https://scholars.lib.ntu.edu.tw/handle/123456789/611850
標題: | Quantifying nonlinear coupling in cerebral haemodynamics using information transfer | 作者: | Rowley A.B. Payne S.J. STEPHEN JOHN PAYNE |
關鍵字: | algorithm;arterial pressure;brain function;brain region;conference paper;entropy;functional magnetic resonance imaging;hemodynamics;mathematical model;near infrared spectroscopy;priority journal | 公開日期: | 2007 | 卷: | 27 | 期: | SUPPL. 1 | 起(迄)頁: | BP04-05W | 來源出版物: | Journal of Cerebral Blood Flow and Metabolism | 摘要: | BACKGROUND: The information theoretical concept of transfer entropy [Schreiber, T. Phys Rev Lett. 2000] was recently used [Katura et al. NeuroImage. 2006.] to assess the influence of systemic variability on spontaneous low frequency oscillations measured in the human brain using diffuse optical tomography. Transfer entropy is a probabilistic technique of assessing general coupling between time series. It has considerable advantages over traditional techniques (such as correlation) as it makes no assumptions about the linearity of the underlying relationship. It was recently shown [Achard, S. Journal of Neuroscience. 2006] that correlation of coefficients of the maximal overlap wavelet packet transform of functional magnetic resonance imaging (fMRI) time series in humans can be used to characterise functional connectivity between regions of the brain by assessing similarity between time series of low frequency (<0.1Hz) oscillations. Importantly, the wavelet packet transform provides a means of separating the time series into components at different scales (frequency bands). When this decomposition has been performed, correlation provides a measure of linear similarity between scales. Replacing this correlation step in the algorithm with a similarity measure based upon transfer entropy relaxes the assumption of linearity and can be used to estimate a directed measure of information transfer between inter scale time series. METHODS: We have developed an estimator for transfer entropy that uses a recently developed unsupervised learning algorithm [Figueiredo et al. IEEE Trans. on Pattern Analysis and Machine Intelligence 2002.] for estimation of the probability density functions of the time series embedding from relatively short duration records. This estimator is applied to quantification of the interrelationship between wavelet transform coefficients of low frequency oscillations measured using near infrared spectroscopy (NIRS) and those observed systemically (for example mean arterial blood pressure (ABP)). RESULTS AND CONCLUSIONS: Use of a Gaussian mixture model to converge on the underlying probability density functions provides a practical estimator for transfer entropy between time series that may be measured using near infrared spectroscopy. The maximal overlap wavelet packet transform is an efficient algorithm for decomposing a time series into coefficients that have highly localised frequency content. Applying the technique to quantification of coupling between NIRS and systemically measured variability provides a measure of the extent to which low frequency behaviour measured using NIRS can be attributed to systemic variability. It is expected that this technique will also be useful to investigate functional connectivity between regions of the brain by assessing scale dependent similarity of low frequency oscillations measured using FMRI without making the assumption of linearity of the underlying coupling relationship. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-36348943397&partnerID=40&md5=452824bda5e76b329f29c0275a3d42f7 https://scholars.lib.ntu.edu.tw/handle/123456789/611850 |
顯示於: | 應用力學研究所 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。