Behavioral and Neural Signatures of Working Memory in Childhood

Rosenberg, Monica D.; Martinez, Steven A.; Rapuano, Kristina M.; Conley, May I.; Cohen, Alexandra O.; Cornejo, M. Daniela; Hagler, Donald J.; Meredith, W.J.; Anderson, Kevin; Wager, Tor D.; Feczko, Eric; Earl, Eric; Fair, Damien A.; Deanna, M; Watts, Richard; Casey, B. J.

doi:10.1523/jneurosci.2841-19.2020

Cited by 64 publications

(73 citation statements)

References 110 publications

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“…The largest available population study including these variables is the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org). At its current stage, ABCD provides a conclusive analysis on mostly prepubertal 10-year-old children demonstrating that processing speed and working memory are not related at this age 17 . Our results corroborate this finding revealing the difference in the maturational timing of the two behavioural measures, and also support an interpretation emphasizing their diverse neural background.…”

Section: Discussionmentioning

confidence: 97%

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Skeletal maturity predicts cognitive abilities in human adolescents

Kovács

Gerván

et al. 2021

Preprint

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Adolescent development is not only shaped by the mere passing of time and accumulating experience, it also depends on pubertal timing and the cascade of maturational processes orchestrated by gonadal hormones. Although individual variability in puberty onset confounds adolescent studies, it has not been efficiently controlled for. Here we introduce ultrasonic bone age assessment to estimate biological maturity and disentangle the independent effects of chronological and biological age on adolescent cognitive abilities. Comparing cognitive performance of participants with different skeletal maturity we uncover the striking impact of biological age on both IQ and specific abilities. We find that biological age has a selective effect on abilities: more mature individuals within the same age group have higher working memory capacity and processing speed, while those with higher chronological age have better verbal abilities, independently of their maturity. Based on our findings, bone age is a promising biomarker for adolescent research.

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Section: Discussionmentioning

confidence: 97%

“…nutritional conditions and secular trends of human growth 3,13,15,17 . The Tanner Scale is based on a post-war longitudinal study carried out in the Harpenden children's home near London, between 1949 and 1971 14,16 .…”

mentioning

confidence: 99%

Skeletal maturity predicts cognitive abilities in human adolescents

Kovács

Gerván

et al. 2021

Preprint

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“…Literature reveals robust relationships between working memory, short-term memory, language skills, and fluid intelligence which are the basis for academic performance. [ 11 ] It is also seen that the relationship between cardiorespiratory fitness and academic achievement is mediated by executive functions such as working memory in school children. [ 12 ]…”

Section: Discussionmentioning

confidence: 99%

Correlation of self-reported sleep duration with working memory of adolescents

Mehta

Kamble

Gadhvi

et al. 2020

J Family Med Prim Care

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Context: With the increasing use of electronic devices and social media, the duration of sleep has consistently reduced in adolescents. Sleep restriction eventually leads to cognitive performance declines. Poor sleep and working memory difficulties are both associated with learning difficulties leading to poor academic performance. Aims: We postulated that decreased sleep duration decreases the working memory of adolescents and eventually their academic performance. Settings and Design: Cross-sectional Study. Methods and Material: The study was conducted on 114 school students; 62 boys and 52 girls (age 13.8 ± 0.91 and 13.65 ± 0.88 years, respectively). Sleep was monitored by self-reported diary. Working memory was tested by the n-back task. The students were given 1-back and 2-back visual tasks in two blocks and accuracy of each of the tests was calculated. Statistical Analysis Used: Prism software was used and Mann-Whitney-U test and Spearman Correlation tests were employed. Results: Sleep duration range was 4.15-12 hours with a mean of 7.63 ± 1.35 hours. The sleep duration in males and females, respectively was 6.94 ± 0.94 hrs. and 8.5 ± 1.31 hrs.; significant ( p = 0.0001). The total n-back score accuracy (1-back and 2-back) was 52.11 ± 17.32% in males and 52.24 ± 17.40% in females ( p = 0.976). Spearman Correlation between sleep-duration and total n-back score was not found to be statistically significant ( p = 0.611). However, the correlation of total n-back score with academic performance was statistically significant. Conclusions: The working memory was not statistically different in males and females, and was not significantly correlated with sleep duration, though it was significantly associated with the academic performance.

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“…We used the average contrast values across two runs. As for behavioral performance for the N-back task, we used the accuracy of the 2-back condition as it is correlated well with performance of cognitive tasks collected outside of the scanner 38 .…”

Section: Methodsmentioning

confidence: 99%

Explainable Machine Learning Approach to Predict and Explain the Relationship between Task-based fMRI and Individual Differences in Cognition

Pornpattananangkul

Bartonicek

Wang

et al. 2020

Preprint

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Predicting individual differences in cognitive processes is crucial, but the ability of task-based fMRI to do so remains dubious, despite decades of costly research. We tested the ability of working-memory fMRI in predicting working-memory, using the Adolescent Brain Cognitive Development (n = 4,350). The conventionally-used mass-univariate approach led to poor out-of-sample prediction (Mean r = .1-.12). However, the multivariate Elastic Net, which draws information across brain regions, enhanced out-of-sample prediction (r = .47) by several folds. The Elastic Net also enabled us to predict cognitive performance from various tasks collected outside of the scanner, highlighting its generalizability. Moreover, using an omics-inspired approach, we combined Elastic Net with permutation, allowing us to statistically infer which brain regions contribute to individual differences while accounting for collinearity. Accordingly, our framework can build an easy-to-interpret predictive fMRI model that transfers knowledge learned from large-scale datasets to smaller samples, akin to polygenic scores in genomics.

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Behavioral and Neural Signatures of Working Memory in Childhood

Cited by 64 publications

References 110 publications

Skeletal maturity predicts cognitive abilities in human adolescents

Skeletal maturity predicts cognitive abilities in human adolescents

Correlation of self-reported sleep duration with working memory of adolescents

Explainable Machine Learning Approach to Predict and Explain the Relationship between Task-based fMRI and Individual Differences in Cognition

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