Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Altered functional connectivity of the default mode network by glucose loading in young, healthy participants

Altered functional connectivity of the default mode network by glucose loading in young, healthy... Background: The functional connectivity of the default mode network (DMN) decreases in patients with Alzheimer’s disease (AD) as well as in patients with type 2 diabetes mellitus ( T2DM). Altered functional connectivity of the DMN is associated with cognitive impairment. T2DM is a known cause of cognitive dysfunction and dementia in the elderly, and studies have established that T2DM is a risk factor for AD. In addition, recent studies with positron emission tomography demonstrated that increased plasma glucose levels decrease neuronal activity, especially in the precu- neus/posterior cingulate cortex (PC/PCC), which is the functional core of the DMN. These findings prompt the ques- tion of how increased plasma glucose levels decrease neuronal activity in the PC/PCC. Given the association among DMN, AD, and T2DM, we hypothesized that increased plasma glucose levels decrease the DMN functional connectiv- ity, thus possibly reducing PC/PCC neuronal activity. We conducted this study to test this hypothesis. Results: Twelve young, healthy participants without T2DM and insulin resistance were enrolled in this study. Each participant underwent resting-state functional magnetic resonance imaging in both fasting and glucose loading conditions to evaluate the DMN functional connectivity. The results showed that the DMN functional connectivity in the PC/PCC was significantly lower in the glucose loading condition than in the fasting condition ( P = 0.014). Conclusions: Together with previous findings, the present results suggest that decreased functional connectivity of the DMN is possibly responsible for reduced PC/PCC neuronal activity in healthy individuals with increased plasma glucose levels. Keywords: Resting-state functional MRI, Default mode network, Glucose, Precuneus, Posterior cingulate Background Its functional connectivity is impaired in patients with The default mode network (DMN), one of the resting- Alzheimer’s disease (AD) [7], and impairment worsens state brain networks, is characterized by hyperactivity with disease progression [8]. Furthermore, altered func- when the brain is not engaged in specific behavioral tasks tional connectivity of the DMN is associated with cog- and low activity when the brain is focused on the external nitive decline [9, 10]. Interestingly, the DMN functional environment [1]. While performing various active tasks connectivity can also decrease in patients with type 2 including novel, non-self-referential, and goal-directed diabetes mellitus (T2DM) [11–13]. Although T2DM, tasks, the functional connectivity of the DMN consist- characterized by insulin resistance and increased plasma ently decreases [2, 3]. Although the mechanisms are not glucose levels, is intuitively far from AD pathophysiology, completely known, the DMN plays an important role T2DM is reportedly associated with cognitive decline in regulating complex cognition and behavior [4–6]. and is a risk factor for AD [14]. Although it is unclear why T2DM is a risk factor for AD, the shared vulnera- bility of the DMN in the two diseases may reveal a func- *Correspondence: ishibashi@pet.tmig.or.jp tional association between them. Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Ishibashi et al. BMC Neurosci (2018) 19:33 Page 2 of 8 The functional connectivity in resting-state brain net - resting-state fMRI to compare the functional connectiv- works is measured by detecting spontaneous fluctuations ity of the DMN in young, healthy participants under fast- in the blood-oxygen-level-dependent (BOLD) signals ing and glucose loading conditions. with functional magnetic resonance imaging (fMRI) [15]. Positive BOLD signals are presumably caused by altered Methods cerebral blood flow, an index of neuronal activity [16]. Research participants Therefore, the functional connectivity in resting-state The study was conducted in accordance with the ten - brain networks may be associated with neuronal activity ets of the Declaration of Helsinki, and was approved by [17]. As one of the most fundamental resting-state brain the Ethics Committee of the Tokyo Metropolitan Insti- networks, the DMN comprises  a set of interconnected tute of Gerontology. After a detailed explanation of the brain regions,  such as the precuneus/posterior cingu- study, each participant provided written informed con- late cortex (PC/PCC), medial prefrontal cortex (MPFC), sent. The study was composed of 12 young, healthy par - and lateral parietotemporal cortex (LPTC), with the PC/ ticipants [six males and six females, age: 30.3 ± 4.6  years PCC being the functional core of the DMN [4, 5]. In AD (mean ± SD), range: 24–36  years]. None of the partici- patients, functional connectivity of the DMN is impaired pants had a history of T2DM, and all were certified to be [7, 8]; glucose metabolism, another index of neuronal healthy based on the results of physical and neurological activity, is compromised primarily in the PC/PCC [18, examinations, medical interviews with a neurologist, and 19]. Therefore, in AD patients, decreased functional con - MRI findings. nectivity of the DMN is possibly associated with reduced neuronal activity in the PC/PCC. Study protocol Resting-state glucose metabolism, measured by fluo - Each participant visited the Tokyo Metropolitan Insti- rine-18-labeled fluorodeoxyglucose ( F-FDG) PET, is tute of Gerontology twice to undergo a resting-state physiologically associated with neuronal activity [20]. fMRI under each of two different conditions: fasting Interestingly, recent studies using F-FDG PET showed and glucose loading. The order in which the partici - that neuronal activity in the PC/PCC significantly pants presented for imaging under the two conditions decreases with increased plasma glucose levels in young, was randomized. Half of the male and half of the female healthy individuals [21] as well as in cognitively normal participants underwent the first and second resting-state elderly individuals [22–24]. The reduction in PC/PCC fMRI sessions under fasting and glucose loading condi- neuronal activity has been shown to occur in cognitively tions, respectively. The other participants underwent normal individuals with plasma glucose levels between the two resting-state fMRI sessions in the reverse order. 100 and 110  mg/dL [25] as well as in individuals devel- The time interval between the two visits was less than oping insulin resistance [26]. Reversibly increasing and 30 days. In the fasting condition, each participant visited decreasing plasma glucose levels decease and increase the institute to undergo a resting-state fMRI after fasting PC/PCC neuronal activity, respectively, in cognitively overnight for at least 8  h. In the glucose loading condi- normal individuals with T2DM [27]. Cerebral blood flow tion, each participant visited the institute without having can also decrease in the PC/PCC as plasma glucose lev- been under any dietary restriction, and was administered els increase [21]. More recently, we measured net glu- 75 g of glucose orally (TRELAN-G75; AY Pharma, Tokyo, cose metabolism using F-FDG PET with arterial blood Japan) approximately 30  min prior to the resting-state sampling in young, healthy individuals under fasting and fMRI. glucose loading conditions, and confirmed that glucose The plasma glucose levels, plasma insulin levels, and loading can reduce glucose metabolism (i.e., neuronal HbA1c values were measured after each resting-state activity), especially in the PC/PCC [28]. These findings fMRI, using ultraviolet absorption spectrophotom- prompt the question of how increased plasma glucose etry, chemiluminescent enzyme immunoassay, and levels decrease neuronal activity, especially in the PC/ latex agglutination, respectively (SRL, Tokyo, Japan). PCC. The homeostasis model assessment of insulin resistance Given the association among the DMN, AD, and (HOMA-IR) was calculated as an index of insulin resist- T2DM, decreased functional connectivity of the DMN ance using the following formula: HOMA-IR = (fasting may be responsible for reduced neuronal activity in the glucose (mmol/L) × fasting insulin (μU/mL))/22.5. PC/PCC. Therefore, we hypothesized that increased plasma glucose levels decrease the functional connec- Magnetic resonance data acquisition tivity of the DMN even in healthy individuals without Imaging data were acquired on a Discovery MR 750w T2DM and insulin resistance, possibly thus reducing PC/ 3.0-T scanner (GE Healthcare, Milwaukee, WI) at PCC neuronal activity. To test this hypothesis, we used the Tokyo Metropolitan Institute of Gerontology. Ishibashi et al. BMC Neurosci (2018) 19:33 Page 3 of 8 High-resolution anatomical data were collected nonlinear