What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits

Neurocognitive Development of Risk Aversion from Early Childhood to Adulthood

Human adults tend to avoid risk. In behavioral economic studies, risk aversion is manifest as a preference for sure gains over uncertain gains. However, children tend to be less averse to risk than adults. Given that many of the brain regions supporting decision-making under risk do not reach maturity until late adolescence or beyond it is possible that mature risk-averse behavior may emerge from the development of decision-making circuitry. To explore this hypothesis, we tested 5- to 8-year-old children, 14- to 16-year-old adolescents, and young adults in a risky-decision task during functional magnetic resonance imaging (fMRI) data acquisition. To our knowledge, this is the youngest sample of children in an fMRI decision-making task. We found a number of decision-related brain regions to increase in activation with age during decision-making, including areas associated with contextual memory retrieval and the incorporation of prior outcomes into the current decision-making strategy, e.g., insula, hippocampus, and amygdala. Further, children who were more risk-averse showed increased activation during decision-making in ventromedial prefrontal cortex and ventral striatum. Our findings indicate that the emergence of adult levels of risk aversion co-occurs with the recruitment of regions supporting decision-making under risk, including the integration of prior outcomes into current decision-making behavior. This pattern of results suggests that individual differences in the development of risk aversion may reflect differences in the maturation of these neural processes

Nucleus Accumbens Mediates Relative Motivation for Rewards in the Absence of Choice

To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed functional magnetic resonance imaging in a large sample of participants while they anticipated earning rewards from two different modalities: monetary and candy rewards. An index for relative motivation toward different reward types was constructed using reaction times to the target for earning rewards. Activation in the nucleus accumbens (NAcc) and anterior insula (aINS) predicted individual variation in relative motivation between our reward modalities. NAcc activation, however, mediated the effects of aINS, indicating the NAcc is the likely source of this relative weighting. These results demonstrate that neural idiosyncrasies in reward efficacy exist even in the absence of explicit choices, and extend the role of NAcc as a critical brain region for such choice-free motivation

Senior Recital

List of performers and performances

Jazz Convocation

Program listing performers and works performe

Jazz Convocation

Program listing performers and works performed

Particulate Matter-Induced Lung Inflammation Increases Systemic Levels of PAI-1 and Activates Coagulation Through Distinct Mechanisms

Exposure of human populations to ambient particulate matter (PM) air pollution significantly contributes to the mortality attributable to ischemic cardiovascular events. We reported that mice treated with intratracheally instilled PM develop a prothrombotic state that requires the release of IL-6 by alveolar macrophages. We sought to determine whether exposure of mice to PM increases the levels of PAI-1, a major regulator of thrombolysis, via a similar or distinct mechanism. mice but was absent in mice treated with etanercept, a TNF-α inhibitor. Treatment with etanercept did not prevent the PM-induced tendency toward thrombus formation.Mice exposed to inhaled PM exhibited a TNF-α-dependent increase in PAI-1 and an IL-6-dependent activation of coagulation. These results suggest that multiple mechanisms link PM-induced lung inflammation with the development of a prothrombotic state

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent component analysis (ICA). We estimated voxel-wise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal–parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity