Brain Iron in Signature Regions Relating to Cognitive Aging in Older Adults: The Taizhou Imaging Study

Brain Iron in Signature Regions Relating to Cognitive Aging in Older Adults: The Taizhou Imaging Study | 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 Brain Iron in Signature Regions Relating to Cognitive Aging in Older Adults: The Taizhou Imaging Study Rui Li, Yi‑Ren Fan, Ying-Zhe Wang, He‑Yang Lu, Pei-Xi Li, Qiang Dong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4425826/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Oct, 2024 Read the published version in Alzheimer's Research & Therapy → Version 1 posted 12 You are reading this latest preprint version Abstract Background Recent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer’s disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in our cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults. Methods In this Taizhou Imaging Study, 770 older adults (mean age 62.0 ± 4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 3 signature regions: aging (AG)-specific regions, AG regions and AD signature meta-ROIs (Fig. 2). The QSM and cortical morphometry means of the above ROIs were also computed. Results Significant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse global cognitive function than did those in the bottom tertile [Table 2; \(\beta\) = -0.104, p = 0.035; \(\beta\) = -0.118, p = 0.020, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict cognitive decline in global cognition and key domains such as attention and visuospatial function (Table 3, Fig. 3; all p < 0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific, and AG signature regions (Fig. 2; all p < 0.05). Conclusion AG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging. Aging signature AD signature iron atrophy cortical thickness cognition Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Cognitive aging, an inevitable, natural process of lifespan, is characterized by a progressive decline in cognitive functions, including processing speed, reasoning, and memory, among elderly individuals[ 1 ]. The rate of cognitive aging varies significantly among individuals, with those experiencing accelerated cognitive decline being at a greater risk of developing dementia[ 2 ]. In the pursuit of understanding cognitive aging, magnetic resonance imaging (MRI) has been instrumental in identifying macroscopic structural changes such as brain atrophy[ 3 ]. These changes have been extensively studied as markers of cognitive decline within community-dwelling elderly populations. However, brain atrophy represents a relatively late-stage manifestation of the cognitive decline process, prompting researchers to seek earlier and more sensitive markers. Iron, a critical element involved in numerous biochemical processes within the brain, has emerged as a potential early biomarker for cognitive decline due to its association with neurodegenerative processes and brain aging[ 4 , 5 ]. Dysregulation of cerebral iron has been implicated in the pathophysiology of several neurodegenerative diseases, suggesting that changes in iron levels may precede brain atrophy[ 6 , 7 ], thus providing a window for earlier detection of cognitive decline. Quantitative susceptibility mapping (QSM) has emerged as a reliable neuroimaging technique that facilitates noninvasive quantification of brain iron levels[ 8 , 9 ]. Mounting evidence from this technique underscores its importance in deciphering the clinical progression of Alzheimer’s disease (AD) and other neurodegenerative diseases[ 4 , 5 , 10 ]. Specifically, iron might accumulate in combination with amyloid-beta (A \(\beta )\) , which has been shown to exacerbate cognitive deterioration[ 11 ]. Recent findings by Spotorno et al. suggest a potential relationship between iron deposition and tau aggregation, which affects brain structure[ 12 ]. Despite the promising insights provided by QSM in neurodegenerative diseases, there remains a gap in its application toward understanding cognitive aging in community-dwelling populations. Here, we conducted the QSM to investigate the potential relationship between iron deposition and cognitive aging in the Taizhou Imaging Study (TIS), a community-based prospective cohort study. First, our analysis focused on regions of interest (ROIs) to explore the association between QSM and cross-sectional and longitudinal cognition. We hypothesized that elevated local cerebral iron in cortical signature regions would be negatively related to cognitive performance. Subsequently, we conducted voxel-based QSM and morphometry analyses to compare the distribution of iron and atrophy across the whole brain among older adults with varying cognitive statuses. This study aimed to evaluate QSM as a potential imaging biomarker for the early detection of cognitive decline. Materials and methods Participants The Taizhou Longitudinal Study (TZL) is an ongoing community-based prospective cohort study focused on multiple chronic diseases in rural older adults. As an ancillary study of the TZL, the TIS included four villages (Hutou, Lubao, Caixiang, and Baima) with the highest response rates; thus, residents were designed to participate in the TIS, as previously described[ 13 ]. Participants from the TIS group were enrolled at baseline upon meeting the following criteria: The exclusion criteria for patients were as follows: (1) aged 45–75 years; (2) resided in Taizhou for more than 10 years; (3) had no cerebrovascular diseases, intracranial tumors, other neurological diseases (including immune, metabolic, toxic, and infectious etiologies), or psychiatric illnesses; and (4) had complete physical, cognitive and imaging examinations. Written informed consent was obtained from all involved participants. The TIS study received ethical approval from the ethics committees of the School of Life Sciences, Fudan University, and the Fudan University Taizhou Institute of Health Sciences. Cognitive assessments Global cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The MoCA was further subdivided into five cognitive domain scores, namely, memory (delayed recall, orientation, digit span forward), language (animal picture naming, sentence repetition), attention (serial 7 s, digit vigilance), executive function (digit span backward, trail-making test, word similarities, category fluency) and visuospatial function (cube draw, clock draw), using a method published previously[ 14 ]. Participants were categorized into three groups according to the MMSE and MoCA cutoff values[ 15 , 16 ]: 1) cognitively normal; 2) mild cognitive dysfunction; and 3) severe cognitive dysfunction. A comprehensive neuropsychological battery assessing the cognitive domains was executed[ 13 ]: (1) Memory: the Chinese version of the Modified Fuld Object Memory Evaluation or Auditory verbal learning test (Huashan version, AVLT-H); (2) Attention: Conflicting Instructions Task (CIT); (3) Execution: Trail Making Test (TMT); (4) Language: Animal Naming Test (ANT); and (5) Visuospatial function: Clock Drawing Test (CDT). Dementia and mild cognitive impairment (MCI) were diagnosed by the consensus of neurologists with the criteria in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5)[ 17 ] and the criteria proposed by Petersen[ 18 ], respectively. All cognitive scores were standardized into Z scores. MRI acquisition and preprocessing MRI data were obtained using 3.0 T MR scanners, including 3D T1-weighted magnetization-prepared rapid gradient echo (MPRAGE), fluid-attenuated inversion recovery (FLAIR), and multiecho gradient-recalled echo (GRE) sequences. All MR images were reviewed by trained neuroradiologists. The detailed MRI sequences at each site are described in Supplemental Table 1. Integration of T1-weighted structural and FLAIR images was applied to improve pial surfaces in the FreeSurfer v7.2.0 pipeline ( http://surfer.nmr.mgh.harvard.edu ). Segmentations were visually inspected for both internal and external surfaces following the ENIGMA Cortical Quality Control Protocol 2.0[ 19 ]. QSM reconstruction QSM reconstruction was conducted using the combined pipeline in Sepia v0.8.1.1 ( https://github.com/kschan0214/sepia )[ 20 ]. In summary, the phase images were spatially unwrapped with a Laplacian-based technique. Binary masks, which are necessary for distinguishing local from background fields, were created via the 'antsBrainExtraction.sh' approach in ANTs (version 2.3.5, https://github.com/ANTsX/ANTs ) based on the magnitude images. The variable-kernels sophisticated harmonic artifact reduction for phase data (V-SHARP) algorithm was employed for background field removal, with a radius of a spherical mean value (SMV) kernel of 12 mm. During this processing step, the masks were eroded by 2 voxels from the edge of the brain. Finally, susceptibility maps were reconstructed using the improved sparse linear equations and least squares (iLSQR) algorithm. To mitigate assumptions about areas being spared in aging and minimize potential errors caused by reference selections, QSM values were not referenced, as suggested by previous studies[ 21 , 22 ]. Signature cortical measurements Assuming that iron accumulation in regions affected by normal aging and AD is relevant to cognitive aging, we computed three cortical signature QSMs (Supplemental Fig. 1): AD signature ROIs based on work by Jack et al[ 23 ] and aging (AG) and aging-specific (AG-specific) signature ROIs proposed by Dickerson and colleagues[ 24 ]. The AD signature meta-ROIs were defined as the entorhinal, fusiform, inferior, and middle temporal cortex regions. The AG signature represents a map of specific brain regions involved in cortical atrophy in normal aging, consisting of inferior, middle, and superior frontal, precentral, fusiform, angular, supramarginal, lateral occipital, cuneus, pericalcarine, and caudal insula cortices. However, it overlaps with some regions previously determined to be associated with AD. The AG-specific signature meta-ROI is composed of bilateral individual ROIs where atrophy is affected by aging only. These include the inferior and dorsomedial frontal, precentral, fusiform, lateral occipital, cuneus, pericalcarine, and caudal insula cortex regions. We also computed MRI-derived markers for neurodegeneration [cortical thickness, gray matter (GM) volume, local gyrification index, surface area] in these signature cortical regions. The estimated total intracranial volume (eTIV) was used to normalize the total brain and GM volume without ventricles to determine global and GM atrophy, respectively. All segments were inferred from anatomical MPRAGE images in the FreeSurfer v7.2.0 framework. The values of the ROIs were averaged across hemispheres for QSM and structural MRI analysis. Voxel-based QSM analyses Bias-corrected magnitude gradient echo images were affinely coregistered to their corresponding bias-corrected MPRAGE volume. Bias correction was performed using the N4 algorithm (ANTs). The MPRAGE images were nonlinearly registered to the MNI space (Montreal Neurological Institute, McGill University, Canada) using the SyN algorithm (ANTs). QSM data were spatially standardized to the MNI space by concatenating the warp of the aforementioned transformations and applying third-order b-spline interpolation. Absolute QSM maps were used for whole-brain analysis to prevent convolution-driven cancellations of spatially adjacent positive/negative susceptibilities. To attenuate the spurious impact of brain boundary effects, a 3D Gaussian kernel with a standard deviation of 3 mm was applied for smoothing, followed by a previously proposed smoothing-compensation strategy[ 22 , 25 ]. The QSM maps were confined to GM regions using probabilistic tissue segments obtained from MPRAGE data using SPM12 tissue segmentation ( http://www.fil.ion.ucl.ac.uk/spm/software/spm12 ). Finally, whole-brain (dementia vs. MCI vs. CN) analysis was carried out using nonparametric