Widespread frontoparietal fMRI activity is greatly affected by changes in criterion placement, not discriminability, during recognition memory and visual detection tests.

Widespread frontoparietal activity is consistently observed in recognition memory tests that compare studied ("target") versus unstudied ("nontarget") responses. However, there are conflicting accounts that ascribe various aspects of frontoparietal activity to mnemonic evidence versus decisional processes. According to Signal Detection Theory, recognition judgments require individuals to decide whether the memory strength of an item exceeds an evidence threshold-the decision criterion-for reporting previously studied items. Yet, most fMRI studies fail to manipulate both memory strength and decision criteria, making it difficult to appropriately identify frontoparietal activity associated with each process. In the current experiment, we manipulated both discriminability and decision criteria across recognition memory and visual detection tests during fMRI scanning to assess how frontoparietal activity is affected by each manipulation. Our findings revealed that maintaining a conservative versus liberal decision criterion drastically affects frontoparietal activity in target versus nontarget response contrasts for both recognition memory and visual detection tests. However, manipulations of discriminability showed virtually no differences in frontoparietal activity in target versus nontarget response or item contrasts. Comparing across task domains, we observed similar modulations of frontoparietal activity across criterion conditions, though the recognition memory task revealed larger activations in both magnitude and spatial extent in these contrasts. Nonetheless, there appears to be some domain specificity in frontoparietal activity associated with the maintenance of a conservative versus liberal criterion. We propose that widespread frontoparietal activity observed in target versus nontarget contrasts is largely attributable to response bias where increased activity may reflect inhibition of a prepotent response, which differs depending on whether a person maintains a conservative versus liberal decision criterion.

Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns.

Prevention neuroscience investigates the brain basis of attitude and behavior change. Over the years, an increasingly structurally and functionally resolved "persuasion network" has emerged. However, current studies have only identified a small handful of neural structures that are commonly recruited during persuasive message processing, and the extent to which these (and other) structures are sensitive to numerous individual difference factors remains largely unknown. In this project we apply a multi-dimensional similarity-based individual differences analysis to explore which individual factors-including characteristics of messages and target audiences-drive patterns of brain activity to be more or less similar across individuals encountering the same anti-drug public service announcements (PSAs). We demonstrate that several ensembles of brain regions show response patterns that are driven by a variety of unique factors. These results are discussed in terms of their implications for neural models of persuasion, prevention neuroscience and message tailoring, and methodological implications for future research.

Recording brain activity can function as an implied social presence and alter neural connectivity.

People often behave differently when they know they are being watched. Here, we report the first investigation of whether such social presence effects also include brain monitoring technology, and also their impacts on the measured neural activity. We demonstrate that merely informing participants that fMRI has the potential to observe (thought-related) brain activity is sufficient to trigger changes in functional connectivity within and between relevant brain networks that have been previously associated selectively with executive and attentional control as well as self-relevant processing, social cognition, and theory of mind. These results demonstrate that an implied social presence, mediated here by recording brain activity with fMRI, can alter brain functional connectivity. These data provide a new manipulation of social attention, as well as shining light on a methodological hazard for researchers using equipment to monitor brain activity.

The Neural Basis of Individual Differences in Directional Sense.

Individuals differ greatly in their ability to learn and navigate through environments. One potential source of this variation is "directional sense" or the ability to identify, maintain, and compare allocentric headings. Allocentric headings are facing directions that are fixed to the external environment, such as cardinal directions. Measures of the ability to identify and compare allocentric headings, using photographs of familiar environments, have shown significant individual and strategy differences; however, the neural basis of these differences is unclear. Forty-five college students, who were highly familiar with a campus environment and ranged in self-reported sense-of-direction, underwent fMRI scans while they completed the Relative Heading task, in which they had to indicate the direction of a series of photographs of recognizable campus buildings (i.e., "target headings") with respect to initial "orienting headings." Large individual differences were found in accuracy and correct decision latencies, with gender, self-reported sense-of-direction, and familiarity with campus buildings all predicting task performance. Using linear mixed models, the directional relationships between headings and the experiment location also impacted performance. Structural scans revealed that lateral orbitofrontal and superior parietal volume were related to task accuracy and decision latency, respectively. Bilateral hippocampus and right presubiculum volume were related to self-reported sense-of-direction. Meanwhile, functional results revealed clusters within the superior parietal lobule, supramarginal gyrus, superior frontal gyrus, lateral orbitofrontal cortex, and caudate among others in which the intensity of activation matched the linear magnitude of the difference between the orienting and target headings. While the retrosplenial cortex and hippocampus have previously been implicated in the coding of allocentric headings, this work revealed that comparing those headings additionally involved frontal and parietal regions. These results provide insights into the neural bases of the variation within human orientation abilities, and ultimately, human navigation.

Small sample sizes reduce the replicability of task-based fMRI studies.

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

Can categorical knowledge be used in visual search?

Smith, Redford, Gent, and Washburn (2005) have proposed a new categorization paradigm called the visual-search categorization task to study how display size affects categorization performance. Their results show that, in a wide range of conditions, category knowledge collapses as soon as multiple stimuli are simultaneously displayed in a scene. This result is surprising and important considering that humans parse and categorize objects from complex scenes on a daily basis. However, Smith et al. only studied one kind of category structure. This article presents the results of three experiments exploring the effect of display size on perceptual categorization as a function of category structure. We show that rule-based and information-integration categories are differently affected by display size in the visual search categorization task. For rule-based structures, target-present and target-absent trials are not much affected by display size. However, the effect of display size is bigger for information-integration category structures, and much more pronounced for target-absent trials than for target-present trials. A follow-up experiment shows that target redundancy (i.e., having more than one target in the display) does not improve performance with information-integration category structures. These results suggest that categories may be learned differently depending on their underlying structure, and that the resulting category representation may influence performance in the visual search categorization task.

Improving resolution of dynamic communities in human brain networks through targeted node removal.

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined "ground truth" communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.

The persuasion network is modulated by drug-use risk and predicts anti-drug message effectiveness.

While a persuasion network has been proposed, little is known about how network connections between brain regions contribute to attitude change. Two possible mechanisms have been advanced. One hypothesis predicts that attitude change results from increased connectivity between structures implicated in affective and executive processing in response to increases in argument strength. A second functional perspective suggests that highly arousing messages reduce connectivity between structures implicated in the encoding of sensory information, which disrupts message processing and thereby inhibits attitude change. However, persuasion is a multi-determined construct that results from both message features and audience characteristics. Therefore, persuasive messages should lead to specific functional connectivity patterns among a priori defined structures within the persuasion network. The present study exposed 28 subjects to anti-drug public service announcements where arousal, argument strength, and subject drug-use risk were systematically varied. Psychophysiological interaction analyses provide support for the affective-executive hypothesis but not for the encoding-disruption hypothesis. Secondary analyses show that video-level connectivity patterns among structures within the persuasion network predict audience responses in independent samples (one college-aged, one nationally representative). We propose that persuasion neuroscience research is best advanced by considering network-level effects while accounting for interactions between message features and target audience characteristics.

Hierarchical control of procedural and declarative category-learning systems.

Substantial evidence suggests that human category learning is governed by the interaction of multiple qualitatively distinct neural systems. In this view, procedural memory is used to learn stimulus-response associations, and declarative memory is used to apply explicit rules and test hypotheses about category membership. However, much less is known about the interaction between these systems: how is control passed between systems as they interact to influence motor resources? Here, we used fMRI to elucidate the neural correlates of switching between procedural and declarative categorization systems. We identified a key region of the cerebellum (left Crus I) whose activity was bidirectionally modulated depending on switch direction. We also identified regions of the default mode network (DMN) that were selectively connected to left Crus I during switching. We propose that the cerebellum-in coordination with the DMN-serves a critical role in passing control between procedural and declarative memory systems.

Trial-by-trial identification of categorization strategy using iterative decision-bound modeling.

Identifying the strategy that participants use in laboratory experiments is crucial in interpreting the results of behavioral experiments. This article introduces a new modeling procedure called iterative decision-bound modeling (iDBM), which iteratively fits decision-bound models to the trial-by-trial responses generated from single participants in perceptual categorization experiments. The goals of iDBM are to identify: (1) all response strategies used by a participant, (2) changes in response strategy, and (3) the trial number at which each change occurs. The new method is validated by testing its ability to identify the response strategies used in noisy simulated data. The benchmark simulation results show that iDBM is able to detect and identify strategy switches during an experiment and accurately estimate the trial number at which the strategy change occurs in low to moderate noise conditions. The new method is then used to reanalyze data from Ell and Ashby (2006). Applying iDBM revealed that increasing category overlap in an information-integration category learning task increased the proportion of participants who abandoned explicit rules, and reduced the number of training trials needed to abandon rules in favor of a procedural strategy. Finally, we discuss new research questions made possible through iDBM.

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

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.

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.

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.

Neural networks underlying the metacognitive uncertainty response.

Humans monitor states of uncertainty that can guide decision-making. These uncertain states are evident behaviorally when humans decline to make a categorization response. Such behavioral uncertainty responses (URs) have also defined the search for metacognition in animals. While a plethora of neuroimaging studies have focused on uncertainty, the brain systems supporting a volitional strategy shift under uncertainty have not been distinguished from those observed in making introspective post-hoc reports of categorization uncertainty. Using rapid event-related fMRI, we demonstrate that the neural activity patterns elicited by humans' URs are qualitatively different from those recruited by associative processes during categorization. Participants performed a one-dimensional perceptual-categorization task in which an uncertainty-response option let them decline to make a categorization response. Uncertainty responding activated a distributed network including prefrontal cortex (PFC), anterior and posterior cingulate cortex (ACC, PCC), anterior insula, and posterior parietal areas; importantly, these regions were distinct from those whose activity was modulated by task difficulty. Generally, our results can be characterized as a large-scale cognitive control network including recently evolved brain regions such as the anterior dorsolateral and medial PFC. A metacognitive theory would view the UR as a deliberate behavioral adjustment rather than just a learned middle category response, and predicts this pattern of results. These neuroimaging results bolster previous behavioral findings, which suggested that different cognitive processes underlie responses due to associative learning versus the declaration of uncertainty. We conclude that the UR represents an elemental behavioral index of metacognition.

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.

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.

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.

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.

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 trial's 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.