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Led by Professor Andrea Cipriani, the course is designed to enhance understanding and expertise in carrying out systematic reviews and meta-analysis. It is aimed at psychiatrists, psychologists, mental health professionals, pharmacists and researchers in neuroscience and related disciplines.
Evidence for specificity of polygenic contributions to attainment in English, maths and science during adolescence
AbstractHow well one does at school is predictive of a wide range of important cognitive, socioeconomic, and health outcomes. The last few years have shown marked advancement in our understanding of the genetic contributions to, and correlations with, academic attainment. However, there exists a gap in our understanding of the specificity of genetic associations with performance in academic subjects during adolescence, a critical developmental period. To address this, the Avon Longitudinal Study of Parents and Children was used to conduct genome-wide association studies of standardised national English (N = 5983), maths (N = 6017) and science (N = 6089) tests. High SNP-based heritabilities (h2SNP) for all subjects were found (41–53%). Further, h2SNP for maths and science remained after removing shared variance between subjects or IQ (N = 3197–5895). One genome-wide significant single nucleotide polymorphism (rs952964, p = 4.86 × 10–8) and four gene-level associations with science attainment (MEF2C, BRINP1, S100A1 and S100A13) were identified. Rs952964 remained significant after removing the variance shared between academic subjects. The findings highlight the benefits of using environmentally homogeneous samples for genetic analyses and indicate that finer-grained phenotyping will help build more specific biological models of variance in learning processes and abilities.
The last decade of laterality research has been bolstered by a significant broadening in theoretical framing and investigative approaches. Comparative research contributions continue to strengthen the position that ancient functional and anatomical brain biases are preserved in modern humans. However, how they unfold over developmental time and contribute to cognitive abilities is still unclear. To make further advances, we must position human brains and behaviors within an evolutionary framework. This includes viewing motor-sensory behavior as an integral part of a developing cognitive system.
Imagining a brighter future: The effect of positive imagery training on mood, prospective mental imagery and emotional bias in older adults
© 2015 The Authors. Positive affect and optimism play an important role in healthy ageing and are associated with improved physical and cognitive health outcomes. This study investigated whether it is possible to boost positive affect and associated positive biases in this age group using cognitive training. The effect of computerised imagery-based cognitive bias modification on positive affect, vividness of positive prospective imagery and interpretation biases in older adults was measured. 77 older adults received 4 weeks (12 sessions) of imagery cognitive bias modification or a control condition. They were assessed at baseline, post-training and at a one-month follow-up. Both groups reported decreased negative affect and trait anxiety, and increased optimism across the three assessments. Imagery cognitive bias modification significantly increased the vividness of positive prospective imagery post-training, compared with the control training. Contrary to our hypothesis, there was no difference between the training groups in negative interpretation bias. This is a useful demonstration that it is possible to successfully engage older adults in computer-based cognitive training and to enhance the vividness of positive imagery about the future in this group. Future studies are needed to assess the longer-term consequences of such training and the impact on affect and wellbeing in more vulnerable groups.
Bringing conceptually discrete, yet functionally related, fields of temporal attentionresearch together within a single volume, this book provides a ...
The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools
Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.
The ability to hold visual information in mind beyond the duration of the initial sensory stimulation critically underpins many higher-level cognitive functions. In particular, visual short-term memory (VSTM) provides the perceptual continuity that is necessary for visual information to guide behavior across short temporal delays. This chapter explores how the mechanisms of attention optimize VSTM. First, it considers how top-down attention biases VSTM encoding to favor information that is most likely to be relevant to behavior. Next, it looks at more recent evidence that top-down attention can also bias representations already stored within VSTM. Flexible allocation of attention within VSTM enables the visual system to prioritize and update stored representations to accommodate changing task demands.
Hemispatial neglect is usually designated a "parietal syndrome." However, neglect can also arise after lesions in the frontal lobes, cingulate gyrus, striatum, and thalamus. These areas belong to an interconnected large-scale network subserving all aspects of spatial attention. This network helps to compile a mental representation of extrapersonal events in terms of their motivational salience, and to generate "kinetic strategies" so that the attentional focus can shift from one target to another. In the human, the left hemisphere controls attention predominantly within the contralateral right hemispace, whereas the right hemisphere controls attention in both hemispaces. Because of this asymmetry, severe contralesional neglect occurs almost exclusively after right hemisphere lesions and encompasses the left side of extrapersonal space. © 2005 Elsevier Inc. All rights reserved.
Intuition suggests that knowing when something is going to happen helps one to focus his resources at that expected point in time and enhance his behavior. Recent experiments have validated this notion, and have begun to reveal the neural systems and mechanisms involved in the temporal orienting of attention. These studies indicate that people are able to use time information flexibly to orient attention selectively to different time intervals. This is achieved via a left-hemisphere weighted parietal-frontal system. Temporal orienting capitalizes on modulation of motor-related mechanisms. Temporal orienting, when contrasted to spatial orienting, illustrates the flexibility of attentional functions in the human brain. Furthermore, undetected effects of temporal expectancies may be pervasive in behavioral and neuro-scientific experiments. © 2005 Elsevier Inc. All rights reserved.
Codesign and development of a primary school based pathway for child anxiety screening and intervention delivery: a protocol, mixed-methods feasibility study.
INTRODUCTION: Anxiety difficulties are among the most common mental health problems in childhood. Despite this, few children access evidence-based interventions, and school may be an ideal setting to improve children's access to treatment. This article describes the design, methods and expected data collection of the Identifying Child Anxiety Through Schools - Identification to Intervention (iCATS i2i) study, which aims to develop acceptable school-based procedures to identify and support child anxiety difficulties. METHODS AND ANALYSIS: iCATS i2i will use a mixed-methods approach to codesign and deliver a set of procedures-or 'pathway'-to improve access to evidence-based intervention for child anxiety difficulties through primary schools in England. The study will consist of four stages, initially involving in-depth interviews with parents, children, school staff and stakeholders (stage 1) to inform the development of the pathway. The pathway will then be administered in two primary schools, including screening, feedback to parents and the offer of treatment where indicated (stage 2), with participating children, parents and school staff invited to provide feedback on their experience (stages 3 and 4). Data will be analysed using Template Analysis. ETHICS AND DISSEMINATION: The iCATS i2i study was approved by the University of Oxford's Research Ethics Committee (REF R64620/RE001). It is expected that this codesign study will lead on to a future feasibility study and, if indicated, a randomised controlled trial. The findings will be disseminated in several ways, including via lay summary report, publication in academic journals and presentation at conferences. By providing information on child, parent, school staff and other stakeholder's experiences, we anticipate that the findings will inform the development of an acceptable evidence-based pathway for identification and intervention for children with anxiety difficulties in primary schools and may also inform broader approaches to screening for and treating youth mental health problems outside of clinics.
Group-Personalized Regression Models for Predicting Mental Health Scores From Objective Mobile Phone Data Streams: Observational Study.
BACKGROUND: Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. OBJECTIVE: The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, "group-personalized" models can be built for individuals based on other individuals to whom they are most similar. METHODS: We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants' smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. RESULTS: The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P