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Holmes, J.

Publications and source records attributed to Holmes, J..

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Are working memory training effects paradigm-specific?

A randomized controlled trial compared complex span and n-back training regimes to investigate the generality of training benefits across materials and paradigms. The memory items and training intensities were equated across programs, providing the first like-with-like comparison of transfer in these two widely-used training paradigms. The stimuli in transfer tests of verbal and visuo-spatial n-back and complex span differed from the trained tasks, but were matched across the untrained paradigms. Pre-to-post changes were observed for untrained n-back tasks following n-back training. Following complex span training there was equivocal evidence for improvements on a verbal complex span task, but no evidence for changes on an untrained visuo-spatial complex span activity. Relative to a no intervention group, the evidence supported no change on an untrained verbal complex span task following either n-back or complex span training. Equivocal evidence was found for improvements on visuo-spatial complex span and verbal and visuo-spatial n-back tasks following both training regimes. Evidence for selective transfer (comparing the two active training groups) was only found for an untrained visuo-spatial n-back task following n-back training. There was no evidence for cross-paradigm transfer. Thus transfer is constrained by working memory paradigm and the nature of individual processes executed within complex span tasks. However, within-paradigm transfer can occur when the change is limited to stimulus category, at least for n-back.

neuroscience

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

Remapping the cognitive and neural profiles of children who struggle at school

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within-group homogeneity and between-group differences, and fails to capture comorbidity. Here we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n=530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behaviour questionnaires. Within the network, we could identify four groups of children: i) children with broad cognitive difficulties, and severe reading, spelling and maths problems; ii) children with age-typical cognitive abilities and learning profiles; iii) children with working memory problems; and iv) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a childs cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole-brain structural connectomes for children from these four groupings (n=184), alongside an additional group of typically developing children (n=36), and identified distinct patterns of brain organisation for each group. This study represents a novel move towards identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.\n\nAuthor NoteThe Centre for Attention Learning and Memory (CALM) research clinic is based at and supported by funding from the MRC Cognition and Brain Sciences Unit, University of Cambridge. The Principal Investigators are Joni Holmes (Head of CALM), Susan Gathercole (Chair of CALM Management Committee), Duncan Astle, Tom Manly and Rogier Kievit. Data collection is assisted by a team of researchers and PhD students at the CBSU. This currently includes: Sarah Bishop, Annie Bryant, Sally Butterfield, Fanchea Daily, Laura Forde, Erin Hawkins, Sinead OBrien, Cliodhna OLeary, Joseph Rennie, and Mengya Zhang. The authors wish to thank the many professionals working in childrens services in the South-East and East of England for their support, and to the children and their families for giving up their time to visit the clinic.\n\nResearch HighlightsO_LIfirst study to apply machine learning to understand heterogeneity in struggling learners\nC_LIO_LIlarge sample of struggling learners that includes children with multiple difficulties\nC_LIO_LIrich phenotyping with detailed behavioural, cognitive, and neuroimaging assessments\nC_LI

neuroscience

Cognition and behaviour in learning difficulties and ADHD: A dimensional approach

BackgroundAcademic underachievement often accompanies the symptoms of inattention and hyperactivity/ impulsivity associated with ADHD. The aim of the present study is to establish whether learning difficulties have the same cognitive origins in this comorbid condition as in children who do not have ADHD.\n\nMethodsParticipants were 163 school-aged children with learning difficulties. Over a third also had a diagnosis of ADHD. Cognition, behaviour and learning attainments were assessed.\n\nResultsThe sample was distinguished by three cognitive and three behavioural dimensions. Learning was equivalently related to cognitive dimensions for children with and without ADHD. A diagnosis of ADHD was associated only with elevated levels of ADHD symptoms and problems with emotional control.\n\nConclusionsDistinct dimensions underpin academic learning and the control of impulsive and emotional behaviour impaired in ADHD. Phonological deficits are associated with learning problems in literacy and maths, and impairments in nonverbal and executive abilities with mathematical learning difficulties. The comorbid condition of ADHD combined with learning difficulties reflects independent deficits in the cognitive dimensions critical for learning and in the control of impulsive and emotional behaviour.

animal behavior and cognition

Data-driven subtyping of behavioural problems associated with ADHD in children

IntroductionMany developmental disorders are associated with deficits in controlling and regulating behaviour. These difficulties are frequently observed across multiple groups of children including children with diagnoses of attention deficit hyperactivity disorder (ADHD), specific learning difficulties, autistic spectrum disorder, or conduct disorder. The co-occurrence of these behavioural problems across disorders typically leads to comorbid diagnoses and can complicate intervention approaches. An alternative to classifying children on the basis of specific diagnostic criteria is to use a data-driven grouping that identifies dimensions of behaviour that meaningfully distinguish groups of children.\n\nMethodsThe sample consisted of 442 children identified by health and educational professionals as having difficulties in attention, learning and/or memory. The current study applied community clustering, a data-driven clustering algorithm, to group children by similarities across scales on a commonly used rating scale, the Conners-3 questionnaire. Further, the current study investigated if the groups identified by the data-driven algorithm could be identified by white matter connectivity using a structural connectomics approach combined with partial least squares analysis.\n\nResultsThe data-driven clustering yielded three distinct groups of children with symptoms of either: (1) elevated inattention and hyperactivity/impulsivity, and poor executive function, (2) learning problems, and (3) aggressive behaviour and problems with peer relationships. These groups were associated with significant inter-individual variation in white matter connectivity of the prefrontal and anterior cingulate cortex.\n\nConclusionIn sum, data-driven classification of executive function difficulties identifies stable groups of children, provides a good account of inter-individual differences, and aligns closely with underlying neurobiological substrates.

neuroscience