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Altered Spontaneous Brain Activity in Type 2 Diabetes: A Resting-State Functional MRI
Study
Running Title: Resting State Brain Activity in Type 2 Diabetes
Ying Cui,1 Yun Jiao,1 Yu Chen Chen,1 Kun Wang,2 Bo Gao,3 Song Wen,1 Shenghong Ju,1
Gao Jun Teng1*
From the 1Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of
Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China;
the 2Medical School of Southeast University, Nanjing, China; and the 3Department of
Radiology, Yuhuangding Hospital, Yantai, China.
*Corresponding author: Gao Jun Teng
Jiangsu Key Laboratory of Molecular and Functional Imaging
Department of Radiology, Zhongda Hospital
Medical School of Southeast University
87 Dingjiaqiao Road, Nanjing210009, Jiangsu, China
Tel.: +86 25 83272121; Fax: +86 25 83311083
E mail: [email protected]
Word Count: 3738
Number of Tables and Figures: 2 tables, 6 figures
Diabetes Publish Ahead of Print, published online December 18, 2013 Diabetes Page 2 of 38
Abstract
Previous research has shown that type 2 diabetes mellitus (T2DM) is associated with an increased risk of cognitive impairment. Patients with impaired cognition often show decreased spontaneous brain activity on resting state functional magnetic resonance imaging
(rs fMRI). This study used rs fMRI to investigate changes in spontaneous brain activity among patients with T2DM and to determine the relationship of these changes with cognitive impairment. T2DM patients (n = 29) and age , sex , and education matched healthy controls
(n = 27) were included in this study. Amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo) values were calculated to represent spontaneous brain activity.
Brain volume and cognition were also evaluated among these participants. Compared with healthy controls, patients with T2DM had significantly decreased ALFF and ReHo values in the occipital lobe and postcentral gyrus. Patients performed worse on several cognitive tests; this impaired cognitive performance was correlated with decreased activity in the cuneus and lingual gyrus in the occipital lobe. Brain volume did not differ between the two groups. The abnormalities of spontaneous brain activity reflected by ALFF and ReHo measurements in the absence of structural changes in T2DM patients may provide insights into the neurological pathophysiology underlying diabetes associated cognitive decline.
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Introduction
Type 2 diabetes mellitus (T2DM) has been shown to be associated with an increased risk of
cognitive impairment (1,2), which primarily manifests as declining memory, information
processing speed (3), attention, and executive function (4). However, the pathophysiological
mechanism of T2DM induced cognitive impairment is still largely unknown (5).
Neuroimaging has proven to be a useful tool for investigating the diabetic brain.
Cerebral atrophy and white matter (WM) lesions, which are commonly reported structural
abnormalities in previous studies, are believed to be modestly associated with
diabetes related cognitive dysfunction (6 8). Magnetic resonance spectroscopy (MRS) was
used to determine the concentration of brain metabolites. A recent MRS study in patients with
T2DM and major depression revealed that abnormal brain metabolite measurements may be
related to mood changes in this population (9). However, little is known about the effects of
diabetes on neural activity. Neural activity is a sensitive measurement that has been observed
to be acutely altered by brain structural lesions (10). Neural abnormalities have been detected
in populations at risk for developing cognitive impairment (11). Based on this evidence,
measures of neural activity may be suited to track the early effects of diabetes on brain
function.
Resting state functional MR imaging (rs fMRI) has been found to be a powerful tool
for evaluating spontaneous neural activity (12). Recently, Musen et al (13) used rs fMRI to
investigate neural functional connectivity (FC) changes in patients with T2DM and found
evidence of altered neural networks in non demented diabetic patients. However, this study
focused only on the FC changes between two distinct brain regions. As the effects of diabetes
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on the brain may be global, a whole brain analysis of brain function in patients with T2DM is needed.
Amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo) analyses are two important methods for depicting the various characteristics of global rs fMRI signals (14). ALFF measures the intensity of neural activity at the single voxel level
(15), whereas ReHo measures the neural synchronization of a given voxel with its neighboring voxels (16). A previous study indicated that ReHo may be more sensitive than
ALFF for detecting regional abnormalities and that ALFF may be complementary to ReHo for measuring global spontaneous activity (17). Therefore, the combination of these two methods may provide more information about the pathophyiological framework in the human brain than either method alone (17).
In this study, combined ALFF and ReHo analyses were applied to investigate global spontaneous neural activity in patients with T2DM. We hypothesized that 1) abnormal ALFF and ReHo values would be detected within specific brain regions; and 2) the spontaneous brain activity abnormalities would be related to impaired cognitive performance and
T2DM related biometric measurements.
Research Design and Methods
Subjects
This study was approved by the local institutional review board and conducted between
September 2012 and June 2013. Written informed consent was obtained from all subjects.
Patients were recruited from the practices of collaborating endocrinologists and from the
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local community via advertisement. Patients who were between 45 and 75 years of age and
who had disease duration of >1 year were qualified for this study. T2DM was defined
according to the latest criteria published by the American Diabetes Association (18). All
patients were closely self monitored and were routinely treated with hypoglycemic agents;
none had any history of hypoglycemic episodes. Control participants were recruited through
advertisements and were matched with T2DM patients with respect to age, sex, and education.
Exclusion criteria for all participants included a history of alcohol or substance abuse;
indication of dementia (defined as a Mini Mental State Examination [MMSE] score ≤24) (19);
history of a brain lesion such as tumor or stroke; psychiatric or neurological disorder
unrelated to diabetes; and contraindications to MR imaging. Control participants were
excluded if they had a fasting blood glucose level ≥7.0 mmol/L; glucose level ≥7.8 mmol/L
after oral glucose tolerance test (OGTT); or a Montreal Cognitive Assessment (MoCA,
Beijing edition) score <26 (20). Participants with vascular risk factors were not excluded (to
improve the generalizability of the study results) (8). A flow chart of the study design is
provided in Supplementary Figure 1.
