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. 2026 Jun 1;18:1778960. doi: 10.3389/fnagi.2026.1778960

Analysis of structural alterations in olfactory-related brain regions in patients with cognitive impairment and the correlation with olfactory function

Xuefang Han 1,†, Xueshan Cao 2,3,†, Lin Liu 1, Ziqi Wang 1, Duo Gao 1, Huandi Lv 1, Haiqing Yang 1, Yiming Wang 2,3, Caixia Cui 1, Shenao Li 1, Qianhang Yang 1, Jingjing Cui 4, Zhengnan Zhang 1, Huixiao Wang 1, Shihan Cheng 1, Bo Song 2,3,*,‡, Zuojun Geng 1,5,*,‡,§
PMCID: PMC13265641  PMID: 42306137

Abstract

Objective

To evaluate the differences in olfactory function, volumes of brain regions related to olfaction, and subregion volumes of the amygdala and hippocampus among the healthy control (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD), as well as the association of volumes and olfactory function.

Methods

Clinical data for the participants included age, gender, education level, Mini-Mental State Examination (MMSE). Participants underwent olfactory function test, including three sub-experiments as namely threshold test (TT), discrimination test (DT) and identification test (IT) and brain magnetic resonance imaging (MRI), including a three dimensions T1-weighted sequence (3D-T1WI). The brain subregions were extracted automatically by the uAI research portal.

Results

A total of 107 participants were recruited, including 35 HC, 28 MCI and 44 AD. The volumes of hippocampal subregion (HC_Body_L and HC_Tail_L) were significant difference in HC and MCI groups while the global hippocampal volumes were not. All tested domains (TOS, TT, DT, and IT) showed statistically significant differences in olfactory function between the three groups. Furthermore, we found only IT showed significant differences across all three pairwise comparisons among the HC, MCI, and AD groups. The hippocampus and amygdala volumes showed the strongest correlation with olfactory function, followed by the PHG, PCC, lOFC and ERC, and more sub-region volumes had moderate-strength correlations with DT, IT, and TOS, according to Spearman correlation analysis.

Conclusion

The olfactory function and the volumes of the hippocampal subnuclei maybe good candidates as early biomarkers of preclinical AD.

Keywords: Alzheimer’s disease, amygdala, hippocampus, magnetic resonance imaging, mild cognitive impairment, olfactory function

1. Introduction

With the sustained growth of economy and significant changes in social demographics, the issue of elderly-related diseases has increasingly drawn attention particularly. Alzheimer’s disease (AD), as a representative disease of neurodegenerative diseases has become a major challenge, affecting the health and quality of life among the elderly population severely. According to the “World Alzheimer’s Disease Report 2018” published by the Alzheimer’s Disease International (ADI), there are at least 50 million people with dementia worldwide, and it is projected to reach 152 million by 2050, with about 60–70 percent of them suffering from Alzheimer’s disease.

AD is a severe neurodegenerative disorder with complex etiology. Currently, due to the lack of effective treatment options, prevention of AD can be accomplished only by retarding the progression of the disease in its presymptomatic stages, thus postponing the onset of clinical symptoms (McDade et al., 2021). China is among the countries with the largest and fastest-growing elderly population globally, and identified as a high-risk nation for AD. According to data released by the Ministry of Civil Affairs at the end of 2022, China’s population aged 60 and above reached 280.04 million, accounting for 19.8% of the total population, and those aged 65 and above numbered 209.78 million, representing 14.9% (Zhi et al., 2025). In 2021, the number of people with Alzheimer’s disease and related dementias (ADRD) in China reached 16.99 million cases (Zhi et al., 2025). At the same time, the economic burden brought by Alzheimer’s disease is equally heavy. Total payments in 2023 for all individuals with Alzheimer’s or other dementias are estimated at $345 billion (Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association, 2023). This shows that AD is a serious threat to the health and quality of life of the elderly, while also brings a heavy burden to families and society. Consequently, early prevention and diagnosis of AD emerges as one of the most crucial endeavors in the field of healthcare today.

Imaging markers based on magnetic resonance imaging (MRI) has a special foreground due to its wide coverage, ease of measurement and non-invasive nature. Especially the structural MRI (sMRI), which based on T1 Weighted Image can assess the brain atrophy being used as an image marker of neurodegenerative changes in brain aging (Aging Biomarker Consortium et al., 2023) and AD (Cummings, 2019). Jobin et al. ‘s research found that the total gray matter volume in the central olfactory processing region of the MCI group was significantly smaller than that of the healthy control group and the SCD group. Especially in the regions such as the piriform cortex, amygdala, entorhinal cortex, and hippocampus. More importantly, this atrophy is specific, and there is no significant difference in the volume of gray matter in non-olfactory areas among the three groups (Jobin et al., 2023). Papadatos et al. also demonstrated that there is a specific association between olfactory function and structural indicators of the olfactory brain region. In the SCD group, olfaction was associated with atrophy of the entorhinal cortex. In the MCI group, olfaction was related to hippocampal volume and the thickness of the right entorhinal cortex (Papadatos and Phillips, 2023).

Besides, previous studies also have shown that olfactory impairment is correlated with the pathological changes of AD (Doty, 2017; Lafaille-Magnan et al., 2017). Olfactory dysfunction gradually worsens with age (Murphy, 2002), and it is present in 85–90% of patients with AD, preceding the cognitive symptoms onset by several years (Schubert et al., 2008; Woodward et al., 2017). In the AD continuum, the onset of olfactory dysfunction begins as early as the subjective cognitive decline (SCD) stage, precedes brain structural changes, cognitive decline and clinical symptoms. The research also shows that the individuals with SCD had poorer olfactory function than healthy elderly, but better than MCI patients (Wang et al., 2021). This indicates that olfactory impairment may be a clinical marker, for the early detection and clinical diagnosis of AD.

At present, there are same reports on the correlation between olfactory function and olfactory related brain regions, but they are all based on individual brain regions. Comprehensive evaluations of olfactory related brain regions, such as amygdala (AMG), hippocampus (HPC), posterior cingulate cortex (PCC), insula (Ins), superior temporal gyrus (STG), entorhinal cortex (ERC), para-hippocampal gyrus (PHG), medial orbitofrontal cortex (mOFC), lateral orbitofrontal cortex (lOFC), have not been seen yet, and studies based on amygdala subregions and hippocampus subregions are even rare. Therefore, this study focuses on olfactory related brain regions as the target to evaluate the differences in olfactory function, brain regions volume, and subregion volumes of the amygdala and hippocampus among the three groups: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Additionally, the study explores the correlation between olfactory function and brain region volumes.

2. Materials and methods

2.1. Participants

The project adheres to the Declaration of Helsinki and has been approved by the Ethics Committee of the Second Hospital of Hebei Medical University. This study was conducted from March 2024 to September 2025 at the Second Hospital of Hebei Medical University. It was diagnosed as HC, MCI or AD by senior neurologists based on the National Institute on Aging and the Alzheimer’s Association (NIA-AA) criteria (2011) (Albert et al., 2011). Exclusion criteria for all participants were a history of psychiatric disorders, cardiovascular or cerebrovascular disease, nasal pathologies, alcohol or substance abuse, steroid treatment, infections, inability to undergo clinical tests or MRI scanning, presence of obvious head motion during scanning or other reasons causing image artifacts. A total of 107 participants were recruited, including 35 HC, 28 MCI and 44 AD, collected demographic information including age, gender, and education level for all participants. All the participants signed the informed consent form. All participants were right-handed and aged between 53 and 81 years.

