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TOMM40 polymorphism is associated with resting-state functional MRI results in patients with Alzheimer’s disease

Xiao, Xuewena; Wei, Jingyaa; Zhang, Weiweia,,b; Jiao, Bina,,c,,d; Liao, Xinxina; Pan, Chuzhenga; Liu, Xiaoyana; Yan, Xinxianga,,c,,d; Tang, Beishaa,,c,,d,,e,,f,,g; Zhang, Youmingb; Wang, Dongcuib; Xing, Wub; Liao, Weihuab; Shen, Lua,,c,,d,,h

doi: 10.1097/WNR.0000000000001297
Clinical Neuroscience

Objective Translocase of outer mitochondrial membrane 40 (TOMM40) encodes translocase of the outer mitochondrial membrane (TOM), which is associated with mitochondrial dysfunction in Alzheimer’s disease (AD). TOMM40 rs157581-G has been reported to increase susceptibility to AD. However, the effect of TOMM40 rs157581-G in resting-state functional MRI (rs-fMRI) on AD has not been studied. Therefore, we aimed to investigate the role of TOMM40 rs157581-G on rs-fMRI results in AD patients.

Methods Twenty-four AD patients were divided into two groups based on TOMM40 rs157581-G status, and clinical and imaging data were compared between the groups.

Results TOMM40 rs157581-G carriers of AD showed decreased regional homogeneity in the left precuneus and decreased amplitude of low-frequency fluctuations in the bilateral temporal poles compared with noncarriers of AD. TOMM40 rs157581-G carriers of AD also showed increased functional connectivity between the right middle occipital gyrus and the left supramarginal gyrus and decreased connectivity between the left superior occipital gyrus and the right transverse temporal gyrus in comparison with TOMM40 rs157581-G noncarriers.

Conclusion We analyzed rs-fMRI characteristics of TOMM40 rs157581-G carriers of AD for the first time, which suggest that TOMM40 rs157581-G plays a harmful role in AD patients.

Departments of aNeurology


cNational Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China

dKey Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China

eParkinson’s Disease Center of Beijing Institute for Brain Disorders, Beijing, China

fCollaborative Innovation Center for Brain Science

gCollaborative Innovation Center for Genetics and Development, Shanghai, China

hKey Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China

Received 1 April 2019 Accepted 2 June 2019

Supplemental Digital Content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website,

Correspondence to Lu Shen, Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China, Tel: +86 731 84327623; fax: +86 731 84327332; e-mail: or

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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Alzheimer’s disease (AD) is characterized by progressive memory loss and other cognitive dysfunctions. AD accounts for 60–80% of dementia worldwide [1]. Although the pathological changes of AD are relatively clear, the exact pathogenesis underlying this disease is not well understood [2].

Resting-state functional MRI (rs-fMRI) measures brain activities in vivo and is one of the most widely used imaging techniques [3]. Two important imaging parameters that are often investigated using rs-fMRI are regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFF). ReHo is a measure of the local similarity between a given voxel and its nearest neighbors in brain [4], while ALFF reflects the intensity of spontaneous neural activities [5].

Functional connectivity is defined as the interregional neural interactions. Common approaches include comparing correlations among various regions of interest or comparing correlations between a ‘seed’ region and other voxels [6]. One of the most well-studied functional networks is the default mode network (DMN), which includes the medial prefrontal cortex, precuneus and posterior cingulate. Impaired functional connectivity within the DMN is a well-established rs-fMRI characteristic of AD [7].