permutation testing (10,000 permutations) with threshold-free cluster enhancement (TFCE) implemented in FSL randomize v2.9 (with ‘-T’ settings, http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomize ). The statistical model included age and sex as nuisance covariates. Significant clusters were reported at FWE-corrected P values < 0.05. Voxel-based gray matter volume analysis All the MPRAGE images were extracted from the brain via ANT and GM segmentation in SPM12. To perform a simultaneous analysis pipeline for voxel-based morphometry (VBM) and QSM, the subsequent procedures were performed and adhered to FSL-VBM routines. First, GM images were nonlinearly registered to the MNI152 template, concatenated and averaged to create a study-specific GM template. Second, all native GM images were then reregistered to this study-specific template using nonlinear registration. Third, each registered GM image was multiplied by the Jacobian of the warp field for modulation to account for volume changes during registration. Fourth, all the modulated registered GM images were then smoothed using a Gaussian kernel with a standard deviation of 3 mm. Finally, we conducted a random analysis and displayed TFCE-based thresholding results with the same permutation testing settings as mentioned above. Statistical analyses All the statistical analyses were performed using R (version 4.3.1) provided by the R Core Team (2023) (R: a language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria). Multiple linear regression models were used to explore the associations between brain iron signatures, as measured by signed QSM-ROIs (cortical and signature brain regions), and various risk factors, MRI markers, and cross-sectional cognitive performance (n = 770). Linear mixed-effect models were utilized to investigate the longitudinal relationship between QSM-ROIs and cognitive function over time (n = 219). To assess the independence of these associations from local cerebral atrophy, models were rerun with additional control for local cortical thickness in each ROI (model 3). Finally, Cox proportional hazards regression was conducted for individuals who had available follow-up cognitive diagnoses (n = 458) to assess the capacity of iron in the aging-specific signature to predict incident dementia. A two-sided P value < 0.05 was considered indicative of a significant difference, and Benjamini‒Hochberg correction was applied for multiple comparisons[ 26 ]. Results Participant characteristics A total of 925 participants aged 45–75 years were enrolled from 2017 to 2022. Among 925 participants, 50 individuals were unable to complete the clinical assessment and MRI scan due to missing clinical tests and SWI scans. The remaining 875 participants who underwent complete clinical assessments and MRI scans were included. Of those participants, 36 individuals with neurological disease, 5 with conflicting cognitive status, and 64 individuals with poor MRI quality were excluded, resulting in a final sample size of 770 individuals for the subsequent analysis (Fig. 1 ). Cognitive follow-up assessments were conducted at two distinct intervals, with 770 participants involved. In the first follow-up period (2019–2020), 293 participants completed the assessment, which was further reduced to 219 participants in the subsequent period (2020–2021). The flow chart of the selection process is shown in Fig. 1 . The demographic information and neuropsychological data of the 770 participants are summarized in Table 1 . We also compared the characteristics between individuals who were followed up and those who were not among the two groups (Supplemental Table 2). Table 1 Baseline demographical, clinical and MRI characteristics of participants Baseline (n = 770) Demographics Age, y 62.0 ± 4.93 Male 327 (42.5) Education, y 5.35 ± 3.80 Cognition Baseline MMSE, score 26.0 (22.0, 28.0) Baseline MoCA, score 18.0 (13.0, 22.0) Follow-up MMSE†, score 25.0 (22.0, 27.0) Follow-up MoCA†, score 17.0 (13.0, 21.0) Follow-up time†, year 2.68 ± 0.39 Neuroimaging Cortical QSM 0.74 ± 0.78 AG-specific signature QSM 2.02 ± 0.93 AG signature QSM 1.03 ± 0.89 AD signature QSM 1.26 ± 1.80 Global atrophy 0.70 ± 0.04 Grey matter atrophy 0.38 ± 0.02 Data are mean ± SD, n (%), or median (interquartile range). Demographic information and clinical characteristics were compared using χ2, Student t-test and Mann-Whitney U-test. † Data was summarized in subjects completed follow-up phase I and II (n = 219). Abbreviations: MMSE = Mini-Mental State Examination; MoCA = Montreal Cognitive Assessment; AG = Aging; AD = Alzheimer's disease; QSM = quantitative susceptibility mapping. Relationship between iron signatures and cross-sectional cognition We initially explored the correlation between age and QSM values across various brain regions. We quantified iron deposition in cortical and AD signature brain regions using QSM values. Within the aging-related brain regions, specific brain areas reported to be affected by atrophy during normal aging (AG) were calculated, as well as brain regions solely affected by aging, not overlapping with AD signature brain regions (AG-specific). As shown in Supplemental Fig. 2, the QSM values in the cortical ( \(\beta\) = 0.098, p = 0.027), AG-specific ( \(\beta\) = -0.091, p = 0.030), AG ( \(\beta\) = 0.097, p = 0.027), and AD signature ( \(\beta\) = 0.109, p = 0.027) regions were all positively correlated with age. These findings suggest that age may be a significant risk factor for increased iron deposition. Subsequently, to investigate the impact of brain iron on cognitive impairment, we first examined the relationship between iron deposition in different brain regions and baseline global cognitive scores. As shown in Table 2 , participants in the upper tertile of cortical QSM presented significantly poorer global cognitive function \((\beta\) = -0.100, p = 0.035) than did those in the bottom tertile after adjusting for sex, age, education, site, eTIV, cognitive status, smoking, drinking, and medical history (Model 2), as well as in the AG-specific signature QSM \((\beta\) = -0.110, p = 0.029). This association persisted even after accounting for cortical thickness (interpreted as local atrophy) of the ROI (model 3; \(\beta\) = -0.104, p = 0.035; \(\beta\) = -0.118, p = 0.020, respectively). However, similar negative correlations between brain iron and global cognition were not observed in the Aging and AD signature regions. In summary, higher QSM values were linked to poorer global cognition, particularly in aging-specific and cortical regions. Nevertheless, no significant correlations were found between QSM in any of the selected ROIs and the ZMoCA. Table 2 Association between localized QSM and baseline global cognition ZMMSE ZMoCA \(\beta\) (SE) P \(\beta\) (SE) P Cortical QSM upper Q2 vs bottom Q1 Model 1 -0.070 (0.041) 0.193 -0.092 (0.043) 0.130 Model 2 -0.068 (0.041) 0.232 -0.096 (0.044) 0.116 Model 3 -0.069 (0.042) 0.209 -0.090 (0.044) 0.168 upper Q3 vs bottom Q1 Model 1 -0.107 (0.041) 0.020 -0.022 (0.044) 0.880 Model 2 -0.100 (0.042) 0.035 -0.013 (0.044) 0.986 Model 3 -0.104 (0.044) 0.035 -0.002 (0.046) 0.986 AG-specific signature QSM upper Q2 vs bottom Q1 Model 1 -0.027 (0.040) 0.503 -0.070 (0.043) 0.206 Model 2 -0.029 (0.041) 0.471 -0.068 (0.043) 0.226 Model 3 -0.032 (0.041) 0.437 -0.064 (0.043) 0.282 upper Q3 vs bottom Q1 Model 1 -0.119 (0.040) 0.013 -0.011 (0.043) 0.880 Model 2 -0.110 (0.041) 0.029 -0.001 (0.043) 0.986 Model 3 -0.118 (0.042) 0.020 0.013 (0.045) 0.986 AG signature QSM upper Q2 vs bottom Q1 Model 1 -0.067 (0.040) 0.193 -0.025 (0.043) 0.561 Model 2 -0.064 (0.041) 0.232 -0.028 (0.043) 0.518 Model 3 -0.068 (0.042) 0.209 -0.020 (0.044) 0.653 upper Q3 vs bottom Q1 Model 1 -0.057 (0.041) 0.222 -0.010 (0.043) 0.880 Model 2 -0.050 (0.041) 0.302 -0.002 (0.044) 0.986 Model 3 -0.057 (0.043) 0.256 0.015 (0.046) 0.986 AD signature QSM upper Q2 vs bottom Q1 Model 1 -0.047 (0.041) 0.325 -0.038 (0.043) 0.509 Model 2 -0.037 (0.041) 0.471 -0.029 (0.044) 0.518 Model 3 -0.036 (0.041) 0.437 -0.028 (0.044) 0.653 upper Q3 vs bottom Q1 Model 1 -0.036 (0.042) 0.395 -0.007 (0.044) 0.880 Model 2 -0.027 (0.042) 0.529 -0.001 (0.045) 0.986 Model 3 -0.024 (0.042) 0.570 0.001 (0.045) 0.986 Standardized Beta coefficient values represent a one unit change in global cognition z-score with a one PPB change in QSM. Model 1 was adjusted for sex, age, years of education, site, eTIV, and cognitive status. Model 2 was additionally adjusted smoking, drinking, hypertension, diabetes, and hyperlipidemia. Model 3 was additionally adjusted cortical thickness of each ROI. Abbreviations: MMSE = Mini-Mental State Examination; MoCA = Montreal Cognitive Assessment; AG = Aging; AD = Alzheimer's disease; QSM = quantitative susceptibility mapping. Benjamini-Hochberg FDR corrected P < .05 are shown bold. Relationship between signature iron and local cortical atrophy Previous studies have confirmed a negative association between QSM values and cortical atrophy in AD patients. To assess the relationship between cortical iron deposition and brain atrophy in normal community-dwelling elderly individuals, we evaluated the associations between QSM values in different brain regions and regional brain volumes. As shown in Fig. 2 and Supplemental Table 3, after adjusting for sex, age, education, site, smoking status, drinking status, history of disease, and cognition, the QSM values in the cortical, AG specific, and AG signature regions remained significantly negatively correlated with cortical thickness (model 3; p < 0.005). Similar significant negative correlations were also observed for other classical indicators reflecting brain atrophy, such as GM volume and the local cortical gyrification index (Fig. 2 ). However, there were no significant correlations between QSM in any of the selected ROIs and surface area. Relationship between signature iron and longitudinal cognition We further analyzed the relationship between brain iron and longitudinal changes in cognitive decline. QSM values in all selected ROIs were associated with cognitive decline as assessed by the MoCA (Table 3 ). In participants who completed baseline assessments and two cognitive follow-ups (n = 219), the annual decrease in MoCA score was negatively correlated with iron deposition in the cortical region ( \(\beta\) = -0.440, p = 0.012), AG-specific signature region \((\beta\) = -0.527, p = 0.004), AG signature region ( \(\beta\) = -0.521, p = 0.004), and AD signature region ( \(\beta\) = -0.365, p = 0.029) after adjusting for sex, age, education, eTIV, smoking status, drinking status, and history of disease (Model 2). These associations with longitudinal changes in the MoCA score remained significant independent of local atrophy after additionally adjusting for the cortical thickness of each ROI (model 3; p < 0.05). Considering the differences in scale sensitivity, we did not observe a relationship between QSM in any of the selected ROIs and longitudinal changes in MMSE scores. Table 3 Association between localized QSM and longitudinal global cognition change in ZMMSE change in ZMoCA \(\beta\) (SE) P \(\beta\) (SE) P Cortical QSM Model 1 -0.162 (0.166) 0.439 -0.433 (0.165) 0.012 Model 2 -0.165 (0.168) 0.435 -0.440 (0.167) 0.012 Model 3 -0.164 (0.168) 0.439 -0.441 (0.167) 0.012 AG-specific signature QSM Model 1 -0.220 (0.165) 0.439 -0.526 (0.164) 0.004 Model 2 -0.222 (0.166) 0.435 -0.527 (0.165) 0.004 Model 3 -0.221 (0.166) 0.439 -0.526 (0.165) 0.004 AG signature QSM Model 1 -0.196 (0.166) 0.439 -0.516 (0.165) 0.004 Model 2 -0.200 (0.168) 0.435 -0.521 (0.167) 0.004 Model 3 -0.198 (0.168) 0.439 -0.521 (0.167) 0.004 AD signature QSM Model 1 -0.087 (0.165) 0.595 -0.354 (0.164) 0.031 Model 2 -0.090 (0.167) 0.592 -0.365 (0.167) 0.029 Model 3 -0.092 (0.168) 0.585 -0.364 (0.167) 0.030 Standardized Beta coefficient values represent a one unit change in global cognition z-score per 1 years with a one