Altered dynamic effective connectivity of the default mode network in type 2 diabetes

Introduction Altered functional connectivity of resting-state functional magnetic resonance imaging (rs-fMRI) within default mode network (DMN) regions has been verified to be closely associated with cognitive decline in patients with Type 2 diabetes mellitus (T2DM), but most studies neglected the fluctuations of brain activities—the dynamic effective connectivity (DEC) within DMN of T2DM is still unknown. Methods For the current investigation, 40 healthy controls (HC) and 36 T2DM patients have been recruited as participants. To examine the variation of DEC between T2DM and HC, we utilized the methodologies of independent components analysis (ICA) and multivariate granger causality analysis (mGCA). Results We found altered DEC within DMN only show decrease in state 1. In addition, the causal information flow of diabetic patients major affected areas which are closely associated with food craving and metabolic regulation, and T2DM patients stayed longer in low activity level and exhibited decreased transition rate between states. Moreover, these changes related negatively with the MoCA scores and positively with HbA1C level. Conclusion Our study may offer a fresh perspective on brain dynamic activities to understand the mechanisms underlying T2DM-related cognitive deficits.

[1]  T. Subramaniam,et al.  Association of early-onset Type 2 diabetes with cognitive impairment is partially mediated by increased pulse pressure. , 2022, Journal of diabetes and its complications.

[2]  Xiaoqi Huang,et al.  Impairments in intrinsic functional networks in type 2 diabetes: A meta-analysis of resting-state functional connectivity , 2022, Frontiers in Neuroendocrinology.

[3]  Xindao Yin,et al.  Aberrant Brain Functional Connectivity Strength and Effective Connectivity in Patients with Type 2 Diabetes Mellitus , 2021, Journal of diabetes research.

[4]  Lin Fan,et al.  Neural Correlates of Causal Inferences in Discourse Understanding and Logical Problem-Solving: A Meta-Analysis Study , 2021, Frontiers in Human Neuroscience.

[5]  Xin Zhang,et al.  Alterations in Dynamic Functional Connectivity in Individuals With Subjective Cognitive Decline , 2021, Frontiers in Aging Neuroscience.

[6]  Weihao Zheng,et al.  Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy , 2020, NeuroImage: Clinical.

[7]  D. Lu,et al.  A Precuneal Causal Loop Mediates External and Internal Information Integration in the Human Brain , 2020, The Journal of Neuroscience.

[8]  Zhiguo Zhang,et al.  Recurrent and concurrent patterns of regional BOLD dynamics and functional connectivity dynamics in cognitive decline , 2020, Alzheimer's research & therapy.

[9]  Yan Bai,et al.  Altered Effective Connectivity of Bilateral Hippocampus in Type 2 Diabetes Mellitus , 2020, Frontiers in Neuroscience.

[10]  Weibo Chen,et al.  Altered functional connectivity of brain regions based on a meta‐analysis in patients with T2DM: A resting‐state fMRI study , 2020, Brain and behavior.

[11]  Zhengjia Dai,et al.  Abnormal dynamic functional connectivity in Alzheimer’s disease , 2020, CNS neuroscience & therapeutics.

[12]  Qing-guo Ma,et al.  The Stroop effect: An activation likelihood estimation meta-analysis in healthy young adults , 2019, Neuroscience Letters.

[13]  Xia Wu,et al.  Altered dynamic functional connectivity in weakly-connected state in major depressive disorder , 2019, Clinical Neurophysiology.

[14]  Hui Lu,et al.  The Medial Prefrontal Cortex in Neurological Diseases. , 2019, Physiological genomics.

[15]  Waldemar Karwowski,et al.  Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review , 2019, Front. Neurosci..

[16]  S. Qiu,et al.  Altered functional connectivity of the posterior cingulate cortex in type 2 diabetes with cognitive impairment , 2019, Brain Imaging and Behavior.

[17]  D. Hu,et al.  Changes in default mode network connectivity in different glucose metabolism status and diabetes duration , 2018, NeuroImage: Clinical.

