crucial role in age-dependent neural remodeling. Although the DMD group exhibited shorter intrinsic timescales at the whole-brain level relative to the HC group, both groups displayed the previously reported gradient distribution pattern of timescales in the human brain(27), where higher-order cortices (e.g., prefrontal cortex) exhibit longer intrinsic timescales, while the occipital visual cortex exhibits shorter intrinsic timescales(28, 29). These findings support the hierarchical functional organization of brain regions, such that information integration time progressively increases from primary to higher-order areas(30). However, children with DMD exhibited significantly shorter intrinsic timescales in the CPL, OFC, insula, and TPO compared with HCs, as well as lower GMV in these regions. The synchronized alterations suggest disruptions of temporal dynamics and structural integrity. The intrinsic timescale, which reflects the temporal window of neural integration(31), is essential for hierarchical information processing. Shorter timescales in these regions suggest reduced capacity to sustain and integrate information over time, which may interfere with higher-order cognitive functions. The lower GMV likely reflects decreased neuronal density and synaptic arborization that impair the integration of local neural circuits(16, 17). In support of previous findings (23), we found that regions with lower GMV exhibit attenuated neural signal autocorrelation, providing a mechanistic basis for the shorter intrinsic timescales observed in these cortical hubs. Synchronized changes in intrinsic timescale and GMV within these regions likely reflect a shared pathophysiological process. Dystrophin deficiency, a hallmark of DMD, disrupts neuronal cytoskeletal stability and synaptic function(32). This deficiency may accelerate neuronal loss and reduce GMV, while impairing synaptic plasticity, which then shortens the temporal window for information integration. Notably, dystrophin isoforms are strongly expressed in the cerebellum, OFC, and hippocampus(33), suggesting region-specific vulnerability in these areas. The cerebellum, historically associated with motor coordination and balance, is increasingly recognized for its roles in advanced cognitive control and visuomotor processes(8). In the context of DMD, dystrophin deficiency in cerebellar Purkinje cells leads to disrupted GABA A receptor clustering(19), which reduces inhibitory synaptic function and alters postsynaptic currents(20). These GABAergic impairments have been implicated in cognitive and behavioral deficits exhibited by mdx mice and DMD patients(34). The intrinsic timescale shortening observed in the present study may reflect accelerated neural signal decay due to impaired GABAergic inhibition(19). Concurrent GMV loss corresponds with the role of Dp140 in neurodevelopment(33, 35); its absence impairs glutamatergic transmission and cerebellar circuit maturation, ultimately resulting in structural atrophy(32). The OFC, insula, TPO, and hippocampus are integral components of the LBM, which is crucial for emotional regulation, flexible behavior, memory, and learning(36). The OFC integrates reward and punishment signals to guide decision-making(37), whereas the insula mediates interoceptive awareness and emotional salience(38). Structural and functional impairments in these brain regions may underlie the social-emotional deficits observed in DMD patients, such as anxiety and social withdrawal(39). The TPO, which serves as a hub for social cognition and semantic memory(40), also exhibited concurrent reductions in intrinsic timescale and GMV. This finding aligns with behavioral research demonstrating impairments in theory of mind and emotional recognition among DMD patients(41). Due to its connectivity with the DMN and limbic structures, the TPO plays a critical role in mediating social information processing. Dysfunction in this region may disrupt social cue integration, thereby exacerbating psychosocial challenges in DMD patients(42). In addition to the synchronized regions, widespread GMV atrophy was observed in the occipital cortex, precuneus, MTG, and thalamus, whereas increased GMV was detected in the DPFC. These findings highlight both degenerative and compensatory mechanisms in DMD. Consistent with previous studies(9, 43), children with DMD exhibited significantly lower TIV than HCs, likely due to dystrophin deficiency-induced neuromorphological impairments. Significant GMV atrophy and its correlations with PRI scores were observed in the precuneus, a key hub of the DMN associated with self-referential thinking and episodic memory(44). The structural integrity of this region is essential for visuospatial buffering and episodic memory retrieval—functions impaired by dystrophin deficiency in DMD(33, 35). The precuneus also integrates sensory inputs and mental representations critical for perceptual reasoning tasks(45). The correlation of PRI scores with GMV in the right precuneus emphasizes its roles in visuospatial reasoning and mental imagery. Preservation of precuneus structural integrity could potentially mitigate visuospatial deficits associated with DMD. WMI scores were correlated with GMV in the left MTG, bilateral CPL, and thalamus. The thalamus—a relay linking the striatum, cortex, and cerebellum—modulates prefrontal activity, integrates sensorimotor signals, and enhances information transfer essential for task maintenance(46). Its atrophy can disrupt corticostriatal circuits, exacerbating executive function deficits in DMD(2). Additionally, the MTG, which is vital for language and semantic memory(47), exhibited lower GMV. Degeneration in this region may underlie the language delays and verbal memory deficits encountered in DMD patients(24). Its role in phonological processing and the contribution of the cerebello-thalamo-cortical circuit to working memory imply that the integrity of these regions supports verbal working memory(48). These correlations suggest that interventions aimed at preserving GMV in regions such as the CPL, MTG, and precuneus could mitigate cognitive decline. For instance, cognitive training programs designed to enhance working memory and visuospatial skills may stimulate neuroplasticity in these regions. GMV atrophy in the occipital cortex corresponds with reports of visual-perceptual deficits in DMD(41). Given the occipital lobe’s role in visual processing and its connectivity with parietal attentional networks, it may be particularly vulnerable to dystrophin deficiency-induced disruptions, potentially leading to visuospatial impairment(33). In contrast to widespread GMV atrophy in DMD, the DPFC, a key region of the frontoparietal and dorsal attention networks involved in executive control, working memory, decision-making, attention regulation, cognitive flexibility, and social cognition(49), exhibited increased GMV. This finding implies compensatory neuroplasticity to offset deficits in other neural networks. In Alzheimer’s disease, similar GMV increases have been associated with gliosis or compensatory neurogenesis(14). In DMD, chronic neuroinflammation driven by dystrophin-deficient microglia may induce reactive astrocytosis, which transiently increases GMV but may compromise functional efficiency(2). However, the adaptive or maladaptive nature of this hypertrophy requires exploration through longitudinal studies. Although this study provided novel insights, several limitations warrant consideration. First, the cross-sectional design precludes causal inferences regarding relationships among GMV, intrinsic timescale, and cognition. Longitudinal studies that track disease progression over time are necessary to address these questions. Second, the sample size is appropriate for a rare disease such as DMD but may limit statistical power for subgroup analyses. Third, data limitations restricted neurocognitive analyses to the cohort-wide DMD population, precluding dystrophin subtype-specific genotype-phenotype correlations. Future research integrating functional MRI, diffusion tensor imaging, and dystrophin expression profiling may help clarify how dystrophin deficiency leads to neural network dysfunction. 