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Adaptive HRF and BF approaches to fNIRS activation analysis.

Tomoyuki Hiroyasu1, Saki Yoshitake2* and Satoru Hiwa1
  • 1 Doshisha University, Faculty of Life and Medical Sciences, Japan
  • 2 Doshisha University, Graduate School of Life and Medical Sciences, Japan

[Background and purpose] Functional Near Infrared Spectroscopy (fNIRS) is a brain imaging facility and is widely used recently. The brain functional activation is determined by measuring blood flow changes. The Generalized Linear Model (GLM) is one of the approaches to analyze brain functional activations [1][2]. This method is also used in functional Magnetic Resonance Imaging (fMRI) analysis. In the GLM, activation is determined by the similarity of regression analysis between a basis function and a test data. The basis function is generated by convolving the variable Hemodynamic Response Function (HRF) with a boxcar function [3] and generally temporal variables are used for these parameters. HRF has several parameters such as first peak delay, second peak delay and ratio of first and second peaks. The boxcar function is related to the experiment design event and weights of boxcar function are uniform. However, we assume parameters of HRF at each signal point are different and weights of boxcar function are also different [3][4]. Thus, in this paper, we apply adaptive HRF and adaptive boxcar function approaches. [Proposed method] The parameters of HRF and weights of boxcar functions are derived by minimizing the difference between base function and experiment data. This operation is performed for all the channels of all subjects. Therefore, the optimum parameters of HRF and weights of boxcar functions are determined at every channel for every subject. The object function is the t-value which is derived using the regression coefficient beta and residual error e. To maximize the t-value, three parameters of HRF and weights of boxcar function are determined. [Method] The proposed method is applied to N-back task experiment. Then the effectiveness of the proposed method is discussed.

References

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Keywords: fNIRS, GLM, HRF, Regression Analysis, n-back

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Neuroimaging

Citation: Hiroyasu T, Yoshitake S and Hiwa S (2016). Adaptive HRF and BF approaches to fNIRS activation analysis.. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00062

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Received: 01 May 2016; Published Online: 18 Jul 2016.

* Correspondence: Ms. Saki Yoshitake, Doshisha University, Graduate School of Life and Medical Sciences, Kyoto, Japan, syoshitake@mis.doshisha.ac.jp