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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Huang,C., Huang,C., Huang,C., Huang,C., Lin,S., Lin,C., Lin,C., Lin,C., & Neuroimaging Initiative,T.A.D.(2016).The effect of network template from normal subjects in the detection of network impairment.Acta Neurobiologiae Experimentalis, 76(4), 294-303.doi:10.21307/ane-2017-028.
Huang,Chun‑Chao., Chun‑Chao Huang, Chun‑Chao Huang, Chun‑Chao Huang, Shang‑Hua Lin, Ching‑Po Lin, Ching‑Po Lin, Ching‑Po Lin, and The Alzheimer’s Disease Neuroimaging Initiative"The effect of network template from normal subjects in the detection of network impairment"Acta Neurobiologiae Experimentalis 76,no.4(2016):294-303doi:10.21307/ane-2017-028.
Huang,Chun‑Chao., Chun‑Chao Huang, Chun‑Chao Huang, Chun‑Chao Huang, Shang‑Hua Lin, Ching‑Po Lin, Ching‑Po Lin, Ching‑Po Lin, and The Alzheimer’s Disease Neuroimaging Initiative"The effect of network template from normal subjects in the detection of network impairment"Acta Neurobiologiae Experimentalis 76.no.4(2016):294-303doi:10.21307/ane-2017-028.
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