Biometric measurements
Medical history and medication use were recorded according to a standardized questionnaire.
Blood pressure levels were measured at three different time points during the interview and
then averaged. Hypertension was defined as a systolic blood pressure >160 mmHg, a
diastolic blood pressure >95 mmHg, or self reported use of blood pressure lowering
medication (3). An OGTT (75 g dextrose monohydrate in 250 mL water) was performed on
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all subjects except those being currently treated for diabetes. Plasma glucose levels at fasting and 2 hours after the OGTT were measured, along with HbA1c and cholesterol levels. BMI and waist circumference were measured and recorded. Insulin resistance was determined by homeostasis model assessment of insulin resistance (HOMA IR) for control subjects and patients not being treated with insulin. Diabetic retinopathy was assessed for all participants except for 4 healthy controls who refused the examination. After papillary dilation, stereoscopic color fundus photographs were taken and graded according to the Wisconsin
Epidemiologic Study of Diabetic Retinopathy (21). Peripheral neuropathy, as defined by the
Diabetes Control and Complications Trial criteria, was assessed for all participants using a method described previously (22).
Cognitive assessment
All participants underwent a battery of neuropsychological tests that covered relevant cognitive domains. Selection of the tests was based on previous literature describing cognitive dysfunction in T2DM patients (3,23,24). The MMSE was used to assess possible dementia (19). The MoCA was used to screen subjects with mild cognitive impairment and assess their general cognition (20). The Auditory Verbal Learning Test (AVLT) and
Rey Osterrieth Complex Figure Test (CFT) (both of which included immediate and delayed recall tasks) were used to assess episodic memory for verbal and visual information. The
Trail Making Test, parts A and B (TMT A and TMT B), was primarily used to evaluate attention and psychomotor speed, and the Clock Drawing Test (CDT) was used to address several relevant cognitive domains including executive function and working memory (24).
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All of the tests took approximately 60 minutes to complete.
MR imaging
All MR images were acquired at the Radiology Department of Zhongda Hospital via a
Siemens 3 T Trio scanner (Erlangen, Germany). Subjects were instructed to keep their eyes
closed but to remain awake, avoid thinking of anything in particular, and keep their heads still
during the scanning. Functional images were obtained using a gradient echo planar sequence
(36 slices; repetition time, 2,000 ms; echo time, 25 ms; slice thickness, 4 mm; flip angle, 90°;
field of view, 240 mm × 240 mm). Structural images were acquired using a T1 weighted
three dimensional spoiled gradient recalled sequence (176 slices; repetition time, 1,900 ms;
echo time, 2.48 ms; slice thickness, 1.0 mm; flip angle, 9°; inversion time, 900 ms; field of
view, 250 × 250 mm; in plane resolution, 256 × 256). Fluid attenuated inversion recovery
(FLAIR) images were also obtained with the following parameters: repetition time, 8,500 ms;
echo time, 94 ms; 20 slices; slice thickness, 5 mm; voxel size, 1.3 × 0.9 × 5 mm3.
Structural images (three dimensional T1 weighted images) were processed using the
VBM8 toolbox software (http://dbm.neuro.uni jena.de/vbm). Images were segmented into
gray matter (GM), white matter (WM), and cerebrospinal fluid using the unified
segmentation model. Statistical parametric mapping between the patient and control groups
was generated. The distribution of brain parenchyma volume (sum of the GM and WM
volumes) was displayed as a box plot. Subjects with brain parenchyma volume values that
were extreme outliers were considered to have abnormal brain volume and excluded from the
study.
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Small vessel disease assessment
White matter hyperintensity (WMH) and lacunar infarcts were assessed on FLAIR images with a method described previously (25). Participants with a rating score >1 (confluence of lesions or diffuse involvement of the entire region) were excluded. Two experienced radiologists blinded to the group allocations performed the ratings separately. Consensus was obtained through discussion between the two raters.
Data preprocessing
FMRI imaging data were preprocessed with the toolbox Data Processing Assistant for
Resting State functional MR imaging (DPARSF; http://www.restfmri.net/forum/DPARSF) through statistical parametric mapping (SPM8; http://www.fil.ion.ucl.ac.uk/spm/) and an rs fMRI data analysis toolkit (REST1.8, http://www.restfmri.net). Slice timing and realignment for head motion correction were performed. Any subjects with head motion >2.0 mm translation or >2.0° rotation in any direction were excluded. The functional images were then spatially normalized to standard coordinates and resampled to 3 × 3 × 3 mm3.
ALFF and ReHo analyses
ALFF and ReHo analyses were performed with REST software as described in previous studies (15,16).
For ALFF analysis, the resampled images were first smoothed with a Gaussian kernel
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of 4 mm. Linear trend and band pass filtering (0.01–0.08 Hz) were performed to remove the
effects of low frequency drift and high frequency noise. The time series were transformed to
the frequency domain using a fast Fourier transform. The square root of the power spectrum
was then calculated and averaged across 0.01 to 0.08 Hz within each voxel to obtain the raw
ALFF value. Subsequently, the global mean ALFF value was calculated by extracting the raw
values from all voxels across the whole brain and averaging them. Finally, the raw ALFF of
each voxel was divided by the global mean ALFF values for standardization. The resulting
ALFF value in a given voxel reflects the degree of its raw ALFF value relative to the average
ALFF value of the whole brain (26).
ReHo analysis was performed on preprocessed images. After linear trend and
band pass filtering were performed, ReHo maps were generated by calculating the
concordance of Kendall’s coefficient of the time series of a given voxel with its 26 nearest
neighbors (16). The ReHo value of each voxel was then standardized by dividing the raw
value by the global mean ReHo value, which was obtained with the same calculation used to
determine the global mean ALFF value. Finally, the data were smoothed with a Gaussian
kernel of 4 mm for further statistical analysis.