Research has shown that mitochondrial dysfunction mediates or even initiates the pathologic cascades in the development of AD [8]. One significant candidate gene in mitochondrial dysfunction is Translocase of outer mitochondrial membrane 40 (TOMM40) gene. TOMM40 is located on chromosome 19 and encodes translocase of the outer mitochondrial membrane (TOM), which is crucial for protein entry into mitochondria [9]. TOMM40 is located 5´-upstream of APOE [10]. Aβ, which contributes to neuronal dysfunction in AD, can be imported into mitochondria through TOM [11]. TOMM40 polymorphisms have been reported to increase susceptibility to AD in both Caucasian and Chinese [12] population. In particular, TOMM40 rs10524523 variable poly-thymine repeats were found to be associated with risk and age at onset of AD [13]. Furthermore, TOMM40 rs157581-G conferred an increased risk for developing AD and mild cognitive impairment (MCI) in patients [14]. The ALFF values of bilateral superior frontal gyrus, bilateral lingual gyrus, and right calcarine sulcus were decreased in MCI patients with TOMM40 rs157581 GG/AG genotype compared to patients with AA genotype [15]. Nevertheless, to date there have been no studies on the relation between the TOMM40 rs157581-G polymorphism and rs-fMRI characteristics in AD patients.

In this study, we investigated the impact of TOMM40 rs157581-G polymorphism on brain activity in AD. To our knowledge, this is the first study to investigate the relation between the TOMM40 rs157581-G and rs-fMRI results in AD patients.

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Materials and methods


Twenty-four patients with AD participated in this study. AD patients were recruited from the memory clinic at the Neurology department, Xiangya Hospital, Central South University, China. All participants met NINDS-ADRDA criteria [16]. This study was performed with the approval of the Medical Research Ethics Committee of Xiangya Hospital and informed consent was obtained from each participant before participation. Detailed clinical characteristics are presented in Table 1.

Table 1

Table 1

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Genomic DNA extraction and SNP genotyping

Genomic DNA was extracted from peripheral blood leukocytes using phenol-chloroform extraction method. The quality and quantity of DNA were assessed with a fluorometer. All DNA samples were normalized to 50 ng/μL. All subjects were screened for SNP rs157581 of TOMM40 by using Sanger sequencing. All primers designed by Primer 5 software were applied to amplify TOMM40 rs157581 by using PCR. The primers and PCR reaction conditions were listed in Supplementary Table S2 (Supplemental digital content 1, PCR products were sequenced using identical forward and reverse primers with BigDye terminator v3.1 sequencing chemistry on an ABI 3730xl DNA analyzer (Applied Biosystems). The DNA sequences were then analyzed by Sequencher software, version 4.2.

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Data acquisition

All of the participants were scanned on GE Signa EXCITE 3.0T MRI scanner (General Electric, Fairfield, Connecticut, USA). All the participants wore MRI-compatible headphones to reduce noise and kept their eyes closed during the MRI scan. The rs-fMRI images were collected using an echo-planar imaging (EPI) sequence with the following scan parameters: repetition time = 2000 ms, echo time = 30 ms, flip angle = 90°, field of view = 220 × 220 mm2, number of slices = 32, slice thickness=3 mm, gap = 1 mm. Three-dimensional T1-weighted magnetization-prepared rapid gradient echo sagittal images were also collected using the same parameters.

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Data preprocessing

The fMRI data were preprocessed using the Data Processing Assistant for Resting-State fMRI (DPARSF), based on Statistical Parametric Mapping (SPM8) ( The data collected during the first 10 time points were removed due to magnetization instability. The remaining 170 images were corrected to adjust for varying acquisition times. Each participant showed head motion of <1.5 mm or rotation <1°. The data were then normalized to the standard EPI template in Montreal Neurological Institute space and resampled to 3× 3 × 3 mm3 resolution. The normalized images were smoothed with a 4 mm full width half maximum function. Finally, nuisance covariates were discarded.

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Regional homogeneity analysis

ReHo is defined as the regional coherence of the voxels in brain based on Kendall’s coefficient of concordance (KCC). The KCC formula can be expressed as

In this equation, W is the KCC ranging from 0 to 1; Ri is the sum rank of the time point; n = 170 is the length of time series and K = 27 is the cluster size. In this study, the parameters ReHo, mReHo (mean ReHo), and smReHo (smooth mean ReHo) were all acquired using DPARSF, and smReHo images were used for statistical analysis.