2.2. Olfactory function test

Olfactory function testing was performed using Humans’ Quick Odour Pens “Sniffin’ Sticks” in a quiet and well-ventilated indoor place, which included three sub-experiments, namely threshold test (TT), discrimination test (DT) and identification test (IT). Odor threshold is the ability to detect an odor at a given concentration wherein the lowest detectable concentration is considered the threshold; Odor discrimination is the ability to distinguish between two or more odors; Odor identification is the detection and recall of a previous smell associated with an individual’s knowledge or experience.

During smell administration, the opened odour pen was set at a distance about 2 cm in front of both nostrils for about 3–4 s. The interval between the different presentations should be around 30 s. A break of about 3 min should be made between the three tests. The complete test took 30–40 min. The olfactory function test result was expressed as the sum of the three sub-test scores. A total score of more than 30 was normal, and a total score of less than 30 indicated the decreased sense of smell.

2.3. MRI data acquisition

Brain MRI data were acquired using a GE Architect 3.0 T MR-scanner with a 48-channel phased-array head coil. Structural images were acquired with high-resolution three dimensions T1- weighted fast field echo structural scans (repetition time 6.2 ms, echo time 2.4 ms, field of view 256 mm × 256 mm, flip angle 8°, and voxel size 1 mm × 1 mm × 1 mm).

The brain subregions of all groups were extracted automatically by the uAI research portal (uRP, https://urp.united-imaging.com). The uRP is a multifunctional platform to perform accurate image processing and analysis (Wu et al., 2023). The processing pipeline involved automatic parcellation of the structural MRI data using a 3D deep learning segmentation model, which achieved a Dice similarity coefficient of 91.06% between the automatically segmented results and ground truth data. Note that the ground truth is initially obtained from FreeSurfer software and then further refined by an experienced rater based on the Desikan–Killiany atlas (Desikan et al., 2006; Fischl, 2012) The image processing flow mainly includes: (a) the bias field correction, (b) removal of the skull, (c) tissue segmentation of white matter, gray matter, and cerebrospinal fluid (CSF), (d) bilateral segmentation, and (e) parcellation of 109 sub-regions. Following the brain parcellation step, brain volumes were automatically calculated.

Different olfactory functions are regulated by different regions of the brain which are also relevant to aging, PD and AD (Dan et al., 2021). The key brain regions in humans and mammalian animal models involved in olfactory function are the entorhinal cortex, hippocampus, insula, orbitofrontal cortex, inferior frontal gyrus, piriform cortex, thalamus, cingulate gyrus, and amygdala (Kehl et al., 2024; Stenwall et al., 2025). Retrospectively, odor identification were associated with the brain volumes of specific frontal (medial frontal, orbitofrontal, insula, and precentral) and temporal areas (middle, superior, inferior, entorhinal cortex, parahippocampal, amygdala, fusiform, and hippocampus), and thalamus (Tian et al., 2023). Based on the above, the following brain regions were selected: intracranial volume (ICV) of the subjects (sum of gray matter, white matter and intracranial cerebrospinal fluid volume), and brain regions related to olfaction including AMG, HPC, PCC, Ins, STG, ERC, PHG, mOFC and lOFC.

The AMG and HPC play a central role in olfactory information processing and odor identification (Kehl et al., 2024). Therefore, we further conducted subregion analysis of the amygdala and hippocampus. The amygdala subregions include lateral nucleus (La), basal nucleus (Ba), central nucleus (Ce), medial nucleus (Me), cortical nucleus (Co), accessory basal nucleus (AB), cortico-amygdaloid transition area (CAT), anterior amygdaloid area (AAA), paralaminar nucleus (PL). The hippocampal subregions includes hippocampal head (HC_Head), hippocampal body (HC_Body), hippocampal tail (HC_Tail), hippocampal parasubiculum (HC_Parasubiculum), hippocampal presubiculum (HC_Presubiculum), hippocampal subiculum (HC_Subiculum), HC_CA1, HC_CA3, HC_CA4, hippocampal granule cell layer of dentate gyrus (HC_GC_DG), hippocampal molecular layer (HC_ Molecular layer), hippocampus amygdala transition area (HC_HATA), hippocampal fimbria (HC_Fimbria), hippocampal fissure (HC_Fissure).

2.4. Statistical analysis

Statistical analysis was performed in SPSS 31.0 software (IBM, Armonk, NY, United States) and R Studio. Data with normal distribution are expressed as mean ± standard deviation (SD), and non-normal distribution are expressed as median (interquartile range). For numerical data, such as age and MMSE, One-way ANOVA or a Kruskal-Wallis test was used to evaluate the differences among HC, MCI and AD groups. For categorical data, such as gender and education, Pearson’s Chi-squared test was used to evaluate differences among HC, MCI and AD groups. Comparisons of olfactory function among the HC, MCI, and AD groups were performed using ANCOVA, with age, gender, and education included as covariates. Comparisons of brain regional and subregional volumes among the HC, MCI, and AD groups were performed using ANCOVA, with age, gender, education, and ICV included as covariates. Post-hoc comparisons were corrected for multiple testing using Bonferroni correction. A Spearman partial correlation analysis was used to evaluate the correlations between olfactory function and brain regional volumes by controlling for age, gender, education and ICV. Statistical significance was considered as p < 0.05.

3. Results

3.1. Basic characteristics of the participants

Figure 1 displays the study’s flow diagram. A total of 35 HC participants, 28 MCI patients and 44 AD patients with high-resolution sMRI data were enrolled. The mean age for the cohort was 66.2 ± 6.5 years. The demographic data for participants by group are shown in Table 1. No significant age or gender differences were observed between the HC, MCI and AD groups (p > 0.05). There were significant differences between the three groups in terms of education attainment and MMSE scores.

Figure 1.

Flowchart summarizing research design in four phases: participants are recruited and divided into HC, MCI, and AD groups; data acquisition includes MRI, demographics, and clinical scales; statistical analyses compare groups on age, gender, MMSE, olfactory function, and brain volume, as well as the correlation between brain regions and olfactory function; the conclusin focuuses on screening early biomarkers of preclinical Alzheimer’s disease.

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Study’s flow diagram.

Table 1.

Statistical analysis of demographic information in groups HC, MCI, and AD.

Index HC (n = 35) MCI (n = 28) AD (n = 44) H/χ2 P P1 P2 P3
Age (years) 63 (61, 67) 66.11 ± 7.12 67.70 ± 7.42 5.682 0.058 – – –
Gender (male/female) 19/16 15/13 22/22 0.167 0.920 – – –
Education
Primary school and below 1 3 10 15.135 <0.001 0.105 <0.001 0.054
Junior high school 6 10 20
Senior high school 18 8 8
University and above 10 7 6
MMSE score 29 (28, 30) 25 (25, 26.75) 16.5(12, 21) 89.788 <0.001 0.001 <0.001 <0.001
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HC, healthy control; MCI, mild cognitive impairment; AD, Alzheimer’s disease; MMSE, Mini-Mental State Examination.

p values for comparison among the three groups.

P1 HC group versus MCI group.

P2 HC group versus AD group.

P3 MCI group versus AD group.

