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High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model

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Abstract

Many functional magnetic resonance imaging (fMRI) studies have indicated that Granger causality analysis (GCA) is a suitable method for revealing causal effects between brain regions. The purpose of the present study was to identify neuroimaging biomarkers with a high sensitivity to amnestic mild cognitive impairment (aMCI). The resting-state fMRI data of 30 patients with Alzheimer’s disease (AD), 14 patients with aMCI, and 18 healthy controls (HC) were evaluated using GCA. This study focused on the “triple networks” concept, a recently proposed higher-order functioning-related brain network model that includes the default-mode network (DMN), salience network (SN), and executive control network (ECN). As expected, GCA techniques were able to reveal differences in connectivity in the three core networks among the three patient groups. The fMRI data were pre-processed using DPARSFA v2.3 and REST v1.8. Voxel-wise GCA was performed using the REST-GCA in the REST toolbox. The directed (excitatory and inhibitory) connectivity obtained from GCA could differentiate among the AD, aMCI and HC groups. This result suggests that analysing the directed connectivity of inter-hemisphere connections represents a sensitive method for revealing connectivity changes observed in patients with aMCI. Specifically, inhibitory within-DMN connectivity from the posterior cingulate cortex (PCC) to the hippocampal formation and from the thalamus to the PCC as well as excitatory within-SN connectivity from the dorsal anterior cingulate cortex (dACC) to the striatum, from the ECN to the DMN, and from the SN to the ECN demonstrated that changes in connectivity likely reflect compensatory effects in aMCI. These findings suggest that changes observed in the triple networks may be used as sensitive neuroimaging biomarkers for the early detection of aMCI.

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Abbreviations

ACC:

Anterior cingulate cortex

AD:

Alzheimer’s disease

ALFF:

Amplitude of low-frequency fluctuations

aMCI:

Amnestic mild cognitive impairment

ANCOVA:

One-way analysis of covariance

ANOVA:

One-way analysis of variance

BOLD:

Blood oxygenation level-dependent

dACC:

Dorsal anterior cingulate cortex

dlPFC:

Dorsolateral prefrontal cortex

DMN:

Default-mode network

ECN:

Executive control network

EPI:

Echo-planar imaging

FC:

Functional connectivity

fMRI:

Functional magnetic resonance imaging

FOV:

Field of view

GCA:

Granger causality analysis

HC:

Healthy controls

IPC:

Inferior parietal cortex

ITC:

Inferior temporal cortex

MMSE:

Mini mental state evaluation

MoCA:

Montreal cognitive scale

MPFC:

Medial prefrontal cortex

MPRAGE:

Magnetization-prepared rapid gradient echo

PCC:

Cingulate cortex

PCC:

Posterior cingulate cortex

PPC:

Lateral posterior parietal cortex

ROI:

Region of interest

SN:

Salience network

TE:

Echo time

TR:

Repetition time

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Acknowledgements

This study is funded by the Zhejiang Provincial Natural Science Foundation of China (no. Y2091289, LY16H180007, LY13H180016) and the Science Foundation from Health Commission of Zhejiang Province (no. 2013RCA001, 2016147373, ZKJ-ZJ-1503). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychiatry, Zhejiang Provincial People’s Hospital, Hangzhou, 310014, China

    Enyan Yu, Zhengluan Liao, Yunfei Tan, Yaju Qiu, Junpeng Zhu, Xinwei Wang, Hong Wang, Yan Chen & Qi Zhang

  2. Center for Cognition and Brain Disorders, Hangzhou Normal University, Zhejiang, Hangzhou, 311121, China

    Zhang Han

  3. Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012, China

    Jue Wang

  4. Department of Radiology, Zhejiang Provincial People’s Hospital, 158, Shangtang Rd, Hangzhou, 310014, China

    Yumei Li, Dewang Mao & Zhongxiang Ding

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  1. Enyan Yu
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  2. Zhengluan Liao
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  3. Yunfei Tan
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  4. Yaju Qiu
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  5. Junpeng Zhu
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  9. Hong Wang
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  12. Yumei Li
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  14. Zhongxiang Ding
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Correspondence to Zhongxiang Ding.

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Conflict of interest

Enyan Yu, Zhengluan Liao, Yunfei Tan, Yaju Qiu, Junpeng Zhu, Zhang Han, Jue Wang, Xinwei Wang, Hong Wang, Yan Chen, Qi Zhang, Yumei Li, Dewang Mao, and Zhongxiang Ding declare that they have no conflict of interest.

Informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and the Helsinki Declaration of 1975, and the applicable revisions at the time of investigation. Informed consent was obtained from all patients. The study was approved by the institutional Ethics Committee number: 2012KY002.

Electronic supplementary material

Supplemental Fig. 1

Position of the three seed points of the triple networks. A, Posterior cingulate cortex (PCC) of the default-mode network (DMN); B, dorsal anterior cingulate cortex dACC of the salience network (SN); and C, dorsolateral prefrontal cortex (dlPFC) of the executive control network (ECN) (JPEG 163 kb)

High Resolution Image (TIFF 1521 kb)

Supplemental Fig. 2

The driving effect from PCC to other brain regions. (JPEG 639 kb)

High Resolution Image (TIFF 7721 kb)

Supplemental Fig. 3

The driving effect to PCC from other brain areas. (JPEG 624 kb)

High Resolution Image (TIFF 7422 kb)

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Yu, E., Liao, Z., Tan, Y. et al. High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model. Brain Imaging and Behavior 13, 1–14 (2019). https://doi.org/10.1007/s11682-017-9727-6

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