Jessica R. Cohen

A unique adolescent response to reward prediction errors

Previous work has shown that human adolescents may be hypersensitive to rewards, but it is not known which aspect of reward processing is responsible for this. We separated decision value and prediction error signals and found that neural prediction error signals in the striatum peaked in adolescence, whereas neural decision value signals varied depending on how value was modeled. This suggests that heightened dopaminergic prediction error responsivity contributes to adolescent reward seeking.

Engagement of large-scale networks is related to individual differences in inhibitory control.

Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examining the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of voxelwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent component analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research.

Measurement and Reliability of Response Inhibition

Response inhibition plays a critical role in adaptive functioning and can be assessed with the Stop-signal task, which requires participants to suppress prepotent motor responses. Evidence suggests that this ability to inhibit a prepotent motor response (reflected as Stop-signal reaction time (SSRT)) is a quantitative and heritable measure of interindividual variation in brain function. Although attention has been given to the optimal method of SSRT estimation, and initial evidence exists in support of its reliability, there is still variability in how Stop-signal task data are treated across samples. In order to examine this issue, we pooled data across three separate studies and examined the influence of multiple SSRT calculation methods and outlier calling on reliability (using Intra-class correlation). Our results suggest that an approach which uses the average of all available sessions, all trials of each session, and excludes outliers based on predetermined lenient criteria yields reliable SSRT estimates, while not excluding too many participants. Our findings further support the reliability of SSRT, which is commonly used as an index of inhibitory control, and provide support for its continued use as a neurocognitive phenotype.

The phenomenology of error processing: The dorsal acc response to stop-signal errors tracks reports of negative affect

A reliable observation in neuroimaging studies of cognitive control is the response of dorsal ACC (dACC) to events that demand increased cognitive control (e.g., response conflicts and performance errors). This observation is apparently at odds with a comparably reliable association of the dACC with the subjective experience of negative affective states such as pain, fear, and anxiety. Whereas “affective” associates of the dACC are based on studies that explicitly manipulate and/or measure the subjective experience of negative affect, the “cognitive” associates of dACC are based on studies using tasks designed to manipulate the demand for cognitive control, such as the Stroop, flanker, and stop-signal tasks. Critically, extant neuroimaging research has not systematically considered the extent to which these cognitive tasks induce negative affective experiences and, if so, to what extent negative affect can account for any variance in the dACC response during task performance. While undergoing fMRI, participants in this study performed a stop-signal task while regularly reporting their experience of performance on several dimensions. We observed that within-subject variability in the dACC response to stop-signal errors tracked changes in subjective frustration throughout task performance. This association remained when controlling for within-subject variability in subjective reports of cognitive engagement and several performance-related variables indexing task difficulty. These results fit with existing models characterizing the dACC as a hub for monitoring ongoing behavior and motivating adjustments when necessary and further emphasize that such a function may be linked to the subjective experience of negative affect.

The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition

A critical feature of the human brain that gives rise to complex cognition is its ability to reconfigure its network structure dynamically and adaptively in response to the environment. Existing research probing task-related reconfiguration of brain network structure has concluded that, although there are many similarities in network structure during an intrinsic, resting state and during the performance of a variety of cognitive tasks, there are meaningful differences as well. In this study, we related intrinsic, resting state network organization to reconfigured network organization during the performance of two tasks: a sequence tapping task, which is thought to probe motor execution and likely engages a single brain network, and an n-back task, which is thought to probe working memory and likely requires coordination across multiple networks. We implemented graph theoretical analyses using functional connectivity data from fMRI scans to calculate whole-brain measures of network organization in healthy young adults. We focused on quantifying measures of network segregation (modularity, system segregation, local efficiency, number of provincial hub nodes) and measures of network integration (global efficiency, number of connector hub nodes). Using these measures, we found converging evidence that local, within-network communication is critical for motor execution, whereas integrative, between-network communication is critical for working memory. These results confirm that the human brain has the remarkable ability to reconfigure its large-scale organization dynamically in response to current cognitive demands and that interpreting reconfiguration in terms of network segregation and integration may shed light on the optimal network structures underlying successful cognition. SIGNIFICANCE STATEMENT The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brains ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.

Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9–30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences.

Automaticity in motor sequence learning does not impair response inhibition

We examined the relationship between automaticity and response inhibition in the serial reaction time (SRT) task to test the common assertion that automatic behavior is ballistic. Participants trained for 3 h on the SRT, using blocks of a second-order conditional sequence interleaved with random blocks. Automaticity was measured using a concurrent secondary letter-counting task. Response inhibition was measured using a stop-signal task. RTs decreased with training, with a greater decrease for sequenced versus random blocks. Training correlated with a decreased RT cost to performing the secondary task concurrently with the SRT, indicating the development of automaticity. Crucially, there was no change in the ability to inhibit responses at the end of training, even in individuals who showed no dual-task interference. These results demonstrate that the ability to inhibit a motor response does not decrease with automaticity, suggesting that some aspects of automatic behavior are not ballistic.

Decoding continuous variables from neuroimaging data: basic and clinical applications.

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participants brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.

Differentiating allocation of resources and conflict detection within attentional control processing

Increasing demands for conflict detection and for allocation of attentional resources increase the need for attentional control. While prior evidence suggests that different cortical regions are preferentially engaged by these two attentional processes, the effect of increasing demand for conflict detection and/or allocation of attentional resources has been relatively unexplored. We designed a novel task (the ‘variable attentional control’– VAC – task) that varies the demand for attentional control by increasing conflict detection and allocation of attentional resources within the same stimuli. We studied 34 subjects who underwent event‐related functional magnetic resonance imaging while performing the VAC task. Increasing demand for attentional control, as reflected by longer reaction time and reduced accuracy, was associated with greater activation in the dorsolateral prefrontal cortex, parietal cortex and dorsal cingulate. Furthermore, an increase in conflict detection was associated with greater dorsal cingulate activity, whereas an increase in demand for allocation of attentional resources implied greater activation in the dorsolateral prefrontal and parietal cortices. In essence, in addition to allowing the exploration of the overall effects of increasing demand for attentional control, our novel task also allowed parsing of the neural components of attentional control into those related to allocation of attentional resources and those related to conflict detection.

Quantifying the Reconfiguration of Intrinsic Networks during Working Memory

Rapid, flexible reconfiguration of connections across brain regions is thought to underlie successful cognitive control. Two intrinsic networks in particular, the cingulo-opercular (CO) and fronto-parietal (FP), are thought to underlie two operations critical for cognitive control: task-set maintenance/tonic alertness and adaptive, trial-by-trial updating. Using functional magnetic resonance imaging, we directly tested whether the functional connectivity of the CO and FP networks was related to cognitive demands and behavior. We focused on working memory because of evidence that during working memory tasks the entire brain becomes more integrated. When specifically probing the CO and FP cognitive control networks, we found that individual regions of both intrinsic networks were active during working memory and, as expected, integration across the two networks increased during task blocks that required cognitive control. Crucially, increased integration between each of the cognitive control networks and a task-related, non-cognitive control network (the hand somatosensory-motor network; SM) was related to increased accuracy. This implies that dynamic reconfiguration of the CO and FP networks so as to increase their inter-network communication underlies successful working memory.