Building the multitasking brain: An integrated perspective on functional brain activation during task-switching and dual-tasking.

Multitasking behavior is associated with well-known performance costs, but the question of why individuals falter when attempting to manage multiple streams of information remains difficult to answer. One reason for this difficulty may be that multitasking costs are often characterized by isolating component processes that are studied largely independently. In this study, we instead integrate two commonly studied substrates of multitasking, task-switching and dual-tasking, within the same procedural context. This method allows not only a direct comparison of performance costs associated with different demand types but also examination of their interaction. We measured functional brain activation in thirty healthy young adults as they completed a block-design version of the task, observing consistent and separable patterns of frontoparietal activation as a function of demand type. Broadly, task-switching was associated with activation of left premotor and inferior parietal regions, and dual-tasking was associated with activation in regions of right prefrontal and inferior parietal cortex. In the interaction condition, we observed a distributed bilateral pattern of activation across the areas associated with each demand in isolation. These results provide both behavioral and neuroimaging evidence that task-switching and dual-tasking demands can be dissociated and contribute to multitasking costs in unique and separable ways.

Individual differences in analogical reasoning revealed by multivariate task-based functional brain imaging.

Although analogical reasoning (AR) plays a central role in higher-level cognition and constitutes a key source of individual differences in intellectual ability, the neural mechanisms that account for individual differences in AR remain to be well characterized. Here we investigated individual differences in AR within a large sample (n = 229), using multivariate fMRI analysis (a simple multiple kernel learning machine). The individual AR capability was positively correlated with activation level in a prefrontal executive network and a visuospatial network. Notably, the best predictors of individual differences in AR within these networks were activation in the dorsomedial prefrontal cortex (response selection) and the lingual gyrus (visual feature mapping). In contrast, AR capability was negatively correlated with activation in the default mode network. The implications of the reported findings are twofold: (i) Individual differences in AR depend on multiple executive and visuospatial brain regions, where their respective contributions are contingent upon the individuals' cognitive skills; (ii) Brain regions associated with individual differences in AR only partially overlap with brain regions sensitive to the associated task demands (i.e., brain regions sensitive to the analogy relational complexity, at the group-level). We discuss implications of such brain organization supporting AR as an example for brain architecture underlying higher-level cognitive processes.

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.

Mindfulness training induces structural connectome changes in insula networks.

Although mindfulness meditation is known to provide a wealth of psychological benefits, the neural mechanisms involved in these effects remain to be well characterized. A central question is whether the observed benefits of mindfulness training derive from specific changes in the structural brain connectome that do not result from alternative forms of experimental intervention. Measures of whole-brain and node-level structural connectome changes induced by mindfulness training were compared with those induced by cognitive and physical fitness training within a large, multi-group intervention protocol (n = 86). Whole-brain analyses examined global graph-theoretical changes in structural network topology. A hypothesis-driven approach was taken to investigate connectivity changes within the insula, which was predicted here to mediate interoceptive awareness skills that have been shown to improve through mindfulness training. No global changes were observed in whole-brain network topology. However, node-level results confirmed a priori hypotheses, demonstrating significant increases in mean connection strength in right insula across all of its connections. Present findings suggest that mindfulness strengthens interoception, operationalized here as the mean insula connection strength within the overall connectome. This finding further elucidates the neural mechanisms of mindfulness meditation and motivates new perspectives about the unique benefits of mindfulness training compared to contemporary cognitive and physical fitness interventions.

Determinants of fluid intelligence in healthy aging: Omega-3 polyunsaturated fatty acid status and frontoparietal cortex structure.

