The effect of network template from normal subjects in the detection of network impairment

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Acta Neurobiologiae Experimentalis

Nencki Institute of Experimental Biology

Polish Neuroscience Society

Subject: Behavioral Sciences , Biomedical Sciences & Nutrition , Life Sciences , Medicine , Neurosciences

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ISSN: 0065-1400
eISSN: 1689-0035

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VOLUME 76 , ISSUE 4 (December 2016) > List of articles

Advertisement The effect of network template from normal subjects in the detection of network impairment

Chun‑Chao Huang / Shang‑Hua Lin / Ching‑Po Lin * / The Alzheimer’s Disease Neuroimaging Initiative

Keywords : functional connectivity, resting‑state network, default mode network, Alzheimer’s disease, mild cognitive impairment, independent component analysis

Citation Information : Acta Neurobiologiae Experimentalis. Volume 76, Issue 4, Pages 294-303, DOI: https://doi.org/10.21307/ane-2017-028

License : (CC BY 4.0)

Published Online: 31-July-2017

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ABSTRACT

This study aimed to provide a simple way to approach group differences by independent component analysis when researching functional connectivity changes of resting‑state network in brain disorders. We used baseline resting state functional magnetic resonance imaging from the Alzheimer’s disease neuroimaging initiative dataset and performed independent component analysis based on different kinds of subject selection, by including two downloaded templates and single‑subject independent component analysis method. All conditions were used to calculate the functional connectivity of the default mode network, and to test group differences and evaluate correlation with cognitive measurements and hippocampal volume. The default mode network functional connectivity results most fitting clinical evaluations were from templates based on young healthy subjects and the worst results were from heterogeneous or more severe disease groups or single‑subject independent component analysis method. Using independent component analysis network maps derived from normal young subjects to extract all individual functional connectivities provides significant correlations with clinical evaluations.

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