Test-retest reliability of fNIRS in resting-state cortical activity and brain network assessment in stroke patients

Abstract

Resting-state functional near infrared spectroscopy (fNIRS) scanning has attracted considerable attention in stroke rehabilitation research in recent years. The aim of this study was to quantify the reliability of fNIRS in cortical activity intensity and brain network metrics among resting-state stroke patients, and to comprehensively evaluate the effects of frequency selection, scanning duration, analysis and preprocessing strategies on test-retest reliability. Nineteen patients with stroke underwent two resting fNIRS scanning sessions with an interval of 24 hours. The haemoglobin signals were preprocessed by principal component analysis, common average reference and haemodynamic modality separation (HMS) algorithm respectively. The cortical activity, functional connectivity level, local network metrics (degree, betweenness and local efficiency) and global network metrics were calculated at 25 frequency scales × 16 time windows. The test-retest reliability of each fNIRS metric was quantified by the intraclass correlation coefficient. The results show that (1) the high-frequency band has higher ICC values than the low-frequency band, and the fNIRS metric is more reliable than at the individual channel level when averaged within the brain region channel, (2) the ICC values of the low-frequency band above the 4-minute scan time are generally higher than 0.5, the local efficiency and global network metrics reach high and excellent reliability levels after 4 min (0.5 < ICC < 0.9), with moderate or even poor reliability for degree and betweenness (ICC < 0.5), (3) HMS algorithm performs best in improving the low-frequency band ICC values. The results indicate that a scanning duration of more than 4 minutes can lead to high reliability of most fNIRS metrics when assessing low-frequency resting brain function in stroke patients. It is recommended to use the global correction method of HMS, and the reporting of degree, betweenness and single channel level should be performed with caution. This paper provides the first comprehensive reference for resting-state experimental design and analysis strategies for fNIRS in stroke rehabilitation.

Priming transcranial direct current stimulation for improving hemiparetic upper limb in patients with subacute stroke: study protocol for a randomised controlled trial

“…fNIRS data acquisition will be performed in a relatively dark room in a relaxed environment. Our study will acquire 10 min of resting-state fNIRS data before and after the intervention and at follow-up 41…”

“…Our study will acquire 10 min of resting-state fNIRS data before and after the intervention and at follow-up. 41 fNIRS data processing The resting-state fNIRS data will be preprocessed using the HomER2 toolbox in MATLAB 2014a (Math-Works), 42 which is similar to the method in our previous work. 39 40 First, the raw signals are converted to changes in the optical density.…”

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons

“…; (11) this conversion from absorption at 800 nm to HbX concentrations at 690 and 830 nm, although not so rigorous, relies solely on the need to have an estimation of the reference values at the beginning of the measurement process. Then, both the baseline and the corresponding concentration changes were converted into absorption coefficients for the two different wavelengths, λ 1 ¼ 690 nm and λ 2 ¼ 830 nm, by the following operation:…”

“…5,6 Due to all this, applications of CW-fNIRS have reached several subfields in medicine, neurology, and neuroscience. [7][8][9][10][11][12] The main objective of CW-fNIRS is to retrieve concentration changes of particularly two chromophores: oxygenated and deoxygenated hemoglobin (usually abbreviated as HbO and HbR, respectively); to do so, a minimum of two different wavelengths must be considered, at both sides of the isosbestic point (∼800 nm) of HbO and HbR. 13 However, some difficulties may arise that mask the desired information that comes from the cortex.…”

Reliability of brain metrics derived from a Time-Domain Functional Near-Infrared Spectroscopy System

“…On the other hand, an observed lack of reliability does not readily invalidate a measure; it may simply indicate that the function of interest was not properly held constant across measurements, or may represent a valid change. While the bulk of non-invasive functional neuroimaging studies has sought to validate derived neural metrics, a growing body of literature has explored the TRR of these measurements in neuropsychiatric conditions such as depression 1,2 , cognitive decline 4,5 , pain 6 , stroke 7 , aging 8 , and ADHD 9 . These studies are helpful to establish the current standard of TRR across common noninvasive neuroimaging modalities (functional Magnetic Resonance Imaging, fMRI; electroencephalography, EEG; and functional Near-Infrared Spectroscopy, fNIRS).…”