APOE ε4-related structural vulnerability in mild cognitive impairment: a subsystem-based analysis of the default mode network

APOE ε4-related structural vulnerability in mild cognitive impairment: a subsystem-based analysis of the default mode network | 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 APOE ε4-related structural vulnerability in mild cognitive impairment: a subsystem-based analysis of the default mode network Xiaoming Yang, Jian Lyu, Jubo Wang, Yu Quan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9447482/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Mild cognitive impairment (MCI) is a critical prodromal stage of progressive cognitive decline. As a core cognitive brain network, the default mode network (DMN) exhibits structural alterations in MCI that are closely associated with cognitive impairment. Apolipoprotein E ε4 (APOE ε4) , the strongest genetic risk factor for pathological cognitive decline, has been shown to disrupt the structural integrity of the DMN’s three core subsystems: the Midline Core, Medial Temporal Lobe (MTL), and Dorsomedial Prefrontal Cortex (DMPFC) subsystems. However, the subsystem-specific effects of APOE ε4 and the cognitive mechanisms through which they operate in MCI remain unclear. Methods We enrolled 176 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including 116 patients with MCI and 60 cognitively normal (CN) individuals, with balanced APOE ε4 carrier status within each diagnostic group. Standardized volumes of eight DMN regions were analyzed using permutational multivariate analysis of variance (PERMANOVA) to test diagnosis-by-genotype interactions, followed by false discovery rate (FDR)-corrected univariate analyses and multiple linear regression to identify regional volume differences. Bootstrap-based mediation analysis and receiver operating characteristic (ROC) analysis were further performed to examine clinical relevance. Results A significant diagnosis-by- APOE ε4 interaction was observed in the global structural pattern of the DMN, primarily driven by the DMPFC and MTL subsystems. Among patients with MCI, APOE ε4 carriers exhibited selective atrophy within the DMPFC subsystem, specifically in the gyrus rectus (REC) and superior temporal pole (TPOsup), relative to non-carriers. Critically, TPOsup atrophy mediated 30.43% of the negative effect of APOE ε4 on global cognition, as measured by the Mini-Mental State Examination (MMSE), and was significantly associated with impaired orientation. Conclusion The TPOsup may represent a key neural hub linking APOE ε4 to cognitive decline in MCI and may serve as a specific imaging marker for risk stratification in this population. Apolipoprotein E ε4 Mild Cognitive Impairment Default Mode Network Dorsomedial Prefrontal Cortex Superior Temporal Pole Mediation Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Alzheimer’s disease (AD) is a neurodegenerative disorder whose prevalence is rising steadily worldwide as populations age.( 1 ) It is estimated that the number of affected individuals may reach approximately 153 million by 2050.( 2 ) This trend is expected to place an enormous burden on healthcare systems, social infrastructure, and the global economy.( 3 ) Importantly, the pathological process of AD may begin 15–20 years before the onset of overt clinical symptoms, thereby providing a critical window for early intervention.( 4 ) Mild cognitive impairment (MCI) is generally regarded as an intermediate stage between normal aging and clinically defined dementia, particularly AD.( 5 , 6 ) Although MCI is a heterogeneous syndrome with multiple possible etiologies,( 7 , 8 ) approximately 10%–15% of individuals with MCI progress to AD each year.( 9 , 10 ) Therefore, a more comprehensive understanding of the neuropathological mechanisms underlying MCI is urgently needed to improve early detection and therapeutic intervention in AD. At the genetic level, the apolipoprotein E (APOE) ε4 allele is the most extensively studied and most common genetic risk factor for AD.( 11 , 12 ) Individuals carrying one copy of APOE ε4 have approximately double the risk of developing AD, whereas those carrying two copies face a 12-fold or greater increase in risk.( 13 , 14 ) Notably, even in the presence of protective factors such as higher educational attainment, the ε4 allele appears to diminish the effectiveness of cognitive reserve, underscoring the relative independence of its neurotoxic effects.( 15 , 16 ) Neuroimaging studies have consistently implicated the default mode network (DMN) as a core brain network involved in the early pathological progression of AD.( 17 – 21 ) Owing to its high metabolic activity at rest, the DMN may be particularly vulnerable to AD-related pathology.( 22 – 24 ) Progressive disruption of the DMN’s structural and functional connectivity has been linked to cognitive decline, highlighting its importance in the preclinical and prodromal stages of AD. To further clarify the functional organization of the DMN, Andrews-Hanna et al. proposed a three-subsystem model comprising: ( 1 ) the Midline Core, which integrates information across the DMN and serves as a central hub for internally directed cognition; ( 2 ) the Medial Temporal Lobe (MTL) subsystem, which supports memory encoding and retrieval and is therefore closely related to memory impairment at the MCI stage;( 25 , 26 ) and ( 3 ) the Dorsomedial Prefrontal Cortex (DMPFC) subsystem, which is involved in affective integration, social cognition, and semantic processing, and may therefore contribute to non-memory impairments in MCI.( 27 , 28 ) Although this model provides a valuable framework for understanding the DMN, several important knowledge gaps remain. Most neuroimaging studies of APOE ε4 have focused on traditionally defined DMN regions such as the hippocampus and posterior cingulate cortex.( 29 – 32 ) First, relatively few studies have explicitly examined whether APOE ε4 exerts subsystem-specific effects within the three-subsystem DMN model, particularly within the DMPFC subsystem. Second, to our knowledge, no study has systematically investigated whether APOE ε4 produces selective neurotoxic effects on the key nodes of DMN subsystems defined by functional and anatomical connectivity, rather than on conventionally defined anatomical regions alone. Of particular importance is whether the relationship between APOE ε4 and cognitive impairment is mediated by structurally vulnerable nodes within these subsystems. These limitations hinder a more comprehensive understanding of the neural pathophysiology of cognitive decline and constrain the identification of more precise neuroimaging biomarkers. To address these limitations, and unlike previous studies that relied on conventional region-of-interest (ROI) templates,( 33 , 34 ) we defined eight ROIs spanning the three DMN subsystems based on the functional architecture of the DMN and the pathological characteristics of AD. These regions include not only classical regions within the Midline Core and MTL subsystems, which are traditionally associated with memory-related pathology, but also less-studied regions within the DMPFC subsystem that may be involved in non-memory cognitive processes such as semantic and language-related functions. Accordingly, the present study systematically examined the differential effects of APOE ε4 on DMN subsystems and further tested whether critical regional structural alterations mediate the association between genetic risk and cognitive performance. We hypothesized that APOE ε4 carriers with MCI would show selective volume reductions in regions belonging to the DMPFC subsystem, and that structural alterations in these regions would partly account for the association between APOE ε4 and cognitive decline. Overall, this study aims to delineate the subsystem-specific structural vulnerabilities associated with APOE ε4 in the prodromal stage of MCI. 2. Methods The data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). Launched in 2003 as a public–private partnership under the leadership of Principal Investigator Dr. Michael W. Weiner, ADNI aims to determine whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessments can be combined to track the progression of MCI and early AD. 2.1. Participants Initial screening included 205 cognitively normal (CN) individuals and 351 patients with MCI from the ADNI database.(35, 36) Participants were excluded if they met any of the following criteria: (1) poor-quality MRI data (C- or D-rated based on interquartile range [IQR]); (2) carriage of the APOE ε2 allele, to minimize its potentially protective effect against APOE ε4 -related risk;(37, 38) or (3) a history of other major neurological disorders, psychiatric illness, or severe head trauma. After applying these criteria, 111 CN participants and 153 patients with MCI remained eligible. Diagnoses were based solely on standardized ADNI clinical criteria.(39) To reduce within-group imbalance, 1:1 nearest-neighbor propensity score matching (PSM) was performed separately within the CN and MCI groups. Carrier status for the APOE ε4 allele was treated as the grouping variable, and age, sex, years of education, and body mass index (BMI) were included as covariates in the propensity score model. The caliper width was set to 0.3 times the standard deviation of the logit of the propensity score. MMSE score was not included as a matching variable because doing so could obscure the true effect of APOE ε4 on cognition and introduce bias into subsequent mediation analyses. After matching, 58 pairs (n = 116) were obtained in the MCI group and 30 pairs (n = 60) in the CN group. Balance diagnostics confirmed that all standardized mean differences (SMDs) were below 0.1. Thus, a total of 176 participants were included in the final analyses (Figure 1). 2.2. MRI Data Acquisition and Processing Baseline T1-weighted structural MRI data were downloaded from the ADNI database. All scans were acquired on 1.5T MRI scanners. Image preprocessing was performed using the Computational Anatomy Toolbox (CAT12, v12.8) implemented in MATLAB R2023a. The preprocessing pipeline was optimized for DMN subsystem analysis and included noise reduction to improve image quality, N4 bias-field correction to reduce intensity inhomogeneity caused by scanner artifacts, and segmentation of gray matter (GM), white matter, and cerebrospinal fluid. Images were spatially normalized to the Montreal Neurological Institute (MNI) 152 template (2 × 2 × 2 mm voxel size) using the DARTEL algorithm, which improves inter-subject registration by modeling fine-grained anatomical differences.(40-47) 2.3. Region of Interest Volume Extraction Based on the three-subsystem DMN model proposed by Andrews-Hanna et al.