5. Conclusion This study revealed multilevel neural disruptions in DMD, characterized by synchronized functional and structural deficits in limbic-sensorimotor networks, as well as extensive GMV alterations associated with cognitive deficits. The distinct neurodevelopmental trajectories and compensatory prefrontal hypertrophy observed highlight the critical role of dystrophin in age-dependent neural remodeling. By integrating INT and voxel-based morphometry, this study established a multimodal framework for decoding neurodevelopmental disorders and identified potential targets for therapeutic intervention. Abbreviations DMD, Duchenne muscular dystrophy INT, intrinsic neural timescale MRI, magnetic resonance imaging rs-fMRI, resting-state functional magnetic resonance imaging VBM, voxel-based morphometry GMV, gray matter volume FSIQ, full-scale intelligence quotient DMN, default mode network ECN, executive control network HCs, healthy controls WISC-IV, Wechsler Intelligence Scale for Children-Fourth Edition VCI, verbal comprehension index PRI, perceptual reasoning index WMI, working memory index PSI, processing speed index 3D-MPRAGE, three-dimensional magnetization-prepared rapid gradient echo DPARSF, Data Processing Assistant for Resting-State fMRI SPM 12, Statistical Parametric Mapping 12 MNI, Montreal Neurological Institute CAT 12, Computational Anatomy Toolbox 12 GM, gray matter WM, white matter CSF, cerebrospinal fluid TIV, total intracranial volume GRF, Gaussian random field CPL, cerebellum posterior lobe OFC, orbitofrontal cortex TPO, temporal pole gyrus DPFC, dorsal prefrontal cortex Declarations Acknowledgments We extend our sincere thanks to all the families and children who participated in the study. Authors’ contributions XN, HX and BC conceived and designed the study. XN, QH, XZ, SZ, KX, YS, ZZ, RX, HF, YR, CL and TX collected the data. XN, SH, YZ, HX and BC analyzed the data, performed the statistical study. XN drafted the manuscript. XN, BC, HX, XC, YG and SH revised the manuscript. All authors read and approved the final manuscript. Funding This research was supported by the National Natural Science Foundation of China (82120108015, 82271981, 82471970, 82402250, 82402251, 82402249, 82304078, 82302168, 824B2052), Sichuan Science and Technology Program (23ZDYF2519, 2023NSFSC1715, 2024NSFSC0652, 2024YFFK0257, 2024YFFK0258, 2025ZNSFSC1772, 2025ZNSFSC1767, 2024YFFK0361), Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1116), Clinical Research Finding of Chinese Society of Cardiovascular Disease (CSC) of 2019 (No. HFCSC2019B01), Postdoctoral Fellowship Program of CPS Funder Grant (GZC20231830), Beijing Medical Award Foundation (24H1187), and Sichuan University Interdisciplinary Innovation Fund. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request. Ethics approval and consent to participate This study received ethical approval from the Medical Research Ethics Committee of West China Second University Hospital, Sichuan University (approval number 2024392). Written informed consent was obtained from all participants or their legal guardians. Consent for publication All participants or their legal guardians provided consent for publication on study enrolment. Competing interests No authors have any personal or financial conflict of interest. References Crisafulli S, Sultana J, Fontana A, Salvo F, Messina S, Trifirò G. Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta-analysis. Orphanet J Rare Dis. 2020;15(1):141. Vaillend C, Aoki Y, Mercuri E, Hendriksen J, Tetorou K, Goyenvalle A, et al. Duchenne muscular dystrophy: recent insights in brain related comorbidities. Nat Commun. 2025;16(1):1298. Lidov HG, Byers TJ, Watkins SC, Kunkel LM. 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Abnormal interhemispheric and intrahemispheric functional connectivity dynamics in drug-naïve first-episode schizophrenia patients with auditory verbal hallucinations. Hum Brain Mapp. 2022;43(14):4347-58. Ma S, Skarica M, Li Q, Xu C, Risgaard RD, Tebbenkamp ATN, et al. Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science. 2022;377(6614):eabo7257. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Mar, 2026 Read the published version in Journal of Neurodevelopmental Disorders → Version 1 posted Editorial decision: Revision requested 22 Sep, 2025 Reviews received at journal 22 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 27 Aug, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 05 Jun, 2025 Submission checks completed at journal 05 Jun, 2025 First submitted to journal 02 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6802845","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469646334,"identity":"9e20a810-f415-43e9-9323-aea106e7b0c1","order_by":0,"name":"Xiaoyu Niu","email":"","orcid":"","institution":"Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Niu","suffix":""},{"id":469646337,"identity":"4d3374e5-6a34-493c-869f-a7d302ba4d2e","order_by":1,"name":"Qin Hu","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, China","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Hu","suffix":""},{"id":469646338,"identity":"6394e8a1-6f30-487b-b4eb-b21f1b1b5f21","order_by":2,"name":"Xinyuan Zhang","email":"","orcid":"","institution":"Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":469646339,"identity":"cabe11e2-cba4-4248-8c29-c5ca5cc49f71","order_by":3,"name":"Suming 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Xu","email":"data:image/png;base64,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","orcid":"","institution":"Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Huayan","middleName":"","lastName":"Xu","suffix":""},{"id":469646351,"identity":"6983af87-b669-4be9-b206-026bea2eb7c3","order_by":15,"name":"Xiaotang Cai","email":"","orcid":"","institution":"Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, China","correspondingAuthor":false,"prefix":"","firstName":"Xiaotang","middleName":"","lastName":"Cai","suffix":""},{"id":469646352,"identity":"31967dd6-e039-493d-a933-c0825d89e2f5","order_by":16,"name":"Bochao Cheng","email":"","orcid":"","institution":"Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Bochao","middleName":"","lastName":"Cheng","suffix":""},{"id":469646353,"identity":"69f51a81-d1cc-4c6a-8075-a4392ef0afe9","order_by":17,"name":"Yingkun Guo","email":"","orcid":"","institution":"Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yingkun","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-06-02 13:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6802845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6802845/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s11689-026-09689-x","type":"published","date":"2026-03-29T16:10:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84528668,"identity":"46fa78a6-bddb-455c-b03b-71db23f9d05c","added_by":"auto","created_at":"2025-06-13 05:44:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114035,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in clinical data, whole-brain volumes, and brain structure–age-related trajectories between DMD and HC groups. (A) Compared with HCs, the DMD group exhibited significantly lower scores in FSIQ, VCI, PRI, and WMI. (B) Whole-brain structural volumes, including TIV, WM, and CSF, were