
Altered Brain Functional Hubs and Connectivity in Type 2 Diabetes Mellitus Patients: A Resting-State fMRI Study
March 2018

Nicola Canessa 
Robi Tacutu 
Jiuquan Zhang 
Jian Wang wangjian_811@yahoo.com 
Liu D 
Duan S 
Zhou C 
Wei P 
Chen L 
Yin X 
Zhang J 
Wang J 
Daihong Liu 
† 
Shanshan Duan 
† 
Chaoyang Zhou 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Ping Wei 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Lihua Chen 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Xuntao Yin 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Jiuquan Zhang 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Jian Wang 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Department of Endocrinology
The Third Affiliation Hospital of Chongqing Medical University
ChongqingChina

Department of Endocrinology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina


Istituto Universitario di Studi Superiori di Pavia (IUSS)
Institute of Biochemistry of the Romanian Academy
Arie Budovsky, Judea R & D Center
Italy, Romania, Israel

Altered Brain Functional Hubs and Connectivity in Type 2 Diabetes Mellitus Patients: A Resting-State fMRI Study

Frontiers in Aging Neuroscience | www.frontiersin.org
155March 201810.3389/fnagi.2018.00055Received: 19 June 2017 Accepted: 19 February 2018 Published: 06 March 2018 Citation:ORIGINAL RESEARCH Edited by: Vadim Fraifeld, Ben-Gurion University of the Negev, Israel Reviewed by: *Correspondence: † These authors have contributed equally to this work. Altered Brain Functional Hubs and Connectivity in Type 2 Diabetes Mellitus Patients: A Resting-State fMRI Study. Front. Aging Neurosci. 10:55. Liu et al. Altered Brain Network in T2DMtype 2 diabetes mellitus, resting-state functional MRI, degree centrality, Granger causality analysis, functional connectivity, cognitive impairment Abbreviations: ACG, anterior cingulate gyrusAVLT, Auditory Verbal Learning TestBMI, body mass indexDC, degree centralityDST, Digit Span TestEPI, echo planar imagingFC, functional connectivityFLAIR, fluid-attenuated inversion recoveryfMRI, functional magnetic resonance imagingFT3, free triiodothyronineFT4, free thyroxineGCA, Granger causality analysisHAMD, Hamilton Depression Rating ScaleHbA 1c , glycosylated hemoglobinHC, healthy controlHDL, high-density lipoproteinHOMA2-IR, updated homeostasis model assessment of insulin resistanceLDL, low-density lipoproteinLOC, lateral occipital corticesMMSE, Mini-Mental State ExaminationMNI, Montreal Neurological InstituteMoCA, Montreal Cognitive AssessmentMP-RAGE, magnetization prepared by rapid-acquisition gradient-echoPreCG, precentral gyrusROI, region of interestT2DM, Type 2 diabetes mellitusTMT, Trail Making TestTSH, thyroid-stimulating hormoneVFT, Verbal Fluency Test
Type 2 diabetes mellitus (T2DM) affects a vast population and is closely associated with cognitive impairment. However, the mechanisms of cognitive impairment in T2DM patients have not been unraveled. Research on the basic units (nodes or hubs and edges) of the brain functional network on the basis of neuroimaging may advance our understanding of the network change pattern in T2DM patients. This study investigated the change patterns of brain functional hubs using degree centrality (DC) analysis and the connectivity among these hubs using functional connectivity and Granger causality analysis. Compared to healthy controls, the DC values were higher in the left anterior cingulate gyrus (ACG) and lower in the bilateral lateral occipital cortices (LOC) and right precentral gyrus (PreCG) in T2DM patients. The functional connectivity between the left ACG and the right PreCG was stronger in T2DM patients, whereas the functional connectivity among the right PreCG and bilateral LOC was weaker. A negative causal effect from the left ACG to left LOC and a positive effect from the left ACG to right LOC were observed in T2DM patients, while in healthy controls, the opposite occurred. Additionally, the reserve of normal brain function in T2DM patients was negatively associated with the elevated glycemic parameters. This study demonstrates that there are brain functional hubs and connectivity alterations that may reflect the aberrant information communication in the brain of T2DM patients. The findings may advance our understanding of the mechanisms of T2DM-related cognitive impairment.

Abbreviations: ACG, anterior cingulate gyrus; AVLT, Auditory Verbal Learning Test; BMI, body mass index; DC, degree centrality; DST, Digit Span Test; EPI, echo planar imaging; FC, functional connectivity; FLAIR, fluid-attenuated inversion recovery; fMRI, functional magnetic resonance imaging; FT3, free triiodothyronine; FT4, free thyroxine; GCA, Granger causality analysis; HAMD, Hamilton Depression Rating Scale; HbA 1c , glycosylated hemoglobin; HC, healthy control; HDL, high-density lipoprotein; HOMA2-IR, updated homeostasis model assessment of insulin resistance; LDL, low-density lipoprotein; LOC, lateral occipital cortices; MMSE, Mini-Mental State Examination; MNI, Montreal Neurological Institute; MoCA, Montreal Cognitive Assessment; MP-RAGE, magnetization prepared by rapid-acquisition gradient-echo; PreCG, precentral gyrus; ROI, region of interest; T2DM, Type 2 diabetes mellitus; TMT, Trail Making Test; TSH, thyroid-stimulating hormone; VFT, Verbal Fluency Test.


INTRODUCTION

Type 2 diabetes mellitus (T2DM) affects 415 million individuals and is predicted to increase to 642 million in 2040, according to the Diabetes Atlas 7th Edition published by the International Diabetes Federation (http://www.idf.org/about-diabetes/factsfigures). Numerous studies have suggested that T2DM is closely associated with cognitive impairment, including the domains of motor function, executive function, processing speed and memory (Palta et al., 2014). Clarification of the underlying mechanism of cognitive impairment in T2DM patients for diagnosis and therapeutic effect estimation is essential before these patients develop dementia.

As a proven informative neuroimaging method, functional magnetic resonance imaging (fMRI) has been extensively applied to investigate alterations of brain function in T2DM patients. In fMRI studies, T2DM patients manifest functional changes in certain brain regions, and these changes are different from those associated with normal aging. For instance, the abnormal amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity (FC) in T2DM patients have been associated with poor performance in cognitive tests (Xia et al., 2013;Chen et al., 2014;Cui et al., 2014Cui et al., , 2015Moheet et al., 2015). These studies have focused on local spontaneous brain activity or have analyzed the FC or network within the selected brain regions based on a priori assumption. According to the graph theory, a network is defined as a set of pairwise relationships between the elements of a system, which formally consists of a set of edges that link a set of nodes (Barabasi and Oltvai, 2004;Petersen and Sporns, 2015). Network analysis offers a new conceptual framework to investigate the network biology of aging (Wolfson et al., 2009;Tacutu et al., 2011), T2DM (Sandor et al., 2017) and neurodegenerative diseases (de Haan et al., 2017) at a variety of levels of scale including genes, proteins, synapses, neurons, neuronal circuits, neuronal populations, and systems (Petersen and Sporns, 2015). Therefore, the aforementioned fMRI findings provide a clue to explore T2DM-related brain dysfunction from the perspective of macroscopic nodes and connectivity at the whole brain level, which may share universal laws of network.

Degree centrality (DC), a measure based on graph theory, provides an approach for identifying the candidate functional hubs in the diabetic brain. DC is defined as the number of links that are strongly correlated to a given voxel or node for a binary graph and enables whole brain analysis at the voxel level, which may avoid the bias caused by selecting brain regions according to a priori assumption (Buckner et al., 2009;Zuo et al., 2012). It also considers the weights of these links for a weighted graph, and the weighted version of DC is more robust against confounding factors (Zuo et al., 2012). Thus, it can quantify the importance of a node to the rest of the brain, and nodes with high DC are defined as hubs (Zuo et al., 2012). Therefore, DC enables an investigation of the complexity and patterns of the brain functional connectome in diseases, including the subsequent analysis of FC between the hubs and the rest of the brain (Cui et al., 2016), or the interactions among these functional hubs in the present study.

Connectivity depicts the relationships among functionally segregated brain systems and can be classified into undirected and directed functional connectivity. Traditional FC is often used to investigate the statistical dependencies among several given seed regions in an undirected manner (Biswal et al., 1995). Directed FC is explicitly used to explore the functional interactions in a directed manner and can be estimated using the Granger causality model (Seth et al., 2015). The positive casual effect and negative causal effect can be quantified with positive and negative signed-path coefficients and interpreted as activation and inhibition, respectively (Hamilton et al., 2011). Both undirected and directed FC are the components of functional integration that are used to analyze functional brain architectures (Friston et al., 2013). By combining the traditional FC and Granger causality analysis (GCA) approaches, more information can be obtained from different perspectives to map the connectivity pattern among the brain functional hubs that are probably affected by T2DM.

In this study, we hypothesized that the aberrant brain function of hubs and their connectivity may contribute to brain dysfunction in T2DM patients. The DC method was applied to identify the candidate functional hubs. Next, the FC and GCA methods were applied to investigate the connectivity among these functional hubs. We also investigated the relationships of brain functional alterations with clinical data and neuropsychological performance. Our study provides evidence to further understand the neurological mechanisms that underlie T2DM-associated cognitive impairment.


MATERIALS AND METHODS


Subjects

T2DM patients were recruited from inpatients and the community and healthy controls (HC) were recruited from the community between December 2013 and December 2015. Forty-seven T2DM patients and 47 healthy controls, who were matched with regard to age, sex, education and body mass index (BMI), were enrolled in the study. Both the T2DM patients and the healthy controls met the following inclusion criteria: (1) between 45 and 70 years old; 2) at least six years of education; and (3) right-handedness. T2DM was diagnosed by endocrinologists according to the criteria published by the World Health Organization in 1999 (Alberti and Zimmet, 1998). The T2DM patients had at least 1 year of disease duration. The exclusion criteria for all participants were as follows: (1) organic disease in the brain, such as stroke, tumor, or a white matter lesion rating score ≥ 2 (Wahlund et al., 2001); (2) physical disability; (3) pregnancy, thyroid dysfunction; (4) signs of dementia (Mini-Mental State Examination [MMSE] score ≤ 24) (Galea and Woodward, 2005), major depression  > 20) (Hamilton, 1960), or other psychiatric disorders; (5) severe hearing or visual impairment; and (6) contraindications to MRI. Patients with T2DM-related complications were also excluded, including diabetic foot, retinopathy, and nephropathy.

The study protocol was approved by the Medical Research Ethics Committee of the Southwest Hospital (Chongqing, China). Written informed consent was obtained from participants after they were informed of the study details. Additionally, the study process strictly obeyed the protocol.


Clinical Data

The following information about the subjects was recorded using a standardized protocol: handedness, height, weight, BMI [weight in kg]/[height in m] 2 ), resting arm arterial blood pressure, medical history and current medications. For T2DM patients, we also recorded the date of diagnosis to calculate the disease duration. Venous blood samples were collected by venipuncture after overnight fasting for the biometric measurements, including glucose-related parameters (fasting plasma glucose, fasting insulin, fasting C-peptide, and glycosylated hemoglobin [ 


Neuropsychological Tests

All participants underwent cognitive status assessments with a battery of neuropsychological tests in a fixed order that assessed their global cognitive level and major cognitive subdomains. The global level of cognition was evaluated with the MMSE and Montreal Cognitive Assessment (MoCA) tests. The depressive state was evaluated with HAMD to exclude cases with major depression. The major cognitive subdomains were evaluated with the following tests: (1) the Trail Making Test (TMT, parts A and B) for executive function and psychomotor speed (Bowie and Harvey, 2006); (2) the Verbal Fluency Test (VFT) for mental flexibility (Diamond, 2013); (3) the Digit Span Test (DST, forwards and backwards) for working memory (Diamond, 2013); and (4) the Auditory Verbal Learning Test (AVLT, including immediate recall, short-term delayed recall, longterm delayed recall, long-term delayed recognition and total score) for episodic memory (Zhao et al., 2015). A trained neuropsychologist administered the battery of tests and was blinded to the group status. Each participant completed the assessment in approximately 60 min.


MRI Data Acquisition

MRI scan was performed on the same day that the clinical data were obtained and the cognitive test was performed. MRI data were acquired using a 3.0-T MR scanner (Trio, Siemens Medical, Erlangen, Germany) and a 12-channel head coil. Subjects were instructed to close their eyes, stay awake and avoid thinking about any topics. Earplugs and cushions were used to alleviate noise influence and restrict head motion, respectively. T2-weighted images: repetition time = 6,000 ms, echo time = 89 ms, flip angle = 120 • , field of view = 230 × 230 mm 2 , slices = 20, thickness = 5.0 mm, matrix = 448 × 448 and voxel size = 0.5 × 0.5 × 5.0 mm 3 . Fluid-attenuated inversion recovery (FLAIR) images: repetition time = 9,000 ms, echo time = 93 ms, flip angle = 130 • , field of view = 220 × 220 mm 2 , slices = 25, thickness = 4.0 mm, matrix = 256 × 256 and voxel size = 0.9 × 0.9 × 4.0 mm 3 . Resting-state functional images were collected using an echo planar imaging (EPI) sequence: repetition time = 2,000 ms, echo time = 30 ms, flip angle = 90 • , field of view = 192 × 192 mm 2 , slices = 36, thickness = 3 mm, matrix = 64 × 64 and voxel size = 3 × 3 × 3 mm 3 ; 240 volumes were transversely acquired. T1-weighted structural images were collected using a volumetric 3D magnetization prepared by rapid-acquisition gradient-echo (MP-RAGE) sequence: repetition time = 1,900 ms, echo time = 2.52 ms, flip angle = 9 • , field of view = 256 × 256 mm 2 , slices = 176, thickness = 1 mm, matrix = 256 × 256 and voxel size = 1 × 1 × 1 mm 3 , sagittally scanned.


MRI Data Analysis

No subjects were excluded after the T2-weighted and FLAIR images were reviewed by two radiologists with at least five years of work experience.

Structural and functional data analyses were performed using toolkits based on Statistical Parametric Mapping 8 software (SPM 8, http://www.fil.ion.ucl.ac.uk/spm). Intracranial tissue segmentation was performed with Voxel Based Morphometry Toolbox 8 software (VBM8, version 435) according to a previously described protocol (Whitwell, 2009). The main steps include spatial normalization to match every subject's T1weighed images to the template image, and segmentation of the intracranial tissue into gray matter, white matter and cerebrospinal fluid, which automatically produces information about the volumes of each part of the brain tissue. Gray matter was smoothed with 4 mm full-width half-maximum.

Functional data were preprocessed using Data Processing Assistant for Resting-State fMRI (DPARSF module v4.1 of Data Processing & Analysis of Brain Imaging v2.1, http://rfmri.org/ dpabi) according to the standard procedure (Yan and Zang, 2010). All DICOM files were converted to NifTI files. Next, the first 10 volumes were removed to enable the subjects to adapt to the scanning environment, especially the noise. Slice-timing was performed to correct the time differences between slices. Realignment was performed to correct head motion, and a report of head motion was created. Any subjects with head motion > 2.0 mm in any direction of x, y, and x or > 2.0 • at any angle were excluded from the subsequent statistical analyses. Friston 24-parameter model was applied to regress out head motion effects (Yan et al., 2013). Other nuisance variables including white matter signal and cerebrospinal fluid signal were regressed out. Individual functional images were normalized into the Montreal Neurological Institute (MNI) space for intersubject comparison. The resulting images were smoothed with 4 mm full-width halfmaximum. Detrending was applied to remove the systematic drift of the baseline signal. The data were bandpass filtered (0.01-0.08 Hz) to reduce physiological noise at other bands of frequency.

Based on preprocessing, DC calculations were performed using DPARSF in a voxel-wise manner with a threshold r > 0.25 in accordance with previous studies (Beucke et al., 2013;Mueller et al., 2013). Peak MNI coordinates of the candidate brain functional hubs were obtained via group comparison of DC maps and considered to be the center of the spherical region of interest (ROI) with a 6-mm radius. Connectivity among the ROIs was analyzed using rs-fMRI data analysis toolkits (REST v1.8, http://www.restfmri.net) in a ROI-wise manner, including FC and GCA analyses. For FC, the correlation coefficients between the ROIs were computed and normalized with Fisher's r-to-z transformation. For GCA, signed-path coefficients between ROIs were computed in a multivariate mode for the subsequent parametric statistical analyses (Hamilton et al., 2011).


Statistical Analysis

Inter-group comparisons of numeric data were conducted using SPSS software (version 20.0; IBM Corp., Armonk, NY) and included demographic data, clinical parameters, and neuropsychological test scores. First, the data distribution was verified using the Kolmogorov-Smirnov test. Second, independent samples of the t-test and Mann-Whitney U test were applied to normally distributed continuous data and to non-normally distributed data, respectively. The sex proportion was examined with the χ 2 test. p < 0.05 indicated statistical significance.

