Spectral clustering-based resting-state network detection approach for functional near-infrared spectroscopy.

Lian Duan,Xiaoqin Mai

doi:10.1364/boe.387919

Abstract

In recent years, studying the resting-state network (RSN) by using functional near-infrared spectroscopy (fNIRS) has received increased attention. The previous resting-state fNIRS studies mainly adopted the seed-based correlation and the independent component analysis to detect RSN. However, these methods have several inherent problems. For example, the seed-based correlation method relies on seed region selection and neglects the interactions among multiple regions. The ICA method usually relies on manual component selection, which requires rich experience from the experimenter. In the present study, we developed a new approach for fNIRS-RSN detection based on spectral clustering. It consists of two steps. First, it calculates the individual-level partition of the fNIRS measurement region by using spectral clustering with an automatically determined cluster number. Second, the individual-level partitioning results are further clustered. Those clusters with high group consistency are determined as RSN clusters. We validated the method by using simulated data and in vivo fNIRS data. The results showed that the proposed method was effective and robust for fNIRS-RSN detection.

Highlights

Human brain is intrinsically organized into networks that consist of multiple spatially remote brain regions whose neural activities are temporally coordinated even during the resting state [1,2]
The present study developed a novel data-driven approach for functional near-infrared spectroscopy (fNIRS)-resting-state network (RSN) detection based on spectral clustering
The results suggested that the method could perfectly find all the RSN clusters (GOF = 1 in all conditions. p < 0.05, Bonferroni-corrected)

Summary

Introduction

Human brain is intrinsically organized into networks that consist of multiple spatially remote brain regions whose neural activities are temporally coordinated even during the resting state [1,2]. The resting-state network (RSN) is believed to serve as the foundation of many basic and critical cognitive functions [3,4,5]. The researchers use functional magnetic resonance imaging (fMRI) to study RSN [7]. In recent years, studying RSN by using functional near-infrared spectroscopy (fNIRS) received increasing attentions [8,9,10,11,12,13,14,15,16,17,18]. Compared with fMRI, fNIRS has many unique advantages such as low-cost, portable, comfortable and insensitive to head motion, making it very suitable for resting-state studies, especially on specific participant groups such as infants [19]

Methods

Results

Conclusion