Transcriptional specialization shapes abnormal cortical morphological similarity gradients in Wilson's disease

Transcriptional specialization shapes abnormal cortical morphological similarity gradients in Wilson's disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptional specialization shapes abnormal cortical morphological similarity gradients in Wilson's disease Yuqi Song, Weiqi Wang, Sheng Hu, Yulong Yang, Chuanfu Li, Kou Xu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6250277/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Neuroimaging studies have revealed structural abnormalities in the brains of individuals with Wilson's disease (WD), particularly within the basal ganglia, and the associated molecular mechanisms have been elucidated. However, the structural damage in the cerebral cortex, along with its underlying biological and molecular processes, remains elusive. Here, we investigated the abnormalities in cortical morphological similarity gradients associated with WD and further unraveled their underlying transcriptional specialization. Methods: First, we analyzed cortical morphological features from structural magnetic resonance imaging scans from 102 WD patients and 90 healthy controls (HCs) and then computed the cortical morphological similarity (MS) connections. Subsequently, the diffusion map embedding approach was employed to investigate the cortical MS gradients. Finally, the differences in MS gradients between WD and HC were analyzed and their underlying clinical relevance and transcriptional specialization were revealed using clinical symptoms and gene expression data, respectively. Results: Compared with HC, WD patients exhibited regional differences across extensive brain networks in both the first and second MS gradients. Alterations in MS gradient alterations correlated with age, neurological symptoms, liver function symptoms, and motor-related processing. Partial least squares (PLS) regression analysis results indicated a significant association between MS gradients and gene expression profiles (PLS components). Gene enrichment analysis showed that the transcriptional specialization of PLS components was enriched in biological processes such as cell projection organization, regulation of protein organization, and GPTase-mediated signal transduction, all of which are relevant to WD. The transcriptional specializations influencing the MS gradient of WD were also enriched in WD's pathological genes associated with WD and other neuropsychiatric risks, such as dystonia and Parkinsonism. Conclusion: Overall, this research offers new perspectives on the neurobiological foundations that govern the emergence of complex neural architectures and associated mental manifestations in WD. Wilson's disease cortical morphological similarity gradient gene expression transcriptional specialization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Mutations in the ATPase copper transporting beta (ATP7B) gene cause Wilson's disease (WD), a hereditary disorder that disrupts copper metabolism. In WD, the impaired function of ATP7B leads to an accumulation of copper in hepatocytes, resulting in liver damage[1]. However, excess copper may disseminate to other tissues in the body, especially the brain, leading to neuropsychiatric manifestations that can severely impact the quality of life of patients[2]. The accumulation of copper adversely affects astrocytes, the blood-brain barrier, and other brain cells, including neurons and oligodendrocytes. Previous studies have identified common brain injury sites in WD, primarily located in subcortical regions such as the basal ganglia, thalamus, cerebellum, and upper brainstem[3-5]. However, encephalopathy associated with WD may extend beyond these areas to involve the cortex[6]. A previous study found that WD patients who did not receive timely diagnosis and treatment were more susceptible to damage in the pons, midbrain, and cortex. Notably, midbrain and cortical lesions were particularly pronounced in WD patients with torsion spasm symptoms[7]. This suggests a potential correlation between cortical damage and certain symptoms of WD. While prior studies predominantly focused on the subcortical structure, there has been little exploration of the cerebral cortex, with only a few studies addressing its connection to cognitive dysfunction[8]. Therefore, the present study seeks to examine the structural damage to the cortex in WD patients and its association with clinical symptoms, as well as the underlying biological mechanisms involved. The cortex is characterized by a highly folded structure rich in neurons and a distinct laminar organization[9, 10]. Research indicates that primary motor regions have a lower density of neurons and neurotransmitter receptors[11, 12], thicker cortical layers[13], and more pronounced laminar flow than sensory regions[14]. The cortical morphological similarity (MS) gradient reflects hierarchical arrangement, illustrating how structural features are interconnected and their potential implications for brain functionality[15]. Neuroimaging studies have associated variations in whole-brain volume and cortical thickness (CT) in WD patients with disease severity, suggesting that these morphometrics may serve as potential biomarkers[16]. Additionally, research has linked the large axes of the cortex to gene expression and microstructure topography[17, 18], indicating that the dimensions and configuration of the cortex are influenced by complete gene expression and microstructural integrity. Even though numerous studies have identified cortical abnormalities in WD patients, there has been a lack of exploration into the synergistic interactions among these morphological indicators and their links to structural and transcriptomic vulnerabilities. To fill this gap, a method for measuring morphological similarity was utilized to develop a comprehensive morphological trend analysis, highlighting the diverse changes and abnormalities in the cerebral cortex. Herein, we constructed cortical similarity connections by integrating various cortical morphological features and subsequently mapped the gradient organization pattern of MS connections, illustrating relationships within cortical structures[15]. We sought to unravel the transcriptional specialization associated with alterations in the cortical morphology in patients with WD. We used structural magnetic resonance imaging to obtain five morphological features: CT, gyrification index (GI), fractal dimension (FD), sulcus depth (SD), and grey matter volume (GM). These metrics facilitated Pearson correlation analysis between pairs of cortical regions of interest (ROI) nodes, leading to the construction of an MS matrix. Next, we used cosine similarity to pinpoint the top 10% of connections and employed the diffusion graph embedding method to calculate the connectome gradient. Additionaly, We evaluated the spatial relationship between changes in the connectome gradient and the entire brain gene expression data from the Allen Human Brain Atlas (AHBA). We also investigated the influence of gene transcriptomic specialization on gradient perturbations. Finally, we conducted a gene enrichment analysis to elucidate transcriptomic associations between known pathogenic WD variants and other neuropsychiatric disorders. Materials and methods Experimental design This study combined brain structural T1 imaging with transcriptome data to explore the association between gene expression and structural gradient perturbations in WD patients versus healthy controls (HCs). The goal was to determine whether gradient perturbations were influenced by the relevant specialized transcriptome (Supplementary Figure 1). We derived five cortical morphological metrics from T1 images and conducted a Z-value analysis to establish connections among them. Pearson correlation coefficients were calculated for corresponding brain regions. To define the MS gradient, we used diffusion graph embedding to identify the spatial axis of interregional structural changes. Finally, partial least squares (PLS) regression analysis was conducted to elucidate the link between changes in structural gradients and gene expression data, followed by gene enrichment and specificity analyses to evaluate the biological processes affected by gradient disturbances. Participants Demographic data were collected from 192 participants, including 102 WD patients (gender distribution: 62% male; age: 27.21 ± 8.229 years) and 90 healthy controls (gender distribution: 66% male; age: 24.98 ± 1.792 years) at the First Affiliated Hospital of Anhui University of Chinese Medicine. Every patient fulfilled the diagnostic standards, which included a ceruloplasmin level under 0.1 g/L, 24-hour urinary copper excretion, and the detection of a Kayser-Fleischer ring via slit lamp examination. The severity of impairment was assessed using the Unified Wilson Disease Rating Scale (UWDRS) examination subscore[19]. Patients with alternative diagnoses or significant medical conditions were excluded. Potential control participants were excluded if they presented with either a documented history of mental health disorders or any other substantial medical comorbidities. The study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine, and written informed consent was obtained from all participants. Demographic and clinical characteristics of the participants are summarized in Table 1. Table 1: Clinical and Demographic Characteristics of Patients WD HC P -value n=102 n=90 Age, years 27.21±8.229 24.98±1.792 0.0127 Gender, male (%) 63（62%） 59（66%） - Education, years 11.82±3.265 17.4±1.505 <0.0001 Disease duration, years 9.344±6.404 - UWDRS-N score 9.843±12.23 - - UWDRS-liver function score 1.696±1.597 - - UWDRS-P score 3.755±3.583 - - UWDRS total score 15.12±14.25 - - 24-hours urinary cooper excretion, ug 851.9±590.8 - - Ceruloplasmin, g/L 0.049±0.036 - - WD, Wilson's disease; HC, healthy control. Data acquisition and preprocessing All participants underwent structural T1 imaging of the brain using a GE MR750 scanner at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine. The T1-3D BRAVO sequence was implemented for T1-weighted image acquisition, configured with specific parameters: TR = 8.16 ms, TE = 3.18 ms, 12° flip angle, 256 × 256 imaging matrix, 256 mm × 256 mm FOV, and 1 mm³ voxel resolution (1 mm slice thickness). A total of 192 T1-weighted images were preprocessed using the Computational Anatomy Toolbox (CAT12, http://www.neuro.uni-jena.de/cat/) for analysis of structural T1 images. This process included cortical modeling, volume segmentation, and the measurement of various structural metrics. The steps undertaken were as follows: (1) adjustment for uneven signal intensity; (2) segmentation of T1-weighted images into gray matter, white matter, and cerebrospinal fluid images; (3) alignment of gray matter images to Montreal Neurological Institute (MNI) space using the Diffeomorphic Anatomical Registration Through Exponential Lie Algebra (DARTELA) algorithm; (4) application of nonlinear modulation to account for brain size variations among individuals; (5) generation of CT maps; (6) automatic correction of topological defects; (7) execution of spherical mapping and alignment; (8) calculation of CT, GI, FD, SD, GM metrics; (9) performance of surface reconstruction and Gaussian smoothing (FWHM); (10) mapping of the five metrics to 360 brain regions of the Human Connectome Project (HCP) atlas[20]. All images were meticulously reviewed for accuracy, with manual corrections applied where necessary. Preprocessing gene expression data Microarray-based gene expression data were sourced from the left AHBA (http://human.brainmap.org)[21]. The data were collected from six donors (mean age: 42.5 years; five males and one female), none with a background of neuropsychiatric or neurological conditions. The dataset included two intact brains and four left