(27) nd the anatomical relevance of the selected regions to the present study aims, eight ROIs were defined (Figure 2). The Midline Core subsystem included the posterior cingulate cortex (PCC) and precuneus (PCUN);(27, 48-51) the MTL subsystem included the hippocampus (HIP), parahippocampal gyrus (PHG), and frontal medial orbital cortex (FMO);(27, 52-54) and the DMPFC subsystem included the gyrus rectus (REC), superior temporal pole (TPOsup), and middle temporal gyrus (MTG).(27, 55-57) Native-space volumes of each ROI were extracted from preprocessed GM maps using the Automated Anatomical Labeling 3 (AAL3) atlas.(47) To account for inter-individual differences in head size, each regional volume was normalized to total intracranial volume (TIV) using the following formula: [Insert Figure 2. Spatial distribution of the regions of interest within the three-subsystem model of the default mode networkl. The selected ROIs are mapped onto the 3D cortical surface and grouped according to their respective subsystems. The medial temporal lobe (MTL) subsystem includes the hippocampus (HIP), parahippocampal gyrus (PHG), and frontal medial orbital (FMO). The dorsomedial prefrontal cortex (DMPFC) subsystem comprises the middle temporal gyrus (MTG), superior temporal pole (TPOsup), and gyrus rectus (REC). The Midline Core Subsystem encompasses the precuneus (PCUN) and posterior cingulate cortex (PCC). L, left hemisphere; R, right hemisphere. ] 2.4. Statistical Analysis All statistical analyses were conducted in R (version 4.2.2), with a two-tailed significance threshold of α = 0.05. 2.4.1. Multivariate brain volume analysis To evaluate the interaction between APOE ε4 carrier status and diagnostic group, as well as their main effects on the combined normalized volumes of all eight ROIs, we first conducted a global multivariate analysis using permutational multivariate analysis of variance (PERMANOVA) implemented with the adonis2 function in the vegan package. Euclidean distance matrices and 10,000 permutations were used. Separate stratified PERMANOVA analyses were also performed for each of the three DMN subsystems. These four tests were Bonferroni-corrected, yielding a significance threshold of α = 0.0125. 2.4.2. Univariate Group Comparisons To identify which specific ROIs contributed to significant multivariate findings, univariate analyses were subsequently performed. Normality of ROI volume distributions was assessed using the Shapiro–Wilk test. For normally distributed data, independent-samples t-tests were used and effect sizes were reported as Cohen’s d; for non-normally distributed data, Mann–Whitney U tests were used and effect sizes were reported as rank-biserial correlations (r_rb). False discovery rate (FDR) correction was applied across all univariate tests, with significance defined as q < 0.05. 2.4.3. Correlation and Multiple Linear Regression Analysis Spearman’s rank correlation was used for preliminary analyses of the associations among ROI volumes, APOE ε4 status, and MMSE scores in the full sample. Multiple linear regression models were then constructed, with normalized ROI volume as the dependent variable and APOE ε4 carrier status as the primary independent variable, while adjusting for sex, handedness, TIV, and hypertension status. Hypertension status was included because a significant baseline group difference was observed (p = 0.015). Variance inflation factors (VIFs) were calculated to assess multicollinearity; all VIF values were below 5. In addition, the number of APOE ε4 alleles (0/1/2) was modeled as a continuous variable to test for a potential gene-dose effect. Finally, associations between altered regional volumes and the five MMSE subdomains—Orientation, Registration, Attention and Calculation, Delayed Recall, and Language and Visuospatial Function—were examined. 2.4.4. Mediation Analysis For variables identified as significant in the regression analyses, mediation models were constructed using the mediation package in R to test whether regional brain volume mediated the relationship between APOE ε4 carrier status and cognitive performance. Nonparametric bootstrap resampling (1,000 iterations) was used to estimate indirect effects and their 95% confidence intervals (CIs). To examine robustness across populations and expand the dynamic range of cognitive performance, mediation analyses were performed both in the MCI subgroup and in the full matched sample. Although the Alzheimer’s Disease Assessment Scale (ADAS) score was strongly negatively correlated with MMSE (r = −0.76, p < 0.001), MMSE was selected as the primary cognitive outcome because it is more commonly used in studies of early cognitive decline. 2.4.5. Exploratory ROC Analysis Receiver operating characteristic (ROC) analysis was performed to evaluate the discriminative ability of structural, clinical, and genetic variables for differentiating MCI from CN. Three logistic regression models were constructed: a clinical model using MMSE score, a neuroimaging model using TPOsup volume, and a combined model including TPOsup volume, MMSE score, and APOE ε4 carrier status. Age, sex, and education were included as covariates in all models. Predicted probabilities were used to generate ROC curves and calculate the area under the curve (AUC). Optimal cutoffs were determined using the maximum Youden index. DeLong’s test was used to compare AUCs across models. 3. Results 3.1. Baseline Demographic and Clinical Characteristics Table 1. Demographic and clinical characteristics After propensity score matching, the final sample comprised 176 participants, including 60 CN individuals and 116 patients with MCI. Within the CN group, 30 participants were APOE ε4 non-carriers and 30 were carriers. Within the MCI group, 58 participants were APOE ε4 non-carriers and 58 were carriers. Among APOE ε4 carriers, the MCI group included 17 homozygotes and 41 heterozygotes, whereas the CN group included 2 homozygotes and 28 heterozygotes. As shown in Table 1, no significant differences were observed between APOE ε4 carriers and non-carriers in age, sex, education, BMI, or TIV within either diagnostic group (all p > 0.05). However, hypertension status differed significantly between APOE ε4 subgroups in the CN group (p = 0.015). Specifically, the proportion of normotensive individuals was higher in the APOE ε4− subgroup (80.0%) than in the APOE ε4+ subgroup (46.7%). Therefore, hypertension status was included as a covariate in subsequent multiple linear regression analyses. 3.2. APOE ε4 Effects on DMN Regional Volumes PERMANOVA revealed a significant multivariate interaction between diagnostic group (CN vs. MCI) and APOE ε4 carrier status on the overall structural pattern of the DMN (F = 3.24, p = 0.007), which remained significant after Bonferroni correction. Subsystem-level analyses indicated that this interaction was primarily driven by the DMPFC subsystem (F = 4.16, p = 0.003) and the MTL subsystem (F = 4.82, p = 0.002), whereas the Midline Core subsystem showed no significant interaction (F = 1.76, p = 0.163). 3.3. Univariate Group Comparisons . Based on the significant multivariate findings, univariate analyses were conducted to identify specific affected regions (Figure 3). In the CN group, APOE ε4 carrier status was not associated with significant volume differences in any ROI after FDR correction (all q > 0.05). In contrast, within the MCI group, APOE ε4 carriers showed significant localized volume reductions in the DMPFC subsystem. Specifically, normalized volumes of the REC and TPOsup were significantly lower in APOE ε4 carriers than in non-carriers (REC: p = 0.011, FDR q = 0.044, d = −0.48, 95% CI [−0.85, −0.11]; TPOsup: p = 0.006, FDR q = 0.044, d = −0.52, 95% CI [−0.89, −0.14]). Within the MTL subsystem, the PHG showed a trend toward lower volume in APOE ε4 carriers (p = 0.055, d = −0.36), but this did not survive FDR correction (q = 0.148). Notably, the hippocampus—traditionally considered a key AD-related structure—did not differ significantly between groups (p = 0.153, q = 0.238). Other DMPFC regions, such as the MTG, also showed no significant differences (all q > 0.05). These findings suggest that the DMPFC subsystem may be more sensitive than the MTL subsystem to APOE ε4 -related neurotoxicity during the early stages of cognitive impairment. [Insert Figure 3 . Comparison of standardized brain region volumes between APOE ε4 carriers and non-carriers within the MCI group. Box and scatter plots illustrate the standardized volumes of eight predefined ROIs for APOE ε4 non-carriers (blue, n=58) and carriers (orange, n=58). The solid horizontal line inside each box represents the median, while the lower and upper hinges correspond to the first and third quartiles. Whiskers extend to the furthest data points within 1.5 × IQR. Individual subject data points are overlaid to show the distribution. Statistical significance was assessed using univariate group comparisons; q-values denote FDR-corrected p-values. Effect sizes are reported as Cohen’s d or rank-biserial correlation ( r_rb ), with 95% confidence intervals enclosed in brackets. Significant volume reductions in APOE ε4 carriers are specifically observed in the gyrus rectus (REC, q = 0.044) and superior temporal pole (TPOsup, q = 0.044).Abbreviations: ROI, region of interest; MCI, Mild Cognitive Impairment; FDR, False Discovery Rate. ] 3.4. Multiple Linear Regression and Sensitivity Analysis of Brain Volume and Clinical Correlations To account for potential confounding factors, multiple linear regression analyses were conducted within the MCI group. After adjusting for sex, handedness, TIV, and hypertension status (Figure 4), APOE ε4 carrier status remained significantly associated with reduced volume in both the TPOsup (β = −0.199, 95% CI [−0.349, −0.050], p = 0.010) and REC (β = −0.185, 95% CI [−0.329, −0.042], p = 0.013). Multicollinearity diagnostics indicated that all VIF values were below 5. In the sensitivity analysis, APOE ε4 allele dosage was modeled as a continuous predictor. A marginal dose-dependent trend was observed for TPOsup volume reduction (Estimate = −0.109, SE = 0.055, p = 0.051). In preliminary correlation analyses across the full sample, TPOsup volume was positively correlated with global cognitive performance as measured by MMSE (Spearman r = 0.202, p = 0.007), suggesting that preserved structural integrity in this region is associated with better cognition. [Figure 4. Multiple linear regression analysis of factors associated with regional brain volumes in the MCI group. The forest plot shows the unstandardized beta (β) coefficients and 95% confidence intervals (CIs) from multiple linear regression models examining the associations of APOE ε4 carrier status ( ε4+ vs. ε4− ), handedness (Right vs. Left), gender (Man vs. Female), hypertension status, and total intracranial volume (TIV) with the normalized volumes of the superior temporal pole (TPOsup) and gyrus rectus (REC) among participants with mild cognitive impairment (MCI) . The solid vertical line at β = 0 indicates the null effect, and the horizontal lines represent the 95% CIs. APOE ε4 positivity was significantly and independently associated with lower TPOsup volume (p = 0.010) and lower REC volume (p = 0.013).] 