Selective hippocampal subfield atrophy mediates cognitive decline in Cushing’s Disease.

Selective hippocampal subfield atrophy mediates cognitive decline in Cushing’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 Selective hippocampal subfield atrophy mediates cognitive decline in Cushing’s Disease. zhebin Feng, Tao Zhou, Kunyu He, Junpeng Xu, Xinyuan Yan, Guosong Shang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7058713/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 Cushing’s disease (CD) provides insight into how prolonged high cortisol exposure affects brain structure. While CD patients show cognitive and emotional symptoms linked to hippocampal function, detailed analysis of hippocampal subfield changes and their correlations with peripheral blood gene expression profiles remains limited. Methods The study included 91 patients with active CD and 53 matched healthy controls who underwent T1-weighted magnetic resonance imaging and comprehensive neuropsychological assessment. We employed voxel-based morphometry, automated segmentation, and shape analysis to evaluate gray matter volume, subfield volumes, and hippocampal morphology. Additionally, RNA sequencing was performed to characterize peripheral blood leukocyte transcriptome profiles in a subgroup of 25 CD patients and 30 matched HCs. Results Compared to controls, CD patients showed decreased hippocampal gray matter volume, particularly in body and tail regions. Specific subfields including presubiculum-body, subiculum-body, CA4-body, and granule cell layer showed significant volume reductions. Shape analysis revealed corresponding surface alterations. Notably, left CA4-body and GC-ML-DG-body volumes mediated the relationship between cortisol levels and cognitive performance, and left CA4-body volume demonstrated a positive correlation with peripheral blood LINC02193 expression. Conclusions . CD patients exhibit distinct patterns of hippocampal atrophy affecting specific subfields, with changes correlating to hormone levels and cognitive symptoms, and discrete hippocampal subfield volumes associated with gene expression profiles. These structural and transcriptomic alterations may serve as potential biomarkers for CD and provide insight into the mechanisms underlying cognitive dysfunction in hypercortisolism. Cushing’s disease hypercortisolism hippocampal subfields cognitive structural image blood transcriptome LINC02193 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Cushing’s disease (CD) occurs when a pituitary tumor produces excess adrenocorticotrophic hormone (ACTH), leading to chronically high cortisol levels[ 1 ]. This makes CD an ideal natural model for studying how prolonged cortisol exposure affects brain structure[ 2 ]. CD patients experience both cognitive deficits (affecting memory[ 3 , 4 ], verbal learning[ 5 ], executive function, and attention[ 3 ]) and psychiatric symptoms[ 6 ] (including depression, anxiety, and somatization). By studying CD patients, researchers can better understand how brain structural changes relate to these cognitive and psychiatric symptoms. The hippocampus, a key component of the limbic system, controls learning, memory, emotion recognition, and attention[ 7 , 8 ]. In CD patients, elevated cortisol levels appear to disrupt hippocampal function and structure, potentially explaining their cognitive and emotional symptoms. This disruption occurs through several mechanisms: reduced formation of new neurons[ 9 ], decreased growth of support cells (astrocytes and oligodendrocytes) [ 10 ], shortened nerve cell connections[ 11 ], and impaired memory processing[ 3 ]. Additionally, high cortisol triggers inflammatory responses that can damage hippocampal tissue, as demonstrated in animal studies[ 12 – 14 ]. Previous research on hippocampal changes in CD has yielded mixed results. Early studies using manual measurements found significant hippocampal shrinkage (27%) that persisted even after treatment[ 15 – 17 ]. More recent studies using advanced imaging techniques (voxel-based morphometry) confirmed reduced hippocampal volume and gray matter density[ 18 – 20 ]. However, some studies found no significant volume differences between CD patients and healthy controls[ 4 , 21 ]. These conflicting findings suggest the need for more detailed analysis of specific hippocampal regions, rather than examining only total volume. The hippocampus is organized along a head-body-tail axis, with each section containing distinct subfields that can respond differently to disease. Examining only total hippocampal volume may obscure important changes in these specific regions. Recent advances in computer-assisted analysis have enabled a more precise study of hippocampal structure. Using automated MRI analysis software developed by Iglesias et al. [ 22 ], researchers can now reliably identify and measure 19 distinct subfields of the hippocampus. These include the parasubiculum, presubiculum (head and body), subiculum (head and body), cornu ammonia regions CA1, CA3, and CA4 (each in head and body), granule cell and molecular layers of the dentate gyrus (GC-ML-DG) (head and body), molecular layers (head and body), hippocampus-amygdala-transition-area (HATA), fimbria, hippocampal tail, and hippocampal fissure. This detailed segmentation method has been validated as stable and effective, allowing researchers to examine subtle structural changes that might be missed in whole-hippocampus analyses[ 23 ]. Studies across various neurological conditions have revealed specific patterns of hippocampal subfield changes. Insomnia severity correlates with reduced CA3/dentate gyrus volume[ 24 ], while depression shows distinct changes in left CA1 volume that predict illness duration[ 25 ]. In temporal lobe epilepsy, volume loss occurs in CA, dentate gyrus, subiculum, and fimbria regions, matching patterns of neuronal loss[ 26 ]. Parkinson’s disease patients with depression show decreased CA3 volume[ 27 ]. Despite these findings in other conditions, research on hippocampal subfield changes in CD remains limited. Transcriptomic profiling serves as a quantitative phenotyping approach to systematically elucidate dysregulated biological pathways in disease progression[ 28 ]. Emerging evidence suggests that blood-derived gene expression signatures may partially reflect cerebral metabolic alterations associated with neurodegenerative disorders, such as Alzheimer's disease[ 29 ]. Compared to invasive brain biopsies and cerebrospinal fluid analyses, peripheral blood biomarkers offer minimally invasive sampling and dynamic monitoring capabilities, rendering them particularly advantageous for clinical investigations of CD. Nevertheless, few prior studies have comprehensively explored the correlation between peripheral blood transcriptomic signatures and hippocampal structural remodeling in CD patients. Our study aims to investigate hippocampal changes in CD patients with chronically elevated cortisol levels. We hypothesize that: (1) CD patients will show reduced gray matter and subfield volumes compared to healthy controls, (2) these volume reductions will correspond to visible structural changes in hippocampal shape, and (3) these structural changes will correlate with both hormone levels and psychiatric symptoms, suggesting that high cortisol may cause psychiatric symptoms through its effects on specific hippocampal regions. Besides, (4) the peripheral blood transcriptome may harbor biomarkers reflecting specific hippocampal subfields in patients with Crohn's disease. 2 Methods 2.1 Participants 102 CD patients and 54 healthy controls (HCs) were recruited from the Department of Neurosurgery, XXX Hospital, between May 2017 and October 2024. After excluding participants (including 11 CD patients and one healthy control) with low-quality magnetic resonance scanning (MRI) images, the current study finally included 144 participants (91 CD patients and 53 HCs). Experienced endocrinologists diagnosed CD patients according to the latest clinical practice guidelines[ 30 ]: clinical characteristics (such as moon face, buffalo hump, purple striae), high levels and abnormal circadian rhythms of relevant hormones (ACTH level at 8:00, reference range < 10.12 pmol/L; cortisol level at 8:00, reference range 198.7–797.5 nmol/L; 24-h urinary free cortisol, reference range 98.0–500.1 nmol/24 h), special tests (negative results in dexamethasone suppression test and low dose dexamethasone suppression test, while positive result in high dose dexamethasone suppression test), inferior petrosal sinus sampling (inferior petrosal sinus to peripheral blood, ratio of ACTH level > 2), and histopathologic diagnosis (ACTH-secreting pituitary adenoma). HCs were recruited from the local community and excluded any current or history of mental disorder by an experienced psychiatrist. All participants were right-handed. This study adhered to the principles outlined in the Declaration of Helsinki and the ethical requirements from the local ethics committee (Ethics Committee Approval No. S2021-677-01). Before participating in the research, every patient provided written informed consent. 2.2 Clinical Data Acquisition For CD patients, peripheral blood cortisol levels were measured at 0:00, 8:00, and 16:00, ACTH levels at 0:00, 8:00, and 16:00, and urinary-free cortisol levels within 24 hours. For HCs, peripheral blood’s cortisol level at 8:00, ACTH level at 8:00, and urinary free cortisol level within 24h were measured. A comprehensive neuropsychological assessment was also included in this study, such as Self-Rating Depression Scale (SDS), Self‐Rating Anxiety Scale (SAS), Montreal Cognitive Assessment (MoCA), and Cushing’s Quality‐of‐Life (QOL) questionnaire (only for CD patients). 2.3 MRI image acquisition High-resolution T1-weighted structural images were acquired using the GE Discovery MR 750w 3.0-Tesla system (8-channel head coil) at XXX Hospital, Beijing. The parameters were shown as follow: repetition time = 6700 ms, echo time = 29 ms, flip angle = 7°, field of view = 256 × 256 mm 2 , voxel size = 1 × 1 × 1 mm 3 . 2.4 Preprocessing and Voxel-based morphometry (VBM) Analysis The image was converted to Nifti format using MRIcron (version 1.0.20190902, https://www.nitrc.org/projects/mricron/ ), and image quality was checked manually. The structure image was preprocessed with the SPM 12 software ( https://www.fil.ion.ucl.ac.uk/spm/software/ ) and the CAT 12 toolbox ( http://www.neuro.uni-jena.de/cat/ ) in the MATLAB environment. In short, the anterior commissure of the structural image should coincide with the origin (0,0,0) of the Montreal Neurological Institute (MNI) space. Then, segmentation (gray matter, white matter, and cerebrospinal fluid)[ 31 ], space normalization (rigid and non-rigid registration to MNI standard space), resampling (1.5×1.5×1.5mm voxel resolution), smoothing (8-mm full-width at half-maximum), and total intracranial volume (TIV) calculation were conducted. After data preprocessing, we assessed the difference in GMV between CD patients and HCs. A general linear model was constructed with sex, age, education year, and TIV as covariates. Apply absolute masking with a threshold of 0.2[ 32 ]. Next, a bilateral hippocampal mask based on the Anatomical Automated Labeling (AAL) template was applied to explore structural changes in the hippocampus. Using the false discovery rate (FWE) to correct for p < 0.05 at the cluster level to reduce Type 1 errors. 