Human working memory capacity develops during childhood and is a strong predictor of future academic performance, in particular, achievements in mathematics and reading. Predicting working memory development is important for the early identification of children at risk for poor cognitive and academic development. Here we show that structural and functional magnetic resonance imaging data explain variance in children's working memory capacity 2 years later, which was unique variance in addition to that predicted using cognitive tests. While current working memory capacity correlated with frontoparietal cortical activity, the future capacity could be inferred from structure and activity in basal ganglia and thalamus. This gives a novel insight into the neural mechanisms of childhood development and supports the idea that neuroimaging can have a unique role in predicting children's cognitive development.
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http://dx.doi.org/10.1523/JNEUROSCI.0842-13.2014 | DOI Listing |
PLoS One
January 2025
Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia.
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions.
View Article and Find Full Text PDFIn the current cybersecurity landscape, Distributed Denial of Service (DDoS) attacks have become a prevalent form of cybercrime. These attacks are relatively easy to execute but can cause significant disruption and damage to targeted systems and networks. Generally, attackers perform it to make reprisal but sometimes this issue can be authentic also.
View Article and Find Full Text PDFNonlinear Dyn
September 2024
Department of Mathematics, University College London, London, UK.
Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. It goes without saying that analyzing time series data holds the key to gaining insight into our day-to-day observations. Among the vast spectrum of time series analysis, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Guangdong Provincial Key Laboratory of Brain Function and Disease, Center for Brain and Mental Well-Being, Department of Psychology, Sun Yat-sen University, Guangzhou, China.
Introduction: Visual short-term memory (VSTM) is a critical indicator of Alzheimer's disease (AD), but whether its neural substrates could adapt to early disease progression and contribute to cognitive resilience in amnestic mild cognitive impairment (aMCI) has been unclear.
Methods: Fifty-five aMCI patients and 68 normal controls (NC) performed a change-detection task and underwent multimodal neuroimaging scanning.
Results: Among the atrophic brain regions in aMCI, VSTM performance correlated with the volume of the right prefrontal cortex (PFC) but not the medial temporal lobe (MTL), and this correlation was mainly present in patients with greater MTL atrophy.
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