Intra-and inter-group analyses of DC maps were conducted using REST software. A one-sample t-test was applied to investigate the DC pattern with the base of "0" in the intragroup. An independent t-test was applied to investigate the DC differences between the groups with age, sex, education, BMI, blood pressure, blood biometric parameters (except fasting glucose, fasting insulin, fasting C-peptide and HbA 1c ) entered as covariates. Gray matter maps were also included as covariates to control the influence of structural changes in the T2DM patients. The resulting maps were corrected for multiple comparisons using AlphaSim (p < 0.001, cluster size > 20 voxels, number of Monte Carlo simulations = 10,000, cluster connection radius: rmm = 5.0).

For the z scores of FC and signed-path coefficients, statistical analyses were performed using SPSS software. A one-sample t-test was applied to investigate the patterns of FC and GCA in each group. An independent sample t-test was applied to investigate the differences between T2DM patients and healthy controls in terms of FC and GCA. The relationships of DC and connectivity with clinical parameters and neuropsychological test scores were explored through partial correlation analyses in SPSS software within the T2DM group. Both the independent sample t-test and the correlation analysis were adjusted with gray matter volumes, and the same covariates that employed in the inter-group analyses of the DC maps were applied to control their possible effect on the results. The independent sample t-test was also controlled for multiple comparisons with Bonferroni correction (p < 0.05/6 for FC and p < 0.05/12 for GCA).


RESULTS


Demographic and Clinical Data Comparison

No significant inter-group differences were found in terms of age, sex, education, BMI, blood pressure, total cholesterol, LDL cholesterol, blood urea nitrogen, cystatin C, uric acid and TSH. The T2DM patients had significantly higher levels of glucoserelated parameters, including fasting plasma glucose, fasting insulin, HbA 1c and HOMA2-IR index (all p < 0.05). The T2DM patients had significantly higher levels of triglycerides and homocysteine but lower fasting C-peptide and serum creatine levels (all p < 0.05). The inter-group differences in FT3 and FT4 were significant (all p < 0.05); however, the levels were within the normal range. The details are presented in Table 1.


Neuropsychological Test Comparison

The T2DM patients exhibited poorer performance on the tests of MoCA, TMT-B, DST forwards and long-term delayed recall of AVLT (all p < 0.05). No significant inter-group differences were observed in the other tests. The details are presented in Table 2.


Brain Volume Comparison

The general volumes of gray matter, white matter and brain parenchyma (the sum of gray and white matter) revealed no significant differences between the two groups ( Table 1).


DC Analysis

Compared to the global mean value, both groups displayed higher DC values in the posterior cingulate cortex, cuneus, angular gyrus, occipital cortex, superior frontal cortex, precentral gyrus and postcentral gyrus (Figures 1A,B). In the T2DM patients, significantly higher DC values were observed in the left anterior cingulate gyrus (ACG), and significantly lower DC values were observed in the bilateral lateral occipital cortices (LOC) and right precentral gyrus (PreCG). Details of the changed brain areas are presented in Table 3 ( Figures 1C,D).


Connectivity Analysis

For the FC analyses, the one-sample t-test suggested that FC existed in both groups among the ROIs, including the left ACG, right PreCG, and bilateral LOC (all p < 0.05; Figures 2A,B). The independent t-test suggested that the FC of the left ACG-right PreCG (p = 0.015) in T2DM patients was significantly stronger than that in the healthy controls, and the FC values of the left LOC-right PreCG (p = 0.011), right LOC-right PreCG (p = 0.019), and left LOC-right LOC (p = 0.001) were significantly weaker than those in the healthy controls (Figures 2C,D). After multiple comparisons correction, the FC of the left LOC-right LOC in T2DM patients was significantly weaker than that in the healthy controls (p < 0.05/6).

With respect to the GCA analyses, the one-sample t-test showed a positive causal effect from the left ACG to the left LOC and a negative causal effect from the left ACG to the right LOC in the healthy controls (Figures 3A,C). However, the conditions were inverted such that a negative causal effect from the left ACG to the left LOC and a positive causal effect from the left ACG to the right LOC were observed in the T2DM patients (Figures 3B,C). Significant differences between the two groups in terms of the two directed functional edges were observed (p < 0.05; Figure 3C). Unfortunately, the independent t-test results of GCA could not bear the multiple comparisons correction (p > 0.05/12).


Correlation Analysis

Signed-path coefficients from the left ACG to the left LOC were negatively correlated with fasting C-peptide levels (ρ = −0.386, p = 0.007; Figure 4A) in T2DM patients. Poor performance on the DST forwards was associated with elevated HbA 1c levels in T2DM patients (HbA 1c [%], ρ = −0.301, p = 0.040; HbA 1c [mmol/mol], ρ = −0.301, p = 0.040; Figure 4B). However, no correlations were observed between disrupted DC or connectivity and lower scores on the neuropsychological tests or disease duration.


DISCUSSION

To explore the possible neurological mechanisms underlying cognitive dysfunction in T2DM patients, we combined the DC, FC and GCA approaches to investigate the changes in the candidate brain functional hubs and their connectivity. The increased DC in left ACG and decreased DC in the occipital areas were consistent with a previous study (Cui et al., 2016). A novel finding was the increased DC in the right PreCG. The changed FC pattern of right PreCG may be associated with impaired preparation and execution of goal-directed actions. The GCA approach further revealed the disordered direct connectivity from the left ACG to bilateral occipital areas. The brain regions where identified hubs are located have been reported to be abnormal in previous studies using other neuroimaging metrics in T2DM patients, which suggests that these brain regions are susceptible to T2DM. The ACG is usually identified as the disturbed brain region across experiments and appeared with increased amplitude of low-frequency fluctuation, regional homogeneity Liu D. et al., 2016), and cerebral blood flow (Cui et al., 2017). The aforementioned  hyperactivity of ACG has usually been regarded as a means to compensate for the cognitive loss and maintain normal cognition (He et al., 2015;Liu D. et al., 2016;Cui et al., 2017). However, the mechanism of the compensation requires further research. In contrast, a decreased amplitude of low-frequency fluctuation and regional homogeneity were observed in the occipital lobe (Xia et al., 2013;Cui et al., 2014;Peng et al., 2016). Decreased connectivity of the PreCG was found with the posterior cingulate cortex  and the thalamic . These brain regions also exhibited gray matter atrophy in T2DM patients (Chen et al., 2012;Garcia-Casares et al., 2014;Peng et al., 2015). We discovered a change in the number of edges that connect the bilateral LOC and right PreCG, which rendered these brain regions candidate functional hubs to be damaged in T2DM patients (Zuo et al., 2012). These findings provided information about the reorganized layout of the brain network with a graph theory-based approach in addition to previous studies. Placing the identified brain functional hubs into the context of brain local networks, the changed FC of the right PreCG may suggest impaired preparation and execution of goal-directed actions. The motor system participates in the constitution of internal representations of sensory information (Crowe et al., 2004) and obtains visually perceived information to prepare a motor response (Merchant and Georgopoulos, 2006). However, the decreased DC and FC values of the right PreCG indicated its decline in communication efficiency with the visual cortex and other brain regions. Additionally, the motor system is closely connected with the ACG which mediates focusing attention on targets to select appropriate actions (Isomura et al., 2003). The increased FC between the ACG and PreCG may be interpreted as brain efforts to guide motor behavior. Notably, the process of goal-directed action is a main constituent of executive function. Moreover, for its motor speed component, the poor executive performance in TMT-B may be the behavioral level evidence of cognitive and motor system dysfunction. The GCA results may further suggest that the reorganization of the brain network in T2DM patients may be associated with the impaired visual information acquisition for executive function. The ACG serves an important role in the top-down control network (Petersen and Posner, 2012). The occipital cortex is an important brain area of vision-related information encoding for working memory which is the primary component of executive function (Bentley et al., 2004;Diamond, 2013). Furthermore, visual cortex can be mediated by the ACG which monitors and resolves conflict by enhancing the to-be-attended information and suppressing distracter stimuli (Petersen and Posner, 2012;Liu Y. et al., 2016). In this study, the disordered excitatory and inhibitory effects from the ACG to the visual cortex suggest that T2DM patients may experience a cognitive impairment in visual information acquisition that is indirectly related to the deficit in executive function. In addition, unlike the scale free organization of molecular networks (Budovsky et al., 2007;Tacutu et al., 2010), the human brain network was proposed to exhibit prominent small-world architecture which facilitates efficient information communication (Liao et al., 2017). The impacts of brain functional hubs and connectivity alterations on the small-world property remains to be further explored.

GCA connections appear at where FC connections exist; however, we observed that there was no overlap of significant alterations between undirected and directed connectivity in the present study. On one hand, undirected FC is usually assessed with the correlation coefficient, which can be regarded as descriptive (Biswal et al., 1995). On the other hand, the directed FC depicted by GCA rests explicitly on the linear vector autoregressive models (Seth et al., 2015). It is difficult to make a direct comparison between correlation data and a model. The non-overlapping alterations between the undirected and directed connectivity may result from the distinction of mathematical theories. It is also reported that internet gaming disorder individuals without FC alterations exhibited impaired GCA connections (Guller et al., 2012). This phenomenon requires a further investigation.

Our finding also suggested that the level of glycemic parameters may be linked to the reserve of normal brain function in T2DM patients. Elevated HbA 1c was negatively correlated  with spontaneous brain activity in the middle temporal gyrus (Xia et al., 2013) and with cognitive performance such as VFT . The higher insulin resistance has been associated with decreased FC between the posterior cingulate cortex and middle temporal gyrus Yang et al., 2016). With the higher elevation of C-peptide in this study, a greater deviation of directed FC was observed in patients than in the healthy controls. Additionally, the patients with lower HbA 1c levels obtained higher scores on the DST forwards. The results were similar to those of several previous studies, which indicated that T2DM patients with elevated glycemic parameters exhibited decreased/inverted brain functional activity and poor cognitive performance (Xia et al., 2013;Chen et al., 2014;Liu D. et al., 2016;Yang et al., 2016). However, the findings of this study did not provide sufficient information about the relationships between the altered brain function and cognitive performance. Additional studies are required to investigate these mysteries.

The main limitations of the present investigation are as follows: First, the study comprised a relatively small population. The inter-group comparisons of FC and GCA were unable to bear the multiple corrections. These factors may restrict its statistical power and the explanation of the results. Second, we deduced the visually related cognition impairment in T2DM patients according to fMRI metrics. However, the lack of assessment on this cognitive domain weakened the basis of our inference. Third, the approach of GCA is controversial due to the poor temporal resolution in fMRI studies. However, the GCA method remains a powerful tool to explore the directed connectivity and helps elucidate the complexity of the brain (Seth et al., 2015). A large-sample study with comprehensive cognitive assessments and the application of new technology, such as compressed sensing (Lustig et al., 2008), to accelerate the sampling may solve these problems in the future. We are also considering the application of other network measurements, for instance, betweenness, closeness, path length (Rubinov and Sporns, 2010), which could be combined with the molecular networks to map the complex biological systems of T2DMrelated cognitive impairment in depth Vidal et al., 2011).


CONCLUSIONS

Our findings suggest that the DC of the left ACG, bilateral LOC and right PreCG, as well as the connectivity among them reflected by FC and GCA in different perspectives, are altered in T2DM patients. These alterations may be associated with the disruption of visual information acquisition and goal-directed action execution, both of which have previously proven to be related to executive function. Patients with lower glycemic parameters may reserve more normal brain functions. This study provides insight into the neurological underpinnings of T2DMrelated cognitive impairment using neuroimaging.


AUTHOR CONTRIBUTIONS

DL contributed to the experiments, data analysis and writing of the manuscript. SD contributed to performing the experiments and writing and revising the manuscript. CZ contributed to the data collection. PW designed the experiment and revised the manuscript. LC contributed to the data analysis and manuscript revision. XY contributed to the manuscript revision. JZ and JW are the guarantors of this study and had complete access to all data in the study. They accept responsibility for the integrity of the data and the accuracy of the data analysis.


FUNDING

The study was supported by the National Natural Science Foundation of China (81471647, 81771814) and the Innovation Fund for Younger Investigators of Southwest Hospital of the Third Military Medical University (SWH2013QN09).

FIGURE 1 |
1DC value distribution of intra-group and inter-group comparisons. (A,B) The spatial distribution of the DC value in the HC group and T2DM group. (C) The significantly altered DC map in the T2DM group. (D) Comparison of DC value between the two groups. AlphaSim corrected (p < 0.001, cluster size > 20 voxels). The color bar denotes the t-value. Error bars define the SEM. ACG, anterior cingulate gyrus; LOC, lateral occipital cortices; PreCG, precentral gyrus; R, right; L, left.

FIGURE 2 |
2FC pattern of intra-group and inter-group comparisons. (A,B) The FC pattern in the HC group and T2DM group. (C) The significantly altered FC in the T2DM group. (D) Comparison of FC z scores between the two groups. *p < 0.05, # p < 0.05/6 (Bonferroni correction). Error bars define the SEM. ACG, anterior cingulate gyrus; LOC, lateral occipital cortices; PreCG, precentral gyrus; R, right; L, left.

FIGURE 3 |
3GCA pattern of intra-group and inter-group comparisons. (A,B) Causal effect patterns of the HC group and T2DM group. The red arrow indicates a positive casual effect and its direction. The blue arrow indicates a negative casual effect and its direction. (C) Comparison of signed-path coefficients between the two groups. *p < 0.05. Error bars define the SEM.

FIGURE 4 |
4Correlations among the connectivity, neuropsychological performance and diabetes-related parameters. (A) Signed-path coefficients of the left ACG to the left LOC vs. C-peptide (ng/mL). (B) DST forwards vs. HbA 1c (%).


The updated homeostasis model assessment of the insulin resistance (HOMA2-IR) index was calculated using the HOMA2 Calculator v2.2.3 software (http://www.dtu.ox.ac. uk/homacalculator/) to evaluate insulin resistance in all subjects.HbA 1c ]), lipoid parameters 
(total cholesterol, triglyceride, high-density lipoprotein [HDL] 
cholesterol, low-density lipoprotein [LDL] cholesterol), renal 
function parameters (blood urea nitrogen, serum creatine, 
uric acid, and cystatin C), thyroid function parameters 
(free triiodothyronine [FT3], free thyroxine [FT4] and 
thyroid-stimulating hormone [TSH]), and homocysteine. 


TABLE 1 |
1Demographic and clinical data of the subjects.T2DM 
HC 
p-value 

Age (years) 
58.66 ± 6.87 
57.36 ± 5.42 
0.312 

Sex (male:female) 
28/19 
25/22 
0.533 a 

Education (years) 
10.60 ± 3.06 
10.77 ± 2.65 
0.773 

T2DM duration (years) 
8.87 ± 6.61 
-
-

BMI (kg/m 2 ) 
25.46 ± 5.11 
23.94 ± 3.87 
0.109 

Gray matter (cm 3 ) 
608.15 ± 58.00 
615.16 ± 51.67 
0.538 

White matter (cm 3 ) 
534.91 ± 64.85 
524.16 ± 66.56 
0.430 

Brain parenchyma (cm 3 ) 
1143.06 ± 114.78 1139.32 ± 107.80 
0.871 

Systolic blood pressure 
(mmHg) 

128.91 ± 16.81 
134.55 ± 17.88 
0.119 

Diastolic blood pressure 
(mmHg) 

79.49 ± 10.01 
80.26 ± 10.25 
0.715 

HbA 1c (%) 
8.26 ± 2.08 
5.63 ± 0.39 
< 0.001 
HbA 1c (mmol/mol) 
66.76 ± 22.80 
38.15 ± 4.33 
< 0.001 
Fasting plasma glucose 
(mmol/L) 

7.47 ± 2.70 
5.22 ± 0.46 
< 0.001 

Fasting insulin (mIU/L) 
14.91 (9.58, 24.54) 11.41 (8.39, 17.10) 
0.035 b 

Fasting C-peptide (ng/mL) 
1.81 ± 1.05 
2.22 ± 0.90 
0.047 

HOMA2-IR 
0.28 (0.19, 0.52) 
0.21 (0.16, 0.32) 
0.015 b 

Total cholesterol (mmol/L) 
4.89 ± 1.04 
5.10 ± 1.02 
0.325 

Triglyceride (mmol/L) 
1.45 (1.05, 1.98) 
1.23 (0.91, 1.48) 
0.023 b 

HDL cholesterol (mmol/L) 
1.06 ± 0.23 
1.41 ± 0.36 
< 0.001 
LDL cholesterol (mmol/L) 
3.16 ± 0.83 
3.27 ± 0.76 
0.489 

Homocysteine (µmol/L) 
16.62 ± 10.03 
10.70 ± 4.10 
< 0.001 
Blood urea nitrogen 
(mmol/L) 

6.36 ± 2.45 
5.72 ± 1.23 
0.113 

Serum creatine (µmol/L) 
65.00 (57.00, 78.00) 78.00 (66.00, 85.00) 0.007 b 

Cystatin C (mg/L) 
0.72 (0.64, 0.91) 
0.76 (0.69, 0.86) 
0.639 b 

Uric acid (µmol/L) 
316.67 ± 79.72 
328.34 ± 69.09 
0.450 

Free triiodothyronine, FT3 
(pmol/L) 

4.31 ± 0.91 
5.13 ± 0.72 
< 0.001 

Free thyroxine, FT4 (pmol/L) 
15.52 ± 2.34 
16.48 ± 1.89 
0.032 

Thyroid-stimulating 
hormone, TSH (mIU/L) 

2.12 ± 0.95 
2.57 ± 1.54 
0.090 

p < 0.05 indicates statistical significance. a χ 2 test for sex (n). b Mann-Whitney U-test for 
non-normally distributed data [median (QR)]. Independent t-test for the other normally 
distributed continuous data (means ± SD). T2DM, type 2 diabetes mellitus; HC, 
healthy control; BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density 
lipoprotein. 