2.5 Segmentation of Hippocampal Subfields FreeSurfer (version 7.3.2, https://surfer.nmr.mgh.harvard.edu/ ) image analysis suite performed cortical reconstruction and volumetric segmentation[ 33 , 34 ]. Briefly, this processing includes motion correction and averaging of multiple volumetric T1 weighted images, removal of non-brain tissue, automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures intensity normalization, tessellation of the gray matter white matter boundary, automated topology correction, and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location. Estimated TIV was calculated. The hippocampus was segmented into 19 subfields using Bayesian inference[ 22 ]. The information on subfields was shown in Table S1 . 2.6 Hippocampal Shape Analysis FSL-FIRST (version 5.0.9, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST/ ) was used for automated segmentation and linear registration to the MNI152 space of the hippocampus[ 35 ]. Image binarization and smoothing were conducted for each sample. Then FIRST modeled the boundary of all structures. The mesh parameterization (including vertices and edges) was performed using the spherical harmonic decomposition point distribution model (SPHARM-PDM). Next, boundary correction was done to decide whether the boundary voxels should belong to the structure or not. Finally, a univariate test at each vertice was used to measure the difference in location between HCs and CD patients[ 36 ]. The displacement vector of CD patients' vertices represented the deformation of the hippocampal surface relative to HCs. 2.7 Mediation Analysis The bootstrapping method was employed to estimate the mediation effect[ 37 ]. Bootstrapping entails repeated random resampling from the existing data to empirically approximate the sampling distribution of a given statistic. This approximated distribution is then utilized to ascertain p-values and construct confidence intervals (with a total of 5000 resamples). Furthermore, this methodology produces supplied confidence intervals (CIs) that undergo bias-corrected and accelerated bootstrapping, ensuring greater accuracy and reliability[ 37 ]. 2.8 Blood Sample Collection, Sequencing, and Data Processing Twenty-five CD patients (27.5% of the 91-case CD cohort) and 30 HCs (56.6% of the 53-subject HCs cohort) were enrolled for peripheral blood transcriptomic profiling. All samples were collected and underwent batch-controlled RNA extraction and transcriptome sequencing (Illumina NovaSeq 6000) to minimize technical variability, with detailed protocols provided in Supplementary Materials. After quality control, a total of n = 15,674 genes were retained for further analysis. Differentially expressed genes (DEGs) were identified from raw read counts (featureCounts v2.0.3) using DESeq2 v1.42.1 and edgeR v3.38.1, with significance thresholds set at | Log 2 (Fold Change) | ≥1 and false discovery rate (FDR) -adjusted p < 0.05 (Benjamini-Hochberg method). Spearman correlation analysis was performed in 25 CD patients to evaluate relationships between subfield volumes and DEG expression, with FDR correction (Benjamini-Hochberg method) independently applied per subfield (significance: FDR-adjusted p < 0.05). Genes showing significant correlations (e.g., left CA4-body volume-associated genes) were used to compute genome-wide Spearman coefficients (n = 15,674 genes), generating ranked lists for gene set enrichment analysis (GSEA) via clusterProfiler v4.10.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets (size: 10–500 genes) were tested using Subramanian's recommended threshold (FDR-adjusted p < 0.05). Visualizations were created with ggplot2 v3.5.1. 2.9 Statistical Analysis Statistical analysis was performed using SPSS 25.0. Two-sample t-test, χ2-test, and One-way analyses of Variance (ANOVA) were performed to assess GroupWise differences in demographics, clinical characteristics, and hippocampal subfield volumes. Mixed-method ANOVA was conducted to evaluate the main effects and interactions of hemisphere and disease on hippocampal subfield volumes. Mauchly’s test of sphericity was conducted to assess the assumption of sphericity for the within-subjects effects. In the above analyses, sex, age, education year, and TIV were included as covariates. For the analysis of the 19 kinds of hippocampal subfield volume, Bonferroni correction was applied as the multiple comparison correction to control Type 1 errors. Mediation analysis was performed to explore the relationship between hormone levels, hippocampal subfield volumes, and neuropsychological scale scores. 3 Results 3.1 Demographics 91 CD patients and 53 HCs were included in this study. No significant differences between CD patients and HCs were found for age, gender, or education years. The average duration of illness was 39.7 months in the CD patients. Compared with HCs, CD patients had higher levels of cortisol and ACTH, more severe anxiety and depression, obvious cognitive impairment, and lower quality of life (Table 1 ). Table 1 Demographic Information of HCs and CD patients Characteristics CD patients, n = 91 HCs, n = 53 p-value Gender, n Male /n Female , % Female 9/82, 90.1% 3/50, 94.3% 0.378 Age, Years, Mean ± SD 38.95 ± 11.86 34.73 ± 10.05 0.131 Education Years, Years, Mean ± SD 11.85 ± 4.17 11.92 ± 2.98 0.905 BMI, kg/m 2 , Mean ± SD 26.57 ± 4.20 22.44 ± 3.18 < 0.001 Duration of Illness, Months, Mean ± SD 39.70 ± 45.42 QOL score, Mean ± SD 35.90 ± 9.98 SDS score, Mean ± SD 43.20 ± 10.73 27.02 ± 4.41 < 0.001 SAS score, Mean ± SD 42.27 ± 11.46 26.89 ± 4.44 < 0.001 MoCA score, Mean ± SD 22.97 ± 4.10 27.79 ± 1.73 < 0.001 cortisol level at 0:00, nmol/L, Mean ± SD 574.70 ± 220.89 cortisol level at 8:00, nmol/L, Mean ± SD 726.31 ± 275.09 356.05 ± 108.40 < 0.001 cortisol level at 16:00, nmol/L, Mean ± SD 656.42 ± 271.54 ACTH level at 0:00, pmol/L, Mean ± SD 15.36 ± 9.73 ACTH level at 8:00, pmol/L, Mean ± SD 20.18 ± 14.68 4.97 ± 3.07 < 0.001 ACTH level at 16:00, pmol/L, Mean ± SD 19.40 ± 13.04 UFC level within 24h, nmol/ 24 h, Mean ± SD 2033.44 ± 1292.97 242.29 ± 117.51 < 0.001 Two-sample t-test(for normal distribution variable) and χ2-test(for classified variable) were performed to assess GroupWise differences in demographics and clinical characteristics. Sections highlighted in bold are statistically significant. 3.2 Voxelwise Statistical Analysis Compared to HCs, two clusters of reduced GMV in the CD patients in the left and right hippocampus were found (both right and left, p < 0.0001). The highest t score (9.6) was found within the cluster located in the left hippocampus, with peak MNI coordinates of -18 mm, -36 mm, and − 6 mm. The cluster size was 1026 voxels. The highest t score in the right hippocampus was 8.8, with peak MNI coordinates of 18 mm, -33 mm, and − 4.5 mm. The cluster size was 991 voxels (Fig. 1 ). No cluster of increased GMV was found in the CD patients. 3.3 Hippocampal Volumetrics Mixed-method ANOVA revealed strong trends toward significance for the main effect for group for the whole hippocampus (F = 25.03, p < 0.001, η 2 p = 0.15) and hippocampal subfields, including presubiculum-head (F = 15.81, p < 0.001, η 2 p = 0.10), presubiculum-body (F = 20.75, p < 0.001, η 2 p = 0.13), subiculum-body (F = 17.89, p < 0.001, η 2 p = 0.12), CA1-body (F = 11.55, p = 0.001, η 2 p = 0.08), CA4-body (F = 23.47, p < 0.001, η 2 p = 0.15), GC-ML-DG-body (F = 34.17, p < 0.001, η 2 p = 0.20), molecular_layer-head (F = 9.71, p = 0.002, η 2 p = 0.07), molecular_layer-body (F = 53.02, p < 0.001, η 2 p = 0.28), and hippocampal tail (F = 22.43, p < 0.001, η 2 p = 0.14). There were no significant differences between the left and right hemispheres, neither the interaction effect of groups nor hemispheres (Table S2 ). Mauchly’s tests indicated no violation of sphericity (p > 0.05) in the above tests. Further analysis using one-way ANOVA confirmed bilateral hippocampal atrophy in CD patients (right p < 0.001 and left p < 0.001) (Table 2 ). Significant volume reductions were found in multiple regions: bilateral presubiculum-body (right p < 0.001 and left p < 0.001), subiculum-body (right p < 0.001 and left p < 0.001), CA4-body (right p < 0.001 and left p < 0.001), GC-ML-DG-body (right p < 0.001 and left p < 0.001), molecular_layer-body (right p < 0.001 and left p < 0.001), hippocampal tail (right p < 0.001 and left p < 0.001), and right parasubiculum (p < 0.001), presubiculum-head (p < 0.001), CA1-body (p < 0.001), molecular-layer-head (p = 0.001), hippocampal-fissure (p = 0.001) had significant difference between HCs and CD patients (Fig. 2 ). All these differences were highly significant (p < 0.001), suggesting widespread but specific patterns of hippocampal atrophy in CD patients. Table 2 Volumetric Between-Group Differences for Hippocampal Subfields Subfields (mm 3 ) CD patients, n = 91 HCs, n = 53 F p η 2 p Left subfields Parasubiculum 57.65 ± 14.27 60.35 ± 9.73 1.072 0.302 0.008 Presubiculum-head 133.69 ± 18.14 141.72 ± 15.38 8.110 0.005 0.056 Presubiculum-body 152.76 ± 28.86 168.59 ± 25.30 15.163 < 0.001 0.099 Subiculum-head 190.01 ± 27.33 200.30 ± 29.26 5.009 0.027 0.035 Subiculum-body 232.08 ± 27.86 248.32 ± 29.35 15.543 < 0.001 0.101 CA1-head 474.23 ± 56.20 492.58 ± 45.32 3.850 0.052 0.027 CA1-body 111.73 ± 18.76 119.85 ± 16.23 6.586 0.011 0.046 CA3-head 105.23 ± 16.15 105.37 ± 12.76 0.002 0.961 < 0.001 CA3-body 78.89 ± 13.25 80.87 ± 11.11 1.161 0.283 0.008 CA4-head 111.81 ± 12.83 116.39 ± 10.75 4.148 0.044 0.029 CA4-body 110.04 ± 11.72 117.20 ± 8.07 19.067 < 0.001 0.121 GC-ML-DG-head 134.56 ± 16.13 140.26 ± 12.86 4.134 0.044 0.029 GC-ML-DG-body 122.39 ± 13.03 131.99 ± 8.85 28.227 < 0.001 0.170 Molecular_layer-head 305.47 ± 32.57 319.46 ± 27.65 7.056 0.009 0.049 Molecular_layer-body 202.37 ± 21.79 223.02 ± 17.21 39.958 < 0.001 0.225 HATA 51.26 ± 9.09 54.26 ± 7.01 3.254 0.073 0.023 Fimbria 83.01 ± 18.25 85.68 ± 11.97 0.540 0.464 0.004 Hippocampal_tail 522.08 ± 75.10 576.33 ± 67.98 20.533 < 0.001 0.130 Hippocampal- fissure 155.34 ± 26.89 148.02 ± 29.45 2.055 0.154 0.015 Whole hippocampus 3179.25 ± 296.72 3382.53 ± 257.70 21.246 < 0.001 0.133 Right subfields Parasubiculum 51.44 ± 10.54 58.15 ± 11.09 13.594 < 0.001 0.090 Presubiculum-head 129.26 ± 16.09 141.62 ± 16.13 21.212 < 0.001 0.133 Presubiculum-body 138.67 ± 26.61 158.40 ± 26.80 21.549 < 0.001 0.135 Subiculum-head 192.41 ± 25.42 207.43 ± 30.65 8.540 0.004 0.058 Subiculum-body 226.94 ± 26.23 243.99 ± 32.81 16.009 < 0.001 0.104 CA1-head 506.69 ± 62.29 533.26 ± 59.98 5.710 0.018 0.040 CA1-body 119.42 ± 14.27 129.09 ± 16.68 13.611 < 0.001 0.090 CA3-head 112.75 ± 16.58 111.79 ± 14.32 0.245 0.621 0.002 CA3-body 85.90 ± 12.03 86.46 ± 12.61 0.189 0.664 0.001 CA4-head 118.30 ± 13.42 123.64 ± 12.81 4.399 0.038 0.031 CA4-body 110.85 ± 11.59 118.51 ± 10.00 20.668 < 0.001 0.130 GC-ML-DG-head 142.54 ± 17.19 149.57 ± 15.39 4.979 0.027 0.035 GC-ML-DG-body 123.38 ± 12.71 133.89 ± 11.75 29.315 < 0.001 0.175 Molecular_layer-head 318.30 ± 33.98 337.89 ± 34.63 10.619 0.001 0.071 Molecular_layer-body 206.67 ± 20.05 229.38 ± 19.92 53.045 < 0.001 0.278 HATA 51.72 ± 8.29 56.21 ± 7.85 9.063 0.003 0.062 Fimbria 83.06 ± 18.18 86.82 ± 17.53 0.661 0.418 0.005 Hippocampal_tail 539.55 ± 74.75 587.63 ± 74.88 19.349 < 0.001 0.123 Hippocampal- fissure 171.50 ± 30.86 153.92 ± 27.32 11.265 0.001 0.075 Whole hippocampus 3257.87 ± 302.23 3493.74 ± 310.61 25.496 < 0.001 0.156 The table shows between-group volumetric differences for individual subfields following analysis of covariance correcting for gender, age, education years, and estimated total intracranial volume. HCs, health controls; CD patients, Cushing’s disease patients. Sections highlighted in bold are statistically significant survived the Bonferroni multiple comparison correction. Bonferroni-corrected significance threshold: 0.0026 η 2 p describes effect size (.01 = low, .06 = moderate, .14 = large). 