Influence of goals on modular brain network organization during working memory

IntroductionTop-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM.MethodsHere, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity–a measure of brain sub-network segregation–that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant).ResultsFirst, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance.DiscussionThese results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM

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

Functional Polymorphism of the Mu-Opioid Receptor Gene (OPRM1) Influences Reinforcement Learning in Humans

Previous reports on the functional effects (i.e., gain or loss of function), and phenotypic outcomes (e.g., changes in addiction vulnerability and stress response) of a commonly occurring functional single nucleotide polymorphism (SNP) of the mu-opioid receptor (OPRM1 A118G) have been inconsistent. Here we examine the effect of this polymorphism on implicit reward learning. We used a probabilistic signal detection task to determine whether this polymorphism impacts response bias to monetary reward in 63 healthy adult subjects: 51 AA homozygotes and 12 G allele carriers. OPRM1 AA homozygotes exhibited typical responding to the rewarded response—that is, their bias to the rewarded stimulus increased over time. However, OPRM1 G allele carriers exhibited a decline in response to the rewarded stimulus compared to the AA homozygotes. These results extend previous reports on the heritability of performance on this task by implicating a specific polymorphism. Through comparison with other studies using this task, we suggest a possible mechanism by which the OPRM1 polymorphism may confer reduced response to natural reward through a dopamine-mediated decrease during positive reinforcement learning

Brain Modularity: A Biomarker of Intervention-related Plasticity

Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition

All the World’s a Stage: A Guide to Conducting School-based Research Remotely

Assessing students in their learning environment continues to be an important avenue for understanding the developing brain. One of the many changes brought on by the COVID-19 pandemic is a shift to virtual and remote learning opportunities. Just as parents, students, and teachers had to adjust to the virtual and hybrid classrooms, so too must researchers adapt their methods and research strategies to work in this new context. Here, we present our experiences conducting behavioral assessments in remote environments. We discuss the challenges presented in a fully virtual environment when neither the teachers nor researchers can physically interact with students, and contrast that with a hybrid environment in which researchers were remote, but teachers and students were together in their physical classroom. By sharing our experiences and insights into how to conduct research with students remotely, we aim to encourage more researchers to take advantage of this new avenue of research that has the potential to increase participation for populations geographically farther from the research team at a lower cost of effort for the research team, which can particularly help increase participation from underrepresented groups

Brain network modularity predicts cognitive training-related gains in young adults

The brain operates via networked activity in separable groups of regions called modules. The quantification of modularity compares the number of connections within and between modules, with high modularity indicating greater segregation, or dense connections within sub-networks and sparse connections between sub-networks. Previous work has demonstrated that baseline brain network modularity predicts executive function outcomes in older adults and patients with traumatic brain injury after cognitive and exercise interventions. In healthy young adults, however, the functional significance of brain modularity in predicting training-related cognitive improvements is not fully understood. Here, we quantified brain network modularity in young adults who underwent cognitive training with casual video games that engaged working memory and reasoning processes. Network modularity assessed at baseline was positively correlated with training-related improvements on untrained tasks. The relationship between baseline modularity and training gain was especially evident in initially lower performing individuals and was not present in a group of control participants that did not show training-related gains. These results suggest that a more modular brain network organization may allow for greater training responsiveness. On a broader scale, these findings suggest that, particularly in low-performing individuals, global network properties can capture aspects of brain function that are important in understanding individual differences in learning