Bidirectional Cortical Gyrification Alterations in Chronic Obstructive Pulmonary Disease: Links to Cognitive Impairment and Global Initiative for Chronic Obstructive Lung Disease Staging

Bidirectional Cortical Gyrification Alterations in Chronic Obstructive Pulmonary Disease: Links to Cognitive Impairment and Global Initiative for Chronic Obstructive Lung Disease Staging | 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 Bidirectional Cortical Gyrification Alterations in Chronic Obstructive Pulmonary Disease: Links to Cognitive Impairment and Global Initiative for Chronic Obstructive Lung Disease Staging Jiajie Chen, Yanrong Chen, Kun Zhang, Kai Xu, Jingping Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7052215/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose Cognitive impairment is a common but poorly understood comorbidity in chronic obstructive pulmonary disease. Although gray matter abnormalities have been observed in this population, the contribution of cortical gyrification—a structural feature linked to cognitive development and brain plasticity—remains unclear. This study aimed to characterize region-specific cortical gyrification alterations and examine their associations with domain-specific cognitive function and disease severity. ‌ Methods We enrolled 59 patients with stable chronic obstructive pulmonary disease and 49 healthy controls who underwent pulmonary function testing, Montreal Cognitive Assessment, and high-resolution T1WI. The Toro's Gyrification Index quantified cortical gyrification. Group comparisons, partial correlations, and multiple linear regression analyses were conducted with adjustments for age, sex, and total intracranial volume. Results Compared to healthy controls, the patient group showed increased Toro's Gyrification Index in the bilateral superior temporal gyrus and right anterior cingulate cortex, and decreased Toro's Gyrification Index in the bilateral lingual gyri ( P < .05). In the patient group, the Toro's Gyrification Index in the left superior temporal gyrus was negatively correlated with abstract thinking ( r = –0.522, P < .001) and attention scores ( r = –0.377, P = .01), and in the right superior temporal gyrus with orientation score ( r = –0.360, P = .02). A regression model combining Toro's Gyrification Index in the left superior temporal and right lingual gyri explained 43% of the variance in the abstract thinking score ( F = 9.73, P < .001). The Global Initiative for Chronic Obstructive Lung Disease stage significantly predicted the right superior temporal gyrus Toro's Gyrification Index ( F = 15.08, P < .001), with higher values observed in patients with disease stages 3 and 4 than stages 1 and 2 ( F = 4.74, P = .005). ‌ Conclusions Chronic obstructive pulmonary disease is associated with region-specific, bidirectional cortical gyrification changes that are closely linked to cognitive impairment and disease severity. Hypergyrification in the superior temporal and lingual gyri might reflect compensatory neural plasticity mechanisms, suggesting these patterns could serve as novel neuroimaging biomarkers for evaluating neurodegenerative changes in chronic obstructive pulmonary disease. chronic obstructive pulmonary disease cortical gyrification superior temporal gyrus lingual gyrus cognitive impairment magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide ( https://www.who.int/ ). Beyond respiratory symptoms, chronic hypoxemia, systemic inflammation, and oxidative stress, COPD might disrupt blood-brain barrier integrity and induce neuroinflammatory cascades, potentially contributing to gray matter abnormalities. 1 , 2 These mechanisms might lead to structural and functional alterations in the central nervous system, 3 potentially contributing to the high prevalence of cognitive impairment observed in patients with COPD. Structural MRI studies have revealed that cognitive impairment in COPD is closely associated with gray matter abnormalities. 4 Most of these studies employed voxel-based morphometry analysis to evaluate these changes, consistently reporting reduced GM volume in brain regions involved in cognition, including the frontal lobe, cingulate cortex, basal ganglia, hippocampus, temporal lobe, and parietal lobe. 4 – 6 These structural changes have been associated with a decline in forced vital capacity, 7 longer disease duration, 5 and poorer cognitive performance. 8 A recent meta-analysis study revealed significant GM abnormalities in the right postcentral, left precentral, and left cingulate gyri in patients with COPD. 4 Another meta-analysis used connectivity modeling to demonstrate co-atrophy patterns between these regions and the insula, suggesting deep neural network involvement. 2 These findings suggest that disease-specific neuroanatomical mechanisms underlie cognitive impairment in COPD. Interestingly, despite the consistently reported widespread regional GM volume reduction, normal total brain volume is unaffected by COPD, 5 , 9 , 10 , hinting at potential compensatory neuroplasticity mechanisms, such as coexisting localized gray matter atrophy with compensatory volumetric increases in other regions. Similar compensatory patterns, characterized by the homeostatic equilibrium between atrophy and hypertrophy, have been observed in other chronic hypoxic conditions. 11 However, the widely used conventional voxel-based morphometry cannot capture cortical folding complexity or distinguish between gyral and sulcal morphological contributions, which might lead to heterogeneous findings. This highlights the need for more surface-based metrics to detect subtle cortical geometry alterations. Cortical gyrification—the folding of the cerebral cortex into intricate patterns of gyri and sulci—is a key neurodevelopmental feature associated with brain connectivity and cognitive capacity. Cortical folds can be quantified using metrics such as the gyrification index (GI) 12 Compared to conventional volumetric analyses, the GI provides a comprehensive evaluation of cortical morphology and enhances the precision in detecting morphological changes. 13 However, previous studies employing the local gyrification index (LGI) in COPD did not detect significant differences, 9 likely due to the limited sensitivity of LGI and surface-based morphometry techniques. 14 In contrast, Toro's Gyrification Index (ToroGI), a curvature-based geometric approach that quantifies the ratio between the cortical surface area and its projected area, effectively captures sulcal depth and gyral height. 15 , 16 This global approach offers enhanced sensitivity in detecting microstructural gyrification alterations while providing a comprehensive assessment of cortical gyrification patterns across entire brain regions or large-scale anatomical areas. ToroGI has demonstrated superior sensitivity in identifying cortical microstructural alterations in conditions such as neuropathic pain 17 and psychiatric disorders. 