significantly higher in the DMD group than in HCs. (C) Group-specific age trajectories diverged: TIV declined with age in DMD but increased in HCs. Both groups exhibited quadratic GM trajectories, and the slope was steeper in the DMD group. WM peaked and then declined in the DMD group, whereas it steadily increased in HCs. CSF exhibited inverse quadratic patterns, rising and then falling in the DMD group, while it decreased before increasing in HCs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DMD, Duchenne muscular dystrophy; HC, healthy control; FSIQ, full-scale intelligence quotient; WMI, Working Memory Index; PRI, Perceptual Reasoning Index; VCI, Verbal Comprehension Index; TIV, total intracranial volume; GM, gray matter; WM, white matter; CSF, cerebrospinal fluid. * indicates statistically significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6802845/v1/d5664e465f92719a61598c69.png"},{"id":84528670,"identity":"6c279403-4555-4a6a-9951-9ea3bc87be58","added_by":"auto","created_at":"2025-06-13 05:44:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":354355,"visible":true,"origin":"","legend":"\u003cp\u003eIntrinsic timescale differences between DMD and HC groups. (A) Spatial distribution maps of intrinsic timescales for children in the DMD and HC groups. At the whole-brain level, the intrinsic timescale was generally shorter in the DMD group than in the HC group. In both groups, the prefrontal lobe exhibited longer intrinsic timescales, whereas the occipital lobe showed shorter timescales. Warm colors indicate longer timescales, and cold colors indicate shorter timescales. (B) Voxel-wise differences in intrinsic timescales between the DMD and HC groups. The DMD group exhibited shorter intrinsic timescales in the bilateral CPL, right OFC, insula, and TPO extending to the hippocampus (GRF-corrected, voxel-wise\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001, cluster-wise \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eAbbreviations: DMD, Duchenne muscular dystrophy; HC, healthy control; CPL, cerebellum posterior lobe; OFC, orbitofrontal cortex; TPO, temporal pole gyrus.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6802845/v1/b25145feb83e5dbda597958e.png"},{"id":84530375,"identity":"e0a03092-0e3e-4274-b9dd-45f1c2c5a348","added_by":"auto","created_at":"2025-06-13 06:00:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183924,"visible":true,"origin":"","legend":"\u003cp\u003eGMV differences between DMD and HC groups. (A) Voxel-wise GMV differences between the DMD and HC groups. Compared with HCs, the DMD group exhibited lower GMV in the bilateral CPL, insula, thalamus, precuneus, right OFC/TPO, and left MTG; it showed higher GMV in the DPFC (GRF-corrected, voxel-wise\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.001, cluster-wise \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). Warm and cold colors denote higher and lower GMV in DMD, respectively. (B) Brain regions with significant GMV differences were localized to large-scale neural networks, predominantly the DMN and LMB. (C) Correlations between GMV and WISC-IV scores in the DMD group. Solid and hollow circles indicate right and left hemispheric regions, respectively; colors distinguish brain regions.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GMV, gray matter volume; DMD, Duchenne muscular dystrophy; HC, healthy control; CPL, cerebellum posterior lobe; OFC, orbitofrontal cortex; TPO, temporal pole gyrus; MTG, middle temporal gyrus; DPFC, dorsal prefrontal cortex; DMN, default mode network; CB, cerebellum; VN, visual network; SMN, sensorimotor network; DAN, dorsal attention network; SN, salience network; LMB, limbic system; FPN, frontoparietal network; WISC-IV, Wechsler Intelligence Scale for Children–Fourth Edition; L, left; R, right.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6802845/v1/8fc47f37fb154e9362a7062a.png"},{"id":105754961,"identity":"eec3b7bc-0f92-43df-b82a-aa3b18aa9055","added_by":"auto","created_at":"2026-03-30 16:23:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1497484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6802845/v1/9bc870b0-ff0c-4c6e-b71f-ed15b2a909af.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive deficits linked to intrinsic timescales and gray matter volume abnormalities in children with Duchenne muscular dystrophy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDuchenne muscular dystrophy (DMD), an X-linked recessive disorder caused by mutations in the dystrophin gene, affects approximately 1 in 3,500-5,000 male births and results in a lack of functional dystrophin protein(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Dystrophin isoforms are expressed in muscle, along with cortical neurons and cerebellar Purkinje cells(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), suggesting involvement in neurocognitive functions(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Individuals with DMD typically exhibit a full-scale intelligence quotient (FSIQ) approximately one standard deviation below the population mean(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Cognitive deficits in attention, executive function and memory are frequently observed, in conjunction with increased prevalence of psychiatric disorders(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These findings indicate widespread neural dysfunction; however, the neurobiological mechanisms linking dystrophin deficiency to cognitive impairment remain unclear, impeding development of targeted interventions.\u003c/p\u003e \u003cp\u003eIn children with DMD, functional magnetic resonance imaging (MRI) studies have demonstrated abnormal hyperactivation in the default mode network (DMN) and executive control network (ECN), along with suppressed somatosensory and cerebellar-visual circuit activities. These patterns are correlated with cognitive impairments(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Gray matter volume (GMV) reductions have been identified in the insula, occipital lobes, and cerebellum, highlighting regional atrophy(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Nevertheless, previous evidences predominantly describe localized functional changes without examining disruptions in the brain\u0026rsquo;s global functional hierarchy. Specialization and hierarchical organization are fundamental aspects of primate cortical architecture(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Neuroimaging and electrophysiological studies have demonstrated that higher-order cortical areas (e.g., prefrontal cortex) exhibit longer intrinsic timescales for sustained cognitive processing; primary sensory cortices display shorter timescales for rapid stimulus detection(\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The intrinsic neural timescale (INT), a recently proposed metric quantifying the temporal window over which a region integrates information, captures this hierarchical organization; it reflects functional specialization across cortical networks and may depend on structural integrity(\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). No published studies have investigated INT in DMD or its relationship with GMV alterations. This research gap remains critical to understanding the structure-function dynamics that underlie cognitive decline.