TABLE 2 |
2Comparison of the neuropsychological test results between the two groups.T2DM 
HC 
p-value 

GENERAL COGNITION 

MMSE 
29.00 (27.00, 29.00) 29.00 (28.00, 29.00) 0.203 a 

MoCA 
23.17 ± 2.88 
24.51 ± 2.31 
0.015 

EXECUTIVE FUNCTION AND PSYCHOMOTOR SPEED 

TMT-A 
74.67 ± 41.11 
64.17 ± 32.59 
0.173 

TMT-B 
183.19 ± 89.90 
142.00 ± 58.35 
0.010 

MENTAL FLEXIBILITY 

VFT 
42.17 ± 7.33 
40.55 ± 7.05 
0.279 

WORKING MEMORY 

DST forwards 
8.83 ± 1.37 
9.60 ± 1.50 
0.011 

DST backwards 
4.00 (3.00, 5.00) 
4.00 (3.00, 4.00) 
0.911 a 

EPISODIC MEMORY 

AVLT immediate recall 
6.46 ± 1.69 
6.86 ± 1.49 
0.226 

AVLT short-term delayed 
recall 

6.77 ± 3.18 
7.74 ± 2.41 
0.096 

AVLT long-term delayed 
recall 

5.40 ± 3.77 
6.72 ± 2.43 
0.047 

AVLT long-term delayed 
recognition 

11.00 (8.00, 13.00) 
12.00 (9.00, 13.00) 
0.327 a 

AVLT total score 
28.97 ± 9.94 
32.34 ± 7.45 
0.065 

p < 0.05 was considered statistically significant. a Mann-Whitney U-test for non-normally 
distributed data [median (QR)]. Independent t-test for the other normally distributed 
continuous data (means ± SD). MMSE, Mini-Mental State Examination; MoCA, Montreal 
Cognitive Assessment; TMT, Trail Making Test; VFT, Verbal Fluency Test; DST, Digital Span 
Test; AVLT, Auditory Verbal Learning Test. 



TABLE 3 |
3Brain regions with significant DC differences between the two groups.Brain regions 
BA 
Peak MNI 
t-value 
Cluster 
(voxels) 
X 
Y 
Z 

1 Left anterior cingulate gyrus 32 
−9 
42 
9 
3.1455 
28 

2 Right lateral occipital cortex 19 
27 
−78 24 −3.1327 
20 

3 Left lateral occipital cortex 
19 −24 −84 21 −3.9907 
147 

4 Right precentral gyrus 
43 
63 
−3 30 −3.4576 
55 

MNI, Montreal Neurological Institute; BA, Broadmann Area. R, Right; L, left; AlphaSim 
corrected (p < 0.001, cluster > 20 voxels). 


Frontiers in Aging Neuroscience | www.frontiersin.org
March 2018 | Volume 10 | Article 55
ACKNOWLEDGMENTSThe authors thank all volunteers who participated in the study and the staff of the Department of Laboratory Medicine and Department of Nuclear Medicine at the Southwest Hospital of the Third Military Medical University (Army Medical University) in Chongqing, China for their selfless and valuable assistance.Conflict of Interest Statement:The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Copyright © 2018 Liu, Duan, Zhou, Wei, Chen, Yin, Zhang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. K G Alberti, P Z Zimmet, 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-SDiabet. Med. 15Alberti, K. G., and Zimmet, P. Z. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 15, 539-553. doi: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S

Network medicine: a network-based approach to human disease. A L Barabasi, N Gulbahce, J Loscalzo, 10.1038/nrg2918Nat. Rev. Genet. 12Barabasi, A. L., Gulbahce, N., and Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68. doi: 10.1038/nrg2918

Network biology: understanding the cell's functional organization. A L Barabasi, Z N Oltvai, 10.1038/nrg1272Nat. Rev. Genet. 5Barabasi, A. L., and Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113. doi: 10.1038/nrg1272

Effects of cholinergic enhancement on visual stimulation, spatial attention, and spatial working memory. P Bentley, M Husain, R J Dolan, 10.1016/S0896-6273(04)00145-XNeuron. 41Bentley, P., Husain, M., and Dolan, R. J. (2004). Effects of cholinergic enhancement on visual stimulation, spatial attention, and spatial working memory. Neuron 41, 969-982. doi: 10.1016/S0896-6273(04)00145-X

Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder. J C Beucke, J Sepulcre, T Talukdar, C Linnman, K Zschenderlein, T Endrass, 10.1001/jamapsychiatry.2013.173JAMA Psychiatry. 70Beucke, J. C., Sepulcre, J., Talukdar, T., Linnman, C., Zschenderlein, K., Endrass, T., et al. (2013). Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder. JAMA Psychiatry 70, 619-629. doi: 10.1001/jamapsychiatry.2013.173

Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. B Biswal, F Z Yetkin, V M Haughton, J S Hyde, 10.1002/mrm.1910340409Magn. Reson. Med. 34Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. doi: 10.1002/mrm.1910340409

Administration and interpretation of the trail making test. C R Bowie, P D Harvey, 10.1038/nprot.2006.390Nat. Protoc. 1Bowie, C. R., and Harvey, P. D. (2006). Administration and interpretation of the trail making test. Nat. Protoc. 1, 2277-2281. doi: 10.1038/nprot.2006.390

Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. R L Buckner, J Sepulcre, T Talukdar, F M Krienen, H Liu, T Hedden, 10.1523/JNEUROSCI.5062-08.2009J. Neurosci. 29Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860-1873. doi: 10.1523/JNEUROSCI.5062-08.2009

Longevity network: construction and implications. A Budovsky, A Abramovich, R Cohen, V Chalifa-Caspi, V Fraifeld, 10.1016/j.mad.2006.11.018Mech. Ageing Dev. 128Budovsky, A., Abramovich, A., Cohen, R., Chalifa-Caspi, V., and Fraifeld, V. (2007). Longevity network: construction and implications. Mech. Ageing Dev. 128, 117-124. doi: 10.1016/j.mad.2006.11.018

Aberrant brain functional connectivity related to insulin resistance in type 2 diabetes: a resting-state fMRI study. Y C Chen, Y Jiao, Y Cui, S A Shang, J Ding, Y Feng, 10.2337/dc13-2127Diabetes Care. 37Chen, Y. C., Jiao, Y., Cui, Y., Shang, S. A., Ding, J., Feng, Y., et al. (2014). Aberrant brain functional connectivity related to insulin resistance in type 2 diabetes: a resting-state fMRI study. Diabetes Care 37, 1689-1696. doi: 10.2337/dc13-2127

Thalamic resting-state functional connectivity: disruption in patients with type 2 diabetes. Metab. Brain Dis. Y C Chen, W Xia, C Qian, J Ding, S Ju, G J Teng, 10.1007/s11011-015-9700-230Chen, Y. C., Xia, W., Qian, C., Ding, J., Ju, S., and Teng, G. J. (2015). Thalamic resting-state functional connectivity: disruption in patients with type 2 diabetes. Metab. Brain Dis. 30, 1227-1236. doi: 10.1007/s11011-015-9700-2

Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL. Z Chen, L Li, J Sun, L Ma, 10.1016/j.ejrad.2011.04.025Eur. J. Radiol. 81Chen, Z., Li, L., Sun, J., and Ma, L. (2012). Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL. Eur. J. Radiol. 81, 1870-1876. doi: 10.1016/j.ejrad.2011.04.025

Participation of primary motor cortical neurons in a distributed network during maze solution: representation of spatial parameters and time-course comparison with parietal area 7a. D A Crowe, M V Chafee, B B Averbeck, A P Georgopoulos, 10.1007/s00221-004-1876-3Exp. Brain Res. 158Crowe, D. A., Chafee, M. V., Averbeck, B. B., and Georgopoulos, A. P. (2004). Participation of primary motor cortical neurons in a distributed network during maze solution: representation of spatial parameters and time-course comparison with parietal area 7a. Exp. Brain Res. 158, 28-34. doi: 10.1007/s00221-004-1876-3

Aberrant functional connectivity of default-mode network in type 2 diabetes patients. Y Cui, Y Jiao, H J Chen, J Ding, B Luo, C Y Peng, 10.1007/s00330-015-3746-8Eur. Radiol. 25Cui, Y., Jiao, Y., Chen, H. J., Ding, J., Luo, B., Peng, C. Y., et al. (2015). Aberrant functional connectivity of default-mode network in type 2 diabetes patients. Eur. Radiol. 25, 3238-3246. doi: 10.1007/s00330-015-3746-8

Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Y Cui, Y Jiao, Y C Chen, K Wang, B Gao, S Wen, 10.2337/db13-0519Diabetes. 63Cui, Y., Jiao, Y., Chen, Y. C., Wang, K., Gao, B., Wen, S., et al. (2014). Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Diabetes 63, 749-760. doi: 10.2337/db13-0519

Disrupted brain connectivity patterns in patients with type 2 diabetes. Y Cui, S F Li, H Gu, Y Z Hu, X Liang, C Q Lu, 10.3174/ajnr.A4858AJNR. Am. J. Neuroradiol. 37Cui, Y., Li, S. F., Gu, H., Hu, Y. Z., Liang, X., Lu, C. Q., et al. (2016). Disrupted brain connectivity patterns in patients with type 2 diabetes. AJNR. Am. J. Neuroradiol. 37, 2115-2122. doi: 10.3174/ajnr.A4858

Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction. Y Cui, X Liang, H Gu, Y Hu, Z Zhao, X Y Yang, 10.1007/s11682-016-9583-9Brain Imaging Behav. 11Cui, Y., Liang, X., Gu, H., Hu, Y., Zhao, Z., Yang, X. Y., et al. (2017). Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction. Brain Imaging Behav. 11, 1248-1257. doi: 10.1007/s11682-016-9583-9

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease. W De Haan, E C W Van Straaten, A A Gouw, C J Stam, 10.1371/journal.pcbi.1005707PLoS Comput. Biol. 131005707de Haan, W., van Straaten, E. C. W., Gouw, A. A., and Stam, C. J. (2017). Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease. PLoS Comput. Biol. 13:e1005707. doi: 10.1371/journal.pcbi.1005707

Executive functions. A Diamond, 10.1146/annurev-psych-113011-143750Annu. Rev. Psychol. 64Diamond, A. (2013). Executive functions. Annu. Rev. Psychol. 64, 135-168. doi: 10.1146/annurev-psych-113011-143750

Analysing connectivity with Granger causality and dynamic causal modelling. K Friston, R Moran, A K Seth, 10.1016/j.conb.2012.11.010Curr. Opin. Neurobiol. 23Friston, K., Moran, R., and Seth, A. K. (2013). Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172-178. doi: 10.1016/j.conb.2012.11.010

Mini-Mental State Examination (MMSE). M Galea, M Woodward, Galea, M., and Woodward, M. (2005). Mini-Mental State Examination (MMSE).

. 10.1016/S0004-9514(05)70034-9Aust. J. Physiother. 51198Aust. J. Physiother. 51:198. doi: 10.1016/S0004-9514(05)70034-9

Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study. N Garcia-Casares, M L Berthier, R E Jorge, P Gonzalez-Alegre, A Gutierrez Cardo, J Rioja Villodres, 10.3233/JAD-131736J. Alzheimers. Dis. 40Garcia-Casares, N., Berthier, M. L., Jorge, R. E., Gonzalez-Alegre, P., Gutierrez Cardo, A., Rioja Villodres, J., et al. (2014). Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study. J. Alzheimers. Dis. 40, 375-386. doi: 10.3233/JAD-131736

Conserved functional connectivity but impaired effective connectivity of thalamocortical circuitry in schizophrenia. Y Guller, G Tononi, B R Postle, 10.1089/brain.2012.0100Brain Connect. 2Guller, Y., Tononi, G., and Postle, B. R. (2012). Conserved functional connectivity but impaired effective connectivity of thalamocortical circuitry in schizophrenia. Brain Connect. 2, 311-319. doi: 10.1089/brain.20 12.0100

Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. J P Hamilton, G Chen, M E Thomason, M E Schwartz, I H Gotlib, 10.1038/mp.2010.46Mol. Psychiatry. 16Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E., and Gotlib, I. H. (2011). Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Mol. Psychiatry 16, 763-772. doi: 10.1038/mp.2010.46

A rating scale for depression. M Hamilton, 10.1136/jnnp.23.1.56J. Neurol. Neurosurg. Psychiatr. 23Hamilton, M. (1960). A rating scale for depression. J. Neurol. Neurosurg. Psychiatr. 23, 56-62. doi: 10.1136/jnnp.23.1.56

Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics. X S He, Z X Wang, Y Z Zhu, N Wang, X Hu, D R Zhang, 10.1007/s00592-014-0618-7Acta Diabetol. 52He, X. S., Wang, Z. X., Zhu, Y. Z., Wang, N., Hu, X., Zhang, D. R., et al. (2015). Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics. Acta Diabetol. 52, 133-142. doi: 10.1007/s00592-014-0618-7

Neural coding of "attention for action" and "response selection" in primate anterior cingulate cortex. Y Isomura, Y Ito, T Akazawa, A Nambu, M Takada, J. Neurosci. 23Isomura, Y., Ito, Y., Akazawa, T., Nambu, A., and Takada, M. (2003). Neural coding of "attention for action" and "response selection" in primate anterior cingulate cortex. J. Neurosci. 23, 8002-8012.

Small-world human brain networks: perspectives and challenges. X Liao, A V Vasilakos, Y He, 10.1016/j.neubiorev.2017.03.018Neurosci. Biobehav. Rev. 77Liao, X., Vasilakos, A. V., and He, Y. (2017). Small-world human brain networks: perspectives and challenges. Neurosci. Biobehav. Rev. 77, 286-300. doi: 10.1016/j.neubiorev.2017.03.018

Aberrant brain regional homogeneity and functional connectivity in middle-aged T2DM patients: a resting-state functional MRI study. D Liu, S Duan, J Zhang, C Zhou, M Liang, X Yin, 10.3389/fnhum.2016.00490Front. Hum. Neurosci. 10490Liu, D., Duan, S., Zhang, J., Zhou, C., Liang, M., Yin, X., et al. (2016). Aberrant brain regional homogeneity and functional connectivity in middle-aged T2DM patients: a resting-state functional MRI study. Front. Hum. Neurosci. 10:490. doi: 10.3389/fnhum.2016.00490

Topdown modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Y Liu, J Bengson, H Huang, G R Mangun, M Ding, 10.1093/cercor/bhu204Cereb. Cortex. 26Liu, Y., Bengson, J., Huang, H., Mangun, G. R., and Ding, M. (2016). Top- down modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Cereb. Cortex 26, 517-529. doi: 10.1093/cercor/bhu204

. M Lustig, D L Donoho, J M Santos, Pauly , J M , Lustig, M., Donoho, D. L., Santos, J. M., and Pauly, J. M. (2008).

