Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment.
In recent years, functional connectomics signatures have been shown to be a very valuable tool in characterizing and differentiating brain disorders from normal controls. However, if the functional connectivity alterations in a brain disease are localized within sub-networks of a connectome, then ac...
| Publicado en: | Brain Imaging & Behavior Vol. 9; no. 4; pp. 663 - 678 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2015
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=111244180&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111244180 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Dec2015 vid: 9 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 111244180 111244180 NLM25355371 111244180 10.1007/s11682-014-9320-1 NLM25355371 111244180 ppf: 663 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Atomic connectomics signatures for characterization and differentiation of mild cognitive impairment. aug: au: Ou, Jinli Xie, Li Li, Xiang Zhu, Dajiang Terry, Douglas Puente, A. Jiang, Rongxin Chen, Yaowu Wang, Lihong Shen, Dinggang Zhang, Jing Miller, L. Liu, Tianming Terry, Douglas P Puente, A Nicholas Miller, L Stephen affil: School of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou China sug: subj: Brain Mapping Methods Cognition Disorders Physiopathology Brain Physiopathology Magnetic Resonance Imaging Methods Female Human Neural Pathways Physiopathology Data Collection Male Relaxation Cognition Disorders Classification Aged Validation Studies Comparative Studies Evaluation Research Multicenter Studies Funding Source Aged: 65+ years Female Male ab: In recent years, functional connectomics signatures have been shown to be a very valuable tool in characterizing and differentiating brain disorders from normal controls. However, if the functional connectivity alterations in a brain disease are localized within sub-networks of a connectome, then accurate identification of such disease-specific sub-networks is critical and this capability entails both fine-granularity definition of connectome nodes and effective clustering of connectome nodes into disease-specific and non-disease-specific sub-networks. In this work, we adopted the recently developed DICCCOL (dense individualized and common connectivity-based cortical landmarks) system as a fine-granularity high-resolution connectome construction method to deal with the first issue, and employed an effective variant of non-negative matrix factorization (NMF) method to pinpoint disease-specific sub-networks, which we called atomic connectomics signatures in this work. We have implemented and applied this novel framework to two mild cognitive impairment (MCI) datasets from two different research centers, and our experimental results demonstrated that the derived atomic connectomics signatures can effectively characterize and differentiate MCI patients from their normal controls. In general, our work contributed a novel computational framework for deriving descriptive and distinctive atomic connectomics signatures in brain disorders. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|