3.2. Olfactory function

Table 2 displays the findings of the measurement and statistical analysis of the total olfactory score (TOS), TT, DT and IT scores. All tested domains (TOS, TT, DT, and IT) showed statistically significant differences in olfactory function between the three groups. We also observed that olfactory function declined with disease progression. With the exception of the comparison in the TT between the gropes of MCI and AD (p = 0.991), the DT between the gropes of HC and MCI (p = 0.137) and the TOS between the gropes of MCI and AD (p = 0.101), post hoc analysis using the Dunn Kruskal-Wallis multiple comparison (p-values adjusted with the Bonferroni method) or LSD method revealed significant differences in olfactory function among all other inter-group comparisons (p < 0.05).

Table 2.

Statistical analysis of the olfactory function in groups HC, MCI, and AD.

Index HC (n = 35) MCI (n = 28) AD (n = 44) F/W p P1 P2 P3
Olfactory function
Threshold test 7.848 ± 0.621 4.935 ± 0.581 4.926 ± 0.546 14.225 <0.001 0.002 0.002 0.991
Discrimination test 10.122 ± 0.424 9.198 ± 0.430 7.159 ± 0.465 23.037 <0.001 0.137 <0.001 0.002
Identification test 10.795 ± 0.526 7.449 ± 0.559 5.615 ± 0.469 25.014 <0.001 <0.001 <0.001 0.042
TOS 28.783 ± 1.292 21.600 ± 1.374 17.718 ± 1.152 18.935 <0.001 <0.001 <0.001 0.101
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p values for comparison among the three groups.

P1 HC group versus MCI group.

P2 HC group versus AD group.

P3 MCI group versus AD group.

3.3. Brain regions volumes

Table 3 displays the findings of the measurement and statistical analysis of the ICV volumes and the olfactory related brain regions. Except for ICV, the volumes of the other olfactory related brain regions all showed intergroup differences between the three groups. Further pairwise comparisons revealed that there were significantly differences between the AD group and the MCI group as well as between the AD group and the HC group in the volumes of both the left and right of AMG, HPC, PCC, Ins, STG, ERC, PHG and lOFC as well as the right side of mOFC. There were no differences between the AD and MCI groups in the left side of mOFC or between the HC and MCI groups in any of the regions.

Table 3.

Statistical analysis of the ICV volumes and the olfactory related brain regions.

MRI measures HC (n = 35) MCI (n = 28) AD (n = 44) F/W p P1 P2 P3
ICV 1,430.237 ± 116.272 1,415.199 ± 115.811 1,395.674 ± 122.754 0.837 0.436 – – –
AMG_L 2.046 ± 0.061 1.991 ± 0.063 1.507 ± 0.040 69.201 <0.001 0.519 <0.001 <0.001
AMG_R 2.086 ± 0.052 2.022 ± 0.056 1.527 ± 0.047 35.410 <0.001 1.000 <0.001 <0.001
HPC_L 3.352 ± 0.073 3.152 ± 0.077 2.486 ± 0.065 40.149 <0.001 0.183 <0.001 <0.001
HPC_R 3.445 ± 0.070 3.262 ± 0.075 2.638 ± 0.063 37.635 <0.001 0.227 <0.001 <0.001
PCC_L 3.073 ± 0.080 3.014 ± 0.064 2.618 ± 0.061 27.491 <0.001 0.565 <0.001 <0.001
PCC_R 3.129 ± 0.073 3.052 ± 0.077 2.643 ± 0.065 13.426 <0.001 1.000 <0.001 <0.001
Ins_L 6.712 ± 0.099 6.733 ± 0.105 6.230 ± 0.089 8.574 <0.001 1.000 0.002 0.001
Ins-R 6.834 ± 0.100 6.740 ± 0.107 6.310 ± 0.090 8.025 <0.001 1.000 0.001 0.008
STG_L 11.374 ± 0.201 11.432 ± 0.213 10.639 ± 0.180 5.070 0.008 1.000 0.032 0.017
STG_R 10.777 ± 0.177 10.749 ± 0.186 10.024 ± 0.146 13.288 0.001 0.912 0.005 0.005
ERC_L 2.075 ± 0.067 1.994 ± 0.068 1.637 ± 0.047 33.044 <0.001 0.393 <0.001 <0.001
ERC_R 1.951 ± 0.064 1.878 ± 0.065 1.571 ± 0.046 26.555 <0.001 0.425 <0.001 <0.001
PHG_L 2.148 ± 0.042 2.044 ± 0.042 1.728 ± 0.030 70.726 <0.001 0.079 <0.001 <0.001
PHG_R 1.993 ± 0.040 1.942 ± 0.042 1.643 ± 0.036 23.324 <0.001 1.000 <0.001 <0.001
lOFC_L 6.726 ± 0.124 6.638 ± 0.129 6.087 ± 0.010 18.345 <0.001 0.621 <0.001 0.002
lOFC_R 6.809 ± 0.091 6.756 ± 0.096 6.373 ± 0.081 7.154 0.001 1.000 0.003 0.009
mOFC_L 5.038 ± 0.079 4.924 ± 0.083 4.688 ± 0.070 5.290 0.007 0.967 0.006 0.101
mOFC_R 5.291 ± 0.080 5.148 ± 0.082 4.795 ± 0.064 23.541 <0.001 0.207 <0.001 0.002
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p values for comparison among the three groups.

P1 HC group versus MCI group.

P2 HC group versus AD group.

P3 MCI group versus AD group.

Table 4 displays the findings of the measurement and statistical analysis of the volumes of the 18 subregions in the left and right amygdalae. Excepted Me_L and PL_L volumes between the HC and AD groups and Me_L, Me_R and PL_L volumes between the MCI and AD groups all other subregion volumes demonstrated significant differences between the HC and AD groups as well as between the MCI and AD groups.

Table 4.

Statistical analysis of volumes in 18 subregions of the left and right amygdala.

MRI measures HC (n = 35) MCI (n = 28) AD (n = 44) F/W p P1 P2 P3
La_L 0.641 ± 0.019 0.643 ± 0.021 0.496 ± 0.013 51.085 <0.001 0.943 <0.001 <0.001
La_R 0.640 ± 0.021 0.646 ± 0.023 0.497 ± 0.015 43.229 <0.001 0.848 <0.001 <0.001
Ba_L 0.451 ± 0.014 0.443 ± 0.014 0.334 ± 0.009 66.722 <0.001 0.687 <0.001 <0.001
Ba_R 0.468 ± 0.015 0.455 ± 0.015 0.346 ± 0.010 59.798 <0.001 0.536 <0.001 <0.001
Ce_L 0.034 ± 0.001 0.031 ± 0.001 0.022 ± 0.001 61.650 <0.001 0.068 <0.001 <0.001
Ce_R 0.035 ± 0.001 0.032 ± 0.001 0.023 ± 0.001 34.393 <0.001 0.148 <0.001 <0.001
Me_L 0.002 ± 0.0002 0.002 ± 0.0002 0.002 ± 0.0002 2.421 0.298 – – –
Me_R 0.003 ± 0.000 0.002 ± 0.000 0.002 ± 0.000 3.181 0.046 0.712 0.040 0.584
Co_L 0.021 ± 0.001 0.019 ± 0.001 0.015 ± 0.000 45.413 <0.001 0.168 <0.001 <0.001
Co_R 0.021 ± 0.001 0.019 ± 0.001 0.015 ± 0.001 25.413 <0.001 0.265 <0.001 <0.001
AB_L 0.258 ± 0.009 0.247 ± 0.009 0.182 ± 0.005 68.156 <0.001 0.378 <0.001 <0.001
AB_R 0.264 ± 0.009 0.252 ± 0.009 0.184 ± 0.006 69.774 <0.001 0.336 <0.001 <0.001
CAT_L 0.150 ± 0.005 0.145 ± 0.005 0.108 ± 0.003 70.969 <0.001 0.384 <0.001 <0.001
CAT_R 0.154 ± 0.005 0.147 ± 0.005 0.108 ± 0.003 64.565 <0.001 0.341 <0.001 <0.001
AAA_L 0.025 ± 0.001 0.024 ± 0.001 0.017 ± 0.001 62.659 <0.001 0.302 <0.001 <0.001
AAA_R 0.027 ± 0.001 0.025 ± 0.001 0.018 ± 0.001 75.653 <0.001 0.153 <0.001 <0.001
PL_L 0.031 ± 0.001 0.032 ± 0.001 0.028 ± 0.001 3.049 0.052 – – –
PL_R 0.036 ± 0.001 0.036 ± 0.001 0.027 ± 0.001 36.924 <0.001 0.953 <0.001 <0.001
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p values for comparison among the three groups.