hemispheres. A total of 58,692 probes were used to measure gene expression in each donor sample, resulting in expression levels for 20,737 genes per sample. We utilized the AHBA processing pipeline to preprocess gene expression data from the brain samples, adhering to the recommended default settings (https://github.com/BMHLab/AHBAprocessing)[20]. Next, each sample was assigned to one of the 180 partitions corresponding to its nearest HCP_MMP1.0 partition (left hemisphere). This methodology yielded 1,290 samples of cortical tissue from 176 distinct areas of the left cortex, with each sample containing expression data for 10,027 genes. Morphological similarity gradient construction We assigned the HCP atlas to the cortical surface of each participant and extracted five feature values from their T1W images. First, a Z-value for each individual was calculated based on the five morphological features. Then, the MS matrix was generated through Pearson correlation analysis of cortical ROI node pairs. Subsequent diffusion map embedding of this matrix yielded the connectome gradient[22], using the top 10% connections per node and cosine similarity measures. Negative values were eliminated by converting the similarity matrix into a normalized angle matrix. Finally, we applied the diffusion graph embedding method to identify the gradient component explaining the majority of variance in structural connectivity, setting the flow shape learning parameter to α = 0.5[23, 24]. Case-control difference analysis of morphological similarity gradient The current study focused on perturbations in the WD first gradient (FG) and secondary gradient (SG). A two-sample t-test was used to evaluate the differences in the MS gradient between WD patients and HCs while controlling for age, sex, and educational background as covariates. Adjustments for multiple comparisons were corrected using the False Discovery Rate (FDR), with a statistical significance threshold established at q < 0.05. Furthermore, we categorized whole-brain parcels into eight distinct brain networks through cortical mapping[17]. We conducted paired two-sample t-tests, accounting for age, education, and gender, to explore differences in brain network-based connectome gradients between HCs and WD patients, with results further adjusted for multiple comparisons. Analysis of the relationship between clinical characteristics and gradient in patients with WD. An analysis was performed to examine the associations between gradients in the affected region and various clinical characteristics, including the UWDRS score (comprising UWDRS-N, UWDRS-P, and liver function score), ceruloplasmin concentration, and 24-hour urinary copper excretion. This was achieved utilizing partial correlations while adjusting for age and sex. Furthermore, we investigated the relationships between gradients in the disturbed region and factors such as age, disease duration, and years of education. Association a nalysis between gene expression and WD gradient changes We analyzed spatial correlations between changes in FG and SG and gene expression in the cortex. PLS regression analysis was conducted to identify the correlations between the FG and SG and regional differences in transcripts of 10,027 genes. The gene expression results were utilized as predictor variables. The first PLS component (PLS-1) and second PLS component (PLS-2) were derived as linear combinations of gene expression levels that exhibited the strongest association with changes in the FG and SG. To evaluate the statistical significance of the primary PLS components, we performed 10,000 permutation tests on the response variables. Additionally, we implemented bootstrap resampling methodology to account for and correct potential estimation errors in the gene weight coefficients for each PLS component[25]. Enrichment analysis Following established protocols, we first ranked genes according to their bootstrap weight absolute values. The highest-ranking 10% of PLS-weighted genes were then analyzed for biological process enrichment using the WebGestalt online toolkit (https://www.webgestalt.org/), following the developer's recommended parameters and statistical thresholds[26]. To quantify functional enrichment, we calculated the enrichment ratio (ER) using the formula: ER = (observed overlaps) / (expected overlaps), where observed overlaps represent the number of PLS-identified genes in a given biological process, and expected overlaps were determined through 1,000 random permutations of gene-process associations. A Bonferroni FDR correction with a q-value threshold of <0.05 was used to determine significant enrichment. Specificity analysis A targeted analysis was performed to determine whether the gene implicated in WD was enriched in PLS components. We also analyzed various neuropsychiatric disorders, revealing an enrichment of risk genes for other neurological conditions in PLS components. The top 100 genes associated with WD, Alzheimer's disease (AD), Dystonia, Parkinson's disease (PD), and Huntington's disease (HD) were separately identified using the GeneCards dataset (https://www.genecards.org/). The disease-associated risk genes are detailed in Supplementary Table 1. The ER for each PLS component was calculated by subtracting the average bootstrap weight of a randomly ordered gene set from that of the candidate gene, and then dividing it by the standard deviation of the ordered gene weights[27]. Significance was assessed by comparing the bootstrap weight of the candidate gene to the bootstrap weights of genes randomly selected from 10,000 permutations. A positive or negative expression ratio (ER) for a specific condition signifies a higher or lower level of expression of the risk gene compared with the baseline expression level, respectively. Statistical analysis Statistical comparisons of MS gradients between HCs and WD patients were conducted using two-sample t-tests, with appropriate adjustments made for the covariates of age, gender, and educational background. A spatial correlation analysis was conducted to assess the relationship between gradient score changes and PLS components in WD. In the specificity analysis, a bootstrap permutation test with 10,000 samples was used to determine the statistical significance of the effect size. A two-tailed P < 0.05 for multiple comparisons or correlations was considered significant after FDR correction for all tests. Results Demographics and clinical characteristics A comparative analysis of the demographic and clinical characteristics between HCs and WD patients is illustrated in Table 1. The results show that WD patients were, on average, older and had lower educational attainment than the HCs. Disruption of the brain network's gradient in WD. The first component of the cortical MS gradient accounted for 13.1% ± 0.4% of the total variance in the WD group, compared with 12.9% ± 0.6% in the HC group (Supplementary Figure 2). The secondary component of the cortical MS gradient accounted for 12.0% ± 0.5% of the total variance in the WD group, compared with 11.6% ± 0.5% in the HC group (Supplementary Figure 2). Global box plot analysis revealed no significant differences in mean FG and SG scores between WD patients and HCs (Figures 1 A and B). The difference in gradient scores between the WD and HC groups was further assessed using a cross-region paired t-test for each brain network system. In the FG analysis, compared with the HC group, the WD group exhibited higher gradient scores in the cingulo-opercular network (CON), auditory network (AN), and dorsal-attention network (DAN) but lower scores in somatomotor network (SMN), visual network (VIS), and ventral-attention network (VAN), with no significant difference between frontoparietal network (FPN) and default mode network (DMN) (FDR correction q < 0.05, Figure 1 A, Supplementary Table 2). However, in the SG analysis, the WD group demonstrated higher gradient scores in DAN and FPN and lower gradient scores in VIS, AN, and SMN, with no significant difference observed in VAN, CON, and DMN (FDR correction q<0.05, Figure 1B, Supplementary Table 3). Alterations in the first gradient associated with clinical features related to WD In the FG analysis, the differential gradient scores for the WD and HC groups showed uneven distribution across various networks, predominantly in CON (frontal, temporal, and parietal cortex), AN (temporal cortex), DAN (occipital cortex), SMN (primary somatic and motor cortex), VIS (primary visual cortex), and VAN (temporal and frontal cortex). Notably, the scores in the frontal cortex of the left CON, the prefrontal and parietal cortex of the right CON, the temporal cortex of the left AN, and the bilateral DAN occipital cortex were higher in the WD group than in the HC group. Conversely, the scores in the parietal cortex of the right SMN, the occipital cortex of the right VIS, the frontal cortex of the left VAN, and the temporal cortex of the right VAN were lower in the WD group than in the HC group (Figure 2A). Regions exhibiting significantly lower gradient scores were negatively correlated with UWDRS (R = -0.2054, P = 0.0383, Figure 2B) and age (R = -0.2528, P = 0.0104, Figure 2B). No significant correlations were found between the gradient score and UWDRS-N, UWDRS-P, liver function score, education level, 24-hour urinary copper excretion, ceruloplasmin concentration, and disease progression. Alterations in the secondary gradient associated with clinical features related to WD In the SG analysis, the differential gradient scores for the WD and HC groups were also distributed across various networks, predominantly within the FPN (frontal and parietal cortex), DAN (temporal cortex), VIS (primary visual cortex), SMN (primary motor cortex), and AN (temporal cortex). Notably, the scores were higher in the temporal cortex of left DAN, the frontal cortex of bilateral FPN, and the parietal cortex of right FPN and lower in the primary visual cortex of left VIS, the temporal cortex of bilateral AN and the primary motor cortex of right SMN in the WD group than in the HC group (Figure 3A). Among them, regions with significantly elevated gradient scores were inversely related to UWDRS (R = -0.1994, P = 0.0445, Figure 3B) and UWDRS-N (R = -0.2391, P = 0.0155, Figure 3B). Meanwhile, liver function scores were negatively correlated with regions that had lower gradient scores (R = -0.3344, P = 0.0006, Figure 3B). There were no notable correlations between the gradient score and UWDRS-P, education level, age, 24-hour urinary copper excretion, ceruloplasmin concentration, and disease progression.. Gene expression alterations for the first gradients related to WD. PLS regression was applied to identify differences in gene expression associated with the anatomical distribution of FG differences in the left hemisphere cortex (Figure 4A). The PLS-1 and PLS-2 are linear combinations of the gene expression values that exhibit the strongest correlation with variations in the MS gradient (permutation test, P < 0.001). PLS-1 accounted for 14.9% of the variance, establishing it as the component with the highest explained variance, while PLS-2 accounted for 14.3% of the variance. PSL-1 was highly expressed in the cortex near SMN, VIS, and AN and lowly expressed in the cortex near CON and DMN (Figure 4A). PLS-2 was highly expressed in the cortex near DMN, CON, and VAN and lowly expressed in the cortex near SMN, VIS, and FPN (Figure 4A). The MS gradient in the cerebral cortex was significantly positively correlated with the PLS-1 and PLS-2 gene expression (PLS-1: R = 0.36. P < 0.001; PLS-2: R = 0.35, P < 0.001, Figure 4B), highlighting the spatial relationship of cortical properties at a microscopic scale. Our gene enrichment analysis showed that PLS-1 plays a regulatory role in negative cell projection processes (FDR < 0.05, Figure 4C), whereas PLS-2 mainly associated with stroma-dependent cell migration, integrin