transformation. The data were skull-stripped using an SPGR sequence (repetition time = 7.648  ms, and spatially smoothed using a 5-mm full width at a half echo time = 3.092  ms, flip angle = 11°, matrix maximum Gaussian kernel, and a high-pass temporal fil - size = 196 × 256 × 256, voxel size = 1.2  mm × 1.0547  ter of 100 s was applied. mm × 1.0547  mm). Whole-brain resting-state fMRI Probabilistic independent component analysis (ICA) data were collected using an echo planar imaging (EPI) was then performed to identify the functional anatomy of sequence (repetition time = 2500 ms, echo time = 30  ms, the DMN, and to create a DMN mask for the subsequent flip angle = 73°, slice thickness = 4  mm, matrix seed-based analysis. A multi-session temporal concat- size = 64 × 64 × 41, FOV = 192  mm × 192  mm). The par - enation approach was applied to all echo planar imaging ticipants were instructed to rest quietly with their eyes sequence images. This approach allowed for a single 2D open and to avoid specific thoughts during the resting- ICA run on the concatenated data matrix to be obtained state fMRI sessions. Subsequently, the procedure was by stacking the 2D data matrices of every data set on manually reviewed to verify that all participants followed top of each other (https ://fsl.fmrib .ox.ac.uk/fsl/fslwi ki/ the instructions correctly.MELOD IC). FSL Melodic was used to carry out infer- ence on the estimated maps using a mixture model and Resting‑state fMRI data processing and independent an alternative hypothesis testing approach. A threshold component analysis (ICA) level of 0.5 was applied to each mixture model probability The resting-state fMRI data were processed using the map. This threshold level implies that a voxel “survives” FMRIB Software Library version 5.0.9 (FSL; Oxford, UK) thresholding as soon as the probability of being in the [29–31]. The first 10 volumes (images) were discarded “active” class exceeds that of being in the “background to avoid transient signal changes before magnetization noise” class, and assumes that the probability of false- reached a steady state and to allow the participants to negative and false-positive findings is equal [33, 34]. Of become accustomed to the fMRI scanning noise [32]. the 25 IC maps created by FSL Melodic, we identified one Then, the following 120 volumes, equivalent to 5  min IC map representing the default mode network (Fig. 1). of resting-state fMRI, were realigned to compensate for motion. Each motion-corrected EPI image was registered Seed‑based analysis and statistical analysis to the corresponding high-resolution SPGR image, and The thresholded IC map, shown in Fig.  1, included the transformed into the Montreal Neurological Institute representative components of the DMN: the PC/PCC, space using a 12-parameter affine transformation and a the MPFC, and the LPTC. These components were Fig. 1 Independent component map representing the default mode network. Independent component analysis was performed on all echo planar imaging sequence images using a multi-session temporal concatenation approach implemented in FSL Melodic. The mixture model probability map was transformed into a Z map. The red-yellow scale represents the magnitude of Z values ranging from 2.36 to 16.13 Ishibashi et al. BMC Neurosci (2018) 19:33 Page 4 of 8 extracted from the IC map and used as a mask for the employed using the difference in Z values between the DMN (Fig.  2a). Using the mask for the DMN as a seed, two conditions as a dependent factor and the order of the mean time series across all voxels within the seed was conditions, gender, HOMA-IR, fasting plasma glucose extracted from each EPI image. A first-level analysis was and insulin levels, and HbA1c values as independent performed for each 4D EPI image. The extracted mean factors. Statistical significance was set at P < 0.05. All time series was set as a covariate. We added the following statistical analyses were conducted using SPSS Statis- variables as nuisance regressors: mean signals of cerebro- tics version 22 (IBM, Armonk, NY). spinal fluid and white matter, and metrics of motion- related artifact created by FSL Mcflirt and Motion Outliers [35, 36]. A one-sample t test was then performed Results as a higher-level analysis for each of the two conditions The demographic characteristics are presented in to assess the within-group functional connectivity of the Table  1. After glucose loading, plasma glucose and DMN, using FSL Feat (https ://fsl.fmrib .ox.ac.uk/fsl/fslwi insulin levels significantly increased (glucose: Z = 2.158, ki/FEAT). Z statistic images were thresholded using clus- P = 0.031, insulin: Z = 3.061, P = 0.002, two-tailed Wil- ters determined by Z > 2.3 and a corrected cluster signifi - coxon signed-rank test). All participants were con- cance of P < 0.05. firmed to be free of T2DM and insulin resistance on the A between-group analysis was then performed to basis of HOMA-IR, fasting plasma glucose levels, and test the hypothesis that increased plasma glucose lev- HbA1c values [37]. els decrease the functional connectivity of the DMN. The results of the one-sample t tests (Z > 2.3, cluster- The central area of the PC/PCC was extracted from the corrected P < 0.05) are shown in Fig.  3. The representa - IC map as shown in Fig.  1 and used as a mask for the tive components of the DMN (PC/PCC, MPFC, and PC/PCC (Fig. 2b). The mask was moved on each Z map LPTC) were detected in the two conditions. The results that was created in the first-level analysis, as described of the between-group analysis of the magnitude of the above. The individual mean Z value within the mask DMN functional connectivity in the PC/PCC are shown was calculated, and used as the index of the magnitude in Fig. 4. The functional connectivity of the DMN in the of the functional connectivity of the DMN in the PC/ PC/PCC was significantly lower in the glucose load - PCC. To assess the effects of glucose loading on the ing condition than in the fasting condition (Z = 2.197, functional connectivity of the DMN in the PC/PCC, Z P = 0.014, one-tailed Wilcoxon signed-rank test). values were compared between the fasting and glucose Multiple regression analyses revealed no significant loading conditions using a one-tailed Wilcoxon signed- factors that may have affected the changes in functional rank test. The null hypothesis was that the functional connectivity between the two conditions [R = 0.176, connectivity of the DMN in the glucose loading condi- F(6, 5) = 0.178, P = 0.971, order of conditions: t = 0.380, tion was not lower than in the fasting condition. Addi- P = 0.719, gender: t = 0.362, P = 0.732, HOM A-IR: tionally, in order to assess whether any factors affected t = 0.041, P = 0.969, fasting plasma glucose: t = 0.310, the changes in the functional connectivity of the DMN P = 0.769, fasting plasma insulin: t = 0.038, P = 0.971, after glucose loading, a multiple regression analysis was HbA1c: t = 0.721, P = 0.503]. Fig. 2 Masks for the representative components of the DMN (a) and PC/PCC (b) in the Montreal Neurological Institute space. The representative components of the DMN were extracted from the IC map shown in Fig. 1, and used as a mask for the DMN (a yellow). The voxels with the highest statistical values were extracted from the IC map shown in Fig. 1, and used as a mask for the PC/PCC (b green). The mask volume for the PC/PCC was 2360 mm . DMN default mode network, IC independent component, PC/PCC precuneus/posterior cingulate cortex Ishibashi et al. BMC Neurosci (2018) 19:33 Page 5 of 8 Table 1 Demographic and clinical characteristics Subject Age Sex HbA1c (%) Fasting Glucose loading Glucose (mg/ Insulin (μU/ HOMA‑IR Glucose (mg/dL) Insulin (μU/mL) dL) mL) 1 34 M 5.2 94 2.7 0.62 126 37.8 2 34 M 5.8 80 6.5 1.28 164 32.2 3 27 M 5.5 84 0.8 0.16 85 4.8 4 32 F 4.7 90 4.4 0.98 96 18.1 5 30 F 5.1 82 3.4 0.69 100 27.8 6 36 F 5.2 87 2.7 0.58 117 33.4 7 23 M 5.4 91 2.4 0.53 115 18.7 8 26 F 4.9 94 4.2 0.98 84 8.0 9 37 F 5.1 87 1.8 0.40 186 87.4 10 32 M 5.1 90 3.4 0.75 123 29.1 11 24 M 4.9 89 1.9 0.42 83 29.0 12 29 F 5.3 82 4.3 0.88 75 26.5 Mean 5.2 87.5 3.2 0.69 112.8 29.4 HOMA-IR homeostasis model assessment of insulin resistance Fig. 3 Within-group functional connectivity of the DMN using a one-sample t test. A seed was placed on the representative components of the DMN as shown in Fig. 2a. The magnitude of the DMN functional connectivity is displayed in the fasting condition (a) and glucose loading condition (b). The threshold was set at Z > 2.3 and cluster-corrected P < 0.05. The rainbow scale represents the magnitude of the Z values. R right, L left, DMN default mode network functional connectivity of the DMN is known to decrease Discussion in patients with T2DM, characterized by insulin resist- The primary objective of this study was to investigate the ance and increased plasma glucose levels [11–13]. To effects of glucose loading on the