Numerous functional neuroimaging studies suggest that widespread bilateral parietal, temporal, and frontal regions are involved in tool-related and pantomimed gesture performance, but the role of these regions in specific aspects of gestural tasks remains unclear. In the largest prospective study of apraxia-related lesions to date, we performed voxel-based lesion-symptom mapping with data from 71 left hemisphere stroke participants to assess the critical neural substrates of three types of actions: gestures produced in response to viewed tools, imitation of tool-specific gestures demonstrated by the examiner, and imitation of meaningless gestures. Thus, two of the three gesture types were tool-related, and two of the three were imitative, enabling pairwise comparisons designed to highlight commonalities and differences. Gestures were scored separately for postural (hand/arm positioning) and kinematic (amplitude/timing) accuracy. Lesioned voxels in the left posterior temporal gyrus were significantly associated with lower scores on the posture component for both of the tool-related gesture tasks. Poor performance on the kinematic component of all three gesture tasks was significantly associated with lesions in left inferior parietal and frontal regions. These data enable us to propose a componential neuroanatomic model of action that delineates the specific components required for different gestural action tasks. Thus, visual posture information and kinematic capacities are differentially critical to the three types of actions studied here: the kinematic aspect is particularly critical for imitation of meaningless movement, capacity for tool-action posture representations are particularly necessary for pantomimed gestures to the sight of tools, and both capacities inform imitation of tool-related movements. These distinctions enable us to advance traditional accounts of apraxia.

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data Highlights d We use data from smartphones and wearables from~7,000 people to compare flu and COVID-19 d While symptoms have some overlap, patients report longer COVID-19 illnesses than flu d Elevated resting heart rate measures are more frequent around illness symptoms onset d It is important to consider flu as a confounder in COVID-19 real-world studies

Two fundamental goals of decision making are to select actions that maximize rewards while minimizing costs and to have strong confidence in the accuracy of a judgment. Neural signatures of these two forms of value: the subjective value (SV) of choice alternatives and the value of the judgment (confidence), have both been observed in ventromedial prefrontal cortex (vmPFC). However, the relationship between these dual value signals and their relative time courses are unknown. Twenty-eight men and women underwent fMRI while performing a two-phase approach-avoidance (Ap-Av) task with mixed-outcomes of monetary rewards paired with painful shock stimuli. Neural responses were measured during offer valuation (offer phase) and choice valuation (commit phase) and analyzed with respect to observed decision outcomes, model-estimated SV and confidence. During the offer phase, vmPFC tracked SV and the decision but not confidence. During the commit phase, vmPFC tracked confidence, computed as the quadratic extension of SV, but not the offer valuation nor the decision. In fact, vmPFC responses from the commit phase were selective for confidence even for reject decisions wherein confidence and SV are inversely related. Conversely, activation of the cognitive control network, including within lateral prefrontal cortex (lPFC) and dorsal anterior cingulate cortex (dACC) was associated with ambivalence, during both the offer and commit phases. Taken together, our results reveal complementary representations in vmPFC during value-based decision making that temporally dissociate such that offer valuation (SV) emerges before decision valuation (confidence).

Substantial evidence suggests that conceptual processing of manipulable objects is associated with potentiation of action. Such data have been viewed as evidence that objects are recognized via access to action features. Many objects, however, are associated with multiple actions. For example, a kitchen timer may be clenched with a power grip to move it, but pinched with a precision grip to use it. The present study tested the hypothesis that action evocation during conceptual object processing is responsive to the visual scene in which objects are presented. Twenty-five healthy adults were asked to categorize object pictures presented in different naturalistic visual contexts that evoke either move- or use-related actions. Categorization judgments (natural vs. artifact) were performed by executing a move- or use-related action (clench vs. pinch) on a response device, and response times were assessed as a function of contextual congruence. Although the actions performed were irrelevant to the categorization judgment, responses were significantly faster when actions were compatible with the visual context. This compatibility effect was largely driven by faster pinch responses when objects were presented in use- compared to move-compatible contexts. The present study is the first to highlight the influence of visual scene on stimulus-response compatibility effects during semantic object processing. These data support the hypothesis that action evocation during conceptual object processing is biased toward context-relevant actions.

Commercial wearable devices are surfacing as an appealing mechanism to detect COVID-19 and potentially other public health threats, due to their widespread use. To assess the validity of wearable devices as population health screening tools, it is essential to evaluate predictive methodologies based on wearable devices by mimicking their real-world deployment. Several points must be addressed to transition from statistically significant differences between infected and uninfected cohorts to COVID-19 inferences on individuals. We demonstrate the strengths and shortcomings of existing approaches on a cohort of 32,198 individuals who experience influenza like illness (ILI), 204 of which report testing positive for COVID-19. We show that, despite commonly made design mistakes resulting in overestimation of performance, when properly designed wearables can be effectively used as a part of the detection pipeline. For example, knowing the week of year, combined with naive randomised test set generation leads to substantial overestimation of COVID-19 classification performance at 0.73 AUROC. However, an average AUROC of only 0.55 ± 0.02 would be attainable in a simulation of real-world deployment, due to the shifting prevalence of COVID-19 and non-COVID-19 ILI to trigger further testing. In this work we show how to train a machine learning model to differentiate ILI days from healthy days, followed by a survey to differentiate COVID-19 from influenza and unspecified ILI based on symptoms. In a forthcoming week, models can expect a sensitivity of 0.50 (0-0.74, 95% CI), while utilising the wearable device to reduce the burden of surveys by 35%. The corresponding false positive rate is 0.22 (0.02-0.47, 95% CI). In the future, serious consideration must be given to the design, evaluation, and reporting of wearable device interventions if they are to be relied upon as part of frequent COVID-19 or other public health threat testing infrastructures.