Amygdala Subregional Atrophy Across ATN-Defined Mild Cognitive Impairment Subgroups

Amygdala Subregional Atrophy Across ATN-Defined Mild Cognitive Impairment Subgroups | 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 Amygdala Subregional Atrophy Across ATN-Defined Mild Cognitive Impairment Subgroups Qianqian Yuan, Darui Zheng, Wenzhang Qi, Xuhong Liang, Yiming Ruan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7378071/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Alzheimer’s disease (AD) pathology begins years before clinical symptoms, with Mild Cognitive Impairment (MCI) as a prodromal stage. The ATN framework (Amyloid, Tau, Neurodegeneration) aids in stratifying MCI risk. While amygdala atrophy is a recognized biomarker, amygdala subregional changes across ATN-defined MCI subgroups remain underexplored. Methods: This study analyzed MRI data and cerebrospinal fluid biomarkers from 134 MCI participants classified into A–T–, A+T–, and A+T+ subgroups using ADNI data. Amygdala volumes were computed and compared among the different groups. Furthermore, we also investigated the relationship between the altered brain regions and cognitive function. Results: Significant atrophy was observed in the A+T+ group within bilateral basal, accessory basal, central nuclei, and right cortical-amygdaloid transition area compared to other groups. Volume reductions in the left central nucleus correlated positively with cognitive scores. Conclusion: Amygdala subregional atrophy, particularly in the central, basal, accessory basal, and cortical-amygdaloid transition nuclei, is linked to AD pathology progression and cognitive decline. The findings suggested the right amygdala’s vulnerability and suggest these subregions as potential early imaging biomarkers for AD progression. Alzheimer's disease amygdala subnuclei Mild Cognitive Impairment ATN structural magnetic resonance imaging Figures Figure 1 Figure 2 1. Introduction Alzheimer’s disease (AD) is the most common neurodegenerative disorder, characterized by progressive memory decline and multiple cognitive impairments [ 1 ]. Its hallmark pathological features include β-amyloid (Aβ) deposition, hyperphosphorylation of tau protein, and consequent neurodegeneration and brain atrophy [ 2 ]. Notably, these pathological changes begin 10–20 years prior to the onset of clinical symptoms [ 3 ], highlighting the critical importance of early identification and intervention in high-risk individuals. Mild Cognitive Impairment (MCI) is widely considered a prodromal stage of AD, positioned between normal aging and dementia[ 4 ]. It is characterized by measurable cognitive decline—such as impairments in memory, attention, executive function, or language—that does not significantly interfere with daily functioning and thus does not meet the diagnostic criteria for dementia [ 5 ]. Traditionally, MCI diagnosis relies on neuropsychological assessments such as the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE), which evaluate multiple cognitive domains[ 6 ]. However, these tools have limitations: their results can be influenced by factors such as education, cultural background, and emotional state, and they primarily reflect behavioral manifestations rather than underlying neuropathology. As a result, they may lack sensitivity in detecting early changes and offer limited predictive value for AD conversion[ 7 ]. Consequently, there is growing interest in objective, biomarker-based diagnostic approaches to improve early detection and risk stratification. In recent years, the ATN framework—based on three core biomarkers: Aβ (A), tau (T), and neurodegeneration (N)—has been widely adopted for the pathological classification of AD. This system enables the stratification of individuals with mild cognitive impairment (MCI) into distinct subgroups, such as A + T+ (high-risk group), A + T– (early pathological stage), and A–T– (low-risk or non-AD group) [ 8 , 9 ]. Detection of protein aggregates typically requires positron emission tomography, a technique that remains largely inaccessible outside of specialized academic centers[ 10 , 11 ]. By contrast, structural magnetic resonance imaging (MRI) offers a more widely available, cost-effective, and less invasive alternative[ 12 , 13 ]. Medial temporal lobe atrophy assessed via structural MRI has been established as a key biomarker for the early diagnosis of both AD and MCI[ 14 ]. Previous studies have also demonstrated that volume reduction in medial temporal structures, including the amygdala, can occur as early as the MCI stage [ 15 – 17 ]. However, few investigations have specifically explored amygdala volume alterations across MCI subgroups defined by the ATN classification, particularly at the subnuclear level. To address this gap, the present study utilized FreeSurfer 7.4.0 to segment amygdala subregions on structural MRI in MCI patients. We aimed to characterize patterns of amygdala atrophy across different ATN-defined MCI subgroups. Additionally, we examined the relationship between these imaging alterations and cognitive performance, using a battery of clinical assessments. This study seeks to provide new insights into the early neurobiological changes in MCI and to identify potential imaging biomarkers for early diagnosis and disease monitoring. We hypothesized that: (1) significant differences in amygdala subregional volumes would be observed among ATN subgroups; and (2) these volumetric alterations would be significantly associated with cognitive function. 2. Methods 2.1 Participants and ATN Classification All data in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu), which provides detailed MCI inclusion and exclusion criteria. Based on previous studies, this study defined abnormal CSF biomarkers using the following thresholds: Aβ42 concentration 24 pg/ml was considered T+[18, 19]. A−T+, and A−T−. In alignment with the A/T/N researcAh framework, the A−T+ group was excluded, as it does not lie within the AD pathological continuum. To ensure methodological consistency across participants, individuals in the A−T+ subgroup were also excluded from the MCI cohort. Accordingly, the final analytical sample comprised 54 A−T−, 28 A+T−, and 52 A+T+ MCI participants. 2.2 Neuropsychological Assessment To assess cognitive function, group comparisons were conducted using composite episodic memory (EM) and executive function (EF) scores. The composite EM score included performance from the Rey Auditory Verbal Learning Test (RAVLT) and its key indicators—RAVLT-immediate, RAVLT-learning, RAVLT-forgetting, and RAVLT-percent forgetting—as well as the word list learning and recognition components of the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), the word recall task from the Mini-Mental State Examination (MMSE), and Logical Memory I from the Wechsler Memory Scale–Revised. The composite EF score included results from the Digit Symbol Substitution and Digit Span Backward tests, Trail Making Test parts A and B, category fluency tasks (animals and vegetables), the Digit Cancellation Test, and the Clock Drawing Test. 2.3 Ethical Approval and Informed Consent The ADNI study received ethical approval from the institutional review boards of all participating institutions. Written informed consent was obtained from all participants or their authorized representatives. More details can be found on the ADNI website (www.adni-info.org). 