3.4 Shape Analysis CD patients showed overall hippocampal shrinkage compared to healthy controls but with distinct patterns in each hemisphere. The left hippocampus primarily showed changes in its lateral body and tail regions, matching the areas of significant volume loss. The right hippocampus displayed more extensive changes, with depression across the lateral head, body, and tail regions. While the medial head of both hippocampi appeared enlarged, this likely reflects expanded cerebrospinal fluid spaces (widened fissures) rather than true hippocampal tissue expansion (Fig. 3 and Figure S1 ). 3.5 Correlation between Hippocampal Volume and Clinical Characteristics For CD patients, partial correlation analyses showed significant correlations between the bilateral whole hippocampus and ACTH level at 8:00 (right p = 0.009 and left p = 0.005). Left GC-ML-DG-body had a positive correlation with the MOCA score (p = 0.024) and a negative correlation with the ACTH level at 8:00 (p = 0.010). No significant correlation was found between right hippocampal subfields and hormone levels or scale scores (Fig. 4 ). Importantly, our mediation analysis revealed that higher morning ACTH levels led to reduced volume in two left hippocampal regions (GC-ML-DG-body and CA4-body), which in turn resulted in poorer cognitive performance. When controlling for these subfield volumes, the direct relationship between ACTH and cognitive performance disappeared, indicating these brain regions fully mediate the effect of high hormone levels on cognition (Fig. 5 ). 3.6 Correlation between Hippocampal Volume and Peripheral Blood Transcriptome Transcriptomic analysis identified 1,578 peripheral blood DEGs (957 upregulated, 621 downregulated; Figure S2 ). Integration of hippocampal subfield volumetry revealed a strong positive correlation between left CA4-body volume and the long non-coding RNA LINC02193 in CD patients (ρ = 0.767, FDR-adjusted p = 0.012). LINC02193 expression was 2.2-fold higher in CD versus HCs (FDR-adjusted p = 0.003). Single-gene GSEA demonstrated significant associations between LINC02193 expression and multiple biological pathways. KEGG analysis showed pronounced negative enrichment in ribosome (NES = − 3.67, FDR-adjusted p < 0.001) and oxidative phosphorylation pathways (NES = − 2.96, FDR-adjusted p < 0.001) (Fig. 6 A, C-D). GO annotation further implicated LINC02193 in cytoplasmic translation (NES = − 3.59, FDR-adjusted p < 0.001) and adenosine triphosphate (ATP) synthesis-coupled electron transport (NES = − 3.04, FDR-adjusted p < 0.001) (Fig. 6 B, E-F). 4 Discussion In this study of 91 CD patients and 53 healthy controls using T1-weighted MRI, we found bilateral reductions in hippocampal gray matter volume, most pronounced in the body and caudal region. The right hippocampus showed more widespread atrophy than the left. Detailed subfield analysis revealed volume reductions in multiple regions, particularly in body and caudal subfields, with shape analysis confirming that structural depressions corresponded to areas of volume loss. Clinical correlations showed that total hippocampal volume was related to morning ACTH levels, while the left GC-ML-DG-body volume correlated with both cognitive performance and ACTH levels. Most importantly, we found that two left hippocampal regions (CA4-body and GC-ML-DG-body) completely mediated the relationship between morning ACTH levels and cognitive performance, suggesting these structural changes represent the pathway through which elevated hormones affect cognition. Our findings of reduced hippocampal volume align with several previous studies[ 15 , 18 – 20 , 38 , 39 ], though past research shows mixed results. While some studies report bilateral hippocampal atrophy in CD patients, others found no significant differences[ 4 , 21 , 40 ]. Our detailed subfield analysis helps explain these contradictions: volume reductions occur primarily in small subfields (parasubiculum, CA1-body, CA4-body, GC-ML-DG), which can be masked when measuring total hippocampal volume. Similar patterns of subfield-specific atrophy have been observed in other conditions with elevated cortisol levels, supporting the selective vulnerability of these regions. Using three complementary methods (VBM, subfield analysis, and shape analysis), we consistently found changes concentrated in the hippocampal body and tail regions. This selective vulnerability of posterior regions is particularly significant given their role in cognition and emotion. The body region contains critical pathways (perforant path, alveus, mossy fibers)[ 41 ] and shows stronger connectivity to the default mode network than anterior regions[ 42 ]. Based on these findings, we hypothesize that high cortisol primarily affects posterior hippocampal regions, disrupting their connectivity and contributing to cognitive and emotional symptoms in CD patients. Our correlation and mediation analyses revealed a clear pathway from hormone elevation to cognitive decline through specific hippocampal regions. Morning ACTH levels correlated with total hippocampal volume, while left GC-ML-DG-body volume was linked to both ACTH levels and cognitive performance. Mediation analysis showed that volume changes in the left CA4-body and GC-ML-DG-body fully explain how elevated ACTH affects cognition. These findings align with known cellular mechanisms. The dentate gyrus (DG), particularly its granule cells, is highly sensitive to cortisol levels[ 43 ], while normal cortisol is necessary for cell survival, chronic elevation impairs neurogenesis and disrupts synaptic plasticity[ 44 ]. Through mossy fibers and fornix connections, CA4/DG regions influence broader cognitive networks[ 41 , 45 ]. Similar patterns of CA4/DG volume reduction appear in other conditions with cognitive impairment[ 46 , 47 ]. These results suggest targeted mechanisms for cognitive decline in CD and identify potential therapeutic targets for future drug development and neuromodulation strategies. While both hemispheres showed changes, right hippocampal atrophy was more widespread. This asymmetry may reflect known functional differences between left and right hippocampi in verbal and spatial processing[ 20 , 48 , 49 ], warranting future investigation of connectivity patterns. LINC02193, a novel long non-coding RNA with uncharacterized function, exhibited marked negative enrichment in ribosomal activity (NES = − 3.67) and oxidative phosphorylation (NES = − 3.04) in CD leukocytes, suggesting its role in cortisol-mediated metabolic dysfunction. Paradoxically, its elevated expression correlated positively with left CA4_body volume—a hippocampal subfield demonstrating significant atrophy in CD—and negatively with cortisol levels. This may indicate complex compensatory protective responses to chronic glucocorticoid toxicity, potentially positioning LINC02193 as a biomarker linking hippocampal structural integrity to cognitive decline. Several key limitations warrant discussion. While our sample size was substantial, the cross-sectional design prevents conclusions about temporal relationships and causality. Technical limitations also affected our imaging analysis. Our use of 3T T1-weighted images, while standard, provides less detail than T2-weighted or 7T imaging for visualizing hippocampal subfields, particularly the molecular layer and CA1 boundaries[ 22 , 33 , 50 ]. Future studies using higher-resolution imaging could improve subfield segmentation accuracy. Quality control presented another challenge. FreeSurfer’s hippocampal segmentation tool lacks standardized quality control procedures, relying primarily on visual inspection. To address this, we implemented CAT12 quality control and excluded images with quality ratings below 80%[ 51 ]. Additionally, volume measurements of smaller structures (GC-DG, CA4, molecular layer) may be less reliable[ 22 ], a common challenge in hippocampal subfield analysis due to their size and variable manual annotation standards. Despite these limitations, FreeSurfer’s automated segmentation offers advantages over global hippocampal analysis[ 52 ], enabling the detection of subtle changes in CD patients while maintaining high stability and reducing manual segmentation bias. Furthermore, mechanistic insights into LINC02193's action in hippocampal cells remain undefined. Future studies integrating longitudinal designs and functional validation are imperative to elucidate its spatiotemporal regulatory roles in CD pathogenesis. Finally, The use of peripheral blood, rather than brain tissue gene expression, in this study may underestimate the spatiotemporal relationship between hippocampal volume changes and genetic factors. In this comprehensive analysis of hippocampal structure in Cushing’s disease, we demonstrated specific patterns of volume reduction and shape changes using multiple imaging methods (VBM, automated segmentation, and shape analysis). Key findings include decreased gray matter and subfield volumes in targeted regions, with corresponding shape alterations. Importantly, changes in specific hippocampal subfields correlated with cognitive impairment, suggesting these structural changes directly contribute to CD symptoms. Additionally, Transcriptome profiling identified a significant positive association between left CA4-body volume and circulating LINC02193 long non-coding RNA expression levels in CD patients. These distinct patterns of hippocampal atrophy under prolonged cortisol exposure not only advance our understanding of CD's effects on brain structure but may also serve as potential biomarkers for the disease. Declarations Funding This work was supported by the National Natural Science Foundation of China (No. 81871087 and No. 82001798) and the Young Talent Project of Chinese PLA General Hospital (No. 20230403). Competing interests The authors declare no competing interests. Ethical approval This study adhered to the principles outlined in the Declaration of Helsinki and the ethical requirements from the ethics committee of Chinese PLA General Hospital . Informed consent Every patient provided written informed consent before participating in the research. Data Availability Some or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request. Disclosure Statement The authors have no relevant financial or non-financial interests to disclose. Author Contribution Writing – original draft: FZB and ZT; Methodology: FZB, XJP, and SGS; Formal analysis: FZB and YXY; Software: FZB, HKY, and SGS; Visualization: FZB, YXY, and LR; Data curation: ZT, LR, and MZG; Validation: ZT; Investigation: HKY and SZX; Resources: XJP and YXG; Conceptualization, Supervision, Funding acquisition, and Project administration: ZYY. All authors read and approved the final manuscript. References Newell-Price J, Bertagna X, Grossman AB, Nieman LK (2006) Cushing's syndrome. Lancet 367(9522):1605–1617. https://doi.org/10.1016/s0140-6736(06)68699-6 van der Werff SJ, Pannekoek JN, Andela CD, Meijer OC, van Buchem MA, Rombouts SA et al (2015) Resting-State Functional Connectivity in Patients with Long-Term Remission of Cushing's Disease. 