18 Building upon previous GM volume findings in COPD and given the limitations of conventional volumetric and LGI approaches, a more sensitive examination of cortical gyrification using ToroGI is warranted. This study employed ToroGI to investigate alterations in cortical gyrification patterns in patients with stable COPD compared to healthy controls. We further examined whether these alterations were associated with disease severity, assessed by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, and cognitive performance, measured by the Montreal Cognitive Assessment. We hypothesized that patients with COPD would exhibit region-specific gyral-sulcal pattern alterations and that ToroGI values in specific regions would correlate with GOLD stages and cognitive scores. METHODS Clinical Data Patients with stable COPD and healthy controls were recruited from the Affiliated Hospital of the Shannxi University of Traditional Chinese Medicine. The inclusion criteria for patients with COPD were as follows: (1) a confirmed diagnosis of COPD based on the GOLD (2024) guidelines; (2) Han Chinese ethnicity and right-handedness; (3) age between 50 and 80 years; (4) education of ≥ 6 years; and (5) no history of substance abuse, including alcohol, illicit drugs, or prescription medications misuse. The exclusion criteria for patients with COPD were: (1) comorbid psychiatric disorders, such as anxiety or depression, as diagnosed by DSM-5; (2) participation in other clinical trials within the past three months; (3) the presence of severe systemic comorbidities, including diabetes mellitus, hepatic insufficiency, cardiac dysfunction (New York Heart Association Class III–IV), moderate-to-severe obstructive sleep apnea, or hypertension; and (4) inability to independently complete neuropsychological assessments. The healthy control subjects were recruited from local communities and hospitals and were matched to the patients with COPD by sex and education level. This study was approved by the Ethics Committee of the Affiliated Hospital of the Shaanxi University of Traditional Chinese Medicine (Approval Number: SZFYIEC-PJ-2021 No. [ 233]) All participants provided written informed consent after receiving a detailed explanation of the research protocol. Clinical Assessments Pulmonary function in patients with COPD was evaluated using standard spirometry, and included measurements of forced vital capacity, forced vital capacity in one second as a percentage of the predicted value, forced expiratory volume in one second, and the volume in one second / forced vital capacity ratio. Neurocognitive performance was assessed using the Montreal Cognitive Assessment. Magnetic Resonance Imaging Acquisition MRI data were acquired using a 3.0T MR scanner (MAGNETOM Skyra, Siemens Healthineers, Erlangen, Germany) equipped with a standard-channel head coil. Conventional T2WI was performed to screen for intracranial lesions. High-resolution 3D T1WI was performed using a magnetization-prepared rapid acquisition gradient-echo sequence with the following parameters: TR = 2,300 ms, TE = 2.9 ms, TI = 900 ms, flip angle = 9°, slice thickness = 1 mm, FOV = 240 × 240 mm, matrix size = 256 × 256, and number of excitations = 1. All MR images were independently reviewed by two experienced neuroradiologists blinded to clinical group assignment. Image Processing Structural data were processed using the Computational Anatomy Toolbox (CAT12.9; r2577, https://neuro-jena.github.io/cat12 ) implemented in SPM12 (Welcome Trust Centre for Neuroimaging, London, UK) running on MATLAB R2024b (MathWorks Inc., Natick, MA, USA). The preprocessing pipeline included the following steps: (1) DICOM-to-NIFTI format conversion; (2) image segmentation into gray matter, white matter, and cerebrospinal fluid; (3) surface-based reconstruction of inner and outer cortical surfaces through three-dimensional modeling of gray-white matter boundaries and pial surfaces, including cortical surface registration and inflation; (4) spatial normalization to standard Montreal Neurological Institute space; (5) computation of ToroGI as a quantification of cortical gyrification complexity; and (6) spatial smoothing with a 25-mm full-width at half-maximum Gaussian kernel. Statistical Analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, USA). Categorical variables were compared using the chi-squared test, and continuous variables were analyzed using two-sample t tests. Data are presented as means ± standard deviations unless otherwise noted. Comparisons of ToroGI values between the COPD and healthy control cohorts were performed using two-sample t -test in the CAT12 statistical module, with age, sex, and total intracranial volume included as covariates. Multiple comparisons were corrected using the Holm-Bonferroni method ( P < .05). Brain regions in ToroGI were labeled according to the Destrieux atlas (2009 version). Partial correlation analyses explored associations between ToroGI values and pulmonary/cognitive measures, adjusting for age, sex, and total intracranial volume. Two multiple linear regression models were constructed: (1) to assess the predictive value of ToroGI for cognitive performance (e.g., Montreal Cognitive Assessment scores); and (2) to examine the association of COPD severity (GOLD stage) on ToroGI values. Additionally, analysis of covariance (ANCOVA) was performed on ToroGI value within identified cortical regions to exam mean differences and characterize regional abnormalities across GOLD subgroups. Statistical significance was set at P < .05. RESULTS Demographics and Clinical Characteristics The study initially recruited 64 patients with COPD and 50 healthy controls. Following quality control of cortical imaging data, 59 patients with COPD and 49 healthy controls were retained for the final analysis. Significant differences were observed in age, cerebrospinal fluid volume, and Montreal Cognitive Assessment scores between the groups ( P .05; Table 1 ). Table 1 Demographic and physiological characteristics of the COPD and HC groups COPD ( n = 59) HC ( n = 49) t/x 2 P Age (years) 63.5 ± 9.1 56.7 ± 8.4 3.1 .003 Education (years) 9.5 ± 3.7 11.7 ± 3.5 –1.5 .13 Sex (Male/Female) 50/9 41/8 0.88 .54 TIV (cm 3 ) 1,492.5 ± 123.4 1,466.7 ± 148.5 0.8 .42 Gray matter (cm 3 ) 628.0 ± 47.1 650.1 ± 62.9 –1.7 .09 White matter (cm 3 ) 503.2 ± 61.5 503.4 ± 56.4 –0.1 .94 Cerebrospinal Fluid (cm 3 ) 363.1 ± 68.7 313.1 ± 77.0 2.8 .006 MoCA 20.8 ± 5.5 25.4 ± 1.8 –4.9 < .001 COPD = chronic obstructive pulmonary disease; HC = healthy control; TIV = total intracranial volume; MoCA = Montreal Cognitive Assessment. Group Difference in Cortical Gyrification Compared to the healthy control group, the COPD group, adjusted for age, sex, and total intracranial volume, showed significant bidirectional alterations in cortical gyrification—higher ToroGI values in the bilateral superior temporal gyrus (STG) and right anterior cingulate cortex (ACC) and lower ToroGI values in the bilateral lingual gyri ( P < .05, Holm-Bonferroni corrected; Fig. 1 , Table 2 ). Table 2 Clusters with significant differences in ToroGI values between the COPD and HC groups Anatomical Region MNI Coordinates Cluster-Size t- value P -value X Y Z Left Hemisphere STG –55 –40 20 1,359 3.8 .001 Lingual gyrus –14 –75 –10 454 –3.5 .027 Right Hemisphere ACC 5 30 25 551 3.5 .015 STG 35 15 –30 101 2.3 .012 Lingual gyrus 17 –69 –6 458 –3.4 .008 COPD = chronic obstructive pulmonary disease; HC = healthy control; ToroGI = Toro's gyrification index; STG = superior temporal gyrus; ACC = anterior cingulate cortex. The results are aftter Holm-Bonferroni correction for multiple comparisons ( P < .05). Associations between Cortical Gyrification and Cognitive Performance in the COPD Group ToroGI values in the left STG correlated negatively with abstract thinking ( r = − 0.522, P < .001, False Discovery Rate (FDR)-corrected) and attention ( r = − 0.377, P = .01, FDR-corrected). In the right STG, they correlated negatively with orientation scores ( r = − 0.360, P = .02, FDR-corrected; Fig. 2 ). Regression Analysis Multiple linear regression models were constructed to further investigate the relationships between cortical morphology and cognitive/clinical parameters. Cognitive Prediction Model Using ToroGI values of brain regions as independent variables and abstract thinking scores as the dependent variables, the regression model revealed a significant predictive association in the right lingual gyrus ( t = 2.05, P = .04, VIF = 1.11) and left STG ( t = − 4.09, P < .001, VIF = 1). The resulting regression equation is: ATS = 12.03 + 6.86 × ToroGI_RLG – 20.17 × ToroGI_LSTG + ε (1) where ATS refers to the abstract thinking score; ToroGI_RLG and ToroGI_LSTG denote ToroGI scores of the right lingual gyrus and left STG, respectively; and ε is an error term. This model explained 43% of the variance (R 2 = 0.43, Durbin-Watson = 2.6, F = 9.73, P < .001). Disease Severity Model Using GOLD stages as the independent variable and ToroGI values as the dependent variable, the regression analysis revealed a significant association