3.5. Mediation Analysis of the Association Between APOE ε4 and Cognitive Function 3.5.1. Primary Mediation Analysis in the MCI Group Because structural changes were predominantly observed in the MCI group, mediation analyses were first performed in this subgroup using bootstrap resampling (1,000 iterations) to test whether volumetric alterations mediated the relationship between APOE ε4 and MMSE score (Figure 5). After adjusting for age and sex, TPOsup volume significantly mediated the association between APOE ε4 carrier status and lower MMSE scores (indirect effect β = −0.38, 95% CI [−0.89, −0.05], p = 0.010), accounting for 30.43% of the total effect (total effect β = −1.19, p = 0.042). Although REC volume was significantly associated with genotype, it was not significantly associated with MMSE score (β = 0.98, p = 0.195) and therefore did not significantly mediate the APOE ε4 –MMSE relationship (p = 0.208). [Insert Figure 5 . Mediation analysis of regional brain volumes on the association between APOE ε4 status and cognitive function. Path diagrams illustrate the potential mediating role of the gyrus rectus (REC, Panel A) and superior temporal pole (TPOsup, Panel B) volumes in the relationship between APOE ε4 carrier status and MMSE scores within the MCI group. The arrows indicate the direction of the modeled effects. Unstandardized path coefficients ( β ) and corresponding p-values are presented adjacent to each path. Significant p -values ( p < 0.05) are highlighted in red. The summary boxes display the unstandardized estimates for the indirect, direct, and total effects, along with the proportion of the effect mediated. (A) The REC volume does not significantly mediate the effect of APOE ε4 on MMSE ( p = 0.208). (B) The TPOsup volume significantly mediates the association between APOE ε4 status and MMSE scores (indirect effect: β = -0.38, p = 0.010), accounting for 30.43% of the total effect.Abbreviations: REC, Gyrus rectus; TPOsup, Superior temporal pole; MMSE, Mini-Mental State Examination.] 3.5.2. Sensitivity Mediation Verification in the Full Sample To broaden the range of cognitive scores and test the robustness of the findings, the mediation analysis was repeated in the full matched sample. TPOsup volume remained a significant mediator (indirect effect β = −0.171, 95% CI [−0.415, −0.002], p = 0.044), accounting for 19.22% of the total effect on cognitive decline (total effect β = −0.889, p = 0.033). 3.6. MMSE Sub-item Regression Analysis To identify which cognitive domain was most closely related to TPOsup atrophy, we examined associations between TPOsup volume and the five MMSE subdomain scores in the full sample. After adjusting for APOE ε4 carrier status, multiple linear regression showed that only the Orientation subscore remained significantly positively associated with TPOsup volume after FDR correction (β = 0.676, p = 0.010, FDR q = 0.049). Although Attention and Calculation and Delayed Recall showed nominal associations with TPOsup volume, these did not survive correction for multiple comparisons (all q > 0.05, Table 2). Table 2. Association between TPOsup volume and MMSE subdomains MMSE Sub -item β SE p FDR q Orientation 0.676 0.259 0.010 0.049 Registration 0.073 0.049 0.141 0.141 Attention & Calculation 0.369 0.186 0.049 0.075 Delayed Recall 0.467 0.212 0.029 0.073 Language & Visuospatial 0.215 0.113 0.060 0.075 Note: N = 176; β , unstandardized regression coefficient; SE, standard error; FDR, false discovery rate. Significant p-values (< 0.05) are highlighted in bold. 3.7. Exploratory ROC Analysis of Combined Metrics for Differentiating MCI To evaluate the discriminative performance of structural, genetic, and clinical markers, exploratory ROC analyses were performed using logistic regression models adjusted for age, sex, and education (Figure 6). MMSE alone showed good discrimination between MCI and CN (AUC = 0.839, 95% CI: 0.780–0.898). In contrast, TPOsup volume alone showed modest performance (AUC = 0.626, 95% CI: 0.541–0.710), and APOE ε4 status alone showed poor performance (AUC = 0.541, 95% CI: 0.450–0.631). The combination of TPOsup volume and APOE ε4 status also performed poorly (AUC = 0.621, 95% CI: 0.536–0.706) and did not significantly improve over TPOsup volume alone (DeLong’s test, p = 0.528). The full model including MMSE, TPOsup volume, and APOE ε4 status achieved the highest AUC (0.847, 95% CI: 0.790–0.904), but this improvement over MMSE alone was not statistically significant (p = 0.362). Importantly, the full model performed significantly better than the model including only TPOsup volume and APOE ε4 (p < 0.001), indicating that its discriminative value was primarily driven by MMSE. [Insert Figure 6. ROC curves for discriminating MCI from CN individuals. The graph illustrates the diagnostic performance of three distinct predictive models: the combined model (red line; incorporating TPOsup volume, APOE ε4 status, and MMSE), MMSE alone (green line), and TPOsup volume alone (blue line). Area under the curve (AUC) and 95% CI are provided for each classifier. Abbreviations: CN, cognitively normal; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; TPOsup, superior temporal pole. ] 4. Discussion The present study examined whether APOE ε4 -related structural alterations in mild cognitive impairment are evenly distributed across the default mode network or are more prominent in specific subsystems. The findings indicate that these effects are not uniform across the DMN. In this matched sample, APOE ε4 -related volumetric differences in the MCI group were most evident in the dorsomedial prefrontal cortex subsystem, particularly in the gyrus rectus and superior temporal pole. Among these regions, TPOsup showed the most consistent association with global cognitive performance and statistically accounted for part of the relationship between APOE ε4 status and MMSE score. Overall, these results support the view that APOE ε4 -related structural vulnerability in MCI is better understood at the subsystem level than at the level of the DMN as a whole. A key contribution of the study is the application of the three-subsystem DMN framework to the question of genetic risk. Previous neuroimaging studies of APOE ε4 have primarily focused on canonical AD-related regions, including the hippocampus, posterior cingulate cortex, and precuneus, because of their established involvement in memory decline and AD pathology.( 58 – 61 ) Although this literature has been highly informative, it has also tended to favor a region-centered view of vulnerability. By contrast, the present analysis was organized around functionally defined DMN subsystems. This approach showed that the interaction between diagnosis and APOE ε4 status was detectable at the level of the overall DMN pattern and was most apparent in the DMPFC and MTL subsystems, but not in the Midline Core. This pattern does not imply that other DMN regions are uninvolved. Rather, it indicates that the structural expression of APOE ε4 in MCI is not evenly distributed across the network and that a subsystem-based framework may capture heterogeneity that is less visible in conventional ROI analyses.( 62 , 63 ) Within this framework, the DMPFC subsystem deserves particular attention. This subsystem has been implicated in semantic processing, self-referential cognition, affective integration, and other higher-order associative functions.( 64 – 66 ) These functions rely on distributed connectivity and substantial metabolic support, both of which are relevant to APOE ε4 -associated pathophysiology.( 67 ) APOE ε4 has been linked to altered lipid transport, reduced amyloid-β clearance, synaptic dysfunction, and neuroinflammatory responses.( 68 – 70 ) Although the present study did not test these mechanisms directly, the regional pattern observed here is broadly consistent with the possibility that association areas within the DMN are sensitive to APOE ε4 -related stress during the MCI stage. In this context, the DMPFC subsystem may be an informative target for further work on prodromal cognitive decline. Among the regions examined, TPOsup emerged as the most clinically relevant in the current dataset. Its volume remained associated with APOE ε4 carrier status after adjustment for covariates, and mediation analysis indicated that it explained a modest but nontrivial proportion of the APOE ε4 -related difference in MMSE score. Given the cross-sectional design, this finding should be interpreted as evidence of statistical mediation rather than proof of a causal pathway. Even so, it suggests that TPOsup occupies a meaningful position between genetic status and clinical performance. This interpretation is anatomically plausible. The superior temporal pole is an association region involved in integrating semantic, contextual, and multimodal information,( 71 – 73 ) and its functional profile is compatible with the idea that structural compromise in this region could contribute to broader cognitive inefficiency in MCI. The MMSE subdomain analysis provides additional context for this interpretation. After correction for multiple comparisons, TPOsup volume was specifically associated with the Orientation subscore. Although orientation is often treated as a relatively broad screening measure, successful performance depends on the integration of temporal, spatial, and situational information rather than on episodic memory alone. This observation aligns with the known functional role and suggests that APOE ε4 -related structural effects in MCI may extend beyond memory-dominant systems.( 72 – 74 ) At the same time, this result should not be overinterpreted. The MMSE offers only a coarse assessment of cognitive domains, and the observed association would benefit from replication using more detailed neuropsychological measures. These findings are also relevant to ongoing discussions about the relative contribution of DMPFC and MTL structures to early APOE ε4 -related neurodegeneration. In the present study, the MTL subsystem showed a significant interaction effect at the multivariate level, but no individual MTL region, including the hippocampus, survived correction for multiple comparisons in univariate analyses. This result should not be taken as evidence against the importance of medial temporal involvement in AD. A more cautious interpretation is that, in this clinically defined and propensity-matched MCI sample, APOE ε4 -related macroscopic volume differences were easier to detect in selected DMPFC regions than in traditional MTL structures. Several factors may contribute to this pattern. Hippocampal changes at this stage may be more readily detected by molecular, microstructural, or longitudinal measures than by cross-sectional volumetry. In addition, clinical heterogeneity within MCI may reduce sensitivity to genotype-related effects in regions that are affected by multiple pathological processes.