activation, synaptic vesicles organization, cell-substrate junction tissue, organelles fusion, positive regulation of protein localization, small GTP-mediated signal transduction, cell-matrix adhesion, and regulation of protein transport (FDR < 0.05, Figure 4C). Further analysis showed that PLS-1 and PLS-2 are enriched with risk genes such as those linked to dystonia (PLS-1: ER = -2.51, FDR = 0.0153; PLS-2: ER = -1.71, FDR = 0.0361), WD (PLS-2: ER = -1.80, FDR = 0.0361), and PD (PLS-2: ER = -1.75, FDR = 0.0361) (Figure 4D). Taken together, our results indicate that both structural impairments and gradient abnormalities in cortical regions might be associated with similar transcriptomic characteristics that are also implicated in neurological motor dysfunction. Gene expression alterations for the secondary gradients related to WD. Partial Least Squares (PLS) regression was performed to identify variations in gene expression associated with the anatomical distribution of secondary gradient differences within the left hemisphere cortex (Figure 5A). PLS-1 accounted for 15.7% of the variance, and thus its variance was the most explained compared with the other components. Meanwhile, PLS-2 accounted for 15.5% of the variance. PSL-1 was highly expressed in the cortex near the CON and DMN, but its expression was low in the cortex near the SMN and VIS (Figure 4A). PLS-2 exhibited specific expression in the cortex near VAN and FPN, but its expression in the cortex near CON was minimal (Figure 5A). The secondary gradient in the cerebral cortex correlated significantly with PLS-1 and PLS-2 gene expression (PLS-1: R=0.3742. P <0.001; PLS-2: R=0.3715, P <0.001, Figure 4B). Gene enrichment analysis showed that PLS-1 was involved in the regulation of membrane potential, vesicle-mediated transport in synapse, regulation of trans-synaptic signaling, regulation of monoatomic ion transport, negative regulation of transport, signal release, regulation of neuron projection development, amine transport, binding regulation, monoamine transport (FDR <0.05, Figure 5C). In contrast, the genes associated with PLS-2 exhibited no significant enrichment. Further analysis showed that PLS-1/2 were not enriched with risk genes linked to dystonia, PD, WD, AD, and HD. Discussion Human cortical expansion exhibits an uneven pattern, underscoring evolutionary differences among various brain regions[28]. The organization of cortical morphology is shaped by microscale features and constrained by evolutionary growth[29]. We identify the main axis of variation in the topological organization through the concept of “gradient organization”, which elucidates the transfer of local information flow from primary sensory and motor areas to regions associated with higher cognitive functions, as well as the shift in disease-related information flow from a singular mode to a transmembrane state. Given that WD is recognized for its diverse neurological and psychiatric manifestations[30, 31], it may disrupt the information flow from sensorimotor processing to cognitive functions. Our findings reveal that individuals with WD exhibit reduced SMN and VIS networks in the FG and SG compared with HCs, while the DAN is increased. Thus, the identified gradients may reflect morphological changes in both primary sensory/motor and higher cognitive cortices in WD patients. At the regional level, our findings showed that WD patients had increased gradient scores in major regions such as the temporal cortex of the left DAN and the occipital cortex of the bilateral DAN. Generally, the DAN is known to uphold attentional stability and contribute to various aspects of human intelligence[32]. In alignment with our research, prior functional studies have demonstrated that enhanced functional connectivity between the left inferior temporal cortex and the right parietal cortex is associated with altered attentional functions in WD patients[33]. Therefore, the disturbance of morphological gradient in the DAN may lead to deficits in attentional capabilities among these patients. Conversely, our study revealed diminished gradient scores in WD patients in key regions such as the parietal cortex of right SMN and the occipital cortex of bilateral VIS. Current research has established that the SMN and VIS are crucial for maintaining postural balance and motor coordination[34, 35]. Mounting evidence suggests that structural or functional impairments in these areas correlate with neurological symptoms observed in WD[7, 36, 37]. Thus, the morphological gradient imbalance in the SMN and VIS regions may play a role in the neurological manifestations of WD. Previous studies suggested that analogous cortical regions in morphological networks share similar cellular structures and are likely to be anatomically interconnected[15]. In the FG analysis, we observed that the region exhibiting a significantly lower gradient score in the WD group was negatively correlated with both the UWDRS and age. This may be due to the disruptions in local information flow in the brain cortex of individuals with WD, which worsens with disease progression and advancing age. In the SG analysis, regions that exhibited significantly elevated gradient scores in the WD group were negatively correlated with UWDRS and UWDRS-N. However, regions with lower gradient scores were negatively associated with liver function scores. It was further confirmed that the MS gradient score in WD patients decreased significantly with worsening clinical symptoms. The imbalance in hierarchical structure may stem from reduced connections among brain networks in the cerebral cortex region of WD patients, leading to more pronounced structural differentiation and diminished axonal connections. Therefore, the cortical MS gradient score serves as an indicator of cortical structural vulnerability in WD patients and may be used as a potential neuroimaging biomarker for assessing the disease and prognosis. The complex pathogenesis of WD suggests that changes in the cortical MS gradient associated with WD may be influenced by several factors, including genetic, neuronal, molecular, and cellular mechanisms. The present study identified spatial associations between alterations in connectome gradients and gene expression patterns, highlighting the transcriptomic specificity associated with WD. The PLS-1-related gene negatively regulates cell projection, inhibiting neuronal connections and developmental pathways. The process of neuronal projection is integral to the transmission of information within the brain, facilitating nerve development and differentiation of nerve cells[38]. WD is characterized by impaired copper transport within cells, leading to an accumulation of copper in the brain over time, which adversely affects the structure of cortical neurons and may result in abnormal neuronal degeneration[39]. Consequently, these findings may elucidate the biological mechanisms underlying the atypical neuronal connections observed in patients with WD. PLS-2 is strongly associated with the positive regulation of protein localization, small Gtpase-mediated signal transduction, and protein transport processes. The localization of proteins within subcellular compartments depends on intricate transport mechanisms that are vital for their functionality in various cellular regions. The ATP7B gene mutation causes WD, with research revealing abnormal subcellular localization and altered properties in ATP7B mutants, which exacerbate clinical symptoms[40]. Another study indicated that the gen associated with WD encodes a copper-transporting P-type ATPase, whose subcellular localization is modulated by copper homeostasis[41]. This underscores the role of gene expression in regulating protein transport for purposes, facilitating the anisotropic differentiation of cell structures across different cortical regions in WD patients and influencing brain morphology to coordinate complex brain functions. Excessive copper accumulation in WD can negatively impact the blood-brain barrier[42]. Small GTPase is a vital signaling molecule involved in maintaining blood-brain barrier integrity[43]. Therefore, PLS-1 is likely to influence nerve cell connectivity, whereas PLS-2 is associated with cortical cell differentiation and signal transduction, thereby contributing to the maintenance of brain homeostasis. Additionally, the differentiation of the transcriptome in WD has been associated with genetic risk factors for Parkinsonism and dystonia, which may elucidate the intricate neurological and psychiatric manifestations observed in patients with WD. Nevertheless, this study has some limitations. Firstly, we utilized publicly available AHBA gene data from the postmortem brains of six neurologically healthy donors, which may restrict our analysis of transcription data related to MS abnormalities. Subject variations affect the correlation between the MS gradient and gene expression in PLS analysis. Future research should include more samples from postmortem WD patients to compare differences between hemispheres. Secondly, there were significant age and educational differences between the groups. While we controlled for age, sex, and education in our analysis, their impact on the results could not be fully eliminated. Therefore, we intend to recruit healthy individuals matched for age, gender, and education to explore neuroanatomical and functional irregularities in WD patients and their association with transcriptional regulation patterns. In conclusion, our study identified variations in the white matter cortical microstructural gradient associated with various clinical phenotypes, likely due to disrupted information flow between primary sensorimotor and higher cognitive regions. A key strength of our research is its integration of multiple structural characteristics, enhancing our understanding of cortical architecture and interconnections in WD patients. Our investigation further explored the relationship between abnormal gradient configurations and transcriptional patterns, elucidating the biological underpinnings of these genetic clusters via enrichment analyses focusing on cellular projections, cortical differentiation processes, and cell-to-cell communication pathways. These findings deepen our understanding of the genetic factors contributing to the morphological irregularities in WD and the biological pathways linked to complex neurological and psychiatric traits. Abbreviations ATP7B ATPase copper transporting beta WD Wilson's disease HCs healthy controls MS morphological similarity PLS Partial least squares CT cortical thickness GI gyrification index FD fractal dimension SD sulcus depth GM grey matter volume ROI regions of interest AHBA Allen Human Brain Atlas UWDRS Unified Wilson Disease Rating Scale HCP Human Connectome Project FG first gradient SG secondary gradient FDR False Discovery Rate ER enrichment ratio AD Alzheimer's disease PD Parkinson's disease HD Huntington's disease VIS visual network SMN somatomotor network AN auditory network DAN dorsal attentional network VAN ventral attentional network FPN frontoparietal network CON cingulo-opercular network DMN default mode network LFS liver function score Declarations Acknowledgements We would like to express our gratitude to the research participants and their families, as well as the Medical Imaging Center of the First Affiliated Hospital of Anhui University of Chinese Medicine. Secondly, we extend our sincere thanks to to the Human Connectome Project (HCP), the Allen Human Brain Atlas (AHBA), and BrainSpan for supplying the neuroimaging and gene expression data employed in this research. Author contributions Yuqi Song: Funding acquisition, methodology, data acquisition, curation, and analysis, literature review, manuscript preparation, and served as the primary contributor and editor in the writing of the manuscript. Weiqi Wang: Methodology, data curation, formal analysis, investigation, supervision, writing