2.4. MRI data acquisition and analysis T1-weighted images were processed with FreeSurfer 7.4.0 using the “recon-all" command line. Briefly, this processing includes motion correction and intensity normalization of T1-weighted images, removal of non-brain tissue using a hybrid watershed/surface deformation procedure, automated Talairach transformation, segmentation of the subcortical white matter (WM) and deep GM volumetric structures, tessellation of the GM WM matter boundary, and derivation of cortical thickness. The “segmentHA_T1. sh'' script was subsequently used to compute the parcellation and volume quantification of different amygdala [28] subregions. The amygdala was segmented into 9 nuclei for each hemisphere: the accessory basal nucleus, the anterior amygdaloid area (AAA), the basal nucleus, the central nucleus, the cortical-amygdaloid transition area, the cortical nucleus, the medial nucleus, the lateral nucleus, and the para-laminar nucleus. The volumes were normalized (divided) by the estimated total intracranial volumes. 2.5. Statistics Analysis of variance and Bonferroni post hoc tests were used to evaluate group differences regarding demographic, neuropsychological, and clinical data. The categorical variables were analyzed using chi-square tests. For the MRI measures, a MANCOVA followed by post-hoc comparison, was applied to test the differences among groups. Age, gender and education level were added to the model to control for their potential confounding effect. Spearman's correlations were conducted to examine possible relationships between the volume of atrophic subfields and the neuropsychological outcomes. All analyses applied false discovery rate (FDR) correction to account for multiple comparisons. 3. Results 3.1. Demographic and clinical features Differences in demographic characteristics, cognitive assessments, and CSF biomarkers among MCI subgroups are presented in Table 1. Significant age differences were observed among the three MCI subgroups, with the A+T+ group being the oldest and the A−T− group the youngest; no significant differences were found in sex distribution or years of education. In terms of cognitive assessments, although MMSE and MoCA scores did not significantly differ among the subgroups, the A+T+ group showed poorer performance across multiple specific cognitive domains. For example, RAVLT-immediate and RAVLT-learning scores were significantly lower in the A+T+ group compared to the A−T− group. EM scores were also lowest in the A+T+ group, with a significant difference compared to the A−T− group. In EF tests, both the A+T− and A+T+ groups scored lower than the A−T− group. 3.2. MRI volume As shown in table 2 and figure 1, the A+T+ group exhibited varying degrees of atrophy in the amygdala and its subregions compared to the A–T– and A+T– groups, including right Basal, right Accessory Basal, right Central, left Accessory Basal, and left Central. The A+T+ group showed significant atrophy in right CATA compared to the A–T– group, and in left Basal compared to the A–T+ group. No significant differences were observed between the A+T– and A–T– groups. 3.2. Correlation analysis of significant brain differences and neuropsychological tests As shown in Figure 2, correlation analysis revealed a positive association between the left Central amygdala and both MoCA (p = 0.0012, r = 0.338) and RAVLT_learning (p = 0.0018, r = 0.311), after Bonferroni correction. 4. Discussion The primary objective of this study was to identify morphological changes in specific amygdala subregions across different ATN subgroups. Although subcortical atrophy of the amygdala was observed across all groups, no significant differences were found between the A + T– and A–T– groups. The affected subregions included bilateral Basal, bilateral Accessory Basal, bilateral Central, and right CATA. As expected, atrophy in some of these subregions was significantly associated with cognitive performance. In line with previous research, our study found that amygdala atrophy progressively worsens as the disease advances[ 20 ]. We found that, compared to both A–T– and A + T– groups, the A + T + group exhibited significant atrophy in the bilateral accessory basal, bilateral central, and right basal subregions of the amygdala. This finding aligns with previous postmortem studies, which reported the presence of isolated neurofibrillary tangles (NFTs) in the central nucleus of the amygdala during the early stages of AD[ 21 , 22 ]. The central nucleus is thought to regulate behavioral responses to potentially harmful stimuli and fear perception through its connections with the hypothalamus, basal forebrain, and brainstem[ 23 , 24 ]. Damage in this region may thus impair normal cognitive functioning in individuals with A + T+. Our correlation analyses further support this: we observed that, compared to the A–T– group, the volume of the central nucleus in A + T + individuals was significantly associated with several cognitive measures, such as RAVLT_learning and MoCA, suggesting a link between progressive tau and Aβ pathology and cognitive decline. Additionally, as the disease advances, other amygdala subnuclei—including the basal and accessory basal nuclei—are also affected, showing the highest NFTs burden[ 22 , 25 ]. Previous studies have also reported that neuronal loss, reflected as atrophy, is more prominent in the central and accessory basal nuclei compared to other amygdala subregions[ 21 , 26 ]. Given its role in updating stimulus–value associations via connections with the orbitofrontal cortex, the basal nucleus is essential for emotional and social cognition[ 27 – 29 ]. Thus, atrophy in this region may contribute to cognitive dysfunction observed in the A + T + group. In addition, our study found that the A + T + group showed significant atrophy in the right CATA region compared to the A–T– group. The CATA is characterized by its dense cholinergic and dopaminergic innervation, distinguishing it from adjacent regions like the piriform cortex and amygdala, and by its direct projections from both the main and accessory olfactory bulbs[ 30 , 31 ]. Due to its unique connectivity and functional roles, damage to the CATA may contribute to cognitive impairments observed in patients[ 32 , 33 ]. Furthermore, we observed that atrophy was more pronounced in the right amygdala subregions compared to the left. Consistent with our findings, Anderson et al. reported that patients with right amygdala damage exhibited impairments in recognizing emotional expressions, whereas patients with left-sided lesions