functional connectiv - the best of our knowledge, this is the first study showing ity of the DMN in young, healthy subjects free of T2DM that after glucose loading, the functional connectivity of and insulin resistance, using resting-state fMRI. The Ishibashi et al. BMC Neurosci (2018) 19:33 Page 6 of 8 connectivity of the DMN by glucose loading physiologi- cally reflects. There are several studies using F-FDG PET, reporting that increased plasma glucose levels decrease glucose metabolism (i.e., neuronal activity), especially in the PC/PCC [24, 28]. In a dynamic F-FDG PET study with arterial blood sampling, which directly measured net glucose metabolism, glucose loading decreased glucose metabolism in DMN-related regions, especially in the PC/PCC, in young, healthy individu- als free of T2DM and insulin resistance [28]. Consider- ing these findings, reduced functional connectivity of the DMN by glucose loading is possibly responsible for reduced neuronal activity in DMN-related regions, espe- cially in the PC/PCC. Interestingly, plasma glucose levels in the prediabetes range of 100–126  mg/dL [39] are associated with cogni- tive decline, as measured using a battery of neuropsy- chological tests [40–42]. There is an inverse association between plasma glucose levels and Mini Mental State Examination scores in individuals at high risk for cardio- vascular disease [43]. In a sample of non-T2DM elderly subjects, individuals with higher plasma glucose levels tended to have lower Mini Mental State Examination scores [40]. A longitudinal study with a median follow- up of 6.8  years showed that higher glucose levels might be related to an increased risk for dementia, even among individuals without T2DM [44]. Although it remains Fig. 4 Differences in the functional connectivity of the DMN in unclear as to why mildly increased plasma glucose lev- the PC/PCC. The y-axis represents the mean Z values in the PC/PCC els induce cognitive decline, the phenomenon may be shown in Fig. 3, which was used for the index of the magnitude of the functional connectivity of the DMN in the PC/PCC. The functional speculated as follows: increased plasma glucose levels connectivity was significantly lower in the glucose loading condition reduce the functional connectivity of the DMN as well compared with the fasting condition (Z = 2.197, P = 0.014, one-tailed as neuronal activity in its components, particularly the Wilcoxon signed-rank test). Closed and open circles represent males PC/PCC, which is a central core for regulating complex and females, respectively. DMN default mode network, PC/PCC cognition and behavior [45, 46]. As a result, subclinical precuneus/posterior cingulate cortex cognitive decline may occur even in individuals without T2DM. This speculation may be important to explain the functional link between T2DM and AD, although future the DMN is decreased even in healthy individuals with- studies are needed to elucidate this hypothesis. out T2DM and insulin resistance. Zhang and colleagues In summary, glucose loading can reduce the DMN recently evaluated the acute effects of insulin admin - functional connectivity and PC/PCC neuronal activ- istration on the resting-state brain network in patients ity in healthy participants. Although the mechanism with T2DM, and showed that insulin administration underlying this phenomenon is unclear, cholinergic and increased the functional connectivity between the hip- glutamatergic neurotransmitter systems may play an pocampus and the DMN [38]. Because insulin adminis- important role in modulating the functional connectivity tration induces a reduction in plasma glucose levels, their of the DMN and neuronal activity in the PC/PCC. This findings could be restated as showing that a decrease in is because both the DMN and PC/PCC are anatomically plasma glucose levels increases the functional connectiv- crucial in regulating complex cognition and behavior ity of the DMN. Thus, their findings from patients with [4–6, 45, 46], and cholinergic and glutamatergic systems T2DM are consistent with our results. However, because are associated with cognitive function [47]. Moreover, the number of participants was relatively small in the cholinergic enhancement is reported to increase neu- present study, our results require  further  validation in a ronal activity in the PC/PCC [48]. Hence, glucose loading future study with a large number of participants. may modulate these neurotransmitter systems, possibly One of the concerns of this study is a lack of under- reducing the functional connectivity of the DMN and standing as to what the reduction in the functional Ishibashi et al. BMC Neurosci (2018) 19:33 Page 7 of 8 neuronal activity in the PC/PCC. However, further inves- Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- tigation is needed to elucidate this speculation. lished maps and institutional affiliations. Received: 11 December 2017 Accepted: 24 May 2018 Conclusions The present study showed that glucose loading reduces the functional connectivity of the DMN in the PC/PCC in young, healthy participants free of T2DM and insulin References resistance. Taken together with the previous knowledge 1. Anticevic A, Cole MW, Murray JD, Corlett PR, Wang XJ, Krystal JH. The role that glucose loading decreases neuronal activity in the of default network deactivation in cognition and disease. Trends Cogn Sci. 2012;16(12):584–92. PC/PCC, the present results suggest that decreased func- 2. Shulman GL, Corbetta M, Fiez JA, Buckner RL, Miezin FM, Raichle ME, tional connectivity of the DMN is possibly responsible Petersen SE. Searching for activations that generalize over tasks. Hum for reduced PC/PCC neuronal activity in healthy individ- Brain Mapp. 1997;5(4):317–22. 3. Raichle ME. The brain’s default mode network. Annu Rev Neurosci. uals with increased plasma glucose levels. 2015;38:433–47. 4. Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correla- Abbreviations tion network analysis. NeuroImage. 2008;42(3):1178–84. DMN: default mode network; AD: Alzheimer’s disease; T2DM: type 2 diabetes 5. Utevsky AV, Smith DV, Huettel SA. Precuneus is a functional core of the mellitus; PC/PCC: precuneus/posterior cingulate cortex; MPFC: medial prefron- default-mode network. J Neurosci. 2014;34(3):932–40. tal cortex; LPTC: lateral parietotemporal cortex; F-FDG: fluorine-18-labeled 6. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shul- fluorodeoxyglucose; PET: positron emission tomography; fMRI: functional man GL. A default mode of brain function. Proc Natl Acad Sci USA. magnetic resonance imaging; HOMA-IR: homeostasis model assessment of 2001;98(2):676–82. insulin resistance. 7. Sperling RA, Laviolette PS, O’Keefe K, O’Brien J, Rentz DM, Pihlajamaki M, Marshall G, Hyman BT, Selkoe DJ, Hedden T, et al. Amyloid deposition Author’s contributions is associated with impaired default network function in older persons KI and KI designed the study. KI, KS, KS, AMT, and KI obtained the data. without dementia. Neuron. 2009;63(2):178–88. KI carried out the data processing. KI, KS, KS, AMT, and KI interpreted the 8. Zhu DC, Majumdar S, Korolev IO, Berger KL, Bozoki AC. Alzheimer’s data. All authors were involved in drafting and revising the manuscript. All disease and amnestic mild cognitive impairment weaken connections authors agreed to be accountable for all aspects of the work in ensuring within the default-mode network: a multi-modal imaging study. J Alzhei- that questions related to the accuracy or integrity of any part of the work are mer’s Dis. 2013;34(4):969–84. appropriately investigated and resolved. All authors read and approved the 9. Wang L, Brier MR, Snyder AZ, Thomas JB, Fagan AM, Xiong C, Benzinger final manuscript. TL, Holtzman DM, Morris JC, Ances BM. Cerebrospinal fluid Abeta42, phosphorylated Tau181, and resting-state functional connectivity. JAMA Author details Neurol. 2013;70(10):1242–8. Research Team for Neuroimaging, Tokyo Metropolitan Institute of Ger- 10. Sheline YI, Raichle ME. Resting state functional connectivity in preclinical ontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan. Depart- Alzheimer’s disease. Biol Psychiatry. 2013;74(5):340–7. ment of Diagnostic Radiology, Tokyo Metropolitan Geriatric Hospital, 35-2 11. Musen G, Jacobson AM, Bolo NR, Simonson DC, Shenton ME, McCartney Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan. RL, Flores VL, Hoogenboom WS. Resting-state brain functional connectiv- ity is altered in type 2 diabetes. Diabetes. 2012;61(9):2375–9. Acknowledgements 12. Zhou H, Lu W, Shi Y, Bai F, Chang J, Yuan Y, Teng G, Zhang Z. Impairments The authors thank Dr. Dara Ghahremani at the Laboratory of Molecular Neuro- in cognition and resting-state connectivity of the hippocampus in elderly imaging, UCLA for the advice and support on analyzing the resting-state fMRI subjects with type 2 diabetes. Neurosci Lett. 2010;473(1):5–10. data, and the people of Research Team for Neuroimaging at the Tokyo Metro- 13. Chen YC, Jiao Y, Cui Y, Shang SA, Ding J, Feng Y, Song W, Ju SH, Teng GJ. politan Institute of Gerontology and Department of Diagnostic Radiology at Aberrant brain functional connectivity related to insulin resistance in type the Tokyo Metropolitan Geriatric Hospital for the technical assistance. 2 diabetes: a resting-state fMRI study. Diabetes Care. 2014;37(6):1689–96. 14. Ohara T, Doi Y, Ninomiya T, Hirakawa Y, Hata J, Iwaki T, Kanba S, Kiyohara Competing interests Y. Glucose tolerance status and risk of dementia in the community: the The authors declare that they have no competing interests. Hisayama study. Neurology. 2011;77(12):1126–34. 15. Rosazza C, Minati L. Resting-state brain networks: literature review and Availability of data and materials clinical applications. Neurol Sci. 2011;32(5):773–85. Ethical restrictions make data unsuitable for public deposition. Requests 16. Heeger DJ, Ress D. What does fMRI tell us about neuronal activity? Nat for data access will be sent to the Ethics Committee of Tokyo Metropolitan Rev Neurosci. 2002;3(2):142–51. Institute of Gerontology. Please contact Kenji Ishibashi (email: ishibashi@pet. 17. Aiello M, Salvatore E, Cachia A, Pappata S, Cavaliere C, Prinster A, Nicolai tmig.or.jp) who will lead the authorization process to make the data available E, Salvatore M, Baron JC, Quarantelli M. Relationship between simulta- upon request. neously acquired resting-state regional cerebral glucose metabolism and functional MRI: a PET/MR hybrid scanner study. NeuroImage. Consent for publication 2015;113:111–21. Not applicable. 18. Friedland RP, Budinger TF, Ganz E, Yano Y, Mathis CA, Koss B, Ober BA, Huesman RH, Derenzo SE. Regional cerebral metabolic alterations in Ethics approval and consent to participate dementia of the Alzheimer type: positron emission tomography with The study was approved by the Ethics Committee of the Tokyo Metropolitan [18F]fluorodeoxyglucose. J Comput Assist Tomogr. 1983;7(4):590–8. Institute of Gerontology (H28-2). After a detailed explanation of the study, 19. Langbaum JB, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, Alexander each participant provided written informed consent. GE, Foster NL, Weiner MW, Koeppe RA, et al. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography Funding images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). This work was supported by Translational Research Grants 2016 of Tokyo NeuroImage. 2009;45(4):1107–16. Metropolitan Institute of Gerontology (to Kenji Ishibashi). Ishibashi et al. BMC Neurosci (2018) 19:33 Page 8 of 8 20. Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomo - mapping of BOLD data on the detection of functional connectiv- graphic measurement of local cerebral glucose metabolic rate in humans ity changes in Alzheimer’s disease and bvFTD. Front Hum Neurosci. with (F-18)2-fluoro-2-deoxy- d -glucose: validation of method. Ann Neurol. 2016;10:680. 1979;6(5):371–88. 35. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but 21. Ishibashi K, Kawasaki K, Ishiwata K, Ishii K. Reduced uptake of F-FDG and systematic correlations in functional connectivity MRI networks arise O-H O in Alzheimer’s disease-related regions after glucose loading. J from subject motion. NeuroImage. 2012;59(3):2142–54. Cereb Blood Flow Metab. 2015;35(8):1380–5. 36. Kohno M, Okita K, Morales AM, Robertson CL, Dean AC, Ghahremani 22. Kawasaki K, Ishii K, Saito Y, Oda K, Kimura Y, Ishiwata K. Influence of mild DG, Sabb FW, Rawson RA, Mandelkern MA, Bilder RM, et al. Midbrain hyperglycemia on cerebral FDG distribution patterns calculated by statis- functional connectivity and ventral striatal dopamine D2-type recep- tical parametric mapping. Ann Nucl Med. 2008;22(3):191–200. tors: link to impulsivity in methamphetamine users. Mol Psychiatry. 23. Burns CM, Chen K, Kaszniak AW, Lee W, Alexander GE, Bandy D, Fleisher 2016;21(11):1554–60. AS, Caselli RJ, Reiman EM. Higher serum glucose levels are associ- 37. Keskin M, Kurtoglu S, Kendirci M, Atabek ME, Yazici C. Homeostasis model ated with cerebral hypometabolism in Alzheimer regions. Neurology. assessment is more reliable than the fasting glucose/insulin ratio and 2013;80(17):1557–64. quantitative insulin sensitivity check index for assessing insulin resistance 24. Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Eec ff ts of glucose, insu- among obese children and adolescents. Pediatrics. 2005;115(4):e500–3. lin, and insulin resistance on cerebral F-FDG distribution in cognitively 38. Zhang H, Hao Y, Manor B, Novak P, Milberg W, Zhang J, Fang J, Novak V. normal older subjects. PLoS ONE. 2017;12(7):e0181400. Intranasal insulin enhanced resting-state functional connectivity of hip- 25. Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Relationship between pocampal regions in type 2 diabetes. Diabetes. 2015;64(3):1025–34. Alzheimer disease-like pattern of F-FDG and fasting plasma glucose 39. American Diabetes A. 2. Classification and diagnosis of diabetes. Diabetes levels in cognitively normal volunteers. J Nucl Med. 2015;56(2):229–33. Care. 2017;40(Suppl 1):S11–24. 26. Baker LD, Cross DJ, Minoshima S, Belongia D, Watson GS, Craft S. Insulin 40. Di Bonito P, Di Fraia L, Di Gennaro L, Vitale A, Lapenta M, Scala A, Iardino resistance and Alzheimer-like reductions in regional cerebral glucose MR, Cusati B, Attino L, Capaldo B. Impact of impaired fasting glucose and metabolism for cognitively normal adults with prediabetes or early type 2 other metabolic factors on cognitive function in elderly people. Nutr diabetes. Arch Neurol. 2011;68(1):51–7. Metab Cardiovasc Dis. 2007;17(3):203–8. 27. Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Plasma glucose levels 41. Yaffe K, Blackwell T, Kanaya AM, Davidowitz N, Barrett-Connor E, Krueger affect cerebral F-FDG distribution in cognitively normal subjects with K. Diabetes, impaired fasting glucose, and development of cognitive diabetes. Clin Nucl Med. 2016;41(6):e274–80. impairment in older women. Neurology. 2004;63(4):658–63. 28. Ishibashi K, Wagatsuma K, Ishiwata K, Ishii K. Alteration of the regional 42. Cukierman-Yaffe T, Gerstein HC, Anderson C, Zhao F, Sleight P, Hilbrich cerebral glucose metabolism in healthy subjects by glucose loading. L, Jackson SH, Yusuf S, Teo K, Investigators OT. Glucose intolerance and Hum Brain Mapp. 2016;37(8):2823–32. diabetes as risk factors for cognitive impairment in people at high 29. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. cardiovascular risk: results from the ONTARGET/TRANSCEND research NeuroImage. 2012;62(2):782–90. programme. Diabetes Res Clin Pract. 2009;83(3):387–93. 30. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, 43. Cukierman-Yaffe T. Diabetes, dysglycemia and cognitive dysfunction. Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, et al. Diabetes Metab Res Rev. 2014;30(5):341–5. Advances in functional and structural MR image analysis and implemen- 44. Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, Haneuse S, tation as FSL. NeuroImage. 2004;23(Suppl 1):S208–19. Craft S, Montine TJ, Kahn SE, et al. Glucose levels and risk of dementia. N 31. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Engl J Med. 2013;369(6):540–8. Beckmann C, Jenkinson M, Smith SM. Bayesian analysis of neuroimaging 45. Cavanna AE, Trimble MR. The precuneus: a review of its functional data in FSL. NeuroImage. 2009;45(1 Suppl):S173–86. anatomy and behavioural correlates. Brain. 2006;129(Pt 3):564–83. 32. Lv XF, Qiu YW, Tian JZ, Xie CM, Han LJ, Su HH, Liu ZY, Peng JP, Lin CL, Wu 46. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition MS, et al. Abnormal regional homogeneity of resting-state brain activity and disease. Brain. 2014;137(Pt 1):12–32. in patients with HBV-related cirrhosis without overt hepatic encepha- 47. Francis PT, Parsons CG, Jones RW. Rationale for combining glutamatergic lopathy. Liver Int. 2013;33(3):375–83. and cholinergic approaches in the symptomatic treatment of Alzheimer’s 33. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting- disease. Expert Rev Neurother. 2012;12(11):1351–65. state connectivity using independent component analysis. Philos Trans R 48. Iizuka T, Kameyama M. Cholinergic enhancement increases regional Soc Lond B Biol Sci. 2005;360(1457):1001–13. cerebral blood flow to the posterior cingulate cortex in mild Alzheimer’s 34. Tuovinen T, Rytty R, Moilanen V, Abou Elseoud A, Veijola J, Remes AM, disease. Geriatr Gerontol Int. 2017;17(6):951–8. Kiviniemi VJ. The effect of gray matter ICA and coefficient of variation Ready to submit your research ? Choose BMC and benefit from: fast, convenient online submission thorough peer review by experienced researchers in your field rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Neuroscience Springer Journals

Altered functional connectivity of the default mode network by glucose loading in young, healthy participants

Download PDF
8 pages

Loading next page...