\u003c/p\u003e \u003cp\u003eTheoretical models suggest that intrinsic timescale gradients arise from structural properties such as synaptic density(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), which are frequently disrupted in neurodevelopmental disorders. Dystrophin deficiency compromises neuronal cytoskeletal stability and synaptic plasticity(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), potentially affecting both GMV and INT. For instance, cerebellar Purkinje cells, which strongly express dystrophin isoforms, exhibit GABAergic dysfunction and atrophy in DMD(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e); these phenomena may disrupt sensorimotor and cognitive processing. Similarly, prefrontal and limbic regions\u0026mdash;critical for executive function and emotional regulation\u0026mdash;might exhibit shortened intrinsic timescales due to dystrophin-related synaptic instability. Despite these plausible links, neuroimaging studies have separately analyzed structure and function in DMD without considering synergistic effects. This limitation hampers a comprehensive understanding of how dystrophin deficiency leads to network-level dysfunction, underscoring the need for multimodal approaches that integrate INT and GMV analyses.\u003c/p\u003e \u003cp\u003eTo address these gaps, the current study evaluated INT gradients, which reflect the brain\u0026rsquo;s functional hierarchy, and GMV alterations in children with DMD, while assessing their correlations with cognitive phenotypes. Based on prior research(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), we hypothesized that children with DMD would exhibit differences in INT and GMV compared with healthy controls (HCs), particularly within brain regions linked to cognitive functions. Additionally, we anticipated overlap between regions exhibiting abnormalities in INT and GMV.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eThis study enrolled 42 male children with genetically or pathologically confirmed DMD from the Neurorehabilitation Department of West China Second University Hospital, along with 30 age- and sex-matched HCs. Both groups met the following inclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) male sex and age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) absence of neuropsychiatric disorders, neurosensory deficits, physical illnesses, or substance dependence. The exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) recent use of antipsychotic or sedative medications and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) contraindications for MRI.\u003c/p\u003e \u003cp\u003eThis study adhered to the principles of the Declaration of Helsinki and received ethical approval from the Medical Research Ethics Committee of West China Second University Hospital, Sichuan University (approval number 2024392). Written informed consent was obtained from all participants or their legal guardians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data acquisition\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Neurodevelopmental assessment\u003c/h2\u003e \u003cp\u003eCognitive function was assessed in all participants using the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV), which evaluated FSIQ, as well as the verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. MRI scanning\u003c/h2\u003e \u003cp\u003eMRI data were acquired using a 3T Siemens MAGNETOM Skyra scanner (Siemens Healthcare, Erlangen, Germany) equipped with a 32-channel head coil. Participants remained supine, awake, and with eyes closed during scanning, while foam padding and earplugs minimized motion and noise. Structural images were obtained using a three-dimensional magnetization-prepared rapid gradient echo (3D-MPRAGE) sequence with the following parameters: repetition time/echo time\u0026thinsp;=\u0026thinsp;2000/2.19 ms, field of view\u0026thinsp;=\u0026thinsp;256 \u0026times; 224 mm\u003csup\u003e2\u003c/sup\u003e, sagittal slices\u0026thinsp;=\u0026thinsp;176, slice thickness\u0026thinsp;=\u0026thinsp;1.0 mm, skip\u0026thinsp;=\u0026thinsp;0 mm, and flip angle\u0026thinsp;=\u0026thinsp;9\u0026deg;. Functional images were acquired using a gradient echo-planar imaging sequence with the following parameters: repetition time/echo time\u0026thinsp;=\u0026thinsp;2000/30 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, field of view\u0026thinsp;=\u0026thinsp;240 \u0026times; 240 mm\u003csup\u003e2\u003c/sup\u003e, voxel size\u0026thinsp;=\u0026thinsp;3.75 \u0026times; 3.75 \u0026times; 5 mm\u003csup\u003e3\u003c/sup\u003e, slices\u0026thinsp;=\u0026thinsp;28, slice thickness\u0026thinsp;=\u0026thinsp;5.0 mm, and a total of 200 volumes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Functional data preprocessing and intrinsic timescale map\u003c/h2\u003e \u003cp\u003eFunctional images were preprocessed using the Data Processing Assistant for Resting-State fMRI (DPARSF), built upon the framework of Statistical Parametric Mapping 12 (SPM 12) and MATLAB 2018a. The key steps included(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e): (i) DICOM-to-NIFTI conversion, removal of the first 10 volumes for signal equilibrium, slice-timing correction, and realignment (excluding participants with translation\u0026thinsp;\u0026gt;\u0026thinsp;2.5 mm or rotation\u0026thinsp;\u0026gt;\u0026thinsp;2.5\u0026deg;; six DMD patients were excluded during this step); (ii) spatial normalization to Montreal Neurological Institute (MNI) space (resampled to 3 \u0026times; 3 \u0026times; 3 mm\u003csup\u003e3\u003c/sup\u003e); (iii) linear detrending and bandpass filtering (0.01\u0026ndash;0.08 Hz) to reduce noise; (iv) removal of nuisance covariates (24 head motion parameters, global signal, white matter, and cerebrospinal fluid signals); and (v) scrubbing and despiking (via 3dDespike) to replace outliers with spline-interpolated values(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe intrinsic timescale was estimated from preprocessed resting state functional MRI data by calculating voxel-wise autocorrelation(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The intrinsic timescale for each voxel was regarded as the sum of positive values from the autocorrelation function, computed from the first lag until the first non-positive coefficient(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), then multiplied by repetition time to standardize temporal resolution. This process was implemented using MATLAB scripts from the RaichleLab GitHub repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/RaichleLab\u003c/span\u003e\u003cspan address=\"https://github.com/RaichleLab\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which generated whole-brain INT maps for each participant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Structural data preprocessing and GMV map\u003c/h2\u003e \u003cp\u003eGMV was calculated from structural MRI data using the Computational Anatomy Toolbox 12 (CAT 12) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat/\u003c/span\u003e\u003cspan address=\"http://www.neuro.uni-jena.de/cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an SPM12 extension based on MATLAB 2018a (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The main steps were(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e): (i) segmentation of structural images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF); (ii) spatial normalization to the MNI template (resampled to 2 \u0026times; 2 \u0026times; 2 mm\u003csup\u003e3\u003c/sup\u003e) and modulation; and (iii) smoothing of modulated GMV maps with a 6-mm full-width at half-maximum Gaussian kernel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical data, along with whole-brain volumes\u0026mdash;including total intracranial volume (TIV), GM, WM, and CSF\u0026mdash;were compared between the two groups using two-sample \u003cem\u003et\u003c/em\u003e-tests in IBM SPSS Statistics (v26.0), with the significance threshold set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Whole-brain volumes were modeled via quadratic regression to establish age-related trajectories.