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Data Repository


This page shows all 172 data sets currently available in our Data repository

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Gene functional networks and autism spectrum characteristics in young people with intellectual disability: a dimensional phenotyping study
Authors:
BAKER, K., BRKIC, D, Ng-Cordell, E., O’BRIEN, S., Scerif, G., ASTLE, D.
Reference:
Molecular Autism
Year of publication:
In Press
CBU number:
8589
Abstract:
Background The relationships between specific genetic aetiology and phenotype in neurodevelopmental disorders are complex and hotly contested. Genes associated with Intellectual disability (ID) can be grouped into networks according to gene function. This study explored whether individuals with ID show differences in autism spectrum characteristics (ASC), depending on the functional network membership of their rare, pathogenic de novo genetic variants. Methods Children and young people with ID of known genetic origin were allocated to two broad functional network groups: synaptic physiology (n=29) or chromatin regulation (n=23). We applied principle components analysis to the Social Responsiveness Scale to map the structure of ASC in this population, and identified three components – Inflexibility, Social Understanding and Social Motivation. We then used Akaike Information Criterion (AIC) to test the best fitting models for predicting ASC components, including demographic factors (age, gender), non-ASC behavioural factors (global adaptive function, anxiety, hyperactivity, inattention) and gene functional networks. Results We found that, when other factors are accounted for, the chromatin regulation group showed higher levels of Inflexibility. We also observed contrasting predictors of ASC within each network group. Within the chromatin regulation group, Social Understanding was associated with inattention, and Social Motivation was predicted by hyperactivity. Within the synaptic group, Social Understanding was associated with hyperactivity, and Social Motivation was linked to anxiety. Limitations Functional network definitions were manually curated based on multiple sources of evidence, but a data-driven approach to classification may be more robust. Sample sizes for rare genetic diagnoses remain small, mitigated by our network-based approach to group comparisons. This is a cross-sectional study across a wide age range, and longitudinal data within focused age groups will be informative of developmental trajectories across network groups. Conclusion We report that gene functional networks can predict Inflexibility, but not other ASC dimensions. Contrasting behavioural associations within each group suggests network-specific developmental pathways from genomic variation to autism. Simple classification of neurodevelopmental disorder genes as high risk or low risk for autism is unlikely to be valid or useful.
Data available, click to request
Relationship between sensitivity to temporal fine structure and spoken language abilities in children with mild-to-moderate sensorineural hearing loss
Authors:
HALLIDAY, L., Cabrera
Reference:
Journal of the Acoustical Society of America
Year of publication:
2020
CBU number:
8588
Abstract:
Children with sensorineural hearing loss show considerable variability in spoken language outcomes. We tested whether specific deficits in supra-threshold auditory perception might contribute to this variability. In a previous study [Halliday, Rosen, Tuomainen, & Calcus, (2019), J. Acoust. Soc. Am, 146, 4299], children with mild-to-moderate sensorineural hearing loss (MMHL) were shown to perform more poorly than normally hearing (NH) controls on measures designed to assess sensitivity to the temporal fine structure (TFS, the rapid oscillations in the amplitude of narrowband signals over short time intervals). However, they performed within normal limits on measures assessing sensitivity to the envelope (E; the slow fluctuations in the overall amplitude). Here, individual differences in unaided sensitivity to TFS accounted for significant variance in the spoken language abilities of children with MMHL, after controlling for nonverbal IQ, family history of language difficulties, and hearing loss severity. Aided sensitivity to TFS and E cues was equally important for children with MMHL, whereas for children with NH, E cues were more important. These findings suggest that deficits in TFS perception may contribute to the variability in spoken language outcomes in children with sensorineural hearing loss.
Data available, click to request
Pitch perception at very high frequencies: On psychometric functions and integration of frequency information
Authors:
Gockel, H., Moore, B.J., CARLYON, R.P.
Reference:
The Journal of the Acoustical Society of America, 148, 3322
Year of publication:
2020
CBU number:
8586
Abstract:
Lau et al. [Lau, Mehta, and Oxenham (2017), J. Neuroscience, 37, 9013-9021] showed that discrimination of the fundamental frequency (F0) of complex tones with components in a high frequency region was better than predicted from the optimal combination of information from the individual harmonics. The predictions depend on the assumption that psychometric functions for frequency discrimination have a slope of 1 at high frequencies. This was tested by measuring psychometric functions for F0 discrimination and frequency discrimination. Difference limens for F0 (F0DLs) and difference limens for frequency (FDLs) for each frequency component were also measured. Complex tones contained harmonics 6-10 and had F0s of 280 or 1400 Hz. Thresholds were measured using 210-ms tones presented diotically in diotic threshold-equalizing noise (TEN) and 1000-ms tones presented diotically in dichotic TEN. The slopes of the psychometric functions were close to 1 for all frequencies and F0s. The ratio of predicted to observed F0DLs was around 1 or smaller for both F0s, i.e. not super-optimal, and was significantly smaller for the low than for the high F0. The results are consistent with the idea that place information alone can convey pitch, but pitch is more salient when phase-locking information is available.
