br Summary br Acknowledgements We thank

Summary
Acknowledgements We thank everyone who helped us launch our eyetracking laboratory: Mehdi Bouhaddou, Lisa Johnson, Sheri Johnson, Jesse Niebaum, Andrew Peckham, Jordan Tharp, and Carter Wendelken. We also acknowledge the following funding sources: German Academic Exchange Service Doctoral Scholarship (M.E.), National Science Foundation Graduate Research Fellowship (B.G-C.), James S. McDonnell Foundation 21st Century Science Initiative Scholar Award in Understanding Human Cognition (S.A.B.), and a Jacobs Foundation Advanced Career Research Fellowship (S.A.B.).
Introduction Infants in resource-poor settings may be frequently exposed to a range of social, environmental, nutritional and pathological insults. Approximately 1 in 2 children are thought to live in poverty (Currie and Almond, 2011; UNICEF, 2013), and 165 million children worldwide are under nourished and stunted (UNICEF, 2013), the majority of whom live in Sub-Saharan Africa or South Asia. According to a recent study, one third of children in developing countries fail to reach their developmental milestones in cognitive and/or socio-emotional growth, with the largest number of affected children in sub- Saharan Africa (McCoy et al., 2016). This means that over 80 million children in low and middle income countries (LMICs) fail to develop a core set of age-appropriate skills that allow them to maintain attention, understand and follow simple directions, communicate and cooperate with others, control aggression, and solve complex problems. The absence of these skills has significant impact on their academic achievement and mental health into adulthood, and as such their potential to lead full and productive lives and support future generations. While many studies suggest that the presence of these risk factors in infancy has a lasting impact throughout the life course (Hackman and Farah, 2009; Martorell et al., 2010; Victora et al., 2008), almost nothing is known about the neural bases of these early deficits. The first 1000days of life are a critical window for Octreotide acetate and nervous system maturation, and impaired development during this time can have a significant impact on cognitive outcome (Cusick and Georgieff, 2012; Mendez and Adair, 1999; Powell et al., 1995). To inform interventions that may reduce the impact of these insults, early detection of atypical neurocognitive function is required. However, to date there has been a lack of suitable methods for use from early infancy (Isaacs, 2013). Investigation of the developing brain in rural field settings has been broadly limited to behavioural assessments (Georgieff, 2007; Sabanathan et al., 2015). However, measurements of behaviour come with some limitations. Firstly, they can only be used to detect effects once they reach the point of observable behaviour, usually in the second year of life or later. For example, whilst behavioural measures have been unable to distinguish between infants with low and high-risk of developing autism (defined by a familial diagnosis) before the first year of life, several recent neuroimaging studies have identified differences in brain function in young infants (Elsabbagh et al., 2012, 2009; Fox et al., 2013; Guiraud et al., 2011; Lloyd-Fox et al., 2013; Luyster et al., 2011; McCleery et al., 2009). Furthermore, work on the relationship between family socio-economic status (SES) and infant brain development has evidenced atypical neural activity using electroencephalography (EEG) in six to nine month old infants from low SES backgrounds in the UK, highlighting the importance of the early-life environment on brain development (Tomalski et al., 2013). Secondly, there are issues relating to the implementation, cultural adaptation and standardisation of behavioural assessments between contrasting populations. For example, many standardised assessment measures are developed and normed within a limited number of high-income countries (Mullen, 1995). Therefore researchers need to develop country-specific norms for these measures or create independent measures and questionnaires for their own populations (i.e. Abubakar et al., 2016; Kariuki et al., 2016; Sabanathan et al., 2015). Adjustments to these measures can produce more robust and reliable datasets within populations, but can also hinder cross-cultural comparison due to issues with measurement equivalence.