in the right STG ( t = 3.88, P < .001). The resulting regression equation is: ToroGI_RSTG = 0.46 + 0.01 × GOLD_Stage + ε (2) where ToroGI_RSTG denotes the ToroGI score of the right STG; GOLD_Stage is the GOLD clinical stage; and ε is an error term. This model accounted for 28% of the variance (R 2 = 0.28, Durbin-Watson = 2.6, F = 15.08, P < .001). Subgroup Analysis by Disease Severity We performed a comparative analysis with analysis of covariance, adjusted for age, sex, and total intracranial volume, to further characterize cortical abnormalities across GOLD stages. Significant differences were found in the right STG among stages ( F = 4.74, P = .005). Bonferroni-corrected post-hoc pairwise comparisons revealed that patients with GOLD stage 1 or 2 had lower ToroGI values than those with GOLD stages 3 and 4 (Fig. 3 ). DISCUSSION This study reveals concurrent hypergyrification and hypogyrification alterations in patients with COPD using neuroimaging. Utilizing ToroGI, we demonstrated the COPD-specific increases in ToroGI occurred in the bilateral STG and right ACC, alongside decreased ToroGI in the bilateral lingual gyri. Furthermore, GOLD stage-dependent differences were found in the right STG; and significant correlations between STG gyrification and cognitive dysfunction. Collectively, these findings demonstrate that structural abnormalities underlying disease progression and cognitive dysfunction in COPD manifest as divergent patterns of hypergyrification and hypogyrification. We observed increased cortical gyrification in the STG and the right ACC in patients with COPD. This finding appears to conflict with Wang et al.’s observation of gray matter atrophy in the right STG using voxel-based morphometry. 8 However, these findings likely reflect different aspects of brain structure: while voxel-based morphometry assesses the macroscopic GM volume, surface-based GI (like ToroGI) measures cortical folding complexity, 12 sensitively capturing curvature changes that volumetric approaches (e.g., voxel-based morphometry) may miss. Consequently, ToroGI offers a new quantitative perspective on COPD-related brain structural abnormalities. According to the mechanical tension hypothesis, intracortical axonal connections influence cortical gyrification, 19 and altered neuronal connectivity can drive region-specific gyrification modifications. 20 Experimental evidence suggests that disrupted afferent fibers may enhance gyrification by altering intracortical axonal tension. 21 While genetic factors can influence sulcal length, 22 cortical gyrification may be more sensitive to environmental factors. 23 The pathological characteristics of COPD, including chronic hypoxia, systemic inflammation, oxidative stress, and hypercapnia, 24 may contribute to brain structural remodeling through neuroinflammatory responses and metabolic disorders. 25 – 27 Neurodevelopmental models suggest that increased local GI could indicate mechanical tension imbalances. While potentially enhancing local processing efficiency, such alterations could impair long-range functional integration. 28 Abnormal cortical gyrification in the STG and ACC might be linked to neuropsychiatric and neurologic disorders. This study revealed negative correlations between STG ToroGI and abstract thinking, attention, and orientation scores. Left STG ToroGI correlated negatively with abstract thinking. The STG contributes directly to abstract cognitive abilities. 29 As key default mode network nodes, bilateral STG regions interact with the medial prefrontal cortex, posterior cingulate cortex, and precuneus to form the neural substrate for introspective cognition, 30 during abstract thinking. 31 Previous studies reported default mode network functional abnormalities and reduced connectivity in COPD, which correlated with cognitive impairment. 32 This finding suggests that excessive STG gyrification might disrupt default mode network functional integration, compromising abstract thinking capacity. A linear regression analysis revealed that the GOLD stage positively predicted the right STG ToroGI. Subgroup analysis showed that the right STG ToroGI in GOLD stages 3 and 4 was significantly higher than in GOLD stages 1 and 2. This finding is consistent with Yin et al., 33 who linked COPD severity to accelerated gray matter atrophy. A diffusion tensor imaging study found decreased WM fiber density and reduced connection strength in the STG region (superior longitudinal and arcuate fasciculi) in COPD, which may relate to gyrification changes. 34 Abnormal cortical expansion and gyrification may influence system-level connectivity and manifest as neuropsychiatric or neurological disorders. 35 , 36 Our findings link the GOLD stage to progressive cortical complexity in COPD, with right STG gyrification changes resembling progressive cortical alterations in neurodegenerative processes. The pattern could serve as a biomarker of COPD severity. We found that ToroGI was significantly decreased in the bilateral lingual gyrus of patients with COPD. Reduced cortical complexity may indicate disrupted functional brain networks, consistent with the tension-based morphogenesis model that links gyrification to cortical connectivity and brain development. 37 This reduced lingual gyrus complexity echoes prior findings of decreased spontaneous neural activity 38 local neural synchrony 34 and reduced white matter integrity in the bilateral lingual gyrus in patients with COPD. 39 A systematic review by Wang et al. 4 also identified altered activity in the right lingual gyrus. This simultaneous structural simplification and functional suppression suggests that ToroGI may be a sensitive marker for degenerative changes in the visual cortex of patients with COPD. The lingual gyrus primarily processes retinal information. Its high cellular activity and metabolic demands, characteristic of the visual system’s millisecond-timescale processing, 40 render it particularly vulnerable to hypoxic damage. 41 Therefore, these structural abnormalities provide direct morphological evidence of chronic hypoxic damage in high-energy-consuming brain regions in COPD. Intriguingly, multivariate linear regression analysis revealed that the lingual gyrus gyrification positively predicted abstract thinking scores. Abstract thinking requires coordinated neural processing across several cortical regions, including the dorsomedial prefrontal cortex, ACC, and visual cortex. 42 Consistent with our findings, previous studies demonstrated that the lingual gyrus GM volume affected divergent thinking performance. 43 Our findings complement existing evidence by suggesting that decreased gyrification complexity in the lingual gyrus may weaken abstract thinking capacity. This study had several limitations. First, its cross-sectional design precluded establishing a causal relationship between cortical changes and COPD progression; longitudinal studies are warranted to elucidate the dynamic evolution of ToroGI alterations. Second, the absence of inflammatory blood marker measurements (e.g., IL-6, TNF-α) limits the depth of mechanistic explanations. Future research could combine resting-state functional connectivity and metabolomics to establish a multidimensional ‘morphology-function-metabolism’ model. Furthermore, the impact of pulmonary rehabilitation on cortical plasticity should be explored. CONCLUSIONS Cortical gyrification alterations in patients with COPD featured STG/ACC hypergyrification correlating with cognitive deficits and disease progression, alongside lingual gyri hypogyrification reflecting structural simplification. This disease-associated STG pattern could be a novel neuroimaging biomarker for COPD-related neurodegeneration, demonstrating ToroGI’s capacity to characterize cortical reorganization. Abbreviations COPD = chronic obstructive pulmonary disease; GM = gray matter; GI = gyrification index; ToroGI = Toro's gyrification index; GOLD = chronic obstructive lung disease; STG = superior temporal gyrus; ACC = anterior cingulate cortex. Declarations Acknowledgements None to declare. Author contributions JJ Chen: Methodology, Formal analysis, Visualization, Writing – original draft. YR Chen: Formal analysis, Supervision, Writing – review & editing. K Zhang: Data curation, Conceptualization. K Xu: Data curation, Conceptualization. JP Zhang: Conceptualization, Supervision, Validation. K Yang: Supervision, Validation. LY He: Supervision, Validation. W Sheng: Writing – review & editing. revised the manuscript. GM Ma: Conceptualization, Data curation, Project administration. CW Jin: Conceptualization, Project administration, Supervision, Writing – review and editing. Funding No funding was received for this study. Data availability Data are available from the corresponding authors upon reasonable request. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent after receiving a detailed explanation of the research protocol. This study had been approved by the Ethics Committee of the Affiliated Hospital of the Shaanxi University of Traditional Chinese Medicine (Approval Number: SZFYIEC-PJ-2021 No. [ 233]). Clinical Trial Number Not applicable. Competing Interests The authors declare no competing interests. Consent for publication Not applicable. References Wang T, Mao L, Wang J, et al. Influencing Factors and Exercise Intervention of Cognitive Impairment in Elderly Patients with Chronic Obstructive Pulmonary Disease. 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Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 11 Aug, 2025 Editor assigned by journal 10 Jul, 2025 Submission checks completed at journal 10 Jul, 2025 First submitted to journal 05 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7052215","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501404387,"identity":"2bde9f73-eb94-4f18-985d-2d4f79ee57f5","order_by":0,"name":"Jiajie Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiajie","middleName":"","lastName":"Chen","suffix":""},{"id":501404388,"identity":"67287998-1256-422f-b9c2-2aa604ad3121","order_by":1,"name":"Yanrong Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yanrong","middleName":"","lastName":"Chen","suffix":""},{"id":501404389,"identity":"e29b5a33-5e49-44c0-86b6-e3a410e05c9b","order_by":2,"name":"Kun Zhang","email":"","orcid":"","institution":"Baoji central hospital","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Zhang","suffix":""},{"id":501404390,"identity":"667c1b08-48f8-46a7-966f-2887cba01d0a","order_by":3,"name":"Kai Xu","email":"","orcid":"","institution":"Baoji central hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Xu","suffix":""},{"id":501404391,"identity":"a501a8e3-bd0c-4b3d-a9b0-dff6bcd847c7","order_by":4,"name":"Jingping Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jingping","middleName":"","lastName":"Zhang","suffix":""},{"id":501404393,"identity":"05b47032-ff41-428b-93c3-801ec99c104a","order_by":5,"name":"Kai Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Yang","suffix":""},{"id":501404394,"identity":"9aa4a076-84a7-4b1a-8789-605a4b9640e8","order_by":6,"name":"Liyu He","email":"","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Liyu","middleName":"","lastName":"He","suffix":""},{"id":501404397,"identity":"9900467b-40bb-4a35-84da-525827e746b0","order_by":7,"name":"Wei Sheng","email":"","orcid":"","institution":"MR Research Collaboration, Siemens Healthineers","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Sheng","suffix":""},{"id":501404399,"identity":"df114760-9db9-4b88-b1c3-200ee6fe441f","order_by":8,"name":"Guangming Ma","email":"","orcid":"","institution":"Affiliated Hospital of the Shaanxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Ma","suffix":""},{"id":501404400,"identity":"55db94c8-a1bc-400e-9652-b472080c0fb3","order_by":9,"name":"Chenwang Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBAC9mYGhgMJBv/kgBSIz0xYC2MzA+OBBwUHjBkYDhOrpYGB+eCDDwcSGyCqidHSznsA6LA76RsOnj8mwVBhndjAfvYAAYfxJQC1PMud2XCYTYLhTHpiA09eAgEtPAZALcy5/QxALYxthxMbJHgMiNKSzgbW8o8ILYIQLYcT+MFaGojQIs0M1pJmCPSLsUXCsXTjNp4c/Fr4+M8Yf/zxx0be4MbBhzc+1FjL9rOfwa8FASQOMDAkAGk2ItUDAX8D8WpHwSgYBaNgZAEAKoVHWWvqGV0AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Chenwang","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2025-07-05 09:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7052215/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7052215/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-025-02125-x","type":"published","date":"2026-01-07T15:59:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89290775,"identity":"a5c348e3-5068-49ed-815f-b4d342a84683","added_by":"auto","created_at":"2025-08-18 12:18:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1153034,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions with significant differences in cortical ToroGI values between the COPD and healthy control (HC) groups.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7052215/v1/74fa2a856f8479a381794eee.png"},{"id":89291613,"identity":"cb2f543f-cf3d-4693-9f68-8d988723fefb","added_by":"auto","created_at":"2025-08-18 12:26:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":766334,"visible":true,"origin":"","legend":"\u003cp\u003eCortical ToroGI values demonstrating significant correlations with cognitive function and pulmonary parameters.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7052215/v1/83686de575e05cf0018cc699.png"},{"id":89290779,"identity":"0ebf047e-a8a1-45da-9075-bf6bf7134c19","added_by":"auto","created_at":"2025-08-18 12:18:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160060,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup differences in ToroGI values in the right STG across GOLD stages.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7052215/v1/cc51afb4dd4e1251467cb2a4.png"},{"id":100070082,"identity":"82a690f6-4075-4c8a-a0a2-85531dcc959d","added_by":"auto","created_at":"2026-01-12 16:16:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3015200,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7052215/v1/94731d7c-e3af-401e-9d74-d23c875e67f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bidirectional Cortical Gyrification Alterations in Chronic Obstructive Pulmonary Disease: Links to Cognitive Impairment and Global Initiative for Chronic Obstructive Lung Disease Staging","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/\u003c/span\u003e\u003cspan address=\"https://www.who.int/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Beyond respiratory symptoms, chronic hypoxemia, systemic inflammation, and oxidative stress, COPD might disrupt blood-brain barrier integrity and induce neuroinflammatory cascades, potentially contributing to gray matter abnormalities.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e These mechanisms might lead to structural and functional alterations in the central nervous system,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e potentially contributing to the high prevalence of cognitive impairment observed in patients with COPD.\u003c/p\u003e\u003cp\u003eStructural MRI studies have revealed that cognitive impairment in COPD is closely associated with gray matter abnormalities.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Most of these studies employed voxel-based morphometry analysis to evaluate these changes, consistently reporting reduced GM volume in brain regions involved in cognition, including the frontal lobe, cingulate cortex, basal ganglia, hippocampus, temporal lobe, and parietal lobe.\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e These structural changes have been associated with a decline in forced vital capacity,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e longer disease duration,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and poorer cognitive performance.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e A recent meta-analysis study revealed significant GM abnormalities in the right postcentral, left precentral, and left cingulate gyri in patients with COPD.