Data available, click to request
A causal role for gastric rhythm in human disgust avoidance
Authors:
NORD, C.L., DALMAIJER, E., Armstrong, T., BAKER, K., DALGLEISH, T.
Reference:
Current Biology
Year of publication:
In Press
CBU number:
8579
Data available, click to request
Human Cognitive Neuroscience As It Is Taught
Authors:
HAUK, O.
Reference:
Frontiers in Psychology, section Educational Psychology
Year of publication:
In Press
CBU number:
8578
Abstract:
Cognitive neuroscience increasingly relies on complex data analysis methods. Researchers in this field come from highly diverse scientific backgrounds, such as psychology, engineering and medicine. This poses challenges with respect to acquisition of appropriate scientific computing and data analysis skills, as well as communication among researchers with different knowledge and skills sets. Are researchers in cognitive neuroscience adequately equipped to address these challenges? Here, we present evidence from an online survey of methods skills. Respondents (n=307) mainly comprised students and post-doctoral researchers working in the cognitive neurosciences. Multiple choice questions addressed a variety of basic and fundamental aspects of neuroimaging data analysis, such as signal analysis, linear algebra, and statistics. We analysed performance with respect to the following factors: undergraduate degree (grouped into Psychology, Methods, Biology), current researcher status (undergraduate student, PhD student, post-doctoral researcher), gender, and self-rated expertise levels. Overall accuracy was 72%. Not surprisingly, the Methods group performed best (87%), followed by Biology (73%) and Psychology (66%). Accuracy increased from undergraduate (59%) to PhD (74%) level, but not from PhD to post-doctoral (74%) level. The difference in performance for the Methods versus non-methods (Psychology/Biology) groups was especially striking for questions related to signal analysis and linear algebra, two areas particularly relevant to neuroimaging research. Self-rated methods expertise was not strongly predictive of performance. The majority of respondents (93%) indicated they would like to receive at least some additional training on the topics covered in this survey. In conclusion, methods skills among junior researchers in cognitive neuroscience can be improved, researchers are aware of this, and there is strong demand for more skills-oriented training opportunities. We hope that this survey will provide an empirical basis for the development of bespoke skills-oriented training programmes in cognitive neuroscience institutions. We will provide practical suggestions on how to achieve this.
Data available, click to request
Proof-of-concept for the autobiographical Memory Flexibility (MemFlex) intervention for Posttraumatic Stress Disorder
Authors:
Moradi, A.R., Piltan, M., Choobin, M.H., Azadfallah, P., WATSON, P., DALGLEISH, T., HITCHCOCK, C.
Reference:
Clinical Psychological Science
Year of publication:
In Press
CBU number:
8577
Data available, click to request
Characterising group-level brain connectivity: a framework using Bayesian exponential random graph models
Authors:
Lehmann, B.C.L., HENSON, R.N., Geerligs, L., Cam-CAN, and White, S.R.
Reference:
NeuroImage
Year of publication:
In Press
CBU number:
8576
Abstract:
The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain’s functional connectivity structure across a group of young individuals and a group of old individuals.
URL:
Data available, click to request
The Strengths and Difficulties Questionnaire Predicts Concurrent Mental Health Difficulties in a Transdiagnostic Sample of Struggling Learners
Authors:
HOLME,S J., GUY, J.
Reference:
Frontiers in Psychology, Developmental Psychology
Year of publication:
In Press
CBU number:
8575
Abstract:
Children and adolescents with developmental problems are at increased risk of experiencing mental health problems. The Strengths and Difficulties Questionnaire (SDQ) is widely used as a screener for detecting mental health difficulties in these populations, but its use thus far has been restricted to groups of children with diagnosed disorders (e.g. ADHD). Transdiagnostic approaches, which focus on symptoms and soften or remove the boundaries between traditional categorical disorders, are increasingly adopted in research and practice. The aim of this study was to assess the potential of the SDQ to detect concurrent mental health problems in a transdiagnostic sample of children. The sample were referred by health and educational professionals for difficulties related to learning (N=389). Some had one diagnosis, others had multiple, but many had no diagnoses. Parent-rated SDQ scores were significantly positively correlated with parent ratings of mental health difficulties on the Revised Child Anxiety and Depression Scale (RCADS). Ratings on the SDQ Emotion subscale significantly predicted the likelihood of having concurrent clinical anxiety and depression scores. Ratings on the Hyperactivity subscale predicted concurrent anxiety levels. These findings suggest the SDQ could be a valuable screening tool for identifying existing mental health difficulties in children recognised as struggling, as it can be in typically developing children and those with specific diagnoses.