\u003c/p\u003e \u003cp\u003eGroup differences in intrinsic timescale and GMV were assessed using two-sample \u003cem\u003et\u003c/em\u003e-tests, with adjustments for age, years of education, duration of corticosteroid treatment, mean framewise displacement, and TIV. Statistical thresholds were set at voxel-wise \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, cluster-wise \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed), with a minimum cluster size of 30 voxels after Gaussian random field (GRF) correction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Correlation analysis\u003c/h2\u003e \u003cp\u003eSpearman rank correlation analysis was conducted to evaluate relationships between WISC-IV scores (FSIQ, VCI, PRI, WMI, PSI) and intrinsic timescale or GMV values in brain regions exhibiting significant group differences.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Demographic and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 36 children with DMD and 30 HCs; there were no significant group differences in age or years of education. Compared with HCs, the DMD group exhibited significantly lower scores in FSIQ, VCI, PRI, and WMI, as well as higher whole-brain structural volumes in TIV, WM, and CSF. Group-specific age trajectories diverged across compartments: TIV declined with age in the DMD group but increased in HCs. Both groups exhibited quadratic GM trajectories, and the slope was steeper in the DMD group. WM peaked and then declined in the DMD group, whereas it steadily increased in HCs. CSF exhibited inverse quadratic patterns, rising and then falling in the DMD group, while it decreased before increasing in HCs. Further details are provided in Table 1 and Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Participant demographic and clinical characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eDMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eSex, M/F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e36/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e30/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e9.50\u0026plusmn;2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e9.23\u0026plusmn;2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eEducation, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e3.42\u0026plusmn;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e2.90\u0026plusmn;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eFull-scale IQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e91.97\u0026plusmn;16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e107.70\u0026plusmn;9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e-4.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eDuration of corticosteroid treatment, months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e19.14\u0026plusmn;15.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eVerbal Comprehension Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e92.31\u0026plusmn;17.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e100.77\u0026plusmn;11.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e-2.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003ePerceptual Reasoning Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e95.28\u0026plusmn;16.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e108.63\u0026plusmn;13.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e-3.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eWorking Memory Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e88.62\u0026plusmn;15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e93.93\u0026plusmn;12.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e-4.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eProcessing Speed Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e97.10\u0026plusmn;17.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e107.13\u0026plusmn;14.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eTotal intracranial volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1580.12\u0026plusmn;112.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e1468.19\u0026plusmn;110.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e4.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eGray matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e804.78\u0026plusmn;68.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e786.23\u0026plusmn;49.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e1.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eWhite matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e508.33\u0026plusmn;45.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e468.83\u0026plusmn;46.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e3.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 34.958%;\"\u003e\n \u003cp\u003eCerebrospinal fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e270.53\u0026plusmn;68.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e213.13\u0026plusmn;35.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.41176%;\"\u003e\n \u003cp\u003e4.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6303%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: DMD, Duchenne muscular dystrophy; HC, healthy control; M, male; F, female; IQ, intelligence quotient. Values are presented as mean \u0026plusmn; standard deviation. * indicates statistically significant difference (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Intrinsic timescale abnormalities in DMD versus HC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2A presents the mean intrinsic timescale maps for the DMD and HC groups. Both groups exhibited comparable whole-brain patterns, such that timescales were longer in the prefrontal lobe and shorter in the occipital lobe. However, the DMD group demonstrated significantly shorter intrinsic timescales in the bilateral cerebellum posterior lobe (CPL), right orbitofrontal cortex (OFC), insula, and temporal pole gyrus (TPO), extending to the hippocampus (Table 2, Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Voxel-wise comparisons of intrinsic timescale and GMV between DMD and HC groups\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 150px;\"\u003e\n \u003cp\u003eBrain region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003eHemisphere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003ePeak MNI coordinate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCluster size (voxels)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePeak T value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e(X, Y, Z)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 569px;\"\u003e\n \u003cp\u003eIntrinsic timescale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 569px;\"\u003e\n \u003cp\u003eDMD \u0026lt; HC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eBilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e18, -54, -63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eTPO+Hippocampus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e44, 3, -15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eInsula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e38, 24, -7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eOFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e34, 24, -7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 569px;\"\u003e\n \u003cp\u003eDMD \u0026gt; HC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 476px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 569px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 569px;\"\u003e\n \u003cp\u003eDMD \u0026lt; HC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e21, -60, -65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eCPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-39, -54, -62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eOFC+TPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e35, 21, -21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-6.