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Amplitude of low-frequency fluctuations analysis

ALFF is the average amplitude value of fluctuations within the range of 0.01–0.08 Hz. The time series of each voxel was converted to the frequency range using Fourier transform (parameters: slope percent = 0%, length = shortest) before calculating the power spectrum. Later, we calculated the square root of the power spectrum for each frequency. The average square root of the power spectrum for all frequencies was the ALFF value. To reduce the effects of individual variations on our results, normalized ALFF values (mALFF) were calculated as the ALFF value per voxel divided by the average of the whole brain ALFF values. In addition, ALFF values were calculated using a standard brain template.

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Functional connectivity calculations

The brain was divided into 116 brain regions based on the automated anatomy marker (AAL) template and these regions were extracted by MarsBaR ( The mean time series of all voxels was calculated for each anatomical brain. We calculated the correlation coefficients of the mean time series among brain regions and converted the correlation coefficients to Z value using Fisher’s transformation. Finally, we calculated functional connectivity based on matrix calculations in MATLAB.

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Statistical analysis

We performed two-sample t-tests to assess differences in ReHo, ALFF, and functional connectivity between TOMM40 rs157581-G carriers and noncarriers of AD, using the statistical analysis function in MATLAB software and REST V 17.0 software (P < 0.01, Alphasim correction). SPSS V19.0 software was used to test the distribution differences of clinical data as well as to analyze the linear correlation between mini mental state examination (MMSE) scores and ReHo/ALFF values. Differences in gender distribution were analyzed by χ2 test and the differences of age distribution were analyzed using an independent two-sample t-test. P < 0.05 was considered statistically significant.

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Demographic and neuropsychological results

Twenty-four patients with AD were enrolled in this study. There were no significant differences in gender and age distribution between TOMM40 rs157581-G carriers and noncarriers of AD (P > 0.05). In addition, MMSE scores were not significantly different between the two groups (P = 0.103) (Table 1).

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Between-group regional homogeneity analyses and its relationship with mini mental state examination

Among AD patients, the carriers of TOMM40 rs157581-G demonstrated decreased ReHo values in left precuneus in comparison with noncarriers (P < 0.001, Alphasim correction) (Fig. 1) (Table 2). There was no significant correlation between ReHo value and MMSE score in left precuneus (r = −0.149; P = 0.643).

Table 2

Table 2

Fig. 1

Fig. 1

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Between-group amplitude of low-frequency fluctuation analyses and its relationship with mini mental state examination

TOMM40 rs157581-G carriers of AD showed decreased ALFF values in bilateral temporal poles compared with noncarriers of AD (Fig. 2) (Table 3). We found that there was a significant correlation between the left temporal pole’s ALFF and MMSE score in rs157581-G carriers of AD (r = 0.562; P = 0.029).

Table 3

Table 3

Fig. 2

Fig. 2

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Between-group whole-brain functional connectivity differences and their relationships with mini mental state examination

We used independent two-sample t-tests to compare differences in whole-brain functional connectivity. The TOMM40 rs157581-G carriers of AD demonstrated decreased functional connectivity between left superior occipital gyrus and right transverse temporal gyrus and increased functional connectivity between right middle occipital gyrus and left supramarginal gyrus (Fig. 3) (Table S1, Supplemental digital content 1, Neither of these functional connectivity pairs were significantly associated with MMSE.

Fig. 3

Fig. 3

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In the present study, we first investigated the association between TOMM40 rs157581-G polymorphism and rs-fMRI results in AD, and our results suggest that TOMM40 rs157581-G can exert a harmful influence on brain activity in AD patients.

Mitochondria are organelles in charge of producing energy, lipid and amino acid metabolism, and self-renewal in neurons, which has an essential role in neurodegenerative diseases, including AD [17]. TOMM40 encodes TOM, which imports proteins from cytoplasm into mitochondria. One genome-wide association study has demonstrated that SNPs within TOMM40 are significantly associated with Aβ42 level, T-tau:Aβ42 ratio, and p-tau:Aβ42 ratio in cerebrospinal fluid [18]. Further, TOMM40 poly-T lengths were associated with the thinness of entorhinal cortex [19], and TOMM40 rs157581-C could increase susceptibility to AD [20]. The above evidence suggests that TOMM40 affects brain activity in AD. To date, no studies have examined the effects of TOMM40 rs157581-G polymorphism on rs-fMRI results in AD.