showed no significant difference from healthy controls[ 34 ]. Similarly, a case study by Wang et al. found that a patient with right amygdala and bilateral anterior cingulate damage following stereotactic brain surgery demonstrated a marked deficit in recognizing fearful faces[ 35 ]. Taken together with our findings, these studies suggest that damage to the right amygdala may play a more critical role in the disruption of recognition abilities[ 36 – 38 ]. Several limitations should be acknowledged in this study. First, the cross-sectional design restricts our ability to infer causal relationships or track longitudinal changes in amygdala subregional atrophy. Second, although we used an advanced segmentation algorithm, the resolution and accuracy of automated subregional parcellation may still be affected by individual anatomical variability. Finally, while we included cognitive measures, other potentially relevant behavioral or neuropsychiatric symptoms were not assessed and may provide additional insights in future research. In summary, this study reveals that specific amygdala subregions, particularly the central, basal, accessory basal, and CATA nuclei, exhibit progressive atrophy across the ATN spectrum. the central nuclei alterations are significantly associated with cognitive decline, suggesting their potential role as imaging biomarkers for early AD progression. Notably, our findings highlight the vulnerability of the right amygdala, reinforcing its critical involvement in emotion recognition and social cognition. These results, consistent with both neuroimaging and neuropathological studies, provide further evidence for the role of amygdala subregional degeneration in the pathophysiology of AD. Declarations Data availability statement The original contributions presented in this study are included in this article material, further inquiries can be directed to the corresponding authors. Ethics statement The studies involving humans were approved by Ethical approval for the ADNI study was granted by the institutional review committees of all participating institutions. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Funding The author(s) declare that no financial support was received for the research and/or publication of this article. Author Contribution Q.Y. contributed substantially to the conception and design of the study, as well as to data acquisition, analysis, and interpretation. D.Z., W.Q., X.L., Y.R., and Y.T. were actively involved in experimental implementation, data processing, and interpretation of results. C.Xu. and C.Xi. provided critical input in study design, guided experimental optimization, and offered substantial revisions to the manuscript to enhance its intellectual content and scientific rigor. 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Tables Table 1: Group Comparisons of Demographic Characteristics, Clinical Scale Scores, and CSF Biomarkers A-T-(54) A+T-(28) A+T+(52) F values(χ2) p values Age (years) 68.97(7.65) 71.68(7.30) 73.16(5.79) 4.982 0.008 a Sex (Male/Female) 20/34 13/15 20/32 0.723 0.697 Years of education 15.91(2.64) 16.46(2.57) 16.33(2.65) 0.534 0.588 MMSE 28.06(1.89) 28.18(1.74) 27.38(2.18) 2.100 0.127 MoCA 23.79(3.06) 23.04(2.52) 22.76(3.61) 1.402 0.250 RAVLT-immediate 38.43(9.42) 34.68(9.21) 33.10(8.94) 4.620 0.012 a RAVLT-learning 4.91(2.24) 4.36(2.09) 3.73(2.39) 3.559 0.031 a RAVLT-forgetting 4.43(3.93) 4.57(2.17) 5.10(2.51) 0.654 0.522 RAVLT-prec-forgetting 46.08(56.25) 57.15(29.42) 65.50(30.51) 2.757 0.067 EM 0.49(5.20) 0.30(1.05) 0.05(0.65) 5.113 0.007 a EF 0.65(0.91) 0.23(1.05) 0.24(0.84) 3.310 0.040 Aβ 42 1484.25(255.03) 726.19(199.64)*** 622.09(159.11) 249.840 <0.001 ab T-tau 201.89(44.35) 178.88(46.61) 383.71(130.72) 73.112 <0.001 ac p-tau 17.36(3.98) 16.30(4.68) 40.58(16.54) 75.415 <0.001 ac Values are presented as mean (standard deviation, SD), unless otherwise specified. MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; RAVLT: Rey Auditory Verbal Learning Test; EM: episodic memory; EF: executive function; Aβ: Amyloid-beta protein; p-tau: phosphorylated tau protein; t-tau: total tau protein; A+T+: abnormal Aβ42 and abnormal p-tau; A+T−: abnormal Aβ42 and normal p-tau; A−T−: normal Aβ42 and normal p-tau. a: comparison between A−T− and A+T+; b: comparison between A−T− and A+T−; c: comparison between A+T− and A+T+. Table 1: amygdala volumetric measures among ATN groups Volume nucleus A-T-(Mean ± SD) A+T-(Mean ± SD) A+T+(Mean ± SD) F-value FDR-p r Lateral 658.31 ± 98.42 655.16 ± 84.57 611.42 ± 98.72 4.103 0.0306 r Basal 442.79 ± 69.88 437.05 ± 62.11 393.45 ± 63.98 7.77 0.0033 ac r Accessory_Basal 254.77 ± 40.26 250.92 ± 38.14 218.86 ± 39.79 10.43 <0.001 ac r AAA 53.95 ± 8.92 53.39 ± 8.47 48.91 ± 7.68 3.908 0.0345 r Central 46.12 ± 11.16 45.30 ± 7.83 37.05 ± 8.70 11.201 <0.001 ac r Medial 20.60 ± 4.24 20.68 ± 6.40 18.07 ± 4.83 3.426 0.045 r Cortical 24.57 ± 4.31 24.26 ± 4.02 21.74 ± 4.32 4.897 0.0171 r CATA 168.98 ± 24.21 165.02 ± 21.05 149.75 ± 25.21 7.298 0.0033 c r Paralaminar 50.33 ± 8.25 49.96 ± 6.47 46.28 ± 7.52 4.708 0.0185 r Whole_amygdala 1720.42 ± 249.02 1701.75 ± 223.80 1545.53 ± 243.70 7.517 0.0033 ac l Lateral 629.93 ± 85.45 622.39 ± 89.92 587.76 ± 84.05 3.282 0.0482 l Basal 420.47 ± 59.24 419.94 ± 63.24 381.96 ± 56.81 5.832 0.0095 c l Accessory_Basal 238.04 ± 35.37 235.71 ± 41.69 210.34 ± 31.85 6.726 0.0054 ac r_AAA 49.94 ± 7.10 49.66 ± 7.68 45.64 ± 6.50 4.437 0.0225 l Central 42.13 ± 8.10 41.30 ± 9.07 34.54 ± 8.29 9.414 <0.001 ac l Medial 18.36 ± 4.68 20.24 ± 8.36 16.49 ± 4.49 3.561 0.0415 l Cortical 22.08 ± 4.15 21.75 ± 5.10 20.28 ± 3.62 1.148 0.3371 l CATA 159.83 ± 22.47 158.83 ± 24.24 146.06 ± 23.08 3.729 0.0378 l Paralaminar 48.54 ± 7.74 49.58 ± 7.16 45.73 ± 7.24 3.32 0.0477 l Whole_amygdala 1629.32 ± 210.64 1619.40 ± 232.90 1488.80 ± 205.77 5.659 0.0095 Abbreviations: AAA = Anterior Amygdaloid Area; CATA=Cortical-Amygdaloid Transition Area; SD: standard deviation; A+T+: abnormal Aβ42 and abnormal p-tau; A+T−: abnormal Aβ42 and normal p-tau; A−T−: normal Aβ42 and normal p-tau. a: comparison between A−T− and A+T+; b: comparison between A−T− and A+T−; c: comparison between A+T− and A+T+. l = left; r = right. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7378071","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509318634,"identity":"64e26b01-e12b-4d8b-b635-0bf6ecff44ec","order_by":0,"name":"Qianqian Yuan","email":"","orcid":"","institution":"Nanjing Chest Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Yuan","suffix":""},{"id":509318635,"identity":"15a25bc2-7993-41a9-8823-e4feb5df3e0e","order_by":1,"name":"Darui Zheng","email":"","orcid":"","institution":"Liyang People's Hospita","correspondingAuthor":false,"prefix":"","firstName":"Darui","middleName":"","lastName":"Zheng","suffix":""},{"id":509318636,"identity":"3658fc93-5673-48bb-be94-3febae9e1ae7","order_by":2,"name":"Wenzhang Qi","email":"","orcid":"","institution":"Nanjing Brain Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenzhang","middleName":"","lastName":"Qi","suffix":""},{"id":509318637,"identity":"c3e18ec8-dfef-43a0-a258-510bce32e110","order_by":3,"name":"Xuhong Liang","email":"","orcid":"","institution":"Sichuan Second Hospital of traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xuhong","middleName":"","lastName":"Liang","suffix":""},{"id":509318638,"identity":"4e276425-8d76-4eb6-b034-b203749f1250","order_by":4,"name":"Yiming Ruan","email":"","orcid":"","institution":"Yixing traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Ruan","suffix":""},{"id":509318639,"identity":"a52a53c0-5fba-4570-a7e3-93b80a1c9c1e","order_by":5,"name":"Yue Tang","email":"","orcid":"","institution":"Nanjing Brain Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Tang","suffix":""},{"id":509318640,"identity":"a11ed7bc-fe65-40b8-bdc7-ffd97ea44895","order_by":6,"name":"Chen Xue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACCQjFw8/ekPiAgQfMMSBKi4xkz4HHBiRpsTG4kfgMyiaghX9287HHvG02PAY3ktOqC2S2JTawN2+TYKi5g9uSO8fSDWe2pfFInnmWdnsGz+3EBp5jZRIMx57h1GIgkWMm8bHtMA/f8Zy02zwgLSARxobDeLTkf5NIbPvPw3Ag/1sxWIv8G0JactiAthzgETiRkMYMsYUHvxaJG2lmkjPOJfMAAzlZGqjFuI0nrdgi4RhuLfwzkp9J85TZ2YOi8jNvz23ZfvbDG298qMGtBRUw9jAwsIEYCURqAIIfxCsdBaNgFIyCkQMAsudTeuOHSXUAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Brain Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Xue","suffix":""},{"id":509318641,"identity":"4f2a87b0-6fba-4934-b212-6ce7d9a6fa5d","order_by":7,"name":"Chaoyong Xiao","email":"","orcid":"","institution":"Nanjing Brain Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chaoyong","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2025-08-15 03:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7378071/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7378071/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90908196,"identity":"6bfa40dc-adcc-49cf-a98a-6d282ca1ba04","added_by":"auto","created_at":"2025-09-09 13:24:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78041,"visible":true,"origin":"","legend":"\u003cp\u003eAmygdala Subregional Comparison Across A+T+, A+T–, and A–T– Groups in Right and Left Hemispheres. Abbreviations: AAA = Anterior Amygdaloid Area; CATA=Cortical-Amygdaloid Transition Area; SD: standard deviation; A+T+: abnormal Aβ42 and abnormal p-tau; A+T−: abnormal Aβ42 and normal p-tau; A−T−: normal Aβ42 and normal p-tau. a: comparison between A−T− and A+T+; b: comparison between A−T− and A+T−; c: comparison between A+T− and A+T+.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7378071/v1/17d476c2841cab8b648ebbab.png"},{"id":90908150,"identity":"533ce32b-42f5-42c7-863f-f91d64d5d12c","added_by":"auto","created_at":"2025-09-09 13:24:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77622,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant positive association between the left Central amygdala and both MoCA and RAVLT_learning. A+T+: abnormal Aβ42 and abnormal p-tau; A+T−: abnormal Aβ42 and normal p-tau; A−T−: normal Aβ42 and normal p-tau.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7378071/v1/f9d5c752c08766047282c5f7.png"},{"id":93731903,"identity":"4c9851b3-b62e-4f66-bb30-bec6d42d80ec","added_by":"auto","created_at":"2025-10-17 02:24:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710535,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7378071/v1/c30d2611-3888-4adf-b29c-a405784bae07.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Amygdala Subregional Atrophy Across ATN-Defined Mild Cognitive Impairment Subgroups","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is the most common neurodegenerative disorder, characterized by progressive memory decline and multiple cognitive impairments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its hallmark pathological features include β-amyloid (Aβ) deposition, hyperphosphorylation of tau protein, and consequent neurodegeneration and brain atrophy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, these pathological changes begin 10\u0026ndash;20 years prior to the onset of clinical symptoms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], highlighting the critical importance of early identification and intervention in high-risk individuals.\u003c/p\u003e\u003cp\u003eMild Cognitive Impairment (MCI) is widely considered a prodromal stage of AD, positioned between normal aging and dementia[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is characterized by measurable cognitive decline\u0026mdash;such as impairments in memory, attention, executive function, or language\u0026mdash;that does not significantly interfere with daily functioning and thus does not meet the diagnostic criteria for dementia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Traditionally, MCI diagnosis relies on neuropsychological assessments such as the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE), which evaluate multiple cognitive domains[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these tools have limitations: their results can be influenced by factors such as education, cultural background, and emotional state, and they primarily reflect behavioral manifestations rather than underlying neuropathology. As a result, they may lack sensitivity in detecting early changes and offer limited predictive value for AD conversion[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, there is growing interest in objective, biomarker-based diagnostic approaches to improve early detection and risk stratification.\u003c/p\u003e\u003cp\u003eIn recent years, the ATN framework\u0026mdash;based on three core biomarkers: Aβ (A), tau (T), and neurodegeneration (N)\u0026mdash;has been widely adopted for the pathological classification of AD. This system enables the stratification of individuals with mild cognitive impairment (MCI) into distinct subgroups, such as A\u0026thinsp;+\u0026thinsp;T+ (high-risk group), A\u0026thinsp;+\u0026thinsp;T\u0026ndash; (early pathological stage), and A\u0026ndash;T\u0026ndash; (low-risk or non-AD group) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Detection of protein aggregates typically requires positron emission tomography, a technique that remains largely inaccessible outside of specialized academic centers[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By contrast, structural magnetic resonance imaging (MRI) offers a more widely available, cost-effective, and less invasive alternative[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMedial temporal lobe atrophy assessed via structural MRI has been established as a key biomarker for the early diagnosis of both AD and MCI[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Previous studies have also demonstrated that volume reduction in medial temporal structures, including the amygdala, can occur as early as the MCI stage [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, few investigations have specifically explored amygdala volume alterations across MCI subgroups defined by the ATN classification, particularly at the subnuclear level. To address this gap, the present study utilized FreeSurfer 7.4.0 to segment amygdala subregions on structural MRI in MCI patients. We aimed to characterize patterns of amygdala atrophy across different ATN-defined MCI subgroups. Additionally, we examined the relationship between these imaging alterations and cognitive performance, using a battery of clinical assessments.