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eInsula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e57, 11, -2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eInsula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-42, 12, -2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-5.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eMTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-65, -30, -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-6.22\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eThalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eBilateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9, -15, 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-5.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ePrecuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e12, -75, 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003ePrecuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-20, -72, 54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eOccipital cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-27, -86, 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e-4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 569px;\"\u003e\n \u003cp\u003eDMD \u0026gt; HC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eDPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e14, 2, 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003eDPFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-17, 5, 69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e4.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: GMV, gray matter volume; DMD, Duchenne muscular dystrophy; HC, healthy control; CPL, cerebellum posterior lobe; TPO, temporal pole gyrus; OFC, orbitofrontal cortex; MTG, middle temporal gyrus; DPFC, dorsal prefrontal cortex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. GMV abnormalities in DMD versus HC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with HCs, the DMD group exhibited lower GMV in the bilateral CPL, right OFC, right TPO, bilateral insula, left MTG, bilateral thalamus, and precuneus; it displayed higher GMV in the dorsal prefrontal cortex (DPFC) (GRF-corrected, voxel-wise\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt; 0.001, cluster-wise \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Table 2, Figure 3A, B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Correlations of GMV and intrinsic timescale with WISC-IV scores in the DMD group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation analyses indicated that GMV in the bilateral CPL, thalamus, and left MTG was positively correlated with WMI scores, whereas GMV in the right precuneus was positively correlated with PRI scores (Figure 3C). No significant correlations between intrinsic timescale and WISC-IV scores were observed in the DMD group.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study integrated advanced methodologies of INT and voxel-based morphometry to comprehensively evaluate the relationship between functional hierarchy disruptions and structural degeneration in children with DMD. The results demonstrated (i) neurocognitive deficits and divergent whole-brain structural aging trajectories, (ii) synchronized abnormalities in intrinsic timescale and GMV in the limbic system and sensorimotor network, (iii) widespread GMV-specific alterations in the visual network, DMN, and dorsal attention network, and (iv) cognitive-specific structural correlations.\u003c/p\u003e\n\u003cp\u003eRelative to HCs, children with DMD exhibited distinctive neurodevelopmental trajectories, which were characterized by global cognitive deficits (e.g., lower FSIQ, VCI, PRI, and WMI), as well as abnormalities in whole-brain volume (TIV, WM, and CSF). The distinct age-related patterns in the DMD and HC groups suggest disrupted neurodevelopmental programming and accelerated neurodegeneration. These findings confirmed that multidimensional cognitive deficits occur in DMD, including reduced FSIQ and domain-specific deficits in memory, language processing, and perceptual reasoning(6, 24). The observed increase in TIV was primarily attributed to WM expansion and CSF enlargement. Although increased TIV might indicate preserved brain growth, CSF enlargement likely reflects compensatory ventriculomegaly resulting from cortical thinning—a pattern that matches neurodegenerative mechanisms observed in Huntington’s disease(25). WM hypertrophy in DMD may result from dystrophin deficiency-related abnormal myelination or reactive gliosis(26). Notably, the absence of observed GM differences may obscure regionally heterogeneous changes, whereby localized GM atrophy coexists with compensatory increases in other regions.\u0026nbsp;Furthermore, age-specific volumetric trajectories underscore critical neurodevelopmental periods, revealing distinctive patterns of whole-brain volume alterations in DMD that contrast with those in HCs. These findings suggest that dystrophin plays a crucial role in age-dependent neural remodeling.\u003c/p\u003e\n\u003cp\u003eAlthough the DMD group exhibited shorter intrinsic timescales at the whole-brain level relative to the HC group, both groups displayed the previously reported gradient distribution pattern of timescales in the human brain(27), where higher-order cortices (e.g., prefrontal cortex) exhibit longer intrinsic timescales, while the occipital visual cortex exhibits shorter intrinsic timescales(28, 29). These findings support the hierarchical functional organization of brain regions, such that information integration time progressively increases from primary to higher-order areas(30). However, children with DMD exhibited significantly shorter intrinsic timescales in the CPL, OFC, insula, and TPO compared with HCs, as well as lower GMV in these regions. The synchronized alterations suggest disruptions of temporal dynamics and structural integrity. The intrinsic timescale, which reflects the temporal window of neural integration(31),\u0026nbsp;is essential for hierarchical information processing. Shorter timescales in these regions suggest reduced capacity to sustain and integrate information over time, which may interfere with higher-order cognitive functions. The lower GMV likely reflects decreased neuronal density and synaptic arborization that impair the integration of local neural circuits(16, 17). In support of previous findings\u0026nbsp;(23), we found that regions with lower GMV exhibit attenuated neural signal autocorrelation, providing a mechanistic basis for the shorter intrinsic timescales observed in these cortical hubs. Synchronized changes in intrinsic timescale and GMV within these regions likely reflect a shared pathophysiological process. Dystrophin deficiency, a hallmark of DMD, disrupts neuronal cytoskeletal stability and synaptic function(32). This deficiency may accelerate neuronal loss and reduce GMV, while impairing synaptic plasticity, which then shortens the temporal window for information integration. Notably, dystrophin isoforms are strongly expressed in the cerebellum, OFC, and hippocampus(33), suggesting region-specific vulnerability in these areas.