 
/lp/springer_journal/altered-functional-connectivity-of-the-default-mode-network-by-glucose-ACB8uzjMEh

References (48)

Publisher
Springer Journals
Copyright
Copyright © 2018 by The Author(s)
Subject
Biomedicine; Neurosciences; Neurobiology; Animal Models
eISSN
1471-2202
DOI
10.1186/s12868-018-0433-0
Publisher site
See Article on Publisher Site

Abstract

Background: The functional connectivity of the default mode network (DMN) decreases in patients with Alzheimer’s disease (AD) as well as in patients with type 2 diabetes mellitus ( T2DM). Altered functional connectivity of the DMN is associated with cognitive impairment. T2DM is a known cause of cognitive dysfunction and dementia in the elderly, and studies have established that T2DM is a risk factor for AD. In addition, recent studies with positron emission tomography demonstrated that increased plasma glucose levels decrease neuronal activity, especially in the precu- neus/posterior cingulate cortex (PC/PCC), which is the functional core of the DMN. These findings prompt the ques- tion of how increased plasma glucose levels decrease neuronal activity in the PC/PCC. Given the association among DMN, AD, and T2DM, we hypothesized that increased plasma glucose levels decrease the DMN functional connectiv- ity, thus possibly reducing PC/PCC neuronal activity. We conducted this study to test this hypothesis. Results: Twelve young, healthy participants without T2DM and insulin resistance were enrolled in this study. Each participant underwent resting-state functional magnetic resonance imaging in both fasting and glucose loading conditions to evaluate the DMN functional connectivity. The results showed that the DMN functional connectivity in the PC/PCC was significantly lower in the glucose loading condition than in the fasting condition ( P = 0.014). Conclusions: Together with previous findings, the present results suggest that decreased functional connectivity of the DMN is possibly responsible for reduced PC/PCC neuronal activity in healthy individuals with increased plasma glucose levels. Keywords: Resting-state functional MRI, Default mode network, Glucose, Precuneus, Posterior cingulate Background Its functional connectivity is impaired in patients with The default mode network (DMN), one of the resting- Alzheimer’s disease (AD) [7], and impairment worsens state brain networks, is characterized by hyperactivity with disease progression [8]. Furthermore, altered func- when the brain is not engaged in specific behavioral tasks tional connectivity of the DMN is associated with cog- and low activity when the brain is focused on the external nitive decline [9, 10]. Interestingly, the DMN functional environment [1]. While performing various active tasks connectivity can also decrease in patients with type 2 including novel, non-self-referential, and goal-directed diabetes mellitus (T2DM) [11–13]. Although T2DM, tasks, the functional connectivity of the DMN consist- characterized by insulin resistance and increased plasma ently decreases [2, 3]. Although the mechanisms are not glucose levels, is intuitively far from AD pathophysiology, completely known, the DMN plays an important role T2DM is reportedly associated with cognitive decline in regulating complex cognition and behavior [4–6]. and is a risk factor for AD [14]. Although it is unclear why T2DM is a risk factor for AD, the shared vulnera- bility of the DMN in the two diseases may reveal a func- *Correspondence: ishibashi@pet.tmig.or.jp tional association between them. Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Ishibashi et al. BMC Neurosci (2018) 19:33 Page 2 of 8 The functional connectivity in resting-state brain net - resting-state fMRI to compare the functional connectiv- works is measured by detecting spontaneous fluctuations ity of the DMN in young, healthy participants under fast- in the blood-oxygen-level-dependent (BOLD) signals ing and glucose loading conditions. with functional magnetic resonance imaging (fMRI) [15]. Positive BOLD signals are presumably caused by altered Methods cerebral blood flow, an index of neuronal activity [16]. Research participants Therefore, the functional connectivity in resting-state The study was conducted in accordance with the ten - brain networks may be associated with neuronal activity ets of the Declaration of Helsinki, and was approved by [17]. As one of the most fundamental resting-state brain the Ethics Committee of the Tokyo Metropolitan Insti- networks, the DMN comprises  a set of interconnected tute of Gerontology. After a detailed explanation of the brain regions,  such as the precuneus/posterior cingu- study, each participant provided written informed con- late cortex (PC/PCC), medial prefrontal cortex (MPFC), sent. The study was composed of 12 young, healthy par - and lateral parietotemporal cortex (LPTC), with the PC/ ticipants [six males and six females, age: 30.3 ± 4.6  years PCC being the functional core of the DMN [4, 5]. In AD (mean ± SD), range: 24–36  years]. None of the partici- patients, functional connectivity of the DMN is impaired pants had a history of T2DM, and all were certified to be [7, 8]; glucose metabolism, another index of neuronal healthy based on the results of physical and neurological activity, is compromised primarily in the PC/PCC [18, examinations, medical interviews with a neurologist, and 19]. Therefore, in AD patients, decreased functional con - MRI findings. nectivity of the DMN is possibly associated with reduced neuronal activity in the PC/PCC. Study protocol Resting-state glucose metabolism, measured by fluo - Each participant visited the Tokyo Metropolitan Insti- rine-18-labeled fluorodeoxyglucose ( F-FDG) PET, is tute of Gerontology twice to undergo a resting-state physiologically associated with neuronal activity [20]. fMRI under each of two different conditions: fasting Interestingly, recent studies using F-FDG PET showed and glucose loading. The order in which the partici - that neuronal activity in the PC/PCC significantly pants presented for imaging under the two conditions decreases with increased plasma glucose levels in young, was randomized. Half of the male and half of the female healthy individuals [21] as well as in cognitively normal participants underwent the first and second resting-state elderly individuals [22–24]. The reduction in PC/PCC fMRI sessions under fasting and glucose loading condi- neuronal activity has been shown to occur in cognitively tions, respectively. The other participants underwent normal individuals with plasma glucose levels between the two resting-state fMRI sessions in the reverse order. 100 and 110  mg/dL [25] as well as in individuals devel- The time interval between the two visits was less than oping insulin resistance [26]. Reversibly increasing and 30 days. In the fasting condition, each participant visited decreasing plasma glucose levels decease and increase the institute to undergo a resting-state fMRI after fasting PC/PCC neuronal activity, respectively, in cognitively overnight for at least 8  h. In the glucose loading condi- normal individuals with T2DM [27]. Cerebral blood flow tion, each participant visited the institute without having can also decrease in the PC/PCC as plasma glucose lev- been under any dietary restriction, and was administered els increase [21]. More recently, we measured net glu- 75 g of glucose orally (TRELAN-G75; AY Pharma, Tokyo, cose metabolism using F-FDG PET with arterial blood Japan) approximately 30  min prior to the resting-state sampling in young, healthy individuals under fasting and fMRI. glucose loading conditions, and confirmed that glucose The plasma glucose levels, plasma insulin levels, and loading can reduce glucose metabolism (i.e., neuronal HbA1c values were measured after each resting-state activity), especially in the PC/PCC [28]. These findings fMRI, using ultraviolet absorption spectrophotom- prompt the question of how increased plasma glucose etry, chemiluminescent enzyme immunoassay, and levels decrease neuronal activity, especially in the PC/ latex agglutination, respectively (SRL, Tokyo, Japan). PCC. The homeostasis model assessment of insulin resistance Given the association among the DMN, AD, and (HOMA-IR) was calculated as an index of insulin resist- T2DM, decreased functional connectivity of the DMN ance using the following formula: HOMA-IR = (fasting may be responsible for reduced neuronal activity in the glucose (mmol/L) × fasting insulin (μU/mL))/22.5. PC/PCC. Therefore, we hypothesized that increased plasma glucose levels decrease the functional connec- Magnetic resonance data acquisition tivity of the DMN even in healthy individuals without Imaging data were acquired on a Discovery MR 750w T2DM and insulin resistance, possibly thus reducing PC/ 3.0-T scanner (GE Healthcare, Milwaukee, WI) at PCC neuronal activity. To test this hypothesis, we used the Tokyo Metropolitan Institute of Gerontology. Ishibashi et al. BMC Neurosci (2018) 19:33 Page 3 of 8 High-resolution anatomical data were collected nonlinear transformation. The data were skull-stripped using an SPGR sequence (repetition time = 7.648  ms, and spatially smoothed using a 5-mm full width at a half echo time = 3.092  ms, flip angle = 11°, matrix maximum Gaussian kernel, and a high-pass temporal fil - size = 196 × 256 × 256, voxel size = 1.2  mm × 1.0547  ter of 100 s was applied. mm × 1.0547  mm). Whole-brain resting-state fMRI Probabilistic independent component analysis (ICA) data were collected using an echo planar imaging (EPI) was then performed to identify the functional anatomy of sequence (repetition time = 2500 ms, echo time = 30  ms, the DMN, and to create a DMN mask for the subsequent flip angle = 73°, slice thickness = 4  mm, matrix seed-based analysis. A multi-session temporal concat- size = 64 × 64 × 41, FOV = 192  mm × 192  mm). The par - enation approach was applied to all echo planar imaging ticipants were instructed to rest quietly with their eyes sequence images. This approach allowed for a single 2D open and to avoid specific thoughts during the resting- ICA run on the concatenated data matrix to be obtained state fMRI sessions. Subsequently, the procedure was by stacking the 2D data matrices of every data set on manually reviewed to verify that all participants followed top of each other (https ://fsl.fmrib .ox.ac.uk/fsl/fslwi ki/ the instructions correctly.MELOD IC). FSL Melodic was used to carry out infer- ence on the estimated maps using a mixture model and Resting‑state fMRI data processing and independent an alternative hypothesis testing approach. A threshold component analysis (ICA) level of 0.5 was applied to each mixture model probability The resting-state fMRI data were processed using the map. This threshold level implies that a voxel “survives” FMRIB Software Library version 5.0.9 (FSL; Oxford, UK) thresholding as soon as the probability of being in the [29–31]. The first 10 volumes (images) were discarded “active” class exceeds that of being in the “background to avoid transient signal changes before magnetization noise” class, and assumes that the probability of false- reached a steady state and to allow the participants to negative and false-positive findings is equal [33, 34]. Of become accustomed to the fMRI scanning noise [32]. the 25 IC maps created by FSL Melodic, we identified one Then, the following 120 volumes, equivalent to 5  min IC map representing the default mode network (Fig. 1). of resting-state fMRI, were realigned to compensate for motion. Each motion-corrected EPI image was registered Seed‑based analysis and statistical analysis to the corresponding high-resolution SPGR image, and The thresholded IC map, shown in Fig.  1, included the transformed into the Montreal Neurological Institute representative components of the DMN: the PC/PCC, space using a 12-parameter affine transformation and a the MPFC, and the LPTC. These components were Fig. 1 Independent component map representing the default mode network. Independent component analysis was performed on all echo planar imaging sequence images using a multi-session temporal concatenation approach implemented in FSL Melodic. The mixture model probability map was transformed into a Z map. The red-yellow scale represents the magnitude of Z values ranging from 2.36 to 16.13 Ishibashi et al. BMC Neurosci (2018) 19:33 Page 4 of 8 extracted from the IC map and used as a mask for the employed using the difference in Z values between the DMN (Fig.  2a). Using the mask for the DMN as a seed, two conditions as a dependent factor and the order of the mean time series across all voxels within the seed was conditions, gender, HOMA-IR, fasting plasma glucose extracted from each EPI image. A first-level analysis was and insulin levels, and HbA1c values as independent performed for each 4D EPI image. The extracted mean factors. Statistical significance was set at P < 0.05. All time series was set as a covariate. We added the following statistical analyses were conducted using SPSS Statis- variables as nuisance regressors: mean signals of cerebro- tics version 22 (IBM, Armonk, NY). spinal fluid and white matter, and metrics of motion- related artifact created by FSL Mcflirt and Motion Outliers [35, 36]. A one-sample t test was then performed Results as a higher-level analysis for each of the two conditions The demographic characteristics are presented in to assess the within-group functional connectivity of the Table  1. After glucose loading, plasma glucose and DMN, using FSL Feat (https ://fsl.fmrib .ox.ac.uk/fsl/fslwi insulin levels significantly increased (glucose: Z = 2.158, ki/FEAT). Z statistic images were thresholded using clus- P = 0.031, insulin: Z = 3.061, P = 0.002, two-tailed Wil- ters determined by Z > 2.3 and a corrected cluster signifi - coxon signed-rank test). All participants were con- cance of P < 0.05. firmed to be free of T2DM and insulin resistance on the A between-group analysis was then performed to basis of HOMA-IR, fasting plasma glucose levels, and test the hypothesis that increased plasma glucose lev- HbA1c values [37]. els decrease the functional connectivity of the DMN. The results of the one-sample t tests (Z > 2.3, cluster- The central area of the PC/PCC was extracted from the corrected P < 0.05) are shown in Fig.  3. The representa - IC map as shown in Fig.  1 and used as a mask for the tive components of the DMN (PC/PCC, MPFC, and PC/PCC (Fig. 2b). The mask was moved on each Z map LPTC) were detected in the two conditions. The results that was created in the first-level analysis, as described of the between-group analysis of the magnitude of the above. The individual mean Z value within the mask DMN functional connectivity in the PC/PCC are shown was calculated, and used as the index of the magnitude in Fig. 4. The functional connectivity of the DMN in the of the functional connectivity of the DMN in the PC/ PC/PCC was significantly lower in the glucose load - PCC. To assess the effects of glucose loading on the ing condition than in the fasting condition (Z = 2.197, functional connectivity of the DMN in the PC/PCC, Z P = 0.014, one-tailed Wilcoxon signed-rank test). values were compared between the fasting and glucose Multiple regression analyses revealed no significant loading conditions using a one-tailed Wilcoxon signed- factors that may have affected the changes in functional rank test. The null hypothesis was that the functional connectivity between the two conditions [R = 0.176, connectivity of the DMN in the glucose loading condi- F(6, 5) = 0.178, P = 0.971, order of conditions: t = 0.380, tion was not lower than in the fasting condition. Addi- P = 0.719, gender: t = 0.362, P = 0.732, HOM A-IR: tionally, in order to assess whether any factors affected t = 0.041, P = 0.969, fasting plasma glucose: t = 0.310, the changes in the functional connectivity of the DMN P = 0.769, fasting plasma insulin: t = 0.038, P = 0.971, after glucose loading, a multiple regression analysis was HbA1c: t = 0.721, P = 0.503]. Fig. 2 Masks for the representative components of the DMN (a) and PC/PCC (b) in the Montreal Neurological Institute space. The representative components of the DMN were extracted from the IC map shown in Fig. 1, and used as a mask for the DMN (a yellow). The voxels with the highest statistical values were extracted from the IC map shown in Fig. 1, and used as a mask for the PC/PCC (b green). The mask volume for the PC/PCC was 2360 mm . DMN default mode network, IC independent component, PC/PCC precuneus/posterior cingulate cortex Ishibashi et al. BMC Neurosci (2018) 19:33 Page 5 of 8 Table 1 Demographic and clinical characteristics Subject Age Sex HbA1c (%) Fasting Glucose loading Glucose (mg/ Insulin (μU/ HOMA‑IR Glucose (mg/dL) Insulin (μU/mL) dL) mL) 1 34 M 5.2 94 2.7 0.62 126 37.8 2 34 M 5.8 80 6.5 1.28 164 32.2 3 27 M 5.5 84 0.8 0.16 85 4.8 4 32 F 4.7 90 4.4 0.98 96 18.1 5 30 F 5.1 82 3.4 0.69 100 27.8 6 36 F 5.2 87 2.7 0.58 117 33.4 7 23 M 5.4 91 2.4 0.53 115 18.7 8 26 F 4.9 94 4.2 0.98 84 8.0 9 37 F 5.1 87 1.8 0.40 186 87.4 10 32 M 5.1 90 3.4 0.75 123 29.1 11 24 M 4.9 89 1.9 0.42 83 29.0 12 29 F 5.3 82 4.3 0.88 75 26.5 Mean 5.2 87.5 3.2 0.69 112.8 29.4 HOMA-IR homeostasis model assessment of insulin resistance Fig. 3 Within-group functional connectivity of the DMN using a one-sample t test. A seed was placed on the representative components of the DMN as shown in Fig. 2a. The magnitude of the DMN functional connectivity is displayed in the fasting condition (a) and glucose loading condition (b). The threshold was set at Z > 2.3 and cluster-corrected P < 0.05. The rainbow scale represents the magnitude of the Z values. R right, L left, DMN default mode network functional connectivity of the DMN is known to decrease Discussion in patients with T2DM, characterized by insulin resist- The primary objective of this study was to investigate the ance and increased plasma glucose