Compressed Sensing MRI. 10.1109/MSP.2007.914728IEEE Signal Process. Mag. 25Compressed Sensing MRI. IEEE Signal Process. Mag. 25, 72-82. doi: 10.1109/MSP.2007.914728

Neurophysiology of perceptual and motor aspects of interception. H Merchant, A P Georgopoulos, 10.1152/jn.00422.2005J. Neurophysiol. 95Merchant, H., and Georgopoulos, A. P. (2006). Neurophysiology of perceptual and motor aspects of interception. J. Neurophysiol. 95, 1-13. doi: 10.1152/jn.00422.2005

Impact of diabetes on cognitive function and brain structure. A Moheet, S Mangia, E R Seaquist, 10.1111/nyas.12807Ann. N.Y. Acad. Sci. 1353Moheet, A., Mangia, S., and Seaquist, E. R. (2015). Impact of diabetes on cognitive function and brain structure. Ann. N.Y. Acad. Sci. 1353, 60-71. doi: 10.1111/nyas.12807

Individual variability in functional connectivity architecture of the human brain. S Mueller, D Wang, M D Fox, B T Yeo, J Sepulcre, M R Sabuncu, 10.1016/j.neuron.2012.12.028Neuron. 77Mueller, S., Wang, D., Fox, M. D., Yeo, B. T., Sepulcre, J., Sabuncu, M. R., et al. (2013). Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586-595. doi: 10.1016/j.neuron.2012. 12.028

Magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. P Palta, A L Schneider, G J Biessels, P Touradji, F Hill-Briggs, 10.1017/S1355617713001483J. Int. Neuropsychol. Soc. 20Palta, P., Schneider, A. L., Biessels, G. J., Touradji, P., and Hill-Briggs, F. (2014). Magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. J. Int. Neuropsychol. Soc. 20, 278-291. doi: 10.1017/S1355617713001483

Cerebral alterations of type 2 diabetes mellitus on MRI: a pilot study. B Peng, Z Chen, L Ma, Y Dai, 10.1016/j.neulet.2015.08.030Neurosci. Lett. 606Peng, B., Chen, Z., Ma, L., and Dai, Y. (2015). Cerebral alterations of type 2 diabetes mellitus on MRI: a pilot study. Neurosci. Lett. 606, 100-105. doi: 10.1016/j.neulet.2015.08.030

Abnormal spontaneous brain activity in type 2 diabetes with and without microangiopathy revealed by regional homogeneity. J Peng, H Qu, J Peng, T Y Luo, F J Lv, L Chen, 10.1016/j.ejrad.2015.12.024Eur. J. Radiol. 85Peng, J., Qu, H., Peng, J., Luo, T. Y., Lv, F. J., Chen, L., et al. (2016). Abnormal spontaneous brain activity in type 2 diabetes with and without microangiopathy revealed by regional homogeneity. Eur. J. Radiol. 85, 607-615. doi: 10.1016/j.ejrad.2015.12.024

The attention system of the human brain: 20 years after. S E Petersen, M I Posner, 10.1146/annurev-neuro-062111-150525Annu. Rev. Neurosci. 35Petersen, S. E., and Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35, 73-89. doi: 10.1146/annurev-neuro-062111-150525

Brain networks and cognitive architectures. S E Petersen, O Sporns, 10.1016/j.neuron.2015.09.027Neuron. 88Petersen, S. E., and Sporns, O. (2015). Brain networks and cognitive architectures. Neuron 88, 207-219. doi: 10.1016/j.neuron.2015.09.027

Complex network measures of brain connectivity: uses and interpretations. M Rubinov, O Sporns, 10.1016/j.neuroimage.2009.10.003Neuroimage. 52Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003

Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. C Sandor, N L Beer, C Webber, 10.1371/journal.pcbi.1005816PLoS Comput. Biol. 131005816Sandor, C., Beer, N. L., and Webber, C. (2017). Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput. Biol. 13:e1005816. doi: 10.1371/journal.pcbi.1005816

Granger causality analysis in neuroscience and neuroimaging. A K Seth, A B Barrett, L Barnett, 10.1523/JNEUROSCI.4399-14.2015J. Neurosci. 35Seth, A. K., Barrett, A. B., and Barnett, L. (2015). Granger causality analysis in neuroscience and neuroimaging. J. Neurosci. 35, 3293-3297. doi: 10.1523/JNEUROSCI.4399-14.2015

The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes. R Tacutu, A Budovsky, V E Fraifeld, 10.1007/s10522-010-9265-8Biogerontology. 11Tacutu, R., Budovsky, A., and Fraifeld, V. E. (2010). The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes. Biogerontology 11, 513-522. doi: 10.1007/s10522-010-9265-8

Molecular links between cellular senescence, longevity and age-related diseases -a systems biology perspective. R Tacutu, A Budovsky, H Yanai, V E Fraifeld, 10.18632/aging.100413Aging. 3Tacutu, R., Budovsky, A., Yanai, H., and Fraifeld, V. E. (2011). Molecular links between cellular senescence, longevity and age-related diseases -a systems biology perspective. Aging 3, 1178-1191. doi: 10.18632/aging.100413

Interactome networks and human disease. M Vidal, M E Cusick, A L Barabasi, 10.1016/j.cell.2011.02.016Cell. 144Vidal, M., Cusick, M. E., and Barabasi, A. L. (2011). Interactome networks and human disease. Cell 144, 986-998. doi: 10.1016/j.cell.2011.02.016

A new rating scale for age-related white matter changes applicable to MRI and CT. L O Wahlund, F Barkhof, F Fazekas, L Bronge, M Augustin, M Sjogren, 10.1161/01.STR.32.6.1318Stroke. 32Wahlund, L. O., Barkhof, F., Fazekas, F., Bronge, L., Augustin, M., Sjogren, M., et al. (2001). A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 32, 1318-1322. doi: 10.1161/01.STR.32. 6.1318

Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J L Whitwell, 10.1523/JNEUROSCI.2160-09.2009J. Neurosci. 29Whitwell, J. L. (2009). Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J. Neurosci. 29, 9661-9664. doi: 10.1523/JNEUROSCI.2160-09.2009

The signaling hubs at the crossroad of longevity and age-related disease networks. M Wolfson, A Budovsky, R Tacutu, V Fraifeld, 10.1016/j.biocel.2008.08.026Int. J. Biochem. Cell Biol. 41Wolfson, M., Budovsky, A., Tacutu, R., and Fraifeld, V. (2009). The signaling hubs at the crossroad of longevity and age-related disease networks. Int. J. Biochem. Cell Biol. 41, 516-520. doi: 10.1016/j.biocel.2008.08.026

Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study. W Xia, S Wang, Z Sun, F Bai, Y Zhou, Y Yang, 10.1016/j.psyneuen.2013.05.012Psychoneuroendocrinology. 38Xia, W., Wang, S., Sun, Z., Bai, F., Zhou, Y., Yang, Y., et al. (2013). Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study. Psychoneuroendocrinology 38, 2493-2501. doi: 10.1016/j.psyneuen.2013.05.012

A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. C G Yan, B Cheung, C Kelly, S Colcombe, R C Craddock, A Di Martino, 10.1016/j.neuroimage.2013.03.004Neuroimage. 76Yan, C. G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., et al. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183-201. doi: 10.1016/j.neuroimage.2013.03.004

DPARSF: a Matlab toolbox for "pipeline" data analysis of resting-state fMRI. C G Yan, Y F Zang, 10.3389/fnsys.2010.00013Front. Syst. Neurosci. 413Yan, C. G., and Zang, Y. F. (2010). DPARSF: a Matlab toolbox for "pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4:13. doi: 10.3389/fnsys.2010.00013

Altered intranetwork and internetwork functional connectivity in type 2 diabetes mellitus with and without cognitive impairment. S Q Yang, Z P Xu, Y Xiong, Y F Zhan, L Y Guo, S Zhang, 10.1038/srep32980Sci. Rep. 632980Yang, S. Q., Xu, Z. P., Xiong, Y., Zhan, Y. F., Guo, L. Y., Zhang, S., et al. (2016). Altered intranetwork and internetwork functional connectivity in type 2 diabetes mellitus with and without cognitive impairment. Sci. Rep. 6:32980. doi: 10.1038/srep32980

Auditory verbal learning test is superior to rey-osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer's disease. Q Zhao, Q Guo, X Liang, M Chen, Y Zhou, D Ding, 10.2174/1567205012666150530202729Curr. Alzheimer Res. 12Zhao, Q., Guo, Q., Liang, X., Chen, M., Zhou, Y., Ding, D., et al. (2015). Auditory verbal learning test is superior to rey-osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer's disease. Curr. Alzheimer Res. 12, 520-526. doi: 10.2174/1567205012666150530202729

Network centrality in the human functional connectome. X N Zuo, R Ehmke, M Mennes, D Imperati, F X Castellanos, O Sporns, 10.1093/cercor/bhr269Cereb. Cortex. 22Zuo, X. N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., et al. (2012). Network centrality in the human functional connectome. Cereb. Cortex 22, 1862-1875. doi: 10.1093/cercor/bhr269

Type 2 diabetes mellitus (T2DM) affects a vast population and is closely associated with cognitive impairment. However, the mechanisms of cognitive impairment in T2DM patients have not been unraveled. Research on the basic units (nodes or hubs and edges) of the brain functional network on the basis of neuroimaging may advance our understanding of the network change pattern in T2DM patients. This study investigated the change patterns of brain functional hubs using degree centrality (DC) analysis and the connectivity among these hubs using functional connectivity and Granger causality analysis. Compared to healthy controls, the DC values were higher in the left anterior cingulate gyrus (ACG) and lower in the bilateral lateral occipital cortices (LOC) and right precentral gyrus (PreCG) in T2DM patients. The functional connectivity between the left ACG and the right PreCG was stronger in T2DM patients, whereas the functional connectivity among the right PreCG and bilateral LOC was weaker. A negative causal effect from the left ACG to left LOC and a positive effect from the left ACG to right LOC were observed in T2DM patients, while in healthy controls, the opposite occurred. Additionally, the reserve of normal brain function in T2DM patients was negatively associated with the elevated glycemic parameters. This study demonstrates that there are brain functional hubs and connectivity alterations that may reflect the aberrant information communication in the brain of T2DM patients. The findings may advance our understanding of the mechanisms of T2DM-related cognitive impairment.
Nicola Canessa  Robi Tacutu  Jiuquan Zhang  Jian Wang wangjian_811@yahoo.com  Liu D  Duan S  Zhou C  Wei P  Chen L  Yin X  Zhang J  Wang J  Daihong Liu  †  Shanshan Duan  †  Chaoyang Zhou 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina
 Ping Wei 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina
 Lihua Chen 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina
 Xuntao Yin 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina
 Jiuquan Zhang 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina
 Jian Wang 
Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina

Department of Endocrinology
The Third Affiliation Hospital of Chongqing Medical University
ChongqingChina

Department of Endocrinology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina
 
Istituto Universitario di Studi Superiori di Pavia (IUSS)
Institute of Biochemistry of the Romanian Academy
Arie Budovsky, Judea R & D Center
Italy, Romania, Israel

Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Department of Radiology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Department of Endocrinology
The Third Affiliation Hospital of Chongqing Medical University
ChongqingChina Department of Endocrinology, Southwest Hospital
Third Military Medical University (Army Medical University)
ChongqingChina Istituto Universitario di Studi Superiori di Pavia (IUSS)
Institute of Biochemistry of the Romanian Academy
Arie Budovsky, Judea R & D Center
Italy, Romania, Israel
K G Alberti,  P Z Zimmet,  A L Barabasi,  N Gulbahce,  J Loscalzo,  A L Barabasi,  Z N Oltvai,  P Bentley,  M Husain,  R J Dolan,  J C Beucke,  J Sepulcre,  T Talukdar,  C Linnman,  K Zschenderlein,  T Endrass,  B Biswal,  F Z Yetkin,  V M Haughton,  J S Hyde,  C R Bowie,  P D Harvey,  R L Buckner,  J Sepulcre,  T Talukdar,  F M Krienen,  H Liu,  T Hedden,  A Budovsky,  A Abramovich,  R Cohen,  V Chalifa-Caspi,  V Fraifeld,  Y C Chen,  Y Jiao,  Y Cui,  S A Shang,  J Ding,  Y Feng,  Y C Chen,  W Xia,  C Qian,  J Ding,  S Ju,  G J Teng,  Z Chen,  L Li,  J Sun,  L Ma,  D A Crowe,  M V Chafee,  B B Averbeck,  A P Georgopoulos,  Y Cui,  Y Jiao,  H J Chen,  J Ding,  B Luo,  C Y Peng,  Y Cui,  Y Jiao,  Y C Chen,  K Wang,  B Gao,  S Wen,  Y Cui,  S F Li,  H Gu,  Y Z Hu,  X Liang,  C Q Lu,  Y Cui,  X Liang,  H Gu,  Y Hu,  Z Zhao,  X Y Yang,  W De Haan,  E C W Van Straaten,  A A Gouw,  C J Stam,  A Diamond,  K Friston,  R Moran,  A K Seth,  M Galea,  M Woodward,  N Garcia-Casares,  M L Berthier,  R E Jorge,  P Gonzalez-Alegre,  A Gutierrez Cardo,  J Rioja Villodres,  Y Guller,  G Tononi,  B R Postle,  J P Hamilton,  G Chen,  M E Thomason,  M E Schwartz,  I H Gotlib,  M Hamilton,  X S He,  Z X Wang,  Y Z Zhu,  N Wang,  X Hu,  D R Zhang,  Y Isomura,  Y Ito,  T Akazawa,  A Nambu,  M Takada,  X Liao,  A V Vasilakos,  Y He,  D Liu,  S Duan,  J Zhang,  C Zhou,  M Liang,  X Yin,  Y Liu,  J Bengson,  H Huang,  G R Mangun,  M Ding,  M Lustig,  D L Donoho,  J M Santos,  Pauly ,  J M ,  H Merchant,  A P Georgopoulos,  A Moheet,  S Mangia,  E R Seaquist,  S Mueller,  D Wang,  M D Fox,  B T Yeo,  J Sepulcre,  M R Sabuncu,  P Palta,  A L Schneider,  G J Biessels,  P Touradji,  F Hill-Briggs,  B Peng,  Z Chen,  L Ma,  Y Dai,  J Peng,  H Qu,  J Peng,  T Y Luo,  F J Lv,  L Chen,  S E Petersen,  M I Posner,  S E Petersen,  O Sporns,  M Rubinov,  O Sporns,  C Sandor,  N L Beer,  C Webber,  A K Seth,  A B Barrett,  L Barnett,  R Tacutu,  A Budovsky,  V E Fraifeld,  R Tacutu,  A Budovsky,  H Yanai,  V E Fraifeld,  M Vidal,  M E Cusick,  A L Barabasi,  L O Wahlund,  F Barkhof,  F Fazekas,  L Bronge,  M Augustin,  M Sjogren,  J L Whitwell,  M Wolfson,  A Budovsky,  R Tacutu,  V Fraifeld,  W Xia,  S Wang,  Z Sun,  F Bai,  Y Zhou,  Y Yang,  C G Yan,  B Cheung,  C Kelly,  S Colcombe,  R C Craddock,  A Di Martino,  C G Yan,  Y F Zang,  S Q Yang,  Z P Xu,  Y Xiong,  Y F Zhan,  L Y Guo,  S Zhang,  Q Zhao,  Q Guo,  X Liang,  M Chen,  Y Zhou,  D Ding,  X N Zuo,  R Ehmke,  M Mennes,  D Imperati,  F X Castellanos,  O Sporns, 
K G P Z A L N J A L Z N P M R J J C J T C K T B F Z V M J S C R P D R L J T F M H T A A R V V Y C Y Y S A J Y Y C W C J S G J Z L J L D A M V B B A P Y Y H J J B C Y Y Y Y C K B S Y S F H Y Z X C Q Y X H Y Z X Y W E C W A A C J A K R A K M M N M L R E P A J Y G B R J P G M E M E I H M X S Z X Y Z N X D R Y Y T A M X A V Y D S J C M X Y J H G R M M D L J M Pauly J M H A P A S E R S D M D B T J M R P A L G J P F B Z L Y J H J T Y F J L S E M I S E O M O C N L C A K A B L R A V E R A H V E M M E A L L O F F L M M J L M A R V W S Z F Y Y C G B C S R C A C G Y F S Q Z P Y Y F L Y S Q Q X M Y D X N R M D F X O
Alberti Zimmet Barabasi Gulbahce Loscalzo Barabasi Oltvai Bentley Husain Dolan Beucke Sepulcre Talukdar Linnman Zschenderlein Endrass Biswal Yetkin Haughton Hyde Bowie Harvey Buckner Sepulcre Talukdar Krienen Liu Hedden Budovsky Abramovich Cohen Chalifa-Caspi Fraifeld Chen Jiao Cui Shang Ding Feng Chen Xia Qian Ding Ju Teng Chen Li Sun Ma Crowe Chafee Averbeck Georgopoulos Cui Jiao Chen Ding Luo Peng Cui Jiao Chen Wang Gao Wen Cui Li Gu Hu Liang Lu Cui Liang Gu Hu Zhao Yang De Haan Van Straaten Gouw Stam Diamond Friston Moran Seth Galea Woodward Garcia-Casares Berthier Jorge Gonzalez-Alegre Gutierrez Cardo Rioja Villodres Guller Tononi Postle Hamilton Chen Thomason Schwartz Gotlib Hamilton He Wang Zhu Wang Hu Zhang Isomura Ito Akazawa Nambu Takada Liao Vasilakos He Liu Duan Zhang Zhou Liang Yin Liu Bengson Huang Mangun Ding Lustig Donoho Santos Merchant Georgopoulos Moheet Mangia Seaquist Mueller Wang Fox Yeo Sepulcre Sabuncu Palta Schneider Biessels Touradji Hill-Briggs Peng Chen Ma Dai Peng Qu Peng Luo Lv Chen Petersen Posner Petersen Sporns Rubinov Sporns Sandor Beer Webber Seth Barrett Barnett Tacutu Budovsky Fraifeld Tacutu Budovsky Yanai Fraifeld Vidal Cusick Barabasi Wahlund Barkhof Fazekas Bronge Augustin Sjogren Whitwell Wolfson Budovsky Tacutu Fraifeld Xia Wang Sun Bai Zhou Yang Yan Cheung Kelly Colcombe Craddock Di Martino Yan Zang Yang Xu Xiong Zhan Guo Zhang Zhao Guo Liang Chen Zhou Ding Zuo Ehmke Mennes Imperati Castellanos Sporns
Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. K G Alberti, P Z Zimmet, 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-SDiabet. Med. 15Alberti, K. G., and Zimmet, P. Z. (1998). Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 15, 539-553. doi: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S Network medicine: a network-based approach to human disease. A L Barabasi, N Gulbahce, J Loscalzo, 10.1038/nrg2918Nat. Rev. Genet. 12Barabasi, A. L., Gulbahce, N., and Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68. doi: 10.1038/nrg2918 Network biology: understanding the cell's functional organization. A L Barabasi, Z N Oltvai, 10.1038/nrg1272Nat. Rev. Genet. 5Barabasi, A. L., and Oltvai, Z. N. (2004). Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113. doi: 10.1038/nrg1272 Effects of cholinergic enhancement on visual stimulation, spatial attention, and spatial working memory. P Bentley, M Husain, R J Dolan, 10.1016/S0896-6273(04)00145-XNeuron. 41Bentley, P., Husain, M., and Dolan, R. J. (2004). Effects of cholinergic enhancement on visual stimulation, spatial attention, and spatial working memory. Neuron 41, 969-982. doi: 10.1016/S0896-6273(04)00145-X Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder. J C Beucke, J Sepulcre, T Talukdar, C Linnman, K Zschenderlein, T Endrass, 10.1001/jamapsychiatry.2013.173JAMA Psychiatry. 70Beucke, J. C., Sepulcre, J., Talukdar, T., Linnman, C., Zschenderlein, K., Endrass, T., et al. (2013). Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder. JAMA Psychiatry 70, 619-629. doi: 10.1001/jamapsychiatry.2013.173 Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. B Biswal, F Z Yetkin, V M Haughton, J S Hyde, 10.1002/mrm.1910340409Magn. Reson. Med. 34Biswal, B., Yetkin, F. Z., Haughton, V. M., and Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537-541. doi: 10.1002/mrm.1910340409 Administration and interpretation of the trail making test. C R Bowie, P D Harvey, 10.1038/nprot.2006.390Nat. Protoc. 1Bowie, C. R., and Harvey, P. D. (2006). Administration and interpretation of the trail making test. Nat. Protoc. 1, 2277-2281. doi: 10.1038/nprot.2006.390 Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. R L Buckner, J Sepulcre, T Talukdar, F M Krienen, H Liu, T Hedden, 10.1523/JNEUROSCI.5062-08.2009J. Neurosci. 29Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860-1873. doi: 10.1523/JNEUROSCI.5062-08.2009 Longevity network: construction and implications. A Budovsky, A Abramovich, R Cohen, V Chalifa-Caspi, V Fraifeld, 10.1016/j.mad.2006.11.018Mech. Ageing Dev. 128Budovsky, A., Abramovich, A., Cohen, R., Chalifa-Caspi, V., and Fraifeld, V. (2007). Longevity network: construction and implications. Mech. Ageing Dev. 128, 117-124. doi: 10.1016/j.mad.2006.11.018 Aberrant brain functional connectivity related to insulin resistance in type 2 diabetes: a resting-state fMRI study. Y C Chen, Y Jiao, Y Cui, S A Shang, J Ding, Y Feng, 10.2337/dc13-2127Diabetes Care. 37Chen, Y. C., Jiao, Y., Cui, Y., Shang, S. A., Ding, J., Feng, Y., et al. (2014). Aberrant brain functional connectivity related to insulin resistance in type 2 diabetes: a resting-state fMRI study. Diabetes Care 37, 1689-1696. doi: 10.2337/dc13-2127 Thalamic resting-state functional connectivity: disruption in patients with type 2 diabetes. Metab. Brain Dis. Y C Chen, W Xia, C Qian, J Ding, S Ju, G J Teng, 10.1007/s11011-015-9700-230Chen, Y. C., Xia, W., Qian, C., Ding, J., Ju, S., and Teng, G. J. (2015). Thalamic resting-state functional connectivity: disruption in patients with type 2 diabetes. Metab. Brain Dis. 30, 1227-1236. doi: 10.1007/s11011-015-9700-2 Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL. Z Chen, L Li, J Sun, L Ma, 10.1016/j.ejrad.2011.04.025Eur. J. Radiol. 81Chen, Z., Li, L., Sun, J., and Ma, L. (2012). Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL. Eur. J. Radiol. 81, 1870-1876. doi: 10.1016/j.ejrad.2011.04.025 Participation of primary motor cortical neurons in a distributed network during maze solution: representation of spatial parameters and time-course comparison with parietal area 7a. D A Crowe, M V Chafee, B B Averbeck, A P Georgopoulos, 10.1007/s00221-004-1876-3Exp. Brain Res. 158Crowe, D. A., Chafee, M. V., Averbeck, B. B., and Georgopoulos, A. P. (2004). Participation of primary motor cortical neurons in a distributed network during maze solution: representation of spatial parameters and time-course comparison with parietal area 7a. Exp. Brain Res. 158, 28-34. doi: 10.1007/s00221-004-1876-3 Aberrant functional connectivity of default-mode network in type 2 diabetes patients. Y Cui, Y Jiao, H J Chen, J Ding, B Luo, C Y Peng, 10.1007/s00330-015-3746-8Eur. Radiol. 25Cui, Y., Jiao, Y., Chen, H. J., Ding, J., Luo, B., Peng, C. Y., et al. (2015). Aberrant functional connectivity of default-mode network in type 2 diabetes patients. Eur. Radiol. 25, 3238-3246. doi: 10.1007/s00330-015-3746-8 Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Y Cui, Y Jiao, Y C Chen, K Wang, B Gao, S Wen, 10.2337/db13-0519Diabetes. 63Cui, Y., Jiao, Y., Chen, Y. C., Wang, K., Gao, B., Wen, S., et al. (2014). Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Diabetes 63, 749-760. doi: 10.2337/db13-0519 Disrupted brain connectivity patterns in patients with type 2 diabetes. Y Cui, S F Li, H Gu, Y Z Hu, X Liang, C Q Lu, 10.3174/ajnr.A4858AJNR. Am. J. Neuroradiol. 37Cui, Y., Li, S. F., Gu, H., Hu, Y. Z., Liang, X., Lu, C. Q., et al. (2016). Disrupted brain connectivity patterns in patients with type 2 diabetes. AJNR. Am. J. Neuroradiol. 37, 2115-2122. doi: 10.3174/ajnr.A4858 Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction. Y Cui, X Liang, H Gu, Y Hu, Z Zhao, X Y Yang, 10.1007/s11682-016-9583-9Brain Imaging Behav. 11Cui, Y., Liang, X., Gu, H., Hu, Y., Zhao, Z., Yang, X. Y., et al. (2017). Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction. Brain Imaging Behav. 11, 1248-1257. doi: 10.1007/s11682-016-9583-9 Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease. W De Haan, E C W Van Straaten, A A Gouw, C J Stam, 10.1371/journal.pcbi.1005707PLoS Comput. Biol. 131005707de Haan, W., van Straaten, E. C. W., Gouw, A. A., and Stam, C. J. (2017). Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease. PLoS Comput. Biol. 13:e1005707. doi: 10.1371/journal.pcbi.1005707 Executive functions. A Diamond, 10.1146/annurev-psych-113011-143750Annu. Rev. Psychol. 64Diamond, A. (2013). Executive functions. Annu. Rev. Psychol. 64, 135-168. doi: 10.1146/annurev-psych-113011-143750 Analysing connectivity with Granger causality and dynamic causal modelling. K Friston, R Moran, A K Seth, 10.1016/j.conb.2012.11.010Curr. Opin. Neurobiol. 23Friston, K., Moran, R., and Seth, A. K. (2013). Analysing connectivity with Granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172-178. doi: 10.1016/j.conb.2012.11.010 Mini-Mental State Examination (MMSE). M Galea, M Woodward, Galea, M., and Woodward, M. (2005). Mini-Mental State Examination (MMSE). . 10.1016/S0004-9514(05)70034-9Aust. J. Physiother. 51198Aust. J. Physiother. 51:198. doi: 10.1016/S0004-9514(05)70034-9 Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study. N Garcia-Casares, M L Berthier, R E Jorge, P Gonzalez-Alegre, A Gutierrez Cardo, J Rioja Villodres, 10.3233/JAD-131736J. Alzheimers. Dis. 40Garcia-Casares, N., Berthier, M. L., Jorge, R. E., Gonzalez-Alegre, P., Gutierrez Cardo, A., Rioja Villodres, J., et al. (2014). Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study. J. Alzheimers. Dis. 40, 375-386. doi: 10.3233/JAD-131736 Conserved functional connectivity but impaired effective connectivity of thalamocortical circuitry in schizophrenia. Y Guller, G Tononi, B R Postle, 10.1089/brain.2012.0100Brain Connect. 2Guller, Y., Tononi, G., and Postle, B. R. (2012). Conserved functional connectivity but impaired effective connectivity of thalamocortical circuitry in schizophrenia. Brain Connect. 2, 311-319. doi: 10.1089/brain.20 12.0100 Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. J P Hamilton, G Chen, M E Thomason, M E Schwartz, I H Gotlib, 10.1038/mp.2010.46Mol. Psychiatry. 16Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E., and Gotlib, I. H. (2011). Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Mol. Psychiatry 16, 763-772. doi: 10.1038/mp.2010.46 A rating scale for depression. M Hamilton, 10.1136/jnnp.23.1.56J. Neurol. Neurosurg. Psychiatr. 23Hamilton, M. (1960). A rating scale for depression. J. Neurol. Neurosurg. Psychiatr. 23, 56-62. doi: 10.1136/jnnp.23.1.56 Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics. X S He, Z X Wang, Y Z Zhu, N Wang, X Hu, D R Zhang, 10.1007/s00592-014-0618-7Acta Diabetol. 52He, X. S., Wang, Z. X., Zhu, Y. Z., Wang, N., Hu, X., Zhang, D. R., et al. (2015). Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics. Acta Diabetol. 52, 133-142. doi: 10.1007/s00592-014-0618-7 Neural coding of "attention for action" and "response selection" in primate anterior cingulate cortex. Y Isomura, Y Ito, T Akazawa, A Nambu, M Takada, J. Neurosci. 23Isomura, Y., Ito, Y., Akazawa, T., Nambu, A., and Takada, M. (2003). Neural coding of "attention for action" and "response selection" in primate anterior cingulate cortex. J. Neurosci. 23, 8002-8012. Small-world human brain networks: perspectives and challenges. X Liao, A V Vasilakos, Y He, 10.1016/j.neubiorev.2017.03.018Neurosci. Biobehav. Rev. 77Liao, X., Vasilakos, A. V., and He, Y. (2017). Small-world human brain networks: perspectives and challenges. Neurosci. Biobehav. Rev. 77, 286-300. doi: 10.1016/j.neubiorev.2017.03.018 Aberrant brain regional homogeneity and functional connectivity in middle-aged T2DM patients: a resting-state functional MRI study. D Liu, S Duan, J Zhang, C Zhou, M Liang, X Yin, 10.3389/fnhum.2016.00490Front. Hum. Neurosci. 10490Liu, D., Duan, S., Zhang, J., Zhou, C., Liang, M., Yin, X., et al. (2016). Aberrant brain regional homogeneity and functional connectivity in middle-aged T2DM patients: a resting-state functional MRI study. Front. Hum. Neurosci. 10:490. doi: 10.3389/fnhum.2016.00490 Topdown modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Y Liu, J Bengson, H Huang, G R Mangun, M Ding, 10.1093/cercor/bhu204Cereb. Cortex. 26Liu, Y., Bengson, J., Huang, H., Mangun, G. R., and Ding, M. (2016). Top- down modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI. Cereb. Cortex 26, 517-529. doi: 10.1093/cercor/bhu204 . M Lustig, D L Donoho, J M Santos, Pauly , J M , Lustig, M., Donoho, D. L., Santos, J. M., and Pauly, J. M. (2008). Compressed Sensing MRI. 10.1109/MSP.2007.914728IEEE Signal Process. Mag. 25Compressed Sensing MRI. IEEE Signal Process. Mag. 25, 72-82. doi: 10.1109/MSP.2007.914728 Neurophysiology of perceptual and motor aspects of interception. H Merchant, A P Georgopoulos, 10.1152/jn.00422.2005J. Neurophysiol. 95Merchant, H., and Georgopoulos, A. P. (2006). Neurophysiology of perceptual and motor aspects of interception. J. Neurophysiol. 95, 1-13. doi: 10.1152/jn.00422.2005 Impact of diabetes on cognitive function and brain structure. A Moheet, S Mangia, E R Seaquist, 10.1111/nyas.12807Ann. N.Y. Acad. Sci. 1353Moheet, A., Mangia, S., and Seaquist, E. R. (2015). Impact of diabetes on cognitive function and brain structure. Ann. N.Y. Acad. Sci. 1353, 60-71. doi: 10.1111/nyas.12807 Individual variability in functional connectivity architecture of the human brain. S Mueller, D Wang, M D Fox, B T Yeo, J Sepulcre, M R Sabuncu, 10.1016/j.neuron.2012.12.028Neuron. 77Mueller, S., Wang, D., Fox, M. D., Yeo, B. T., Sepulcre, J., Sabuncu, M. R., et al. (2013). Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586-595. doi: 10.1016/j.neuron.2012. 12.028 Magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. P Palta, A L Schneider, G J Biessels, P Touradji, F Hill-Briggs, 10.1017/S1355617713001483J. Int. Neuropsychol. Soc. 20Palta, P., Schneider, A. L., Biessels, G. J., Touradji, P., and Hill-Briggs, F. (2014). Magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains. J. Int. Neuropsychol. Soc. 20, 278-291. doi: 10.1017/S1355617713001483 Cerebral alterations of type 2 diabetes mellitus on MRI: a pilot study. B Peng, Z Chen, L Ma, Y Dai, 10.1016/j.neulet.2015.08.030Neurosci. Lett. 606Peng, B., Chen, Z., Ma, L., and Dai, Y. (2015). Cerebral alterations of type 2 diabetes mellitus on MRI: a pilot study. Neurosci. Lett. 606, 100-105. doi: 10.1016/j.neulet.2015.08.030 Abnormal spontaneous brain activity in type 2 diabetes with and without microangiopathy revealed by regional homogeneity. J Peng, H Qu, J Peng, T Y Luo, F J Lv, L Chen, 10.1016/j.ejrad.2015.12.024Eur. J. Radiol. 85Peng, J., Qu, H., Peng, J., Luo, T. Y., Lv, F. J., Chen, L., et al. (2016). Abnormal spontaneous brain activity in type 2 diabetes with and without microangiopathy revealed by regional homogeneity. Eur. J. Radiol. 85, 607-615. doi: 10.1016/j.ejrad.2015.12.024 The attention system of the human brain: 20 years after. S E Petersen, M I Posner, 10.1146/annurev-neuro-062111-150525Annu. Rev. Neurosci. 35Petersen, S. E., and Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35, 73-89. doi: 10.1146/annurev-neuro-062111-150525 Brain networks and cognitive architectures. S E Petersen, O Sporns, 10.1016/j.neuron.2015.09.027Neuron. 88Petersen, S. E., and Sporns, O. (2015). Brain networks and cognitive architectures. Neuron 88, 207-219. doi: 10.1016/j.neuron.2015.09.027 Complex network measures of brain connectivity: uses and interpretations. M Rubinov, O Sporns, 10.1016/j.neuroimage.2009.10.003Neuroimage. 52Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003 Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. C Sandor, N L Beer, C Webber, 10.1371/journal.pcbi.1005816PLoS Comput. Biol. 131005816Sandor, C., Beer, N. L., and Webber, C. (2017). Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network. PLoS Comput. Biol. 13:e1005816. doi: 10.1371/journal.pcbi.1005816 Granger causality analysis in neuroscience and neuroimaging. A K Seth, A B Barrett, L Barnett, 10.1523/JNEUROSCI.4399-14.2015J. Neurosci. 35Seth, A. K., Barrett, A. B., and Barnett, L. (2015). Granger causality analysis in neuroscience and neuroimaging. J. Neurosci. 35, 3293-3297. doi: 10.1523/JNEUROSCI.4399-14.2015 The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes. R Tacutu, A Budovsky, V E Fraifeld, 10.1007/s10522-010-9265-8Biogerontology. 11Tacutu, R., Budovsky, A., and Fraifeld, V. E. (2010). The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes. Biogerontology 11, 513-522. doi: 10.1007/s10522-010-9265-8 Molecular links between cellular senescence, longevity and age-related diseases -a systems biology perspective. R Tacutu, A Budovsky, H Yanai, V E Fraifeld, 10.18632/aging.100413Aging. 3Tacutu, R., Budovsky, A., Yanai, H., and Fraifeld, V. E. (2011). Molecular links between cellular senescence, longevity and age-related diseases -a systems biology perspective. Aging 3, 1178-1191. doi: 10.18632/aging.100413 Interactome networks and human disease. M Vidal, M E Cusick, A L Barabasi, 10.1016/j.cell.2011.02.016Cell. 144Vidal, M., Cusick, M. E., and Barabasi, A. L. (2011). Interactome networks and human disease. Cell 144, 986-998. doi: 10.1016/j.cell.2011.02.016 A new rating scale for age-related white matter changes applicable to MRI and CT. L O Wahlund, F Barkhof, F Fazekas, L Bronge, M Augustin, M Sjogren, 10.1161/01.STR.32.6.1318Stroke. 32Wahlund, L. O., Barkhof, F., Fazekas, F., Bronge, L., Augustin, M., Sjogren, M., et al. (2001). A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 32, 1318-1322. doi: 10.1161/01.STR.32. 6.1318 Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J L Whitwell, 10.1523/JNEUROSCI.2160-09.2009J. Neurosci. 29Whitwell, J. L. (2009). Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J. Neurosci. 29, 9661-9664. doi: 10.1523/JNEUROSCI.2160-09.2009 The signaling hubs at the crossroad of longevity and age-related disease networks. M Wolfson, A Budovsky, R Tacutu, V Fraifeld, 10.1016/j.biocel.2008.08.026Int. J. Biochem. Cell Biol. 41Wolfson, M., Budovsky, A., Tacutu, R., and Fraifeld, V. (2009). The signaling hubs at the crossroad of longevity and age-related disease networks. Int. J. Biochem. Cell Biol. 41, 516-520. doi: 10.1016/j.biocel.2008.08.026 Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study. W Xia, S Wang, Z Sun, F Bai, Y Zhou, Y Yang, 10.1016/j.psyneuen.2013.05.012Psychoneuroendocrinology. 38Xia, W., Wang, S., Sun, Z., Bai, F., Zhou, Y., Yang, Y., et al. (2013). Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study. Psychoneuroendocrinology 38, 2493-2501. doi: 10.1016/j.psyneuen.2013.05.012 A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. C G Yan, B Cheung, C Kelly, S Colcombe, R C Craddock, A Di Martino, 10.1016/j.neuroimage.2013.03.004Neuroimage. 76Yan, C. G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., et al. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183-201. doi: 10.1016/j.neuroimage.2013.03.004 DPARSF: a Matlab toolbox for "pipeline" data analysis of resting-state fMRI. C G Yan, Y F Zang, 10.3389/fnsys.2010.00013Front. Syst. Neurosci. 413Yan, C. G., and Zang, Y. F. (2010). DPARSF: a Matlab toolbox for "pipeline" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4:13. doi: 10.3389/fnsys.2010.00013 Altered intranetwork and internetwork functional connectivity in type 2 diabetes mellitus with and without cognitive impairment. S Q Yang, Z P Xu, Y Xiong, Y F Zhan, L Y Guo, S Zhang, 10.1038/srep32980Sci. Rep. 632980Yang, S. Q., Xu, Z. P., Xiong, Y., Zhan, Y. F., Guo, L. Y., Zhang, S., et al. (2016). Altered intranetwork and internetwork functional connectivity in type 2 diabetes mellitus with and without cognitive impairment. Sci. Rep. 6:32980. doi: 10.1038/srep32980 Auditory verbal learning test is superior to rey-osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer's disease. Q Zhao, Q Guo, X Liang, M Chen, Y Zhou, D Ding, 10.2174/1567205012666150530202729Curr. Alzheimer Res. 12Zhao, Q., Guo, Q., Liang, X., Chen, M., Zhou, Y., Ding, D., et al. (2015). Auditory verbal learning test is superior to rey-osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer's disease. Curr. Alzheimer Res. 12, 520-526. doi: 10.2174/1567205012666150530202729 Network centrality in the human functional connectome. X N Zuo, R Ehmke, M Mennes, D Imperati, F X Castellanos, O Sporns, 10.1093/cercor/bhr269Cereb. Cortex. 22Zuo, X. N., Ehmke, R., Mennes, M., Imperati, D., Castellanos, F. X., Sporns, O., et al. (2012). Network centrality in the human functional connectome. Cereb. Cortex 22, 1862-1875. doi: 10.1093/cercor/bhr269
Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation Network medicine: a network-based approach to human disease Network biology: understanding the cell's functional organization Effects of cholinergic enhancement on visual stimulation, spatial attention, and spatial working memory Abnormally high degree connectivity of the orbitofrontal cortex in obsessive-compulsive disorder Functional connectivity in the motor cortex of resting human brain using echo-planar MRI Administration and interpretation of the trail making test Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease Longevity network: construction and implications Aberrant brain functional connectivity related to insulin resistance in type 2 diabetes: a resting-state fMRI study Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL Participation of primary motor cortical neurons in a distributed network during maze solution: representation of spatial parameters and time-course comparison with parietal area 7a Aberrant functional connectivity of default-mode network in type 2 diabetes patients Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study Disrupted brain connectivity patterns in patients with type 2 diabetes Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease Executive functions Analysing connectivity with Granger causality and dynamic causal modelling Structural and functional brain changes in middle-aged type 2 diabetic patients: a cross-sectional study Conserved functional connectivity but impaired effective connectivity of thalamocortical circuitry in schizophrenia Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data A rating scale for depression Hyperactivation of working memory-related brain circuits in newly diagnosed middle-aged type 2 diabetics Neural coding of "attention for action" and "response selection" in primate anterior cingulate cortex Small-world human brain networks: perspectives and challenges Aberrant brain regional homogeneity and functional connectivity in middle-aged T2DM patients: a resting-state functional MRI study Topdown modulation of neural activity in anticipatory visual attention: control mechanisms revealed by simultaneous EEG-fMRI Compressed Sensing MRI Neurophysiology of perceptual and motor aspects of interception Impact of diabetes on cognitive function and brain structure Individual variability in functional connectivity architecture of the human brain Magnitude of cognitive dysfunction in adults with type 2 diabetes: a meta-analysis of six cognitive domains and the most frequently reported neuropsychological tests within domains Cerebral alterations of type 2 diabetes mellitus on MRI: a pilot study Abnormal spontaneous brain activity in type 2 diabetes with and without microangiopathy revealed by regional homogeneity The attention system of the human brain: 20 years after Brain networks and cognitive architectures Complex network measures of brain connectivity: uses and interpretations Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network Granger causality analysis in neuroscience and neuroimaging The NetAge database: a compendium of networks for longevity, age-related diseases and associated processes Molecular links between cellular senescence, longevity and age-related diseases -a systems biology perspective Interactome networks and human disease A new rating scale for age-related white matter changes applicable to MRI and CT Voxel-based morphometry: an automated technique for assessing structural changes in the brain The signaling hubs at the crossroad of longevity and age-related disease networks Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics DPARSF: a Matlab toolbox for "pipeline" data analysis of resting-state fMRI Altered intranetwork and internetwork functional connectivity in type 2 diabetes mellitus with and without cognitive impairment Auditory verbal learning test is superior to rey-osterrieth complex figure memory for predicting mild cognitive impairment to Alzheimer's disease Network centrality in the human functional connectome
Diabet. Med Nat. Rev. Genet Nat. Rev. Genet Neuron JAMA Psychiatry Magn. Reson. Med Nat. Protoc J. Neurosci Mech. Ageing Dev Diabetes Care Thalamic resting-state functional connectivity: disruption in patients with type 2 diabetes. Metab. Brain Dis Eur. J. Radiol Exp. Brain Res Eur. Radiol Diabetes AJNR. Am. J. Neuroradiol Brain Imaging Behav PLoS Comput. Biol Annu. Rev. Psychol Curr. Opin. Neurobiol Mini-Mental State Examination (MMSE) Aust. J. Physiother J. Alzheimers. Dis Brain Connect Mol. Psychiatry J. Neurol. Neurosurg. Psychiatr Acta Diabetol J. Neurosci Neurosci. Biobehav. Rev Front. Hum. Neurosci Cereb. Cortex IEEE Signal Process. Mag J. Neurophysiol Ann. N.Y. Acad. Sci Neuron J. Int. Neuropsychol. Soc Neurosci. Lett Eur. J. Radiol Annu. Rev. Neurosci Neuron Neuroimage PLoS Comput. Biol J. Neurosci Biogerontology Aging Cell Stroke J. Neurosci Int. J. Biochem. Cell Biol Psychoneuroendocrinology Neuroimage Front. Syst. Neurosci Sci. Rep Curr. Alzheimer Res Cereb. Cortex

