A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments.

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

Prefrontal cortical activation associated with prospective memory while walking around a real-world street environment

Rostral PFC (area 10) activation is common during prospective memory (PM) tasks. But it is not clear what mental processes these activations index. Three candidate explanations from cognitive neuroscience theory are: (i) monitoring of the environment; (ii) spontaneous intention retrieval; (iii) a combination of the two. These explanations make different predictions about the temporal and spatial patterns of activation that would be seen in rostral PFC in naturalistic settings. Accordingly, we plotted functional events in PFC using portable fNIRS while people were carrying out a PM task outside the lab and responding to cues when they were encountered, to decide between these explanations. Nineteen people were asked to walk around a street in London, U.K. and perform various tasks while also remembering to respond to prospective memory (PM) cues when they detected them. The prospective memory cues could be either social (involving greeting a person) or non-social (interacting with a parking meter) in nature. There were also a number of contrast conditions which allowed us to determine activation specifically related to the prospective memory components of the tasks. We found that maintaining both social and non-social intentions was associated with widespread activation within medial and right hemisphere rostral prefrontal cortex (BA 10), in agreement with numerous previous lab-based fMRI studies of prospective memory. In addition, increased activation was found within lateral prefrontal cortex (BA 45 and 46) when people were maintaining a social intention compared to a non-social one. The data were then subjected to a GLM-based method for automatic identification of functional events (AIDE), and the position of the participants at the time of the activation events were located on a map of the physical space. The results showed that the spatial and temporal distribution of these events was not random, but aggregated around areas in which the participants appeared to retrieve their future intentions (i.e., where they saw intentional cues), as well as where they executed them. Functional events were detected most frequently in BA 10 during the PM conditions compared to other regions and tasks. Mobile fNIRS can be used to measure higher cognitive functions of the prefrontal cortex in “real world” situations outside the laboratory in freely ambulant individuals. The addition of a “brain-first” approach to the data permits the experimenter to determine not only when haemodynamic changes occur, but also where the participant was when it happened. This can be extremely valuable when trying to link brain and cognition

Contributions of Human Prefrontal Cortex to the Recogitation of Thought

Human beings have a unique ability to not only verbally articulate past and present experiences, as well as potential future ones, but also evaluate the mental representations of such things. Some evaluations do little good, in that they poorly reflect facts, create needless emotional distress, and contribute to the obstruction of personal goals, whereas some evaluations are the converse: They are grounded in logic, empiricism, and pragmatism and, therefore, are functional rather than dysfunctional. The aim of non-pharmacological mental health interventions is to revise dysfunctional thoughts into more adaptive, healthier ones; however, the neurocognitive mechanisms driving cognitive change have hitherto remained unclear. Therefore, this thesis examines the role of the prefrontal cortex (PFC) in this aspect of human higher cognition using the relatively new method of functional near-infrared spectroscopy (fNIRS). Chapter 1 advances recogitation as the mental ability on which cognitive restructuring largely depends, concluding that, as a cognitive task, it is a form of open-ended human problem-solving that uses metacognitive and reasoning faculties. Because these faculties share similar executive resources, Chapter 2 discusses the systems in the brain involved in controlled information processing, specifically the nature of executive functions and their neural bases. Chapter 3 builds on these ideas to propose an information-processing model of recogitation, which predicts the roles of different subsystems localized within the PFC and elsewhere in the context of emotion regulation. This chapter also highlights several theoretical and empirical challenges to investigating this neurocognitive theory and proposes some solutions, such as to use experimental designs that are more ecologically valid. Chapter 4 focuses on a neuroimaging method that is best suited to investigating questions of spatial localization in ecological experiments, namely functional near-infrared spectroscopy (fNIRS). Chapter 5 then demonstrates a novel approach to investigating the neural bases of interpersonal interactions in clinical settings using fNIRS. Chapter 6 explores physical activity as a ‘bottom-up’ approach to upregulating the PFC, in that it might help clinical populations with executive deficits to regulate their mental health from the ‘top-down’. Chapter 7 addresses some of the methodological issues of investigating clinical interactions and physical activity in more naturalistic settings by assessing an approach to recovering functional events from observed brain data. Chapter 8 draws several conclusions about the role of the PFC in improving psychological as well as physiological well-being, particularly that rostral PFC is inextricably involved in the cognitive effort to modulate dysfunctional thoughts, and proposes some important future directions for ecological research in cognitive neuroscience; for example, psychotherapy is perhaps too physically stagnant, so integrating exercise into treatment environments might boost the effectiveness of intervention strategies

Towards neuroscience of the everyday world (NEW) using functional near infrared spectroscopy

