Data Analysis Method for Neuroimaging Data: Task-Related Component Analysis and Its Applications to fNIRS Data

Abstract

Experimental results should guarantee their reproducibility for the objective nature of science. Neuroimaging data, however, often contain artifactual components that are not pertinent directly to neural activations in question, thereby impeding the reproducibility of experimental results. Signal processing or data analysis methods play a crucial role in removing such artifactual components and extracting relevant neural activations. We here provide a concise overview of data analysis methods with an emphasis on functional near-infrared spectroscopy (fNIRS) and discuss their advantages and disadvantages. Then our analysis method, task-related component analysis (TRCA), that maximizes the block-by-block reproducibility of a signal in one condition is proposed. TRCA is formulated as a generalized eigenvalue problem and is extended to several useful forms including an online recursive algorithm and one that takes channel-by-channel delays into account. Finally extensive applications of TRCA to synthetic data and fNIRS data of a finger-tapping task and a working-memory task are presented. Although originally motivated for fNIRS data analysis, the concept of signal reproducibility has a broad implication and we expect that TRCA has a wide range of applications in biophysical data analysis.