PPB change in QSM. Model 1 was adjusted for sex, age, years of education and eTIV. Model 2 was additionally adjusted smoking, drinking, hypertension, diabetes, and hyperlipidemia. Model 3 was additionally adjusted cortical thickness of each ROI. Abbreviations: MMSE = Mini-Mental State Examination; MoCA = Montreal Cognitive Assessment; AG = Aging; AD = Alzheimer's disease; QSM = quantitative susceptibility mapping. Benjamini-Hochberg FDR corrected P < .05 are shown bold. Analyses of domain-specific cognitive scores (from MoCA components) also confirmed such negative associations in all ROI-QSM values. Decreased attention was predicted by brain iron levels in cortical (model 3; \(\beta\) = -0.380, p = 0.039), AG-specific ( \(\beta\) = -0.356, p = 0.039), AG signature ( \(\beta\) = -0.401, p = 0.039) and AD signature ( \(\beta\) = -0.347, p = 0.039) regions, independent of atrophy, as depicted in Fig. 3 . Additionally, a negative association was also observed between the rate of change in visuospatial function and cortical as well as all signature QSMs (model 3; \(\beta\) = -0.464, p = 0.008; \(\beta\) = -0.335, p = 0.045; \(\beta\) = -0.481, p = 0.008; \(\beta\) = -0.491, p = 0.008, respectively). Regarding language, QSM in the AG-specific signature region was predictive of a steeper decline in Model 2 ( \(\beta\) = -0.421, p = 0.046) and Model 3 ( \(\beta\) = -0.418, p = 0.048). However, no significant correlations were found between QSM values in any of the selected ROIs and episodic memory or executive function (Supplemental Table 4). Iron metabolism patterns across cognitive diagnoses: voxel-based QSM analysis To further analyze the differences in iron metabolism patterns among distinct cognitive diagnoses, voxelwise comparisons of QSM values were conducted among the dementia, MCI, and CN groups. (Fig. 4 , Supplemental Table 5). Within the dementia group, elevated QSM values were observed in five distinct clusters compared to those in the CN group. Pronounced abnormalities were identified in the left frontal pole/middle frontal gyrus/superior frontal gyrus ( p = 0.018); left paracingulate gyrus/superior frontal gyrus/frontal pole ( p = 0.040); left paracingulate gyrus/medial frontal cortex/cingulate gyrus ( p = 0.041); left caudate/accumbens/putamen/subcallosal cortex ( p = 0.036); and left frontal pole ( p = 0.049). In addition, the dementia group exhibited greater QSM in the left middle frontal gyrus/superior frontal gyrus/frontal pole than did the MCI group ( p = 0.031). There were no significant regions where the MCI group had higher QSM values than did the HC group. Furthermore, VBM analysis revealed no discernible differences in atrophy across the aforementioned groups at a whole-brain FDR-corrected p < 0.05. Discussion In this study, our focus was on iron deposition in regions selectively associated with aging and AD, aiming to evaluate its correlation with baseline cognition and assess its potential value in predicting future cognitive decline processes. Here, for the first time, we present evidence linking increased iron accumulation in signature brain regions to exacerbated cognitive decline and structural brain alterations in a community-based cohort. The whole-brain approach allows for the mapping of iron distribution, revealing increased iron load in dementia patients across the frontal, paracingulate, and cingulate cortex, as well as in deep gray matter structures such as the caudate, accumbens, and putamen. In agreement with previous studies indicating iron-related cognitive dysfunction[ 27 – 29 ], our findings support the potential of brain iron accumulation as a neuroimaging marker for the early assessment of cognitive decline in normal aging. The pathological deposition of cerebral iron significantly contributes to the cascade of neurodegenerative processes. Excessive iron accelerates the production of reactive oxygen species (ROS), leading to oxidative stress that damages neuronal lipids, proteins, and DNA[ 30 ]. Furthermore, the interaction of iron with activated microglia promotes neuroinflammatory responses, exacerbating neuronal damage[ 31 ]. Compounding these effects, iron dysregulation influences the pathology of key neurodegenerative proteins, such as tau and A \(\beta\) , by facilitating tau hyperphosphorylation[ 32 ] and enhancing A \(\beta\) aggregation[ 33 ], thus contributing to the hallmark features of diseases such as AD. The role of iron in exacerbating oxidative damage, inflammation, and protein aggregation pathways suggests its potential as a critical factor in the pathophysiology of neurodegeneration. Iron deposition in the brain plays a pivotal role in the progression of neurodegenerative changes and may lead to brain atrophy through several interrelated mechanisms. Iron overload may facilitate brain atrophy through ferroptosis, a nonapoptotic cell death pathway, by catalyzing reactive oxygen species production and promoting lipid peroxidation, leading to neuronal damage and cell death[ 34 ]. Moreover, the interaction of iron with critical proteins, including tau, exacerbates their pathological aggregation, further implicating iron in the progression of AD, which facilitates neuronal damage and brain atrophy[ 35 ]. Additionally, iron-induced neuroinflammation, characterized by activated microglia and the release of proinflammatory cytokines, accelerates brain tissue loss[ 36 ]. A seven-year follow-up longitudinal study by Daugherty et al.[ 37 ] reported that increased iron levels, particularly in the putamen, predict accelerated brain shrinkage in 32 older adults. In support of this, our research revealed that iron accumulation correlates with atrophy in signature brain regions, affecting cortical thickness, gray matter volume, and the LGI. Although the causal relationship between iron accumulation and brain atrophy, particularly in the context of aging and AD, remains to be fully elucidated, further investigation into the role of iron in the neurodegenerative cascade is necessary. The sensitivity of QSM in neuroimaging studies offers promising insights into the distribution of iron levels in key brain regions, including the hippocampus, amygdala, and caudate, in AD patients[ 38 ]. Moreover, widespread increased magnetic susceptibility across the cortical ribbon, asymmetrically covering the left hemisphere cerebral cortex, caudate nucleus, putamen, and partial cerebellar cortex, which was demonstrated in another study[ 39 ], points to a complex pattern of neurodegeneration that QSM uniquely captures. Specifically, subcortical iron accumulation has been proposed as a potential biomarker for subcortical vascular MCI[ 40 ]. However, our whole-brain volumetry analysis did not align with these QSM findings, suggesting that overall, QSM may be more sensitive than conventional structural MRI in detecting abnormalities in MCI and dementia patients and could also be an indication that QSM might capture early pathological changes before volumetric losses are evident. The phenomenon of brain iron accumulation, while critical to the pathology of cognitive decline, remains only partially understood. Its development is influenced by a constellation of factors, including genetic predispositions that disrupt normal iron metabolism and regulatory mechanisms. Notably, conditions such as neurodegeneration with brain iron accumulation (NBIA) underscore the genetic component of this pathology[ 41 ]. Age-related factors also play a vital role, with evidence suggesting that the brain's ability to regulate iron diminishes with age, leading to iron accumulation in specific regions associated with motor and cognitive functions[ 5 ]. Our investigation confirmed the age-associated accumulation of iron in cortical and all AG- and AD-Signatures regions, aligning with multiple studies that have demonstrated a widespread pattern of iron load across various subcortical structures (e.g., the GP, putamen, amygdala, hippocampus, SN, and RN) and cortical regions (e.g., all lobes and the entorhinal, ITG, SMF and IOF cortex) during aging[ 42 – 44 ]. Furthermore, dysfunction of the blood-brain barrier (BBB) represents a crucial mechanism for abnormal iron deposition, particularly in neurodegenerative conditions such as Parkinson's disease (PD)[ 45 ]. Collectively, these findings highlight the multifaceted nature of brain iron accumulation and its implications for neurodegenerative diseases. The negative findings from the FAIRPARK-II trial[ 46 ], where iron chelation therapy with deferiprone led to clinical worsening in PD patients, highlighted the necessity of carefully navigating the delicate balance between the indispensable physiological role of iron and its propensity to inflict damage when present in excess. This revelation does not diminish the significance of our research but rather emphasizes the need for a sophisticated approach to dissect the intricate interactions among iron-related neurodegenerative mechanisms. Our study specifically addresses the issue of iron accumulation and its correlation with cognitive decline in signature brain regions, aiming to uncover biomarkers for early detection and intervention. This work holds promise for revealing novel avenues for understanding and treating neurodegeneration in aging populations. This study is subject to several limitations. First, not all participants completed consecutive follow-ups from baseline, resulting in a relatively limited number of subjects for the longitudinal analyses. This lack of consistency increases the risk of false-negative associations. Second, due to the absence of continuous scans in the current study, we were unable to assess the dynamics of iron accumulation and its relationship with brain shrinkage and cognitive changes. Further investigations with larger longitudinal datasets are therefore warranted. Third, this study did not incorporate cerebrospinal fluid or plasma biomarker evidence of cerebral amyloid and tau pathology (e.g., A \(\beta\) 42, A \(\beta\) 42/40 ratio, total-tau, and p-tau), which could have provided valuable insights into iron-related mechanisms. Further research is needed to explore these potential connections. Finally, while QSM is sensitive to variations in brain iron content, it is important to note that magnetic susceptibility, as measured by QSM, may also be influenced by other metals (e.g., copper, manganese aluminum, and calcium)[ 47 ], myelin[ 48 ] and cellular packing density[ 49 ]. Variations in QSM reconstruction, spatial standardization, and other procedures may introduce biases that could impact the generalizability of our study findings. Therefore, these factors should be carefully considered when interpreting the results. Conclusion Overall, this study revealed that our distinctive signature QSMs were capable of identifying individuals at risk of cognitive decline during normal aging. The spatial accumulation of iron correlates with dementia, offering novel insights into the role of iron deposition in the aging population. Although iron deposition in specific brain regions has been extensively studied, the signature patterns of iron accumulation in age-related brain areas still warrant further investigation. Declarations Acknowledgments The authors thank the study participants and the staff of Huashan Hospital Fudan University and the Fudan University Taizhou Institute of Health Sciences for assistance in neighborhood outreach and engagement in support of participant recruitment. Authors’ contributions R.L. and Yr.F. were responsible for the study design, statistical analysis, and writing of the original draft of the manuscript. Yz. W., Hy. L, and Px. L participated in the data collection and figure preparation. Q.D. and Yf. J reviewed and edited the manuscript. Xd.C. and M.C. engaged in the study design and study supervision. All the authors contributed to the final version of the paper. Funding The study was supported by the following agencies: the Ministry of Science and Technology of China (2021ZD0201806), the National Key R&D Program of China (2021YFC2500100), the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), the Key Research and Development Plans of Jiangsu Province, China (BE2021696), and the Shanghai Municipal Science and Technology Major Project (2023SHZDZX02). Availability of data and materials The datasets supporting this study's findings were obtained from the TIS cohort, which is available from the corresponding author upon request to any qualified investigator subject to a data use agreement (Mei Cui, e-mail: [email protected] ). Ethics approval and consent to participate All the TIS participants provided informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Deary IJ, Corley J, Gow AJ, Harris SE, Houlihan LM, Marioni RE, et al. 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Ayton S, Portbury S, Kalinowski P, Agarwal P, Diouf I, Schneider JA, et al. Regional brain iron associated with deterioration in alzheimer’s disease: A large cohort study and theoretical significance. Alzheimers Dement. 2021;17(7):1244–56. Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, et al. Ferroptosis: An iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72. Xia C, Makaretz SJ, Caso C, McGinnis S, Gomperts SN, Sepulcre J, et al. Association of in vivo [18F]AV-1451 tau PET imaging results with cortical atrophy and symptoms in typical and atypical alzheimer disease. JAMA Neurol. 2017;74(4):427–36. Ndayisaba A, Kaindlstorfer C, Wenning GK. Iron in neurodegeneration - cause or consequence? Front Neurosci. 2019;13:180. Daugherty AM, Raz N. Accumulation of iron in the putamen predicts its shrinkage in healthy older adults: A multi-occasion longitudinal study. NeuroImage. 2016;128:11–20. Kan H, Uchida Y, Arai N, Ueki Y, Aoki T, Kasai H, et al. Simultaneous voxel-based magnetic susceptibility and morphometry analysis using magnetization-prepared spoiled turbo multiple gradient echo. NMR Biomed. 2020;33(5):e4272. Yang A, Du L, Gao W, Liu B, Chen Y, Wang Y, et al. Associations of cortical iron accumulation with cognition and cerebral atrophy in alzheimer’s disease. Quant Imag Med Surg. 2022;12(9):4570–86. Sun Y, Ge X, Han X, Cao W, Wang Y, Ding W, et al. Characterizing brain iron deposition in patients with subcortical vascular mild cognitive impairment using quantitative susceptibility mapping: A potential biomarker. Front Aging Neurosci. 2017;9:81. Gregory A, Hayflick SJ. Genetics of neurodegeneration with brain iron accumulation. Curr Neurol Neurosci. 2011;11(3):254–61. Chen L, Soldan A, Oishi K, Faria A, Zhu Y, Albert M, et al. Quantitative susceptibility mapping of brain iron and β-amyloid in MRI and PET in cognitively normal older adults. Radiology. 2021;298(2):353–62. Acosta-Cabronero J, Betts MJ, Cardenas-Blanco A, Yang S, Nestor PJ. In vivo MRI mapping of brain iron deposition across the adult lifespan. J Neurosci. 2016;36(2):364–74. Burgetova R, Dusek P, Burgetova A, Pudlac A, Vaneckova M, Horakova D, et al. Age-related magnetic susceptibility changes in deep grey matter and cerebral cortex of normal young and middle-aged adults depicted by whole brain analysis. Quant Imag Med Surg. 2021;11(9):3906–19. Olmedo-Díaz S, Estévez-Silva H, Orädd G, Af Bjerkén S, Marcellino D, Virel A. An altered blood-brain barrier contributes to brain iron accumulation and neuroinflammation in the 6-OHDA rat model of parkinson’s disease. Neuroscience. 2017;362:141–51. Devos D, Labreuche J, Rascol O, Corvol J-C, Duhamel A, Guyon Delannoy P, et al. Trial of deferiprone in parkinson’s disease. N Engl J Med. 2022;387(22):2045–55. Krebs N, Langkammer C, Goessler W, Ropele S, Fazekas F, Yen K, et al. Assessment of trace elements in human brain using inductively coupled plasma mass spectrometry. J Trace Elem Med Biol. 2014;28(1):1–7. Fukunaga M, Li T-Q, Van Gelderen P, De Zwart JA, Shmueli K, Yao B, et al. Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast. Proc Natl Acad Sci U S A. 2010;107(8):3834–9. Zhao Y, Wen J, Cross AH, Yablonskiy DA. On the relationship between cellular and hemodynamic properties of the human brain cortex throughout adult lifespan. NeuroImage. 2016;133:417–29. Additional Declarations No competing interests reported. Supplementary Files 515Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 02 Oct, 2024 Read the published version in Alzheimer's Research & Therapy → Version 1 posted Editorial decision: Revision requested 11 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviews received at journal 09 Jul, 2024 Reviews received at journal 27 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers invited by journal 20 Jun, 2024 Editor assigned by journal 15 May, 2024 Submission checks completed at journal 15 May, 2024 First submitted to journal 15 May, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4425826","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304793689,"identity":"2be4bd79-6165-4420-bd25-8d25bfde2e21","order_by":0,"name":"Rui Li","email":"","orcid":"","institution":"Huashan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Li","suffix":""},{"id":304793690,"identity":"3a836fce-110a-4b09-bf81-6a917b3c11d6","order_by":1,"name":"Yi‑Ren Fan","email":"","orcid":"","institution":"Huashan 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14:29:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4425826/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4425826/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13195-024-01575-9","type":"published","date":"2024-10-02T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57517170,"identity":"ffc31a1e-fb48-4bf8-8928-5e94469bdbab","added_by":"auto","created_at":"2024-05-31 20:15:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52157,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4425826/v1/77fdd988c4af1610567ac8a8.png"},{"id":57517171,"identity":"214bc0d2-ad46-4375-9ede-a7cac352f4c4","added_by":"auto","created_at":"2024-05-31 20:15:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between MRI markers of local cortical atrophy and QSM in signature regions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4425826/v1/95d375a0cef610addc31f7c0.png"},{"id":57517172,"identity":"9e28ce29-974d-40df-a844-e27d8884de0e","added_by":"auto","created_at":"2024-05-31 20:15:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353597,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between localized QSM and longitudinal domain-specific cognitive score changes\u003c/p\u003e\n\u003cp\u003e(A-B) Trajectory plots illustrate the impact of baseline QSM on the change in longitudinal attention and visuospatial function. The annual rate of domain-specific cognitive decline was calculated for each individual and plotted against the baseline localized QSM value. Estimated cognitive trajectories with 95% confidence intervals (CIs) are displayed. The model is adjusted for sex, age, years of education, smoking, drinking, hypertension, diabetes, and hyperlipidemia, eTIV and cortical thickness of each ROI (Supplementary Table 2; model 3).\u003c/p\u003e\n\u003cp\u003eAbbreviations: AG = Aging; AD = Alzheimer's disease; QSM = quantitative susceptibility mapping.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4425826/v1/25a24fc0f639be40d9ea0043.png"},{"id":57517174,"identity":"9be12155-f778-4083-9af7-3f85bdefc5f1","added_by":"auto","created_at":"2024-05-31 20:15:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":969665,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of increased brain iron in dementia compared to CN and MCI\u003c/p\u003e\n\u003cp\u003e(A) Red/yellow clusters represent significantly higher QSM values in dementia group than in CN group. (B) absolute QSM was greater in the dementia group than in the MCI group. The results were overlaid onto the study-wise anatomical template in the MNI (Montreal Neurological Institute template) space and displayed in radiological orientation.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CN = cognitively normal; FWE = family-wise error; MCI = mild cognitive impairment; QSM = quantitative susceptibility mapping.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4425826/v1/997b79c76606b59d1a8a3a48.png"},{"id":66097020,"identity":"03937d1f-3932-47f4-803f-74927b218b59","added_by":"auto","created_at":"2024-10-07 16:12:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2457176,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4425826/v1/59362236-548d-4b63-8084-a75226300ee2.pdf"},{"id":57517173,"identity":"fd369bc1-5647-4195-a4d1-cbe627d00463","added_by":"auto","created_at":"2024-05-31 20:15:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4750961,"visible":true,"origin":"","legend":"","description":"","filename":"515Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4425826/v1/3f7b1613a4a61e8db987d08c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain Iron in Signature Regions Relating to Cognitive Aging in Older Adults: The Taizhou Imaging Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCognitive aging, an inevitable, natural process of lifespan, is characterized by a progressive decline in cognitive functions, including processing speed, reasoning, and memory, among elderly individuals[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The rate of cognitive aging varies significantly among individuals, with those experiencing accelerated cognitive decline being at a greater risk of developing dementia[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the pursuit of understanding cognitive aging, magnetic resonance imaging (MRI) has been instrumental in identifying macroscopic structural changes such as brain atrophy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These changes have been extensively studied as markers of cognitive decline within community-dwelling elderly populations. However, brain atrophy represents a relatively late-stage manifestation of the cognitive decline process, prompting researchers to seek earlier and more sensitive markers.\u003c/p\u003e \u003cp\u003eIron, a critical element involved in numerous biochemical processes within the brain, has emerged as a potential early biomarker for cognitive decline due to its association with neurodegenerative processes and brain aging[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Dysregulation of cerebral iron has been implicated in the pathophysiology of several neurodegenerative diseases, suggesting that changes in iron levels may precede brain atrophy[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], thus providing a window for earlier detection of cognitive decline.\u003c/p\u003e \u003cp\u003eQuantitative susceptibility mapping (QSM) has emerged as a reliable neuroimaging technique that facilitates noninvasive quantification of brain iron levels[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mounting evidence from this technique underscores its importance in deciphering the clinical progression of Alzheimer\u0026rsquo;s disease (AD) and other neurodegenerative diseases[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Specifically, iron might accumulate in combination with amyloid-beta (A\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta )\\)\u003c/span\u003e\u003c/span\u003e, which has been shown to exacerbate cognitive deterioration[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent findings by Spotorno et al. suggest a potential relationship between iron deposition and tau aggregation, which affects brain structure[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the promising insights provided by QSM in neurodegenerative diseases, there remains a gap in its application toward understanding cognitive aging in community-dwelling populations. Here, we conducted the QSM to investigate the potential relationship between iron deposition and cognitive aging in the Taizhou Imaging Study (TIS), a community-based prospective cohort study. First, our analysis focused on regions of interest (ROIs) to explore the association between QSM and cross-sectional and longitudinal cognition. We hypothesized that elevated local cerebral iron in cortical signature regions would be negatively related to cognitive performance. Subsequently, we conducted voxel-based QSM and morphometry analyses to compare the distribution of iron and atrophy across the whole brain among older adults with varying cognitive statuses. This study aimed to evaluate QSM as a potential imaging biomarker for the early detection of cognitive decline.