\u003c/p\u003e\n\u003cp\u003eThe cerebellum, historically associated with motor coordination and balance, is increasingly recognized for its roles in advanced cognitive control and visuomotor processes(8). In the context of DMD, dystrophin deficiency in cerebellar Purkinje cells leads to disrupted GABA\u003csub\u003eA\u003c/sub\u003e receptor clustering(19), which reduces inhibitory synaptic function and alters postsynaptic currents(20). These GABAergic impairments have been implicated in cognitive and behavioral deficits exhibited by \u003cem\u003emdx\u003c/em\u003e mice and DMD patients(34). The intrinsic timescale shortening observed in the present study may reflect accelerated neural signal decay due to impaired GABAergic inhibition(19). Concurrent GMV loss corresponds with the role of Dp140 in neurodevelopment(33, 35); its absence impairs glutamatergic transmission and cerebellar circuit maturation, ultimately resulting in structural atrophy(32). The OFC, insula, TPO, and hippocampus are integral components of the LBM, which is crucial for emotional regulation, flexible behavior, memory, and learning(36). The OFC integrates reward and punishment signals to guide decision-making(37), whereas the insula mediates interoceptive awareness and emotional salience(38). Structural and functional impairments in these brain regions may underlie the social-emotional deficits observed in DMD patients, such as anxiety and social withdrawal(39). The TPO, which serves as a hub for social cognition and semantic memory(40), also exhibited concurrent reductions in intrinsic timescale and GMV. This finding aligns with behavioral research demonstrating impairments in theory of mind and emotional recognition among DMD patients(41). Due to its connectivity with the DMN and limbic structures, the TPO plays a critical role in mediating social information processing. Dysfunction in this region may disrupt social cue integration, thereby exacerbating psychosocial challenges in DMD patients(42).\u003c/p\u003e\n\u003cp\u003eIn addition to the synchronized regions, widespread GMV atrophy was observed in the occipital cortex, precuneus, MTG, and thalamus, whereas increased GMV was detected in the DPFC. These findings highlight both degenerative and compensatory mechanisms in DMD. Consistent with previous studies(9, 43), children with DMD exhibited significantly lower TIV than HCs, likely due to dystrophin deficiency-induced neuromorphological impairments.\u003c/p\u003e\n\u003cp\u003eSignificant GMV atrophy and its correlations with PRI scores were observed in the precuneus, a key hub of the DMN associated with self-referential thinking and episodic memory(44). The structural integrity of this region is essential for visuospatial buffering and episodic memory retrieval—functions impaired by dystrophin deficiency in DMD(33, 35). The precuneus also integrates sensory inputs and mental representations critical for perceptual reasoning tasks(45). The correlation of PRI scores with GMV in the right precuneus emphasizes its roles in visuospatial reasoning and mental imagery. Preservation of precuneus structural integrity could potentially mitigate visuospatial deficits associated with DMD. WMI scores were correlated with GMV in the left MTG, bilateral CPL, and thalamus. The thalamus—a relay linking the striatum, cortex, and cerebellum—modulates prefrontal activity, integrates sensorimotor signals, and enhances information transfer essential for task maintenance(46). Its atrophy can disrupt corticostriatal circuits, exacerbating executive function deficits in DMD(2). Additionally, the MTG, which is vital for language and semantic memory(47), exhibited lower GMV. Degeneration in this region may underlie the language delays and verbal memory deficits encountered in DMD patients(24). Its role in phonological processing and the contribution of the cerebello-thalamo-cortical circuit to working memory imply that the integrity of these regions supports verbal working memory(48). These correlations suggest that interventions aimed at preserving GMV in regions such as the CPL, MTG, and precuneus could mitigate cognitive decline. For instance, cognitive training programs designed to enhance working memory and visuospatial skills may stimulate neuroplasticity in these regions. GMV atrophy in the occipital cortex corresponds with reports of visual-perceptual deficits in DMD(41). Given the occipital lobe’s role in visual processing and its connectivity with parietal attentional networks, it may be particularly vulnerable to dystrophin deficiency-induced disruptions, potentially leading to visuospatial impairment(33).\u003c/p\u003e\n\u003cp\u003eIn contrast to widespread GMV atrophy in DMD, the DPFC, a key region of the frontoparietal and dorsal attention networks involved in executive control, working memory, decision-making, attention regulation, cognitive flexibility, and social cognition(49), exhibited increased GMV. This finding implies compensatory neuroplasticity to offset deficits in other neural networks. In Alzheimer’s disease, similar GMV increases have been associated with gliosis or compensatory neurogenesis(14). In DMD, chronic neuroinflammation driven by dystrophin-deficient microglia may induce reactive astrocytosis, which transiently increases GMV but may compromise functional efficiency(2). However, the adaptive or maladaptive nature of this hypertrophy requires exploration through longitudinal studies.\u003c/p\u003e\n\u003cp\u003eAlthough this study provided novel insights, several limitations warrant consideration. First, the cross-sectional design precludes causal inferences regarding relationships among GMV, intrinsic timescale, and cognition. Longitudinal studies that track disease progression over time are necessary to address these questions. Second, the sample size is appropriate for a rare disease such as DMD but may limit statistical power for subgroup analyses. Third, data limitations restricted neurocognitive analyses to the cohort-wide DMD population, precluding dystrophin subtype-specific genotype-phenotype correlations. Future research integrating functional MRI, diffusion tensor imaging, and dystrophin expression profiling may help clarify how dystrophin deficiency leads to neural network dysfunction.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study revealed multilevel neural disruptions in DMD, characterized by synchronized functional and structural deficits in limbic-sensorimotor networks, as well as extensive GMV alterations associated with cognitive deficits. The distinct neurodevelopmental trajectories and compensatory prefrontal hypertrophy observed highlight the critical role of dystrophin in age-dependent neural remodeling. By integrating INT and voxel-based morphometry, this study established a multimodal framework for decoding neurodevelopmental disorders and identified potential targets for therapeutic intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDMD, Duchenne muscular dystrophy\u003c/p\u003e\n\u003cp\u003eINT, intrinsic neural timescale\u003c/p\u003e\n\u003cp\u003eMRI, magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003ers-fMRI, resting-state functional magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eVBM, voxel-based morphometry\u003c/p\u003e\n\u003cp\u003eGMV, gray matter volume\u003c/p\u003e\n\u003cp\u003eFSIQ, full-scale intelligence quotient\u003c/p\u003e\n\u003cp\u003eDMN, default mode network\u003c/p\u003e\n\u003cp\u003eECN, executive control network\u003c/p\u003e\n\u003cp\u003eHCs, healthy controls\u003c/p\u003e\n\u003cp\u003eWISC-IV, Wechsler Intelligence Scale for Children-Fourth Edition\u003c/p\u003e\n\u003cp\u003eVCI, verbal comprehension index\u003c/p\u003e\n\u003cp\u003ePRI, perceptual reasoning index\u003c/p\u003e\n\u003cp\u003eWMI, working memory index\u003c/p\u003e\n\u003cp\u003ePSI, processing speed index\u003c/p\u003e\n\u003cp\u003e3D-MPRAGE, three-dimensional magnetization-prepared rapid gradient echo\u003c/p\u003e\n\u003cp\u003eDPARSF, Data Processing Assistant for Resting-State fMRI\u003c/p\u003e\n\u003cp\u003eSPM 12, Statistical