\u003c/p\u003e\u003cp\u003eThis study seeks to provide new insights into the early neurobiological changes in MCI and to identify potential imaging biomarkers for early diagnosis and disease monitoring. We hypothesized that: (1) significant differences in amygdala subregional volumes would be observed among ATN subgroups; and (2) these volumetric alterations would be significantly associated with cognitive function.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Participants and ATN Classification\u003c/p\u003e\n\u003cp\u003eAll data in this study were obtained from the Alzheimer\u0026apos;s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu), which provides detailed MCI inclusion and exclusion criteria. Based on previous studies, this study defined abnormal CSF biomarkers using the following thresholds: A\u0026beta;42 concentration \u0026lt;977 pg/ml was considered A+, and phosphorylated tau (p-tau) concentration \u0026gt;24 pg/ml was considered T+[18, 19]. A\u0026minus;T+, and A\u0026minus;T\u0026minus;. In alignment with the A/T/N researcAh framework, the A\u0026minus;T+ group was excluded, as it does not lie within the AD pathological continuum. To ensure methodological consistency across participants, individuals in the A\u0026minus;T+ subgroup were also excluded from the MCI cohort. Accordingly, the final analytical sample comprised 54 A\u0026minus;T\u0026minus;, 28 A+T\u0026minus;, and 52 A+T+ MCI participants.\u003c/p\u003e\n\u003cp\u003e2.2 Neuropsychological Assessment\u003c/p\u003e\n\u003cp\u003eTo assess cognitive function, group comparisons were conducted using composite episodic memory (EM) and executive function (EF) scores. The composite EM score included performance from the Rey Auditory Verbal Learning Test (RAVLT) and its key indicators\u0026mdash;RAVLT-immediate, RAVLT-learning, RAVLT-forgetting, and RAVLT-percent forgetting\u0026mdash;as well as the word list learning and recognition components of the Alzheimer\u0026rsquo;s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), the word recall task from the Mini-Mental State Examination (MMSE), and Logical Memory I from the Wechsler Memory Scale\u0026ndash;Revised. The composite EF score included results from the Digit Symbol Substitution and Digit Span Backward tests, Trail Making Test parts A and B, category fluency tasks (animals and vegetables), the Digit Cancellation Test, and the Clock Drawing Test.\u003c/p\u003e\n\u003cp\u003e2.3 Ethical Approval and Informed Consent\u003c/p\u003e\n\u003cp\u003eThe ADNI study received ethical approval from the institutional review boards of all participating institutions. Written informed consent was obtained from all participants or their authorized representatives. More details can be found on the ADNI website (www.adni-info.org).\u003c/p\u003e\n\u003cp\u003e2.4. MRI data acquisition and analysis\u003c/p\u003e\n\u003cp\u003eT1-weighted images were processed with FreeSurfer 7.4.0 using the \u0026ldquo;recon-all\u0026quot; command line. Briefly, this processing includes motion correction and intensity normalization of T1-weighted images, removal of non-brain tissue using a hybrid watershed/surface deformation procedure, automated Talairach transformation, segmentation of the subcortical white matter (WM) and deep GM volumetric structures, tessellation of the GM WM matter boundary, and derivation of cortical thickness. The \u0026ldquo;segmentHA_T1. sh\u0026apos;\u0026apos; script was subsequently used to compute the parcellation and volume quantification of different amygdala [28] subregions. The amygdala was segmented into 9 nuclei for each hemisphere: the accessory basal nucleus, the anterior amygdaloid area (AAA), the basal nucleus, the central nucleus, the cortical-amygdaloid transition area, the cortical nucleus, the medial nucleus, the lateral nucleus, and the para-laminar nucleus. The volumes were normalized (divided) by the estimated total intracranial volumes.\u003c/p\u003e\n\u003cp\u003e2.5. Statistics\u003c/p\u003e\n\u003cp\u003eAnalysis of variance and Bonferroni post hoc tests were used to evaluate group differences regarding demographic, neuropsychological, and clinical data. The categorical variables were analyzed using chi-square tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the MRI measures, a MANCOVA followed by post-hoc comparison, was applied to test the differences among groups. Age, gender and education level were added to the model to control for their potential confounding effect. Spearman\u0026apos;s correlations were conducted to examine possible relationships between the volume of atrophic subfields and the neuropsychological outcomes. All analyses applied false discovery rate (FDR) correction to account for multiple comparisons.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Demographic and clinical features\u003c/p\u003e\n\u003cp\u003eDifferences in demographic characteristics, cognitive assessments, and CSF biomarkers among MCI subgroups are presented in Table 1. Significant age differences were observed among the three MCI subgroups, with the A+T+ group being the oldest and the A−T− group the youngest; no significant differences were found in sex distribution or years of education.\u003c/p\u003e\n\u003cp\u003eIn terms of cognitive assessments, although MMSE and MoCA scores did not significantly differ among the subgroups, the A+T+ group showed poorer performance across multiple specific cognitive domains. For example, RAVLT-immediate and RAVLT-learning scores were significantly lower in the A+T+ group compared to the A−T− group. EM scores were also lowest in the A+T+ group, with a significant difference compared to the A−T− group. In EF tests, both the A+T− and A+T+ groups scored lower than the A−T− group.\u003c/p\u003e\n\u003cp\u003e3.2. MRI volume\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in table 2 and figure 1, the A+T+ group exhibited varying degrees of atrophy in the amygdala and its subregions compared to the A–T– and A+T– groups, including right Basal, right Accessory Basal, right Central, left Accessory Basal, and left Central. The A+T+ group showed significant atrophy in right CATA compared to the A–T– group, and in left Basal compared to the A–T+ group. No significant differences were observed between the A+T– and A–T– groups.\u003c/p\u003e\n\u003cp\u003e3.2. Correlation analysis of significant brain differences and neuropsychological tests\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, correlation analysis revealed a positive association between the left Central amygdala and both MoCA (p = 0.0012, r = 0.338) and RAVLT_learning (p = 0.0018, r = 0.311), after Bonferroni correction.