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eThe Taizhou Longitudinal Study (TZL) is an ongoing community-based prospective cohort study focused on multiple chronic diseases in rural older adults. As an ancillary study of the TZL, the TIS included four villages (Hutou, Lubao, Caixiang, and Baima) with the highest response rates; thus, residents were designed to participate in the TIS, as previously described[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParticipants from the TIS group were enrolled at baseline upon meeting the following criteria:\u003c/p\u003e \u003cp\u003eThe exclusion criteria for patients were as follows: (1) aged 45\u0026ndash;75 years; (2) resided in Taizhou for more than 10 years; (3) had no cerebrovascular diseases, intracranial tumors, other neurological diseases (including immune, metabolic, toxic, and infectious etiologies), or psychiatric illnesses; and (4) had complete physical, cognitive and imaging examinations. Written informed consent was obtained from all involved participants. The TIS study received ethical approval from the ethics committees of the School of Life Sciences, Fudan University, and the Fudan University Taizhou Institute of Health Sciences.\u003c/p\u003e \u003cp\u003eCognitive assessments\u003c/p\u003e \u003cp\u003eGlobal cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The MoCA was further subdivided into five cognitive domain scores, namely, memory (delayed recall, orientation, digit span forward), language (animal picture naming, sentence repetition), attention (serial 7 s, digit vigilance), executive function (digit span backward, trail-making test, word similarities, category fluency) and visuospatial function (cube draw, clock draw), using a method published previously[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Participants were categorized into three groups according to the MMSE and MoCA cutoff values[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]: 1) cognitively normal; 2) mild cognitive dysfunction; and 3) severe cognitive dysfunction.\u003c/p\u003e \u003cp\u003eA comprehensive neuropsychological battery assessing the cognitive domains was executed[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]: (1) Memory: the Chinese version of the Modified Fuld Object Memory Evaluation or Auditory verbal learning test (Huashan version, AVLT-H); (2) Attention: Conflicting Instructions Task (CIT); (3) Execution: Trail Making Test (TMT); (4) Language: Animal Naming Test (ANT); and (5) Visuospatial function: Clock Drawing Test (CDT). Dementia and mild cognitive impairment (MCI) were diagnosed by the consensus of neurologists with the criteria in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and the criteria proposed by Petersen[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], respectively. All cognitive scores were standardized into Z scores.\u003c/p\u003e \u003cp\u003eMRI acquisition and preprocessing\u003c/p\u003e \u003cp\u003eMRI data were obtained using 3.0 T MR scanners, including 3D T1-weighted magnetization-prepared rapid gradient echo (MPRAGE), fluid-attenuated inversion recovery (FLAIR), and multiecho gradient-recalled echo (GRE) sequences. All MR images were reviewed by trained neuroradiologists. The detailed MRI sequences at each site are described in Supplemental Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eIntegration of T1-weighted structural and FLAIR images was applied to improve pial surfaces in the FreeSurfer v7.2.0 pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu\u003c/span\u003e\u003cspan address=\"http://surfer.nmr.mgh.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Segmentations were visually inspected for both internal and external surfaces following the ENIGMA Cortical Quality Control Protocol 2.0[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eQSM reconstruction\u003c/p\u003e \u003cp\u003eQSM reconstruction was conducted using the combined pipeline in Sepia v0.8.1.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/kschan0214/sepia\u003c/span\u003e\u003cspan address=\"https://github.com/kschan0214/sepia\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In summary, the phase images were spatially unwrapped with a Laplacian-based technique. Binary masks, which are necessary for distinguishing local from background fields, were created via the 'antsBrainExtraction.sh' approach in ANTs (version 2.3.5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ANTsX/ANTs\u003c/span\u003e\u003cspan address=\"https://github.com/ANTsX/ANTs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) based on the magnitude images. The variable-kernels sophisticated harmonic artifact reduction for phase data (V-SHARP) algorithm was employed for background field removal, with a radius of a spherical mean value (SMV) kernel of 12 mm. During this processing step, the masks were eroded by 2 voxels from the edge of the brain. Finally, susceptibility maps were reconstructed using the improved sparse linear equations and least squares (iLSQR) algorithm. To mitigate assumptions about areas being spared in aging and minimize potential errors caused by reference selections, QSM values were not referenced, as suggested by previous studies[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSignature cortical measurements\u003c/p\u003e \u003cp\u003eAssuming that iron accumulation in regions affected by normal aging and AD is relevant to cognitive aging, we computed three cortical signature QSMs (Supplemental Fig.\u0026nbsp;1): AD signature ROIs based on work by Jack et al[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and aging (AG) and aging-specific (AG-specific) signature ROIs proposed by Dickerson and colleagues[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The AD signature meta-ROIs were defined as the entorhinal, fusiform, inferior, and middle temporal cortex regions. The AG signature represents a map of specific brain regions involved in cortical atrophy in normal aging, consisting of inferior, middle, and superior frontal, precentral, fusiform, angular, supramarginal, lateral occipital, cuneus, pericalcarine, and caudal insula cortices. However, it overlaps with some regions previously determined to be associated with AD. The AG-specific signature meta-ROI is composed of bilateral individual ROIs where atrophy is affected by aging only. These include the inferior and dorsomedial frontal, precentral, fusiform, lateral occipital, cuneus, pericalcarine, and caudal insula cortex regions.\u003c/p\u003e \u003cp\u003eWe also computed MRI-derived markers for neurodegeneration [cortical thickness, gray matter (GM) volume, local gyrification index, surface area] in these signature cortical regions. The estimated total intracranial volume (eTIV) was used to normalize the total brain and GM volume without ventricles to determine global and GM atrophy, respectively. All segments were inferred from anatomical MPRAGE images in the FreeSurfer v7.2.0 framework. The values of the ROIs were averaged across hemispheres for QSM and structural MRI analysis.\u003c/p\u003e \u003cp\u003eVoxel-based QSM analyses\u003c/p\u003e \u003cp\u003eBias-corrected magnitude gradient echo images were affinely coregistered to their corresponding bias-corrected MPRAGE volume. Bias correction was performed using the N4 algorithm (ANTs). The MPRAGE images were nonlinearly registered to the MNI space (Montreal Neurological Institute, McGill University, Canada) using the SyN algorithm (ANTs). QSM data were spatially standardized to the MNI space by concatenating the warp of the aforementioned transformations and applying third-order b-spline interpolation. Absolute QSM maps were used for whole-brain analysis to prevent convolution-driven cancellations of spatially adjacent positive/negative susceptibilities. To attenuate the spurious impact of brain boundary effects, a 3D Gaussian kernel with a standard deviation of 3 mm was applied for smoothing, followed by a previously proposed smoothing-compensation strategy[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The QSM maps were confined to GM regions using probabilistic tissue segments obtained from MPRAGE data using SPM12 tissue segmentation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/software/spm12\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm/software/spm12\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, whole-brain (dementia vs. MCI vs. CN) analysis was carried out using nonparametric permutation testing (10,000 permutations) with threshold-free cluster enhancement (TFCE) implemented in FSL randomize v2.9 (with \u0026lsquo;-T\u0026rsquo; settings, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomize\u003c/span\u003e\u003cspan address=\"http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Randomize\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The statistical model included age and sex as nuisance covariates. Significant clusters were reported at FWE-corrected P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eVoxel-based gray matter volume analysis\u003c/p\u003e \u003cp\u003eAll the MPRAGE images were extracted from the brain via ANT and GM segmentation in SPM12. To perform a simultaneous analysis pipeline for voxel-based morphometry (VBM) and QSM, the subsequent procedures were performed and adhered to FSL-VBM routines. First, GM images were nonlinearly registered to the MNI152 template, concatenated and averaged to create a study-specific GM template. Second, all native GM images were then reregistered to this study-specific template using nonlinear registration. Third, each registered GM image was multiplied by the Jacobian of the warp field for modulation to account for volume changes during registration. Fourth, all the modulated registered GM images were then smoothed using a Gaussian kernel with a standard deviation of 3 mm. Finally, we conducted a random analysis and displayed TFCE-based thresholding results with the same permutation testing settings as mentioned above.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eAll the statistical analyses were performed using R (version 4.3.1) provided by the R Core Team (2023) (R: a language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria). Multiple linear regression models were used to explore the associations between brain iron signatures, as measured by signed QSM-ROIs (cortical and signature brain regions), and various risk factors, MRI markers, and cross-sectional cognitive performance (n\u0026thinsp;=\u0026thinsp;770). Linear mixed-effect models were utilized to investigate the longitudinal relationship between QSM-ROIs and cognitive function over time (n\u0026thinsp;=\u0026thinsp;219). To assess the independence of these associations from local cerebral atrophy, models were rerun with additional control for local cortical thickness in each ROI (model 3). Finally, Cox proportional hazards regression was conducted for individuals who had available follow-up cognitive diagnoses (n\u0026thinsp;=\u0026thinsp;458) to assess the capacity of iron in the aging-specific signature to predict incident dementia. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered indicative of a significant difference, and Benjamini‒Hochberg correction was applied for multiple comparisons[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e \u003cp\u003eA total of 925 participants aged 45\u0026ndash;75 years were enrolled from 2017 to 2022. Among 925 participants, 50 individuals were unable to complete the clinical assessment and MRI scan due to missing clinical tests and SWI scans. The remaining 875 participants who underwent complete clinical assessments and MRI scans were included. Of those participants, 36 individuals with neurological disease, 5 with conflicting cognitive status, and 64 individuals with poor MRI quality were excluded, resulting in a final sample size of 770 individuals for the subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cognitive follow-up assessments were conducted at two distinct intervals, with 770 participants involved. In the first follow-up period (2019\u0026ndash;2020), 293 participants completed the assessment, which was further reduced to 219 participants in the subsequent period (2020\u0026ndash;2021). The flow chart of the selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The demographic information and neuropsychological data of the 770 participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We also compared the characteristics between individuals who were followed up and those who were not among the two groups (Supplemental Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline demographical, clinical and MRI characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;770)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327 (42.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline MMSE, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.0 (22.0, 28.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline MoCA, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.0 (13.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up MMSE\u0026dagger;, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 (22.0, 27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up MoCA\u0026dagger;, score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.0 (13.0, 21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time\u0026dagger;, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeuroimaging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortical QSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG-specific signature QSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAG signature QSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD signature QSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrey matter atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, n (%), or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eDemographic information and clinical characteristics were compared using χ2, Student t-test and Mann-Whitney U-test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u0026dagger; Data was summarized in subjects completed follow-up phase I and II (n\u0026thinsp;=\u0026thinsp;219).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination; MoCA\u0026thinsp;=\u0026thinsp;Montreal Cognitive Assessment; AG\u0026thinsp;=\u0026thinsp;Aging; AD\u0026thinsp;=\u0026thinsp;Alzheimer's disease; QSM\u0026thinsp;=\u0026thinsp;quantitative susceptibility mapping.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRelationship between iron signatures and cross-sectional cognition\u003c/p\u003e \u003cp\u003eWe initially explored the correlation between age and QSM values across various brain regions. We quantified iron deposition in cortical and AD signature brain regions using QSM values. Within the aging-related brain regions, specific brain areas reported to be affected by atrophy during normal aging (AG) were calculated, as well as brain regions solely affected by aging, not overlapping with AD signature brain regions (AG-specific). As shown in Supplemental Fig.\u0026nbsp;2, the QSM values in the cortical (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = 0.098, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), AG-specific (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.091, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), AG (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = 0.097, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), and AD signature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = 0.109, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) regions were all positively correlated with age. These findings suggest that age may be a significant risk factor for increased iron deposition.\u003c/p\u003e \u003cp\u003eSubsequently, to investigate the impact of brain iron on cognitive impairment, we first examined the relationship between iron deposition in different brain regions and baseline global cognitive scores. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, participants in the upper tertile of cortical QSM presented significantly poorer global cognitive function \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.100, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) than did those in the bottom tertile after adjusting for sex, age, education, site, eTIV, cognitive status, smoking, drinking, and medical history (Model 2), as well as in the AG-specific signature QSM \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.110, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). This association persisted even after accounting for cortical thickness (interpreted as local atrophy) of the ROI (model 3; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.104, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.118, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020, respectively). However, similar negative correlations between brain iron and global cognition were not observed in the Aging and AD signature regions. In summary, higher QSM values were linked to poorer global cognition, particularly in aging-specific and cortical regions. Nevertheless, no significant correlations were found between QSM in any of the selected ROIs and the ZMoCA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between localized QSM and baseline global cognition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eZMMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eZMoCA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCortical QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q2 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.070 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.092 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.068 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.096 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.069 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.090 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q3 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.107 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.022 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.100 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.013 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.104 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAG-specific signature QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q2 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.027 (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.070 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.029 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.068 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.032 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.064 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q3 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.119 (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.011 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.110 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.118 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAG signature QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q2 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.067 (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.025 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.064 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.028 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.068 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.020 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q3 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.057 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.010 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.050 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.057 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD signature QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q2 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.047 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.038 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.037 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.029 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.036 (0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.028 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eupper Q3 vs bottom Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.036 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.007 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.027 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.024 (0.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eStandardized Beta coefficient values represent a one unit change in global cognition z-score with a one PPB change in QSM.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1 was adjusted for sex, age, years of education, site, eTIV, and cognitive status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2 was additionally adjusted smoking, drinking, hypertension, diabetes, and hyperlipidemia.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 3 was additionally adjusted cortical thickness of each ROI.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination; MoCA\u0026thinsp;=\u0026thinsp;Montreal Cognitive Assessment; AG\u0026thinsp;=\u0026thinsp;Aging; AD\u0026thinsp;=\u0026thinsp;Alzheimer's disease; QSM\u0026thinsp;=\u0026thinsp;quantitative susceptibility mapping.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBenjamini-Hochberg FDR corrected P\u0026thinsp;\u0026lt;\u0026thinsp;.05 are shown bold.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRelationship between signature iron and local cortical atrophy\u003c/p\u003e \u003cp\u003ePrevious studies have confirmed a negative association between QSM values and cortical atrophy in AD patients. To assess the relationship between cortical iron deposition and brain atrophy in normal community-dwelling elderly individuals, we evaluated the associations between QSM values in different brain regions and regional brain volumes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplemental Table\u0026nbsp;3, after adjusting for sex, age, education, site, smoking status, drinking status, history of disease, and cognition, the QSM values in the cortical, AG specific, and AG signature regions remained significantly negatively correlated with cortical thickness (model 3; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Similar significant negative correlations were also observed for other classical indicators reflecting brain atrophy, such as GM volume and the local cortical gyrification index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, there were no significant correlations between QSM in any of the selected ROIs and surface area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRelationship between signature iron and longitudinal cognition\u003c/p\u003e \u003cp\u003eWe further analyzed the relationship between brain iron and longitudinal changes in cognitive decline. QSM values in all selected ROIs were associated with cognitive decline as assessed by the MoCA (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In participants who completed baseline assessments and two cognitive follow-ups (n\u0026thinsp;=\u0026thinsp;219), the annual decrease in MoCA score was negatively correlated with iron deposition in the cortical region (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.440, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), AG-specific signature region \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.527, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), AG signature region (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.521, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and AD signature region (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.365, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) after adjusting for sex, age, education, eTIV, smoking status, drinking status, and history of disease (Model 2). These associations with longitudinal changes in the MoCA score remained significant independent of local atrophy after additionally adjusting for the cortical thickness of each ROI (model 3; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Considering the differences in scale sensitivity, we did not observe a relationship between QSM in any of the selected ROIs and longitudinal changes in MMSE scores.