Home assessment of visual working memory in pre‐schoolers reveals associations between behaviour, brain activation and parent reports of life stress

McKay, Courtney; Shing, Yee Lee; Rafetseder, Eva; Wijeakumar, Sobanawartiny

doi:10.1111/desc.13094

Cited by 9 publications

(11 citation statements)

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“…Probe geometry was designed by collating regions of interest (ROI) from previous fNIRS and fMRI literature (Brod et al, 2017; Wijeakumar et al, 2015). Probe geometry consisted of four channels each on the left and right frontal cortices, and three channels each on the left and right parietal cortices (McKay et al, 2021). Note that short-source-detector channels were not used to regress scalp hemodynamics as all the channels were directed toward maximising coverage of the frontal and parietal cortices.…”

Section: Methodsmentioning

confidence: 99%

“…All children were remunerated with £10 and a toy upon completion of each time point measurement. As part of the overall procedure for the project, children also completed tasks on visual working memory, counterfactual reasoning, and associative memory, while parents filled in questionnaires collecting data on demographics, child behaviour, and life stress (data not included here, see details in McKay et al, 2021).…”

Section: School Achievement Packs (T2 Only)mentioning

confidence: 99%

“…Our first question sought to determine whether entering formal schooling leads to increased engagement of the neural networks underlying response inhibition and response monitoring. To answer this question, we employed a portable functional near-infrared spectroscopy (fNIRS) system, which allowed us to collect data on children in their homes (see more details in McKay et al, 2021). This system has several advantages over other imaging modalities as it is non-invasive, cost-effective, portable, and fairly easy to use with young children.…”

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confidence: 99%

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Disentangling Age and Schooling Effects on Inhibitory Control Development: An fNIRS Investigation

McKay

Wijeakumar

Rafesetder

et al. 2021

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Children show marked improvements in executive functioning (EF) between 4 and 7 years of age. In many societies, this time period coincides with the start of formal school education, in which children are required to follow rules in a structured environment, drawing heavily on EF processes such as inhibitory control. This study aimed to investigate the longitudinal development of two aspects of inhibitory control, namely response inhibition and response monitoring and their neural correlates. Specifically, we examined how their longitudinal development may differ by schooling experience, and their potential significance in predicting academic outcomes. Longitudinal data was collected in two groups of children at their homes. At T1, all children were roughly 4.5 years of age and neither group had attended formal schooling. One year later at T2, one group (P1, 40 children) had completed one full year of schooling while the other group (KG, 40 children) had stayed in kindergarten. Behavioural and brain activation data (measured with functional near-infrared spectroscopy, fNIRS) in response to a Go/No-Go task and measures of academic achievement were collected. We found that P1 children, compared to KG children, showed a greater change over time in activation related to response monitoring in the bilateral frontal cortex. The change in left frontal activation difference showed a small positive association with mathematical ability, suggesting certain functional relevance of response monitoring for academic performance. Overall, the school environment is important in shaping the development of the neural network underlying performance monitoring.

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Section: Methodsmentioning

confidence: 99%

Section: School Achievement Packs (T2 Only)mentioning

confidence: 99%

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confidence: 99%

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Disentangling Age and Schooling Effects on Inhibitory Control Development: An fNIRS Investigation

McKay

Wijeakumar

Rafesetder

et al. 2021

Preprint

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“…The data presented in this case study are part of a longitudinal study investigating the effects of schooling on neurocognitive function. 36 …”

