The role of prediction and outcomes in adaptive cognitive control

Humans adaptively perform actions to achieve their goals. This flexible behaviour requires two core abilities: the ability to anticipate the outcomes of candidate actions and the ability to select and implement actions in a goal-directed manner. The ability to predict outcomes has been extensively researched in reinforcement learning paradigms, but this work has often focused on simple actions that are not embedded in hierarchical and sequential structures that are characteristic of goal-directed human behaviour. On the other hand, the ability to select actions in accordance with high-level task goals, particularly in the presence of alternative responses and salient distractors, has been widely researched in cognitive control paradigms. Cognitive control research, however, has often paid less attention to the role of action outcomes. The present review attempts to bridge these accounts by proposing an outcome-guided mechanism for selection of extended actions. Our proposal builds on constructs from the hierarchical reinforcement learning literature, which emphasises the concept of reaching and evaluating informative states, i.e., states that constitute subgoals in complex actions. We develop an account of the neural mechanisms that allow outcome-guided action selection to be achieved in a network that relies on projections from cortical areas to the basal ganglia and back-projections from the basal ganglia to the cortex. These cortico-basal ganglia-thalamo-cortical ‘loops’ allow convergence – and thus integration – of information from non-adjacent cortical areas (for example between sensory and motor representations). This integration is essential in action sequences, for which achieving an anticipated sensory state signals the successful completion of an action. We further describe how projection pathways within the basal ganglia allow selection between representations, which may pertain to movements, actions, or extended action plans. The model lastly envisages a role for hierarchical projections from the striatum to dopaminergic midbrain areas that enable more rostral frontal areas to bias the selection of inputs from more posterior frontal areas via their respective representations in the basal ganglia.This work is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Grant BB/I019847/1, awarded to NY and FW

Adult energy requirements predicted from doubly labeled water

BACKGROUND: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician\u27s best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. METHODS: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. RESULTS: Sixty-four different linear and nonlinear regression models were developed. The adjusted R for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application

The Burr November 2012

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Optic nerve sheath diameter on computed tomography is correlated with simultaneously measured intracranial pressure in patients with severe traumatic brain injury

Purpose: Assess the relationship between optic nerve sheath diameter (ONSD) measured on bedside portable computed tomography (CT) scans and simultaneously measured intracranial pressure (ICP) in patients with severe traumatic brain injury. Methods: Retrospective cohort study of 57 patients admitted between 2009 and 2013. Linear and logistic regression were used to model the correlation and discrimination between ONSD and ICP or intracranial hypertension, respectively. Results: The cohort had a mean age of 40 years (SD 16) and a median admission Glasgow coma score of 7 (IQR 4-10). The between-rater agreement by intraclass coefficient was 0.89 (95 % CI 0.83-0.93, P < 0.001). The mean ONSD was 6.7 mm (SD 0.75) and the mean ICP during CT was 21.3 mmHg (SD 8.4). Using linear regression, there was a strong correlation between ICP and ONSD (r = 0.74, P < 0.001). ONSD had an area under the curve to discriminate elevated ICP ( 6520 mmHg vs. <20 mmHg) of 0.83 (95 % CI 0.73-0.94). Using a cutoff of 6.0 mm, ONSD had a sensitivity of 97 %, specificity of 42 %, positive predictive value of 67 %, and a negative predictive value of 92 %. Comparing linear regression models, ONSD was a much stronger predictor of ICP (R 2 of 0.56) compared to other CT features (R 2 of 0.21). Conclusions: Simultaneous measurement of ONSD on CT and ICP were strongly correlated and ONSD was discriminative for intracranial hypertension. ONSD was much more predictive of ICP than other CT features. There was excellent agreement between raters in measuring ONSD. \ua9 2014 Springer-Verlag and ESICM