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Supplementary Files Supplementarymaterials.docx Supplementarytables.docx FigS1.jpg FigS2.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7058713","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485644027,"identity":"362e1e7e-e0a7-4a4c-93f4-4f7957a5d0f3","order_by":0,"name":"zhebin Feng","email":"","orcid":"","institution":"Department of Neurosurgery, 1st Medical Center of Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"zhebin","middleName":"","lastName":"Feng","suffix":""},{"id":485644028,"identity":"e6898c44-6084-4ef5-a98b-d2134b74f779","order_by":1,"name":"Tao Zhou","email":"","orcid":"","institution":"Department of 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2","display":"","copyAsset":false,"role":"figure","size":4427511,"visible":true,"origin":"","legend":"\u003cp\u003eHippocampal subfield analysis in CD patients versus controls\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7058713/v1/30c84f5d2f00e7e7231455f0.jpg"},{"id":87272607,"identity":"1048a29e-e9b8-4330-ac51-a4215b6bdf33","added_by":"auto","created_at":"2025-07-22 08:29:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5559229,"visible":true,"origin":"","legend":"\u003cp\u003eThe morphological changes of the hippocampus of CD patients\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7058713/v1/f04b1a537c5bdedba6c0c2f6.jpg"},{"id":87272606,"identity":"ddd9c76a-376f-4fa8-88f1-e32574afc8b9","added_by":"auto","created_at":"2025-07-22 08:29:47","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4967086,"visible":true,"origin":"","legend":"\u003cp\u003ePartial correlation analysis of hormone levels, scale scores, and volumes of hippocampal subfields\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7058713/v1/8b72ada9444a4d5812ec59c3.jpg"},{"id":87269171,"identity":"ec2e2650-aa11-4c95-85c1-1ad6b0adbcb5","added_by":"auto","created_at":"2025-07-22 08:13:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1810368,"visible":true,"origin":"","legend":"\u003cp\u003eMediation effect of hippocampal subfields between hormone levels and scale scores\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7058713/v1/bec58d48a5c85dca56b540df.jpg"},{"id":87270678,"identity":"9f940995-17b4-4ecb-a005-add7d9427e65","added_by":"auto","created_at":"2025-07-22 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08:13:48","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29696599,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7058713/v1/711631ea1d03e309f5a34e3b.jpg"},{"id":87269192,"identity":"3d9d612e-55a6-48e1-b887-048934914dd4","added_by":"auto","created_at":"2025-07-22 08:13:47","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3560270,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7058713/v1/a75d39cac061a6d57bc33d07.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Selective hippocampal subfield atrophy mediates cognitive decline in Cushing’s Disease.","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCushing\u0026rsquo;s disease (CD) occurs when a pituitary tumor produces excess adrenocorticotrophic hormone (ACTH), leading to chronically high cortisol levels[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This makes CD an ideal natural model for studying how prolonged cortisol exposure affects brain structure[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CD patients experience both cognitive deficits (affecting memory[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], verbal learning[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], executive function, and attention[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]) and psychiatric symptoms[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] (including depression, anxiety, and somatization). By studying CD patients, researchers can better understand how brain structural changes relate to these cognitive and psychiatric symptoms.\u003c/p\u003e\u003cp\u003eThe hippocampus, a key component of the limbic system, controls learning, memory, emotion recognition, and attention[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In CD patients, elevated cortisol levels appear to disrupt hippocampal function and structure, potentially explaining their cognitive and emotional symptoms. This disruption occurs through several mechanisms: reduced formation of new neurons[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], decreased growth of support cells (astrocytes and oligodendrocytes) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], shortened nerve cell connections[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and impaired memory processing[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, high cortisol triggers inflammatory responses that can damage hippocampal tissue, as demonstrated in animal studies[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research on hippocampal changes in CD has yielded mixed results. Early studies using manual measurements found significant hippocampal shrinkage (27%) that persisted even after treatment[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. More recent studies using advanced imaging techniques (voxel-based morphometry) confirmed reduced hippocampal volume and gray matter density[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, some studies found no significant volume differences between CD patients and healthy controls[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These conflicting findings suggest the need for more detailed analysis of specific hippocampal regions, rather than examining only total volume.\u003c/p\u003e\u003cp\u003eThe hippocampus is organized along a head-body-tail axis, with each section containing distinct subfields that can respond differently to disease. Examining only total hippocampal volume may obscure important changes in these specific regions. Recent advances in computer-assisted analysis have enabled a more precise study of hippocampal structure. Using automated MRI analysis software developed by Iglesias et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], researchers can now reliably identify and measure 19 distinct subfields of the hippocampus. These include the parasubiculum, presubiculum (head and body), subiculum (head and body), cornu ammonia regions CA1, CA3, and CA4 (each in head and body), granule cell and molecular layers of the dentate gyrus (GC-ML-DG) (head and body), molecular layers (head and body), hippocampus-amygdala-transition-area (HATA), fimbria, hippocampal tail, and hippocampal fissure. This detailed segmentation method has been validated as stable and effective, allowing researchers to examine subtle structural changes that might be missed in whole-hippocampus analyses[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eStudies across various neurological conditions have revealed specific patterns of hippocampal subfield changes. Insomnia severity correlates with reduced CA3/dentate gyrus volume[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while depression shows distinct changes in left CA1 volume that predict illness duration[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In temporal lobe epilepsy, volume loss occurs in CA, dentate gyrus, subiculum, and fimbria regions, matching patterns of neuronal loss[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Parkinson\u0026rsquo;s disease patients with depression show decreased CA3 volume[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite these findings in other conditions, research on hippocampal subfield changes in CD remains limited.\u003c/p\u003e\u003cp\u003eTranscriptomic profiling serves as a quantitative phenotyping approach to systematically elucidate dysregulated biological pathways in disease progression[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Emerging evidence suggests that blood-derived gene expression signatures may partially reflect cerebral metabolic alterations associated with neurodegenerative disorders, such as Alzheimer's disease[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Compared to invasive brain biopsies and cerebrospinal fluid analyses, peripheral blood biomarkers offer minimally invasive sampling and dynamic monitoring capabilities, rendering them particularly advantageous for clinical investigations of CD. Nevertheless, few prior studies have comprehensively explored the correlation between peripheral blood transcriptomic signatures and hippocampal structural remodeling in CD patients.\u003c/p\u003e\u003cp\u003eOur study aims to investigate hippocampal changes in CD patients with chronically elevated cortisol levels. We hypothesize that: (1) CD patients will show reduced gray matter and subfield volumes compared to healthy controls, (2) these volume reductions will correspond to visible structural changes in hippocampal shape, and (3) these structural changes will correlate with both hormone levels and psychiatric symptoms, suggesting that high cortisol may cause psychiatric symptoms through its effects on specific hippocampal regions. Besides, (4) the peripheral blood transcriptome may harbor biomarkers reflecting specific hippocampal subfields in patients with Crohn's disease.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003e102 CD patients and 54 healthy controls (HCs) were recruited from the Department of Neurosurgery, XXX Hospital, between May 2017 and October 2024. After excluding participants (including 11 CD patients and one healthy control) with low-quality magnetic resonance scanning (MRI) images, the current study finally included 144 participants (91 CD patients and 53 HCs). Experienced endocrinologists diagnosed CD patients according to the latest clinical practice guidelines[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]: clinical characteristics (such as moon face, buffalo hump, purple striae), high levels and abnormal circadian rhythms of relevant hormones (ACTH level at 8:00, reference range\u0026thinsp;\u0026lt;\u0026thinsp;10.12 pmol/L; cortisol level at 8:00, reference range 198.7\u0026ndash;797.5 nmol/L; 24-h urinary free cortisol, reference range 98.0\u0026ndash;500.1 nmol/24 h), special tests (negative results in dexamethasone suppression test and low dose dexamethasone suppression test, while positive result in high dose dexamethasone suppression test), inferior petrosal sinus sampling (inferior petrosal sinus to peripheral blood, ratio of ACTH level\u0026thinsp;\u0026gt;\u0026thinsp;2), and histopathologic diagnosis (ACTH-secreting pituitary adenoma). HCs were recruited from the local community and excluded any current or history of mental disorder by an experienced psychiatrist. All participants were right-handed. This study adhered to the principles outlined in the Declaration of Helsinki and the ethical requirements from the local ethics committee (Ethics Committee Approval No. S2021-677-01). Before participating in the research, every patient provided written informed consent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Clinical Data Acquisition\u003c/h2\u003e\u003cp\u003eFor CD patients, peripheral blood cortisol levels were measured at 0:00, 8:00, and 16:00, ACTH levels at 0:00, 8:00, and 16:00, and urinary-free cortisol levels within 24 hours. For HCs, peripheral blood\u0026rsquo;s cortisol level at 8:00, ACTH level at 8:00, and urinary free cortisol level within 24h were measured. A comprehensive neuropsychological assessment was also included in this study, such as Self-Rating Depression Scale (SDS), Self‐Rating Anxiety Scale (SAS), Montreal Cognitive Assessment (MoCA), and Cushing\u0026rsquo;s Quality‐of‐Life (QOL) questionnaire (only for CD patients).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 MRI image acquisition\u003c/h2\u003e\u003cp\u003eHigh-resolution T1-weighted structural images were acquired using the GE Discovery MR 750w 3.0-Tesla system (8-channel head coil) at XXX Hospital, Beijing. The parameters were shown as follow: repetition time\u0026thinsp;=\u0026thinsp;6700 ms, echo time\u0026thinsp;=\u0026thinsp;29 ms, flip angle\u0026thinsp;=\u0026thinsp;7\u0026deg;, field of view\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm\u003csup\u003e2\u003c/sup\u003e, voxel size\u0026thinsp;=\u0026thinsp;1 \u0026times; 1 \u0026times; 1 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Preprocessing and Voxel-based morphometry (VBM) Analysis\u003c/h2\u003e\u003cp\u003eThe image was converted to Nifti format using MRIcron (version 1.0.20190902, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/mricron/\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/mricron/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and image quality was checked manually. The structure image was preprocessed with the SPM 12 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the CAT 12 toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat/\u003c/span\u003e\u003cspan address=\"http://www.neuro.uni-jena.de/cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in the MATLAB environment. In short, the anterior commissure of the structural image should coincide with the origin (0,0,0) of the Montreal Neurological Institute (MNI) space. Then, segmentation (gray matter, white matter, and cerebrospinal fluid)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], space normalization (rigid and non-rigid registration to MNI standard space), resampling (1.5\u0026times;1.5\u0026times;1.5mm voxel resolution), smoothing (8-mm full-width at half-maximum), and total intracranial volume (TIV) calculation were conducted.\u003c/p\u003e\u003cp\u003eAfter data preprocessing, we assessed the difference in GMV between CD patients and HCs. A general linear model was constructed with sex, age, education year, and TIV as covariates. Apply absolute masking with a threshold of 0.2[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Next, a bilateral hippocampal mask based on the Anatomical Automated Labeling (AAL) template was applied to explore structural changes in the hippocampus. Using the false discovery rate (FWE) to correct for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at the cluster level to reduce Type 1 errors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Segmentation of Hippocampal Subfields\u003c/h2\u003e\u003cp\u003eFreeSurfer (version 7.3.2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) image analysis suite performed cortical reconstruction and volumetric segmentation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Briefly, this processing includes motion correction and averaging of multiple volumetric T1 weighted images, removal of non-brain tissue, automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures intensity normalization, tessellation of the gray matter white matter boundary, automated topology correction, and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location. Estimated TIV was calculated. The hippocampus was segmented into 19 subfields using Bayesian inference[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The information on subfields was shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Hippocampal Shape Analysis\u003c/h2\u003e\u003cp\u003eFSL-FIRST (version 5.0.9, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST/\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIRST/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for automated segmentation and linear registration to the MNI152 space of the hippocampus[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Image binarization and smoothing were conducted for each sample. Then FIRST modeled the boundary of all structures. The mesh parameterization (including vertices and edges) was performed using the spherical harmonic decomposition point distribution model (SPHARM-PDM). Next, boundary correction was done to decide whether the boundary voxels should belong to the structure or not. Finally, a univariate test at each vertice was used to measure the difference in location between HCs and CD patients[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The displacement vector of CD patients' vertices represented the deformation of the hippocampal surface relative to HCs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Mediation Analysis\u003c/h2\u003e\u003cp\u003eThe bootstrapping method was employed to estimate the mediation effect[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Bootstrapping entails repeated random resampling from the existing data to empirically approximate the sampling distribution of a given statistic. This approximated distribution is then utilized to ascertain p-values and construct confidence intervals (with a total of 5000 resamples). Furthermore, this methodology produces supplied confidence intervals (CIs) that undergo bias-corrected and accelerated bootstrapping, ensuring greater accuracy and reliability[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Blood Sample Collection, Sequencing, and Data Processing\u003c/h2\u003e\u003cp\u003eTwenty-five CD patients (27.5% of the 91-case CD cohort) and 30 HCs (56.6% of the 53-subject HCs cohort) were enrolled for peripheral blood transcriptomic profiling. All samples were collected and underwent batch-controlled RNA extraction and transcriptome sequencing (Illumina NovaSeq 6000) to minimize technical variability, with detailed protocols provided in Supplementary Materials. After quality control, a total of n\u0026thinsp;=\u0026thinsp;15,674 genes were retained for further analysis. Differentially expressed genes (DEGs) were identified from raw read counts (featureCounts v2.0.3) using DESeq2 v1.42.1 and edgeR v3.38.1, with significance thresholds set at | Log\u003csub\u003e2\u003c/sub\u003e(Fold Change) | \u0026ge;1 and false discovery rate (FDR) -adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Benjamini-Hochberg method).\u003c/p\u003e\u003cp\u003eSpearman correlation analysis was performed in 25 CD patients to evaluate relationships between subfield volumes and DEG expression, with FDR correction (Benjamini-Hochberg method) independently applied per subfield (significance: FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Genes showing significant correlations (e.g., left CA4-body volume-associated genes) were used to compute genome-wide Spearman coefficients (n\u0026thinsp;=\u0026thinsp;15,674 genes), generating ranked lists for gene set enrichment analysis (GSEA) via clusterProfiler v4.10.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets (size: 10\u0026ndash;500 genes) were tested using Subramanian's recommended threshold (FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Visualizations were created with ggplot2 v3.5.1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS 25.0. Two-sample t-test, χ2-test, and One-way analyses of Variance (ANOVA) were performed to assess GroupWise differences in demographics, clinical characteristics, and hippocampal subfield volumes. Mixed-method ANOVA was conducted to evaluate the main effects and interactions of hemisphere and disease on hippocampal subfield volumes. Mauchly\u0026rsquo;s test of sphericity was conducted to assess the assumption of sphericity for the within-subjects effects. In the above analyses, sex, age, education year, and TIV were included as covariates. For the analysis of the 19 kinds of hippocampal subfield volume, Bonferroni correction was applied as the multiple comparison correction to control Type 1 errors. Mediation analysis was performed to explore the relationship between hormone levels, hippocampal subfield volumes, and neuropsychological scale scores.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographics\u003c/h2\u003e\u003cp\u003e91 CD patients and 53 HCs were included in this study. No significant differences between CD patients and HCs were found for age, gender, or education years. The average duration of illness was 39.7 months in the CD patients. Compared with HCs, CD patients had higher levels of cortisol and ACTH, more severe anxiety and depression, obvious cognitive impairment, and lower quality of life (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 Information of HCs and CD patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCD patients, n\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHCs, n\u0026thinsp;=\u0026thinsp;53\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n\u003csub\u003eMale\u003c/sub\u003e/n\u003csub\u003eFemale\u003c/sub\u003e, % Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9/82, 90.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3/50, 94.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.378\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, Years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.95\u0026thinsp;\u0026plusmn;\u0026thinsp;11.