( 75 – 77 ) The subsystem-based framework may therefore complement, rather than replace, the conventional focus on the MTL. Several limitations should be considered when interpreting the present results. First, MCI was defined using clinical criteria rather than biomarker-confirmed AD pathology. As a result, etiological heterogeneity cannot be excluded, and some participants may have had non-AD or mixed pathologies. This limitation is relevant to any structural imaging study of MCI and should temper claims about disease specificity. Second, the sample was drawn from ADNI and consisted largely of highly educated participants with limited demographic diversity, which may restrict generalizability. Third, because the study was cross-sectional, the mediation analysis should be interpreted cautiously. It identifies a statistically plausible pathway but cannot establish temporal order or causal direction. Fourth, ROI selection was theory-driven and limited to eight DMN regions. This improves interpretability but does not capture the full anatomical complexity of the DMN or its interactions with other large-scale networks. These considerations do not negate the main findings, but they define the scope within which the conclusions should be interpreted. These limitations also point to several priorities for future research. Longitudinal studies are needed to determine whether TPOsup atrophy progresses with clinical worsening and whether it contributes to the prediction of conversion from MCI to dementia beyond standard cognitive measures. The integration of amyloid and tau biomarkers would help clarify whether the observed DMPFC-related pattern is specifically linked to AD-related pathology or reflects a broader susceptibility to cognitive decline. Replication in more diverse cohorts will also be important for testing the robustness of the findings across differences in ancestry, education, and clinical context. Finally, multimodal studies combining structural MRI with connectivity and molecular markers may help explain why TPOsup appears particularly sensitive to APOE ε4 -related effects in this stage of impairment. 5. Conclusion In summary, the present study provides evidence that APOE ε4 -related structural variation in mild cognitive impairment is not uniformly distributed across the default mode network, but is more apparent within specific subsystems, particularly the DMPFC subsystem in the current dataset. Among the regions examined, the superior temporal pole showed the most consistent relationship with both APOE ε4 carrier status and cognitive performance, and statistically accounted for part of the association between genetic risk and global cognition. These findings support the utility of a subsystem-based DMN framework for characterizing the neuroanatomical expression of APOE ε4 in prodromal cognitive decline and suggest that TPOsup may be a relevant region for further investigation. Declarations 6. Acknowledgments We thank the Alzheimer's Disease Neuroimaging Initiative (ADNI) for providing the data used in this study. Data collection and sharing for ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, with primary support from the National Institutes of Health (Grant U01 AG024904) and the Department of Defense (Award W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. 7. Ethics approval and consent to participate The data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) database. The ADNI study was approved by the Institutional Review Boards of all participating institutions, and written informed consent was obtained from all participants or their legally authorized representatives. Because the present study involved secondary analysis of de-identified, publicly available data, additional ethics approval was not required at our institution. This study complied with the relevant ADNI data use agreements. 8. Consent to participate Not applicable. 9. Consent for publication Not applicable. 10. Clinical trial number Not applicable. 11. Author Contributions Xiaoming Yang: Writing – original draft, Investigation and Data curation. Jian Lyu: Software, Validation and Conceptualization. Jubo Wang: Methodology. Yu Quan: Writing – review & editing, Supervision. 12. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. 13. Declaration of conflicting interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 14. Generative AI statement The authors affirm that Generative AI was not used in the preparation of this work. 15. 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Classification and epidemiology of MCI. Clinics in geriatric medicine. 2013;29(4):10.1016/j. cger. 2013.07. 003. Additional Declarations No competing interests reported. Supplementary Files supplement.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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11:32:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curves for discriminating MCI from CN individuals.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9447482/v1/7d7973d25378afec2877a5f1.png"},{"id":109068131,"identity":"e98cbbc9-76ea-4321-967b-00c715408cca","added_by":"auto","created_at":"2026-05-12 10:03:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":985010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9447482/v1/0939cd6c-ec4d-432f-8bac-f94d7dc71a59.pdf"},{"id":108939481,"identity":"0075ee6f-905f-4c0f-a3c4-dc83c34df873","added_by":"auto","created_at":"2026-05-11 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Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a neurodegenerative disorder whose prevalence is rising steadily worldwide as populations age.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) It is estimated that the number of affected individuals may reach approximately 153\u0026nbsp;million by 2050.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) This trend is expected to place an enormous burden on healthcare systems, social infrastructure, and the global economy.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Importantly, the pathological process of AD may begin 15\u0026ndash;20 years before the onset of overt clinical symptoms, thereby providing a critical window for early intervention.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Mild cognitive impairment (MCI) is generally regarded as an intermediate stage between normal aging and clinically defined dementia, particularly AD.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Although MCI is a heterogeneous syndrome with multiple possible etiologies,(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) approximately 10%\u0026ndash;15% of individuals with MCI progress to AD each year.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Therefore, a more comprehensive understanding of the neuropathological mechanisms underlying MCI is urgently needed to improve early detection and therapeutic intervention in AD.\u003c/p\u003e \u003cp\u003eAt the genetic level, the \u003cem\u003eapolipoprotein E (APOE) ε4\u003c/em\u003e allele is the most extensively studied and most common genetic risk factor for AD.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Individuals carrying one copy of \u003cem\u003eAPOE ε4\u003c/em\u003e have approximately double the risk of developing AD, whereas those carrying two copies face a 12-fold or greater increase in risk.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Notably, even in the presence of protective factors such as higher educational attainment, the \u003cem\u003eε4\u003c/em\u003e allele appears to diminish the effectiveness of cognitive reserve, underscoring the relative independence of its neurotoxic effects.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eNeuroimaging studies have consistently implicated the default mode network (DMN) as a core brain network involved in the early pathological progression of AD.(\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Owing to its high metabolic activity at rest, the DMN may be particularly vulnerable to AD-related pathology.(\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Progressive disruption of the DMN\u0026rsquo;s structural and functional connectivity has been linked to cognitive decline, highlighting its importance in the preclinical and prodromal stages of AD. To further clarify the functional organization of the DMN, Andrews-Hanna et al. proposed a three-subsystem model comprising: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the Midline Core, which integrates information across the DMN and serves as a central hub for internally directed cognition; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the Medial Temporal Lobe (MTL) subsystem, which supports memory encoding and retrieval and is therefore closely related to memory impairment at the MCI stage;(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the Dorsomedial Prefrontal Cortex (DMPFC) subsystem, which is involved in affective integration, social cognition, and semantic processing, and may therefore contribute to non-memory impairments in MCI.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAlthough this model provides a valuable framework for understanding the DMN, several important knowledge gaps remain. Most neuroimaging studies of APOE ε4 have focused on traditionally defined DMN regions such as the hippocampus and posterior cingulate cortex.(\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) First, relatively few studies have explicitly examined whether APOE ε4 exerts subsystem-specific effects within the three-subsystem DMN model, particularly within the DMPFC subsystem. Second, to our knowledge, no study has systematically investigated whether APOE ε4 produces selective neurotoxic effects on the key nodes of DMN subsystems defined by functional and anatomical connectivity, rather than on conventionally defined anatomical regions alone. Of particular importance is whether the relationship between APOE ε4 and cognitive impairment is mediated by structurally vulnerable nodes within these subsystems. These limitations hinder a more comprehensive understanding of the neural pathophysiology of cognitive decline and constrain the identification of more precise neuroimaging biomarkers.