P1 HC group versus MCI group.

P2 HC group versus AD group.

P3 MCI group versus AD group.

Table 5 displays the findings of the measurement and statistical analysis of the volumes of the 28 subregions in the left and right hippocampal. There were no statistically significant differences in the volumes of HATA_L, HATA_R and HC_Fissure_R across the HC, MCI, and AD groups. There were statistically significant differences between the HC and MCI groups only in the volumes of the HC_Body_L and HC_Tail_L subregions. All other subregion volumes showed significant differences between the HC and AD groups, as well as between the MCI and AD groups, excepted of HC_Presubiculum_R and HC_ HC_Fissure_L volumes between the HC and AD groups.

Table 5.

Statistical analysis of volumes in 28 subregions of the left and right hippocampal.

MRI measures HC (n = 35) MCI (n = 28) AD (n = 44) F/W p P1 P2 P3
HC_Head_L 1.607 ± 0.040 1.558 ± 0.042 1.189 ± 0.035 34.655 <0.001 1.000 <0.001 <0.001
HC_Head_R 1.688 ± 0.039 1.617 ± 0.041 1.278 ± 0.035 33.176 <0.001 0.633 <0.001 <0.001
HC_Body_L 1.148 ± 0.021 1.079 ± 0.030 0.835 ± 0.027 70.131 <0.001 0.047 <0.001 <0.001
HC_Body_R 1.154 ± 0.027 1.106 ± 0.029 0.864 ± 0.024 34.148 <0.001 0.672 <0.001 <0.001
HC_Tail_L 0.538 ± 0.013 0.479 ± 0.017 0.376 ± 0.157 50.717 <0.001 0.005 <0.001 <0.001
HC_Tail_R 0.543 ± 0.016 0.491 ± 0.017 0.388 ± 0.015 23.993 <0.001 0.093 <0.001 <0.001
HC_Parasubiculum_L 0.038 ± 0.002 0.038 ± 0.002 0.026 ± 0.002 10.410 <0.001 1.000 <0.001 <0.001
HC_Parasubiculum_R 0.036 ± 0.003 0.035 ± 0.003 0.024 ± 0.002 14.406 <0.001 0.759 0.004 0.009
HC_Presubiculum_L 0.322 ± 0.009 0.308 ± 0.010 0.217 ± 0.008 38.951 <0.001 0.907 <0.001 <0.001
HC_Presubiculum_R 0.288 ± 0.009 0.282 ± 0.010 0.207 ± 0.008 26.382 <0.001 1.000 0.050 0.045
HC_Subiculum_L 0.418 ± 0.013 0.395 ± 0.013 0.295 ± 0.008 72.578 <0.001 0.216 <0.001 <0.001
HC_Subiculum_R 0.433 ± 0.010 0.407 ± 0.011 0.312 ± 0.009 42.296 <0.001 0.217 <0.001 <0.001
HC_CA1_L 0.644 ± 0.014 0.621 ± 0.022 0.484 ± 0.017 48.602 <0.001 0.356 <0.001 <0.001
HC_CA1_R 0.703 ± 0.020 0.666 ± 0.020 0.546 ± 0.014 44.932 <0.001 0.199 <0.001 <0.001
HC_CA3_L 0.208 ± 0.005 0.193 ± 0.008 0.157 ± 0.007 29.955 <0.001 0.127 <0.001 0.001
HC_CA3_R 0.218 ± 0.007 0.213 ± 0.007 0.166 ± 0.006 18.768 <0.001 1.000 <0.001 <0.001
HC_CA4_L 0.233 ± 0.005 0.225 ± 0.005 0.187 ± 0.005 23.093 <0.001 0.802 <0.001 <0.001
HC_CA4_R 0.246 ± 0.004 0.236 ± 0.006 0.196 ± 0.006 40.081 <0.001 0.167 <0.001 <0.001
HC_GC_DG_L 0.278 ± 0.006 0.273 ± 0.007 0.223 ± 0.006 22.598 <0.001 1.000 <0.001 <0.001
HC_GC_DG_R 0.292 ± 0.008 0.282 ± 0.008 0.234 ± 0.006 37.830 <0.001 0.362 <0.001 <0.001
HC_Molecular layer_L 0.527 ± 0.009 0.500 ± 0.016 0.377 ± 0.012 83.950 <0.001 0.127 <0.001 <0.001
HC_Molecular layer_R 0.552 ± 0.013 0.529 ± 0.013 0.408 ± 0.011 38.320 <0.001 0.696 <0.001 <0.001
HC_HATA_L 0.015 ± 0.002 0.014 ± 0.002 0.011 ± 0.002 1.644 0.440 – – –
HC_HATA_R 0.016 ± 0.002 0.012 ± 0.002 0.012 ± 0.002 1.916 0.384 – – –
HC_Fimbria_L 0.070 ± 0.004 0.062 ± 0.004 0.043 ± 0.003 14.095 <0.001 0.422 <0.001 0.001
HC_Fimbria_R 0.054 ± 0.004 0.055 ± 0.004 0.034 ± 0.003 10.684 <0.001 1.000 <0.001 <0.001
HC_Fissure_L 0.090 ± 0.005 0.074 ± 0.005 0.094 ± 0.004 4.584 0.012 0.085 1.000 0.014
HC_Fissure_R 0.103 ± 0.007 0.095 ± 0.006 0.112 ± 0.006 3.897 0.142 – – –
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p values for comparison among the three groups.

P1 HC group versus MCI group.

P2 HC group versus AD group.

P3 MCI group versus AD group.

Furthermore, we observed that most volumes of olfactory-related brain regions, amygdala subregions, and hippocampal subregions decreased with decreasing cognitive function. The volumes of Ins_L, STG_L, La_L, La_R, PL_L, and HC_Fimbria_R were slightly larger in the MCI group than in the HC group, but there were no significant differences (p > 0.05).

3.4. Correlation between olfactory function and brain region volume

The correlations between olfactory function and brain region volumes were examined using Spearman partial correlation analysis, controlling for age, gender, education, and ICV as covariates. The outcomes are shown in Figure 2.

Figure 2.

