Benjamin O. Turner

Cortical and basal ganglia contributions to habit learning and automaticity

In the 20th century it was thought that novel behaviors are mediated primarily in cortex and that the development of automaticity is a process of transferring control to subcortical structures. However, evidence supports the view that subcortical structures, such as the striatum, make significant contributions to initial learning. More recently, there has been increasing evidence that neurons in the associative striatum are selectively activated during early learning, whereas those in the sensorimotor striatum are more active after automaticity has developed. At the same time, other recent reports indicate that automatic behaviors are striatum- and dopamine-independent, and might be mediated entirely within cortex. Resolving this apparent conflict should be a major goal of future research.

Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses.

Use of multivoxel pattern analysis (MVPA) to predict the cognitive state of a subject during task performance has become a popular focus of fMRI studies. The input to these analyses consists of activation patterns corresponding to different tasks or stimulus types. These activation patterns are fairly straightforward to calculate for blocked trials or slow event-related designs, but for rapid event-related designs the evoked BOLD signal for adjacent trials will overlap in time, complicating the identification of signal unique to specific trials. Rapid event-related designs are often preferred because they allow for more stimuli to be presented and subjects tend to be more focused on the task, and thus it would be beneficial to be able to use these types of designs in MVPA analyses. The present work compares 8 different models for estimating trial-by-trial activation patterns for a range of rapid event-related designs varying by interstimulus interval and signal-to-noise ratio. The most effective approach obtains each trials estimate through a general linear model including a regressor for that trial as well as another regressor for all other trials. Through the analysis of both simulated and real data we have found that this model shows some improvement over the standard approaches for obtaining activation patterns. The resulting trial-by-trial estimates are more representative of the true activation magnitudes, leading to a boost in classification accuracy in fast event-related designs with higher signal-to-noise. This provides the potential for fMRI studies that allow simultaneous optimization of both univariate and MVPA approaches.

Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs

Despite growing interest in multi-voxel pattern analysis (MVPA) methods for fMRI, a major problem remains--that of generating estimates in rapid event-related (ER) designs, where the BOLD responses of temporally adjacent events will overlap. While this problem has been investigated for methods that reduce each event to a single parameter per voxel (Mumford et al., 2012), most of these methods make strong parametric assumptions about the shape of the hemodynamic response, and require exact knowledge of the temporal profile of the underlying neural activity. A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully. In addition to enabling a more accurate estimate of ER responses, this allows for the extension of the standard classification paradigm into the temporal domain (e.g., Mourão-Miranda et al., 2007). However, existing methods in this class were developed for use with block and slow ER data, and there has not yet been an exploration of how to adapt such methods to data collected using rapid ER designs. Here, we demonstrate that the use of multiple parameters preserves or improves classification accuracy, while additionally providing information on the evolution of class discrimination. Additionally, we explore an alternative to the method of Mourão-Miranda et al. tailored to use in rapid ER designs that yields equivalent classification accuracies, but is better at unmixing responses to temporally adjacent events. The current work paves the way for wider adoption of spatiotemporal classification analyses, and greater use of MVPA with rapid ER designs.

Neural Predictors of Message Effectiveness during Counterarguing in Antidrug Campaigns

A substantial amount of research has focused on predicting the effectiveness of persuasive messages. However, characteristics of both the message itself and its receiver can impact theoretically predicted effects. For example, recent work published in this journal demonstrated that issue involvement modulates the relationship between message sensation value (MSV) and argument strength (AS). When exposed to anti-cannabis public service announcements (PSAs), high-drug-risk individuals rate these messages as having low effectiveness regardless of variation in MSV and AS. Accordingly, for high-risk individuals, MSV and AS lose their predictive power in message design; moreover, the all too common use of high MSV, high AS PSAs to dissuade drug use may be ineffective, as high-risk viewers are more likely to engage in counterarguing. In this paper, we use functional magnetic resonance imaging to investigate the neural correlates of counterarguing. Subsequently, we employ a brain-as-predictor approach using neural activation and self-report data to predict message effectiveness in two independent samples. We demonstrate that by adding two neural predictors within the middle frontal gyrus and superior temporal gyrus to self-report data, the prediction accuracy of message effectiveness in high-drug-risk individuals during counterarguing can reach, and even surpass, the prediction accuracy for low-drug-risk individuals.

Hemispheric lateralization in reasoning

A growing body of evidence suggests that reasoning in humans relies on a number of related processes whose neural loci are largely lateralized to one hemisphere or the other. A recent review of this evidence concluded that the patterns of lateralization observed are organized according to two complementary tendencies. The left hemisphere attempts to reduce uncertainty by drawing inferences or creating explanations, even at the cost of ignoring conflicting evidence or generating implausible explanations. Conversely, the right hemisphere aims to reduce conflict by rejecting or refining explanations that are no longer tenable in the face of new evidence. In healthy adults, the hemispheres work together to achieve a balance between certainty and consistency, and a wealth of neuropsychological research supports the notion that upsetting this balance results in various failures in reasoning, including delusions. However, support for this model from the neuroimaging literature is mixed. Here, we examine the evidence for this framework from multiple research domains, including an activation likelihood estimation analysis of functional magnetic resonance imaging studies of reasoning. Our results suggest a need to either revise this model as it applies to healthy adults or to develop better tools for assessing lateralization in these individuals.