Data available, click to request
Realistic precision & accuracy of online experiment platforms, web-browsers, and devices
Authors:
ANWYL-IRVINE, A., DALMAIJER, E.S., Hodges, N., Evershed, J.K.
Reference:
Behaviour Research Methods
Year of publication:
In Press
CBU number:
8571
Abstract:
Due to increasing ease-of-use and ability to quickly collect large samples, online behavioral research is currently booming. With this popularity, it is important that researchers are aware of who online participants are, and what devices and software they use to access experiments. While it is somewhat obvious that these factors can impact data quality, it remains unclear how big this problem is. To understand how these characteristics impact experiment presentation and data quality, we performed a battery of automated tests on a number of realistic setups. We investigated how different web-building platforms (Gorilla v.20190828, jsPsych v6.0.5, Lab.js v19.1.0, and psychoJS/PsychoPy3 v3.1.5), browsers (Chrome, Edge, Firefox, and Safari), and operating systems (macOS and Windows 10) impact display time across 30 different frame durations for each software combination. We then employed a robot actuator in realistic setups to measure response recording across aforementioned platforms, and between different keyboard types (desktop and integrated laptop). Finally, we analysed data from over 200 000 participants on their demographics, technology, and software to provide context to our findings. We found that modern web-platforms provide a reasonable accuracy and precision for display duration and manual response time, and that no single platform stands out as the best in all features and conditions. In addition, our online participant analysis shows what equipment they are likely to use. Introduction Conducting behavioural research online has vastly increased in the last few years. For instance, the number of papers tracked by Web of Science with the keywords ‘MTurk’ or ‘Mechanical Turk’ (Amazon’s popular platform for accessing online participants or workers, available since 2005) was 642 in 2018, over a five-fold increase over five years from 121 publications in 2013 (Figure 1). While scientist do not exclusively use MTurk for psychological experiments, it is indicative of a trend. For example, Bohannon (2016) reported that published MTurk studies in social science increased from 61 in 2011 to 1200 in 2015 – an almost 20-fold increase. A unique problem with internet-based testing is its reliance on participants’ hardware and software. Researchers who are used to lab-based testing will be intimately familiar with their computer, stimulus software, and hardware, for response collection. At the very least, they can be sure that all participants are tested using the very same system. For online testing, the exact opposite is true: Participants use their own computer (desktop, laptop, tablet, or even phone), with their own operating system, and access experiments through a variety of web browsers. In addition to participant degrees of freedom, researchers can choose between various options to generate experiments. These vary from programming libraries (e.g. jsPsych) to graphical experiment builders (e.g. Gorilla Experiment Builder), and come with their own idiosyncrasies with respect to timing, presentation of visual and auditory stimuli, and response collection.
Data available, click to request
Collecting Big Data with Small Screens: Group-Tests of Children’s Cognition with Touchscreen Tablets are Reliable and Valid
Authors:
BIGNARDI, G., DALMAIJER, E.S., ANWYL-IRVINE, A., and ASTLE, D.E.
Reference:
Behaviour Research Methods
Year of publication:
In Press
CBU number:
8570
Abstract:
Collecting experimental cognitive data with young children usually requires undertaking one-on one assessments which can be expensive and time-consuming. In addition, there is increasing acknowledgement of the importance of collecting larger samples for improving statistical power (Button et al., 2013), and reproducing exploratory findings (Open Science Collaboration, 2015). One way both these goals can be achieved more easily, even with a small team of researchers, is to utilize group testing. In this paper, we evaluate the results from a novel tablet application developed for the Resilience in Education and Development (RED) Study. The RED-app includes 12 cognitive tasks designed for groups of children aged 7-13 to independently complete during a one-hour school lesson. The quality of the data collected was high despite the lack of one-on-one engagement with participants. Most outcomes from the tablet showed moderate or high reliability, estimated using internal consistency metrics. Tablet-measured cognitive abilities also explained more than 50% of variance in teacher-rated academic achievement. Overall, the results suggest that tablet-based, group cognitive assessments of children are an efficient, reliable and valid method of collecting the large datasets that modern psychology requires. We have open-sourced the scripts and materials used to make the application, so that they can be adapted and used by others
Data available, click to request


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