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between localized QSM and longitudinal global cognition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003echange in ZMMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003echange in ZMoCA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCortical QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.162 (0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.433 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.165 (0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.440 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.164 (0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.441 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAG-specific signature QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.220 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.526 (0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.222 (0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.527 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.221 (0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.526 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAG signature QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.196 (0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.516 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.200 (0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.521 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.198 (0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.521 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAD signature QSM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.087 (0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.354 (0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.090 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.365 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.092 (0.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.364 (0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eStandardized Beta coefficient values represent a one unit change in global cognition z-score per 1 years with a one PPB change in QSM.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was adjusted for sex, age, years of education and eTIV.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2 was additionally adjusted smoking, drinking, hypertension, diabetes, and hyperlipidemia.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3 was additionally adjusted cortical thickness of each ROI.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination; MoCA\u0026thinsp;=\u0026thinsp;Montreal Cognitive Assessment; AG\u0026thinsp;=\u0026thinsp;Aging; AD\u0026thinsp;=\u0026thinsp;Alzheimer's disease; QSM\u0026thinsp;=\u0026thinsp;quantitative susceptibility mapping.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBenjamini-Hochberg FDR corrected P\u0026thinsp;\u0026lt;\u0026thinsp;.05 are shown bold.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalyses of domain-specific cognitive scores (from MoCA components) also confirmed such negative associations in all ROI-QSM values. Decreased attention was predicted by brain iron levels in cortical (model 3; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.380, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), AG-specific (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.356, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), AG signature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.401, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) and AD signature (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.347, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) regions, independent of atrophy, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Additionally, a negative association was also observed between the rate of change in visuospatial function and cortical as well as all signature QSMs (model 3; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.464, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.335, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.481, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.491, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, respectively). Regarding language, QSM in the AG-specific signature region was predictive of a steeper decline in Model 2 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.421, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) and Model 3 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.418, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). However, no significant correlations were found between QSM values in any of the selected ROIs and episodic memory or executive function (Supplemental Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIron metabolism patterns across cognitive diagnoses: voxel-based QSM analysis\u003c/p\u003e \u003cp\u003eTo further analyze the differences in iron metabolism patterns among distinct cognitive diagnoses, voxelwise comparisons of QSM values were conducted among the dementia, MCI, and CN groups. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplemental Table\u0026nbsp;5). Within the dementia group, elevated QSM values were observed in five distinct clusters compared to those in the CN group. Pronounced abnormalities were identified in the left frontal pole/middle frontal gyrus/superior frontal gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018); left paracingulate gyrus/superior frontal gyrus/frontal pole (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040); left paracingulate gyrus/medial frontal cortex/cingulate gyrus (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041); left caudate/accumbens/putamen/subcallosal cortex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036); and left frontal pole (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). In addition, the dementia group exhibited greater QSM in the left middle frontal gyrus/superior frontal gyrus/frontal pole than did the MCI group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). There were no significant regions where the MCI group had higher QSM values than did the HC group. Furthermore, VBM analysis revealed no discernible differences in atrophy across the aforementioned groups at a whole-brain FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, our focus was on iron deposition in regions selectively associated with aging and AD, aiming to evaluate its correlation with baseline cognition and assess its potential value in predicting future cognitive decline processes. Here, for the first time, we present evidence linking increased iron accumulation in signature brain regions to exacerbated cognitive decline and structural brain alterations in a community-based cohort. The whole-brain approach allows for the mapping of iron distribution, revealing increased iron load in dementia patients across the frontal, paracingulate, and cingulate cortex, as well as in deep gray matter structures such as the caudate, accumbens, and putamen. In agreement with previous studies indicating iron-related cognitive dysfunction[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], our findings support the potential of brain iron accumulation as a neuroimaging marker for the early assessment of cognitive decline in normal aging.\u003c/p\u003e \u003cp\u003eThe pathological deposition of cerebral iron significantly contributes to the cascade of neurodegenerative processes. Excessive iron accelerates the production of reactive oxygen species (ROS), leading to oxidative stress that damages neuronal lipids, proteins, and DNA[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, the interaction of iron with activated microglia promotes neuroinflammatory responses, exacerbating neuronal damage[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Compounding these effects, iron dysregulation influences the pathology of key neurodegenerative proteins, such as tau and A\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e, by facilitating tau hyperphosphorylation[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and enhancing A\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e aggregation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], thus contributing to the hallmark features of diseases such as AD. The role of iron in exacerbating oxidative damage, inflammation, and protein aggregation pathways suggests its potential as a critical factor in the pathophysiology of neurodegeneration.\u003c/p\u003e \u003cp\u003eIron deposition in the brain plays a pivotal role in the progression of neurodegenerative changes and may lead to brain atrophy through several interrelated mechanisms. Iron overload may facilitate brain atrophy through ferroptosis, a nonapoptotic cell death pathway, by catalyzing reactive oxygen species production and promoting lipid peroxidation, leading to neuronal damage and cell death[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, the interaction of iron with critical proteins, including tau, exacerbates their pathological aggregation, further implicating iron in the progression of AD, which facilitates neuronal damage and brain atrophy[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, iron-induced neuroinflammation, characterized by activated microglia and the release of proinflammatory cytokines, accelerates brain tissue loss[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A seven-year follow-up longitudinal study by Daugherty et al.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] reported that increased iron levels, particularly in the putamen, predict accelerated brain shrinkage in 32 older adults. In support of this, our research revealed that iron accumulation correlates with atrophy in signature brain regions, affecting cortical thickness, gray matter volume, and the LGI. Although the causal relationship between iron accumulation and brain atrophy, particularly in the context of aging and AD, remains to be fully elucidated, further investigation into the role of iron in the neurodegenerative cascade is necessary.\u003c/p\u003e \u003cp\u003eThe sensitivity of QSM in neuroimaging studies offers promising insights into the distribution of iron levels in key brain regions, including the hippocampus, amygdala, and caudate, in AD patients[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, widespread increased magnetic susceptibility across the cortical ribbon, asymmetrically covering the left hemisphere cerebral cortex, caudate nucleus, putamen, and partial cerebellar cortex, which was demonstrated in another study[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], points to a complex pattern of neurodegeneration that QSM uniquely captures. Specifically, subcortical iron accumulation has been proposed as a potential biomarker for subcortical vascular MCI[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, our whole-brain volumetry analysis did not align with these QSM findings, suggesting that overall, QSM may be more sensitive than conventional structural MRI in detecting abnormalities in MCI and dementia patients and could also be an indication that QSM might capture early pathological changes before volumetric losses are evident.