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.73\u0026thinsp;\u0026plusmn;\u0026thinsp;10.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Years, Years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of Illness, Months, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.70\u0026thinsp;\u0026plusmn;\u0026thinsp;45.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQOL score, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.90\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDS score, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43.20\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAS score, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42.27\u0026thinsp;\u0026plusmn;\u0026thinsp;11.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.89\u0026thinsp;\u0026plusmn;\u0026thinsp;4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoCA score, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecortisol level at 0:00, nmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e574.70\u0026thinsp;\u0026plusmn;\u0026thinsp;220.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecortisol level at 8:00, nmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e726.31\u0026thinsp;\u0026plusmn;\u0026thinsp;275.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e356.05\u0026thinsp;\u0026plusmn;\u0026thinsp;108.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecortisol level at 16:00, nmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e656.42\u0026thinsp;\u0026plusmn;\u0026thinsp;271.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACTH level at 0:00, pmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.36\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACTH level at 8:00, pmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.18\u0026thinsp;\u0026plusmn;\u0026thinsp;14.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACTH level at 16:00, pmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUFC level within 24h, nmol/\u003c/p\u003e\u003cp\u003e24 h, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2033.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1292.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e242.29\u0026thinsp;\u0026plusmn;\u0026thinsp;117.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTwo-sample t-test(for normal distribution variable) and χ2-test(for classified variable) were performed to assess GroupWise differences in demographics and clinical characteristics. Sections highlighted in bold are statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Voxelwise Statistical Analysis\u003c/h2\u003e\u003cp\u003eCompared to HCs, two clusters of reduced GMV in the CD patients in the left and right hippocampus were found (both right and left, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The highest t score (9.6) was found within the cluster located in the left hippocampus, with peak MNI coordinates of -18 mm, -36 mm, and \u0026minus;\u0026thinsp;6 mm. The cluster size was 1026 voxels. The highest t score in the right hippocampus was 8.8, with peak MNI coordinates of 18 mm, -33 mm, and \u0026minus;\u0026thinsp;4.5 mm. The cluster size was 991 voxels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No cluster of increased GMV was found in the CD patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Hippocampal Volumetrics\u003c/h2\u003e\u003cp\u003eMixed-method ANOVA revealed strong trends toward significance for the main effect for group for the whole hippocampus (F\u0026thinsp;=\u0026thinsp;25.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.15) and hippocampal subfields, including presubiculum-head (F\u0026thinsp;=\u0026thinsp;15.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.10), presubiculum-body (F\u0026thinsp;=\u0026thinsp;20.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.13), subiculum-body (F\u0026thinsp;=\u0026thinsp;17.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.12), CA1-body (F\u0026thinsp;=\u0026thinsp;11.55, p\u0026thinsp;=\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.08), CA4-body (F\u0026thinsp;=\u0026thinsp;23.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.15), GC-ML-DG-body (F\u0026thinsp;=\u0026thinsp;34.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.20), molecular_layer-head (F\u0026thinsp;=\u0026thinsp;9.71, p\u0026thinsp;=\u0026thinsp;0.002, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.07), molecular_layer-body (F\u0026thinsp;=\u0026thinsp;53.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.28), and hippocampal tail (F\u0026thinsp;=\u0026thinsp;22.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.14). There were no significant differences between the left and right hemispheres, neither the interaction effect of groups nor hemispheres (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Mauchly\u0026rsquo;s tests indicated no violation of sphericity (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in the above tests.\u003c/p\u003e\u003cp\u003eFurther analysis using one-way ANOVA confirmed bilateral hippocampal atrophy in CD patients (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Significant volume reductions were found in multiple regions: bilateral presubiculum-body (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), subiculum-body (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CA4-body (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), GC-ML-DG-body (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), molecular_layer-body (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hippocampal tail (right p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and left p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and right parasubiculum (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), presubiculum-head (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CA1-body (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), molecular-layer-head (p\u0026thinsp;=\u0026thinsp;0.001), hippocampal-fissure (p\u0026thinsp;=\u0026thinsp;0.001) had significant difference between HCs and CD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All these differences were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting widespread but specific patterns of hippocampal atrophy in CD patients.\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\u003eVolumetric Between-Group Differences for Hippocampal Subfields\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubfields (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCD patients, n\u0026thinsp;=\u0026thinsp;91\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHCs, n\u0026thinsp;=\u0026thinsp;53\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eη\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft subfields\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParasubiculum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e57.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e60.35\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresubiculum-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e133.69\u0026thinsp;\u0026plusmn;\u0026thinsp;18.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e141.72\u0026thinsp;\u0026plusmn;\u0026thinsp;15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresubiculum-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e152.76\u0026thinsp;\u0026plusmn;\u0026thinsp;28.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e168.59\u0026thinsp;\u0026plusmn;\u0026thinsp;25.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubiculum-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e190.01\u0026thinsp;\u0026plusmn;\u0026thinsp;27.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e200.30\u0026thinsp;\u0026plusmn;\u0026thinsp;29.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubiculum-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e232.08\u0026thinsp;\u0026plusmn;\u0026thinsp;27.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e248.32\u0026thinsp;\u0026plusmn;\u0026thinsp;29.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA1-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e474.23\u0026thinsp;\u0026plusmn;\u0026thinsp;56.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e492.58\u0026thinsp;\u0026plusmn;\u0026thinsp;45.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA1-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e111.73\u0026thinsp;\u0026plusmn;\u0026thinsp;18.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e119.85\u0026thinsp;\u0026plusmn;\u0026thinsp;16.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA3-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e105.23\u0026thinsp;\u0026plusmn;\u0026thinsp;16.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e105.37\u0026thinsp;\u0026plusmn;\u0026thinsp;12.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA3-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e78.89\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e80.87\u0026thinsp;\u0026plusmn;\u0026thinsp;11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA4-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e111.81\u0026thinsp;\u0026plusmn;\u0026thinsp;12.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e116.39\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA4-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e110.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e117.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGC-ML-DG-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e134.56\u0026thinsp;\u0026plusmn;\u0026thinsp;16.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e140.26\u0026thinsp;\u0026plusmn;\u0026thinsp;12.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGC-ML-DG-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e122.39\u0026thinsp;\u0026plusmn;\u0026thinsp;13.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e131.99\u0026thinsp;\u0026plusmn;\u0026thinsp;8.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular_layer-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e305.47\u0026thinsp;\u0026plusmn;\u0026thinsp;32.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e319.46\u0026thinsp;\u0026plusmn;\u0026thinsp;27.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular_layer-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e202.37\u0026thinsp;\u0026plusmn;\u0026thinsp;21.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e223.02\u0026thinsp;\u0026plusmn;\u0026thinsp;17.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHATA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e51.26\u0026thinsp;\u0026plusmn;\u0026thinsp;9.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e54.26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFimbria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e83.01\u0026thinsp;\u0026plusmn;\u0026thinsp;18.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e85.68\u0026thinsp;\u0026plusmn;\u0026thinsp;11.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHippocampal_tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e522.08\u0026thinsp;\u0026plusmn;\u0026thinsp;75.