\u003c/p\u003e \u003cp\u003eTo address these limitations, and unlike previous studies that relied on conventional region-of-interest (ROI) templates,(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) we defined eight ROIs spanning the three DMN subsystems based on the functional architecture of the DMN and the pathological characteristics of AD. These regions include not only classical regions within the Midline Core and MTL subsystems, which are traditionally associated with memory-related pathology, but also less-studied regions within the DMPFC subsystem that may be involved in non-memory cognitive processes such as semantic and language-related functions. Accordingly, the present study systematically examined the differential effects of \u003cem\u003eAPOE ε4\u003c/em\u003e on DMN subsystems and further tested whether critical regional structural alterations mediate the association between genetic risk and cognitive performance. We hypothesized that \u003cem\u003eAPOE ε4\u003c/em\u003e carriers with MCI would show selective volume reductions in regions belonging to the DMPFC subsystem, and that structural alterations in these regions would partly account for the association between \u003cem\u003eAPOE ε4\u003c/em\u003e and cognitive decline. Overall, this study aims to delineate the subsystem-specific structural vulnerabilities associated with APOE ε4 in the prodromal stage of MCI.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe data used in this study were obtained from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). Launched in 2003 as a public\u0026ndash;private partnership under the leadership of Principal Investigator Dr. Michael W. Weiner, ADNI aims to determine whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessments can be combined to track the progression of MCI and early AD.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eInitial screening included 205 cognitively normal (CN) individuals and 351 patients with MCI from the ADNI database.(35, 36) Participants were excluded if they met any of the following criteria: (1) poor-quality MRI data (C- or D-rated based on interquartile range [IQR]); (2) carriage of the\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;2\u003c/em\u003e allele, to minimize its potentially protective effect against \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e-related risk;(37, 38) or (3) a history of other major neurological disorders, psychiatric illness, or severe head trauma. After applying these criteria, 111 CN participants and 153 patients with MCI remained eligible. Diagnoses were based solely on standardized ADNI clinical criteria.(39)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo reduce within-group imbalance, 1:1 nearest-neighbor propensity score matching (PSM) was performed separately within the CN and MCI groups. Carrier status for the \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003eallele was treated as the grouping variable, and age, sex, years of education, and body mass index (BMI) were included as covariates in the propensity score model. The caliper width was set to 0.3 times the standard deviation of the logit of the propensity score. MMSE score was not included as a matching variable because doing so could obscure the true effect of \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003eon cognition and introduce bias into subsequent mediation analyses. After matching, 58 pairs (n = 116) were obtained in the MCI group and 30 pairs (n = 60) in the CN group. Balance diagnostics confirmed that all standardized mean differences (SMDs) were below 0.1. Thus, a total of 176 participants were included in the final analyses (Figure 1).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eMRI Data Acquisition and Processing\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eBaseline T1-weighted structural MRI data were downloaded from the ADNI database. All scans were acquired on 1.5T MRI scanners. Image preprocessing was performed using the Computational Anatomy Toolbox (CAT12, v12.8) implemented in MATLAB R2023a. The preprocessing pipeline was optimized for DMN subsystem analysis and included noise reduction to improve image quality, N4 bias-field correction to reduce intensity inhomogeneity caused by scanner artifacts, and segmentation of gray matter (GM), white matter, and cerebrospinal fluid. Images were spatially normalized to the Montreal Neurological Institute (MNI) 152 template (2 \u0026times; 2 \u0026times; 2 mm voxel size) using the DARTEL algorithm, which improves inter-subject registration by modeling fine-grained anatomical differences.(40-47)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eRegion of Interest Volume Extraction\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eBased on the three-subsystem DMN model proposed by Andrews-Hanna et al.(27) nd the anatomical relevance of the selected regions to the present study aims, eight ROIs were defined (Figure 2). The Midline Core subsystem included the posterior cingulate cortex (PCC) and precuneus (PCUN);(27, 48-51) the MTL subsystem included the hippocampus (HIP), parahippocampal gyrus (PHG), and frontal medial orbital cortex (FMO);(27, 52-54) and the DMPFC subsystem included the gyrus rectus (REC), superior temporal pole (TPOsup), and middle temporal gyrus (MTG).(27, 55-57) Native-space volumes of each ROI were extracted from preprocessed GM maps using the Automated Anatomical Labeling 3 (AAL3) atlas.(47) To account for inter-individual differences in head size, each regional volume was normalized to total intracranial volume (TIV) using the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 2. Spatial distribution of the regions of interest within the three-subsystem model of the default mode networkl.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe selected ROIs are mapped onto the 3D cortical surface and grouped according to their respective subsystems. The medial temporal lobe (MTL) subsystem includes the hippocampus (HIP), parahippocampal gyrus (PHG), and frontal medial orbital (FMO). The dorsomedial prefrontal cortex (DMPFC) subsystem comprises the middle temporal gyrus (MTG), superior temporal pole (TPOsup), and gyrus rectus (REC). The Midline Core Subsystem encompasses the precuneus (PCUN) and posterior cingulate cortex (PCC). L, left hemisphere; R, right hemisphere.\u003cstrong\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e2.4. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted in R (version 4.2.2), with a two-tailed significance threshold of \u0026alpha; = 0.05.\u003c/p\u003e\n\u003ch3\u003e2.4.1.\u0026nbsp; \u0026nbsp;Multivariate brain volume analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate the interaction between \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carrier status and diagnostic group, as well as their main effects on the combined normalized volumes of all eight ROIs, we first conducted a global multivariate analysis using permutational multivariate analysis of variance (PERMANOVA) implemented with the adonis2 function in the vegan package. Euclidean distance matrices and 10,000 permutations were used. Separate stratified PERMANOVA analyses were also performed for each of the three DMN subsystems. These four tests were Bonferroni-corrected, yielding a significance threshold of \u0026alpha; = 0.0125.\u003c/p\u003e\n\u003ch3\u003e2.4.2.\u0026nbsp; \u0026nbsp;Univariate Group Comparisons\u003c/h3\u003e\n\u003cp\u003eTo identify which specific ROIs contributed to significant multivariate findings, univariate analyses were subsequently performed. Normality of ROI volume distributions was assessed using the Shapiro\u0026ndash;Wilk test. For normally distributed data, independent-samples t-tests were used and effect sizes were reported as Cohen\u0026rsquo;s d; for non-normally distributed data, Mann\u0026ndash;Whitney U tests were used and effect sizes were reported as rank-biserial correlations (r_rb). False discovery rate (FDR) correction was applied across all univariate tests, with significance defined as q \u0026lt; 0.05.\u003c/p\u003e\n\u003ch3\u003e2.4.3. \u0026nbsp; Correlation and Multiple Linear Regression Analysis\u003c/h3\u003e\n\u003cp\u003eSpearman\u0026rsquo;s rank correlation was used for preliminary analyses of the associations among ROI volumes, APOE \u0026epsilon;4 status, and MMSE scores in the full sample. Multiple linear regression models were then constructed, with normalized ROI volume as the dependent variable and APOE \u0026epsilon;4 carrier status as the primary independent variable, while adjusting for sex, handedness, TIV, and hypertension status. Hypertension status was included because a significant baseline group difference was observed (p = 0.015). Variance inflation factors (VIFs) were calculated to assess multicollinearity; all VIF values were below 5. In addition, the number of APOE \u0026epsilon;4 alleles (0/1/2) was modeled as a continuous variable to test for a potential gene-dose effect. Finally, associations between altered regional volumes and the five MMSE subdomains\u0026mdash;Orientation, Registration, Attention and Calculation, Delayed Recall, and Language and Visuospatial Function\u0026mdash;were examined.\u003c/p\u003e\n\u003ch3\u003e2.4.4. \u0026nbsp; Mediation Analysis\u003c/h3\u003e\n\u003cp\u003eFor variables identified as significant in the regression analyses, mediation models were constructed using the mediation package in R to test whether regional brain volume mediated the relationship between \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarrier status and cognitive performance. Nonparametric bootstrap resampling (1,000 iterations) was used to estimate indirect effects and their 95% confidence intervals (CIs). To examine robustness across populations and expand the dynamic range of cognitive performance, mediation analyses were performed both in the MCI subgroup and in the full matched sample. Although the Alzheimer\u0026rsquo;s Disease Assessment Scale (ADAS) score was strongly negatively correlated with MMSE (r = \u0026minus;0.76, p \u0026lt; 0.001), MMSE was selected as the primary cognitive outcome because it is more commonly used in studies of early cognitive decline.\u003c/p\u003e\n\u003ch3\u003e2.4.5. \u0026nbsp; Exploratory ROC Analysis\u003c/h3\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) analysis was performed to evaluate the discriminative ability of structural, clinical, and genetic variables for differentiating MCI from CN. Three logistic regression models were constructed: a clinical model using MMSE score, a neuroimaging model using TPOsup volume, and a combined model including TPOsup volume, MMSE score, and \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarrier status. Age, sex, and education were included as covariates in all models. Predicted probabilities were used to generate ROC curves and calculate the area under the curve (AUC). Optimal cutoffs were determined using the maximum Youden index. DeLong\u0026rsquo;s test was used to compare AUCs across models.