期刊
2016 THIRD EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2016)
卷 -, 期 -, 页码 203-208出版社
IEEE
DOI: 10.1109/ENIC.2016.36
关键词
fMRI; functional connectivity patterns; dynamic time warping; warping path; classification
资金
- Hungarian Brain Research Program [KTIA_13_NAP-A-U18]
- National Research, Development and Innovation Office - NKFIH [PD 111710]
- Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences
- [SVV-2015-260222]
Synchronized spontaneous low frequency fluctuations of the so called BOLD signal, as measured by functional Magnetic Resonance Imaging (fMRI), are known to represent the functional connections of different brain areas. 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 and the usage of the DTW algorithm has further advantages: beside the DTW distance, the algorithm generates the warping path, i.e. the time-delay function between the compared two time-series. In this paper, we propose to use the relative length of the warping path as classification feature and demonstrate that the warping path itself carries important information when classifying patients according to cannabis addiction. We discuss biomedical relevance of our findings as well.
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