Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data
Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data
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多通道 fNIRS 数据中 ROI 确定问题决策模式分析的有效性
DOI：
10.1007/s10015-017-0362-5
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Mitsunori MIKI and Tomoyuki HIROYASU
中科院分区：
文献类型：
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作者：
Satoru HIWA;Mitsunori MIKI and Tomoyuki HIROYASU
当使用功能近红外光谱（fNIRS）数据来检测大脑活动时，感兴趣区域（ROI）的确定是必要的。为了从多个fNIRS通道中提取ROI，我们研究了将决策模式分析应用于fNIRS数据集的有效性。这将数据集分类为具有相似特征的聚类。对于每个聚类，数据集被分解为均值向量和特征向量的线性组合。将其应用于fNIRS信号，可以使用平均向量来表示血红蛋白（Hb）的变化，并且将特征向量解释为构成任意信号的信号分量。通过将这些向量与脑功能的理论模型相关联来表征这些向量，有助于我们理解Hb变化发生的位置以及Hb变化的类型反映了fNIRS数据中的脑活动。在观看立体图像期间测量的fNIRS数据的决策模式分析确定了与注意力控制相关的右额下回周围的ROI，以及与行动和预测决策相关的额叶关联区。我们的实验结果表明，从决策模式分析获得的信息可以帮助定量和定性的ROI确定。
Region of interest (ROI) determination is necessary when using functional near-infrared spectroscopy (fNIRS) data to detect brain activity. To extract ROIs from multiple fNIRS channels, we investigated the validity of applying decision mode analysis to the fNIRS dataset. This classifies a dataset into clusters with similar features. For each cluster, the dataset is decomposed into a mean vector and a linear combination of eigenvectors. Applying this to fNIRS signals, the mean vector can be used to represent change in hemoglobin (Hb), and the eigenvectors interpreted as a signal component constructing the arbitrary signal. Characterizing these vectors by correlating them with a theoretical model of brain function aids our understanding of where Hb changes occur and what type of Hb changes reflect brain activity in fNIRS data. Decision mode analysis of fNIRS data measured during viewing stereoscopic images identified ROIs around the right inferior frontal gyrus associated with attentional control, and frontal association area associated with decision on action and prediction. Our experimental results showed that information obtained from decision mode analysis can aid quantitative and qualitative ROI determination.