Statistical analysis
Demographic and clinical variables and cognitive performance scores were compared
between the two groups using SPSS software (ver. 18.0; SPSS, Inc., Chicago, IL, USA). An
independent two sample t test was used for continuous variables and a χ2 test was used for
proportions. P values <0.05 were considered to be statistically significant.
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Within-group analysis. To explore the within group ALFF and ReHo patterns, one sample t tests were performed on the individual ALFF and ReHo maps for each group using REST Statistical Analysis (written by Chaogan Yan, www.restfmri.net). To display the most significant results and reflect the intrinsic nature of these two algorithms, a conservative statistical significance was set at P <0.005 and a cluster size of 24 voxels, which corresponded to a corrected P <0.005 (multiple comparisons with family wise error using
AFNI’s AlphaSim program, http://afni.nih.gov/afni/docpdf/AlphaSim.pdf).
Between-group analysis. To investigate the between group differences of ALFF and
ReHo values, two sample t tests were performed with the REST software (within a GM mask). Age, sex, and education levels were imported as covariates. To exclude the possible effects of structural differences (27), we also stratified the modulated GM maps obtained from VBM analysis. Vascular risk factors (hyperlipidemia, waist circumference, hypertension, and scores of WMH and lacunar infarcts) were also controlled for to exclude possible confounding effects. The statistical threshold was set at P <0.01 and a minimum cluster size of 22 voxels, which corresponded to a corrected P <0.01 (AlphaSim correction).
Correlation analysis. To identify the association between regional ALFF and ReHo abnormalities, a bivariate correlation was performed between these two measurements.
Briefly, the average ALFF and ReHo values of brain regions with significant differences were individually extracted and correlated with one another.
To investigate the relationship between ALFF/ReHo values, cognitive performance, and diabetes related parameters (FPG, HbA1c, HOMA IR, and disease duration), Pearson correlation analyses were performed in a voxel wise manner with the REST software.
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Analyses were adjusted for the same covariates as those controlled in the two sample t tests.
Because the results could be easily affected by noise, a conservative statistical threshold was
set at P <0.005 (after AlphaSim correction) to explore the most significant correlations
among whole brain voxels.
To verify and extend the results of voxel wise analyses, we performed a further
correlation analysis based on regions of interest (ROIs). Briefly, regions showing significant
differences were specified within an automated anatomical labeling (AAL) template
(http://neuro.imm.dtu.dk/services/brededatabase/index_roi_tzouriomazoyer.html), and the
mean ALFF and ReHo values for each ROI were extracted. Partial correlations between
extracted values, cognition, and diabetes related variables were calculated using the same
covariates as in the voxel wise analyses.
Results
A total of 58 participants (30 patients and 28 healthy controls) were recruited for this study.
One patient and one healthy control were excluded because of excessive head movement.
Thus, a total of 56 subjects were included in the final data analysis.
Demographic and cognitive characteristics
Insulin treated patients comprised 21% of the diabetes group (HbA1c, 8.6% ± 1.5%); the
remaining patients were treated with oral antidiabetic agents (58%; HbA1c, 7.9% ± 1.9%) or
dietary restriction only (21%; HbA1c, 7.2% ± 1.3%). The patient and control groups did not
differ in terms of age, sex, or education (Table 1). No significant differences were observed in
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total cholesterol, blood pressure, cerebral small vessel disease, or MMSE scores between the two groups. Patients with T2DM had significantly higher FPG, HOMA IR, HbA1c, and waist circumference than the control group (all P <0.01). Eight patients were diagnosed with retinopathy, but all had background non proliferative change only. Eight patients were diagnosed with peripheral neuropathy. In terms of cognitive assessment, patients had a significantly lower MoCA score than controls, suggesting that their general cognition was impaired. Patients also performed significantly worse than controls on the TMT A, TMT B, and CFT delayed recall tests, indicating that cognitive decrements of these patients are more likely to involve the domains of information processing speed, attention, and visual memory.
Structural results
No participants were excluded because of severe atrophy. No significant difference was observed in GM or WM volume between the patients and controls; overall, however, the GM and WM volumes in the diabetes group were lower than those in the control group (see details in Supplementary Table 1).
ALFF and ReHo analyses
In both groups, standardized ALFF and ReHo values in the posterior cingulate cortex (PCC), precuneus (PCu), and medial prefrontal cortex were significantly higher than the global mean values (Fig. 1).
In T2DM patients, the ALFF and ReHo values were significantly decreased in the occipital lobe (bilateral lingual gyrus, right fusiform, left cuneus, and right calcarine cortex)
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and parietal regions (left postcentral gyrus [PoCG]) (Fig. 2, Table 2). The ReHo values were
also decreased significantly in the thalamas/caudate (Fig. 2B, Table 2).
ALFF and ReHo values in the posterior lobe of cerebellum (PLC) were higher in
diabetic patients than in controls (Table 2). Increased ALFF and ReHo values were also found
in the anterior cingulate cortex and frontal lobe, respectively (Fig. 2).
Correlation analysis
Bivariate correlation analyses indicated that the ALFF and ReHo values extracted from the
occipital lobe and left PoCG were significantly correlated with each other (Fig. 3).
Voxel wise correlation analyses revealed that there were significant correlations
between decreased neural activity in the occipital lobe and impaired neurocognitive
performance (ie, CFT delayed recall test and TMT B) (corrected P <0.005) (Fig. 4). For
example, ALFF and ReHo values in the cuneus and PCu at the occipital lobe were positively
correlated with CFT delayed score and negatively correlated with time spent on TMT B.
HOMA IR in the diabetes group was found to be negatively correlated with neural activity in
the parietal, frontal, and temporal lobes and the lingual gyrus. However, no such correlations
were detected in the control group. Detailed coordinates of these correlations are provided in
Supplementary Table 2.
ROI based correlation analyses indicated that spontaneous neural activity in the
cuneus and lingual gyrus had significant correlations with cognitive performance (Fig. 5).