of the original draft, review, and editing. Sheng Hu, Yulong Yang: Methodology, Writing - reviewing and editing; Chuanfu Li: methodology, data acquisition, curation; Kou Xu and Zilong Li: made charts and analyzed the data. Taohua Wei and Wenming Yang: Funding acquisition, Methodology, Writing - reviewing and editing. All authors read and approved the final manuscript. Funding This study was supported by the Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China (Grant No. U22A20366), the National Natural Science Foundation of China (Grant No. 82305185), the Anhui Provincial Traditional Chinese Medicine Science and Technology Research Project (Grant No. 202303a07020004), the Anhui University Collaborative Innovation Project (Grant No. GXXT-2020-025), and the Anhui Provincial Clinical Medicine Research Transformation Project (Grant No. 202204295107020066). Data availability The data and code supporting the findings of this study are available from the corresponding author on reasonable request. The gene expression data used for transcriptional analysis can be found in the ABHA database (https://human.brain-map.org/static/download). Ethics approval and consent to participate This study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Approval No. 2024-AH-60-02) and obtained written informed consent from all participants. Consent for publication Not applicable. Competing interests The authors declare that there are no competing interests associated with this research. Author details 1 The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China 2 Key Laboratory of Xin'An Medicine, Ministry of Education, Hefei, Anhui, China 3 School of Medical Information Engineering, Anhui University of Chinese Medicine; Hefei, Anhui, China References Członkowska A, Litwin T, Dusek P, Ferenci P, Lutsenko S, Medici V, Rybakowski JK, Weiss KH, Schilsky ML: Wilson disease . Nat Rev Dis Primers 2018, 4 (1):21. Bandmann O, Weiss KH, Kaler SG: Wilson's disease and other neurological copper disorders . Lancet Neurol 2015, 14 (1):103-113. 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Margulies DS, Ghosh SS, Goulas A, Falkiewicz M, Huntenburg JM, Langs G, Bezgin G, Eickhoff SB, Castellanos FX, Petrides M et al : Situating the default-mode network along a principal gradient of macroscale cortical organization . Proc Natl Acad Sci U S A 2016, 113 (44):12574-12579. Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong SJ, Seidlitz J, Bullmore ET, Evans AC, Misic B, Margulies DS et al : Microstructural and functional gradients are increasingly dissociated in transmodal cortices . PLoS Biol 2019, 17 (5):e3000284. Larivière S, Vos de Wael R, Hong SJ, Paquola C, Tavakol S, Lowe AJ, Schrader DV, Bernhardt BC: Multiscale Structure-Function Gradients in the Neonatal Connectome . Cereb Cortex 2020, 30 (1):47-58. Whitaker KJ, Vértes PE, Romero-Garcia R, Váša F, Moutoussis M, Prabhu G, Weiskopf N, Callaghan MF, Wagstyl K, Rittman T et al : Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome . Proc Natl Acad Sci U S A 2016, 113 (32):9105-9110. Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B: WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs . Nucleic Acids Res 2019, 47 (W1):W199-w205. Lee HM, Hong SJ, Gill R, Caldairou B, Wang I, Zhang JG, Deleo F, Schrader D, Bartolomei F, Guye M et al : Multimodal mapping of regional brain vulnerability to focal cortical dysplasia . Brain 2023, 146 (8):3404-3415. Hill J, Inder T, Neil J, Dierker D, Harwell J, Van Essen D: Similar patterns of cortical expansion during human development and evolution . Proc Natl Acad Sci U S A 2010, 107 (29):13135-13140. Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, Wagstyl K, Kirkpatrick Reardon P, Clasen L, Liu S et al : Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation . Neuron 2018, 97 (1):231-247.e237. Mohr I, Pfeiffenberger J, Eker E, Merle U, Poujois A, Ala A, Weiss KH: Neurological worsening in Wilson disease - clinical classification and outcome . J Hepatol 2023, 79 (2):321-328. Zimbrean P, Seniów J: Cognitive and psychiatric symptoms in Wilson disease . Handb Clin Neurol 2017, 142 :121-140. Vossel S, Geng JJ, Fink GR: Dorsal and ventral attention systems: distinct neural circuits but collaborative roles . Neuroscientist 2014, 20 (2):150-159. Han Y, Cheng H, Toledo JB, Wang X, Li B, Han Y, Wang K, Fan Y: Impaired functional default mode network in patients with mild neurological Wilson's disease . Parkinsonism Relat Disord 2016, 30 :46-51. Buch ER, Liew SL, Cohen LG: Plasticity of Sensorimotor Networks: Multiple Overlapping Mechanisms . Neuroscientist 2017, 23 (2):185-196. Leopold DA: Primary visual cortex: awareness and blindsight . Annu Rev Neurosci 2012, 35 :91-109. Südmeyer M, Pollok B, Hefter H, Gross J, Butz M, Wojtecki L, Timmermann L, Schnitzler A: Synchronized brain network underlying postural tremor in Wilson's disease . Mov Disord 2006, 21 (11):1935-1940. Wang A, Dong T, Wei T, Wu H, Yang Y, Ding Y, Li C, Yang W: Large-scale networks changes in Wilson's disease associated with neuropsychiatric impairments: a resting-state functional magnetic resonance imaging study . BMC Psychiatry 2023, 23 (1):805. Sherman SM, Guillery RW: The role of the thalamus in the flow of information to the cortex . Philos Trans R Soc Lond B Biol Sci 2002, 357 (1428):1695-1708. Shribman S, Bocchetta M, Sudre CH, Acosta-Cabronero J, Burrows M, Cook P, Thomas DL, Gillett GT, Tsochatzis EA, Bandmann O et al : Neuroimaging correlates of brain injury in Wilson's disease: a multimodal, whole-brain MRI study . Brain 2022, 145 (1):263-275. Huster D, Kühne A, Bhattacharjee A, Raines L, Jantsch V, Noe J, Schirrmeister W, Sommerer I, Sabri O, Berr F et al : Diverse functional properties of Wilson disease ATP7B variants . Gastroenterology 2012, 142 (4):947-956.e945. Lutsenko S, Cooper MJ: Localization of the Wilson's disease protein product to mitochondria . Proc Natl Acad Sci U S A 1998, 95 (11):6004-6009. Borchard S, Raschke S, Zak KM, Eberhagen C, Einer C, Weber E, Müller SM, Michalke B, Lichtmannegger J, Wieser A et al : Bis-choline tetrathiomolybdate prevents copper-induced blood-brain barrier damage . Life Sci Alliance 2022, 5 (3). DeOre BJ, Partyka PP, Fan F, Galie PA: CD44 mediates shear stress mechanotransduction in an in vitro blood-brain barrier model through small GTPases RhoA and Rac1 . Faseb j 2022, 36 (5):e22278. Additional Declarations No competing interests reported. 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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-6250277","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466287965,"identity":"fe39a14c-8c43-4380-8f95-ddfe1c00ae5a","order_by":0,"name":"Yuqi Song","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Song","suffix":""},{"id":466287966,"identity":"29ff0677-c5b7-4f33-bd75-4fce93801263","order_by":1,"name":"Weiqi Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Weiqi","middleName":"","lastName":"Wang","suffix":""},{"id":466287967,"identity":"a711f94d-dd87-4a27-9cc1-e8346da06382","order_by":2,"name":"Sheng Hu","email":"","orcid":"","institution":"School of Medical Information Engineering, Anhui University of Chinese Medicine; Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Hu","suffix":""},{"id":466287971,"identity":"289db8eb-89f3-4cd3-b7a9-9e665bc00737","order_by":3,"name":"Yulong Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Yulong","middleName":"","lastName":"Yang","suffix":""},{"id":466287974,"identity":"7f448e96-32d6-4db6-a8be-20296f20d723","order_by":4,"name":"Chuanfu Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Chuanfu","middleName":"","lastName":"Li","suffix":""},{"id":466287975,"identity":"b08488fa-3562-4639-8a4f-534e827ac4b3","order_by":5,"name":"Kou Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Kou","middleName":"","lastName":"Xu","suffix":""},{"id":466287976,"identity":"09d47f01-9edd-456c-a35d-0e15c83cba1b","order_by":6,"name":"Zilong Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Zilong","middleName":"","lastName":"Li","suffix":""},{"id":466287977,"identity":"87d1d269-01ba-4eba-a927-35219ffe23d7","order_by":7,"name":"Taohua Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":false,"prefix":"","firstName":"Taohua","middleName":"","lastName":"Wei","suffix":""},{"id":466287979,"identity":"b4631006-f233-49b1-bda9-02b887ab5d13","order_by":8,"name":"Wenming Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYPACGzk2ZuYDBz5UEK8lzZiPvS3x4IwzxGs5nDiP54zxYd4WItQaHD97+DVvTpoxm0TOhwO8DQzy/GIHCGg5k5dmzbsN6BeJ3A0HJHcwGM6cnUBAy4EcM+PcbSBbgFoMzzAkGNwmpOX8G5CWw4ltEjkPDiS2EaPlRo7xY7AWnjMMBw4So0Xyxhsz5r8gh7G3GRxsOCNB2C9853OMP84Eel++mfnx5z8VNvL80gS0KBxgYJNA4kvgVAkH8g0MzB8IKxsFo2AUjIIRDQCmzUwP7KqVJwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, China","correspondingAuthor":true,"prefix":"","firstName":"Wenming","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-03-18 07:08:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6250277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6250277/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84337806,"identity":"da055f25-bf92-4a07-a2e0-ca2e17492958","added_by":"auto","created_at":"2025-06-10 18:02:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in gradient scores between WD and HC groups at the brain network level.\u003c/strong\u003e Group-level connected group gradients for HCs (n = 90) and WD patients (n = 102) were computed based on the group mean MS matrix. \u003cstrong\u003e(A) \u003c/strong\u003eFG\u003cstrong\u003e \u003c/strong\u003eare based on systematic box-and-line plots of gradients. \u003cstrong\u003e(B)\u003c/strong\u003e SG based on systematic gradient box-and-line plots. Using a paired two-sample t-test, differences in system-based connected group gradients between patients with HCs and WD were assessed, including age, gender, and education level as covariates.\"*\" indicates P \u0026lt; 0.05, \"**\" indicates P \u0026lt; 0.01, \"***\" indicates P \u0026lt; 0.001 (FDR-corrected, two-tailed tests for HC-WD differences). VIS, visual network; SMN, somatomotor network; AN, auditory network; DAN, dorsal attentional network; VAN, ventral attentional network; FPN, frontoparietal network; CON, cingulo-opercular network; DMN, default mode network; HC, healthy control; WD, Wilson's disease; FG, first gradient; SG, secondary gradient; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/8d00face552e64200ab47a6c.jpg"},{"id":84337810,"identity":"0d64d2df-3127-4346-876c-b127e21485d1","added_by":"auto","created_at":"2025-06-10 18:02:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in the FG and their relationship with clinical variables.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eComparative analyses of cortical regions between HCs and WD patients revealed differences across various systems, with “warm” indicating higher and “cold” lower gradient scores in WD. Two-sample t-tests, incorporating age, gender, and education as covariates, were conducted to assess differences between groups, with statistical significance set at an FDR parcel level-corrected q \u0026lt; 0.05. \u003cstrong\u003e(B)\u003c/strong\u003e Relationships between FG scores and both UWDRS scores and age measures HC, healthy control; WD, Wilson's disease; FG, first gradient; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/6b5ae1b055bbcb8bfb0a1f32.jpg"},{"id":84337808,"identity":"0e0dd45d-16bc-4c9b-83d9-fdcb507f3620","added_by":"auto","created_at":"2025-06-10 18:02:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferences in the SG and their relationship with clinical variables.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Comparative analysesof cortical regions between HCs and WD patients revealed differences across various systems, with “warm” indicating higher and “cold” lower gradient scores in WD. Two-sample t-tests, adjusting for age, gender, and education, were conducted to assess differences between groups, with statistical significance set at FDR-corrected q \u0026lt; 0.05.