levels [11–13]. To effects of glucose loading on the functional connectiv - the best of our knowledge, this is the first study showing ity of the DMN in young, healthy subjects free of T2DM that after glucose loading, the functional connectivity of and insulin resistance, using resting-state fMRI. The Ishibashi et al. BMC Neurosci (2018) 19:33 Page 6 of 8 connectivity of the DMN by glucose loading physiologi- cally reflects. There are several studies using F-FDG PET, reporting that increased plasma glucose levels decrease glucose metabolism (i.e., neuronal activity), especially in the PC/PCC [24, 28]. In a dynamic F-FDG PET study with arterial blood sampling, which directly measured net glucose metabolism, glucose loading decreased glucose metabolism in DMN-related regions, especially in the PC/PCC, in young, healthy individu- als free of T2DM and insulin resistance [28]. Consider- ing these findings, reduced functional connectivity of the DMN by glucose loading is possibly responsible for reduced neuronal activity in DMN-related regions, espe- cially in the PC/PCC. Interestingly, plasma glucose levels in the prediabetes range of 100–126  mg/dL [39] are associated with cogni- tive decline, as measured using a battery of neuropsy- chological tests [40–42]. There is an inverse association between plasma glucose levels and Mini Mental State Examination scores in individuals at high risk for cardio- vascular disease [43]. In a sample of non-T2DM elderly subjects, individuals with higher plasma glucose levels tended to have lower Mini Mental State Examination scores [40]. A longitudinal study with a median follow- up of 6.8  years showed that higher glucose levels might be related to an increased risk for dementia, even among individuals without T2DM [44]. Although it remains Fig. 4 Differences in the functional connectivity of the DMN in unclear as to why mildly increased plasma glucose lev- the PC/PCC. The y-axis represents the mean Z values in the PC/PCC els induce cognitive decline, the phenomenon may be shown in Fig. 3, which was used for the index of the magnitude of the functional connectivity of the DMN in the PC/PCC. The functional speculated as follows: increased plasma glucose levels connectivity was significantly lower in the glucose loading condition reduce the functional connectivity of the DMN as well compared with the fasting condition (Z = 2.197, P = 0.014, one-tailed as neuronal activity in its components, particularly the Wilcoxon signed-rank test). Closed and open circles represent males PC/PCC, which is a central core for regulating complex and females, respectively. DMN default mode network, PC/PCC cognition and behavior [45, 46]. As a result, subclinical precuneus/posterior cingulate cortex cognitive decline may occur even in individuals without T2DM. This speculation may be important to explain the functional link between T2DM and AD, although future the DMN is decreased even in healthy individuals with- studies are needed to elucidate this hypothesis. out T2DM and insulin resistance. Zhang and colleagues In summary, glucose loading can reduce the DMN recently evaluated the acute effects of insulin admin - functional connectivity and PC/PCC neuronal activ- istration on the resting-state brain network in patients ity in healthy participants. Although the mechanism with T2DM, and showed that insulin administration underlying this phenomenon is unclear, cholinergic and increased the functional connectivity between the hip- glutamatergic neurotransmitter systems may play an pocampus and the DMN [38]. Because insulin adminis- important role in modulating the functional connectivity tration induces a reduction in plasma glucose levels, their of the DMN and neuronal activity in the PC/PCC. This findings could be restated as showing that a decrease in is because both the DMN and PC/PCC are anatomically plasma glucose levels increases the functional connectiv- crucial in regulating complex cognition and behavior ity of the DMN. Thus, their findings from patients with [4–6, 45, 46], and cholinergic and glutamatergic systems T2DM are consistent with our results. However, because are associated with cognitive function [47]. Moreover, the number of participants was relatively small in the cholinergic enhancement is reported to increase neu- present study, our results require  further  validation in a ronal activity in the PC/PCC [48]. Hence, glucose loading future study with a large number of participants. may modulate these neurotransmitter systems, possibly One of the concerns of this study is a lack of under- reducing the functional connectivity of the DMN and standing as to what the reduction in the functional Ishibashi et al. BMC Neurosci (2018) 19:33 Page 7 of 8 neuronal activity in the PC/PCC. However, further inves- Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- tigation is needed to elucidate this speculation. lished maps and institutional affiliations. Received: 11 December 2017 Accepted: 24 May 2018 Conclusions The present study showed that glucose loading reduces the functional connectivity of the DMN in the PC/PCC in young, healthy participants free of T2DM and insulin References resistance. Taken together with the previous knowledge 1. Anticevic A, Cole MW, Murray JD, Corlett PR, Wang XJ, Krystal JH. The role that glucose loading decreases neuronal activity in the of default network deactivation in cognition and disease. Trends Cogn Sci. 2012;16(12):584–92. PC/PCC, the present results suggest that decreased func- 2. Shulman GL, Corbetta M, Fiez JA, Buckner RL, Miezin FM, Raichle ME, tional connectivity of the DMN is possibly responsible Petersen SE. Searching for activations that generalize over tasks. Hum for reduced PC/PCC neuronal activity in healthy individ- Brain Mapp. 1997;5(4):317–22. 3. Raichle ME. The brain’s default mode network. Annu Rev Neurosci. uals with increased plasma glucose levels. 2015;38:433–47. 4. Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correla- Abbreviations tion network analysis. NeuroImage. 2008;42(3):1178–84. DMN: default mode network; AD: Alzheimer’s disease; T2DM: type 2 diabetes 5. Utevsky AV, Smith DV, Huettel SA. Precuneus is a functional core of the mellitus; PC/PCC: precuneus/posterior cingulate cortex; MPFC: medial prefron- default-mode network. J Neurosci. 2014;34(3):932–40. tal cortex; LPTC: lateral parietotemporal cortex; F-FDG: fluorine-18-labeled 6. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shul- fluorodeoxyglucose; PET: positron emission tomography; fMRI: functional man GL. A default mode of brain function. Proc Natl Acad Sci USA. magnetic resonance imaging; HOMA-IR: homeostasis model assessment of 2001;98(2):676–82. insulin resistance. 7. Sperling RA, Laviolette PS, O’Keefe K, O’Brien J, Rentz DM, Pihlajamaki M, Marshall G, Hyman BT, Selkoe DJ, Hedden T, et al. Amyloid deposition Author’s contributions is associated with impaired default network function in older persons KI and KI designed the study. KI, KS, KS, AMT, and KI obtained the data. without dementia. Neuron. 2009;63(2):178–88. KI carried out the data processing. KI, KS, KS, AMT, and KI interpreted the 8. Zhu DC, Majumdar S, Korolev IO, Berger KL, Bozoki AC. Alzheimer’s data. All authors were involved in drafting and revising the manuscript. All disease and amnestic mild cognitive impairment weaken connections authors agreed to be accountable for all aspects of the work in ensuring within the default-mode network: a multi-modal imaging study. J Alzhei- that questions related to the accuracy or integrity of any part of the work are mer’s Dis. 2013;34(4):969–84. appropriately investigated and resolved. All authors read and approved the 9. Wang L, Brier MR, Snyder AZ, Thomas JB, Fagan AM, Xiong C, Benzinger final manuscript. TL, Holtzman DM, Morris JC, Ances BM. Cerebrospinal fluid Abeta42, phosphorylated Tau181, and resting-state functional connectivity. JAMA Author details Neurol. 2013;70(10):1242–8. Research Team for Neuroimaging, Tokyo Metropolitan Institute of Ger- 10. Sheline YI, Raichle ME. Resting state functional connectivity in preclinical ontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan. Depart- Alzheimer’s disease. Biol Psychiatry. 2013;74(5):340–7. ment of Diagnostic Radiology, Tokyo Metropolitan Geriatric Hospital, 35-2 11. Musen G, Jacobson AM, Bolo NR, Simonson DC, Shenton ME, McCartney Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan. RL, Flores VL, Hoogenboom WS. Resting-state brain functional connectiv- ity is altered in type 2 diabetes. Diabetes. 2012;61(9):2375–9. Acknowledgements 12. Zhou H, Lu W, Shi Y, Bai F, Chang J, Yuan Y, Teng G, Zhang Z. Impairments The authors thank Dr. Dara Ghahremani at the Laboratory of Molecular Neuro- in cognition and resting-state connectivity of the hippocampus in elderly imaging, UCLA for the advice and support on analyzing the resting-state fMRI subjects with type 2 diabetes. Neurosci Lett. 2010;473(1):5–10. data, and the people of Research Team for Neuroimaging at the Tokyo Metro- 13. Chen YC, Jiao Y, Cui Y, Shang SA, Ding J, Feng Y, Song W, Ju SH, Teng GJ. politan Institute of Gerontology and Department of Diagnostic Radiology at Aberrant brain functional connectivity related to insulin resistance in type the Tokyo Metropolitan Geriatric Hospital for the technical assistance. 