Making Disciples: The Effects of Technology Integration Coaching

This paper describes a pilot study of collegial coaching for technology integration at two private Christian schools. Two students nearing completion of a Master’s in Education in Curriculum and Instruction with a Specialization in Instructional Technology each coached three fellow teachers, self-described as digital immigrants, to integrate technology into their teaching. The coaches spent an average of 15 hours per teacher brainstorming, teaching, and facilitating technology integration. Information obtained from a variety of data sources (interviews, a post-coaching questionnaire, a focus group, and analyses of journals kept by both coaches and coached teachers) revealed the positive effects of their collegial coaching and suggested ideas for optimizing coaching for technology integration

Probabilistic segmentation propagation from uncertainty in registration

In this paper we propose a novel approach for incorporating measures of spatial uncertainty which are derived from non-rigid registration, into propagated segmentation labels. In current approaches to segmentation via label propagation, a point-estimate of the registration parameters is used. However, this is limited by the registration accuracy achieved. In this work, we derive local measurements of the uncertainty of a non-rigid mapping from a probabilistic registration framework. This allows us to consider the set of probable locations for a segmentation label to hold. We demonstrate the use of this method on the propagation of accurately delineated cortical labels in inter-subject brain MRI using the NIREP dataset. We find that accounting for the spatial uncertainty of the mapping increases the sensitivity of correctly classifying anatomical labels

Posttraumatic stress disorder in mothers of individuals with Anorexia Nervosa.

This pilot study aimed to investigate the prevalence of maternal posttraumatic stress (PTSD), anxiety and depressive symptoms and what aspects of having a child with anorexia nervosa (AN) are perceived as traumatic by the mothers. Thirty-one mothers of daughters with AN completed a range of self-report questionnaires and a structured clinical interview. Higher rates of anxiety, depression and PTSD symptoms were found in mothers than in the general UK population. Mothers reported acute traumatic stressors during the course of AN, such as 'thinking their daughter was about to die or dead', 'seeing or consenting to their daughter being tube-fed'. We also found that mothers rated chronic aspects of their daughter's AN as very severely distressing, such as 'worrying about permanent damage to daughter's health'. Results suggest that maternal mental health should be kept in mind during assessments of individuals with eating disorders and referral for a service in their own right might be indicated

A symmetric multivariate leakage correction for MEG connectomes

AbstractAmbiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections

Interpretable many-class decoding for MEG

Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but have limited decoding performance. On the other hand, full epoch multiclass decoding models, commonly used for brain–computer interface (BCI) applications, can provide better decoding performance. However interpretation methods for such models have been designed with a low number of classes in mind. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. Crucially, we introduce a way of doing supervised dimensionality reduction of input features within a neural network optimised for the classification task, improving performance substantially. We demonstrate the approach on 3 different many-class task-MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal

Group-level brain decoding with deep learning

Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode)