Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures

Abstract

Flourishing is an important criterion to assess wellbeing, however, controversies remain, particularly around assessing it with self-report measures. Due to this reason, to be able to understand the underlying neural mechanisms of well-being, researchers often utilize neuroimaging techniques. However, rather than individual answers, previous neuroimaging studies using statistical approaches provided an answer in average sense. To overcome these problems, we applied machine learning techniques to discriminate 43 highly flourishing from regular flourishing individuals by using a publicly available resting state functional near infrared spectroscopy (rs-fNIRS) dataset to get an answer in individual level. We utilized both Pearson’s correlation (CC) and Dynamic Time Warping (DTW) algorithm to estimate functional connectivity from rs-fNIRS data on temporo-parieto-occipital region as input to nine different machine learning algorithms. Our results revealed that by utilizing oxyhemoglobin concentration change with Pearson’s correlation (CC – ΔHbO) and deoxy hemoglobin concentration change with dynamic time warping (DTW – ΔHb), we could be 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. This finding suggests that temporo-parieto-occipital regional based resting state 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.

Decoding Articulation Motor Imagery Using Early Connectivity Information in the Motor Cortex: A Functional Near-Infrared Spectroscopy Study

“…In this study, functional connectivity features were extracted for the classification tasks because functional connectivity can reflect information propagation. The temporal correlation of regional hemodynamics was estimated by Pearson's correlation which was commonly used to quantify functional connectivity between different brain regions in fNIRS-based studies [26], [27], [28], [29]. The Pearson correlation coefficient is used to measure the degree of linear correlation between two numerical variables.…”

A functional model explaining the dynamics of flourishing during adolescence: an interpretative phenomenological analysis

“…Nevertheless, Kwong and Hayes (2017) have studied flourishing with its three aspects-capability to remain in control when challenged, interest in learning new things and ability to follow through with plans. Research suggests that flourishing is not only an important criterion for assessing well-being (Eken, 2021), but also it serves as a marker for behavioural and social well-being (Lippman et al, 2011;Moore and Lippman, 2006). Recently Whitaker et al (2021) have observed that greater childhood family connection is associated with greater flourishing in US adults across levels of childhood adversity.…”