Section: Demonstration Of the Pipeline On An Example Dataset From Nirssportmentioning

confidence: 99%

Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

et al. 2021

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Significance: Image reconstruction of fNIRS data is a useful technique for transforming channel-based fNIRS into a volumetric representation and managing spatial variance based on optode location. We present an innovative integrated pipeline for image reconstruction of fNIRS data using either MRI templates or individual anatomy.Aim: We demonstrate a pipeline with accompanying code to allow users to clean and prepare optode location information, prepare and standardize individual anatomical images, create the light model, run the 3D image reconstruction, and analyze data in group space.Approach: We synthesize a combination of new and existing software packages to create a complete pipeline, from raw data to analysis.Results: This pipeline has been tested using both templates and individual anatomy, and on data from different fNIRS data collection systems. We show high temporal correlations between channel-based and image-based fNIRS data. In addition, we demonstrate the reliability of this pipeline with a sample dataset that included 74 children as part of a longitudinal study taking place in Scotland. We demonstrate good correspondence between data in channel space and image reconstructed data. Conclusions:The pipeline presented here makes a unique contribution by integrating multiple tools to assemble a complete pipeline for image reconstruction in fNIRS. We highlight further issues that may be of interest to future software developers in the field.

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“…We believe this may occur when children become overwhelmed by the memory demands for a given array and disengage from the task. There is some neurophysiological evidence to support this (Fukuda et al, 2010;Reyes et al, 2020;McKay et al, 2021). If this is the case, then the array size that produces maximum capacity (i.e., the optimal array size) should vary by age, with younger children reaching maximum capacity for smaller array sizes, and older children reaching maximum capacity for large array sizes independent of capacity estimates.…”

Section: Assessing Capacity Across Multiple Set Sizesmentioning

confidence: 99%

Unsupervised Online Assessment of Visual Working Memory in 4- to 10-Year-Old Children: Array Size Influences Capacity Estimates and Task Performance

2021

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The events of the COVID-19 Pandemic forced many psychologists to abandon lab-based approaches and embrace online experimental techniques. Although lab-based testing will always be the gold standard of experimental precision, several protocols have evolved to enable supervised online testing for paradigms that require direct observation and/or interaction with participants. However, many tasks can be completed online in an unsupervised way, reducing reliance on lab-based resources (e.g., personnel and equipment), increasing flexibility for families, and reducing participant anxiety and/or demand characteristics. The current project demonstrates the feasibility and utility of unsupervised online testing by incorporating a classic change-detection task that has been well-validated in previous lab-based research. In addition to serving as proof-of-concept, our results demonstrate that large online samples are quick and easy to acquire, facilitating novel research questions and speeding the dissemination of results. To accomplish this, we assessed visual working memory (VWM) in 4- to 10-year-old children in an unsupervised online change-detection task using arrays of 1–4 colored circles. Maximum capacity (max K) was calculated across the four array sizes for each child, and estimates were found to be on-par with previously published lab-based findings. Importantly, capacity estimates varied markedly across array size, with estimates derived from larger arrays systematically underestimating VWM capacity for our youngest participants. A linear mixed effect analysis (LME) confirmed this observation, revealing significant quadratic trends for 4- through 7-year-old children, with capacity estimates that initially increased with increasing array size and subsequently decreased, often resulting in estimates that were lower than those obtained from smaller arrays. Follow-up analyses demonstrated that these regressions may have been based on explicit guessing strategies for array sizes perceived too difficult to attempt for our youngest children. This suggests important interactions between VWM performance, age, and array size, and further suggests estimates such as optimal array size might capture both quantitative aspects of VWM performance and qualitative effects of attentional engagement/disengagement. Overall, findings suggest that unsupervised online testing of VWM produces reasonably good estimates and may afford many benefits over traditional lab-based testing, though efforts must be made to ensure task comprehension and compliance.

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Home assessment of visual working memory in pre‐schoolers reveals associations between behaviour, brain activation and parent reports of life stress

Abstract: Visual working memory (VWM) is a short-term storage system responsible for detecting changes in the world as they occur. VWM capacity is reliably predictive of cognitive functions (Fukuda et al.

Cited by 9 publications

References 65 publications

Disentangling Age and Schooling Effects on Inhibitory Control Development: An fNIRS Investigation

Disentangling Age and Schooling Effects on Inhibitory Control Development: An fNIRS Investigation

Processing pipeline for image reconstructed fNIRS analysis using both MRI templates and individual anatomy

Unsupervised Online Assessment of Visual Working Memory in 4- to 10-Year-Old Children: Array Size Influences Capacity Estimates and Task Performance

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