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e576.33\u0026thinsp;\u0026plusmn;\u0026thinsp;67.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHippocampal-\u003c/p\u003e\u003cp\u003efissure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e155.34\u0026thinsp;\u0026plusmn;\u0026thinsp;26.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e148.02\u0026thinsp;\u0026plusmn;\u0026thinsp;29.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole hippocampus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3179.25\u0026thinsp;\u0026plusmn;\u0026thinsp;296.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3382.53\u0026thinsp;\u0026plusmn;\u0026thinsp;257.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight subfields\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParasubiculum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e51.44\u0026thinsp;\u0026plusmn;\u0026thinsp;10.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e58.15\u0026thinsp;\u0026plusmn;\u0026thinsp;11.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresubiculum-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e129.26\u0026thinsp;\u0026plusmn;\u0026thinsp;16.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e141.62\u0026thinsp;\u0026plusmn;\u0026thinsp;16.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresubiculum-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e138.67\u0026thinsp;\u0026plusmn;\u0026thinsp;26.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e158.40\u0026thinsp;\u0026plusmn;\u0026thinsp;26.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubiculum-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e192.41\u0026thinsp;\u0026plusmn;\u0026thinsp;25.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e207.43\u0026thinsp;\u0026plusmn;\u0026thinsp;30.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubiculum-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e226.94\u0026thinsp;\u0026plusmn;\u0026thinsp;26.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e243.99\u0026thinsp;\u0026plusmn;\u0026thinsp;32.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA1-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e506.69\u0026thinsp;\u0026plusmn;\u0026thinsp;62.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e533.26\u0026thinsp;\u0026plusmn;\u0026thinsp;59.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA1-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e119.42\u0026thinsp;\u0026plusmn;\u0026thinsp;14.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e129.09\u0026thinsp;\u0026plusmn;\u0026thinsp;16.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.611\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA3-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e112.75\u0026thinsp;\u0026plusmn;\u0026thinsp;16.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e111.79\u0026thinsp;\u0026plusmn;\u0026thinsp;14.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA3-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85.90\u0026thinsp;\u0026plusmn;\u0026thinsp;12.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e86.46\u0026thinsp;\u0026plusmn;\u0026thinsp;12.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA4-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e118.30\u0026thinsp;\u0026plusmn;\u0026thinsp;13.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e123.64\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA4-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e110.85\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e118.51\u0026thinsp;\u0026plusmn;\u0026thinsp;10.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGC-ML-DG-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e142.54\u0026thinsp;\u0026plusmn;\u0026thinsp;17.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e149.57\u0026thinsp;\u0026plusmn;\u0026thinsp;15.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGC-ML-DG-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e123.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e133.89\u0026thinsp;\u0026plusmn;\u0026thinsp;11.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular_layer-head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e318.30\u0026thinsp;\u0026plusmn;\u0026thinsp;33.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e337.89\u0026thinsp;\u0026plusmn;\u0026thinsp;34.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMolecular_layer-body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e206.67\u0026thinsp;\u0026plusmn;\u0026thinsp;20.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e229.38\u0026thinsp;\u0026plusmn;\u0026thinsp;19.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHATA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e51.72\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e56.21\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFimbria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e83.06\u0026thinsp;\u0026plusmn;\u0026thinsp;18.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e86.82\u0026thinsp;\u0026plusmn;\u0026thinsp;17.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHippocampal_tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e539.55\u0026thinsp;\u0026plusmn;\u0026thinsp;74.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e587.63\u0026thinsp;\u0026plusmn;\u0026thinsp;74.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHippocampal-\u003c/p\u003e\u003cp\u003efissure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e171.50\u0026thinsp;\u0026plusmn;\u0026thinsp;30.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e153.92\u0026thinsp;\u0026plusmn;\u0026thinsp;27.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhole hippocampus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3257.87\u0026thinsp;\u0026plusmn;\u0026thinsp;302.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3493.74\u0026thinsp;\u0026plusmn;\u0026thinsp;310.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe table shows between-group volumetric differences for individual subfields following analysis of covariance correcting for gender, age, education years, and estimated total intracranial volume.\u003c/p\u003e\u003cp\u003eHCs, health controls; CD patients, Cushing\u0026rsquo;s disease patients.\u003c/p\u003e\u003cp\u003eSections highlighted in bold are statistically significant survived the Bonferroni multiple comparison correction. Bonferroni-corrected significance threshold: 0.0026\u003c/p\u003e\u003cp\u003eη\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e describes effect size (.01\u0026thinsp;=\u0026thinsp;low, .06\u0026thinsp;=\u0026thinsp;moderate, .14\u0026thinsp;=\u0026thinsp;large).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Shape Analysis\u003c/h2\u003e\u003cp\u003eCD patients showed overall hippocampal shrinkage compared to healthy controls but with distinct patterns in each hemisphere. The left hippocampus primarily showed changes in its lateral body and tail regions, matching the areas of significant volume loss. The right hippocampus displayed more extensive changes, with depression across the lateral head, body, and tail regions. While the medial head of both hippocampi appeared enlarged, this likely reflects expanded cerebrospinal fluid spaces (widened fissures) rather than true hippocampal tissue expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Correlation between Hippocampal Volume and Clinical Characteristics\u003c/h2\u003e\u003cp\u003eFor CD patients, partial correlation analyses showed significant correlations between the bilateral whole hippocampus and ACTH level at 8:00 (right p\u0026thinsp;=\u0026thinsp;0.009 and left p\u0026thinsp;=\u0026thinsp;0.005). Left GC-ML-DG-body had a positive correlation with the MOCA score (p\u0026thinsp;=\u0026thinsp;0.024) and a negative correlation with the ACTH level at 8:00 (p\u0026thinsp;=\u0026thinsp;0.010). No significant correlation was found between right hippocampal subfields and hormone levels or scale scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eImportantly, our mediation analysis revealed that higher morning ACTH levels led to reduced volume in two left hippocampal regions (GC-ML-DG-body and CA4-body), which in turn resulted in poorer cognitive performance. When controlling for these subfield volumes, the direct relationship between ACTH and cognitive performance disappeared, indicating these brain regions fully mediate the effect of high hormone levels on cognition (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Correlation between Hippocampal Volume and Peripheral Blood Transcriptome\u003c/h2\u003e\u003cp\u003eTranscriptomic analysis identified 1,578 peripheral blood DEGs (957 upregulated, 621 downregulated; Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Integration of hippocampal subfield volumetry revealed a strong positive correlation between left CA4-body volume and the long non-coding RNA LINC02193 in CD patients (ρ\u0026thinsp;=\u0026thinsp;0.767, FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.012). LINC02193 expression was 2.2-fold higher in CD versus HCs (FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSingle-gene GSEA demonstrated significant associations between LINC02193 expression and multiple biological pathways. KEGG analysis showed pronounced negative enrichment in ribosome (NES\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.67, FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and oxidative phosphorylation pathways (NES\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.96, FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, C-D). GO annotation further implicated LINC02193 in cytoplasmic translation (NES\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.59, FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and adenosine triphosphate (ATP) synthesis-coupled electron transport (NES\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.04, FDR-adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, E-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study of 91 CD patients and 53 healthy controls using T1-weighted MRI, we found bilateral reductions in hippocampal gray matter volume, most pronounced in the body and caudal region. The right hippocampus showed more widespread atrophy than the left. Detailed subfield analysis revealed volume reductions in multiple regions, particularly in body and caudal subfields, with shape analysis confirming that structural depressions corresponded to areas of volume loss. Clinical correlations showed that total hippocampal volume was related to morning ACTH levels, while the left GC-ML-DG-body volume correlated with both cognitive performance and ACTH levels. Most importantly, we found that two left hippocampal regions (CA4-body and GC-ML-DG-body) completely mediated the relationship between morning ACTH levels and cognitive performance, suggesting these structural changes represent the pathway through which elevated hormones affect cognition.