INTRODUCTION: Accumulating evidence indicates that cognitive decline depends not only upon changes in brain health, but critically, also upon nutritional status. Decline in fluid intelligence, one of the most debilitating aspects of cognitive aging, has been linked to omega-3 polyunsaturated fatty acid (PUFA) status; however, it is not known whether this phenomenon results from specific omega-3 PUFAs acting on particular aspects of brain health. Therefore, this study aims to explore whether particular patterns of omega-3 PUFAs influence fluid intelligence by supporting specific neural structures. METHODS: We measured six plasma phospholipid omega-3 PUFAs, fluid intelligence, and regional gray matter volume in the frontal and parietal cortices in 100 cognitively intact older adults (65-75 years old). A four-step mediation analysis was implemented using principal component analysis and multivariate linear regressions, adjusted for age, gender, education, and body mass index. RESULTS: The mediation analysis revealed that one pattern of omega-3 PUFAs, consisting of alpha-linolenic acid, stearidonic acid, and eicosatrienoic acid, was linked to fluid intelligence, and that total gray matter volume of the left frontoparietal cortex (FPC) fully mediated the relationship between this omega-3 PUFA pattern and fluid intelligence. DISCUSSION: These data demonstrate that fluid intelligence may be optimally supported by specific omega-3 PUFAs through preservation of FPC gray matter structure in cognitively intact older adults. This report provides novel evidence for the benefits of particular omega-3 PUFA patterns on fluid intelligence and underlying gray matter structure.

Aerobic Fitness Explains Individual Differences in the Functional Brain Connectome of Healthy Young Adults.

A wealth of neuroscience evidence demonstrates that aerobic fitness enhances structural brain plasticity, promoting the development of gray matter volume and maintenance of white matter integrity within networks for executive function, attention, learning, and memory. However, the role of aerobic fitness in shaping the functional brain connectome remains to be established. The present work therefore investigated the effects of aerobic fitness (as measured by VO2max) on individual differences in whole-brain functional connectivity assessed from resting state fMRI data. Using a connectome-wide association study, we identified significant brain-fitness relationships within a large sample of healthy young adults (N = 242). The results revealed several regions within frontal, temporal, parietal, and cerebellar cortex, having significant association with aerobic fitness. We further characterized the influence of these regions on 7 intrinsic connectivity networks, demonstrating the greatest association with networks that are known to mediate the beneficial effects of aerobic fitness on executive function (frontoparietal network), attention and learning (dorsal and ventral attention network), and memory (default mode network). In addition, we provide evidence that connectivity strength between these regions and the frontoparietal network is predictive of individuals' fluid intelligence.

Predictors of Memory in Healthy Aging: Polyunsaturated Fatty Acid Balance and Fornix White Matter Integrity.

Recent evidence demonstrates that age and disease-related decline in cognition depends not only upon degeneration in brain structure and function, but also on dietary intake and nutritional status. Memory, a potential preclinical marker of Alzheimer's disease, is supported by white matter integrity in the brain and dietary patterns high in omega-3 and omega-6 polyunsaturated fatty acids. However, the extent to which memory is supported by specific omega-3 and omega-6 polyunsaturated fatty acids, and the degree to which this relationship is reliant upon microstructure of particular white matter regions is not known. This study therefore examined the cross-sectional relationship between empirically-derived patterns of omega-3 and omega-6 polyunsaturated fatty acids (represented by nutrient biomarker patterns), memory, and regional white matter microstructure in healthy, older adults. We measured thirteen plasma phospholipid omega-3 and omega-6 polyunsaturated fatty acids, memory, and regional white matter microstructure in 94 cognitively intact older adults (65 to 75 years old). A three-step mediation analysis was implemented using multivariate linear regressions, adjusted for age, gender, education, income, depression status, and body mass index. The mediation analysis revealed that a mixture of plasma phospholipid omega-3 and omega-6 polyunsaturated fatty acids is linked to memory and that white matter microstructure of the fornix fully mediates the relationship between this pattern of plasma phospholipid polyunsaturated fatty acids and memory. These results suggest that memory may be optimally supported by a balance of plasma phospholipid omega-3 and omega-6 polyunsaturated fatty acids through the preservation of fornix white matter microstructure in cognitively intact older adults. This report provides novel evidence for the benefits of plasma phospholipid omega-3 and omega-6 polyunsaturated fatty acid balance on memory and underlying white matter microstructure.