\u003cstrong\u003e (B) \u003c/strong\u003eCorrelation between SG scores and UWDRS, UWDRS-N, and liver function scores. HC, healthy control; WD, Wilson's disease; LFS, liver function score. SG, secondary gradient; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/02d08eb7697cf9d133f7ab5d.jpg"},{"id":84338915,"identity":"f21ca274-4a75-450b-89d5-0d5a5e977a52","added_by":"auto","created_at":"2025-06-10 18:10:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":377700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlterations in the first gradient associated with cortical transcriptional patterns in WD\u003c/strong\u003e PLS regression analyzed gradient-gene expression relationships. \u003cstrong\u003e(A) \u003c/strong\u003eColor-scaled PLS-1/2 scores illustrate cortical gene expression patterns, with an overall weighted average expression level of 10,027. \u003cstrong\u003e(B)\u003c/strong\u003eCorrelation of PLS scores with FG scores.\u003cstrong\u003e (C) \u003c/strong\u003eGene enrichment analysis revealed that genes associated with PLS-1 are enriched for functions related to the negative regulation of cellular projections that influence neuronal cell junctions and developmental pathways. In contrast, PLS-2 was enriched for the positive regulation of protein localization, small GTPase-mediated signaling, protein translocation, and cellular matrix adhesion processes. The volcano plot illustrates the log2ER on the x-axis and -log10(FDR) on the y-axis. The color coding denotes the number of genes associated with biological processes that overlap with the top 10% of genes from the input list. The dashed line represents an FDR of 0.05. \u003cstrong\u003e(D)\u003c/strong\u003eSpecificity analysis revealed that PLS-1 and PLS-2 are linked to genes associated with dystonia, while PLS-2 is linked to genes associated with WD and Parkinson's disease. The significance of the observed effect size was evaluated using a bootstrap permutation test comprising 10,000 iterations, followed by an FDR correction to adjust for multiple comparisons. The dashed line indicates an FDR threshold of 0.05. PLS, partial least squares; FG, first gradient; FDR, false discovery rate.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/c5b30f427bbdf3c3857e080d.jpg"},{"id":84337807,"identity":"417a7ba8-9434-4ae9-90ef-ecc3f3789487","added_by":"auto","created_at":"2025-06-10 18:02:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":258578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlterations in the secondary gradient associated with cortical transcriptional patterns in WD \u003c/strong\u003eThe PLS regression analysis was conducted to determine the association of variations in gradients with gene expression. \u003cstrong\u003e(A)\u003c/strong\u003e Gene expression profiles within the cortical region. The color scale represents the scores of PLS-1 and PLS-2, with a weighted average expression level of 10,027. \u003cstrong\u003e(B)\u003c/strong\u003eCorrelation of PLS scores with secondary gradient scores. \u003cstrong\u003e(C)\u003c/strong\u003e Genes related to PLS-1 were enriched in the regulation of membrane potential, vesicle-mediated transport in synapse, regulation of trans-synaptic signaling, regulation of monoatomic ion transport, negative regulation of transport, signal release, regulation of neuron projection development, amine transport, binding regulation, monoamine transport. In the volcano plot, the x-axis denotes log2ER, whereas the y-axis denotes -log10(FDR). Color scale shows gene count overlapping with top 10% input genes in biological processes. The dashed line represents an FDR of 0.05. PLS, partial least squares; SG secondary score.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/89a123ec691876803e6e61b5.jpg"},{"id":84459138,"identity":"03f674e6-630f-44c4-b648-16f2b9f3a5b2","added_by":"auto","created_at":"2025-06-12 08:32:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3886736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/0a76f7da-ed73-4d45-b9d9-9e6e4c8f7cb1.pdf"},{"id":84338914,"identity":"c6540c73-f887-485b-8417-f836f63834b3","added_by":"auto","created_at":"2025-06-10 18:10:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":170406,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6250277/v1/05de35b42d4dbd0f4a5c5cb4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptional specialization shapes abnormal cortical morphological similarity gradients in Wilson's disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMutations in the ATPase copper transporting beta (ATP7B) gene cause Wilson\u0026apos;s disease (WD), a hereditary disorder that disrupts copper metabolism. In WD, the impaired function of ATP7B leads to an accumulation of copper in hepatocytes, resulting in liver damage[1]. However, excess copper may disseminate to other tissues in the body, especially the brain, leading to neuropsychiatric manifestations that can severely impact the quality of life of patients[2].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The accumulation of copper adversely affects astrocytes, the blood-brain barrier, and other brain cells, including neurons and oligodendrocytes. Previous studies have identified common brain injury sites in WD, primarily located in subcortical regions such as the basal ganglia, thalamus, cerebellum, and upper brainstem[3-5]. However, encephalopathy associated with WD may extend beyond these areas to involve the cortex[6]. A previous study found that WD patients who did not receive timely diagnosis and treatment were more susceptible to damage in the pons, midbrain, and cortex. Notably, midbrain and cortical lesions were particularly pronounced in WD patients with torsion spasm symptoms[7]. This suggests a potential correlation between cortical damage and certain symptoms of WD. While prior studies predominantly focused on the subcortical structure, there has been little exploration of the cerebral cortex, with only a few studies addressing its connection to cognitive dysfunction[8]. Therefore, the present study seeks to examine the structural damage to the cortex in WD patients and its association with clinical symptoms, as well as the underlying biological mechanisms involved.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The cortex is characterized by a highly folded structure rich in neurons and a distinct laminar organization[9, 10]. Research indicates that primary motor regions have a lower density of neurons and neurotransmitter receptors[11, 12], thicker cortical layers[13], and more pronounced laminar flow than sensory regions[14]. The cortical morphological similarity (MS) gradient reflects hierarchical arrangement, illustrating how structural features are interconnected and their potential implications for brain functionality[15]. Neuroimaging studies have associated variations in whole-brain volume and cortical thickness (CT) in WD patients with disease severity, suggesting that these morphometrics may serve as potential biomarkers[16]. Additionally, research has linked the large axes of the cortex to gene expression and microstructure topography[17, 18], indicating that the dimensions and configuration of the cortex are influenced by complete gene expression and microstructural integrity. Even though numerous studies have identified cortical abnormalities in WD patients, there has been a lack of exploration into the synergistic interactions among these morphological indicators and their links to structural and transcriptomic vulnerabilities. To fill this gap, a method for measuring morphological similarity was utilized to develop a comprehensive morphological trend analysis, highlighting the diverse changes and abnormalities in the cerebral cortex.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Herein, we constructed cortical similarity connections by integrating various cortical morphological features and subsequently mapped the gradient organization pattern of MS connections, illustrating relationships within cortical structures[15]. We sought to unravel the transcriptional specialization associated with alterations in the cortical morphology in patients with WD. We used structural magnetic resonance imaging to obtain five morphological features: CT, gyrification index (GI), fractal dimension (FD), sulcus depth (SD), and grey matter volume (GM). These metrics facilitated Pearson correlation analysis between pairs of cortical regions of interest (ROI) nodes, leading to the construction of an MS matrix. Next, we used cosine similarity to pinpoint the top 10% of connections and employed the diffusion graph embedding method to calculate the connectome gradient. Additionaly, We evaluated the spatial relationship between changes in the connectome gradient and the entire brain gene expression data from the Allen Human Brain Atlas (AHBA). We also investigated the influence of gene transcriptomic specialization on gradient perturbations. Finally, we conducted a gene enrichment analysis to elucidate transcriptomic associations between known pathogenic WD variants and other neuropsychiatric disorders.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eExperimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study combined brain structural T1 imaging with transcriptome data to explore the association between gene expression and structural gradient perturbations in WD patients versus healthy controls (HCs). The goal was to determine whether gradient perturbations were influenced by the relevant specialized transcriptome (Supplementary Figure 1). We derived five cortical morphological metrics from T1 images and conducted a Z-value analysis to establish connections among them. Pearson correlation coefficients were calculated for corresponding brain regions. To define the MS gradient, we used diffusion graph embedding to identify the spatial axis of interregional structural changes. Finally, partial least squares (PLS) regression analysis was conducted to elucidate the link between changes in structural gradients and gene expression data, followed by gene enrichment and specificity analyses to evaluate the biological processes affected by gradient disturbances.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic data were collected from 192 participants, including 102 WD patients (gender distribution: 62% male; age: 27.21 \u0026plusmn; 8.229 years) and 90 healthy controls (gender distribution: 66% male; age: 24.98 \u0026plusmn; 1.792 years) at the First Affiliated Hospital of Anhui University of Chinese Medicine. Every patient fulfilled the diagnostic standards, which included a ceruloplasmin level under 0.1 g/L, 24-hour urinary copper excretion, and the detection of a Kayser-Fleischer ring via slit lamp examination. The severity of impairment was assessed using the Unified Wilson Disease Rating Scale (UWDRS) examination subscore[19]. Patients with alternative diagnoses or significant medical conditions were excluded. Potential control participants were excluded if they presented with either a documented history of mental health disorders or any other substantial medical comorbidities. The study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine, and written informed consent was obtained from all participants. Demographic and clinical characteristics of the participants are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eClinical and Demographic Characteristics of Patients\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003eWD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003en=102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003en=90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e27.21\u0026plusmn;8.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e24.98\u0026plusmn;1.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e0.