\u003c/p\u003e\u003cp\u003eOur findings of reduced hippocampal volume align with several previous studies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], though past research shows mixed results. While some studies report bilateral hippocampal atrophy in CD patients, others found no significant differences[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our detailed subfield analysis helps explain these contradictions: volume reductions occur primarily in small subfields (parasubiculum, CA1-body, CA4-body, GC-ML-DG), which can be masked when measuring total hippocampal volume. Similar patterns of subfield-specific atrophy have been observed in other conditions with elevated cortisol levels, supporting the selective vulnerability of these regions.\u003c/p\u003e\u003cp\u003eUsing three complementary methods (VBM, subfield analysis, and shape analysis), we consistently found changes concentrated in the hippocampal body and tail regions. This selective vulnerability of posterior regions is particularly significant given their role in cognition and emotion. The body region contains critical pathways (perforant path, alveus, mossy fibers)[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and shows stronger connectivity to the default mode network than anterior regions[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Based on these findings, we hypothesize that high cortisol primarily affects posterior hippocampal regions, disrupting their connectivity and contributing to cognitive and emotional symptoms in CD patients.\u003c/p\u003e\u003cp\u003eOur correlation and mediation analyses revealed a clear pathway from hormone elevation to cognitive decline through specific hippocampal regions. Morning ACTH levels correlated with total hippocampal volume, while left GC-ML-DG-body volume was linked to both ACTH levels and cognitive performance. Mediation analysis showed that volume changes in the left CA4-body and GC-ML-DG-body fully explain how elevated ACTH affects cognition. These findings align with known cellular mechanisms. The dentate gyrus (DG), particularly its granule cells, is highly sensitive to cortisol levels[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], while normal cortisol is necessary for cell survival, chronic elevation impairs neurogenesis and disrupts synaptic plasticity[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Through mossy fibers and fornix connections, CA4/DG regions influence broader cognitive networks[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Similar patterns of CA4/DG volume reduction appear in other conditions with cognitive impairment[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These results suggest targeted mechanisms for cognitive decline in CD and identify potential therapeutic targets for future drug development and neuromodulation strategies.\u003c/p\u003e\u003cp\u003eWhile both hemispheres showed changes, right hippocampal atrophy was more widespread. This asymmetry may reflect known functional differences between left and right hippocampi in verbal and spatial processing[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], warranting future investigation of connectivity patterns.\u003c/p\u003e\u003cp\u003eLINC02193, a novel long non-coding RNA with uncharacterized function, exhibited marked negative enrichment in ribosomal activity (NES\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.67) and oxidative phosphorylation (NES\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.04) in CD leukocytes, suggesting its role in cortisol-mediated metabolic dysfunction. Paradoxically, its elevated expression correlated positively with left CA4_body volume\u0026mdash;a hippocampal subfield demonstrating significant atrophy in CD\u0026mdash;and negatively with cortisol levels. This may indicate complex compensatory protective responses to chronic glucocorticoid toxicity, potentially positioning LINC02193 as a biomarker linking hippocampal structural integrity to cognitive decline.\u003c/p\u003e\u003cp\u003eSeveral key limitations warrant discussion. While our sample size was substantial, the cross-sectional design prevents conclusions about temporal relationships and causality. Technical limitations also affected our imaging analysis. Our use of 3T T1-weighted images, while standard, provides less detail than T2-weighted or 7T imaging for visualizing hippocampal subfields, particularly the molecular layer and CA1 boundaries[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Future studies using higher-resolution imaging could improve subfield segmentation accuracy. Quality control presented another challenge. FreeSurfer\u0026rsquo;s hippocampal segmentation tool lacks standardized quality control procedures, relying primarily on visual inspection. To address this, we implemented CAT12 quality control and excluded images with quality ratings below 80%[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Additionally, volume measurements of smaller structures (GC-DG, CA4, molecular layer) may be less reliable[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a common challenge in hippocampal subfield analysis due to their size and variable manual annotation standards. Despite these limitations, FreeSurfer\u0026rsquo;s automated segmentation offers advantages over global hippocampal analysis[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], enabling the detection of subtle changes in CD patients while maintaining high stability and reducing manual segmentation bias. Furthermore, mechanistic insights into LINC02193's action in hippocampal cells remain undefined. Future studies integrating longitudinal designs and functional validation are imperative to elucidate its spatiotemporal regulatory roles in CD pathogenesis. Finally, The use of peripheral blood, rather than brain tissue gene expression, in this study may underestimate the spatiotemporal relationship between hippocampal volume changes and genetic factors.\u003c/p\u003e\u003cp\u003eIn this comprehensive analysis of hippocampal structure in Cushing\u0026rsquo;s disease, we demonstrated specific patterns of volume reduction and shape changes using multiple imaging methods (VBM, automated segmentation, and shape analysis). Key findings include decreased gray matter and subfield volumes in targeted regions, with corresponding shape alterations. Importantly, changes in specific hippocampal subfields correlated with cognitive impairment, suggesting these structural changes directly contribute to CD symptoms. Additionally, Transcriptome profiling identified a significant positive association between left CA4-body volume and circulating LINC02193 long non-coding RNA expression levels in CD patients. These distinct patterns of hippocampal atrophy under prolonged cortisol exposure not only advance our understanding of CD's effects on brain structure but may also serve as potential biomarkers for the disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the National Natural Science Foundation of China (No. 81871087 and No. 82001798) and the Young Talent Project of Chinese PLA General Hospital (No. 20230403).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This study adhered to the principles outlined in the Declaration of Helsinki and the ethical requirements from the \u003cstrong\u003eethics committee of Chinese PLA General Hospital\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e Every patient provided written informed consent before participating in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e Some or all datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWriting \u0026ndash; original draft: FZB and ZT; Methodology: FZB, XJP, and SGS; Formal analysis: FZB and YXY; Software: FZB, HKY, and SGS; Visualization: FZB, YXY, and LR; Data curation: ZT, LR, and MZG; Validation: ZT; Investigation: HKY and SZX; Resources: XJP and YXG; Conceptualization, Supervision, Funding acquisition, and Project administration: ZYY. 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NeuroImage 101:494\u0026ndash;512. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2014.04.054\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2014.04.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Cushing’s disease, hypercortisolism, hippocampal subfields, cognitive, structural image, blood transcriptome, LINC02193","lastPublishedDoi":"10.21203/rs.3.rs-7058713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7058713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Cushing’s disease (CD) provides insight into how prolonged high cortisol exposure affects brain structure. While CD patients show cognitive and emotional symptoms linked to hippocampal function, detailed analysis of hippocampal subfield changes and their correlations with peripheral blood gene expression profiles remains limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e The study included 91 patients with active CD and 53 matched healthy controls who underwent T1-weighted magnetic resonance imaging and comprehensive neuropsychological assessment. We employed voxel-based morphometry, automated segmentation, and shape analysis to evaluate gray matter volume, subfield volumes, and hippocampal morphology. Additionally, RNA sequencing was performed to characterize peripheral blood leukocyte transcriptome profiles in a subgroup of 25 CD patients and 30 matched HCs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Compared to controls, CD patients showed decreased hippocampal gray matter volume, particularly in body and tail regions. Specific subfields including presubiculum-body, subiculum-body, CA4-body, and granule cell layer showed significant volume reductions. Shape analysis revealed corresponding surface alterations. Notably, left CA4-body and GC-ML-DG-body volumes mediated the relationship between cortisol levels and cognitive performance, and left CA4-body volume demonstrated a positive correlation with peripheral blood LINC02193 expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. CD patients exhibit distinct patterns of hippocampal atrophy affecting specific subfields, with changes correlating to hormone levels and cognitive symptoms, and discrete hippocampal subfield volumes associated with gene expression profiles. These structural and transcriptomic alterations may serve as potential biomarkers for CD and provide insight into the mechanisms underlying cognitive dysfunction in hypercortisolism.\u003c/p\u003e","manuscriptTitle":"Selective hippocampal subfield atrophy mediates cognitive decline in Cushing’s Disease.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 08:13:42","doi":"10.21203/rs.3.rs-7058713/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":"4ea36ea3-d8dc-45fb-ae90-b5cfebd53498","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T13:23:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 08:13:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7058713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7058713","identity":"rs-7058713","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}