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eBaseline Demographic and Clinical Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTable 1. Demographic and clinical characteristics\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e\u003cimg 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\"\u003e\u003c/strong\u003e\u003c/em\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eAfter propensity score matching, the final sample comprised 176 participants, including 60 CN individuals and 116 patients with MCI. Within the CN group, 30 participants were\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;4\u003c/em\u003e non-carriers and 30 were carriers. Within the MCI group, 58 participants were \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e non-carriers and 58 were carriers. Among \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarriers, the MCI group included 17 homozygotes and 41 heterozygotes, whereas the CN group included 2 homozygotes and 28 heterozygotes.\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 1,\u0026nbsp;\u003c/strong\u003eno significant differences were observed between \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarriers and non-carriers in age, sex, education, BMI, or TIV within either diagnostic group (all p \u0026gt; 0.05). However, hypertension status differed significantly between \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e subgroups in the CN group (p = 0.015). Specifically, the proportion of normotensive individuals was higher in the \u003cem\u003eAPOE \u0026epsilon;4\u0026minus;\u003c/em\u003e subgroup (80.0%) than in the \u003cem\u003eAPOE \u0026epsilon;4+\u003c/em\u003e subgroup (46.7%). Therefore, hypertension status was included as a covariate in subsequent multiple linear regression analyses.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e\u003cem\u003e\u0026nbsp;Effects on DMN Regional Volumes\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003ePERMANOVA revealed a significant multivariate interaction between diagnostic group (CN vs. MCI) and \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carrier status on the overall structural pattern of the DMN (F = 3.24, p = 0.007), which remained significant after Bonferroni correction. Subsystem-level analyses indicated that this interaction was primarily driven by the DMPFC subsystem (F = 4.16, p = 0.003) and the MTL subsystem (F = 4.82, p = 0.002), whereas the Midline Core subsystem showed no significant interaction (F = 1.76, p = 0.163).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.3. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eUnivariate Group Comparisons\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eBased on the significant multivariate findings, univariate analyses were conducted to identify specific affected regions (Figure 3). In the CN group, \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarrier status was not associated with significant volume differences in any ROI after FDR correction (all q \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn contrast, within the MCI group, \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers showed significant localized volume reductions in the DMPFC subsystem. Specifically, normalized volumes of the REC and TPOsup were significantly lower in \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers than in non-carriers (REC: p = 0.011, FDR q = 0.044, d = \u0026minus;0.48, 95% CI [\u0026minus;0.85, \u0026minus;0.11]; TPOsup: p = 0.006, FDR q = 0.044, d = \u0026minus;0.52, 95% CI [\u0026minus;0.89, \u0026minus;0.14]). Within the MTL subsystem, the PHG showed a trend toward lower volume in \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarriers (p = 0.055, d = \u0026minus;0.36), but this did not survive FDR correction (q = 0.148). Notably, the hippocampus\u0026mdash;traditionally considered a key AD-related structure\u0026mdash;did not differ significantly between groups (p = 0.153, q = 0.238). Other DMPFC regions, such as the MTG, also showed no significant differences (all q \u0026gt; 0.05). These findings suggest that the DMPFC subsystem may be more sensitive than the MTL subsystem to \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e-related neurotoxicity during the early stages of cognitive impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Comparison of standardized brain region volumes between \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers and non-carriers within the MCI group.\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBox and scatter plots illustrate the standardized volumes of eight predefined ROIs for \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003enon-carriers (blue, n=58) and carriers (orange, n=58). The solid horizontal line inside each box represents the median, while the lower and upper hinges correspond to the first and third quartiles. Whiskers extend to the furthest data points within 1.5 \u0026times; IQR. Individual subject data points are overlaid to show the distribution. Statistical significance was assessed using univariate group comparisons; q-values denote FDR-corrected p-values. Effect sizes are reported as Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e or rank-biserial correlation (\u003cem\u003er_rb\u003c/em\u003e), with 95% confidence intervals enclosed in brackets. Significant volume reductions in \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carriers are specifically observed in the gyrus rectus (REC, \u003cem\u003eq\u003c/em\u003e = 0.044) and superior temporal pole (TPOsup, \u003cem\u003eq\u003c/em\u003e = 0.044).Abbreviations: ROI, region of interest; MCI, Mild Cognitive Impairment; FDR, False Discovery Rate.\u003cstrong\u003e]\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.4. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026nbsp;Multiple Linear Regression and Sensitivity Analysis of Brain Volume and Clinical Correlations\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo account for potential confounding factors, multiple linear regression analyses were conducted within the MCI group. After adjusting for sex, handedness, TIV, and hypertension status (Figure 4), \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carrier status remained significantly associated with reduced volume in both the TPOsup (\u0026beta; = \u0026minus;0.199, 95% CI [\u0026minus;0.349, \u0026minus;0.050], p = 0.010) and REC (\u0026beta; = \u0026minus;0.185, 95% CI [\u0026minus;0.329, \u0026minus;0.042], p = 0.013). Multicollinearity diagnostics indicated that all VIF values were below 5.\u003c/p\u003e\n\u003cp\u003eIn the sensitivity analysis, \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e allele dosage was modeled as a continuous predictor. A marginal dose-dependent trend was observed for TPOsup volume reduction (Estimate = \u0026minus;0.109, SE = 0.055, p = 0.051). In preliminary correlation analyses across the full sample, TPOsup volume was positively correlated with global cognitive performance as measured by MMSE (Spearman r = 0.202, p = 0.007), suggesting that preserved structural integrity in this region is associated with better cognition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 4. Multiple linear regression analysis of factors associated with regional brain volumes in the MCI group.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe forest plot shows the unstandardized beta (\u0026beta;) coefficients and 95% confidence intervals (CIs) from multiple linear regression models examining the associations of \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carrier status (\u003cem\u003e\u0026epsilon;4+\u0026nbsp;\u003c/em\u003evs. \u003cem\u003e\u0026epsilon;4\u0026minus;\u003c/em\u003e), handedness (Right vs. Left), gender (Man vs. Female), hypertension status, and total intracranial volume (TIV) with the normalized volumes of the superior temporal pole (TPOsup) and gyrus rectus (REC) among participants with mild cognitive impairment (MCI) . The solid vertical line at \u0026beta; = 0 indicates the null effect, and the horizontal lines represent the 95% CIs. \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003epositivity was significantly and independently associated with lower TPOsup volume (p = 0.010) and lower REC volume (p = 0.013).]\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.5. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eMediation Analysis of the Association Between APOE \u0026epsilon;4 and Cognitive Function\u003c/em\u003e\u003c/h2\u003e\n\u003ch3\u003e3.5.1.\u0026nbsp; \u0026nbsp;Primary Mediation Analysis in the MCI Group\u003c/h3\u003e\n\u003cp\u003eBecause structural changes were predominantly observed in the MCI group, mediation analyses were first performed in this subgroup using bootstrap resampling (1,000 iterations) to test whether volumetric alterations mediated the relationship between\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003eand MMSE score (Figure 5). After adjusting for age and sex, TPOsup volume significantly mediated the association between \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarrier status and lower MMSE scores (indirect effect \u0026beta; = \u0026minus;0.38, 95% CI [\u0026minus;0.89, \u0026minus;0.05], p = 0.010), accounting for 30.43% of the total effect (total effect \u0026beta; = \u0026minus;1.19, p = 0.042). Although REC volume was significantly associated with genotype, it was not significantly associated with MMSE score (\u0026beta; = 0.98, p = 0.195) and therefore did not significantly mediate the\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;4\u003c/em\u003e\u0026ndash;MMSE relationship (p = 0.208).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e. Mediation analysis of regional brain volumes on the association between \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e status and cognitive function.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePath diagrams illustrate the potential mediating role of the gyrus rectus (REC, Panel A) and superior temporal pole (TPOsup, Panel B) volumes in the relationship between \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e carrier status and MMSE scores within the MCI group. The arrows indicate the direction of the modeled effects. Unstandardized path coefficients (\u003cem\u003e\u0026beta;\u003c/em\u003e) and corresponding p-values are presented adjacent to each path. Significant \u003cem\u003ep\u003c/em\u003e-values (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) are highlighted in red. The summary boxes display the unstandardized estimates for the indirect, direct, and total effects, along with the proportion of the effect mediated. (A) The REC volume does not significantly mediate the effect of \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003eon MMSE (\u003cem\u003ep\u003c/em\u003e = 0.208). (B) The TPOsup volume significantly mediates the association between \u003cem\u003eAPOE \u0026epsilon;4\u003c/em\u003e status and MMSE scores (indirect effect: \u003cem\u003e\u0026beta;\u003c/em\u003e = -0.38, \u003cem\u003ep\u003c/em\u003e = 0.010), accounting for 30.43% of the total effect.Abbreviations: REC, Gyrus rectus; TPOsup, Superior temporal pole; MMSE, Mini-Mental State Examination.]