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eGender, male (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e63（62%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e59（66%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eEducation, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e11.82\u0026plusmn;3.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e17.4\u0026plusmn;1.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eDisease duration, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.344\u0026plusmn;6.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eUWDRS-N score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e9.843\u0026plusmn;12.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eUWDRS-liver function score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e1.696\u0026plusmn;1.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eUWDRS-P score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e3.755\u0026plusmn;3.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eUWDRS total score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e15.12\u0026plusmn;14.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003e24-hours urinary cooper excretion, ug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e851.9\u0026plusmn;590.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003eCeruloplasmin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e0.049\u0026plusmn;0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWD, Wilson\u0026apos;s disease; HC, healthy control.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData acquisition and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants underwent structural T1 imaging of the brain using a GE MR750 scanner at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine. The T1-3D BRAVO sequence was implemented for T1-weighted image acquisition, configured with specific parameters: TR = 8.16 ms, TE = 3.18 ms, 12\u0026deg;\u0026nbsp;flip angle, 256\u0026nbsp;\u0026times;\u0026nbsp;256 imaging matrix, 256 mm\u0026nbsp;\u0026times;\u0026nbsp;256 mm FOV, and 1 mm\u0026sup3;\u0026nbsp;voxel resolution (1 mm slice thickness).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;A total of 192 T1-weighted images were preprocessed using the Computational Anatomy Toolbox (CAT12, http://www.neuro.uni-jena.de/cat/) for analysis of structural T1 images. This process included cortical modeling, volume segmentation, and the measurement of various structural metrics. The steps undertaken were as follows: (1) adjustment for uneven signal intensity; (2) segmentation of T1-weighted images into gray matter, white matter, and cerebrospinal fluid images; (3) alignment of gray matter images to Montreal Neurological Institute (MNI) space using the Diffeomorphic Anatomical Registration Through Exponential Lie Algebra (DARTELA) algorithm; (4) application of nonlinear modulation to account for brain size variations among individuals; (5) generation of CT maps; (6) automatic correction of topological defects; (7) execution of spherical mapping and alignment; (8) calculation of CT, GI, FD, SD, GM metrics; (9) performance of surface reconstruction and Gaussian smoothing (FWHM); (10) mapping of the five metrics to 360 brain regions of the Human Connectome Project (HCP) atlas[20]. All images were meticulously reviewed for accuracy, with manual corrections applied where necessary.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePreprocessing gene expression data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicroarray-based gene expression data were sourced from the left AHBA (http://human.brainmap.org)[21]. The data were collected from six donors (mean age: 42.5 years; five males and one female), none with a background of neuropsychiatric or neurological conditions. The dataset included two intact brains and four left hemispheres. A total of 58,692 probes were used to measure gene expression in each donor sample, resulting in expression levels for 20,737 genes per sample. We utilized the AHBA processing pipeline to preprocess gene expression data from the brain samples, adhering to the recommended default settings (https://github.com/BMHLab/AHBAprocessing)[20]. Next, each sample was assigned to one of the 180 partitions corresponding to its nearest HCP_MMP1.0 partition\u0026nbsp;(left hemisphere). This methodology yielded 1,290 samples of cortical tissue from 176 distinct areas of the left cortex, with each sample containing expression data for 10,027 genes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eMorphological similarity gradient construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe assigned the HCP atlas to the cortical surface of each participant and extracted five feature values from their T1W images. First, a Z-value for each individual was calculated based on the five morphological features. Then, the MS matrix was generated through Pearson correlation analysis of cortical ROI node pairs. Subsequent diffusion map embedding of this matrix yielded the connectome gradient[22], using the top 10% connections per node and cosine similarity measures. Negative values were eliminated by converting the similarity matrix into a normalized angle matrix. Finally, we applied the diffusion graph embedding method to identify the gradient component explaining the majority of variance in structural connectivity, setting the flow shape learning parameter to \u0026alpha; = 0.5[23, 24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCase-control difference analysis of morphological similarity gradient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study focused on perturbations in the WD first gradient (FG) and secondary gradient (SG). A two-sample t-test was used to evaluate the differences in the MS gradient between WD patients and HCs while controlling for age, sex, and educational background as covariates. Adjustments for multiple comparisons were corrected using the False Discovery Rate (FDR), with a statistical significance threshold established at q \u0026lt; 0.05. Furthermore, we categorized whole-brain parcels into eight distinct brain networks through cortical mapping[17]. We conducted paired two-sample t-tests, accounting for age, education, and gender, to explore differences in brain network-based connectome gradients between HCs and WD patients, with results further adjusted for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAnalysis of the relationship between clinical characteristics and gradient in patients with WD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis was performed to examine the associations between gradients in the affected region and various clinical characteristics, including the UWDRS score (comprising UWDRS-N, UWDRS-P, and liver function score), ceruloplasmin concentration, and 24-hour urinary copper excretion. This was achieved utilizing partial correlations while adjusting for age and sex. Furthermore, we investigated the relationships between gradients in the disturbed region and factors such as age, disease duration, and years of education.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAssociation a\u003c/strong\u003e\u003cstrong\u003enalysis between gene expression and WD gradient changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed spatial correlations between changes in FG and SG and gene expression in the cortex. PLS regression analysis was conducted to identify the correlations between the FG and SG and regional differences in transcripts of 10,027 genes. The gene expression results were utilized as predictor variables. The first PLS component (PLS-1) and second PLS component (PLS-2) were derived as linear combinations of gene expression levels that exhibited the strongest association with changes in the FG and SG. To evaluate the statistical significance of the primary PLS components, we performed 10,000 permutation tests on the response variables. Additionally, we implemented bootstrap resampling methodology to account for and correct potential estimation errors in the gene weight coefficients for each PLS component[25].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEnrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing established protocols, we first ranked genes according to their bootstrap weight absolute values. The highest-ranking 10% of PLS-weighted genes were then analyzed for biological process enrichment using the WebGestalt online toolkit (https://www.webgestalt.org/), following the developer\u0026apos;s recommended parameters and statistical thresholds[26]. To quantify functional enrichment, we calculated the enrichment ratio (ER) using the formula: ER = (observed overlaps) / (expected overlaps), where observed overlaps represent the number of PLS-identified genes in a given biological process, and expected overlaps were determined through 1,000 random permutations of gene-process associations. A Bonferroni FDR correction with a q-value threshold of \u0026lt;0.05 was used to determine significant enrichment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSpecificity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA targeted analysis was performed to determine whether the gene implicated in WD was enriched in PLS components. We also analyzed various neuropsychiatric disorders,\u0026nbsp;revealing an enrichment of risk genes for other neurological conditions in PLS components. The top 100 genes associated with WD, Alzheimer\u0026apos;s disease (AD), Dystonia, Parkinson\u0026apos;s disease (PD), and Huntington\u0026apos;s disease (HD) were separately identified using the GeneCards dataset (https://www.genecards.org/). The disease-associated risk genes are detailed in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The ER for each PLS component was calculated by subtracting the average bootstrap weight of a randomly ordered gene set from that of the candidate gene, and then dividing it by the standard deviation of the ordered gene weights[27]. Significance was assessed by comparing the bootstrap weight of the candidate gene to the bootstrap weights of genes randomly selected from 10,000 permutations. A positive or negative expression ratio (ER) for a specific condition signifies a higher or lower level of expression of the risk gene compared with the baseline expression level, respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical comparisons of MS gradients between HCs and WD patients were conducted using two-sample t-tests, with appropriate adjustments made for the covariates of age, gender, and educational background. A spatial correlation analysis was conducted to assess the relationship between gradient score changes and PLS components in WD. In the specificity analysis, a bootstrap permutation test with 10,000 samples was used to determine the statistical significance of the effect size. A two-tailed P \u0026lt; 0.05 for multiple comparisons or correlations was considered significant after FDR correction for all tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e \u003cstrong\u003eand clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparative analysis of the demographic and clinical characteristics between HCs and WD patients is illustrated in Table 1. The results show that WD patients were, on average, older and had lower educational attainment than the HCs.