Heatmap showing partial Spearman correlation coefficients between olfactory function measures (TOS, TT, DT, IT) and various brain regions, controlling for age, gender, education, and intracranial volume. Strongest positive correlations occur in the hippocampus, particularly HPC_R and HPC_L with DT and IT measures. A color scale bar on the right indicates correlation strength from 0.0 (lightest) to 0.4 (darkest red). Statistical significance is marked with asterisks, where one asterisk is p less than 0.05, two for p less than 0.01, and three for p less than 0.001.

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Correlation analysis between olfactory function and the olfactory related brain region volumes.

The figure shows that all nine brain regions—aside from STG_L and PHG_R—have a positive correlation with olfaction function. The relationship between TT and volumes is rather weak. Furthermore, the DT, IT and TOS all have relatively strongest correlation with HPC_R (r = 0.46, 0.46, 0.42), HPC_L (r = 0.45, 0.42, 0.39), AMG_R (r = 0.38, 0.37, 0.32) and AMG_L (r = 0.37, 0.34, 0.30). Besides, PHG_L, PCC_L, PCC_R, lOFC_L and ERC_L also exhibit a certain degree of correlation with DT, IT, and TOS.

Figure 3 displays the correlation analysis between olfactory function and hippocampal subregion volumes. Olfactory threshold, odor discrimination ability, or identification ability do not appear to be correlated with HC_Fissure. Only the HC_tail_R (r = 0.25) and HC_CA3_R (r = 0.22) showed a weak correlation with olfactory thresholds, according to the results of the correlation between hippocampal subregion volumes and olfactory threshold. Brain regions within the hippocampal subregion exhibiting moderate-strength correlations with DT include HC_Tail_R (r = 0.43), HC_Tail_L (r = 0.42), HC_Subiculum_R (r = 0.44), HC_Molecular_layer_R (r = 0.44), HC_Molecular_ layer_L (r = 0.44), HC_Head_R (r = 0.43), HC_Head_L (r = 0.44), HC_GC_DG_L (r = 0.40), HC_CA4_L (r = 0.4), HC_CA3_R (r = 0.42), HC_CA3_L (r = 0.42), HC_CA1_R (r = 0.43), HC_CA1_L (r = 0.45), HC_Body_R (r = 0.45), HC_Body_L (r = 0.40). Brain regions within the hippocampal subregion exhibiting moderate-strength correlations with IT include HC_Tail_R (r = 0.47), HC_Tail_L (r = 0.48), HC_Molecular_layer_R (r = 0.45), HC_Molecular_ layer_L (r = 0.43), HC_Head_R (r = 0.41), HC_CA3_R (r = 0.44), HC_CA3_L (r = 0.42), HC_CA1_R (r = 0.40), HC_Body_R (r = 0.45), HC_Body_L (r = 0.41). Brain regions within the hippocampal subregion exhibiting moderate-strength correlations with TOS include HC_Tail_R (r = 0.45), HC_Tail_L (r = 0.42), HC_Molecular_layer_R (r = 0.41), HC_Molecular_ layer_L (r = 0.40), HC_CA3_R (r = 0.43), HC_CA3_L (r = 0.40), HC_Body_R (r = 0.41).

Figure 3.

Heatmap showing partial Spearman correlations between olfactory function types(TOS, TT, DT, IT) and hippocampal subregions, controlling for age, gender, education, and intracranial volume. Red hues indicate positive correlations, with significance levels marked byasterisks. Spearman’s rho color scale ranges from 0.0 (lightest) to 0.4 (red).

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Correlation analysis between hippocampal subregion volumes and olfactory function.

Figure 4 displays the correlation analysis between olfactory function and the volumes of the amygdala subregion. The amygdala subregion volumes had a weaker correlation with TT and were not statistically significant (p > 0.05). Brain regions within the amygdala subregion exhibiting moderate-strength correlations with DT include LA_R (r = 0.41), CTA_R (r = 0.46), CTA_L (r = 0.40), BA_R (r = 0.40), AB_R (r = 0.44), AB_L (r = 0.41). Brain regions within the amygdala subregion exhibiting moderate-strength correlations with IT include CO_R (r = 0.42), CE_R (r = 0.41), CTA_R (r = 0.44), AB_R (r = 0.42), AAA_R (r = 0.44), AAA_L (r = 0.41). Multiple amygdala subregions exhibit weak volumetric correlations with TOS.

Figure 4.

Heatmap showing partial Spearman correlation coefficients between amygdala subregions and olfactory function measures (TOS, T, DT, IT), controlled for age, gender, education, and intracranial volume. Red hues indicate positive correlations, with significance levels marked by asterisks. Spearman's rho color scale ranges from negative 0.1 (light purple) to 0.4 (red).

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Correlation analysis between amygdala subregion volumes and olfactory function.

4. Discussion

The onset and progression of Alzheimer’s disease is a lengthy process characterized by significant individual variation, beginning with asymptomatic pathological changes in the brain, followed by increasing pathological burden, and leading to the emergence of clinical symptoms and continuous progression (Alzheimer’s and Dementia: The Journal of the Alzheimer’s Association, 2023). The pathological changes of AD begin to occur silently in the brain more than a decade before clinical symptoms appear. These changes first selectively affect specific brain regions, then spread along neuroanatomical networks, ultimately leading to widespread brain atrophy and irreversible cognitive decline. However, what has long troubled us is that AD remains incurable. Furthermore, because AD is often diagnosed late, interventional trials aimed at slowing its progression are frequently implemented too late to yield significant benefits (Mehta et al., 2017). If intervention or prevention were initiated before the onset of clinical symptoms, it might effectively slow the progression of the disease (McDade et al., 2021). Therefore, identifying effective biomarkers is crucial for the early recognition and diagnosis of AD and represents a current priority in scientific research.

In the current study, the main goal was to investigate changes in the olfactory function and the volumes of brain regions related to olfaction, in patients with AD and MCI compared with HC. Subregional analyses were conducted for brain regions closely related to cognitive function, such as the amygdala and hippocampus. It is anticipated that imaging biomarkers for the effective and early diagnosis of the Alzheimer’s disease continuum will be discovered.

Through analysis of demographic data, we found that the proportion of individuals with higher education was higher in the HC group than in both the MCI and AD groups. Just as the Lancet previously reported that higher education or continue cognitive activity would prevent and delay dementia by reducing dementia neuropathology, reducing stress and inflammation, building cognitive and brain reserve. Therefore the people with more childhood education and higher educational attainment have a reduced dementia risk (Livingston et al., 2024).

Olfactory dysfunction is common in AD and other neurodegenerative diseases (McLaren and Kawaja, 2024). This study further support the notion that olfactory function is related to cognitive impairment and compared to cognitively normal research participants, patients with cognitive abnormalities exhibited reduced olfactory function, as demonstrated in the study by Tian Q and Christina S. Dintica (Dintica et al., 2019; Tian et al., 2023).

As we all know, accumulation of pathological deposition of Amyloid-beta (Aβ), phosphorylated tau, and α-synuclein in the brain has been considered as markers of neurodegenerative diseases including Alzheimer’s, as well as physiological markers of brain aging (Mattsson et al., 2009; Silva and Haggarty, 2020; Zhang et al., 2021; Sengupta and Kayed, 2022; Leuzy et al., 2025). A Systematic Review and Meta-Analysis of several biological mechanisms related to the associations between olfactory identification ability and recognition found that olfactory identification ability was negatively correlated with Aβ PET and CSF total tau (Tu et al., 2020). These studies have also confirmed from a pathological perspective that the reduction of olfactory function is positively correlated with the decline in cognitive degree.