ReHo was first proposed by Zang in 2004. ReHo is associated with brain local activity and can indicate aberrant local functional connectivity in brain, so it has been widely used in studies of neurological diseases, including AD [21]. Precuneus belongs to the DMN and plays an important role in cognition [22]. Previous study has demonstrated that AD showed decreased ReHo in medial prefrontal cortex and bilateral precuneus [21]. The present study showed that ReHo was decreased in left precuneus in TOMM40 rs157581-G carriers of AD. Because TOMM40 rs157581-G is a risk SNP for AD, this result might indicate a significant functional rs-fMRI characteristic of TOMM40 rs157581-G carriers of AD, further suggesting the harmful role of TOMM40 rs157581-G in AD.

Low-frequency (0.01–0.08 Hz) fluctuations are highly synchronous in brain, the average amplitude of these fluctuations, ALFF, is widely applied in studies on various neurological diseases [23]. Of note, the temporal lobe has been significantly associated with cognitive function [24]. Our study found that ALFF was reduced in temporal lobes of TOMM40 rs157581-G carriers of AD, which indicated that TOMM40 rs157581-G may influence temporal lobe activity in AD. In addition, we found a positive correlation between the ALFF in left temporal pole and MMSE score, suggesting the temporal lobe is vulnerable brain region in TOMM40 rs157581-G carriers of AD.

Furthermore, we found that TOMM40 rs157581-G carriers of AD showed decreased functional connectivity between left superior occipital gyrus and right transverse temporal gyrus. Because occipital gyrus is within the visual processing center, and no patients in our study had visual problems, this decreased function connectivity result is of limited value. Functional brain imaging studies have shown that the supramarginal gyrus involves in memory formation [25]. Our results revealed that the functional connectivity between right middle occipital gyrus and left supramarginal gyrus was increased in TOMM40 rs157581-G carriers of AD, which indicates a compensatory role of this functional connectivity.

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Our study showed that ReHo, ALFF, and functional connectivity varied between TOMM40 rs157581-G carriers and TOMM40 rs157581-G noncarriers of AD. To the best of our knowledge, this is the first study to report that TOMM40 rs157581-G carriers of AD demonstrated different rs-fMRI results when compared with rs157581-G noncarriers of AD, which suggest TOMM40 rs157581-G is harmful in AD patients.

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This study was supported by the National Natural Science Foundation of China (No. 81471295 and No. 81671075 to Lu Shen) and the National Key Plan for Scientific Research and Development of China (No. 2016YFC1306000 to Beisha Tang).

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Conflicts of interest

There are no conflicts of interest.