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDisruption of the brain network\u0026apos;s gradient in WD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first component of the cortical MS gradient accounted for 13.1%\u0026nbsp;\u0026plusmn;\u0026nbsp;0.4% of the total variance in the WD group, compared with 12.9%\u0026nbsp;\u0026plusmn;\u0026nbsp;0.6% in the HC group (Supplementary Figure 2). The secondary component of the cortical MS gradient accounted for 12.0% \u0026plusmn; 0.5% of the total variance in the WD group, compared with 11.6% \u0026plusmn; 0.5% in the HC group (Supplementary Figure 2). Global box plot analysis revealed no significant differences in mean FG and SG scores between WD patients and HCs (Figures 1 A and B). The difference in gradient scores between the WD and HC groups was further assessed using a cross-region paired t-test for each brain network system. In the FG analysis, compared with the HC group, the WD group exhibited higher gradient scores in the cingulo-opercular network (CON), auditory network (AN), and dorsal-attention network (DAN) but lower scores in somatomotor network (SMN), visual network (VIS), and ventral-attention network (VAN), with no significant difference between frontoparietal network (FPN) and default mode network (DMN) (FDR correction q \u0026lt; 0.05, Figure 1 A, Supplementary Table 2). However, in the SG analysis, the WD group demonstrated higher gradient scores in DAN and FPN and lower gradient scores in VIS, AN, and SMN, with no significant difference observed in VAN, CON, and DMN (FDR correction q\u0026lt;0.05, Figure 1B, Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAlterations in the first gradient associated with clinical features related to WD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the FG analysis, the differential gradient scores for the WD and HC groups showed uneven distribution across various networks, predominantly in CON (frontal, temporal, and parietal cortex), AN (temporal cortex), DAN (occipital cortex), SMN (primary somatic and motor cortex), VIS (primary visual cortex), and VAN (temporal and frontal cortex). Notably, the scores in the frontal cortex of the left CON, the prefrontal and parietal cortex of the right CON, the temporal cortex of the left AN, and the bilateral DAN occipital cortex were higher in the WD group than in the HC group. Conversely, the scores in the parietal cortex of the right SMN, the occipital cortex of the right VIS, the frontal cortex of the left VAN, and the temporal cortex of the right VAN were lower\u0026nbsp;in the WD group than in the HC group (Figure 2A). Regions exhibiting significantly lower gradient scores were negatively correlated with UWDRS (R = -0.2054, P = 0.0383, Figure 2B) and age (R = -0.2528, P = 0.0104, Figure 2B). No significant correlations were found between the gradient score and UWDRS-N, UWDRS-P, liver function score, education level, 24-hour urinary copper excretion, ceruloplasmin concentration, and disease progression.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAlterations in the secondary gradient associated with clinical features related to WD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the SG analysis, the differential gradient scores for the WD and HC groups were also distributed across various networks, predominantly within the FPN (frontal and parietal cortex), DAN (temporal cortex), VIS (primary visual cortex), SMN (primary motor cortex), and AN (temporal cortex). Notably, the scores were higher in the temporal cortex of left DAN, the frontal cortex of bilateral FPN, and the parietal cortex of right FPN and lower in the primary visual cortex of left VIS, the temporal cortex of bilateral AN and the primary motor cortex of right SMN in the WD group than in the HC group (Figure 3A). Among them, regions with significantly elevated gradient scores were inversely related to UWDRS (R = -0.1994, P = 0.0445, Figure 3B) and UWDRS-N (R = -0.2391, P = 0.0155, Figure 3B). Meanwhile, liver function scores were negatively correlated with regions that had lower gradient scores (R = -0.3344, P = 0.0006, Figure 3B). There were no notable correlations between the gradient score and UWDRS-P, education level, age, 24-hour urinary copper excretion, ceruloplasmin concentration, and disease progression..\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eGene expression alterations for the first gradients related to WD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePLS regression was applied to identify differences in gene expression associated with the anatomical distribution of FG differences in the left hemisphere cortex (Figure 4A). The PLS-1 and PLS-2 are linear combinations of the gene expression values that exhibit the strongest correlation with variations in the MS gradient (permutation test, P \u0026lt; 0.001). PLS-1 accounted for 14.9% of the variance, establishing it as the component with the highest explained variance, while PLS-2 accounted for 14.3% of the variance. PSL-1 was highly expressed in the cortex near SMN, VIS, and AN and lowly expressed in the cortex near CON and DMN (Figure 4A). PLS-2 was highly expressed in the cortex near DMN, CON, and VAN and lowly expressed in the cortex near SMN, VIS, and FPN (Figure 4A). The MS gradient in the cerebral cortex was significantly positively correlated with the PLS-1 and PLS-2 gene expression (PLS-1: R = 0.36. P \u0026lt; 0.001; PLS-2: R = 0.35, P \u0026lt; 0.001, Figure 4B), highlighting the spatial relationship of cortical properties at a microscopic scale. Our gene enrichment analysis showed that PLS-1 plays a regulatory role in negative cell projection processes (FDR \u0026lt; 0.05, Figure 4C), whereas PLS-2 mainly associated with stroma-dependent cell migration, integrin activation, synaptic vesicles organization, cell-substrate junction tissue, organelles fusion, positive regulation of protein localization, small GTP-mediated signal transduction, cell-matrix adhesion, and regulation of protein transport (FDR \u0026lt; 0.05, Figure 4C). Further analysis showed that PLS-1 and PLS-2 are enriched with risk genes such as those linked to dystonia (PLS-1: ER = -2.51, FDR = 0.0153; PLS-2: ER = -1.71, FDR = 0.0361), WD (PLS-2: ER = -1.80, FDR = 0.0361), and PD (PLS-2: ER = -1.75, FDR = 0.0361) (Figure 4D). Taken together, our results indicate that both structural impairments and gradient abnormalities in cortical regions might be associated with similar transcriptomic characteristics that are also implicated in neurological motor dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene expression alterations for the secondary gradients related to WD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePartial Least Squares (PLS) regression was performed to identify variations in gene expression associated with the anatomical distribution of secondary gradient differences within the left hemisphere cortex (Figure 5A). PLS-1 accounted for 15.7% of the variance, and thus its variance was the most explained compared with the other components. Meanwhile, PLS-2 accounted for 15.5% of the variance. PSL-1 was highly expressed in the cortex near the CON and DMN, but its expression was low in the cortex near the SMN and VIS (Figure 4A). PLS-2 exhibited specific expression in the cortex near VAN and FPN, but its expression in the cortex near CON was minimal (Figure 5A). The secondary gradient in the cerebral cortex correlated significantly with PLS-1 and PLS-2 gene expression (PLS-1: R=0.3742. P \u0026lt;0.001; PLS-2: R=0.3715, P \u0026lt;0.001, Figure 4B). Gene enrichment analysis showed that PLS-1 was involved in the regulation of membrane potential, vesicle-mediated transport in synapse, regulation of trans-synaptic signaling, regulation of monoatomic ion transport, negative regulation of transport, signal release, regulation of neuron projection development, amine transport, binding regulation, monoamine transport (FDR \u0026lt;0.05, Figure 5C). In contrast, the genes associated with PLS-2 exhibited no significant enrichment. Further analysis showed that PLS-1/2 were not enriched with risk genes linked to dystonia, PD, WD, AD, and HD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHuman cortical expansion exhibits an uneven pattern, underscoring evolutionary differences among various brain regions[28]. The organization of cortical morphology is shaped by microscale features and constrained by evolutionary growth[29]. We identify the main axis of variation in the topological organization through the concept of \u0026ldquo;gradient organization\u0026rdquo;, which elucidates the transfer of local information flow from primary sensory and motor areas to regions associated with higher cognitive functions, as well as the shift in disease-related information flow from a singular mode to a transmembrane state. Given that WD is recognized for its diverse neurological and psychiatric manifestations[30, 31], it may disrupt the information flow from sensorimotor processing to cognitive functions. Our findings reveal that individuals with WD exhibit reduced SMN and VIS networks in the FG and SG compared with HCs, while the DAN is increased. Thus, the identified gradients may reflect morphological changes in both primary sensory/motor and higher cognitive cortices in WD patients.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;At the regional level, our findings showed that WD patients had increased gradient scores in major regions such as the temporal cortex of the left DAN and the occipital cortex of the bilateral DAN.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenerally, the DAN is known to uphold attentional stability and contribute to various aspects of human intelligence[32]. In alignment with our research, prior functional studies have demonstrated that enhanced functional connectivity between the left inferior temporal cortex and the right parietal cortex is associated with altered attentional functions in WD patients[33].\u0026nbsp;Therefore, the disturbance of morphological gradient in the DAN may lead to deficits in attentional capabilities among these patients. Conversely, our study revealed diminished gradient scores in WD patients in key regions such as the parietal cortex of right SMN and the occipital cortex of bilateral VIS. Current research has established that the SMN and VIS are crucial for maintaining postural balance and motor coordination[34, 35]. Mounting evidence suggests that structural or functional impairments in these areas correlate with neurological symptoms observed in WD[7, 36, 37]. Thus, the morphological gradient imbalance in the SMN and VIS regions may play a role in the neurological manifestations of WD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Previous studies suggested that analogous cortical regions in morphological networks share similar cellular structures and are likely to be anatomically interconnected[15]. In the FG analysis, we observed that the region exhibiting a significantly lower gradient score in the WD group was negatively correlated with both the UWDRS and age. This may be due to the disruptions in local information flow in the brain cortex of individuals with WD, which worsens with disease progression and advancing age. In the SG analysis, regions that exhibited significantly elevated gradient scores in the WD group were negatively correlated with UWDRS and UWDRS-N. However, regions with lower gradient scores were negatively associated with liver function scores. It was further confirmed that the MS gradient score in WD patients decreased significantly with worsening clinical symptoms. The imbalance in hierarchical structure may stem from reduced connections among brain networks in the cerebral cortex region of WD patients, leading to more pronounced structural differentiation and diminished axonal connections. Therefore, the cortical MS gradient score serves as an indicator of cortical structural vulnerability in WD patients and may be used as a potential neuroimaging biomarker for assessing the disease and prognosis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The complex pathogenesis of WD suggests that changes in the cortical MS gradient associated with WD may be influenced by several factors, including genetic, neuronal, molecular, and cellular mechanisms. The present study identified spatial associations between alterations in connectome gradients and gene expression patterns, highlighting the transcriptomic specificity associated with WD. The PLS-1-related gene negatively regulates cell projection, inhibiting neuronal connections and developmental pathways. The process of neuronal projection is integral to the transmission