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Another meta-analysis used connectivity modeling to demonstrate co-atrophy patterns between these regions and the insula, suggesting deep neural network involvement.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e These findings suggest that disease-specific neuroanatomical mechanisms underlie cognitive impairment in COPD.\u003c/p\u003e\u003cp\u003eInterestingly, despite the consistently reported widespread regional GM volume reduction, normal total brain volume is unaffected by COPD,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, hinting at potential compensatory neuroplasticity mechanisms, such as coexisting localized gray matter atrophy with compensatory volumetric increases in other regions. Similar compensatory patterns, characterized by the homeostatic equilibrium between atrophy and hypertrophy, have been observed in other chronic hypoxic conditions.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e However, the widely used conventional voxel-based morphometry cannot capture cortical folding complexity or distinguish between gyral and sulcal morphological contributions, which might lead to heterogeneous findings. This highlights the need for more surface-based metrics to detect subtle cortical geometry alterations.\u003c/p\u003e\u003cp\u003eCortical gyrification—the folding of the cerebral cortex into intricate patterns of gyri and sulci—is a key neurodevelopmental feature associated with brain connectivity and cognitive capacity. Cortical folds can be quantified using metrics such as the gyrification index (GI)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Compared to conventional volumetric analyses, the GI provides a comprehensive evaluation of cortical morphology and enhances the precision in detecting morphological changes.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e However, previous studies employing the local gyrification index (LGI) in COPD did not detect significant differences,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e likely due to the limited sensitivity of LGI and surface-based morphometry techniques.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eIn contrast, Toro's Gyrification Index (ToroGI), a curvature-based geometric approach that quantifies the ratio between the cortical surface area and its projected area, effectively captures sulcal depth and gyral height.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e This global approach offers enhanced sensitivity in detecting microstructural gyrification alterations while providing a comprehensive assessment of cortical gyrification patterns across entire brain regions or large-scale anatomical areas. ToroGI has demonstrated superior sensitivity in identifying cortical microstructural alterations in conditions such as neuropathic pain\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and psychiatric disorders.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Building upon previous GM volume findings in COPD and given the limitations of conventional volumetric and LGI approaches, a more sensitive examination of cortical gyrification using ToroGI is warranted.\u003c/p\u003e\u003cp\u003eThis study employed ToroGI to investigate alterations in cortical gyrification patterns in patients with stable COPD compared to healthy controls. We further examined whether these alterations were associated with disease severity, assessed by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, and cognitive performance, measured by the Montreal Cognitive Assessment. We hypothesized that patients with COPD would exhibit region-specific gyral-sulcal pattern alterations and that ToroGI values in specific regions would correlate with GOLD stages and cognitive scores.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eClinical Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients with stable COPD and healthy controls were recruited from the Affiliated Hospital of the Shannxi University of Traditional Chinese Medicine. The inclusion criteria for patients with COPD were as follows: (1) a confirmed diagnosis of COPD based on the GOLD (2024) guidelines; (2) Han Chinese ethnicity and right-handedness; (3) age between 50 and 80 years; (4) education of ≥ 6 years; and (5) no history of substance abuse, including alcohol, illicit drugs, or prescription medications misuse. The exclusion criteria for patients with COPD were: (1) comorbid psychiatric disorders, such as anxiety or depression, as diagnosed by DSM-5; (2) participation in other clinical trials within the past three months; (3) the presence of severe systemic comorbidities, including diabetes mellitus, hepatic insufficiency, cardiac dysfunction (New York Heart Association Class III–IV), moderate-to-severe obstructive sleep apnea, or hypertension; and (4) inability to independently complete neuropsychological assessments. The healthy control subjects were recruited from local communities and hospitals and were matched to the patients with COPD by sex and education level. This study was approved by the Ethics Committee of the Affiliated Hospital of the Shaanxi University of Traditional Chinese Medicine (Approval Number: SZFYIEC-PJ-2021 No. [ 233]) All participants provided written informed consent after receiving a detailed explanation of the research protocol.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical Assessments\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePulmonary function in patients with COPD was evaluated using standard spirometry, and included measurements of forced vital capacity, forced vital capacity in one second as a percentage of the predicted value, forced expiratory volume in one second, and the volume in one second / forced vital capacity ratio. Neurocognitive performance was assessed using the Montreal Cognitive Assessment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMagnetic Resonance Imaging Acquisition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMRI data were acquired using a 3.0T MR scanner (MAGNETOM Skyra, Siemens Healthineers, Erlangen, Germany) equipped with a standard-channel head coil. Conventional T2WI was performed to screen for intracranial lesions. High-resolution 3D T1WI was performed using a magnetization-prepared rapid acquisition gradient-echo sequence with the following parameters: TR = 2,300 ms, TE = 2.9 ms, TI = 900 ms, flip angle = 9°, slice thickness = 1 mm, FOV = 240 × 240 mm, matrix size = 256 × 256, and number of excitations = 1. All MR images were independently reviewed by two experienced neuroradiologists blinded to clinical group assignment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage Processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStructural data were processed using the Computational Anatomy Toolbox (CAT12.9; r2577, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://neuro-jena.github.io/cat12\u003c/span\u003e\u003cspan address=\"https://neuro-jena.github.io/cat12\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) implemented in SPM12 (Welcome Trust Centre for Neuroimaging, London, UK) running on MATLAB R2024b (MathWorks Inc., Natick, MA, USA). The preprocessing pipeline included the following steps: (1) DICOM-to-NIFTI format conversion; (2) image segmentation into gray matter, white matter, and cerebrospinal fluid; (3) surface-based reconstruction of inner and outer cortical surfaces through three-dimensional modeling of gray-white matter boundaries and pial surfaces, including cortical surface registration and inflation; (4) spatial normalization to standard Montreal Neurological Institute space; (5) computation of ToroGI as a quantification of cortical gyrification complexity; and (6) spatial smoothing with a 25-mm full-width at half-maximum Gaussian kernel.\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, NY, USA). Categorical variables were compared using the chi-squared test, and continuous variables were analyzed using two-sample \u003cem\u003et\u003c/em\u003e tests. Data are presented as means ± standard deviations unless otherwise noted.