Parahippocampal Cortex Mediates the Relationship between Lutein and Crystallized Intelligence in Healthy, Older Adults.

Introduction: Although, diet has a substantial influence on the aging brain, the relationship between dietary nutrients and aspects of brain health remains unclear. This study examines the neural mechanisms that mediate the relationship between a carotenoid important for brain health across the lifespan, lutein, and crystallized intelligence in cognitively intact older adults. We hypothesized that higher serum levels of lutein are associated with better performance on a task of crystallized intelligence, and that this relationship is mediated by gray matter structure of regions within the temporal cortex. This investigation aims to contribute to a growing line of evidence, which suggests that particular nutrients may slow or prevent aspects of cognitive decline by targeting specific features of brain aging. Methods: We examined 76 cognitively intact adults between the ages of 65 and 75 to investigate the relationship between serum lutein, tests of crystallized intelligence (measured by the Wechsler Abbreviated Scale of Intelligence), and gray matter volume of regions within the temporal cortex. A three-step mediation analysis was implemented using multivariate linear regressions to control for age, sex, education, income, depression status, and body mass index. Results: The mediation analysis revealed that gray matter thickness of one region within the temporal cortex, the right parahippocampal cortex (Brodmann's Area 34), partially mediates the relationship between serum lutein and crystallized intelligence. Conclusion: These results suggest that the parahippocampal cortex acts as a mediator of the relationship between serum lutein and crystallized intelligence in cognitively intact older adults. Prior findings substantiate the individual relationships reported within the mediation, specifically the links between (i) serum lutein and temporal cortex structure, (ii) serum lutein and crystallized intelligence, and (iii) parahippocampal cortex structure and crystallized intelligence. This report demonstrates a novel structural mediation between lutein status and crystallized intelligence, and therefore provides further evidence that specific nutrients may slow or prevent features of cognitive decline by hindering particular aspects of brain aging. Future work should examine the potential mechanisms underlying this mediation, including the antioxidant, anti-inflammatory, and membrane modulating properties of lutein.

Dissociable brain biomarkers of fluid intelligence.

Cognitive neuroscience has long sought to understand the biological foundations of human intelligence. Decades of research have revealed that general intelligence is correlated with two brain-based biomarkers: the concentration of the brain biochemical N-acetyl aspartate (NAA) measured by proton magnetic resonance spectroscopy (MRS) and total brain volume measured using structural MR imaging (MRI). However, the relative contribution of these biomarkers in predicting performance on core facets of human intelligence remains to be well characterized. In the present study, we sought to elucidate the role of NAA and brain volume in predicting fluid intelligence (Gf). Three canonical tests of Gf (BOMAT, Number Series, and Letter Sets) and three working memory tasks (Reading, Rotation, and Symmetry span tasks) were administered to a large sample of healthy adults (n=211). We conducted exploratory factor analysis to investigate the factor structure underlying Gf independent from working memory and observed two Gf components (verbal/spatial and quantitative reasoning) and one working memory component. Our findings revealed a dissociation between two brain biomarkers of Gf (controlling for age and sex): NAA concentration correlated with verbal/spatial reasoning, whereas brain volume correlated with quantitative reasoning and working memory. A follow-up analysis revealed that this pattern of findings is observed for males and females when analyzed separately. Our results provide novel evidence that distinct brain biomarkers are associated with specific facets of human intelligence, demonstrating that NAA and brain volume are independent predictors of verbal/spatial and quantitative facets of Gf.

Declarative strategies persist under increased cognitive load.

