Threshold for Computing Generalized Model of Default Mode Network Connectivity

被引：0
|
作者：
Rasheed, Waqas [1 ]
Tang, Tong Boon [1 ]
Hamid, Nor Hisham [1 ]
机构：
[1] Univ Teknol PETRONAS, Dept Elect & Elect Engn, CISIR, Bandar Seri Iskandar 32610, Perak Darul Rid, Malaysia
来源：
2016 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS) | 2016年
关键词：
Neuroimaging; Magnetoencephalography; Default Mode Network; Generalized Coherence Model; Threshold; MAGNETOENCEPHALOGRAPHY MEG; FUNCTIONAL CONNECTIVITY; SIMULTANEOUS EEG; BRAIN;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Functional connectivity is becoming popular as a second opinion for neurosurgeons and specialists in order to decide on the need for surgical resection, or prescribing medication and appraise prognosis. Neuroimaging modalities such as fMRI, fNIRS, PET, and EEG provide functional connectivity estimation. MEG is the most recent trend in functional connectivity assessment research as it gives more accurate results. The magnetic signals are not disrupted by volume conduction, as in EEG. Besides a reasonable spatial resolution, it offers an extraordinary temporal resolution. However there is a need of a generalized model for default mode network connectivity using MEG. This paper presents a novel method for generating a generalized model and discusses significance of threshold levels in assessing synchronization of activity from various brain regions.
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页数：4
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