of information within the brain, facilitating nerve development and differentiation of nerve cells[38]. WD is characterized by impaired copper transport within cells, leading to an accumulation of copper in the brain over time, which adversely affects the structure of cortical neurons and may result in abnormal neuronal degeneration[39]. Consequently, these findings may elucidate the biological mechanisms underlying the atypical neuronal connections observed in patients with WD. PLS-2 is strongly associated with the positive regulation of protein localization, small Gtpase-mediated signal transduction, and protein transport processes. The localization of proteins within subcellular compartments depends on intricate transport mechanisms that are vital for their functionality in various cellular regions. The ATP7B gene mutation causes WD, with research revealing abnormal subcellular localization and altered properties in ATP7B mutants, which exacerbate clinical symptoms[40]. Another study indicated that the gen associated with WD encodes a copper-transporting P-type ATPase, whose subcellular localization is modulated by copper homeostasis[41]. This underscores the role of gene expression in regulating protein transport for purposes, facilitating the anisotropic differentiation of cell structures across different cortical regions in WD patients and influencing brain morphology to coordinate complex brain functions. Excessive copper accumulation in WD can negatively impact the blood-brain barrier[42]. Small GTPase is a vital signaling molecule involved in maintaining blood-brain barrier integrity[43]. Therefore, PLS-1 is likely to influence nerve cell connectivity, whereas PLS-2 is associated with cortical cell differentiation and signal transduction, thereby contributing to the maintenance of brain homeostasis. Additionally, the differentiation of the transcriptome in WD has been associated with genetic risk factors for Parkinsonism and dystonia, which may elucidate the intricate neurological and psychiatric manifestations observed in patients with WD.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Nevertheless, this study has some limitations. Firstly, we utilized publicly available AHBA gene data from the postmortem brains of six neurologically healthy donors, which may restrict our analysis of transcription data related to MS abnormalities. Subject variations affect the correlation between the MS gradient and gene expression in PLS analysis. Future research should include more samples from postmortem WD patients to compare differences between hemispheres. Secondly, there were significant age and educational differences between the groups. While we controlled for age, sex, and education in our analysis, their impact on the results could not be fully eliminated. Therefore, we intend to recruit healthy individuals matched for age, gender, and education to explore neuroanatomical and functional irregularities in WD patients and their association with transcriptional regulation patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In conclusion, our study identified variations in the white matter cortical microstructural gradient associated with various clinical phenotypes, likely due to disrupted information flow between primary sensorimotor and higher cognitive regions. A key strength of our research is its integration of multiple structural characteristics, enhancing our understanding of cortical architecture and interconnections in WD patients. Our investigation further explored the relationship between abnormal gradient configurations and transcriptional patterns, elucidating the biological underpinnings of these genetic clusters via enrichment analyses focusing on cellular projections, cortical differentiation processes, and cell-to-cell communication pathways. These findings deepen our understanding of the genetic factors contributing to the morphological irregularities in WD and the biological pathways linked to complex neurological and psychiatric traits.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eATP7B ATPase copper transporting beta\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWD \u0026nbsp; Wilson\u0026apos;s disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCs \u0026nbsp; healthy controls\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMS \u0026nbsp; morphological similarity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLS \u0026nbsp; Partial least squares\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCT \u0026nbsp; cortical thickness\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGI \u0026nbsp; gyrification index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFD \u0026nbsp; fractal dimension\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; sulcus depth\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGM \u0026nbsp; grey matter volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROI \u0026nbsp;regions of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAHBA \u0026nbsp;Allen Human Brain Atlas\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUWDRS \u0026nbsp;Unified Wilson Disease Rating Scale\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCP \u0026nbsp;Human Connectome Project\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFG \u0026nbsp;first gradient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSG \u0026nbsp;secondary gradient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR \u0026nbsp;False Discovery Rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eER \u0026nbsp;enrichment ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAD \u0026nbsp;Alzheimer\u0026apos;s disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePD \u0026nbsp;Parkinson\u0026apos;s disease\u003c/p\u003e\n\u003cp\u003eHD \u0026nbsp;Huntington\u0026apos;s disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVIS \u0026nbsp; visual network\u003c/p\u003e\n\u003cp\u003eSMN \u0026nbsp; somatomotor network\u003c/p\u003e\n\u003cp\u003eAN \u0026nbsp;auditory network\u003c/p\u003e\n\u003cp\u003eDAN \u0026nbsp; dorsal attentional network\u003c/p\u003e\n\u003cp\u003eVAN \u0026nbsp; ventral attentional network\u003c/p\u003e\n\u003cp\u003eFPN \u0026nbsp; frontoparietal network\u003c/p\u003e\n\u003cp\u003eCON \u0026nbsp; cingulo-opercular network\u003c/p\u003e\n\u003cp\u003eDMN \u0026nbsp;default mode network\u003c/p\u003e\n\u003cp\u003eLFS \u0026nbsp; liver function score\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the research participants and their families, as well as the Medical Imaging Center of the First Affiliated Hospital of Anhui University of Chinese Medicine. Secondly, we extend our sincere thanks to to the Human Connectome Project (HCP), the Allen Human Brain Atlas (AHBA), and BrainSpan for supplying the neuroimaging and gene expression data employed in this research.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuqi Song: Funding acquisition, methodology, data acquisition, curation, and analysis, literature review, manuscript preparation, and served as the primary contributor and editor in the writing of the manuscript. Weiqi Wang: Methodology, data curation, formal analysis, investigation, supervision, writing of the original draft, review, and editing. Sheng Hu, Yulong Yang: Methodology, Writing - reviewing and editing; Chuanfu Li: methodology, data acquisition, curation; Kou Xu and Zilong Li: made charts and analyzed the data. Taohua Wei and Wenming Yang: Funding acquisition, Methodology, Writing - reviewing and editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China (Grant No. U22A20366), the National Natural Science Foundation of China (Grant No. 82305185), the Anhui Provincial Traditional Chinese Medicine Science and Technology Research Project (Grant No. 202303a07020004), the Anhui University Collaborative Innovation Project (Grant No. GXXT-2020-025), and the Anhui Provincial Clinical Medicine Research Transformation Project (Grant No. 202204295107020066).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and code supporting the findings of this study are available from the corresponding author on reasonable request. The gene expression data used for transcriptional analysis can be found in the ABHA database (https://human.brain-map.org/static/download).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Chinese Medicine (Approval No. 2024-AH-60-02) and obtained written informed consent from all participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests associated with this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor 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\u003cstrong\u003e36\u003c/strong\u003e(5):e22278.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wilson's disease, cortical morphological similarity gradient, gene expression, transcriptional specialization","lastPublishedDoi":"10.21203/rs.3.rs-6250277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6250277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eNeuroimaging studies have revealed structural abnormalities in the brains of individuals with Wilson's disease (WD), particularly within the basal ganglia, and the associated molecular mechanisms have been elucidated. However, the structural damage in the cerebral cortex, along with its underlying biological and molecular processes, remains elusive. Here, we investigated the abnormalities in cortical morphological similarity gradients associated with WD and further unraveled their underlying transcriptional specialization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eFirst, we analyzed cortical morphological features from structural magnetic resonance imaging scans from 102 WD patients and 90 healthy controls (HCs) and then computed the cortical morphological similarity (MS) connections. Subsequently, the diffusion map embedding approach was employed to investigate the cortical MS gradients. Finally, the differences in MS gradients between WD and HC were analyzed and their underlying clinical relevance and transcriptional specialization were revealed using clinical symptoms and gene expression data, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Compared with HC, WD patients exhibited regional differences across extensive brain networks in both the first and second MS gradients. Alterations in MS gradient alterations correlated with age, neurological symptoms, liver function symptoms, and motor-related processing. Partial least squares (PLS) regression analysis results indicated a significant association between MS gradients and gene expression profiles (PLS components). Gene enrichment analysis showed that the transcriptional specialization of PLS components was enriched in biological processes such as cell projection organization, regulation of protein organization, and GPTase-mediated signal transduction, all of which are relevant to WD. The transcriptional specializations influencing the MS gradient of WD were also enriched in WD's pathological genes associated with WD and other neuropsychiatric risks, such as dystonia and Parkinsonism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOverall, this research offers new perspectives on the neurobiological foundations that govern the emergence of complex neural architectures and associated mental manifestations in WD.\u003c/p\u003e","manuscriptTitle":"Transcriptional specialization shapes abnormal cortical morphological similarity gradients in Wilson's disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 18:02:31","doi":"10.21203/rs.3.rs-6250277/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e10a9db-8850-4628-b100-351a6baf9953","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T08:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 18:02:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6250277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6250277","identity":"rs-6250277","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}