FIGURE 1 |
1DC value distribution of intra-group and inter-group comparisons. (A,B) The spatial distribution of the DC value in the HC group and T2DM group. (C) The significantly altered DC map in the T2DM group. (D) Comparison of DC value between the two groups. AlphaSim corrected (p < 0.001, cluster size > 20 voxels). The color bar denotes the t-value. Error bars define the SEM. ACG, anterior cingulate gyrus; LOC, lateral occipital cortices; PreCG, precentral gyrus; R, right; L, left. 
FIGURE 2 |
2FC pattern of intra-group and inter-group comparisons. (A,B) The FC pattern in the HC group and T2DM group. (C) The significantly altered FC in the T2DM group. (D) Comparison of FC z scores between the two groups. *p < 0.05, # p < 0.05/6 (Bonferroni correction). Error bars define the SEM. ACG, anterior cingulate gyrus; LOC, lateral occipital cortices; PreCG, precentral gyrus; R, right; L, left. 
FIGURE 3 |
3GCA pattern of intra-group and inter-group comparisons. (A,B) Causal effect patterns of the HC group and T2DM group. The red arrow indicates a positive casual effect and its direction. The blue arrow indicates a negative casual effect and its direction. (C) Comparison of signed-path coefficients between the two groups. *p < 0.05. Error bars define the SEM. 
FIGURE 4 |
4Correlations among the connectivity, neuropsychological performance and diabetes-related parameters. (A) Signed-path coefficients of the left ACG to the left LOC vs. C-peptide (ng/mL). (B) DST forwards vs. HbA 1c (%). 

The updated homeostasis model assessment of the insulin resistance (HOMA2-IR) index was calculated using the HOMA2 Calculator v2.2.3 software (http://www.dtu.ox.ac. uk/homacalculator/) to evaluate insulin resistance in all subjects.HbA 1c ]), lipoid parameters 
(total cholesterol, triglyceride, high-density lipoprotein [HDL] 
cholesterol, low-density lipoprotein [LDL] cholesterol), renal 
function parameters (blood urea nitrogen, serum creatine, 
uric acid, and cystatin C), thyroid function parameters 
(free triiodothyronine [FT3], free thyroxine [FT4] and 
thyroid-stimulating hormone [TSH]), and homocysteine. 
 
TABLE 1 |
1Demographic and clinical data of the subjects.T2DM 
HC 
p-value 

Age (years) 
58.66 ± 6.87 
57.36 ± 5.42 
0.312 

Sex (male:female) 
28/19 
25/22 
0.533 a 

Education (years) 
10.60 ± 3.06 
10.77 ± 2.65 
0.773 

T2DM duration (years) 
8.87 ± 6.61 
-
-

BMI (kg/m 2 ) 
25.46 ± 5.11 
23.94 ± 3.87 
0.109 

Gray matter (cm 3 ) 
608.15 ± 58.00 
615.16 ± 51.67 
0.538 

White matter (cm 3 ) 
534.91 ± 64.85 
524.16 ± 66.56 
0.430 

Brain parenchyma (cm 3 ) 
1143.06 ± 114.78 1139.32 ± 107.80 
0.871 

Systolic blood pressure 
(mmHg) 

128.91 ± 16.81 
134.55 ± 17.88 
0.119 

Diastolic blood pressure 
(mmHg) 

79.49 ± 10.01 
80.26 ± 10.25 
0.715 

HbA 1c (%) 
8.26 ± 2.08 
5.63 ± 0.39 
< 0.001 
HbA 1c (mmol/mol) 
66.76 ± 22.80 
38.15 ± 4.33 
< 0.001 
Fasting plasma glucose 
(mmol/L) 

7.47 ± 2.70 
5.22 ± 0.46 
< 0.001 

Fasting insulin (mIU/L) 
14.91 (9.58, 24.54) 11.41 (8.39, 17.10) 
0.035 b 

Fasting C-peptide (ng/mL) 
1.81 ± 1.05 
2.22 ± 0.90 
0.047 

HOMA2-IR 
0.28 (0.19, 0.52) 
0.21 (0.16, 0.32) 
0.015 b 

Total cholesterol (mmol/L) 
4.89 ± 1.04 
5.10 ± 1.02 
0.325 

Triglyceride (mmol/L) 
1.45 (1.05, 1.98) 
1.23 (0.91, 1.48) 
0.023 b 

HDL cholesterol (mmol/L) 
1.06 ± 0.23 
1.41 ± 0.36 
< 0.001 
LDL cholesterol (mmol/L) 
3.16 ± 0.83 
3.27 ± 0.76 
0.489 

Homocysteine (µmol/L) 
16.62 ± 10.03 
10.70 ± 4.10 
< 0.001 
Blood urea nitrogen 
(mmol/L) 

6.36 ± 2.45 
5.72 ± 1.23 
0.113 

Serum creatine (µmol/L) 
65.00 (57.00, 78.00) 78.00 (66.00, 85.00) 0.007 b 

Cystatin C (mg/L) 
0.72 (0.64, 0.91) 
0.76 (0.69, 0.86) 
0.639 b 

Uric acid (µmol/L) 
316.67 ± 79.72 
328.34 ± 69.09 
0.450 

Free triiodothyronine, FT3 
(pmol/L) 

4.31 ± 0.91 
5.13 ± 0.72 
< 0.001 

Free thyroxine, FT4 (pmol/L) 
15.52 ± 2.34 
16.48 ± 1.89 
0.032 

Thyroid-stimulating 
hormone, TSH (mIU/L) 

2.12 ± 0.95 
2.57 ± 1.54 
0.090 

p < 0.05 indicates statistical significance. a χ 2 test for sex (n). b Mann-Whitney U-test for 
non-normally distributed data [median (QR)]. Independent t-test for the other normally 
distributed continuous data (means ± SD). T2DM, type 2 diabetes mellitus; HC, 
healthy control; BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density 
lipoprotein. 

 
TABLE 2 |
2Comparison of the neuropsychological test results between the two groups.T2DM 
HC 
p-value 

GENERAL COGNITION 

MMSE 
29.00 (27.00, 29.00) 29.00 (28.00, 29.00) 0.203 a 

MoCA 
23.17 ± 2.88 
24.51 ± 2.31 
0.015 

EXECUTIVE FUNCTION AND PSYCHOMOTOR SPEED 

TMT-A 
74.67 ± 41.11 
64.17 ± 32.59 
0.173 

TMT-B 
183.19 ± 89.90 
142.00 ± 58.35 
0.010 

MENTAL FLEXIBILITY 

VFT 
42.17 ± 7.33 
40.55 ± 7.05 
0.279 

WORKING MEMORY 

DST forwards 
8.83 ± 1.37 
9.60 ± 1.50 
0.011 

DST backwards 
4.00 (3.00, 5.00) 
4.00 (3.00, 4.00) 
0.911 a 

EPISODIC MEMORY 

AVLT immediate recall 
6.46 ± 1.69 
6.86 ± 1.49 
0.226 

AVLT short-term delayed 
recall 

6.77 ± 3.18 
7.74 ± 2.41 
0.096 

AVLT long-term delayed 
recall 

5.40 ± 3.77 
6.72 ± 2.43 
0.047 

AVLT long-term delayed 
recognition 

11.00 (8.00, 13.00) 
12.00 (9.00, 13.00) 
0.327 a 

AVLT total score 
28.97 ± 9.94 
32.34 ± 7.45 
0.065 

p < 0.05 was considered statistically significant. a Mann-Whitney U-test for non-normally 
distributed data [median (QR)]. Independent t-test for the other normally distributed 
continuous data (means ± SD). MMSE, Mini-Mental State Examination; MoCA, Montreal 
Cognitive Assessment; TMT, Trail Making Test; VFT, Verbal Fluency Test; DST, Digital Span 
Test; AVLT, Auditory Verbal Learning Test. 