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe primary objective of this study was to identify morphological changes in specific amygdala subregions across different ATN subgroups. Although subcortical atrophy of the amygdala was observed across all groups, no significant differences were found between the A\u0026thinsp;+\u0026thinsp;T\u0026ndash; and A\u0026ndash;T\u0026ndash; groups. The affected subregions included bilateral Basal, bilateral Accessory Basal, bilateral Central, and right CATA. As expected, atrophy in some of these subregions was significantly associated with cognitive performance.\u003c/p\u003e\u003cp\u003eIn line with previous research, our study found that amygdala atrophy progressively worsens as the disease advances[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. We found that, compared to both A\u0026ndash;T\u0026ndash; and A\u0026thinsp;+\u0026thinsp;T\u0026ndash; groups, the A\u0026thinsp;+\u0026thinsp;T\u0026thinsp;+\u0026thinsp;group exhibited significant atrophy in the bilateral accessory basal, bilateral central, and right basal subregions of the amygdala. This finding aligns with previous postmortem studies, which reported the presence of isolated neurofibrillary tangles (NFTs) in the central nucleus of the amygdala during the early stages of AD[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The central nucleus is thought to regulate behavioral responses to potentially harmful stimuli and fear perception through its connections with the hypothalamus, basal forebrain, and brainstem[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Damage in this region may thus impair normal cognitive functioning in individuals with A\u0026thinsp;+\u0026thinsp;T+. Our correlation analyses further support this: we observed that, compared to the A\u0026ndash;T\u0026ndash; group, the volume of the central nucleus in A\u0026thinsp;+\u0026thinsp;T\u0026thinsp;+\u0026thinsp;individuals was significantly associated with several cognitive measures, such as RAVLT_learning and MoCA, suggesting a link between progressive tau and Aβ pathology and cognitive decline.\u003c/p\u003e\u003cp\u003eAdditionally, as the disease advances, other amygdala subnuclei\u0026mdash;including the basal and accessory basal nuclei\u0026mdash;are also affected, showing the highest NFTs burden[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Previous studies have also reported that neuronal loss, reflected as atrophy, is more prominent in the central and accessory basal nuclei compared to other amygdala subregions[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given its role in updating stimulus\u0026ndash;value associations via connections with the orbitofrontal cortex, the basal nucleus is essential for emotional and social cognition[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, atrophy in this region may contribute to cognitive dysfunction observed in the A\u0026thinsp;+\u0026thinsp;T\u0026thinsp;+\u0026thinsp;group.\u003c/p\u003e\u003cp\u003eIn addition, our study found that the A\u0026thinsp;+\u0026thinsp;T\u0026thinsp;+\u0026thinsp;group showed significant atrophy in the right CATA region compared to the A\u0026ndash;T\u0026ndash; group. The CATA is characterized by its dense cholinergic and dopaminergic innervation, distinguishing it from adjacent regions like the piriform cortex and amygdala, and by its direct projections from both the main and accessory olfactory bulbs[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Due to its unique connectivity and functional roles, damage to the CATA may contribute to cognitive impairments observed in patients[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, we observed that atrophy was more pronounced in the right amygdala subregions compared to the left. Consistent with our findings, Anderson et al. reported that patients with right amygdala damage exhibited impairments in recognizing emotional expressions, whereas patients with left-sided lesions showed no significant difference from healthy controls[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similarly, a case study by Wang et al. found that a patient with right amygdala and bilateral anterior cingulate damage following stereotactic brain surgery demonstrated a marked deficit in recognizing fearful faces[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Taken together with our findings, these studies suggest that damage to the right amygdala may play a more critical role in the disruption of recognition abilities[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged in this study. First, the cross-sectional design restricts our ability to infer causal relationships or track longitudinal changes in amygdala subregional atrophy. Second, although we used an advanced segmentation algorithm, the resolution and accuracy of automated subregional parcellation may still be affected by individual anatomical variability. Finally, while we included cognitive measures, other potentially relevant behavioral or neuropsychiatric symptoms were not assessed and may provide additional insights in future research.\u003c/p\u003e\u003cp\u003eIn summary, this study reveals that specific amygdala subregions, particularly the central, basal, accessory basal, and CATA nuclei, exhibit progressive atrophy across the ATN spectrum. the central nuclei alterations are significantly associated with cognitive decline, suggesting their potential role as imaging biomarkers for early AD progression. Notably, our findings highlight the vulnerability of the right amygdala, reinforcing its critical involvement in emotion recognition and social cognition. These results, consistent with both neuroimaging and neuropathological studies, provide further evidence for the role of amygdala subregional degeneration in the pathophysiology of AD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in this article material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by Ethical approval for the ADNI study was granted by the institutional review committees of all participating institutions. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no financial support was received for the research and/or publication of this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.Y. contributed substantially to the conception and design of the study, as well as to data acquisition, analysis, and interpretation. D.Z., W.Q., X.L., Y.R., and Y.T. were actively involved in experimental implementation, data processing, and interpretation of results. C.Xu. and C.Xi. provided critical input in study design, guided experimental optimization, and offered substantial revisions to the manuscript to enhance its intellectual content and scientific rigor.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eScheltens, P., B. De Strooper, M. Kivipelto, H. Holstege, G. 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Walker, \u003cem\u003eIt Is All in the Right Amygdala: Increased Synaptic Plasticity and Perineuronal Nets in Male, But Not Female, Juvenile Rat Pups after Exposure to Early-Life Stress.