Assessment of flourishing levels of individuals by using resting-state fNIRS with different functional connectivity measures. (July 2021)

• Record Type: Journal Article
• Authors: Eken, Aykut
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Resting-state -- fNIRS -- Flourishing -- Well-being -- Dynamic time warping -- Functional connectivity -- Machine learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102645
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Flourishing is an criterion to assess well-being levels of individuals. Our objective was to find potential biomarkers to classify flourishing levels of individuals using rs-fNIRS and ML. Functional connectivity measures [Dynamic Time Warping (DTW) & Pearson Correlation (CC)] were used to ΔHbO and ΔHb signals. Highest accuracy was found by fusing DTW-ΔHb and CC-ΔHbO. Also, DTW-ΔHb set gave better accuracy compared to CC-ΔHbO set. Abstract: Flourishing is an important criterion for assessing well-being, however, controversy remains, especially while assessing it with self-report measures. Therefore, to understand the underlying neural mechanisms of well-being, researchers often use neuroimaging techniques. However, previous neuroimaging studies using conventional statistical approaches provided answers in an average sense rather than individual answers. In this study, we used machine learning algorithms to classify highly flourishing from normally flourishing individuals using a publicly available resting-state functional near-infrared spectroscopy (rs-fNIRS) dataset collected from 43 participants to obtain an answer for individual level. We utilized both the Pearson's correlation (CC) and the Dynamic Time Warping (DTW) algorithm to estimate functional connectivity matrices from the rs-fNIRS data on the temporo-parieto-occipital region and used them as input for machine learning algorithms. Our results showed that we were able to classify flourishing individuals with 90 % accuracy with AUC 0.90 and 0.93 using Nearest Neighbor and Radial Basis Kernel Support Vector Machine using oxyhemoglobin concentration change with Pearson's correlation (CC – ΔHbO) and deoxy hemoglobin concentration change with dynamic time warping (DTW – ΔHb). This finding suggests that temporo-parieto-occipital region-based resting-state functional connectivity might be a potential biomarker to identify the levels of flourishing and using both connectivity measures might allow us to find different potential biomarkers.