Higher ALFF and ReHo values in the cuneus were related to less time on TMT B (ALFF, R =
–0.451, P = 0.035; ReHo, R = –0.535, P = 0.010). Both values in the lingual gyrus were also
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negatively correlated with TMT B (ALFF, R = –0.435, P = 0.043; ReHo, R = –0.500, P =
0.018). CFT delay scores were correlated with ReHo values in the cuneus (R = 0.403, P =
0.041). Among diabetic patients, HOMA IR was found to be significantly correlated with neural activity in the cuneus (R = –0.469, P = 0.037).
Effects of retinopathy and neuropathy
We performed an analysis to explore the possible effects of retinopathy on neural activity in the visual cortex and the effects of neuropathy on neural activity in the PoCG. First, we divided the patients into two groups: patients with and patients without retinopathy. ALFF and ReHo values were extracted from three brain regions in the visual cortex and compared between these two groups and the control subjects. Subsequently, patients were divided into another two groups: patients with and patients without neuropathy. ALFF and ReHo values were extracted from the PoCG and compared between these two groups and the control subjects. Results showed that the mean ALFF and ReHo values at the occipital lobe in the control group were significantly higher than the values in the diabetes groups with and without retinopathy. No such difference was observed between the two diabetes groups (Fig.
6A and B). ALFF and ReHo values extracted from the PoCG also did not differ between patients with and without neuropathy (Fig. 6C and D).
Discussion
This study demonstrated decreased neural activity in several specific brain regions in T2DM patients versus control subjects. These neural abnormalities were primarily found in the
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occipital lobe and PoCG and were related to the impaired cognitive performance seen in
patients.
ALFF and ReHo analyses have been used to investigate the intrinsic neuropathology
of various mental disorders (17,26,28,29). These two methods are based on different
neurophysiology mechanisms, with ALFF analysis demonstrating neural intensity and ReHo
analysis demonstrating neural coherence. In this study, abnormal neural activity was detected
by both methods in several brain regions. The co existing functional intensity and coherence
abnormalities in these regions may represent more severe functional changes than those
reflected by a single method. Therefore, we believe our results represent reliable information
that is necessary for understanding cognitive decline in T2DM.
Significantly decreased ALFF and ReHo values in the occipital lobe and the PoCG in
T2DM patients are the major findings in this study. Previous fMRI studies indicated that
decreased neural activity in the occipital area and PoCG were related to visual impairment
(30) and sensory loss (31), respectively. Diabetes is known to be associated with retinopathy
and neuropathy that can lead to visual and sensory impairment. In this study, most of the
patients did not have clinical visual or sensory changes, suggesting that decreased neural
activity may be an early change that occurs before the appearance of clinically measurable
symptoms. It will be interesting to follow these patients over time to determine the clinical
significances of these findings.
In the correlation analysis, neural abnormalities in the cuneus and lingual gyrus were
found to be related to impaired cognitive performance on TMT B and CFT delayed tests in
the diabetes group. The cuneus is the center of inhibitory control (32), whereas the lingual
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gyrus is responsible for visual memory (33). Decreased neuronal activity in the cuneus and lingual gyrus may therefore have contributed to patients’ poor performance on related cognitive tests such as the TMT B and CFT delayed tests. These region specific neural cognition relationships support our hypothesis that neural activity abnormalities play an important role in diabetes related cognitive dysfunction.
In this study, HOMA IR was negatively correlated with neural activity in the frontal, temporal, and parietal regions in T2DM patients but not in the control group. These results are compatible with those from a positron emission tomography (PET) study in which insulin resistance was associated with reduced brain metabolism in T2DM patients (34). Interestingly, a similar pattern of hypometabolism has been observed in patients with early Alzheimer’s disease (AD) (35). These results suggest that increased insulin resistance may be a risk of developing AD that is associated with reduced neural activity in diabetic subjects. Our results showed no correlation between blood glucose and neural activity changes, suggesting that blood glucose was not a main contributor to the neural activity difference, at least during the period of the current study.
A previous rs fMRI study suggested that reduced neural activity in AD patients was predominantly located in the PCC, medial temporal lobe, and several other regions (26). In our study with T2DM patients, significant hypo activity was found mainly in the occipital lobe and PoCG. Although T2DM is a known risk factor for dementia (36), the relationship between T2DM and AD is still under debate. The Rotterdam study suggested that there is an increased risk for the development of AD in patients with T2DM (37). However, a population based study failed to find such a risk in elderly patients with T2DM; this study did
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demonstrate a two fold higher risk of vascular dementia (VaD) among diabetic patients (38).
A recent systematic review of 14 longitudinal studies indicated that the risks for both AD and
VaD were increased in diabetic patients (39). Our study suggests that increased insulin
resistance might be a potential risk factor for the development of AD; however, further
studies are needed to test this relationship.
The current study has several limitations. First, the diabetic subjects were on various
medications that may have effects on neural activities. Further studies should include
medication naïve subjects to rule out this possible bias. Second, the small sample size in the
current study may have reduced our ability to detect more neural activity changes. Third,
there are no diagnostic criteria for diabetes related cognitive dysfunction, and this lack of
objective and specific neurocognitive assessment limited our interpretation of the results.
Should such criteria become available, they would help identify patients who are at risk of
developing dementia and allow researchers to explore their brain patterns. Finally, our study
did not measure cerebrovascular reactivity (CVR) because of technical limitations.
Combining CO2 stimulation with BOLD MR mapping is proved a potential tool for detecting
CVR changes (40) and may further assist our understanding of the cognitive changes that
occur in diabetic patients.
In conclusion, our combined ALFF and ReHo analyses demonstrated a significant
decrease in spontaneous brain activity in various brain regions prior to structural changes in
patients with T2DM. These decreased neural activities were mainly located in the occipital
lobe and PoCG and were correlated with impaired cognitive functioning. This study provides
a new approach to investigating brain function abnormalities in T2DM patients and enhances
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our understanding of the relationship between neural abnormalities and cognitive dysfunction in diabetic patients.
