Biology subjects

The CALM Team,

Publications and source records attributed to The CALM Team,.

3 recordsLinked to original sources

bioRxiv2023

The entropy of resting-state neural dynamics is a marker of general cognitive ability in childhood

1Resting-state network activity has been associated with the emergence of individual differences across childhood development. However, due to the limitations of time-averaged representations of neural activity, little is known about how cognitive and behavioural variability relates to the rapid spatiotemporal dynamics of these networks. Magnetoencephalography (MEG), which records neural activity at a millisecond timescale, can be combined with Hidden Markov Modelling (HMM) to track the spatial and temporal characteristics of transient neural states. We applied HMMs to resting-state MEG data from (n = 46) children aged 8-13, who were also assessed on their cognitive ability and across multiple parent-report measures of behaviour. We found that entropy-related properties of participants resting-state time-courses were positively associated with cognitive ability. Additionally, cognitive ability was positively correlated with the probability of transitioning into HMM states involving fronto-parietal and somatomotor activation, and negatively associated with a state distinguished by default-mode network suppression. We discuss how using dynamical measures to characterise rapid, spontaneous patterns of brain activity can shed new light on neurodevelopmental processes implicated in the emergence of cognitive differences in childhood. Significance StatementThere is increasing evidence that the function of resting-state brain networks contributes to individual differences in cognition and behaviour across development. However, the relationship between dynamic, transient patterns of switching between resting-state networks and neurodevelopmental diversity is largely unknown. Here, we show that cognitive ability in childhood is related to the complexity of resting-state brain dynamics. Additionally, we demonstrate that the probability of transitioning into and remaining in certain states of brain network activity predicts individual differences in cognitive ability.

neuroscience↗

bioRxiv2020

Bridging brain and cognition: A multilayer network analysis of brain structural covariance and general intelligence in a developmental sample of struggling learners

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

neuroscience↗

bioRxiv2018

A transdiagnostic study of children with problems of attention, learning and memory (CALM)

BackgroundA substantial proportion of the school-age population experience cognitive-related learning difficulties. Not all children who struggle at school receive a diagnosis, yet their problems are sufficient to warrant additional support. Understanding the causes of learning difficulties is the key to developing effective prevention and intervention strategies for struggling learners. The aim of this project is to apply a transdiagnostic approach to children with cognitive developmental difficulties related to learning to discover the underpinning mechanisms of learning problems.\n\nMethods / DesignA cohort of 1000 children aged 5 to 18 years is being recruited. The sample consists of 800 children with problems in attention, learning and / memory, as identified by a health or educational professional, and 200 typically-developing children recruited from the same schools as those with difficulties. All children are completing assessments of cognition, including tests of phonological processing, short-term and working memory, attention, executive function and processing speed. Their parents/ carers are completing questionnaires about the childs family history, communication skills, mental health and behaviour. Children are invited for an optional MRI brain scan and are asked to provide an optional DNA sample (saliva).\n\nHypothesis-free data-driven methods will be used to identify the cognitive, behavioural and neural dimensions of learning difficulties. Machine-learning approaches will be used to map the multi-dimensional space of the cognitive, neural and behavioural measures to identify clusters of children with shared profiles. Finally, group comparisons will be used to test theories of development and disorder.\n\nDiscussionOur multi-systems approach to identifying the causes of learning difficulties in a heterogeneous sample of struggling learners provides a novel way to enhance our understanding of the common and complex needs of the majority of children who struggle at school. Our broad recruitment criteria targeting all children with cognitive learning problems, irrespective of diagnoses and comorbidities, are novel and make our sample unique. Our dataset will also provide a valuable resource of genetic, imaging and cognitive developmental data for the scientific community.

neuroscience↗