\u003c/p\u003e\n\u003ch3\u003e3.5.2. \u0026nbsp; Sensitivity Mediation Verification in the Full Sample\u003c/h3\u003e\n\u003cp\u003eTo broaden the range of cognitive scores and test the robustness of the findings, the mediation analysis was repeated in the full matched sample. TPOsup volume remained a significant mediator (indirect effect \u0026beta; = \u0026minus;0.171, 95% CI [\u0026minus;0.415, \u0026minus;0.002], p = 0.044), accounting for 19.22% of the total effect on cognitive decline (total effect \u0026beta; = \u0026minus;0.889, p = 0.033).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.6. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eMMSE Sub-item Regression Analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo identify which cognitive domain was most closely related to TPOsup atrophy, we examined associations between TPOsup volume and the five MMSE subdomain scores in the full sample. After adjusting for \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003ecarrier status, multiple linear regression showed that only the Orientation subscore remained significantly positively associated with TPOsup volume after FDR correction (\u0026beta; = 0.676, p = 0.010, FDR q = 0.049). Although Attention and Calculation and Delayed Recall showed nominal associations with TPOsup volume, these did not survive correction for multiple comparisons (all q \u0026gt; 0.05, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Association between TPOsup volume and MMSE subdomains\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSE Sub\u003c/strong\u003e\u003cstrong\u003e-item\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR \u003cem\u003eq\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eOrientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eRegistration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 203px;\"\u003e\n \u003cp\u003eAttention\u0026nbsp;\u0026amp;\u0026nbsp;Calculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eDelayed Recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eLanguage \u0026amp; Visuospatial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: N = 176; \u003cem\u003e\u0026beta;\u003c/em\u003e, unstandardized regression coefficient; SE, standard error; FDR, false discovery rate. Significant p-values (\u0026lt; 0.05) are highlighted in bold.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.7. \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003cem\u003eExploratory ROC Analysis of Combined Metrics for Differentiating MCI\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo evaluate the discriminative performance of structural, genetic, and clinical markers, exploratory ROC analyses were performed using logistic regression models adjusted for age, sex, and education (Figure 6). MMSE alone showed good discrimination between MCI and CN (AUC = 0.839, 95% CI: 0.780\u0026ndash;0.898). In contrast, TPOsup volume alone showed modest performance (AUC = 0.626, 95% CI: 0.541\u0026ndash;0.710), and APOE \u0026epsilon;4 status alone showed poor performance (AUC = 0.541, 95% CI: 0.450\u0026ndash;0.631). The combination of TPOsup volume and\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;4\u003c/em\u003e status also performed poorly (AUC = 0.621, 95% CI: 0.536\u0026ndash;0.706) and did not significantly improve over TPOsup volume alone (DeLong\u0026rsquo;s test, p = 0.528). The full model including MMSE, TPOsup volume, and\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;4\u003c/em\u003e status achieved the highest AUC (0.847, 95% CI: 0.790\u0026ndash;0.904), but this improvement over MMSE alone was not statistically significant (p = 0.362). Importantly, the full model performed significantly better than the model including only TPOsup volume and \u003cem\u003eAPOE \u0026epsilon;4\u0026nbsp;\u003c/em\u003e(p \u0026lt; 0.001), indicating that its discriminative value was primarily driven by MMSE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Insert Figure 6. ROC curves for discriminating MCI from CN individuals.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe graph illustrates the diagnostic performance of three distinct predictive models: \u0026nbsp;the combined model (red line; incorporating TPOsup volume,\u003cem\u003e\u0026nbsp;APOE \u0026epsilon;4\u003c/em\u003e status, and MMSE), MMSE alone (green line), and TPOsup volume alone (blue line). Area under the curve (AUC) and 95% CI are provided for each classifier. Abbreviations: CN, cognitively normal; MCI, mild cognitive impairment; MMSE, Mini-Mental State Examination; TPOsup, superior temporal pole.\u003cstrong\u003e]\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study examined whether \u003cem\u003eAPOE ε4\u003c/em\u003e-related structural alterations in mild cognitive impairment are evenly distributed across the default mode network or are more prominent in specific subsystems. The findings indicate that these effects are not uniform across the DMN. In this matched sample, \u003cem\u003eAPOE ε4\u003c/em\u003e-related volumetric differences in the MCI group were most evident in the dorsomedial prefrontal cortex subsystem, particularly in the gyrus rectus and superior temporal pole. Among these regions, TPOsup showed the most consistent association with global cognitive performance and statistically accounted for part of the relationship between \u003cem\u003eAPOE ε4\u003c/em\u003e status and MMSE score. Overall, these results support the view that \u003cem\u003eAPOE ε4\u003c/em\u003e-related structural vulnerability in MCI is better understood at the subsystem level than at the level of the DMN as a whole.\u003c/p\u003e \u003cp\u003eA key contribution of the study is the application of the three-subsystem DMN framework to the question of genetic risk. Previous neuroimaging studies of \u003cem\u003eAPOE ε4\u003c/em\u003e have primarily focused on canonical AD-related regions, including the hippocampus, posterior cingulate cortex, and precuneus, because of their established involvement in memory decline and AD pathology.(\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) Although this literature has been highly informative, it has also tended to favor a region-centered view of vulnerability. By contrast, the present analysis was organized around functionally defined DMN subsystems. This approach showed that the interaction between diagnosis and \u003cem\u003eAPOE ε4\u003c/em\u003e status was detectable at the level of the overall DMN pattern and was most apparent in the DMPFC and MTL subsystems, but not in the Midline Core. This pattern does not imply that other DMN regions are uninvolved. Rather, it indicates that the structural expression of \u003cem\u003eAPOE ε4\u003c/em\u003e in MCI is not evenly distributed across the network and that a subsystem-based framework may capture heterogeneity that is less visible in conventional ROI analyses.(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWithin this framework, the DMPFC subsystem deserves particular attention. This subsystem has been implicated in semantic processing, self-referential cognition, affective integration, and other higher-order associative functions.(\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) These functions rely on distributed connectivity and substantial metabolic support, both of which are relevant to \u003cem\u003eAPOE ε4\u003c/em\u003e-associated pathophysiology.(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) \u003cem\u003eAPOE ε4\u003c/em\u003e has been linked to altered lipid transport, reduced amyloid-β clearance, synaptic dysfunction, and neuroinflammatory responses.(\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e) Although the present study did not test these mechanisms directly, the regional pattern observed here is broadly consistent with the possibility that association areas within the DMN are sensitive to \u003cem\u003eAPOE ε4\u003c/em\u003e-related stress during the MCI stage. In this context, the DMPFC subsystem may be an informative target for further work on prodromal cognitive decline.\u003c/p\u003e \u003cp\u003eAmong the regions examined, TPOsup emerged as the most clinically relevant in the current dataset. Its volume remained associated with \u003cem\u003eAPOE ε4\u003c/em\u003e carrier status after adjustment for covariates, and mediation analysis indicated that it explained a modest but nontrivial proportion of the \u003cem\u003eAPOE ε4\u003c/em\u003e-related difference in MMSE score. Given the cross-sectional design, this finding should be interpreted as evidence of statistical mediation rather than proof of a causal pathway. Even so, it suggests that TPOsup occupies a meaningful position between genetic status and clinical performance. This interpretation is anatomically plausible. The superior temporal pole is an association region involved in integrating semantic, contextual, and multimodal information,(\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e) and its functional profile is compatible with the idea that structural compromise in this region could contribute to broader cognitive inefficiency in MCI.\u003c/p\u003e \u003cp\u003eThe MMSE subdomain analysis provides additional context for this interpretation. After correction for multiple comparisons, TPOsup volume was specifically associated with the Orientation subscore. Although orientation is often treated as a relatively broad screening measure, successful performance depends on the integration of temporal, spatial, and situational information rather than on episodic memory alone. This observation aligns with the known functional role and suggests that \u003cem\u003eAPOE ε4\u003c/em\u003e-related structural effects in MCI may extend beyond memory-dominant systems.(\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) At the same time, this result should not be overinterpreted. The MMSE offers only a coarse assessment of cognitive domains, and the observed association would benefit from replication using more detailed neuropsychological measures.