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1. Alzheimer's Association. 2016 Alzheimer’s disease facts and figures. Alzheimers Dement. 2016; 12:459–509
2. Scheltens P, Blennow K, Breteler MM, de Strooper B, Frisoni GB, Salloway S, Van der Flier WM. Alzheimer’s disease. Lancet. 2016; 388:505–517
3. Liao X, Huang M, Xing W, Wu X, Liao W, Wang X, et al. Resting state fMRI studies in SPG4-linked hereditary spastic paraplegia. J Neurol Sci. 2018; 15:384
4. Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI data analysis. Neuroimage. 2004; 22:394–400
5. Yang H, Long XY, Yang Y, Yan H, Zhu CZ, Zhou XP, et al. Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage. 2007; 36:144–152
6. Zhao Q, Li H, Yu X, Huang F, Wang Y, Liu L, et al. Abnormal resting-state functional connectivity of insular subregions and disrupted correlation with working memory in adults with attention deficit/hyperactivity disorder. Front Psychiatry. 2017; 8:200
7. Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: is there potential for an imaging biomarker? Neuroimage Clin. 2018; 18:849–870
8. Swerdlow RH. Mitochondria and mitochondrial cascades in Alzheimer’s disease. J Alzheimers Dis. 2018; 62:1403–1416
9. Humphries AD, Streimann IC, Stojanovski D, Johnston AJ, Yano M, Hoogenraad NJ, Ryan MT. Dissection of the mitochondrial import and assembly pathway for human tom40 J Biol Chem. 2005; 280:11535–11543
10. Nishimura A, Nonomura H, Tanaka S, Yoshida M, Maruyama Y, Aritomi Y, et al. Characterization of APOE and TOMM40 allele frequencies in the Japanese population. Alzheimers Dement (N Y). 2017; 3:524–530
11. Hansson Petersen CA, Alikhani N, Behbahani H, Wiehager B, Pavlov PF, Alafuzoff I, et al. The amyloid beta-peptide is imported into mitochondria via the TOM import machinery and localized to mitochondrial cristae. Proc Natl Acad Sci U S A. 2008; 105:13145–13150
12. Jiao B, Liu X, Zhou L, Wang MH, Zhou Y, Xiao T, et al. Polygenic Analysis of Late-Onset Alzheimer’s Disease from Mainland China. PLoS One. 2015; 10:e0144898
13. Roses AD, Lutz MW, Crenshaw DG, Grossman I, Saunders AM, Gottschalk WK. TOMM40 and APOE: requirements for replication studies of association with age of disease onset and enrichment of a clinical trial. Alzheimers Dement. 2013; 9:132–136
14. Bagnoli S, Piaceri I, Tedde A, Bessi V, Bracco L, Sorbi S, Nacmias B. TOMM40 polymorphisms in italian Alzheimer’s disease and frontotemporal dementia patients. Neurol Sci. 2013; 34:995–998
15. Liu X, Bai F, Yue C, Shi Y, Yu H, Luo B, Zhang Z. The association between TOMM40 gene polymorphism and spontaneous brain activity in amnestic mild cognitive impairment. J Neurol. 2014; 261:1499–1507
16. Dubois B, Feldman HH, Jacova C, Dekosky ST, Barberger-Gateau P, Cummings J, et al. Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria. Lancet Neurol. 2007; 6:734–746
17. Swerdlow RH, Burns JM, Khan SM. The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives. Biochim Biophys Acta. 2014; 1842:1219–1231
18. Liu C, Chyr J, Zhao W, Xu Y, Ji Z, Tan H, et al; Alzheimer’s Disease Neuroimaging Initiative. Genome-wide association and mechanistic studies indicate that immune response contributes to Alzheimer’s disease development. Front Genet. 2018; 9:410
19. Burggren AC, Mahmood Z, Harrison TM, Siddarth P, Miller KJ, Small GW, et al. Hippocampal thinning linked to longer TOMM40 poly-T variant lengths in the absence of the APOE ε4 variant. Alzheimers Dement. 2017; 13:739–748
20. Takei N, Miyashita A, Tsukie T, Arai H, Asada T, Imagawa M, et al; Japanese Genetic Study Consortium for Alzheimer Disease. Genetic association study on in and around the APOE in late-onset Alzheimer disease in Japanese. Genomics. 2009; 93:441–448
21. Zhang Z, Liu Y, Jiang T, Zhou B, An N, Dai H, et al. Altered spontaneous activity in Alzheimer’s disease and mild cognitive impairment revealed by regional homogeneity. Neuroimage. 2012; 59:1429–1440
22. Sestieri C, Corbetta M, Romani GL, Shulman GL. Episodic memory retrieval, parietal cortex, and the default mode network: functional and topographic analyses. J Neurosci. 2011; 31:4407–4420
23. Zhang K, Tang Y, Meng L, Zhu L, Zhou X, Zhao Y, et al. The Effects of SNCA rs894278 on Resting-State Brain Activity in Parkinson’s Disease. Front Neurosci. 2019; 13:47
24. Collins JA, Montal V, Hochberg D, Quimby M, Mandelli ML, Makris N, et al. Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia. Brain. 2017; 140:457–471
25. Schaal NK, Pollok B, Banissy MJ. Hemispheric differences between left and right supramarginal gyrus for pitch and rhythm memory. Sci Rep. 2017; 7:42456

Alzheimer’s disease; resting-state functional MRI; TOMM40

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