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Acknowledgements
This work was supported by a grant from the Major State Basic Research Development
Program of China (973 Program) (Nos. 2013CB733800, 2013CB733803) and National
Natural Science Foundation of China General Projects (No. 81271739).
No potential conflicts of interest relevant to this article were reported.
Y.C. collected the data, performed the analysis, and wrote the manuscript. Y.J. made
contributions to the design of the study and revised the manuscript for intellectual content.
Y.C.C. and K.W. collected the data and contributed to the discussion. B.G, S.W., and S.H.J.
contributed to the discussion and manuscript revision. G.J.T. is the guarantor of this work and,
as such, had full access to all the data in the study and takes responsibility for the integrity of
the data and the accuracy of the data analysis.
The authors acknowledge Wen Qing Xia, Department of Endocrinology, Zhong da
Hospital, Nanjing, China, for assisting with the data collection. The authors thank Matthew
Tam, Department of Radiology, Southend University Hospital, Essex, England, and Han Yue
Xu, Macalester College, Saint Paul, Minnesota, United States, for editing the manuscript. Our
special thanks are given to Weiping Wang, imaging institute of The Cleveland Clinic, Ohio,
United States, for his critical revision of the manuscript for intellectual content.
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post processing methodologies for spontaneous brain activity. MAGMA 2010;23:289 307 15. Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, Tian LX, Jiang TZ, Wang YF. Altered baseline brain activity in children with ADHD revealed by resting state functional MRI. Brain Dev 2007;29:83 91 16. Zang Y, Jiang T, Lu Y, He Y, Tian L. Regional homogeneity approach to fMRI data analysis. Neuroimage 2004;22:394 400 17. An L, Cao QJ, Sui MQ, Sun L, Zou QH, Zang YF, Wang YF. Local synchronization and amplitude of the fluctuation of spontaneous brain activity in attention deficit/hyperactivity disorder: a resting state fMRI study. Neurosci Bull 2013 [Epub ahead of print] 18. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2013;36:S67 S74 19. Galea M, Woodward M. Mini Mental State Examination (MMSE). Austr J Physiother 2005;51:198 20. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, Cummings JL, Chertkow H. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695 699 21. Klein R, Klein BE, Magli YL, Brothers RJ, Meuer SM, Moss SE, Davis MD. An alternative method of grading diabetic retinopathy. Ophthalmology 1986;93:1183 1187 22. Ryan CM, Geckle MO. Circumscribed cognitive dysfunction in middle aged adults with type 2 diabetes. Diabetes Care 2000;23:1486 1493 23. Reijmer YD, van den Berg E, Ruis C, Jaap Kappelle LJ, Biessels GJ. Cognitive dysfunction in patients with type 2 diabetes. Diabetes Metab Res Rev 2010;26:507 519 24. Zhou H, Lu W, Shi Y, Bai F, Chang J, Yuan Y, Teng G, Zhang Z. Impairments in cognition and resting state connectivity of the hippocampus in elderly subjects with type 2 diabetes. Neurosci Lett 2010;473:5 10 25. Wahlund LO, Barkhof F, Fazekas F, Bronge L, Augustin M, Sjogren M, Wallin A, Ader H, Leys D, Pantoni L, Pasquier F, Erkinjuntti T, Scheltens P; European Task Force on Age Related White Matter Changes. A new rating scale for age related white matter changes applicable to MRI and CT. Stroke 2001;32:1318 1322 26. Wang Z, Yan C, Zhao C, Qi Z, Zhou W, Lu J, He Y, Li K. Spatial patterns of intrinsic brain activity in mild cognitive impairment and Alzheimer's disease: a resting state functional MRI study. Hum Brain Mapp 2011;32:1720 1740 27. Oakes TR, Fox AS, Johnstone T, Chung MK, Kalin N, Davidson RJ. Integrating VBM into the General Linear Model with voxelwise anatomical covariates. Neuroimage 2007;34:500 508 28. Liu CH, Ma X, Wu X, Zhang Y, Zhou FC, Li F, Tie CL, Dong J, Wang YJ, Yang Z, Wang CY. Regional homogeneity of resting state brain abnormalities in bipolar and unipolar depression. Prog Neuropsychopharmacol Biol Psychiatry 2013;41:52 29 29. Chen HJ, Zhu XQ, Jiao Y, Li PC, Wang Y, Teng GJ. Abnormal baseline brain activity in low grade hepatic encephalopathy: a resting state fMRI study. J Neurol Sci
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2012;318:140 145 30. Liu Y, Liang P, Duan Y, Jia X, Wang F, Yu C, Qin W, Dong H, Ye J, Li K. Abnormal baseline brain activity in patients with neuromyelitis optica: a resting state fMRI study. Eur Jo Radiol 2011;80:407 411 31. Luo C, Chen Q, Huang R, Chen X, Chen K, Huang X, Tang H, Gong Q, Shang HF. Patterns of spontaneous brain activity in amyotrophic lateral sclerosis: a resting state fMRI study. PLoS One 2012;7:e45470 32. Haldane M, Cunningham G, Androutsos C, Frangou S. Structural brain correlates of response inhibition in Bipolar Disorder I. J Psychopharmacol 2008;22:138 143 33. Bogousslavsky J, Miklossy J, Deruaz JP, Assal G, Regli F. Lingual and fusiform gyri in visual processing: a clinico pathologic study of superior altitudinal hemianopia. J Neurol Neurosurg Psychiatry 1987;50:607 614 34. Baker LD, Cross DJ, Minoshima S, Belongia D, Watson G, Craft