The neuropathological basis of olfactory dysfunction in AD is thought to be due mainly to accumulations of disease-related lesions, especially tau pathology in AD, that occur in the olfactory bulb and primary olfactory sensory cortices of the cerebrum (Arnold et al., 1991; Olfactory tau pathology in Alzheimer disease and mild cognitive impairment-PubMed, n.d.). PHFtau, α-synuclein and amyloid-β lesions occur early and severely in cerebral regions of the olfactory system and they have also been observed in olfactory epithelium (OE) (Arnold et al., 2010). Therefore, we hypothesize that the volume of olfactory-related brain regions will also be altered with the occurrence of cognitive impairment.

The ICV was no difference among the three groups. However, the results indicated that the volume of olfactory-related brain regions differed significantly among the three groups. The results of this study confirm that the volumes of AMG, HPC, PCC, Ins, STG, ERC, PHG, mOFC and lOFC were all reduced in the AD group compared with the MCI group and the HC group. The volumes of Ins_L, STG_L, La_L, La_R, PL_L, and HC_Fimbria_R were slightly larger in the MCI group than in the HC group, but there were no significant differences. According to existing literatures, many neuroimaging investigations have identified atrophy of the subcortical structure, as a distinctive feature of AD and MCI (Mijares Diaz et al., 2025). However, some studies have reported different findings. Specifically, Yang et al. observed surface area and local gyrification index in certain cortical regions were increased in the MCI group, such as the transverse temporal gyrus, superior temporal gyrus, and insula (Yang et al., 2023). These morphological changes may account for the relative increases in volumetric measures. We propose that this may result from structural remodeling or compensatory mechanisms during the early stages of the disease process. Olfactory functions, including TT, DT, IT, and TOS, gradually decreased over the course of disease progression, although some differences between groups were not statistically significant. Just as the research by Tian Q higher odor identification scores were associated with greater brain volumes mostly in specific areas, such as orbitofrontal, insula, middle, inferior, entorhinal cortex, amygdala, hippocampus, thalamus, posterior cingulate, corpus callosum, and occipital white matter (Tian et al., 2023). Furthermore, we found that only IT showed significant differences across all three pairwise comparisons among the HC, MCI, and AD groups, suggesting that IT has relatively stronger discriminative ability among the three groups. This finding is consistent with previous literature, which has reported that among the three aspects of olfactory function, odor identification exhibits the strongest correlation with the risk of dementia (Trapp et al., 2023; Xie et al., 2024). Our research findings add to the existing literature and further support the concept that olfaction function is related to cognitive impairment.

Through Spearman partial correlation analysis between brain regions volumes with olfactory function, we found that the volumes of the hippocampus and amygdala showed the strongest correlation with olfactory function and followed by the PHG, PCC, lOFC and ERC. This was in line with previous studies reported that higher odor identification scores were significantly associated with slower rates of brain atrophy in specific frontal (medial frontal, orbitofrontal, insula, and precentral) and temporal areas (middle, superior, inferior, entorhinal cortex, parahippocampal, amygdala, fusiform, and hippocampus) (Tian et al., 2023). Presence of AD neuropathological alterations such as large numbers of neuropil threads and neurofibrillary tangles, in the entorhinal cortex, hippocampus and other temporal regions, lead to an inability to store and retrieve memories of smell, and thereby to correctly identify odors (Attems et al., 2005). amygdala and hippocampal are also part of brain areas that typically show atrophy in AD, that is, AD signature regions, exhibiting large numbers of amyloid plaques and neurofibrillary tangles. We speculate that may account for the strong association between the two areas and olfactory function.

Therefore, we further analyzed the volumes of the amygdala and hippocampal subregions and found that the volumes of hippocampal subregions (HC_Body_L and HC_Tail_L) were significant difference in HC and MCI groups while the global hippocampal volumes were not. Hippocampal subfield volumes were found to be correlated with amyloid status (Aß42/40 negative versus positive) (Yildirim et al., 2023). Early autopsy studies also had shown that in Braak stages I and II, the hippocampus (CA1) is already affected, albeit mildly (Braak and Braak, 1991) and at later AD stages additional subnuclei were affected (Tsuchiya and Kosaka, 1990; Vogt et al., 1990). Although the reported findings are not fully consistent with our study, both suggest that analysis at the level of hippocampal subfields provides greater meaningful information than whole-hippocampal volumetry. Early researches on the hippocampal subregion could only be segmented into seven parts and the atlas only included right hippocampal subfields and the research reported the fimbria and fissure volumes were not significantly different between the amnestic mild cognitive impairment (aMCI) and the elderly control (EC) group (Hanseeuw et al., 2011). However, our study divided the two sides of the hippocampus into 14 parts respectively, and found that HC_Body_L and HC_Tail_L volumes were significantly smaller in the MCI than in HC group. The total volumes of brain regions may mask the early changes in the subregions, and the subregion measurements detect lesions earlier, or atrophy occurs earlier (Salman et al., 2024). Through Spearman partial correlation analysis between amygdala and hippocampal subregion volumes with olfactory function we found that compared with the total volumes of brain regions, more sub-region volumes have moderate-strength correlations with DT, IT, and TOS. Maybe the olfactory function and the volumes of subnuclei are good candidates as early biomarkers of preclinical AD.

5. Limitations

There are several potential limitations should be noted. First, in our study, only volume measurements were analyzed. Previous research clarified the atrophy rate of cortical thickness increases with progression of the disease from the preclinical to clinical stage. Besides, cortical thinning in subjects with HC converted to MCI was significantly faster than that in subjects with stable HC (Kulason et al., 2020; Henzen et al., 2025). Younes et al. found that the surface area of the entorhinal cortex also changes with age through differential morphological measurement indicators based on MRI (Younes et al., 2014). Therefore, we speculate that cortical thickness and surface area may also be imaging markers for the early diagnosis of AD continuum. In subsequent research, we will further analyze the relationship between structural indicators such as cortical thickness, cortical surface area, and cortical curvature and the AD continuum. Second, our research results are based on a limited sample size. Further studies in larger cohorts are needed to confirm these results and determine whether these indicators could be good candidates as early biomarkers for preclinical AD.

6. Conclusion

Our study identified the significance of olfactory function, especially IT, as well as the specific nuclei—HC_Body_L and HC_Tail_L of hippocampus, in the recognition of cognitive impairment by analyzing the hippocampus subnuclei volumes and the olfactory function among the three groups. Segmenting hippocampal subfields indeed allow improved sensitivity to MCI compared with total hippocampus volumetry. These findings are supported by both traditional neuropathological and neuroimaging studies. Therefore, we should highlight the studying of the relevance of the subnuclei and olfactory function in cognitive impairment.

Acknowledgments

We would like to thank all participants for their study participations and thank medical staff of the neurology department of the Second Hospital of Hebei Medical University, for clinical data.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. This research was supported by the S&T Program of Hebei, Grant Nos. 21377733D and 242W7709Z.

Footnotes

Edited by: Lucas Caldi Gomes, NanoTemper Technologies GmbH, Germany

Reviewed by: Gloria Benítez-King, Instituto Nacional de Psiquiatría Ramón de la Fuente Muñiz (INPRFM), Mexico

Mark Albers, Massachusetts General Hospital and Harvard Medical School, United States

Data availability statement

The datasets supporting the conclusions of this article are not readily available because the data also form part of an ongoing study. Requests to access the datasets should be directed to XH, 28804712@hebmu.edu.cn.