\u003c/em\u003e J Neurosci, 2020. \u003cstrong\u003e40\u003c/strong\u003e(43): p. 8276-8291.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Group Comparisons of Demographic Characteristics, Clinical Scale Scores, and CSF Biomarkers\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"708\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-T-(54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA+T-(28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA+T+(52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eF values(\u0026chi;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.97(7.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.68(7.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.16(5.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex (Male/Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20/32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYears of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.91(2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.46(2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.33(2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.06(1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.18(1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.38(2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.79(3.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.04(2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.76(3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAVLT-immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.43(9.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.68(9.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.10(8.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAVLT-learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.91(2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.36(2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.73(2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.031\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAVLT-forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.43(3.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.57(2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.10(2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRAVLT-prec-forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.08(56.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.15(29.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.50(30.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.49(5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.30(1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05(0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.65(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.23(1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.24(0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026beta;\u003csub\u003e42\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1484.25(255.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e726.19(199.64)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e622.09(159.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e249.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT-tau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e201.89(44.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178.88(46.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e383.71(130.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e73.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep-tau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.36(3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.30(4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.58(16.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as mean (standard deviation, SD), unless otherwise specified. MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; RAVLT: Rey Auditory Verbal Learning Test; EM: episodic memory; EF: executive function; A\u0026beta;: Amyloid-beta protein; p-tau: phosphorylated tau protein; t-tau: total tau protein; A+T+: abnormal A\u0026beta;42 and abnormal p-tau; A+T\u0026minus;: abnormal A\u0026beta;42 and normal p-tau; A\u0026minus;T\u0026minus;: normal A\u0026beta;42 and normal p-tau. a: comparison between A\u0026minus;T\u0026minus; and A+T+; b: comparison between A\u0026minus;T\u0026minus; and A+T\u0026minus;; c: comparison between A+T\u0026minus; and A+T+.\u003c/p\u003e\n\u003cp\u003eTable 1:\u0026nbsp;amygdala volumetric measures\u0026nbsp;among ATN groups\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"675\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eVolume nucleus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eA-T-(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003eA+T-(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eA+T+(Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eF-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eFDR-p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Lateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e658.31 \u0026plusmn; 98.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e655.16 \u0026plusmn; 84.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e611.42 \u0026plusmn; 98.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Basal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e442.79 \u0026plusmn; 69.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e437.05 \u0026plusmn; 62.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e393.45 \u0026plusmn; 63.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0033\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Accessory_Basal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e254.77 \u0026plusmn; 40.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e250.92 \u0026plusmn; 38.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e218.86 \u0026plusmn; 39.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er AAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e53.95 \u0026plusmn; 8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e53.39 \u0026plusmn; 8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e48.91 \u0026plusmn; 7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e46.12 \u0026plusmn; 11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e45.30 \u0026plusmn; 7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e37.05 \u0026plusmn; 8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e11.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e20.60 \u0026plusmn; 4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e20.68 \u0026plusmn; 6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e18.07 \u0026plusmn; 4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Cortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e24.57 \u0026plusmn; 4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e24.26 \u0026plusmn; 4.