S. Insulin resistance and Alzheimer like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes. Arch Neurol 2010;68:51 57 35. Mosconi L, Sorbi S, de Leon MJ, Li Y, Nacmias B, Myoung PS, Tsui W, Ginestroni A, Bessi V, Fayyazz M, Caffarra P, Pupi A. Hypometabolism exceeds atrophy in presymptomatic early onset familial Alzheimer's disease. J Nucl Med 2006;47:1778 1786 36. van den Berg E, Kessels RP, Kappelle LJ, de Haan EH, Biessels GJ; Utrecht Diabetic Encephalopathy Study Group. Type 2 diabetes, cognitive function and dementia: vascular and metabolic determinants. Drugs Today (Barc) 2006;42:741 754 37. Ott A, Stolk RP, van Harskamp F, Pols HA, Hofman A, Breteler MM. Diabetes mellitus and the risk of dementia: the Rotterdam Study. Neurology 1999;53:1937 1942 38. Hassing LB, Johansson B, Nilsson SE, Berg S, Pedersen NL, Gatz M, McClearn G. Diabetes mellitus is a risk factor for vascular dementia, but not for Alzheimer's disease: a population based study of the oldest old. Int Psychogeriatr 2002;14:239 248 39. Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol 2006;5:64 74 40. Spano VR, Mandell DM, Poublanc J, Sam K, Battisti Charbonney A, Pucci O, Han JS, Crawley AP, Fisher JA, Mikulis DJ. CO2 blood oxygen level–dependent MR mapping of cerebrovascular reserve in a clinical population: safety, tolerability, and technical feasibility. Radiology 2013;266:592 598
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TABLE 1
Demographic, clinical, and cognitive characteristics of study patients and controls
T2DM Patients Control subjects P value (n = 29) (n = 27)
Age (years) 58.3 ± 7.3 57.8 ± 5.9 0.78
Sex (male/female)* 14/15 11/16 0.60
Education (years) 10.4 ± 4.0 10.2 ± 2.5 0.80
Disease duration (years) 9.3 ± 3.8 — —
Fasting glucose (mmol/L)† 7.8 ± 2.2 5.4 ± 0.4 <0.01
HbA1c (% [mmol/mol])† 7.9 ± 1.7 (63 ± 18.6) 5.6 ± 0.4 (38 ± 4.4) <0.01
HOMA IR 3.3 ± 1.2 2.5 ± 1.0 <0.01
Insulin treatments 6 (21) — —
Retinopathy (background) 8 (28) 0 —
Diabetic peripheral neuropathy 8 (28) 0 —
Vascular risk factors
Systolic BP (mmHg) 133 ± 14 130 ± 12 0.33
Diastolic BP (mmHg) 84 ± 12 83 ± 9.5 0.74
Anti hypertensive medications 8 (28) 5 (19) —
Waist circumference (cm)† 91.7 ± 9.8 85.4 ± 9.2 0.03
Total cholesterol (mmol/L) 5.4 ± 1.1 5.2 ± 1.0 0.31
Cholesterol lowering medications 3 (10) 1 (4) —
Cerebral vessel disease
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WMH 1 (0 6) 0 (0 7) 0.82
Lacunar infarcts 6 (21) 3 (11) 0.47
Cognitive performance
MMSE 28.3 ± 1.4 29.0 ± 1.1 0.07
MoCA† 23.6 ± 2.9 27.3 ± 1.1 <0.01
Trail making test A (s)† 72.8 ± 24.3 58.9 ± 14.6 0.01
Trail making test B (s)† 174.8 ± 59.1 144.4 ± 51.3 0.01
AVLT 6.2 ± 1.3 6.3 ± 1.6 0.81
AVLT delayed recall (20 min) 6.1 ± 2.5 6.3 ± 2.5 0.82
Clock drawing test 3.5 ± 0.6 3.7 ± 0.5 0.12
CFT delayed recall (20 min)† 13.3 ± 6.2 19.7 ± 5.5 <0.01
Data are mean ± SD, n (%), or median (range). *The P value for proportions was obtained by
χ2 test. †P <0.05. BP, blood pressure; WMH, white matter hyperintensity; MMSE,
Mini Mental State Examination; MoCA, Montreal Cognitive Assessment; AVLT, Auditory
Verbal Learning Test; CFT, Complex Figure Test.
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TABLE 2
Differences in ALFF and ReHo values between the patient and control groups (P <0.01,
AlphaSim corrected)
Brain region BA MNI coordinates Voxels Maximal
X Y Z t value*
ALFF differences
R Lingual gyrus/Fusiform 18 24 –75 –6 138 –3.81
L Lingual gyrus 18 –6 –75 –3 27 –3.43
L Middle occipital gyrus/Cuneus 18/19 18 –84 –15 28 –4.14
R Calcarine cortex 30 18 –63 12 25 –3.53
L PoCG 3/1 –18 –30 78 34 –4.01
R ACC — 9 9 24 41 +3.95
R PLC — 18 –81 –51 40 +3.93
ReHo differences
R Lingual gyrus/Fusiform 18 30 –81 –12 74 –3.59
L Lingual gyrus/Cuneus 18/30 –3 –72 0 31 –3.57
R Calcarine cortex 30 21 –57 6 25 –3.24
L PoCG 3/4 –15 –30 75 35 –4.25
R MTG 37 48 –66 0 28 –3.40
B Thalamus/Caudate 79 9 –3 12 79 –4.81
R PLC — 30 –57 –30 91 +3.94
L MFG — –12 33 42 32 +4.19
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Comparisons were performed at P <0.01, corrected for multiple comparisons. *Negative t values: patients with T2DM < control subjects; positive t values: patients with T2DM > control subjects. BA, Brodmann area; MNI, Montreal Neurological Institute; x, y, z, coordinates of primary peak locations in the MNI space; PoCG, postcentral gyrus; ACC, anterior cingulate cortex; PLC, posterior lobe of cerebellum; MTG, middle temporal gyrus;
MFG, medial frontal gyrus.
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FIG. 1. Representative one sample t test results of ALFF and ReHo maps (P <0.005,
AlphaSim corrected). Within each group, standardized ALFF and ReHo values in the
posterior cingulate cortex (PCC), the adjacent precuneus (PCu), and the medial prefrontal
cortex (MPFC) were significantly higher than the global mean values in both groups. Other
regions, such as the inferior parietal lobe (IPL) and bilateral occipital lobes, also had higher
spontaneous activity. Note that the brain regions were mainly part of the default mode
network, which demonstrated the correctness of our data analysis. R indicates the right, and L
indicates the left.
