The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities

Examining predictive coding accounts of typical and autistic neurocognitive development

Predictive coding has emerged as a prominent theoretical framework for understanding perception and its neural underpinnings. There has been a recent surge of interest in the predictive coding framework across the mind sciences. However, comparatively little of the research in this field has investigated the neural underpinnings of predictive coding in young neurotypical and autistic children. This paper provides an overview of predictive coding accounts of typical and autistic neurocognitive development and includes a review of the current electrophysiological evidence supporting these accounts. Based on the current evidence, it is clear that more research in pediatrics is needed to evaluate predictive coding accounts of neurocognitive development fully. If supported, these accounts could have wide-ranging practical implications for pedagogy, parenting, artificial intelligence, and clinical approaches to helping autistic children manage the barrage of everyday sensory information.

From the lab to the field: acceptability of using electroencephalography with Indian preschool children

Background: Measurement of social and cognitive brain development using electroencephalography (EEG) offers the potential for early identification of children with elevated risk of developmental delay. However, there have been no published reports of how acceptable EEG technology is to parents and children within communities, especially in low-resource contexts such as in low and middle income countries (LMICs), which is an important question for the potential scalability of these assessments. We use a mixed-methods approach to examine whether EEG assessments are acceptable to children and their caregivers in a low resource community setting in India. Methods: We assessed the acceptability of neurophysiology research and Braintools (a novel neurodevelopmental assessment toolkit using concurrent EEG and eye-tracking technology) using: 1) a child engagement measure, 2) interviews with caregivers (n=8); 3) survey about caregiver's experience (n=36). Framework analysis was used to analyse interview data. Results: A high level of child engagement in EEG tasks was demonstrated, with children's gaze at the screen during the task averaging at 85.4% (±12.06%) of the task time. External distractions and noise during the tasks were measured, but not found to significantly effect child's attention to the screen during EEG tasks. Key topics were examined using the framework analysis: 1) parental experience of the assessment; and 2) the acceptability of research. From topic 1, four sub-themes were identified: i) caregivers' experience of the assessment, ii) caregivers' perception of child's experience of assessment, iii) logistical barriers and facilitators to participation, and iv) recommendations for improvement. Results from interviews and the survey indicated acceptability for gaze-controlled EEG research for parents and children. From topic 2, three themes were identified: i) caregivers' understanding of the research, ii) barriers to participation, and iii) facilitators to participation. Barriers to participation mainly included logistical challenges, such as geographic location and time, whereas involvement of the wider family in decision making was highlighted as an important facilitator to partake in the research. Conclusions: We demonstrate for the first time the acceptability of conducting neurodevelopmental assessments using concurrent EEG and eye-tracking in preschool children in uncontrolled community LMIC settings. This kind of research appears to be acceptable to the community and we identify potential barriers and facilitators of this research, thus allowing for future large scale research projects to be conducted investigating neurodevelopment and risk factors for suboptimal development in LMICs.

Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.