Ethics statement

The studies involving humans were approved by The Ethics Committee of the Second Hospital of Hebei Medical University. 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.

Author contributions

XH: Writing – original draft, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. XC: Visualization, Writing – review & editing, Methodology, Formal analysis. LL: Methodology, Data curation, Writing – original draft. ZW: Writing – original draft, Methodology, Data curation. DG: Supervision, Conceptualization, Writing – review & editing. HL: Writing – original draft, Conceptualization, Project administration, Methodology, Supervision. HY: Writing – review & editing, Conceptualization, Project administration, Funding acquisition. YW: Writing – original draft, Visualization. CC: Writing – original draft, Data curation. SL: Writing – original draft, Data curation. QY: Writing – original draft, Data curation. JC: Writing – review & editing, Software. ZZ: Writing – original draft, Data curation, Software. HW: Data curation, Investigation, Writing – original draft. SC: Writing – original draft, Investigation, Data curation. BS: Methodology, Writing – review & editing, Formal analysis, Visualization. ZG: Funding acquisition, Conceptualization, Project administration, Writing – review & editing.

Conflict of interest

JC was employed by Shanghai United Imaging Intelligence, Co., Ltd.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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References

  1. Aging Biomarker Consortium, et al. (2023). Biomarkers of aging. Sci. China Life Sci. 66, 893–1066. doi: 10.1007/s11427-023-2305-0, [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Albert M. S., DeKosky S. T., Dickson D., Dubois B., Feldman H. H., Fox N. C., et al. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7, 270–279. doi: 10.1016/j.jalz.2011.03.008, [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association (2023). 2023 Alzheimer’s Disease Facts and Figures, vol. 19 Washington: National Institutes of Health. [DOI] [PubMed] [Google Scholar]
  4. Arnold S. E., Lee E. B., Moberg P. J., Stutzbach L., Kazi H., Han L. Y., et al. (2010). Olfactory epithelium amyloid-beta and paired helical filament-tau pathology in Alzheimer disease. Ann. Neurol. 67, 462–469. doi: 10.1002/ana.21910, [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Arnold S. E., Hyman B. T., Flory J., Damasio A. R., Van Hoesen G. W. (1991). The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer’s disease. Cerebral Cortex (New York, N.Y.: 1991) 1, 103–116. doi: 10.1093/cercor/1.1.103 [DOI] [PubMed] [Google Scholar]
  6. Attems J., Lintner F., Jellinger K. A. (2005). Olfactory involvement in aging and Alzheimer’s disease: An autopsy study. J. Alzheimer's Dis 7, 149–157. doi: 10.3233/JAD-2005-7208, [DOI] [PubMed] [Google Scholar]
  7. Braak H., Braak E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259. doi: 10.1007/BF00308809, [DOI] [PubMed] [Google Scholar]
  8. Cummings J. (2019). The National Institute on Aging—Alzheimer’s association framework on Alzheimer’s disease: application to clinical trials. Alzheimers Dement. 15, 172–178. doi: 10.1016/j.jalz.2018.05.006, [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dan X., Wechter N., Gray S., Mohanty J. G., Croteau D. L., Bohr V. A. (2021). Olfactory dysfunction in aging and neurodegenerative diseases. Ageing Res. Rev. 70:101416. doi: 10.1016/j.arr.2021.101416, [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Desikan R. S., Ségonne F., Fischl B., Quinn B. T., Dickerson B. C., Blacker D., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980. doi: 10.1016/j.neuroimage.2006.01.021, [DOI] [PubMed] [Google Scholar]
  11. Dintica C. S., Marseglia A., Rizzuto D., Wang R., Seubert J., Arfanakis K., et al. (2019). Impaired olfaction is associated with cognitive decline and neurodegeneration in the brain. Neurology 92, e700–e709. doi: 10.1212/WNL.0000000000006919, [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Doty R. L. (2017). Olfactory dysfunction in neurodegenerative diseases: is there a common pathological substrate? Lancet Neurol. 16, 478–488. doi: 10.1016/S1474-4422(17)30123-0, [DOI] [PubMed] [Google Scholar]
  13. Fischl B. (2012). FreeSurfer. NeuroImage 62, 774–781. doi: 10.1016/j.neuroimage.2012.01.021, [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hanseeuw B. J., van Leemput K., Kavec M., Grandin C., Seron X., Ivanoiu A. (2011). Mild cognitive impairment: differential atrophy in the hippocampal subfields. Am. J. Neuroradiol. 32, 1658–1661. doi: 10.3174/ajnr.A2589, [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Henzen N. A., Abdulkadir A., Reinhardt J., Blatow M., Kressig R. W., Krumm S. (2025). Automated segmentation for cortical thickness of the medial perirhinal cortex. Sci. Rep. 15:14903. doi: 10.1038/s41598-025-98399-w, [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Jobin B., Boller B., Frasnelli J. (2023). Smaller grey matter volume in the central olfactory system in mild cognitive impairment. Exp. Gerontol. 183:112325. doi: 10.1016/j.exger.2023.112325, [DOI] [PubMed] [Google Scholar]
  17. Kehl M. S., Mackay S., Ohla K., Schneider M., Borger V., Surges R., et al. (2024). Single-neuron representations of odours in the human brain. Nature 634, 626–634. doi: 10.1038/s41586-024-08016-5, [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kulason S., Xu E., Tward D. J., Bakker A., Albert M., Younes L., et al. (2020). Entorhinal and transentorhinal atrophy in preclinical Alzheimer’s disease. Front. Neurosci. 14:804. doi: 10.3389/fnins.2020.00804, [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Lafaille-Magnan M.-E., Poirier J., Etienne P., Tremblay-Mercier J., Frenette J., Rosa-Neto P., et al. (2017). Odor identification as a biomarker of preclinical AD in older adults at risk. Neurology 89, 327–335. doi: 10.1212/WNL.0000000000004159, [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Leuzy A., Bollack A., Pellegrino D., Teunissen C. E., la Joie R., Rabinovici G. D., et al. (2025). Considerations in the clinical use of amyloid PET and CSF biomarkers for Alzheimer’s disease. Alzheimers Dement. 21:e14528. doi: 10.1002/alz.14528, [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Livingston G., Huntley J., Liu K. Y., Costafreda S. G., Selbæk G., Alladi S., et al. (2024). Dementia prevention, intervention, and care: 2024 report of the lancet standing commission. Lancet 404, 572–628. doi: 10.1016/S0140-6736(24)01296-0, [DOI] [PubMed] [Google Scholar]
  22. Mattsson N., Zetterberg H., Hansson O., Andreasen N., Parnetti L., Jonsson M., et al. (2009). CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302, 385–393. doi: 10.1001/jama.2009.1064, [DOI] [PubMed] [Google Scholar]
  23. McDade E., Llibre-Guerra J. J., Holtzman D. M., Morris J. C., Bateman R. J. (2021). The informed road map to prevention of Alzheimer disease: A call to arms. Mol. Neurodegener. 16:49. doi: 10.1186/s13024-021-00467-y, [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. McLaren A. M. R., Kawaja M. D. (2024). Olfactory dysfunction and Alzheimer’s disease: a review. J. Alzheimer's Dis 99, 811–827. doi: 10.3233/JAD-231377 [DOI] [PubMed] [Google Scholar]
