Using Expectancy Theory to quantitatively dissociate the neural representation of motivation from its influential factors in the human brain: An fMRI study.

Researchers have yet to apply a formal operationalized theory of motivation to neurobiology that would more accurately and precisely define neural activity underlying motivation. We overcome this challenge with the novel application of the Expectancy Theory of Motivation to human fMRI to identify brain activity that explicitly reflects motivation. Expectancy Theory quantitatively describes how individual constructs determine motivation by defining motivation force as the product of three variables: expectancy - belief that effort will better performance; instrumentality - belief that successful performance leads to particular outcome, and valence - outcome desirability. Here, we manipulated information conveyed by reward-predicting cues such that relative cue-evoked activity patterns could be statistically mapped to individual Expectancy Theory variables. The variable associated with activity in any voxel is only reported if it replicated between two groups of healthy participants. We found signals in midbrain, ventral striatum, sensorimotor cortex, and visual cortex that specifically map to motivation itself, rather than other factors. This is important because, for the first time, it empirically clarifies approach motivation neural signals during reward anticipation. It also highlights the effectiveness of the application of Expectancy Theory to neurobiology to more precisely and accurately probe motivation neural correlates than has been achievable previously.

Using "big data" to guide implementation of a web and mobile adaptive learning platform for medical students.

BACKGROUND: Adaptive learning platforms (ALPs) can revolutionize medical education by making learning more efficient, but their potential has not been realized because students do not use them persistently. METHODS: We applied educational data mining methods to study United States medical students who used an ALP called Osmosis ( www.osmosis.org ) from 1 August 2014 to 31 July 2015. Multivariate logistic regressions modeled persistence on Osmosis as the dependent variable and Osmosis-collected variables as predictors. RESULTS: The 6787 students included in our analysis responded to a total of 887,193 items, with 2138 (31.5%) using Osmosis persistently. Number of items per student, mobile device use, subscription payment, and group membership were independently associated with persisting (p < 0.001 in all models). Persistent users rated quality more favorably (p < 0.01) but were not more confident in answer selections (p = 0.80). While persisters were more accurate than non-persisters (55% (SD 18%) vs 52% (SD 22%), p < 0.001), after adjusting for number of items, lower accuracy was associated with persistent use (OR 0.93 [95% CI 0.90-0.97], p < 0.01). CONCLUSIONS: Our study of a large sample of U.S. medical students illustrates big data medical education research and provides guidance for improving implementation of ALPs and further investigation.

Using Commercial Activity Monitors to Measure Gait in Patients with Suspected iNPH: Implications for Ambulatory Monitoring.

OBJECTIVES: This study seeks to validate the use of activity monitors to detect and record gait abnormalities, potentially identifying patients with idiopathic normal pressure hydrocephalus (iNPH) prior to the onset of cognitive or urinary symptoms. METHODS: This study compared the step counts of four common activity monitors (Omron Step Counter HJ-113, New Lifestyles 2000, Nike Fuelband, and Fitbit Ultra) to an observed step count in 17 patients with confirmed iNPH. RESULTS: Of the four devices, the Fitbit Ultra (Fitbit, Inc., San Francisco, CA) provided the most accurate step count. The correlation with the observed step count was significantly higher (p<0.009) for the Fitbit Ultra than for any of the other three devices. CONCLUSIONS: These preliminary findings suggest that existing activity monitors have variable efficacy in the iNPH patient population and that the MEMS tri-axial accelerometer and algorithm of the Fitbit Ultra provides the most accurate gait measurements of the four devices tested.

Subjective socioeconomic status predicts human ventral striatal responses to social status information.

The enormous influence of hierarchical rank on social interactions [1] suggests that neural mechanisms exist to process status-related information [2] and ascribe value to it. The ventral striatum is prominently implicated in processing value and salience, independent of hedonic properties [3, 4], and a functional magnetic resonance imaging (fMRI) study of social status perception in humans demonstrated that viewing higher-ranked compared to lower-ranked individuals evokes a ventral striatal response [5], indicative of a greater assignment of value/salience to higher status. Consistent with this interpretation, nonhuman primates value information associated with higher-ranked conspecifics more than lower-ranked, as illustrated using a choice paradigm in which monkeys preferentially take the opportunity to view high-status monkeys [6]. Interestingly, this status-related value assignment in nonhuman primates is influenced by one's own hierarchical rank: high-status monkeys preferentially attend to conspecifics of high status, whereas low-status monkeys will also attend to other low-status monkeys [7]. Complementary to these findings, using fMRI and a social status judgment task in humans, we suggest a neurobiological mechanism by which one's own relative hierarchical rank influences the value attributed to particular social status information by demonstrating that one's subjective socioeconomic status differentially influences ventral striatal activity during processing of status-related information.

Regionally specific cortical thinning in children with sickle cell disease.

Sickle cell disease (SCD) is a chronic disease with a significant rate of neurological complications in the first decade of life. In this retrospective study, cortical thickness was examined in children with SCD who had no detectable abnormalities on conventional magnetic resonance imaging/magnetic resonance angiography. Regional differences in cortical thickness from SCD were explored using age-matched healthy controls as comparison. A comparison analysis was done for SCD (n = 28) and controls (n = 29) based on age (5-11; 12-21 years), due to the age-dependent variation in cortex maturation. Distinct regions of thinning were found in SCD patients in both age groups. The number, spatial extent, and significance (P < 0.001) of these areas of thinning were increased in the older SCD group. Regions of interest (ROIs) were defined on the areas of highly significant thinning in the older group and then mapped onto the younger cohort; a multiparametric linear regression analysis of the ROI data demonstrated significant (P < 0.001) cortical thinning in SCD subjects, with the largest regions of thinning in the precuneus and the posterior cingulate. The regionally specific differences suggest that cortical thickness may serve as a marker for silent insults in SCD and hence may be a useful tool for identifying SCD patients at risk for neurological sequelae.