FIG. 2. ALFF (A) and ReHo (B) differences between T2DM patients and healthy controls (P
<0.01, AlphaSim corrected).
A, Compared with healthy subjects, patients with T2DM showed significantly decreased
ALFF in the postcentral gyrus (PoCG), calcarine cortex, and bilateral lingual gyrus (blue) and
increased ALFF in the anterior cingulate cortex (ACC) (red) and posterior lobe of cerebellum
(PLC) (data not shown). B, Compared with healthy subjects, patients with T2DM showed
significantly decreased ReHo in the PoCG, bilateral thalamus/caudate, calcarine cortex,
middle temporal gyrus (MTG), and bilateral lingual gyrus (blue) and increased ReHo in the
medial frontal gyrus (MFG) (red) and PLC (data not shown). Color scale denotes the t value;
R indicates the right.
FIG. 3. Correlations between the ALFF and ReHo values extracted from significantly
different regions (bivariate correlation). The mean ALFF values had a significant positive
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correlation with the mean ReHo values in representative regions, including the left postcentral gyrus (A, R = 0.384, P = 0.040), the right occipital lobe (B, R = 0.544, P = 0.002), the left occipital lobe (C, R = 0.462, P = 0.012), and the calcarine cortex (D, R = 0.813, P <
0.01).
FIG. 4. Representative results of correlation analysis in T2DM patients (P <0.005, AlphaSim corrected).
A and B, correlation maps of ALFF/ReHo values and Complex Figure Test (CFT) delayed recall test score. Significant positive correlations were found in the left cuneus, the left precuneus (PCu), and the calcarine cortex (red). C and D, correlation maps of ALFF/ReHo values and performance on Trail Making Test, part B (TMT B). Significant negative correlations were found in bilateral cuneus and PCu (blue). E and F, correlation maps of
ALFF/ReHo values and homeostasis model assessment of insulin resistance (HOMA IR).
Neural activity in the parietal lobe, frontal lobe, middle temporal gyrus (MTG), and lingual gyrus was negatively correlated with HOMA IR (blue). R indicates the right; color scale denotes R value.
FIG. 5. Correlations between the mean ALFF and ReHo values in the cuneus and lingual gyrus and homeostasis model assessment of insulin resistance (HOMA IR)/neurological performance (partial correlation with age, sex, education, and vascular risk factors controlled).
Images in the middle are the cuneus and lingual gyrus masks extracted from the automated anatomical labeling (AAL) template. The mean ReHo values in the cuneus (black spots) were
Page 29 of 38 Diabetes
found to be significantly correlated with HOMA IR (P = 0.037), the Complex Figure Test
(CFT) delayed score (P = 0.041), and Trail Making Test, part B (TMT B) (P = 0.010). The
mean ReHo values in the lingual gyrus (white spots) were also correlated with TMT B (P =
0.018). ALFF values in both the cuneus (black triangles) and lingual gyrus (white triangles)
were negatively correlated with TMT B (P = 0.035 for cuneus, P = 0.043 for lingual gyrus).
FIG. 6. Differences in ALFF and ReHo values among the three groups (ie, patients with
retinopathy/neuropathy, patients without retinopathy/neuropathy, and control group). Low
mean ALFF and ReHo values indicate decreased neural activity.
A and B, The mean ALFF and ReHo values extracted from three brain regions of the occipital
lobe among controls were all significantly higher than values in both of the diabetes groups.
However, the two diabetes groups (with and without retinopathy) did not differ from each
other. C and D, The mean ALFF and ReHo values extracted from the left postcentral gyrus
(PoCG) also did not differ between patients with and patients without peripheral neuropathy.
*P <0.05.
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FIG. 1. Representative one-sample t-test results of ALFF and ReHo maps (P <0.005, AlphaSim corrected). Within each group, standardized ALFF and ReHo values in the posterior cingulate cortex (PCC), the adjacent precuneus (PCu), and the medial prefrontal cortex (MPFC) were significantly higher than the global mean values in both groups. Other regions, such as the inferior parietal lobe (IPL) and bilateral occipital lobes, also had higher spontaneous activity. Note that the brain regions were mainly part of the default-mode network, which demonstrated the correctness of our data analysis. R indicates the right, and L indicates the left. 86x83mm (300 x 300 DPI)
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FIG. 2. ALFF (A) and ReHo (B) differences between T2DM patients and healthy controls (P <0.01, AlphaSim corrected). A, Compared with healthy subjects, patients with T2DM showed significantly decreased ALFF in the postcentral gyrus (PoCG), calcarine cortex, and bilateral lingual gyrus (blue) and increased ALFF in the anterior cingulate cortex (ACC) (red) and posterior lobe of cerebellum (PLC) (data not shown). B, Compared with healthy subjects, patients with T2DM showed significantly decreased ReHo in the PoCG, bilateral thalamus/caudate, calcarine cortex, middle temporal gyrus (MTG), and bilateral lingual gyrus (blue) and increased ReHo in the medial frontal gyrus (MFG) (red) and PLC (data not shown). Color scale denotes the t value; R indicates the right. 193x208mm (300 x 300 DPI)
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FIG. 3. Correlations between the ALFF and ReHo values extracted from significantly different regions (bivariate correlation). The mean ALFF values had a significant positive correlation with the mean ReHo values in representative regions, including the left postcentral gyrus (A, R = 0.384, P = 0.040), the right occipital lobe (B, R = 0.544, P = 0.002), the left occipital lobe (C, R = 0.462, P = 0.012), and the calcarine cortex (D, R = 0.813, P < 0.01). 104x61mm (300 x 300 DPI)
