Biology subjects

Astle, D. E.

Publications and source records attributed to Astle, D. E..

4 recordsLinked to original sources

bioRxiv2018

A randomised exploratory investigation of the effects of Attention vs Working Memory Training on cognitive performance and everyday functioning following stroke.

Difficulties with attention are common following stroke and are associated with poor outcome. Home-based online cognitive training may have to the potential to provide an efficient and effective way to improve attentional functions in such patients. Little work has been carried out to assess the efficacy of this approach in stroke patients, and the lack of studies with active control conditions and rigorous evaluations of cognitive functioning pre and post training means understanding is limited as to whether and how such interventions may be effective. Here we compare the effects of 20 days of active cognitive training using either novel Selective Attention Training (SAT) or commercial Working Memory Training (WMT) programme, versus a waitlist control group, on a wide range of attentional and working memory tasks, as well as on self-reported everyday functioning. We demonstrate separable effects of each of the active training conditions, with SAT leading to improvements in both spatial and non-spatial aspects of attention and WMT leading to improvements only on very closely related working memory tasks. Both training groups reported improvements in everyday functioning, which were associated with improvements in attentional functions, suggesting that improving attention may be of particular importance in maximising functional recovery in this patient group.

clinical trials↗

bioRxiv2018

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↗

bioRxiv2017

Data-driven brain-types and their cognitive consequences

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N=313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in FA of the left and right cingulum. These different brain types had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network.\n\nGraphical abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC=\"FIGDIR/small/237859_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (34K):\norg.highwire.dtl.DTLVardef@135e807org.highwire.dtl.DTLVardef@145673org.highwire.dtl.DTLVardef@137cd67org.highwire.dtl.DTLVardef@8e2cff_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2017

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↗