Parametric Mapping 12\u003c/p\u003e\n\u003cp\u003eMNI, Montreal Neurological Institute\u003c/p\u003e\n\u003cp\u003eCAT 12, Computational Anatomy Toolbox 12\u003c/p\u003e\n\u003cp\u003eGM, gray matter\u003c/p\u003e\n\u003cp\u003eWM, white matter\u003c/p\u003e\n\u003cp\u003eCSF, cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003eTIV, total intracranial volume\u003c/p\u003e\n\u003cp\u003eGRF, Gaussian random field\u003c/p\u003e\n\u003cp\u003eCPL, cerebellum posterior lobe\u003c/p\u003e\n\u003cp\u003eOFC, orbitofrontal cortex\u003c/p\u003e\n\u003cp\u003eTPO, temporal pole gyrus\u003c/p\u003e\n\u003cp\u003eDPFC, dorsal prefrontal cortex\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere thanks to all the families and children who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXN, HX and BC conceived and designed the study. XN, QH, XZ, SZ, KX, YS, ZZ, RX, HF, YR, CL and TX collected the data. XN, SH, YZ, HX and BC analyzed the data, performed the statistical study. XN drafted the manuscript. XN, BC, HX, XC, YG and SH revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (82120108015, 82271981, 82471970, 82402250, 82402251, 82402249, 82304078, 82302168, 824B2052), Sichuan Science and Technology Program (23ZDYF2519, 2023NSFSC1715, 2024NSFSC0652, 2024YFFK0257, 2024YFFK0258, 2025ZNSFSC1772, 2025ZNSFSC1767, 2024YFFK0361), Natural Science Foundation of Chongqing (CSTB2024NSCQ-MSX1116), Clinical Research Finding of Chinese Society of Cardiovascular Disease (CSC) of 2019 (No. HFCSC2019B01), Postdoctoral Fellowship Program of CPS Funder Grant (GZC20231830), Beijing Medical Award Foundation (24H1187), and Sichuan University Interdisciplinary Innovation Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the Medical Research Ethics Committee of West China Second University Hospital, Sichuan University (approval number 2024392). Written informed consent was obtained from all participants or their legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants or their legal guardians provided consent for publication on study enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo authors have any personal or financial conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCrisafulli S, Sultana J, Fontana A, Salvo F, Messina S, Trifir\u0026ograve; G. Global epidemiology of Duchenne muscular dystrophy: an updated systematic review and meta-analysis. Orphanet J Rare Dis. 2020;15(1):141.\u003c/li\u003e\n \u003cli\u003eVaillend C, Aoki Y, Mercuri E, Hendriksen J, Tetorou K, Goyenvalle A, et al. Duchenne muscular dystrophy: recent insights in brain related comorbidities. 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Molecular and cellular evolution of the primate dorsolateral prefrontal cortex. Science. 2022;377(6614):eabo7257.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neurodevelopmental-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jndd","sideBox":"Learn more about [Journal of Neurodevelopmental Disorders](http://jneurodevdisorders.biomedcentral.com/)","snPcode":"11689","submissionUrl":"https://submission.nature.com/new-submission/11689/3","title":"Journal of Neurodevelopmental Disorders","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Duchenne muscular dystrophy, Intrinsic neural timescale, Gray matter volume, Cognitive deficits, Network dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-6802845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6802845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Duchenne muscular dystrophy (DMD) is associated with cognitive deficits and neural abnormalities, but the brain’s global functional hierarchy and its interaction with structural changes remain unclear. This study integrated intrinsic neural timescale (INT) and cerebral structural properties to examine relationships among cognitive function, functional hierarchy, and structural integrity in children with DMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Thirty-six children with DMD and 30 healthy control children underwent magnetic resonance imaging (MRI), including T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI).INT and voxel-based morphometry (VBM) analyses were conducted to assess intrinsic timescales and gray matter volume (GMV). The statistical parametric mapping toolkit was utilized for two-sample t-tests with Gaussian random field correction. Statistical significance thresholds were voxel-wise \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001 and cluster-wise \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. Spearman correlation analysis was further performed to identify associations between cognitive scores and neural abnormalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Children with DMD exhibited impaired cognitive function, and distinct neurodevelopmental trajectories, characterized by synchronized shorter INT and lower GMV in limbic-sensorimotor networks; widespread GMV atrophy in the visual, default mode, and dorsal attention networks. GMV in multiple regions was positively correlated with working memory and perceptual reasoning scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e These findings suggest that dystrophin deficiency induces synchronized functional-structural deficits and aberrant neurodevelopmental trajectories, offering insights into neurodevelopmental abnormalities in children with DMD. The integration of INT and GMV provides a novel framework for decoding hierarchical network dysfunction and morphological plasticity changes in DMD, identifying potential targets for cognitive intervention.\u003c/p\u003e","manuscriptTitle":"Cognitive deficits linked to intrinsic timescales and gray matter volume abnormalities in children with Duchenne muscular dystrophy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 05:44:24","doi":"10.21203/rs.3.rs-6802845/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-22T15:31:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-22T14:30:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T09:40:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239115956862652088829230170865757570153","date":"2025-08-27T08:30:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87666720478971072948321843311985935119","date":"2025-06-12T13:28:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T09:37:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T12:11:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-05T12:09:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neurodevelopmental Disorders","date":"2025-06-02T13:50:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neurodevelopmental-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jndd","sideBox":"Learn more about [Journal of Neurodevelopmental Disorders](http://jneurodevdisorders.biomedcentral.com/)","snPcode":"11689","submissionUrl":"https://submission.nature.com/new-submission/11689/3","title":"Journal of Neurodevelopmental Disorders","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11bd4c05-1ba2-4fba-b073-41666336484c","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:17:58+00:00","versionOfRecord":{"articleIdentity":"rs-6802845","link":"https://doi.org/10.1186/s11689-026-09689-x","journal":{"identity":"journal-of-neurodevelopmental-disorders","isVorOnly":false,"title":"Journal of Neurodevelopmental Disorders"},"publishedOn":"2026-03-29 16:10:35","publishedOnDateReadable":"March 29th, 2026"},"versionCreatedAt":"2025-06-13 05:44:24","video":"","vorDoi":"10.1186/s11689-026-09689-x","vorDoiUrl":"https://doi.org/10.1186/s11689-026-09689-x","workflowStages":[]},"version":"v1","identity":"rs-6802845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6802845","identity":"rs-6802845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}