Cognition and Brain Sciences Unit

About: Cognition and Brain Sciences Unit is a facility organization based out in Cambridge, United Kingdom. It is known for research contribution in the topics: Cognition & Semantic memory. The organization has 801 authors who have published 3055 publications receiving 257962 citations.

Caveats and nuances of model-based and model-free representational connectivity analysis

Abstract: Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarising activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions and transformation of representational structure across brain regions. The paper is accompanied by scripts that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

Training flexible conceptual retrieval in post-stroke aphasia.

Abstract: Semantic therapy in post-stroke aphasia typically focusses on strengthening links between conceptual representations and their lexical-articulatory forms to aid word retrieval. However, research has shown that semantic deficits in this group can affect both verbal and non-verbal tasks, particularly in patients with deregulated retrieval as opposed to degraded knowledge. This study, therefore, aimed to facilitate semantic cognition in a sample of such patients with post-stroke semantic aphasia (SA) by training the identification of both strong and weak semantic associations and providing explicit pictorial feedback that demonstrated both common and more unusual ways of linking concepts together. We assessed the effects of this training on (i) trained and untrained items; and (ii) trained and untrained tasks in eleven individuals with SA. In the training task, the SA group showed improvement with practice, particularly for trained items. A similar untrained task using pictorial stimuli (Camel and Cactus Test) also improved. Together, these results suggest that semantic training can be beneficial in patients with SA and may show some degree of generalization to untrained situations. Future research should seek to understand which patients are most likely to benefit from this type of training.

Lesion site and therapy time predict responses to a therapy for anomia after stroke: a prognostic model development study

Abstract: BACKGROUND: Stroke is a leading cause of disability, and language impairments (aphasia) after stroke are both common and particularly feared Most stroke survivors with aphasia exhibit anomia (difficulties with naming common objects), but while many therapeutic interventions for anomia have been proposed, treatment effects are typically much larger in some patients than others Here, we asked whether that variation might be more systematic, and even predictable, than previously thought METHODS: 18 patients, each at least 6 months after left hemisphere stroke, engaged in a computerised treatment for their anomia over a 6 week period Using only: (a) the patients' initial accuracy when naming (to-be) trained items; (b) the hours of therapy that they devoted to the therapy; and (c) whole-brain lesion location data, derived from structural MRI; we developed Partial Least Squares regression models to predict the patients' improvements on treated items, and tested them in cross-validation RESULTS: Somewhat surprisingly, the best model included only lesion location data and the hours of therapy undertaken In cross-validation, this model significantly out-performed the null model, in which the prediction for each patient was simply the mean treatment effect of the group This model also made promisingly accurate predictions in absolute terms: the correlation between empirical and predicted treatment response was 062 (95%CI: 027, 095) DISCUSSION: Our results indicate that individuals' variation in response to anomia treatment are, at least somewhat, systematic and predictable, from the interaction between where and how much lesion damage they have suffered, and the time they devoted to the therapy