2 diabetes: a resting-state fMRI study. Diabetes Care. 2014;37(6):1689–96. 14. Ohara T, Doi Y, Ninomiya T, Hirakawa Y, Hata J, Iwaki T, Kanba S, Kiyohara Competing interests Y. Glucose tolerance status and risk of dementia in the community: the The authors declare that they have no competing interests. Hisayama study. Neurology. 2011;77(12):1126–34. 15. Rosazza C, Minati L. Resting-state brain networks: literature review and Availability of data and materials clinical applications. Neurol Sci. 2011;32(5):773–85. Ethical restrictions make data unsuitable for public deposition. Requests 16. Heeger DJ, Ress D. What does fMRI tell us about neuronal activity? Nat for data access will be sent to the Ethics Committee of Tokyo Metropolitan Rev Neurosci. 2002;3(2):142–51. Institute of Gerontology. Please contact Kenji Ishibashi (email: ishibashi@pet. 17. Aiello M, Salvatore E, Cachia A, Pappata S, Cavaliere C, Prinster A, Nicolai tmig.or.jp) who will lead the authorization process to make the data available E, Salvatore M, Baron JC, Quarantelli M. Relationship between simulta- upon request. neously acquired resting-state regional cerebral glucose metabolism and functional MRI: a PET/MR hybrid scanner study. NeuroImage. Consent for publication 2015;113:111–21. Not applicable. 18. Friedland RP, Budinger TF, Ganz E, Yano Y, Mathis CA, Koss B, Ober BA, Huesman RH, Derenzo SE. Regional cerebral metabolic alterations in Ethics approval and consent to participate dementia of the Alzheimer type: positron emission tomography with The study was approved by the Ethics Committee of the Tokyo Metropolitan [18F]fluorodeoxyglucose. J Comput Assist Tomogr. 1983;7(4):590–8. Institute of Gerontology (H28-2). After a detailed explanation of the study, 19. Langbaum JB, Chen K, Lee W, Reschke C, Bandy D, Fleisher AS, Alexander each participant provided written informed consent. GE, Foster NL, Weiner MW, Koeppe RA, et al. Categorical and correlational analyses of baseline fluorodeoxyglucose positron emission tomography Funding images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). This work was supported by Translational Research Grants 2016 of Tokyo NeuroImage. 2009;45(4):1107–16. Metropolitan Institute of Gerontology (to Kenji Ishibashi). Ishibashi et al. BMC Neurosci (2018) 19:33 Page 8 of 8 20. Phelps ME, Huang SC, Hoffman EJ, Selin C, Sokoloff L, Kuhl DE. Tomo - mapping of BOLD data on the detection of functional connectiv- graphic measurement of local cerebral glucose metabolic rate in humans ity changes in Alzheimer’s disease and bvFTD. Front Hum Neurosci. with (F-18)2-fluoro-2-deoxy- d -glucose: validation of method. Ann Neurol. 2016;10:680. 1979;6(5):371–88. 35. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but 21. Ishibashi K, Kawasaki K, Ishiwata K, Ishii K. Reduced uptake of F-FDG and systematic correlations in functional connectivity MRI networks arise O-H O in Alzheimer’s disease-related regions after glucose loading. J from subject motion. NeuroImage. 2012;59(3):2142–54. Cereb Blood Flow Metab. 2015;35(8):1380–5. 36. Kohno M, Okita K, Morales AM, Robertson CL, Dean AC, Ghahremani 22. Kawasaki K, Ishii K, Saito Y, Oda K, Kimura Y, Ishiwata K. Influence of mild DG, Sabb FW, Rawson RA, Mandelkern MA, Bilder RM, et al. Midbrain hyperglycemia on cerebral FDG distribution patterns calculated by statis- functional connectivity and ventral striatal dopamine D2-type recep- tical parametric mapping. Ann Nucl Med. 2008;22(3):191–200. tors: link to impulsivity in methamphetamine users. Mol Psychiatry. 23. Burns CM, Chen K, Kaszniak AW, Lee W, Alexander GE, Bandy D, Fleisher 2016;21(11):1554–60. AS, Caselli RJ, Reiman EM. Higher serum glucose levels are associ- 37. Keskin M, Kurtoglu S, Kendirci M, Atabek ME, Yazici C. Homeostasis model ated with cerebral hypometabolism in Alzheimer regions. Neurology. assessment is more reliable than the fasting glucose/insulin ratio and 2013;80(17):1557–64. quantitative insulin sensitivity check index for assessing insulin resistance 24. Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Eec ff ts of glucose, insu- among obese children and adolescents. Pediatrics. 2005;115(4):e500–3. lin, and insulin resistance on cerebral F-FDG distribution in cognitively 38. Zhang H, Hao Y, Manor B, Novak P, Milberg W, Zhang J, Fang J, Novak V. normal older subjects. PLoS ONE. 2017;12(7):e0181400. Intranasal insulin enhanced resting-state functional connectivity of hip- 25. Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Relationship between pocampal regions in type 2 diabetes. Diabetes. 2015;64(3):1025–34. Alzheimer disease-like pattern of F-FDG and fasting plasma glucose 39. American Diabetes A. 2. Classification and diagnosis of diabetes. Diabetes levels in cognitively normal volunteers. J Nucl Med. 2015;56(2):229–33. Care. 2017;40(Suppl 1):S11–24. 26. Baker LD, Cross DJ, Minoshima S, Belongia D, Watson GS, Craft S. Insulin 40. Di Bonito P, Di Fraia L, Di Gennaro L, Vitale A, Lapenta M, Scala A, Iardino resistance and Alzheimer-like reductions in regional cerebral glucose MR, Cusati B, Attino L, Capaldo B. Impact of impaired fasting glucose and metabolism for cognitively normal adults with prediabetes or early type 2 other metabolic factors on cognitive function in elderly people. Nutr diabetes. Arch Neurol. 2011;68(1):51–7. Metab Cardiovasc Dis. 2007;17(3):203–8. 27. Ishibashi K, Onishi A, Fujiwara Y, Ishiwata K, Ishii K. Plasma glucose levels 41. Yaffe K, Blackwell T, Kanaya AM, Davidowitz N, Barrett-Connor E, Krueger affect cerebral F-FDG distribution in cognitively normal subjects with K. Diabetes, impaired fasting glucose, and development of cognitive diabetes. Clin Nucl Med. 2016;41(6):e274–80. impairment in older women. Neurology. 2004;63(4):658–63. 28. Ishibashi K, Wagatsuma K, Ishiwata K, Ishii K. Alteration of the regional 42. Cukierman-Yaffe T, Gerstein HC, Anderson C, Zhao F, Sleight P, Hilbrich cerebral glucose metabolism in healthy subjects by glucose loading. L, Jackson SH, Yusuf S, Teo K, Investigators OT. Glucose intolerance and Hum Brain Mapp. 2016;37(8):2823–32. diabetes as risk factors for cognitive impairment in people at high 29. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. cardiovascular risk: results from the ONTARGET/TRANSCEND research NeuroImage. 2012;62(2):782–90. programme. Diabetes Res Clin Pract. 2009;83(3):387–93. 30. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, 43. Cukierman-Yaffe T. Diabetes, dysglycemia and cognitive dysfunction. Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, et al. Diabetes Metab Res Rev. 2014;30(5):341–5. Advances in functional and structural MR image analysis and implemen- 44. Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, Haneuse S, tation as FSL. NeuroImage. 2004;23(Suppl 1):S208–19. Craft S, Montine TJ, Kahn SE, et al. Glucose levels and risk of dementia. N 31. Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Engl J Med. 2013;369(6):540–8. Beckmann C, Jenkinson M, Smith SM. Bayesian analysis of neuroimaging 45. Cavanna AE, Trimble MR. The precuneus: a review of its functional data in FSL. NeuroImage. 2009;45(1 Suppl):S173–86. anatomy and behavioural correlates. Brain. 2006;129(Pt 3):564–83. 32. Lv XF, Qiu YW, Tian JZ, Xie CM, Han LJ, Su HH, Liu ZY, Peng JP, Lin CL, Wu 46. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition MS, et al. Abnormal regional homogeneity of resting-state brain activity and disease. Brain. 2014;137(Pt 1):12–32. in patients with HBV-related cirrhosis without overt hepatic encepha- 47. Francis PT, Parsons CG, Jones RW. Rationale for combining glutamatergic lopathy. Liver Int. 2013;33(3):375–83. and cholinergic approaches in the symptomatic treatment of Alzheimer’s 33. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting- disease. Expert Rev Neurother. 2012;12(11):1351–65. state connectivity using independent component analysis. Philos Trans R 48. Iizuka T, Kameyama M. Cholinergic enhancement increases regional Soc Lond B Biol Sci. 2005;360(1457):1001–13. cerebral blood flow to the posterior cingulate cortex in mild Alzheimer’s 34. Tuovinen T, Rytty R, Moilanen V, Abou Elseoud A, Veijola J, Remes AM, disease. Geriatr Gerontol Int. 2017;17(6):951–8. Kiviniemi VJ. The effect of gray matter ICA and coefficient of variation Ready to submit your research ? Choose BMC and benefit from: fast, convenient online submission thorough peer review by experienced researchers in your field rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions

Journal

BMC Neuroscience – Springer Journals

Published: May 31, 2018

There are no references for this article.