  25. Mehta D., Jackson R., Paul G., Shi J., Sabbagh M. (2017). Why do trials for Alzheimer’s disease drugs keep failing? A discontinued drug perspective for 2010-2015. Expert Opin. Invest. Drugs 26, 735–739. doi: 10.1080/13543784.2017.1323868, [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Mijares Diaz F., Orlando A., Schneider A. L. C., Pike J. R., Jack C. R., Deal J. A., et al. (2025). Differences in brain volume in individuals with mild cognitive impairment and normal cognition across different anatomical regions: the atherosclerosis risk in communities (ARIC) study. J. Alzheimer's Dis. 104, 43–51. doi: 10.1177/13872877251313816, [DOI] [PubMed] [Google Scholar]
  27. Murphy C. (2002). Prevalence of olfactory impairment in older adults. JAMA 288, 2307–2312. doi: 10.1001/jama.288.18.2307, [DOI] [PubMed] [Google Scholar]
  28. Olfactory tau pathology in alzheimer disease and mild cognitive impairment-PubMed (n.d.). Available online at: https://pubmed.ncbi.nlm.nih.gov/17140156/ (Accessed June 12, 2025).
  29. Papadatos Z., Phillips N. A. (2023). Olfactory function reflects episodic memory performance and atrophy in the medial temporal lobe in individuals at risk for Alzheimer’s disease. Neurobiol. Aging 128, 33–42. doi: 10.1016/j.neurobiolaging.2023.04.001, [DOI] [PubMed] [Google Scholar]
  30. Salman Y., Gérard T., Huyghe L., Colmant L., Quenon L., Malotaux V., et al. (2024). Amygdala atrophies in specific subnuclei in preclinical Alzheimer’s disease. Alzheimers Dement. 20, 7205–7219. doi: 10.1002/alz.14235, [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Schubert C. R., Carmichael L. L., Murphy C., Klein B. E. K., Klein R., Cruickshanks K. J. (2008). Olfaction and the 5-year incidence of cognitive impairment in an epidemiological study of older adults. J. Am. Geriatr. Soc. 56, 1517–1521. doi: 10.1111/j.1532-5415.2008.01826.x, [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Sengupta U., Kayed R. (2022). Amyloid β, tau, and α-Synuclein aggregates in the pathogenesis, prognosis, and therapeutics for neurodegenerative diseases. Prog. Neurobiol. 214:102270. doi: 10.1016/j.pneurobio.2022.102270, [DOI] [PubMed] [Google Scholar]
  33. Silva M. C., Haggarty S. J. (2020). Tauopathies: deciphering disease mechanisms to develop effective therapies. Int. J. Mol. Sci. 21:8948. doi: 10.3390/ijms21238948, [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Stenwall A., Uggla A. L., Weibust D., Fahlström M., Ryttlefors M., Latini F. (2025). The bulb, the brain and the being: new insights into olfactory system anatomy, organization and connectivity. Brain Sci. 15:368. doi: 10.3390/brainsci15040368, [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Tian Q., An Y., Kitner-Triolo M. H., Davatzikos C., Studenski S. A., Ferrucci L., et al. (2023). Associations of olfaction with longitudinal trajectories of brain volumes and neuropsychological function in older adults. Neurology 100, e964–e974. doi: 10.1212/WNL.0000000000201646, [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Trapp W., Heid A., Röder S., Wimmer F., Hajak G. (2023). “Mmm, smells like coffee!”: how a brief odor identification test could help to identify people with mild cognitive impairment and dementia. Brain Sci. 13:1052. doi: 10.3390/brainsci13071052, [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Tsuchiya K., Kosaka K. (1990). Neuropathological study of the amygdala in presenile Alzheimer’s disease. J. Neurol. Sci. 100, 165–173. doi: 10.1016/0022-510X(90)90029-M, [DOI] [PubMed] [Google Scholar]
  38. Tu L., Lv X., Fan Z., Zhang M., Wang H., Yu X. (2020). Association of odor identification ability with amyloid-β and tau burden: a systematic review and meta-analysis. Front. Neurosci. 14:586330. doi: 10.3389/fnins.2020.586330, [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Vogt L. J. K., Hyman B. T., van Hoesen G. W., Damasio A. R. (1990). Pathological alterations in the amygdala in Alzheimer’s disease. Neuroscience 37, 377–385. doi: 10.1016/0306-4522(90)90408-V [DOI] [PubMed] [Google Scholar]
  40. Wang Q., Chen B., Zhong X., Zhou H., Zhang M., Mai N., et al. (2021). Olfactory dysfunction is already present with subjective cognitive decline and deepens with disease severity in the Alzheimer’s disease Spectrum. J. Alzheimer's Dis 79, 585–595. doi: 10.3233/JAD-201168, [DOI] [PubMed] [Google Scholar]
  41. Woodward M. R., Amrutkar C. V., Shah H. C., Benedict R. H. B., Rajakrishnan S., Doody R. S., et al. (2017). Validation of olfactory deficit as a biomarker of Alzheimer disease. Neurol. Clin. Pract. 7, 5–14. doi: 10.1212/CPJ.0000000000000293, [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wu J., Xia Y., Wang X., Wei Y., Liu A., Innanje A., et al. (2023). uRP: An integrated research platform for one-stop analysis of medical images. Front. Radiol. 3:1153784. doi: 10.3389/fradi.2023.1153784, [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Xie B., Yang S., Hao Y., Sun Y., Li L., Guo C., et al. (2024). Impaired olfactory identification in dementia-free individuals is associated with the functional abnormality of the precuneus. Neurobiol. Dis. 194:106483. doi: 10.1016/j.nbd.2024.106483, [DOI] [PubMed] [Google Scholar]
  44. Yang J., Liang L., Wei Y., Liu Y., Li X., Huang J., et al. (2023). Altered cortical and subcortical morphometric features and asymmetries in the subjective cognitive decline and mild cognitive impairment. Front. Neurol. 14:1297028. doi: 10.3389/fneur.2023.1297028, [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yildirim Z., Delen F., Berron D., Baumeister H., Ziegler G., Schütze H., et al. (2023). Brain reserve contributes to distinguishing preclinical Alzheimer’s stages 1 and 2. Alzheimer's Res. Ther. 15:43. doi: 10.1186/s13195-023-01187-9, [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Younes L., Albert M., Miller M. I. (2014). Inferring changepoint times of medial temporal lobe morphometric change in preclinical alzheimer’s disease. NeuroImage: Clin. 5, 178–187. doi: 10.1016/j.nicl.2014.04.009, [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Zhang K., Mizuma H., Zhang X., Takahashi K., Jin C., Song F., et al. (2021). PET imaging of neural activity, β-amyloid, and tau in normal brain aging. Eur. J. Nucl. Med. Mol. Imaging 48, 3859–3871. doi: 10.1007/s00259-021-05230-5, [DOI] [PubMed] [Google Scholar]
  48. Zhi N., Ren R., Qi J., Liu X., Yun Z., Lin S., et al. (2025). The China Alzheimer report 2025. Gen. Psychiatry 38:e102020. doi: 10.1136/gpsych-2024-102020, [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets supporting the conclusions of this article are not readily available because the data also form part of an ongoing study. Requests to access the datasets should be directed to XH, 28804712@hebmu.edu.cn.

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