\u003c/p\u003e \u003cp\u003eThe phenomenon of brain iron accumulation, while critical to the pathology of cognitive decline, remains only partially understood. Its development is influenced by a constellation of factors, including genetic predispositions that disrupt normal iron metabolism and regulatory mechanisms. Notably, conditions such as neurodegeneration with brain iron accumulation (NBIA) underscore the genetic component of this pathology[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Age-related factors also play a vital role, with evidence suggesting that the brain's ability to regulate iron diminishes with age, leading to iron accumulation in specific regions associated with motor and cognitive functions[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Our investigation confirmed the age-associated accumulation of iron in cortical and all AG- and AD-Signatures regions, aligning with multiple studies that have demonstrated a widespread pattern of iron load across various subcortical structures (e.g., the GP, putamen, amygdala, hippocampus, SN, and RN) and cortical regions (e.g., all lobes and the entorhinal, ITG, SMF and IOF cortex) during aging[\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, dysfunction of the blood-brain barrier (BBB) represents a crucial mechanism for abnormal iron deposition, particularly in neurodegenerative conditions such as Parkinson's disease (PD)[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Collectively, these findings highlight the multifaceted nature of brain iron accumulation and its implications for neurodegenerative diseases.\u003c/p\u003e \u003cp\u003eThe negative findings from the FAIRPARK-II trial[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], where iron chelation therapy with deferiprone led to clinical worsening in PD patients, highlighted the necessity of carefully navigating the delicate balance between the indispensable physiological role of iron and its propensity to inflict damage when present in excess. This revelation does not diminish the significance of our research but rather emphasizes the need for a sophisticated approach to dissect the intricate interactions among iron-related neurodegenerative mechanisms. Our study specifically addresses the issue of iron accumulation and its correlation with cognitive decline in signature brain regions, aiming to uncover biomarkers for early detection and intervention. This work holds promise for revealing novel avenues for understanding and treating neurodegeneration in aging populations.\u003c/p\u003e \u003cp\u003eThis study is subject to several limitations. First, not all participants completed consecutive follow-ups from baseline, resulting in a relatively limited number of subjects for the longitudinal analyses. This lack of consistency increases the risk of false-negative associations. Second, due to the absence of continuous scans in the current study, we were unable to assess the dynamics of iron accumulation and its relationship with brain shrinkage and cognitive changes. Further investigations with larger longitudinal datasets are therefore warranted. Third, this study did not incorporate cerebrospinal fluid or plasma biomarker evidence of cerebral amyloid and tau pathology (e.g., A\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e42, A\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e42/40 ratio, total-tau, and p-tau), which could have provided valuable insights into iron-related mechanisms. Further research is needed to explore these potential connections. Finally, while QSM is sensitive to variations in brain iron content, it is important to note that magnetic susceptibility, as measured by QSM, may also be influenced by other metals (e.g., copper, manganese aluminum, and calcium)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], myelin[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and cellular packing density[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Variations in QSM reconstruction, spatial standardization, and other procedures may introduce biases that could impact the generalizability of our study findings. Therefore, these factors should be carefully considered when interpreting the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, this study revealed that our distinctive signature QSMs were capable of identifying individuals at risk of cognitive decline during normal aging. The spatial accumulation of iron correlates with dementia, offering novel insights into the role of iron deposition in the aging population. Although iron deposition in specific brain regions has been extensively studied, the signature patterns of iron accumulation in age-related brain areas still warrant further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors thank the study participants and the staff of Huashan Hospital Fudan University and the Fudan University Taizhou Institute of Health Sciences for assistance in neighborhood outreach and engagement in support of participant recruitment.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eR.L. and Yr.F. were responsible for the study design, statistical analysis, and writing of the original draft of the manuscript. Yz. W., Hy. L, and Px. L participated in the data collection and figure preparation. Q.D. and Yf. J reviewed and edited the manuscript. Xd.C. and M.C. engaged in the study design and study supervision. All the authors contributed to the final version of the paper.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study was supported by the following agencies: the Ministry of Science and Technology of China (2021ZD0201806), the National Key R\u0026amp;D Program of China (2021YFC2500100), the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), the Key Research and Development Plans of Jiangsu Province, China (BE2021696), and the Shanghai Municipal Science and Technology Major Project (2023SHZDZX02).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets supporting this study\u0026apos;s findings were obtained from the TIS cohort, which is available from the corresponding author upon request to any qualified investigator subject to a data use agreement (Mei Cui, e-mail: [email protected]).\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll the TIS participants provided informed consent.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeary IJ, Corley J, Gow AJ, Harris SE, Houlihan LM, Marioni RE, et al. 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NeuroImage. 2016;133:417\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aging signature, AD signature, iron, atrophy, cortical thickness, cognition","lastPublishedDoi":"10.21203/rs.3.rs-4425826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4425826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecent magnetic resonance imaging (MRI) studies have established that brain iron accumulation might accelerate cognitive decline in Alzheimer\u0026rsquo;s disease (AD) patients. Both normal aging and AD are associated with cerebral atrophy in specific regions. However, no studies have investigated aging- and AD-selective iron deposition-related cognitive changes during normal aging. Here, we applied quantitative susceptibility mapping (QSM) to detect iron levels in our cortical signature regions and assessed the relationships among iron, atrophy, and cognitive changes in older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this Taizhou Imaging Study, 770 older adults (mean age 62.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.93 years, 57.5% women) underwent brain MRI to measure brain iron and atrophy, of whom 219 underwent neuropsychological tests nearly every 12 months for up to a mean follow-up of 2.68 years. Global cognition was assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Domain-specific cognitive scores were obtained from MoCA subscore components. Regional analyses were performed for cortical regions and 3 signature regions: aging (AG)-specific regions, AG regions and AD signature meta-ROIs (Fig.\u0026nbsp;2). The QSM and cortical morphometry means of the above ROIs were also computed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSignificant associations were found between QSM levels and cognitive scores. In particular, after adjusting for cortical thickness of regions of interest (ROIs), participants in the upper tertile of the cortical and AG-specific signature QSM exhibited worse global cognitive function than did those in the bottom tertile [Table\u0026nbsp;2; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.104, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\beta\\)\u003c/span\u003e\u003c/span\u003e = -0.118, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020, respectively]. Longitudinal analysis suggested that QSM values in all ROIs might predict cognitive decline in global cognition and key domains such as attention and visuospatial function (Table\u0026nbsp;3, Fig.\u0026nbsp;3; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, iron levels were negatively correlated with classic MRI markers of cortical atrophy (cortical thickness, gray matter volume, and local gyrification index) in total, AG-specific, and AG signature regions (Fig.\u0026nbsp;2; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAG- and AD-selective iron deposition was associated with atrophy and cognitive decline in elderly people, highlighting its potential as a neuroimaging marker for cognitive aging.\u003c/p\u003e","manuscriptTitle":"Brain Iron in Signature Regions Relating to Cognitive Aging in Older Adults: The Taizhou Imaging Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-31 20:15:05","doi":"10.21203/rs.3.rs-4425826/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-11T06:40:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-10T19:23:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-09T14:54:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-27T21:38:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4037355647551388827065342984499388243","date":"2024-06-25T02:28:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132993842522716362388707796387788938532","date":"2024-06-24T14:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271462568558495018497598995748197109384","date":"2024-06-21T13:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171732332438327415418655142877044498354","date":"2024-06-20T17:39:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-20T16:59:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-16T03:39:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-16T03:39:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Alzheimer's Research \u0026 Therapy","date":"2024-05-15T14:28:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59173859-d810-457d-ab2b-71209a3f68ed","owner":[],"postedDate":"May 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-07T16:05:11+00:00","versionOfRecord":{"articleIdentity":"rs-4425826","link":"https://doi.org/10.1186/s13195-024-01575-9","journal":{"identity":"alzheimers-research-and-therapy","isVorOnly":false,"title":"Alzheimer's Research \u0026 Therapy"},"publishedOn":"2024-10-02 15:58:16","publishedOnDateReadable":"October 2nd, 2024"},"versionCreatedAt":"2024-05-31 20:15:05","video":"","vorDoi":"10.1186/s13195-024-01575-9","vorDoiUrl":"https://doi.org/10.1186/s13195-024-01575-9","workflowStages":[]},"version":"v1","identity":"rs-4425826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4425826","identity":"rs-4425826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}