 
TABLE 3 |
3Brain regions with significant DC differences between the two groups.Brain regions 
BA 
Peak MNI 
t-value 
Cluster 
(voxels) 
X 
Y 
Z 

1 Left anterior cingulate gyrus 32 
−9 
42 
9 
3.1455 
28 

2 Right lateral occipital cortex 19 
27 
−78 24 −3.1327 
20 

3 Left lateral occipital cortex 
19 −24 −84 21 −3.9907 
147 

4 Right precentral gyrus 
43 
63 
−3 30 −3.4576 
55 

MNI, Montreal Neurological Institute; BA, Broadmann Area. R, Right; L, left; AlphaSim 
corrected (p < 0.001, cluster > 20 voxels). 


DC value distribution of intra-group and inter-group comparisons. (A,B) The spatial distribution of the DC value in the HC group and T2DM group. (C) The significantly altered DC map in the T2DM group. (D) Comparison of DC value between the two groups. AlphaSim corrected (p < 0.001, cluster size > 20 voxels). The color bar denotes the t-value. Error bars define the SEM. ACG, anterior cingulate gyrus; LOC, lateral occipital cortices; PreCG, precentral gyrus; R, right; L, left. FC pattern of intra-group and inter-group comparisons. (A,B) The FC pattern in the HC group and T2DM group. (C) The significantly altered FC in the T2DM group. (D) Comparison of FC z scores between the two groups. *p < 0.05, # p < 0.05/6 (Bonferroni correction). Error bars define the SEM. ACG, anterior cingulate gyrus; LOC, lateral occipital cortices; PreCG, precentral gyrus; R, right; L, left. GCA pattern of intra-group and inter-group comparisons. (A,B) Causal effect patterns of the HC group and T2DM group. The red arrow indicates a positive casual effect and its direction. The blue arrow indicates a negative casual effect and its direction. (C) Comparison of signed-path coefficients between the two groups. *p < 0.05. Error bars define the SEM. Correlations among the connectivity, neuropsychological performance and diabetes-related parameters. (A) Signed-path coefficients of the left ACG to the left LOC vs. C-peptide (ng/mL). (B) DST forwards vs. HbA 1c (%). The updated homeostasis model assessment of the insulin resistance (HOMA2-IR) index was calculated using the HOMA2 Calculator v2.2.3 software (http://www.dtu.ox.ac. uk/homacalculator/) to evaluate insulin resistance in all subjects. Demographic and clinical data of the subjects. Comparison of the neuropsychological test results between the two groups. Brain regions with significant DC differences between the two groups.
Abbreviations: ACG, anterior cingulate gyrus; AVLT, Auditory Verbal Learning Test; BMI, body mass index; DC, degree centrality; DST, Digit Span Test; EPI, echo planar imaging; FC, functional connectivity; FLAIR, fluid-attenuated inversion recovery; fMRI, functional magnetic resonance imaging; FT3, free triiodothyronine; FT4, free thyroxine; GCA, Granger causality analysis; HAMD, Hamilton Depression Rating Scale; HbA 1c , glycosylated hemoglobin; HC, healthy control; HDL, high-density lipoprotein; HOMA2-IR, updated homeostasis model assessment of insulin resistance; LDL, low-density lipoprotein; LOC, lateral occipital cortices; MMSE, Mini-Mental State Examination; MNI, Montreal Neurological Institute; MoCA, Montreal Cognitive Assessment; MP-RAGE, magnetization prepared by rapid-acquisition gradient-echo; PreCG, precentral gyrus; ROI, region of interest; T2DM, Type 2 diabetes mellitus; TMT, Trail Making Test; TSH, thyroid-stimulating hormone; VFT, Verbal Fluency Test. Type 2 diabetes mellitus (T2DM) affects 415 million individuals and is predicted to increase to 642 million in 2040, according to the Diabetes Atlas 7th Edition published by the International Diabetes Federation (http://www.idf.org/about-diabetes/factsfigures). Numerous studies have suggested that T2DM is closely associated with cognitive impairment, including the domains of motor function, executive function, processing speed and memory (Palta et al., 2014). Clarification of the underlying mechanism of cognitive impairment in T2DM patients for diagnosis and therapeutic effect estimation is essential before these patients develop dementia. As a proven informative neuroimaging method, functional magnetic resonance imaging (fMRI) has been extensively applied to investigate alterations of brain function in T2DM patients. In fMRI studies, T2DM patients manifest functional changes in certain brain regions, and these changes are different from those associated with normal aging. For instance, the abnormal amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity (FC) in T2DM patients have been associated with poor performance in cognitive tests (Xia et al., 2013;Chen et al., 2014;Cui et al., 2014Cui et al., , 2015Moheet et al., 2015). These studies have focused on local spontaneous brain activity or have analyzed the FC or network within the selected brain regions based on a priori assumption. According to the graph theory, a network is defined as a set of pairwise relationships between the elements of a system, which formally consists of a set of edges that link a set of nodes (Barabasi and Oltvai, 2004;Petersen and Sporns, 2015). Network analysis offers a new conceptual framework to investigate the network biology of aging (Wolfson et al., 2009;Tacutu et al., 2011), T2DM (Sandor et al., 2017) and neurodegenerative diseases (de Haan et al., 2017) at a variety of levels of scale including genes, proteins, synapses, neurons, neuronal circuits, neuronal populations, and systems (Petersen and Sporns, 2015). Therefore, the aforementioned fMRI findings provide a clue to explore T2DM-related brain dysfunction from the perspective of macroscopic nodes and connectivity at the whole brain level, which may share universal laws of network. Degree centrality (DC), a measure based on graph theory, provides an approach for identifying the candidate functional hubs in the diabetic brain. DC is defined as the number of links that are strongly correlated to a given voxel or node for a binary graph and enables whole brain analysis at the voxel level, which may avoid the bias caused by selecting brain regions according to a priori assumption (Buckner et al., 2009;Zuo et al., 2012). It also considers the weights of these links for a weighted graph, and the weighted version of DC is more robust against confounding factors (Zuo et al., 2012). Thus, it can quantify the importance of a node to the rest of the brain, and nodes with high DC are defined as hubs (Zuo et al., 2012). Therefore, DC enables an investigation of the complexity and patterns of the brain functional connectome in diseases, including the subsequent analysis of FC between the hubs and the rest of the brain (Cui et al., 2016), or the interactions among these functional hubs in the present study. Connectivity depicts the relationships among functionally segregated brain systems and can be classified into undirected and directed functional connectivity. Traditional FC is often used to investigate the statistical dependencies among several given seed regions in an undirected manner (Biswal et al., 1995). Directed FC is explicitly used to explore the functional interactions in a directed manner and can be estimated using the Granger causality model (Seth et al., 2015). The positive casual effect and negative causal effect can be quantified with positive and negative signed-path coefficients and interpreted as activation and inhibition, respectively (Hamilton et al., 2011). Both undirected and directed FC are the components of functional integration that are used to analyze functional brain architectures (Friston et al., 2013). By combining the traditional FC and Granger causality analysis (GCA) approaches, more information can be obtained from different perspectives to map the connectivity pattern among the brain functional hubs that are probably affected by T2DM. In this study, we hypothesized that the aberrant brain function of hubs and their connectivity may contribute to brain dysfunction in T2DM patients. The DC method was applied to identify the candidate functional hubs. Next, the FC and GCA methods were applied to investigate the connectivity among these functional hubs. We also investigated the relationships of brain functional alterations with clinical data and neuropsychological performance. Our study provides evidence to further understand the neurological mechanisms that underlie T2DM-associated cognitive impairment. T2DM patients were recruited from inpatients and the community and healthy controls (HC) were recruited from the community between December 2013 and December 2015. Forty-seven T2DM patients and 47 healthy controls, who were matched with regard to age, sex, education and body mass index (BMI), were enrolled in the study. Both the T2DM patients and the healthy controls met the following inclusion criteria: (1) between 45 and 70 years old; 2) at least six years of education; and (3) right-handedness. T2DM was diagnosed by endocrinologists according to the criteria published by the World Health Organization in 1999 (Alberti and Zimmet, 1998). The T2DM patients had at least 1 year of disease duration. The exclusion criteria for all participants were as follows: (1) organic disease in the brain, such as stroke, tumor, or a white matter lesion rating score ≥ 2 (Wahlund et al., 2001); (2) physical disability; (3) pregnancy, thyroid dysfunction; (4) signs of dementia (Mini-Mental State Examination [MMSE] score ≤ 24) (Galea and Woodward, 2005), major depression  > 20) (Hamilton, 1960), or other psychiatric disorders; (5) severe hearing or visual impairment; and (6) contraindications to MRI. Patients with T2DM-related complications were also excluded, including diabetic foot, retinopathy, and nephropathy. The study protocol was approved by the Medical Research Ethics Committee of the Southwest Hospital (Chongqing, China). Written informed consent was obtained from participants after they were informed of the study details. Additionally, the study process strictly obeyed the protocol. The following information about the subjects was recorded using a standardized protocol: handedness, height, weight, BMI [weight in kg]/[height in m] 2 ), resting arm arterial blood pressure, medical history and current medications. For T2DM patients, we also recorded the date of diagnosis to calculate the disease duration. Venous blood samples were collected by venipuncture after overnight fasting for the biometric measurements, including glucose-related parameters (fasting plasma glucose, fasting insulin, fasting C-peptide, and glycosylated hemoglobin [  All participants underwent cognitive status assessments with a battery of neuropsychological tests in a fixed order that assessed their global cognitive level and major cognitive subdomains. The global level of cognition was evaluated with the MMSE and Montreal Cognitive Assessment (MoCA) tests. The depressive state was evaluated with HAMD to exclude cases with major depression. The major cognitive subdomains were evaluated with the following tests: (1) the Trail Making Test (TMT, parts A and B) for executive function and psychomotor speed (Bowie and Harvey, 2006); (2) the Verbal Fluency Test (VFT) for mental flexibility (Diamond, 2013); (3) the Digit Span Test (DST, forwards and backwards) for working memory (Diamond, 2013); and (4) the Auditory Verbal Learning Test (AVLT, including immediate recall, short-term delayed recall, longterm delayed recall, long-term delayed recognition and total score) for episodic memory (Zhao et al., 2015). A trained neuropsychologist administered the battery of tests and was blinded to the group status. Each participant completed the assessment in approximately 60 min. MRI scan was performed on the same day that the clinical data were obtained and the cognitive test was performed. MRI data were acquired using a 3.0-T MR scanner (Trio, Siemens Medical, Erlangen, Germany) and a 12-channel head coil. Subjects were instructed to close their eyes, stay awake and avoid thinking about any topics. Earplugs and cushions were used to alleviate noise influence and restrict head motion, respectively. T2-weighted images: repetition time = 6,000 ms, echo time = 89 ms, flip angle = 120 • , field of view = 230 × 230 mm 2 , slices = 20, thickness = 5.0 mm, matrix = 448 × 448 and voxel size = 0.5 × 0.5 × 5.0 mm 3 . Fluid-attenuated inversion recovery (FLAIR) images: repetition time = 9,000 ms, echo time = 93 ms, flip angle = 130 • , field of view = 220 × 220 mm 2 , slices = 25, thickness = 4.0 mm, matrix = 256 × 256 and voxel size = 0.9 × 0.9 × 4.0 mm 3 . Resting-state functional images were collected using an echo planar imaging (EPI) sequence: repetition time = 2,000 ms, echo time = 30 ms, flip angle = 90 • , field of view = 192 × 192 mm 2 , slices = 36, thickness = 3 mm, matrix = 64 × 64 and voxel size = 3 × 3 × 3 mm 3 ; 240 volumes were transversely acquired. T1-weighted structural images were collected using a volumetric 3D magnetization prepared by rapid-acquisition gradient-echo (MP-RAGE) sequence: repetition time = 1,900 ms, echo time = 2.52 ms, flip angle = 9 • , field of view = 256 × 256 mm 2 , slices = 176, thickness = 1 mm, matrix = 256 × 256 and voxel size = 1 × 1 × 1 mm 3 , sagittally scanned. No subjects were excluded after the T2-weighted and FLAIR images were reviewed by two radiologists with at least five years of work experience. Structural and functional data analyses were performed using toolkits based on Statistical Parametric Mapping 8 software (SPM 8, http://www.fil.ion.ucl.ac.uk/spm). Intracranial tissue segmentation was performed with Voxel Based Morphometry Toolbox 8 software (VBM8, version 435) according to a previously described protocol (Whitwell, 2009). The main steps include spatial normalization to match every subject's T1weighed images to the template image, and segmentation of the intracranial tissue into gray matter, white matter and cerebrospinal fluid, which automatically produces information about the volumes of each part of the brain tissue. Gray matter was smoothed with 4 mm full-width half-maximum. Functional data were preprocessed using Data Processing Assistant for Resting-State fMRI (DPARSF module v4.1 of Data Processing & Analysis of Brain Imaging v2.1, http://rfmri.org/ dpabi) according to the standard procedure (Yan and Zang, 2010). All DICOM files were converted to NifTI files. Next, the first 10 volumes were removed to enable the subjects to adapt to the scanning environment, especially the noise. Slice-timing was performed to correct the time differences between slices. Realignment was performed to correct head motion, and a report of head motion was created. Any subjects with head motion > 2.0 mm in any direction of x, y, and x or > 2.0 • at any angle were excluded from the subsequent statistical analyses. Friston 24-parameter model was applied to regress out head motion effects (Yan et al., 2013). Other nuisance variables including white matter signal and cerebrospinal fluid signal were regressed out. Individual functional images were normalized into the Montreal Neurological Institute (MNI) space for intersubject comparison. The resulting images were smoothed with 4 mm full-width halfmaximum. Detrending was applied to remove the systematic drift of the baseline signal. The data were bandpass filtered (0.01-0.08 Hz) to reduce physiological noise at other bands of frequency. Based on preprocessing, DC calculations were performed using DPARSF in a voxel-wise manner with a threshold r > 0.25 in accordance with previous studies (Beucke et al., 2013;Mueller et al., 2013). Peak MNI coordinates of the candidate brain functional hubs were obtained via group comparison of DC maps and considered to be the center of the spherical region of interest (ROI) with a 6-mm radius. Connectivity among the ROIs was analyzed using rs-fMRI data analysis toolkits (REST v1.8, http://www.restfmri.net) in a ROI-wise manner, including FC and GCA analyses. For FC, the correlation coefficients between the ROIs were computed and normalized with Fisher's r-to-z transformation. For GCA, signed-path coefficients between ROIs were computed in a multivariate mode for the subsequent parametric statistical analyses (Hamilton et al., 2011). Inter-group comparisons of numeric data were conducted using SPSS software (version 20.0; IBM Corp., Armonk, NY) and included demographic data, clinical parameters, and neuropsychological test scores. First, the data distribution was verified using the Kolmogorov-Smirnov test. Second, independent samples of the t-test and Mann-Whitney U test were applied to normally distributed continuous data and to non-normally distributed data, respectively. The sex proportion was examined with the χ 2 test. p < 0.05 indicated statistical significance. Intra-and inter-group analyses of DC maps were conducted using REST software. A one-sample t-test was applied to investigate the DC pattern with the base of "0" in the intragroup. An independent t-test was applied to investigate the DC differences between the groups with age, sex, education, BMI, blood pressure, blood biometric parameters (except fasting glucose, fasting insulin, fasting C-peptide and HbA 1c ) entered as covariates. Gray matter maps were also included as covariates to control the influence of structural changes in the T2DM patients. The resulting maps were corrected for multiple comparisons using AlphaSim (p < 0.001, cluster size > 20 voxels, number of Monte Carlo simulations = 10,000, cluster connection radius: rmm = 5.0). For the z scores of FC and signed-path coefficients, statistical analyses were performed using SPSS software. A one-sample t-test was applied to investigate the patterns of FC and GCA in each group. An independent sample t-test was applied to investigate the differences between T2DM patients and healthy controls in terms of FC and GCA. The relationships of DC and connectivity with clinical parameters and neuropsychological test scores were explored through partial correlation analyses in SPSS software within the T2DM group. Both the independent sample t-test and the correlation analysis were adjusted with gray matter volumes, and the same covariates that employed in the inter-group analyses of the DC maps were applied to control their possible effect on the results. The independent sample t-test was also controlled for multiple comparisons with Bonferroni correction (p < 0.05/6 for FC and p < 0.05/12 for GCA). No significant inter-group differences were found in terms of age, sex, education, BMI, blood pressure, total cholesterol, LDL cholesterol, blood urea nitrogen, cystatin C, uric acid and TSH. The T2DM patients had significantly higher levels of glucoserelated parameters, including fasting plasma glucose, fasting insulin, HbA 1c and HOMA2-IR index (all p < 0.05). The T2DM patients had significantly higher levels of triglycerides and homocysteine but lower fasting C-peptide and serum creatine levels (all p < 0.05). The inter-group differences in FT3 and FT4 were significant (all p < 0.05); however, the levels were within the normal range. The details are presented in Table 1. The T2DM patients exhibited poorer performance on the tests of MoCA, TMT-B, DST forwards and long-term delayed recall of AVLT (all p < 0.05). No significant inter-group differences were observed in the other tests. The details are presented in Table 2. The general volumes of gray matter, white matter and brain parenchyma (the sum of gray and white matter) revealed no significant differences between the two groups ( Table 1). Compared to the global mean value, both groups displayed higher DC values in the posterior cingulate cortex, cuneus, angular gyrus, occipital cortex, superior frontal cortex, precentral gyrus and postcentral gyrus (Figures 1A,B). In the T2DM patients, significantly higher DC values were observed in the left anterior cingulate gyrus (ACG), and significantly lower DC values were observed in the bilateral lateral occipital cortices (LOC) and right precentral gyrus (PreCG). Details of the changed brain areas are presented in Table 3 ( Figures 1C,D). For the FC analyses, the one-sample t-test suggested that FC existed in both groups among the ROIs, including the left ACG, right PreCG, and bilateral LOC (all p < 0.05; Figures 2A,B). The independent t-test suggested that the FC of the left ACG-right PreCG (p = 0.015) in T2DM patients was significantly stronger than that in the healthy controls, and the FC values of the left LOC-right PreCG (p = 0.011), right LOC-right PreCG (p = 0.019), and left LOC-right LOC (p = 0.001) were significantly weaker than those in the healthy controls (Figures 2C,D). After multiple comparisons correction, the FC of the left LOC-right LOC in T2DM patients was significantly weaker than that in the healthy controls (p < 0.05/6). With respect to the GCA analyses, the one-sample t-test showed a positive causal effect from the left ACG to the left LOC and a negative causal effect from the left ACG to the right LOC in the healthy controls (Figures 3A,C). However, the conditions were inverted such that a negative causal effect from the left ACG to the left LOC and a positive causal effect from the left ACG to the right LOC were observed in the T2DM patients (Figures 3B,C). Significant differences between the two groups in terms of the two directed functional edges were observed (p < 0.05; Figure 3C). Unfortunately, the independent t-test results of GCA could not bear the multiple comparisons correction (p > 0.05/12). Signed-path coefficients from the left ACG to the left LOC were negatively correlated with fasting C-peptide levels (ρ = −0.386, p = 0.007; Figure 4A) in T2DM patients. Poor performance on the DST forwards was associated with elevated HbA 1c levels in T2DM patients (HbA 1c [%], ρ = −0.301, p = 0.040; HbA 1c [mmol/mol], ρ = −0.301, p = 0.040; Figure 4B). However, no correlations were observed between disrupted DC or connectivity and lower scores on the neuropsychological tests or disease duration. To explore the possible neurological mechanisms underlying cognitive dysfunction in T2DM patients, we combined the DC, FC and GCA approaches to investigate the changes in the candidate brain functional hubs and their connectivity. The increased DC in left ACG and decreased DC in the occipital areas were consistent with a previous study (Cui et al., 2016). A novel finding was the increased DC in the right PreCG. The changed FC pattern of right PreCG may be associated with impaired preparation and execution of goal-directed actions. The GCA approach further revealed the disordered direct connectivity from the left ACG to bilateral occipital areas. The brain regions where identified hubs are located have been reported to be abnormal in previous studies using other neuroimaging metrics in T2DM patients, which suggests that these brain regions are susceptible to T2DM. The ACG is usually identified as the disturbed brain region across experiments and appeared with increased amplitude of low-frequency fluctuation, regional homogeneity Liu D. et al., 2016), and cerebral blood flow (Cui et al., 2017). The aforementioned  hyperactivity of ACG has usually been regarded as a means to compensate for the cognitive loss and maintain normal cognition (He et al., 2015;Liu D. et al., 2016;Cui et al., 2017). However, the mechanism of the compensation requires further research. In contrast, a decreased amplitude of low-frequency fluctuation and regional homogeneity were observed in the occipital lobe (Xia et al., 2013;Cui et al., 2014;Peng et al., 2016). Decreased connectivity of the PreCG was found with the posterior cingulate cortex  and the thalamic . These brain regions also exhibited gray matter atrophy in T2DM patients (Chen et al., 2012;Garcia-Casares et al., 2014;Peng et al., 2015). We discovered a change in the number of edges that connect the bilateral LOC and right PreCG, which rendered these brain regions candidate functional hubs to be damaged in T2DM patients (Zuo et al., 2012). These findings provided information about the reorganized layout of the brain network with a graph theory-based approach in addition to previous studies. Placing the identified brain functional hubs into the context of brain local networks, the changed FC of the right PreCG may suggest impaired preparation and execution of goal-directed actions. The motor system participates in the constitution of internal representations of sensory information (Crowe et al., 2004) and obtains visually perceived information to prepare a motor response (Merchant and Georgopoulos, 2006). However, the decreased DC and FC values of the right PreCG indicated its decline in communication efficiency with the visual cortex and other brain regions. Additionally, the motor system is closely connected with the ACG which mediates focusing attention on targets to select appropriate actions (Isomura et al., 2003). The increased FC between the ACG and PreCG may be interpreted as brain efforts to guide motor behavior. Notably, the process of goal-directed action is a main constituent of executive function. Moreover, for its motor speed component, the poor executive performance in TMT-B may be the behavioral level evidence of cognitive and motor system dysfunction. The GCA results may further suggest that the reorganization of the brain network in T2DM patients may be associated with the impaired visual information acquisition for executive function. The ACG serves an important role in the top-down control network (Petersen and Posner, 2012). The occipital cortex is an important brain area of vision-related information encoding for working memory which is the primary component of executive function (Bentley et al., 2004;Diamond, 2013). Furthermore, visual cortex can be mediated by the ACG which monitors and resolves conflict by enhancing the to-be-attended information and suppressing distracter stimuli (Petersen and Posner, 2012;Liu Y. et al., 2016). In this study, the disordered excitatory and inhibitory effects from the ACG to the visual cortex suggest that T2DM patients may experience a cognitive impairment in visual information acquisition that is indirectly related to the deficit in executive function. In addition, unlike the scale free organization of molecular networks (Budovsky et al., 2007;Tacutu et al., 2010), the human brain network was proposed to exhibit prominent small-world architecture which facilitates efficient information communication (Liao et al., 2017). The impacts of brain functional hubs and connectivity alterations on the small-world property remains to be further explored. GCA connections appear at where FC connections exist; however, we observed that there was no overlap of significant alterations between undirected and directed connectivity in the present study. On one hand, undirected FC is usually assessed with the correlation coefficient, which can be regarded as descriptive (Biswal et al., 1995). On the other hand, the directed FC depicted by GCA rests explicitly on the linear vector autoregressive models (Seth et al., 2015). It is difficult to make a direct comparison between correlation data and a model. The non-overlapping alterations between the undirected and directed connectivity may result from the distinction of mathematical theories. It is also reported that internet gaming disorder individuals without FC alterations exhibited impaired GCA connections (Guller et al., 2012). This phenomenon requires a further investigation. Our finding also suggested that the level of glycemic parameters may be linked to the reserve of normal brain function in T2DM patients. Elevated HbA 1c was negatively correlated  with spontaneous brain activity in the middle temporal gyrus (Xia et al., 2013) and with cognitive performance such as VFT . The higher insulin resistance has been associated with decreased FC between the posterior cingulate cortex and middle temporal gyrus Yang et al., 2016). With the higher elevation of C-peptide in this study, a greater deviation of directed FC was observed in patients than in the healthy controls. Additionally, the patients with lower HbA 1c levels obtained higher scores on the DST forwards. The results were similar to those of several previous studies, which indicated that T2DM patients with elevated glycemic parameters exhibited decreased/inverted brain functional activity and poor cognitive performance (Xia et al., 2013;Chen et al., 2014;Liu D. et al., 2016;Yang et al., 2016). However, the findings of this study did not provide sufficient information about the relationships between the altered brain function and cognitive performance. Additional studies are required to investigate these mysteries. The main limitations of the present investigation are as follows: First, the study comprised a relatively small population. The inter-group comparisons of FC and GCA were unable to bear the multiple corrections. These factors may restrict its statistical power and the explanation of the results. Second, we deduced the visually related cognition impairment in T2DM patients according to fMRI metrics. However, the lack of assessment on this cognitive domain weakened the basis of our inference. Third, the approach of GCA is controversial due to the poor temporal resolution in fMRI studies. However, the GCA method remains a powerful tool to explore the directed connectivity and helps elucidate the complexity of the brain (Seth et al., 2015). A large-sample study with comprehensive cognitive assessments and the application of new technology, such as compressed sensing (Lustig et al., 2008), to accelerate the sampling may solve these problems in the future. We are also considering the application of other network measurements, for instance, betweenness, closeness, path length (Rubinov and Sporns, 2010), which could be combined with the molecular networks to map the complex biological systems of T2DMrelated cognitive impairment in depth Vidal et al., 2011). Our findings suggest that the DC of the left ACG, bilateral LOC and right PreCG, as well as the connectivity among them reflected by FC and GCA in different perspectives, are altered in T2DM patients. These alterations may be associated with the disruption of visual information acquisition and goal-directed action execution, both of which have previously proven to be related to executive function. Patients with lower glycemic parameters may reserve more normal brain functions. This study provides insight into the neurological underpinnings of T2DMrelated cognitive impairment using neuroimaging. DL contributed to the experiments, data analysis and writing of the manuscript. SD contributed to performing the experiments and writing and revising the manuscript. CZ contributed to the data collection. PW designed the experiment and revised the manuscript. LC contributed to the data analysis and manuscript revision. XY contributed to the manuscript revision. JZ and JW are the guarantors of this study and had complete access to all data in the study. They accept responsibility for the integrity of the data and the accuracy of the data analysis. The study was supported by the National Natural Science Foundation of China (81471647, 81771814) and the Innovation Fund for Younger Investigators of Southwest Hospital of the Third Military Medical University (SWH2013QN09).
INTRODUCTION MATERIALS AND METHODS Subjects Clinical Data Neuropsychological Tests MRI Data Acquisition MRI Data Analysis Statistical Analysis RESULTS Demographic and Clinical Data Comparison Neuropsychological Test Comparison Brain Volume Comparison DC Analysis Connectivity Analysis Correlation Analysis DISCUSSION CONCLUSIONS AUTHOR CONTRIBUTIONS FUNDING FIGURE 1 | FIGURE 2 | FIGURE 3 | FIGURE 4 | TABLE 1 | TABLE 2 | TABLE 3 |
HbA 1c ]), lipoid parameters 
(total cholesterol, triglyceride, high-density lipoprotein [HDL] 
cholesterol, low-density lipoprotein [LDL] cholesterol), renal 
function parameters (blood urea nitrogen, serum creatine, 
uric acid, and cystatin C), thyroid function parameters 
(free triiodothyronine [FT3], free thyroxine [FT4] and 
thyroid-stimulating hormone [TSH]), and homocysteine. 
 T2DM 
HC 
p-value 