[18]  Satrajit S. Ghosh,et al.  FMRIPrep: a robust preprocessing pipeline for functional MRI , 2018, Nature Methods.

[19]  Frank B. Hu,et al.  Global aetiology and epidemiology of type 2 diabetes mellitus and its complications , 2018, Nature Reviews Endocrinology.

[20]  K. Khunti,et al.  Type 2 diabetes , 2017, The Lancet.

[21]  Tianzi Jiang,et al.  Correspondent Functional Topography of the Human Left Inferior Parietal Lobule at Rest and Under Task Revealed Using Resting‐State fMRI and Coactivation Based Parcellation , 2017, Human brain mapping.

[22]  Meiling Li,et al.  Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure , 2017, Human brain mapping.

[23]  Kewei Chen,et al.  Dysfunctional organization of default mode network before memory impairments in type 2 diabetes , 2016, Psychoneuroendocrinology.

[24]  Naveed Sattar,et al.  The changing face of diabetes complications. , 2016, The lancet. Diabetes & endocrinology.

[25]  Senqing Qi,et al.  Resting-state functional connectivity of the default mode network associated with happiness. , 2016, Social cognitive and affective neuroscience.

[26]  H. Rao,et al.  Blood Pressure is Associated With Cerebral Blood Flow Alterations in Patients With T2DM as Revealed by Perfusion Functional MRI , 2015, Medicine.

[27]  W. Milberg,et al.  Inflammation-associated declines in cerebral vasoreactivity and cognition in type 2 diabetes , 2015, Neurology.

[28]  Chee-Ming Ting,et al.  Estimating Effective Connectivity from fMRI Data Using Factor-based Subspace Autoregressive Models , 2015, IEEE Signal Processing Letters.

[29]  Yun Jiao,et al.  Aberrant functional connectivity of default-mode network in type 2 diabetes patients , 2015, European Radiology.

[30]  August B. Smit,et al.  Optogenetic dissection of medial prefrontal cortex circuitry , 2014, Front. Syst. Neurosci..

[31]  Yufeng Zang,et al.  DynamicBC: A MATLAB Toolbox for Dynamic Brain Connectome Analysis , 2014, Brain Connect..

[32]  K. M. Deneen,et al.  Altered effective connectivity patterns of the default mode network in Alzheimer's disease: An fMRI study , 2014, Neuroscience Letters.

[33]  Shihui Han,et al.  Reminders of mortality decrease midcingulate activity in response to others' suffering. , 2014, Social cognitive and affective neuroscience.

[34]  L. Kappelle,et al.  Dementia and cognitive decline in type 2 diabetes and prediabetic stages: towards targeted interventions. , 2014, The lancet. Diabetes & endocrinology.

[35]  M. Corbetta,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[36]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[37]  Viviana Betti,et al.  Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain , 2013, Neuron.

[38]  He Li,et al.  Amnestic mild cognitive impairment: topological reorganization of the default-mode network. , 2013, Radiology.

[39]  M. Seghier The Angular Gyrus , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[40]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[41]  M. Ding,et al.  Granger causal influence predicts BOLD activity levels in the default mode network , 2011, Human brain mapping.

[42]  William W. Graves,et al.  Neural Systems for Reading Aloud: A Multiparametric Approach , 2009, Cerebral cortex.

[43]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[44]  V. Calhoun,et al.  Changes in the interaction of resting‐state neural networks from adolescence to adulthood , 2009, Human brain mapping.

[45]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[46]  Bruce L. Miller,et al.  An Overview on Primary Progressive Aphasia and Its Variants , 2006, Behavioural neurology.

[47]  Jin Fan,et al.  The activation of attentional networks , 2005, NeuroImage.

[48]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[49]  Erik B. Erhardt,et al.  Supplementary Material to: Tracking Whole-Brain Connectivity Dynamics in the Resting State. , 2013 .

[50]  Zhenyu Liu,et al.  Exploring the effective connectivity of resting state networks in Mild Cognitive Impairment: An fMRI study combining ICA and multivariate Granger causality analysis , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.