Exploring the effective connectivity of resting state networks in mild cognitive impairment: an fMRI study combining ICA and multivariate Granger causality analysis. 2012

Zhenyu Liu, and Lijun Bai, and Ruwei Dai, and Chongguang Zhong, and Hu Wang, and Youbo You, and Wenjuan Wei, and Jie Tian
Intelligent Medical Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China. liuzhenyu@fingerpass.net.cn

Mild cognitive impairment (MCI) was recognized as the prodromal stage of Alzheimer's disease (AD). Recent neuroimaging studies have shown that the cognitive and memory decline in AD and MCI patients is coupled with abnormal functions of focal brain regions and disrupted functional connectivity between distinct brain regions, as well as losses of small-world attributes. However, the causal interactions among the spatially isolated but function-related resting state networks (RSNs) are still largely unexplored in MCI patients. In this study, we first identified eight RSNs by independent components analysis (ICA) from resting state functional MRI data of 16 MCI patients and 18 age-matched healthy subjects respectively. Then, we performed a multivariate Granger causality analysis (mGCA) to evaluate the effective connectivity among the RSNs. We found that MCI patients exhibited decreased causal interactions among the RSNs in both intensity and quantity compared with normal controls. Results from mGCA indicated that the causal interactions involving the default mode network (DMN) became weaker in MCI patients, while stronger causal connectivity emerged related to the memory network and executive control network. Our findings suggested that the DMN played a less important role in MCI patients. Increased causal connectivity of the memory network and executive control network may elucidate the dysfunctional and compensatory processes in the brain networks of MCI patients. These preliminary findings may be helpful for further understanding the pathological mechanisms of MCI and provide a new clue to explore the neurophysiological mechanisms of MCI.

UI MeSH Term Description Entries
D008279 Magnetic Resonance Imaging Non-invasive method of demonstrating internal anatomy based on the principle that atomic nuclei in a strong magnetic field absorb pulses of radiofrequency energy and emit them as radiowaves which can be reconstructed into computerized images. The concept includes proton spin tomographic techniques. Chemical Shift Imaging,MR Tomography,MRI Scans,MRI, Functional,Magnetic Resonance Image,Magnetic Resonance Imaging, Functional,Magnetization Transfer Contrast Imaging,NMR Imaging,NMR Tomography,Tomography, NMR,Tomography, Proton Spin,fMRI,Functional Magnetic Resonance Imaging,Imaging, Chemical Shift,Proton Spin Tomography,Spin Echo Imaging,Steady-State Free Precession MRI,Tomography, MR,Zeugmatography,Chemical Shift Imagings,Echo Imaging, Spin,Echo Imagings, Spin,Functional MRI,Functional MRIs,Image, Magnetic Resonance,Imaging, Magnetic Resonance,Imaging, NMR,Imaging, Spin Echo,Imagings, Chemical Shift,Imagings, Spin Echo,MRI Scan,MRIs, Functional,Magnetic Resonance Images,Resonance Image, Magnetic,Scan, MRI,Scans, MRI,Shift Imaging, Chemical,Shift Imagings, Chemical,Spin Echo Imagings,Steady State Free Precession MRI
D008297 Male Males
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D003072 Cognition Disorders Disorders characterized by disturbances in mental processes related to learning, thinking, reasoning, and judgment. Overinclusion,Disorder, Cognition,Disorders, Cognition
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000368 Aged A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available. Elderly
D000369 Aged, 80 and over Persons 80 years of age and older. Oldest Old
D015984 Causality The relating of causes to the effects they produce. Causes are termed necessary when they must always precede an effect and sufficient when they initiate or produce an effect. Any of several factors may be associated with the potential disease causation or outcome, including predisposing factors, enabling factors, precipitating factors, reinforcing factors, and risk factors. Causation,Enabling Factors,Multifactorial Causality,Multiple Causation,Predisposing Factors,Reinforcing Factors,Causalities,Causalities, Multifactorial,Causality, Multifactorial,Causation, Multiple,Causations,Causations, Multiple,Enabling Factor,Factor, Enabling,Factor, Predisposing,Factor, Reinforcing,Factors, Enabling,Factors, Predisposing,Factors, Reinforcing,Multifactorial Causalities,Multiple Causations,Predisposing Factor,Reinforcing Factor
D015999 Multivariate Analysis A set of techniques used when variation in several variables are studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables. Analysis, Multivariate,Multivariate Analyses

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