Neuroimaging with functional near infrared spectroscopy: From formation to interpretation

Abstract

Functional Near Infrared Spectroscopy (fNIRS) is gaining momentum as a functional neuroimaging modality to investigate the cerebral hemodynamics subsequent to neural metabolism. As other neuroimaging modalities, it is neuroscience's tool to understand brain systems functions at behaviour and cognitive levels. To extract useful knowledge from functional neuroimages it is critical to understand the series of transformations applied during the process of the information retrieval and how they bound the interpretation. This process starts with the irradiation of the head tissues with infrared light to obtain the raw neuroimage and proceeds with computational and statistical analysis revealing hidden associations between pixels intensities and neural activity encoded to end up with the explanation of some particular aspect regarding brain function.To comprehend the overall process involved in fNIRS there is extensive literature addressing each individual step separately. This paper overviews the complete transformation sequence through image formation, reconstruction and analysis to provide an insight of the final functional interpretation.

Initial-Dip Existence and Estimation in Relation to DPF and Data Drift

“…It is common practice to apply low pass filter and de-trending algorithms to get rid of physiological noises and drift in observed optical data (Abdelnour and Huppert, 2009; Ye et al, 2009; Cui et al, 2010; Shah and Seghouane, 2014; Metz et al, 2015; Yin et al, 2015; Herrera-Vega et al, 2017). As for as physiological noises are concerned, most frequently used filtering range is with cut-off frequency of 0.5 Hz low pass filter and 0.01 for high pass filter to remove these unnecessary signals.…”

“…As the depth of initial dip is very small as compared to main peak, thus even if small drift is present in the data, the initial dip could not be found and in case where initial dip features are selected for BCI algorithm, the false decision could be expected and possible. A typical methodology for correction of drift is de-trending (Herrera-Vega et al, 2017). High-pass filtering is another fruitful way of removing low frequency drift in HbO signal (Cui et al, 2010).…”

Contrast Agents Delivery: An Up-to-Date Review of Nanodiagnostics in Neuroimaging

“…The changes in oxygenated haemoglobin and deoxygenated haemoglobin are regarded as an indicator for variations in the regional cerebral blood volume [65]. The study of brain function through fNIRS requires a good acquaintance with how the diffuse optical neuroimage encodes the information related to it [66].…”

A learnable EEG channel selection method for MI-BCI using efficient channel attention

“…A brain-computer Interface (BCI) provides an interface between users and external devices by converting brain signals into commands (Fadel et al, 2020). Electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS), et al, are commonly used to acquire signals from the brain (Herrera-Vega et al, 2017;Berger et al, 2019;Saha et al, 2021). Among these techniques, EEG is currently one of the most popular brain-imaging techniques for its non-invasive nature, portability, and low cost (Abiri et al, 2019;Aggarwal and Chugh, 2022).…”