\u003c/p\u003e\u003cp\u003eComparisons of ToroGI values between the COPD and healthy control cohorts were performed using two-sample \u003cem\u003et\u003c/em\u003e-test in the CAT12 statistical module, with age, sex, and total intracranial volume included as covariates. Multiple comparisons were corrected using the Holm-Bonferroni method (\u003cem\u003eP\u003c/em\u003e \u0026lt; .05). Brain regions in ToroGI were labeled according to the Destrieux atlas (2009 version).\u003c/p\u003e\u003cp\u003ePartial correlation analyses explored associations between ToroGI values and pulmonary/cognitive measures, adjusting for age, sex, and total intracranial volume. Two multiple linear regression models were constructed: (1) to assess the predictive value of ToroGI for cognitive performance (e.g., Montreal Cognitive Assessment scores); and (2) to examine the association of COPD severity (GOLD stage) on ToroGI values. Additionally, analysis of covariance (ANCOVA) was performed on ToroGI value within identified cortical regions to exam mean differences and characterize regional abnormalities across GOLD subgroups. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e \u0026lt; .05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eDemographics and Clinical Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study initially recruited 64 patients with COPD and 50 healthy controls. Following quality control of cortical imaging data, 59 patients with COPD and 49 healthy controls were retained for the final analysis. Significant differences were observed in age, cerebrospinal fluid volume, and Montreal Cognitive Assessment scores between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), while they were statistically comparable in education, sex, smoking history, total intracranial volume, GM volume, and WM volume (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and physiological characteristics of the COPD and HC groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPD (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et/x\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.7\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (Male/Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIV (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,492.5\u0026thinsp;\u0026plusmn;\u0026thinsp;123.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,466.7\u0026thinsp;\u0026plusmn;\u0026thinsp;148.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGray matter (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e628.0\u0026thinsp;\u0026plusmn;\u0026thinsp;47.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e650.1\u0026thinsp;\u0026plusmn;\u0026thinsp;62.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite matter (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e503.2\u0026thinsp;\u0026plusmn;\u0026thinsp;61.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e503.4\u0026thinsp;\u0026plusmn;\u0026thinsp;56.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrospinal Fluid (cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e363.1\u0026thinsp;\u0026plusmn;\u0026thinsp;68.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e313.1\u0026thinsp;\u0026plusmn;\u0026thinsp;77.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCOPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; HC\u0026thinsp;=\u0026thinsp;healthy control; TIV\u0026thinsp;=\u0026thinsp;total intracranial volume; MoCA\u0026thinsp;=\u0026thinsp;Montreal Cognitive Assessment.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGroup Difference in Cortical Gyrification\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompared to the healthy control group, the COPD group, adjusted for age, sex, and total intracranial volume, showed significant bidirectional alterations in cortical gyrification\u0026mdash;higher ToroGI values in the bilateral superior temporal gyrus (STG) and right anterior cingulate cortex (ACC) and lower ToroGI values in the bilateral lingual gyri (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, Holm-Bonferroni corrected; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClusters with significant differences in ToroGI values between the COPD and HC groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAnatomical Region\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMNI Coordinates\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCluster-Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003et-\u003c/em\u003evalue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft Hemisphere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingual gyrus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight Hemisphere\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLingual gyrus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eCOPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease; HC\u0026thinsp;=\u0026thinsp;healthy control; ToroGI\u0026thinsp;=\u0026thinsp;Toro's gyrification index; STG\u0026thinsp;=\u0026thinsp;superior temporal gyrus; ACC\u0026thinsp;=\u0026thinsp;anterior cingulate cortex.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe results are aftter Holm-Bonferroni correction for multiple comparisons (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations between Cortical Gyrification and Cognitive Performance in the COPD Group\u003c/b\u003e\u003c/p\u003e\u003cp\u003eToroGI values in the left STG correlated negatively with abstract thinking (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.522, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, False Discovery Rate (FDR)-corrected) and attention (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.377, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01, FDR-corrected). In the right STG, they correlated negatively with orientation scores (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.360, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02, FDR-corrected; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegression Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultiple linear regression models were constructed to further investigate the relationships between cortical morphology and cognitive/clinical parameters.\u003c/p\u003e\u003cp\u003eCognitive Prediction Model\u003c/p\u003e\u003cp\u003eUsing ToroGI values of brain regions as independent variables and abstract thinking scores as the dependent variables, the regression model revealed a significant predictive association in the right lingual gyrus (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04, VIF\u0026thinsp;=\u0026thinsp;1.11) and left STG (\u003cem\u003et\u003c/em\u003e = \u0026minus;\u0026thinsp;4.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, VIF\u0026thinsp;=\u0026thinsp;1). The resulting regression equation is:\u003c/p\u003e\u003cp\u003eATS\u0026thinsp;=\u0026thinsp;12.03\u0026thinsp;+\u0026thinsp;6.86 \u0026times; ToroGI_RLG \u0026ndash; 20.17 \u0026times; ToroGI_LSTG\u0026thinsp;+\u0026thinsp;ε (1)\u003c/p\u003e\u003cp\u003ewhere ATS refers to the abstract thinking score; ToroGI_RLG and ToroGI_LSTG denote ToroGI scores of the right lingual gyrus and left STG, respectively; and ε is an error term. This model explained 43% of the variance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.43, Durbin-Watson\u0026thinsp;=\u0026thinsp;2.6, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003eDisease Severity Model\u003c/p\u003e\u003cp\u003eUsing GOLD stages as the independent variable and ToroGI values as the dependent variable, the regression analysis revealed a significant association in the right STG (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The resulting regression equation is:\u003c/p\u003e\u003cp\u003eToroGI_RSTG\u0026thinsp;=\u0026thinsp;0.46\u0026thinsp;+\u0026thinsp;0.01 \u0026times; GOLD_Stage\u0026thinsp;+\u0026thinsp;ε (2)\u003c/p\u003e\u003cp\u003ewhere ToroGI_RSTG denotes the ToroGI score of the right STG; GOLD_Stage is the GOLD clinical stage; and ε