Published in final edited form as: Curr Opin Biomed Eng. 2021 June ; 18: doi:10.1016/j.cobme.2021.100272.Functional near-infrared spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability, and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional magnetic resonance imaging, expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and electroencephalography for routine, increasingly unconstrained, and mobile brain imaging is leading toward a new domain that we term “Neuroscience of the Everyday World” (NEW). In this light, we review recent advances in hardware, study design, and signal processing, and discuss challenges and future directions.U01EB029856 - National Institutes of HealthAccepted manuscrip

The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience

The past few decades have seen a rapid increase in the use of functional near‐infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state‐of‐the‐art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience

Social cognition in the age of human–robot interaction

Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desire to perceive social cues in the environment, as well as the promise of robots enhancing our daily lives. However, a strong mismatch still exists between our expectations and the reality of social robots. We argue that careful delineation of the neurocognitive mechanisms supporting human–robot interaction will enable us to gather insights critical for optimising social encounters between humans and robots. To achieve this, the field must incorporate human neuroscience tools including mobile neuroimaging to explore long-term, embodied human–robot interaction in situ. New analytical neuroimaging approaches will enable characterisation of social cognition representations on a finer scale using sensitive and appropriate categorical comparisons (human, animal, tool, or object). The future of social robotics is undeniably exciting, and insights from human neuroscience research will bring us closer to interacting and collaborating with socially sophisticated robots

Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience.

Funder: European Association for Research on Learning and InstructionFunder: Jacobs Foundation; Id: http://dx.doi.org/10.13039/501100003986As the field of educational neuroscience continues to grow, questions have emerged regarding the ecological validity and applicability of this research to educational practice. Recent advances in mobile neuroimaging technologies have made it possible to conduct neuroscientific studies directly in naturalistic learning environments. We propose that embedding mobile neuroimaging research in a cycle (Matusz, Dikker, Huth, & Perrodin, 2019), involving lab-based, seminaturalistic, and fully naturalistic experiments, is well suited for addressing educational questions. With this review, we take a cautious approach, by discussing the valuable insights that can be gained from mobile neuroimaging technology, including electroencephalography and functional near-infrared spectroscopy, as well as the challenges posed by bringing neuroscientific methods into the classroom. Research paradigms used alongside mobile neuroimaging technology vary considerably. To illustrate this point, studies are discussed with increasingly naturalistic designs. We conclude with several ethical considerations that should be taken into account in this unique area of research

Are advanced methods necessary to improve infant fNIRS data analysis? An assessment of baseline-corrected averaging, general linear model (GLM) and multivariate pattern analysis (MVPA) based approaches

In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying more advanced statistical analyses to infant fNIRS data and compared the most commonly used baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches, to show how the conclusions one would draw based on these different analysis approaches converge or differ. The different analysis methods were tested using a face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured in thirty 4-to-6-month-old infants. By including more standard approaches together with recent machine learning techniques, we aim to inform the fNIRS community on alternative ways to analyse infant fNIRS datasets

Future Applications of Real-World Neuroimaging to Clinical Psychology

Clinical neuroimaging has largely been limited to examining the neurophysiological outcomes of treatments for psychiatric conditions rather than the neurocognitive mechanisms by which these outcomes are brought about as a function of clinical strategies, and the cognitive neuroscientific research aiming to investigate these mechanisms in nonclinical and clinical populations has been ecologically challenged by the extent to which tasks represent and generalize to intervention strategies. However, recent technological and methodological advancements to neuroimaging techniques such as functional near-infrared spectroscopy and functional near-infrared spectroscopy-based hyperscanning provide novel opportunities to investigate the mechanisms of change in more naturalistic and interactive settings, representing a unique prospect for improving our understanding of the intra- and interbrain systems supporting the recogitation of dysfunctional cognitive operations

Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience

Neuroimaging and neuropsychological methods have contributed much toward an understanding of the information processing systems of the human brain in the last few decades, but to what extent do cognitive neuroscientific findings represent and generalize to the inter- and intra-brain dynamics engaged in adapting to naturalistic situations? If it is not marked, and experimental designs lack ecological validity, then this stands to potentially impact the practical applications of a paradigm. In no other domain is this more important to acknowledge than in human clinical neuroimaging research, wherein reduced ecological validity could mean a loss in clinical utility. One way to improve the generalizability and representativeness of findings is to adopt a more “real-world” approach to the development and selection of experimental designs and neuroimaging techniques to investigate the clinically-relevant phenomena of interest. For example, some relatively recent developments to neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) make it possible to create experimental designs using naturalistic tasks that would otherwise not be possible within the confines of a conventional laboratory. Mental health, cognitive interventions, and the present challenges to investigating the brain during treatment are discussed, as well as how the ecological use of fNIRS might be helpful in bridging the explanatory gaps to understanding the cultivation of mental health