Divergent hemispheric reasoning strategies: reducing uncertainty versus resolving inconsistency

Converging lines of evidence from diverse research domains suggest that the left and right hemispheres play distinct, yet complementary, roles in inferential reasoning. Here, we review research on split-brain patients, brain-damaged patients, delusional patients, and healthy individuals that suggests that the left hemisphere tends to create explanations, make inferences, and bridge gaps in information, while the right hemisphere tends to detect conflict, update beliefs, support mental set-shifts, and monitor and inhibit behavior. Based on this evidence, we propose that the left hemisphere specializes in creating hypotheses and representing causality, while the right hemisphere specializes in evaluating hypotheses, and rejecting those that are implausible or inconsistent with other evidence. In sum, we suggest that, in the domain of inferential reasoning, the left hemisphere strives to reduce uncertainty while the right hemisphere strives to resolve inconsistency. The hemispheres’ divergent inferential reasoning strategies may contribute to flexible, complex reasoning in the healthy brain, and disruption in these systems may explain reasoning deficits in the unhealthy brain.

Individual Differences in Dynamic Functional Brain Connectivity across the Human Lifespan

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism—hypergraph cardinality—we investigate individual variations in two separate, complementary data sets. The first data set (“multi-task”) consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set (“age-memory”), in which 95 individuals, aged 18–75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.

Age-dependent changes in task-based modular organization of the human brain

Abstract As humans age, cognition and behavior change significantly, along with associated brain function and organization. Aging has been shown to decrease variability in functional magnetic resonance imaging (fMRI) signals, and to affect the modular organization of human brain function. In this work, we use complex network analysis to investigate the dynamic community structure of large‐scale brain function, asking how evolving communities interact with known brain systems, and how the dynamics of communities and brain systems are affected by age. We analyze dynamic networks derived from fMRI scans of 104 human subjects performing a word memory task, and determine the time‐evolving modular structure of these networks by maximizing the multislice modularity, thereby identifying distinct communities, or sets of brain regions with strong intra‐set functional coherence. To understand how community structure changes over time, we examine the number of communities as well as the flexibility, or the likelihood that brain regions will switch between communities. We find a significant positive correlation between age and both these measures: younger subjects tend to have less fragmented and more coherent communities, and their brain regions tend to change communities less often during the memory task. We characterize the relationship of community structure to known brain systems by the recruitment coefficient, or the probability of a brain region being grouped in the same community as other regions in the same system. We find that regions associated with cingulo‐opercular, somatosensory, ventral attention, and subcortical circuits have a significantly higher recruitment coefficient in younger subjects. This indicates that the within‐system functional coherence of these specific systems during the memory task declines with age. Such a correspondence does not exist for other systems (e.g. visual and default mode), whose recruitment coefficients remain relatively uniform across ages. These results confirm that the dynamics of functional community structure vary with age, and demonstrate methods for investigating how aging differentially impacts the functional organization of different brain systems. HighlightsDynamics of task‐active brain communities are quantified in adults of varying ages.Community number and node flexibility are positively associated with subject age.Dynamics of intrinsic functional networks are differentially modulated by age.No correspondence is found between age or brain dynamics and task performance.

One dataset, many conclusions: BOLD variability’s complicated relationships with age and motion artifacts

In recent years, the variability of the blood-oxygen level dependent (BOLD) signal has received attention as an informative measure in its own right. At the same time, there has been growing concern regarding the impact of motion in fMRI, particularly in the domain of resting state studies. Here, we demonstrate that, not only does motion (among other confounds) exert an influence on the results of a BOLD variability analysis of task-related fMRI data—but, that the exact method used to deal with this influence has at least as large an effect as the motion itself. This sensitivity to relatively minor methodological changes is particularly concerning as studies begin to take on a more applied bent, and the risk of mischaracterizing the relationship between BOLD variability and various individual difference variables (for instance, disease progression) acquires real-world relevance.

Number of events and reliability in fMRI

Relatively early in the history of fMRI, research focused on issues of power and reliability, with an important line concerning the establishment of optimal procedures for experimental design in order to maximize the various statistical properties of such designs. However, in recent years, tasks wherein events are defined only a posteriori, on the basis of behavior, have become increasingly common. Although these designs enable a much wider array of questions to be answered, they are not amenable to the tight control afforded by designs with events defined entirely a priori, and little work has assessed issues of power and reliability in such designs. We demonstrate how differences in numbers of events—as can occur with a posteriori event definition—affect reliability, both through simulation and in real data.