Impaired cognitive plasticity and goal-directed control in adolescent obsessive-compulsive disorder.

BACKGROUND: Youths with obsessive-compulsive disorder (OCD) experience severe distress and impaired functioning at school and at home. Critical cognitive domains for daily functioning and academic success are learning, memory, cognitive flexibility and goal-directed behavioural control. Performance in these important domains among teenagers with OCD was therefore investigated in this study. METHODS: A total of 36 youths with OCD and 36 healthy comparison subjects completed two memory tasks: Pattern Recognition Memory (PRM) and Paired Associates Learning (PAL); as well as the Intra-Extra Dimensional Set Shift (IED) task to quantitatively gauge learning as well as cognitive flexibility. A subset of 30 participants of each group also completed a Differential-Outcome Effect (DOE) task followed by a Slips-of-Action Task, designed to assess the balance of goal-directed and habitual behavioural control. RESULTS: Adolescent OCD patients showed a significant learning and memory impairment. Compared with healthy comparison subjects, they made more errors on PRM and PAL and in the first stages of IED involving discrimination and reversal learning. Patients were also slower to learn about contingencies in the DOE task and were less sensitive to outcome devaluation, suggesting an impairment in goal-directed control. CONCLUSIONS: This study advances the characterization of juvenile OCD. Patients demonstrated impairments in all learning and memory tasks. We also provide the first experimental evidence of impaired goal-directed control and lack of cognitive plasticity early in the development of OCD. The extent to which the impairments in these cognitive domains impact academic performance and symptom development warrants further investigation.This work was funded by a Wellcome Trust Grant (grant number 089589/Z/09/Z) awarded to TW Robbins, BJ Everitt, AC Roberts, JW Dalley, and BJ Sahakian, and a Wellcome Trust Senior Investigator Award (104631/Z/14/Z) to TW Robbins. The research was conducted in the Behavioural and Clinical Neuroscience Institute, which is supported by a joint award from the Medical Research Council and Wellcome Trust (G00001354). J Gottwald was supported by a BCNI MRC PhD studentship and St John’s College, Cambridge

High-Density Microwell Chip for Culture and Analysis of Stem Cells

With recent findings on the role of reprogramming factors on stem cells, in vitro screening assays for studying (de)-differentiation is of great interest. We developed a miniaturized stem cell screening chip that is easily accessible and provides means of rapidly studying thousands of individual stem/progenitor cell samples, using low reagent volumes. For example, screening of 700,000 substances would take less than two days, using this platform combined with a conventional bio-imaging system. The microwell chip has standard slide format and consists of 672 wells in total. Each well holds 500 nl, a volume small enough to drastically decrease reagent costs but large enough to allow utilization of standard laboratory equipment. Results presented here include weeklong culturing and differentiation assays of mouse embryonic stem cells, mouse adult neural stem cells, and human embryonic stem cells. The possibility to either maintain the cells as stem/progenitor cells or to study cell differentiation of stem/progenitor cells over time is demonstrated. Clonality is critical for stem cell research, and was accomplished in the microwell chips by isolation and clonal analysis of single mouse embryonic stem cells using flow cytometric cell-sorting. Protocols for practical handling of the microwell chips are presented, describing a rapid and user-friendly method for the simultaneous study of thousands of stem cell cultures in small microwells. This microwell chip has high potential for a wide range of applications, for example directed differentiation assays and screening of reprogramming factors, opening up considerable opportunities in the stem cell field

Feasibility of using solar energy as a source of renewable energy in mauritius under collaboration of DIREKT

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Research data supporting "Association of Environmental Uncertainty With Altered Decision-making and Learning Mechanisms in Youths With Obsessive-Compulsive Disorder"

In the 'PRL' folder, there is a 'core' and a 'support' folder. The raw data are saved in 'core' as 'probrev_forstan.csv'. The data in this file are collected from 103 adolescents (50 with OCD and 53 without) who completed a probabilistic reversal learning task (details in paper). The file contains information about subject ID (patient_num), trials, chosen stimulus per trial (chosen_stim), whether stimulus 1 was chosen (stim1_chosen), whether subject chose correct stimulus (chosen_correctly), task feedback (1 - positive, 0 - negative; fdbk), group (1 - Control, 0 - OCD). This data are in a format ready to undergo computational modelling. To run the models, use the Runner.R file in the 'core' folder. The models themselves are in .stan format. Next, in the 'WCST' folder, the raw data (in the wcst_data.txt) file are obtained from 73 adolescents (27 with OCD 46 without) who completed a Wisconsin Card Sorting Task (details in paper). Relevant columns in the wcst_data file are subject ID (subnum), trial (trial), whether the card chosen matches the test card on colour (corr_col), shape (corr_shape) and number (corr_name), group (0 -OCD, 1 - CTL), whether card was chosen correctly (corr). The main script for fitting the computational models to data is WCST_JAGS_2021.R. Models themselves are in .txt files (files that end in _dnormsd_groupdiff).Wellcome Trust grant 104631/Z/14/Z

Classifying the activity states of small vertebrates using automated VHF telemetry

Abstract The most basic behavioural states of animals can be described as active or passive. While high‐resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which a combination of automatic radiotracking and machine learning is used to distinguish between active and passive behaviour in small vertebrates fitted with lightweight transmitters (3 million signals from very‐high‐frequency (VHF) telemetry from two forest‐dwelling bat species (Myotis bechsteinii [n = 52] and Nyctalus leisleri [n = 20]) to train and test a random forest model in assigning either active or passive behaviour to VHF‐tagged individuals. The generalisability of the model was demonstrated by recording and classifying the behaviour of tagged birds and by simulating the effect of different activity levels with the help of humans carrying transmitters. The model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F1 0.96–0.98). We provide an ecological case‐study demonstrating the potential of this automated monitoring tool. We used the trained models to compare differences in the daily activity patterns of two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night‐time activity of M. bechsteinii was relatively constant. These results show that subtle differences in the timing of species' activity can be distinguished using our method. Our approach can classify VHF‐signal patterns into fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radiotracking method, we provide the trained random forest models together with an R package that includes all necessary data processing functionalities. In combination with state‐of‐the‐art open‐source automated radiotracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation

Classifying the activity states of small vertebrates using automated VHF telemetry

The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states. A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98). The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method. Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation