期刊
2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
卷 -, 期 -, 页码 245-249出版社
IEEE
关键词
fMRI; functional connectivity networks; dynamic time warping; classification
资金
- Hungarian Brain Research Program [KTIA 13 NAP-A-F18]
- National Research, Development and Innovation Office [NKFIH PD 111710]
- [SVV-2015-260222]
The synchronized spontaneous low frequency fluctuations of the BOLD signal, as captured by functional MRI measurements, is known to represent the functional connections of different brain areas. The aforementioned MRI measurements result in high-dimensional time series, the dimensions of which correspond to the activity of different brain regions. Recently we have shown that Dynamic Time Warping (DTW) distance can be used as a similarity measure between BOLD signals of brain regions as an alternative of the traditionally used correlation coefficient. We have characterized the new metric's stability in multiple measurements, and between subjects in homogenous groups. In this paper we investigated the DTW metric's sensitivity and demonstrated that DTW-based models outperform correlation-based models in resting-state fMRI data classification tasks. Additionally, we show that functional connectivity networks resulting from DTW-based models as compared to the correlation-based models are more stable and sensitive to differences between healthy subjects and patient groups.
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