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FIG. 4. Representative results of correlation analysis in T2DM patients (P <0.005, AlphaSim corrected). A and B, correlation maps of ALFF/ReHo values and Complex Figure Test (CFT)-delayed recall test score. Significant positive correlations were found in the left cuneus, the left precuneus (PCu), and the calcarine cortex (red). C and D, correlation maps of ALFF/ReHo values and performance on Trail-Making Test, part B (TMT-B). Significant negative correlations were found in bilateral cuneus and PCu (blue). E and F, correlation maps of ALFF/ReHo values and homeostasis model assessment of insulin resistance (HOMA-IR). Neural activity in the parietal lobe, frontal lobe, middle temporal gyrus (MTG), and lingual gyrus was negatively correlated with HOMA-IR (blue). R indicates the right; color scale denotes R value. 77x68mm (300 x 300 DPI)
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FIG. 5. Correlations between the mean ALFF and ReHo values in the cuneus and lingual gyrus and homeostasis model assessment of insulin resistance (HOMA-IR)/neurological performance (partial correlation with age, sex, education, and vascular risk factors controlled). Images in the middle are the cuneus and lingual gyrus masks extracted from the automated anatomical labeling (AAL) template. The mean ReHo values in the cuneus (black spots) were found to be significantly correlated with HOMA-IR (P = 0.037), the Complex Figure Test (CFT)-delayed score (P = 0.041), and Trail-Making Test, part B (TMT-B) (P = 0.010). The mean ReHo values in the lingual gyrus (white spots) were also correlated with TMT-B (P = 0.018). ALFF values in both the cuneus (black triangles) and lingual gyrus (white triangles) were negatively correlated with TMT-B (P = 0.035 for cuneus, P = 0.043 for lingual gyrus). 157x137mm (300 x 300 DPI)
Page 35 of 38 Diabetes
FIG. 6. Differences in ALFF and ReHo values among the three groups (ie, patients with retinopathy/neuropathy, patients without retinopathy/neuropathy, and control group). Low mean ALFF and ReHo values indicate decreased neural activity. A and B, The mean ALFF and ReHo values extracted from three brain regions of the occipital lobe among controls were all significantly higher than values in both of the diabetes groups. However, the two diabetes groups (with and without retinopathy) did not differ from each other. C and D, The mean ALFF and ReHo values extracted from the left postcentral gyrus (PoCG) also did not differ between patients with and patients without peripheral neuropathy. *P <0.05.
125x87mm (300 x 300 DPI)
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Supplementary Data
Supplementary Table 1. Comparison of the brain volumes between the patients with T2DM and healthy controls. Mean gray matter (GM) volume and white matter (WM) volume were calculated based on VBM8 toolbox and displayed in the table below. Brain parenchyma volume was calculated as the sum of GM and WM volumes.
Volume T2DM Patients Control Subjects P Value
Gray Matter 586.9±53.9 588.1±46.1 0.93
White Matter 537.1±48.8 541.3±47.9 0.75
Brain Parenchyma 1124.0±92.9 1129.4±82.4 0.82
Data are presented as mean±(SD).
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Supplementary Table 2. Regions showing significant correlation between ALFF/ReHo and neurological performance/HOMA-IR in a voxel-wise manner (P < 0.005, AlphaSim corrected).
MNI Maximal Brain Region BA coordinates Voxels R value X Y Z Correlation between ALFF and CFT-delay score L Cuneus/Calcarine cortex 17 -15 -75 6 24 +0.72 L PCu 31 0 -72 24 22 +0.75 Correlation between ReHo and CFT-delay score L Calcarine cortex/Lingual gyrus 30/18 27 -60 3 59 +0.64 L PCu 31 -3 -75 21 60 +0.66 L MTG — -57 -51 -3 26 -0.74 Correlation between ALFF and TMT-B R Cuneus/PCu 7/19 18 -81 39 34 -0.74 L Cuneus/PCu 7/19 -9 -78 33 32 -0.72 Correlation between ReHo and TMT-B R Cuneus/Pcu 7/31 18 -78 27 81 -0.73 L Cuneus/Pcu 7/19 -12 -78 33 24 -0.75 L STG 22 -66 -6 0 28 -0.66 Correlation between ALFF and HOMA-IR R SPL 7 33 -66 48 39 -0.84 L SPL 7 -33 -63 60 28 -0.68 R IFG 47 -15 18 -27 48 -0.80 R MTG — 54 -36 -15 23 -0.68 R Lingual gyrus 18 30 -96 -15 41 -0.63 Correlation between ReHo and HOMA-IR R SPL 7 33 -66 45 58 -0.77 L MFG 8 33 24 42 60 -0.76 R MTG 21 -54 -63 18 81 -0.78 R Lingual gryus 18 33 -93 -21 52 -0.78
BA, Brodmann area; MNI, Montreal Neurological Institute; x, y, z, coordinates of primary peak locations in the MNI space; negative t values: T2DM < control (Ctrl) subjects; positive t values: T2DM > Ctrl. R, right; L, left. CFT, complex figure test; PCu, precuneus; MTG, middle temporal gyrus; TMT-B, trail making test, part B; STG, superior temporal gyrus; IPL, inferior parietal lobe; MFG, middle frontal gyrus; Comparisons were performed at P < 0.005, corrected by multiple comparisons.
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Supplementary Figure 1. Flow chart of the study design and timeline. The flow chart is divided into three parts. A, Enrollment of the subjects. B, Biometric measurements. C, FMRI scanning and data analysis.
45-75 years old Patients with T2DM (n=30) Inclusion Right-handed Control subjects (n=28) No MR contraindications
A
S Fasting blood sample taken Fasting glucose, HbA1, (8:00 a.m.) Fasting insulin, Cholesterol
Blood pressure measurement
Cognitive assessment
Postprandial glucose Blood sample taken after OGTT Blood pressure measurement (10:00 a.m.) B
MR scan (30 min) (5:00 p.m.) Blood pressure measurement Excluded (n=2) Head motion Discomfort Patients (n=29)
Control subjects (n=27)
Verification Retinopathy Data analysis Results Neuropathy C