Abstract
Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that “brain-first” rather than “behaviour-first” analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging.
Highlights
Functional Near Infrared Spectroscopy is a neuroimaging technique able to measure concentration changes in oxygenated (HbO2) and deoxygenated (HHb) haemoglobin secondary to neuronal activation
The “Synthetic functional Near-Infrared Spectroscopy (fNIRS) Data” section presents the results obtained from the application of the Automatic IDentification of functional Events (AIDE) algorithm to synthetic fNIRS data, while the “Lab-based fNIRS Data” and “Real-world fNIRS Data” sections show the results of the use of the AIDE algorithm for the identification of functional events in a conventional block-design experiment and a real-world prospective memory (PM) task
The novel AIDE algorithm was developed with the aim of providing a tool to support the behavioural analysis of video recordings by statistically detecting functional event onsets from fNIRS data
Summary
Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique able to measure concentration changes in oxygenated (HbO2) and deoxygenated (HHb) haemoglobin secondary to neuronal activation. Whilst fNIRS is a relatively new neuroimaging method, over the last 20 years it has become a popular tool for clinical and psychological applications (Boas et al, 2014), being extensively used to monitor brain activity in response to a wide variety of cognitive tasks. Thanks to its being non-invasive, portable and robust to motion artifacts, fNIRS is suitable: (i) for a wide variety of populations (e.g., clinical patients, infants, elderly people), (ii) for bedside monitoring, and (iii) for those experimental situations that cannot be recreated within the physical constraints of an fMRI scanner because require the volunteer to have unconstraint physical movements (Scholkmann et al, 2014a; Quaresima and Ferrari, 2016). The development of wireless, miniaturized and fiberless fNIRS systems, opens up the way to more ecological applications in neuroscience, especially for those situations in which experiments conducted in the real-world are needed (Burgess et al, 2006; McKendrick et al, 2016; Pinti et al, 2015a)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