\u003c/p\u003e \u003cp\u003eThese findings are also relevant to ongoing discussions about the relative contribution of DMPFC and MTL structures to early \u003cem\u003eAPOE ε4\u003c/em\u003e-related neurodegeneration. In the present study, the MTL subsystem showed a significant interaction effect at the multivariate level, but no individual MTL region, including the hippocampus, survived correction for multiple comparisons in univariate analyses. This result should not be taken as evidence against the importance of medial temporal involvement in AD. A more cautious interpretation is that, in this clinically defined and propensity-matched MCI sample, \u003cem\u003eAPOE ε4\u003c/em\u003e-related macroscopic volume differences were easier to detect in selected DMPFC regions than in traditional MTL structures. Several factors may contribute to this pattern. Hippocampal changes at this stage may be more readily detected by molecular, microstructural, or longitudinal measures than by cross-sectional volumetry. In addition, clinical heterogeneity within MCI may reduce sensitivity to genotype-related effects in regions that are affected by multiple pathological processes.(\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) The subsystem-based framework may therefore complement, rather than replace, the conventional focus on the MTL.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the present results. First, MCI was defined using clinical criteria rather than biomarker-confirmed AD pathology. As a result, etiological heterogeneity cannot be excluded, and some participants may have had non-AD or mixed pathologies. This limitation is relevant to any structural imaging study of MCI and should temper claims about disease specificity. Second, the sample was drawn from ADNI and consisted largely of highly educated participants with limited demographic diversity, which may restrict generalizability. Third, because the study was cross-sectional, the mediation analysis should be interpreted cautiously. It identifies a statistically plausible pathway but cannot establish temporal order or causal direction. Fourth, ROI selection was theory-driven and limited to eight DMN regions. This improves interpretability but does not capture the full anatomical complexity of the DMN or its interactions with other large-scale networks. These considerations do not negate the main findings, but they define the scope within which the conclusions should be interpreted.\u003c/p\u003e \u003cp\u003eThese limitations also point to several priorities for future research. Longitudinal studies are needed to determine whether TPOsup atrophy progresses with clinical worsening and whether it contributes to the prediction of conversion from MCI to dementia beyond standard cognitive measures. The integration of amyloid and tau biomarkers would help clarify whether the observed DMPFC-related pattern is specifically linked to AD-related pathology or reflects a broader susceptibility to cognitive decline. Replication in more diverse cohorts will also be important for testing the robustness of the findings across differences in ancestry, education, and clinical context. Finally, multimodal studies combining structural MRI with connectivity and molecular markers may help explain why TPOsup appears particularly sensitive to \u003cem\u003eAPOE ε4\u003c/em\u003e-related effects in this stage of impairment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, the present study provides evidence that \u003cem\u003eAPOE ε4\u003c/em\u003e-related structural variation in mild cognitive impairment is not uniformly distributed across the default mode network, but is more apparent within specific subsystems, particularly the DMPFC subsystem in the current dataset. Among the regions examined, the superior temporal pole showed the most consistent relationship with both \u003cem\u003eAPOE ε4\u003c/em\u003e carrier status and cognitive performance, and statistically accounted for part of the association between genetic risk and global cognition. These findings support the utility of a subsystem-based DMN framework for characterizing the neuroanatomical expression of \u003cem\u003eAPOE ε4\u003c/em\u003e in prodromal cognitive decline and suggest that TPOsup may be a relevant region for further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e6. \u0026nbsp; \u0026nbsp;Acknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the Alzheimer\u0026apos;s Disease Neuroimaging Initiative (ADNI) for providing the data used in this study. Data collection and sharing for ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, with primary support from the National Institutes of Health (Grant U01 AG024904) and the Department of Defense (Award W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer\u0026rsquo;s Association; Alzheimer\u0026rsquo;s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research \u0026amp; Development, LLC.; Johnson \u0026amp; Johnson Pharmaceutical Research \u0026amp; Development LLC.; Lumosity; Lundbeck; Merck \u0026amp; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer\u0026rsquo;s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp;\u0026nbsp;Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe data used in this study were obtained from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI-1) database. The ADNI study was approved by the Institutional Review Boards of all participating institutions, and written informed consent was obtained from all participants or their legally authorized representatives. Because the present study involved secondary analysis of de-identified, publicly available data, additional ethics approval was not required at our institution. This study complied with the relevant ADNI data use agreements.\u003c/p\u003e\n\u003cp\u003e8.\u0026nbsp; \u0026nbsp;\u0026nbsp;Consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e9.\u0026nbsp; \u0026nbsp;\u0026nbsp;Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e10.\u0026nbsp; Clinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e11.\u0026nbsp;\u0026nbsp;Author Contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaoming Yang:\u003c/strong\u003e Writing \u0026ndash; original draft, Investigation and Data curation.\u003cstrong\u003e\u0026nbsp;Jian Lyu:\u003c/strong\u003e Software, Validation and Conceptualization. \u003cstrong\u003eJubo Wang:\u003c/strong\u003e Methodology.\u003cstrong\u003e\u0026nbsp;Yu Quan:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Supervision.\u003c/p\u003e\n\u003cp\u003e12.\u0026nbsp;\u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e13.\u0026nbsp;\u0026nbsp;Declaration of conflicting interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e14.\u0026nbsp;\u0026nbsp;Generative AI statement\u003c/p\u003e\n\u003cp\u003eThe authors affirm that Generative AI was not used in the preparation of this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e15.\u0026nbsp;\u0026nbsp;Data availability statement \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available in the figshare repository under the permanent identifier 10.6084/m9.figshare.30625913.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLi X, Feng X, Sun X, Hou N, Han F, Liu Y. 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Clinics in geriatric medicine. 2013;29(4):10.1016/j. cger. 2013.07. 003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Apolipoprotein E ε4, Mild Cognitive Impairment, Default Mode Network, Dorsomedial Prefrontal Cortex, Superior Temporal Pole, Mediation Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9447482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9447482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMild cognitive impairment (MCI) is a critical prodromal stage of progressive cognitive decline. As a core cognitive brain network, the default mode network (DMN) exhibits structural alterations in MCI that are closely associated with cognitive impairment. \u003cem\u003eApolipoprotein E ε4 (APOE ε4)\u003c/em\u003e, the strongest genetic risk factor for pathological cognitive decline, has been shown to disrupt the structural integrity of the DMN\u0026rsquo;s three core subsystems: the Midline Core, Medial Temporal Lobe (MTL), and Dorsomedial Prefrontal Cortex (DMPFC) subsystems. However, the subsystem-specific effects of \u003cem\u003eAPOE ε4\u003c/em\u003e and the cognitive mechanisms through which they operate in MCI remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe enrolled 176 participants from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI), including 116 patients with MCI and 60 cognitively normal (CN) individuals, with balanced APOE ε4 carrier status within each diagnostic group. Standardized volumes of eight DMN regions were analyzed using permutational multivariate analysis of variance (PERMANOVA) to test diagnosis-by-genotype interactions, followed by false discovery rate (FDR)-corrected univariate analyses and multiple linear regression to identify regional volume differences. Bootstrap-based mediation analysis and receiver operating characteristic (ROC) analysis were further performed to examine clinical relevance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA significant diagnosis-by-\u003cem\u003eAPOE ε4\u003c/em\u003e interaction was observed in the global structural pattern of the DMN, primarily driven by the DMPFC and MTL subsystems. Among patients with MCI, \u003cem\u003eAPOE ε4\u003c/em\u003e carriers exhibited selective atrophy within the DMPFC subsystem, specifically in the gyrus rectus (REC) and superior temporal pole (TPOsup), relative to non-carriers. Critically, TPOsup atrophy mediated 30.43% of the negative effect of \u003cem\u003eAPOE ε4\u003c/em\u003e on global cognition, as measured by the Mini-Mental State Examination (MMSE), and was significantly associated with impaired orientation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe TPOsup may represent a key neural hub linking \u003cem\u003eAPOE ε4\u003c/em\u003e to cognitive decline in MCI and may serve as a specific imaging marker for risk stratification in this population.\u003c/p\u003e","manuscriptTitle":"APOE ε4-related structural vulnerability in mild cognitive impairment: a subsystem-based analysis of the default mode network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:07:55","doi":"10.21203/rs.3.rs-9447482/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-27T14:43:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T14:36:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T04:18:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2026-04-17T09:45:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c74d2ed1-db4d-4748-b37f-96fa0425f05d","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T05:07:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:07:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9447482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9447482","identity":"rs-9447482","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}