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e21.74 \u0026plusmn; 4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er CATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e168.98 \u0026plusmn; 24.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e165.02 \u0026plusmn; 21.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e149.75 \u0026plusmn; 25.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0033\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Paralaminar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e50.33 \u0026plusmn; 8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e49.96 \u0026plusmn; 6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e46.28 \u0026plusmn; 7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er Whole_amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1720.42 \u0026plusmn; 249.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1701.75 \u0026plusmn; 223.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1545.53 \u0026plusmn; 243.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0033\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Lateral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e629.93 \u0026plusmn; 85.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e622.39 \u0026plusmn; 89.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e587.76 \u0026plusmn; 84.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Basal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e420.47 \u0026plusmn; 59.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e419.94 \u0026plusmn; 63.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e381.96 \u0026plusmn; 56.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0095\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Accessory_Basal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e238.04 \u0026plusmn; 35.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e235.71 \u0026plusmn; 41.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e210.34 \u0026plusmn; 31.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0054\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003er_AAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e49.94 \u0026plusmn; 7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e49.66 \u0026plusmn; 7.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e45.64 \u0026plusmn; 6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e42.13 \u0026plusmn; 8.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e41.30 \u0026plusmn; 9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e34.54 \u0026plusmn; 8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003eac\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Medial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e18.36 \u0026plusmn; 4.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e20.24 \u0026plusmn; 8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e16.49 \u0026plusmn; 4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Cortical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e22.08 \u0026plusmn; 4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e21.75 \u0026plusmn; 5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e20.28 \u0026plusmn; 3.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.3371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el CATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e159.83 \u0026plusmn; 22.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e158.83 \u0026plusmn; 24.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e146.06 \u0026plusmn; 23.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Paralaminar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e48.54 \u0026plusmn; 7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e49.58 \u0026plusmn; 7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e45.73 \u0026plusmn; 7.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003el Whole_amygdala\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1629.32 \u0026plusmn; 210.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1619.40 \u0026plusmn; 232.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1488.80 \u0026plusmn; 205.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e0.0095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: AAA = Anterior Amygdaloid Area; CATA=Cortical-Amygdaloid Transition Area; SD: standard deviation; A+T+: abnormal A\u0026beta;42 and abnormal p-tau; A+T\u0026minus;: abnormal A\u0026beta;42 and normal p-tau; A\u0026minus;T\u0026minus;: normal A\u0026beta;42 and normal p-tau. a: comparison between A\u0026minus;T\u0026minus; and A+T+; b: comparison between A\u0026minus;T\u0026minus; and A+T\u0026minus;; c: comparison between A+T\u0026minus; and A+T+. l = left; r = right.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer's disease, amygdala subnuclei, Mild Cognitive Impairment, ATN, structural magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-7378071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7378071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Alzheimer’s disease (AD) pathology begins years before clinical symptoms, with Mild Cognitive Impairment (MCI) as a prodromal stage. The ATN framework (Amyloid, Tau, Neurodegeneration) aids in stratifying MCI risk. While amygdala atrophy is a recognized biomarker, amygdala subregional changes across ATN-defined MCI subgroups remain underexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study analyzed MRI data and cerebrospinal fluid biomarkers from 134 MCI participants classified into A–T–, A+T–, and A+T+ subgroups using ADNI data. Amygdala volumes were computed and compared among the different groups. Furthermore, we also investigated the relationship between the altered brain regions and cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eSignificant atrophy was observed in the A+T+ group within bilateral basal, accessory basal, central nuclei, and right cortical-amygdaloid transition area compared to other groups. Volume reductions in the left central nucleus correlated positively with cognitive scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAmygdala subregional atrophy, particularly in the central, basal, accessory basal, and cortical-amygdaloid transition nuclei, is linked to AD pathology progression and cognitive decline. The findings suggested the right amygdala’s vulnerability and suggest these subregions as potential early imaging biomarkers for AD progression.\u003c/p\u003e","manuscriptTitle":"Amygdala Subregional Atrophy Across ATN-Defined Mild Cognitive Impairment Subgroups","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 13:23:37","doi":"10.21203/rs.3.rs-7378071/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccb55d7b-3390-4a90-9f10-34ee41868736","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T02:24:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 13:23:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7378071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7378071","identity":"rs-7378071","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}