Dataset Information

Multiple gates on working memory.

ABSTRACT: The contexts for action may be only transiently visible, accessible, and relevant. The corticobasal ganglia (BG) circuit addresses these demands by allowing the right motor plans to drive action at the right times, via a BG-mediated gate on motor representations. A long-standing hypothesis posits these same circuits are replicated in more rostral brain regions to support gating of cognitive representations. Key evidence now supports the prediction that BG can act as a gate on the input to working memory, as a gate on its output, and as a means of reallocating working memory representations rendered irrelevant by recent events. These discoveries validate key tenets of many computational models, circumscribe motor and cognitive models of recurrent cortical dynamics alone, and identify novel directions for research on the mechanisms of higher-level cognition.

SUBMITTER: Chatham CH

PROVIDER: S-EPMC4692183 | biostudies-other | 2015 Feb

REPOSITORIES: biostudies-other

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Multiple gates on working memory.

Chatham Christopher H CH Badre David D

Current opinion in behavioral sciences 20150201

The contexts for action may be only transiently visible, accessible, and relevant. The corticobasal ganglia (BG) circuit addresses these demands by allowing the right motor plans to drive action at the right times, via a BG-mediated <i>gate</i> on motor representations. A long-standing hypothesis posits these same circuits are replicated in more rostral brain regions to support gating of cognitive representations. Key evidence now supports the prediction that BG can act as a gate on the <i>input ...[more]

PMID: 26719851

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Project description:Introduction: Individuals with multiple sclerosis (MS) are vulnerable to deficits in working memory (WM), but the search for neural correlates of WM within circumscribed areas has been inconclusive. Given the widespread neural alterations observed in MS, predictive modeling approaches that capitalize on whole-brain connectivity may better capture individual differences in WM. Materials and Methods: We applied connectome-based predictive modeling to functional magnetic resonance imaging data from WM tasks in two independent samples with relapsing-remitting MS. In the internal sample (ninternal = 36), cross-validation was used to train a model to predict accuracy on the Paced Visual Serial Addition Test from functional connectivity. We hypothesized that this MS-specific model would successfully predict performance on the N-back task in the validation cohort (nvalidation = 36). In addition, we assessed the generalizability of existing WM networks derived in healthy young adults to these samples, and we explored anatomical differences between the healthy and MS networks. Results: We successfully derived an MS-specific predictive model of WM in the internal sample (full: rs = 0.47, permuted p = 0.011), but the predictions were not significant in the validation cohort (rs = -0.047; p = 0.78, mean squared error [MSE] = 0.006, R2 = -2.21%). In contrast, the healthy networks successfully predicted WM in both MS samples (internal: rs = 0.33 p = 0.049, MSE = 0.009, R2 = 13.4%; validation cohort: rs = 0.46, p = 0.005, MSE = 0.005, R2 = 16.9%), demonstrating their translational potential. Discussion: Functional networks identified in a large sample of healthy individuals predicted significant variance in WM in MS. Networks derived in small samples of people with MS may have limited generalizability, potentially due to disease-related heterogeneity. The robustness of models derived in large clinical samples warrants further investigation. ClinicalTrials.gov ID: NCT03244696.

Dataset Information

Multiple gates on working memory.

Publications

Multiple gates on working memory.

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