is an error term. This model accounted for 28% of the variance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.28, Durbin-Watson\u0026thinsp;=\u0026thinsp;2.6, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup Analysis by Disease Severity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed a comparative analysis with analysis of covariance, adjusted for age, sex, and total intracranial volume, to further characterize cortical abnormalities across GOLD stages. Significant differences were found in the right STG among stages (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005). Bonferroni-corrected post-hoc pairwise comparisons revealed that patients with GOLD stage 1 or 2 had lower ToroGI values than those with GOLD stages 3 and 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study reveals concurrent hypergyrification and hypogyrification alterations in patients with COPD using neuroimaging. Utilizing ToroGI, we demonstrated the COPD-specific increases in ToroGI occurred in the bilateral STG and right ACC, alongside decreased ToroGI in the bilateral lingual gyri. Furthermore, GOLD stage-dependent differences were found in the right STG; and significant correlations between STG gyrification and cognitive dysfunction. Collectively, these findings demonstrate that structural abnormalities underlying disease progression and cognitive dysfunction in COPD manifest as divergent patterns of hypergyrification and hypogyrification.\u003c/p\u003e\u003cp\u003eWe observed increased cortical gyrification in the STG and the right ACC in patients with COPD. This finding appears to conflict with Wang et al.\u0026rsquo;s observation of gray matter atrophy in the right STG using voxel-based morphometry.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e However, these findings likely reflect different aspects of brain structure: while voxel-based morphometry assesses the macroscopic GM volume, surface-based GI (like ToroGI) measures cortical folding complexity,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e sensitively capturing curvature changes that volumetric approaches (e.g., voxel-based morphometry) may miss. Consequently, ToroGI offers a new quantitative perspective on COPD-related brain structural abnormalities.\u003c/p\u003e\u003cp\u003eAccording to the mechanical tension hypothesis, intracortical axonal connections influence cortical gyrification,\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and altered neuronal connectivity can drive region-specific gyrification modifications.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Experimental evidence suggests that disrupted afferent fibers may enhance gyrification by altering intracortical axonal tension.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e While genetic factors can influence sulcal length,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e cortical gyrification may be more sensitive to environmental factors.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e The pathological characteristics of COPD, including chronic hypoxia, systemic inflammation, oxidative stress, and hypercapnia,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e may contribute to brain structural remodeling through neuroinflammatory responses and metabolic disorders.\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Neurodevelopmental models suggest that increased local GI could indicate mechanical tension imbalances. While potentially enhancing local processing efficiency, such alterations could impair long-range functional integration.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003eAbnormal cortical gyrification in the STG and ACC might be linked to neuropsychiatric and neurologic disorders.\u003c/p\u003e\u003cp\u003eThis study revealed negative correlations between STG ToroGI and abstract thinking, attention, and orientation scores. Left STG ToroGI correlated negatively with abstract thinking. The STG contributes directly to abstract cognitive abilities.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e As key default mode network nodes, bilateral STG regions interact with the medial prefrontal cortex, posterior cingulate cortex, and precuneus to form the neural substrate for introspective cognition,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e during abstract thinking.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Previous studies reported default mode network functional abnormalities and reduced connectivity in COPD, which correlated with cognitive impairment.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e This finding suggests that excessive STG gyrification might disrupt default mode network functional integration, compromising abstract thinking capacity.\u003c/p\u003e\u003cp\u003eA linear regression analysis revealed that the GOLD stage positively predicted the right STG ToroGI. Subgroup analysis showed that the right STG ToroGI in GOLD stages 3 and 4 was significantly higher than in GOLD stages 1 and 2. This finding is consistent with Yin et al.,\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e who linked COPD severity to accelerated gray matter atrophy. A diffusion tensor imaging study found decreased WM fiber density and reduced connection strength in the STG region (superior longitudinal and arcuate fasciculi) in COPD, which may relate to gyrification changes.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003eAbnormal cortical expansion and gyrification may influence system-level connectivity and manifest as neuropsychiatric or neurological disorders.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Our findings link the GOLD stage to progressive cortical complexity in COPD, with right STG gyrification changes resembling progressive cortical alterations in neurodegenerative processes. The pattern could serve as a biomarker of COPD severity.\u003c/p\u003e\u003cp\u003eWe found that ToroGI was significantly decreased in the bilateral lingual gyrus of patients with COPD. Reduced cortical complexity may indicate disrupted functional brain networks, consistent with the tension-based morphogenesis model that links gyrification to cortical connectivity and brain development.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e This reduced lingual gyrus complexity echoes prior findings of decreased spontaneous neural activity\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e local neural synchrony\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and reduced white matter integrity in the bilateral lingual gyrus in patients with COPD. \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e A systematic review by Wang et al.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e also identified altered activity in the right lingual gyrus. This simultaneous structural simplification and functional suppression suggests that ToroGI may be a sensitive marker for degenerative changes in the visual cortex of patients with COPD. The lingual gyrus primarily processes retinal information. Its high cellular activity and metabolic demands, characteristic of the visual system\u0026rsquo;s millisecond-timescale processing,\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e render it particularly vulnerable to hypoxic damage.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Therefore, these structural abnormalities provide direct morphological evidence of chronic hypoxic damage in high-energy-consuming brain regions in COPD.\u003c/p\u003e\u003cp\u003eIntriguingly, multivariate linear regression analysis revealed that the lingual gyrus gyrification positively predicted abstract thinking scores. Abstract thinking requires coordinated neural processing across several cortical regions, including the dorsomedial prefrontal cortex, ACC, and visual cortex.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Consistent with our findings, previous studies demonstrated that the lingual gyrus GM volume affected divergent thinking performance.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Our findings complement existing evidence by suggesting that decreased gyrification complexity in the lingual gyrus may weaken abstract thinking capacity.\u003c/p\u003e\u003cp\u003eThis study had several limitations. First, its cross-sectional design precluded establishing a causal relationship between cortical changes and COPD progression; longitudinal studies are warranted to elucidate the dynamic evolution of ToroGI alterations. Second, the absence of inflammatory blood marker measurements (e.g., IL-6, TNF-α) limits the depth of mechanistic explanations. Future research could combine resting-state functional connectivity and metabolomics to establish a multidimensional \u0026lsquo;morphology-function-metabolism\u0026rsquo; model. Furthermore, the impact of pulmonary rehabilitation on cortical plasticity should be explored.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eCortical gyrification alterations in patients with COPD featured STG/ACC hypergyrification correlating with cognitive deficits and disease progression, alongside lingual gyri hypogyrification reflecting structural simplification. This disease-associated STG pattern could be a novel neuroimaging biomarker for COPD-related neurodegeneration, demonstrating ToroGI\u0026rsquo;s capacity to characterize cortical reorganization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOPD = chronic obstructive pulmonary disease; GM = gray matter; GI = gyrification index; ToroGI = Toro\u0026apos;s gyrification index; GOLD = chronic obstructive lung disease; STG = superior temporal gyrus; ACC = anterior cingulate cortex.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJJ Chen: Methodology, Formal analysis, Visualization, Writing – original draft. YR Chen: Formal analysis, Supervision, Writing – review \u0026amp; editing. K Zhang: Data curation, Conceptualization. K Xu: Data curation, Conceptualization. JP Zhang: Conceptualization, Supervision, Validation. K Yang: Supervision, Validation. LY He: Supervision, Validation. W Sheng: Writing – review \u0026amp; editing. revised the manuscript. GM Ma: Conceptualization, Data curation, Project administration. CW Jin: Conceptualization, Project administration, Supervision, Writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent after receiving a detailed explanation of the research protocol. This study had been approved by the Ethics Committee of the Affiliated Hospital of the Shaanxi University of Traditional Chinese Medicine (Approval Number: SZFYIEC-PJ-2021 No. [ 233]). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang T, Mao L, Wang J, et al. 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Gray Matter Volume of the Lingual Gyrus Mediates the Relationship between Inhibition Function and Divergent Thinking. \u003cem\u003eFront Psychol\u003c/em\u003e 2016;7:1532\u003cbr\u003e \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"chronic obstructive pulmonary disease, cortical gyrification, superior temporal gyrus, lingual gyrus, cognitive impairment, magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-7052215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7052215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eCognitive impairment is a common but poorly understood comorbidity in chronic obstructive pulmonary disease. Although gray matter abnormalities have been observed in this population, the contribution of cortical gyrification—a structural feature linked to cognitive development and brain plasticity—remains unclear. This study aimed to characterize region-specific cortical gyrification alterations and examine their associations with domain-specific cognitive function and disease severity.\u003c/p\u003e\n\u003cp\u003e‌ \u003cstrong\u003eMethods\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eWe enrolled 59 patients with stable chronic obstructive pulmonary disease and 49 healthy controls who underwent pulmonary function testing, Montreal Cognitive Assessment, and high-resolution T1WI. The Toro's Gyrification Index quantified cortical gyrification. Group comparisons, partial correlations, and multiple linear regression analyses were conducted with adjustments for age, sex, and total intracranial volume.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Compared to healthy controls, the patient group showed increased Toro's Gyrification Index in the bilateral superior temporal gyrus and right anterior cingulate cortex, and decreased Toro's Gyrification Index in the bilateral lingual gyri (\u003cem\u003eP \u003c/em\u003e\u0026lt; .05). In the patient group, the Toro's Gyrification Index in the left superior temporal gyrus was negatively correlated with abstract thinking (\u003cem\u003er\u003c/em\u003e = –0.522, \u003cem\u003eP\u003c/em\u003e \u0026lt; .001) and attention scores (\u003cem\u003er\u003c/em\u003e = –0.377, \u003cem\u003eP\u003c/em\u003e = .01), and in the right superior temporal gyrus with orientation score (\u003cem\u003er\u003c/em\u003e = –0.360, \u003cem\u003eP\u003c/em\u003e = .02). A regression model combining Toro's Gyrification Index in the left superior temporal and right lingual gyri explained 43% of the variance in the abstract thinking score (\u003cem\u003eF\u003c/em\u003e = 9.73, \u003cem\u003eP\u003c/em\u003e \u0026lt; .001). The Global Initiative for Chronic Obstructive Lung Disease stage significantly predicted the right superior temporal gyrus Toro's Gyrification Index (\u003cem\u003eF\u003c/em\u003e = 15.08, \u003cem\u003eP\u003c/em\u003e \u0026lt; .001), with higher values observed in patients with disease stages 3 and 4 than stages 1 and 2 (\u003cem\u003eF\u003c/em\u003e = 4.74, \u003cem\u003eP\u003c/em\u003e = .005).\u003c/p\u003e\n\u003cp\u003e‌\u003cstrong\u003eConclusions\u003c/strong\u003e Chronic obstructive pulmonary disease is associated with region-specific, bidirectional cortical gyrification changes that are closely linked to cognitive impairment and disease severity. Hypergyrification in the superior temporal and lingual gyri might reflect compensatory neural plasticity mechanisms, suggesting these patterns could serve as novel neuroimaging biomarkers for evaluating neurodegenerative changes in chronic obstructive pulmonary disease.\u003c/p\u003e","manuscriptTitle":"Bidirectional Cortical Gyrification Alterations in Chronic Obstructive Pulmonary Disease: Links to Cognitive Impairment and Global Initiative for Chronic Obstructive Lung Disease Staging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 12:18:15","doi":"10.21203/rs.3.rs-7052215/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T07:46:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T05:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248155193090468886891564734859182106883","date":"2025-10-30T05:27:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204183070111073063136540483714489508611","date":"2025-09-19T14:19:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T08:06:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330511431764073918965257184991147649457","date":"2025-08-26T10:03:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-11T09:22:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-10T12:53:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-10T12:50:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-07-05T09:49:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffdbf84e-7e37-4f91-a502-c27090a8911d","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:10:05+00:00","versionOfRecord":{"articleIdentity":"rs-7052215","link":"https://doi.org/10.1186/s12880-025-02125-x","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2026-01-07 15:59:09","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-08-18 12:18:15","video":"","vorDoi":"10.1186/s12880-025-02125-x","vorDoiUrl":"https://doi.org/10.1186/s12880-025-02125-x","workflowStages":[]},"version":"v1","identity":"rs-7052215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7052215","identity":"rs-7052215","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}