Age (years) 
58.66 ± 6.87 
57.36 ± 5.42 
0.312 

Sex (male:female) 
28/19 
25/22 
0.533 a 

Education (years) 
10.60 ± 3.06 
10.77 ± 2.65 
0.773 

T2DM duration (years) 
8.87 ± 6.61 
-
-

BMI (kg/m 2 ) 
25.46 ± 5.11 
23.94 ± 3.87 
0.109 

Gray matter (cm 3 ) 
608.15 ± 58.00 
615.16 ± 51.67 
0.538 

White matter (cm 3 ) 
534.91 ± 64.85 
524.16 ± 66.56 
0.430 

Brain parenchyma (cm 3 ) 
1143.06 ± 114.78 1139.32 ± 107.80 
0.871 

Systolic blood pressure 
(mmHg) 

128.91 ± 16.81 
134.55 ± 17.88 
0.119 

Diastolic blood pressure 
(mmHg) 

79.49 ± 10.01 
80.26 ± 10.25 
0.715 

HbA 1c (%) 
8.26 ± 2.08 
5.63 ± 0.39 
< 0.001 
HbA 1c (mmol/mol) 
66.76 ± 22.80 
38.15 ± 4.33 
< 0.001 
Fasting plasma glucose 
(mmol/L) 

7.47 ± 2.70 
5.22 ± 0.46 
< 0.001 

Fasting insulin (mIU/L) 
14.91 (9.58, 24.54) 11.41 (8.39, 17.10) 
0.035 b 

Fasting C-peptide (ng/mL) 
1.81 ± 1.05 
2.22 ± 0.90 
0.047 

HOMA2-IR 
0.28 (0.19, 0.52) 
0.21 (0.16, 0.32) 
0.015 b 

Total cholesterol (mmol/L) 
4.89 ± 1.04 
5.10 ± 1.02 
0.325 

Triglyceride (mmol/L) 
1.45 (1.05, 1.98) 
1.23 (0.91, 1.48) 
0.023 b 

HDL cholesterol (mmol/L) 
1.06 ± 0.23 
1.41 ± 0.36 
< 0.001 
LDL cholesterol (mmol/L) 
3.16 ± 0.83 
3.27 ± 0.76 
0.489 

Homocysteine (µmol/L) 
16.62 ± 10.03 
10.70 ± 4.10 
< 0.001 
Blood urea nitrogen 
(mmol/L) 

6.36 ± 2.45 
5.72 ± 1.23 
0.113 

Serum creatine (µmol/L) 
65.00 (57.00, 78.00) 78.00 (66.00, 85.00) 0.007 b 

Cystatin C (mg/L) 
0.72 (0.64, 0.91) 
0.76 (0.69, 0.86) 
0.639 b 

Uric acid (µmol/L) 
316.67 ± 79.72 
328.34 ± 69.09 
0.450 

Free triiodothyronine, FT3 
(pmol/L) 

4.31 ± 0.91 
5.13 ± 0.72 
< 0.001 

Free thyroxine, FT4 (pmol/L) 
15.52 ± 2.34 
16.48 ± 1.89 
0.032 

Thyroid-stimulating 
hormone, TSH (mIU/L) 

2.12 ± 0.95 
2.57 ± 1.54 
0.090 

p < 0.05 indicates statistical significance. a χ 2 test for sex (n). b Mann-Whitney U-test for 
non-normally distributed data [median (QR)]. Independent t-test for the other normally 
distributed continuous data (means ± SD). T2DM, type 2 diabetes mellitus; HC, 
healthy control; BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density 
lipoprotein. 

 T2DM 
HC 
p-value 

GENERAL COGNITION 

MMSE 
29.00 (27.00, 29.00) 29.00 (28.00, 29.00) 0.203 a 

MoCA 
23.17 ± 2.88 
24.51 ± 2.31 
0.015 

EXECUTIVE FUNCTION AND PSYCHOMOTOR SPEED 

TMT-A 
74.67 ± 41.11 
64.17 ± 32.59 
0.173 

TMT-B 
183.19 ± 89.90 
142.00 ± 58.35 
0.010 

MENTAL FLEXIBILITY 

VFT 
42.17 ± 7.33 
40.55 ± 7.05 
0.279 

WORKING MEMORY 

DST forwards 
8.83 ± 1.37 
9.60 ± 1.50 
0.011 

DST backwards 
4.00 (3.00, 5.00) 
4.00 (3.00, 4.00) 
0.911 a 

EPISODIC MEMORY 

AVLT immediate recall 
6.46 ± 1.69 
6.86 ± 1.49 
0.226 

AVLT short-term delayed 
recall 

6.77 ± 3.18 
7.74 ± 2.41 
0.096 

AVLT long-term delayed 
recall 

5.40 ± 3.77 
6.72 ± 2.43 
0.047 

AVLT long-term delayed 
recognition 

11.00 (8.00, 13.00) 
12.00 (9.00, 13.00) 
0.327 a 

AVLT total score 
28.97 ± 9.94 
32.34 ± 7.45 
0.065 

p < 0.05 was considered statistically significant. a Mann-Whitney U-test for non-normally 
distributed data [median (QR)]. Independent t-test for the other normally distributed 
continuous data (means ± SD). MMSE, Mini-Mental State Examination; MoCA, Montreal 
Cognitive Assessment; TMT, Trail Making Test; VFT, Verbal Fluency Test; DST, Digital Span 
Test; AVLT, Auditory Verbal Learning Test. 

 Brain regions 
BA 
Peak MNI 
t-value 
Cluster 
(voxels) 
X 
Y 
Z 

1 Left anterior cingulate gyrus 32 
−9 
42 
9 
3.1455 
28 

2 Right lateral occipital cortex 19 
27 
−78 24 −3.1327 
20 

3 Left lateral occipital cortex 
19 −24 −84 21 −3.9907 
147 

4 Right precentral gyrus 
43 
63 
−3 30 −3.4576 
55 

MNI, Montreal Neurological Institute; BA, Broadmann Area. R, Right; L, left; AlphaSim 
corrected (p < 0.001, cluster > 20 voxels). 