When humans simultaneously execute multiple tasks, performance on individual tasks suffers. Complementing existing theories, this article poses a novel question to investigate interactions between memory systems supporting multi-tasking performance: When a primary and dual task both recruit declarative learning and memory systems, does simultaneous performance of both tasks impair primary task performance because learning in the declarative system is reduced, or because control of the primary task is passed to slower procedural systems? To address this question, participants were trained on either a perceptual categorization task believed to rely on procedural learning or one of three different categorization tasks believed to rely on declarative learning. Task performance was examined with and without a simultaneous dual task thought to recruit working memory and executive attention. To test whether the categories were learned procedurally or declaratively, the response keys were switched after a learning criterion had been reached. Large impairments in performance after switching the response keys are taken to indicate procedural learning, and small impairments are taken to indicate declarative learning. Our results suggest that the declarative memory categorization tasks (regardless of task difficulty) were learned by declarative systems, regardless of whether they were learned under dual-task conditions.

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.

Anterior cingulate cortex mediates the relationship between O3PUFAs and executive functions in APOE e4 carriers.

INTRODUCTION: Although diet has a substantial influence on the aging brain, the relationship between biomarkers of diet and aspects of brain health remains unclear. This study examines the neural mechanisms that mediate the relationship between omega-3 polyunsaturated fatty acids (O3PUFAs) and executive functions in at-risk (APOE e4 carriers), cognitively intact older adults. We hypothesized that higher levels of O3PUFAs are associated with better performance in a particular component of the executive functions, namely cognitive flexibility, and that this relationship is mediated by gray matter volume of a specific region thought to be important for cognitive flexibility, the anterior cingulate cortex. METHODS: We examined 40 cognitively intact adults between the ages of 65 and 75 with the APOE e4 polymorphism to investigate the relationship between biomarkers of O3PUFAs, tests of cognitive flexibility (measured by the Delis-Kaplan Executive Function System Trail Making Test), and gray matter volume within regions of the prefrontal cortex (PFC). RESULTS: A mediation analysis revealed that gray matter volume within the left rostral anterior cingulate cortex partially mediates the relationship between O3PUFA biomarkers and cognitive flexibility. CONCLUSION: These results suggest that the anterior cingulate cortex acts as a mediator of the relationship between O3PUFAs and cognitive flexibility in cognitively intact adults thought to be at risk for cognitive decline. Through their link to executive functions and neuronal measures of PFC volume, O3PUFAs show potential as a nutritional therapy to prevent dysfunction in the aging brain.

Lesion Mapping the Four-Factor Structure of Emotional Intelligence.

Emotional intelligence (EI) refers to an individual's ability to process and respond to emotions, including recognizing the expression of emotions in others, using emotions to enhance thought and decision making, and regulating emotions to drive effective behaviors. Despite their importance for goal-directed social behavior, little is known about the neural mechanisms underlying specific facets of EI. Here, we report findings from a study investigating the neural bases of these specific components for EI in a sample of 130 combat veterans with penetrating traumatic brain injury. We examined the neural mechanisms underlying experiential (perceiving and using emotional information) and strategic (understanding and managing emotions) facets of EI. Factor scores were submitted to voxel-based lesion symptom mapping to elucidate their neural substrates. The results indicate that two facets of EI (perceiving and managing emotions) engage common and distinctive neural systems, with shared dependence on the social knowledge network, and selective engagement of the orbitofrontal and parietal cortex for strategic aspects of emotional information processing. The observed pattern of findings suggests that sub-facets of experiential and strategic EI can be characterized as separable but related processes that depend upon a core network of brain structures within frontal, temporal and parietal cortex.

Lesion mapping of social problem solving.

Accumulating neuroscience evidence indicates that human intelligence is supported by a distributed network of frontal and parietal regions that enable complex, goal-directed behaviour. However, the contributions of this network to social aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 144) that investigates the neural bases of social problem solving (measured by the Everyday Problem Solving Inventory) and examine the degree to which individual differences in performance are predicted by a broad spectrum of psychological variables, including psychometric intelligence (measured by the Wechsler Adult Intelligence Scale), emotional intelligence (measured by the Mayer, Salovey, Caruso Emotional Intelligence Test), and personality traits (measured by the Neuroticism-Extraversion-Openness Personality Inventory). Scores for each variable were obtained, followed by voxel-based lesion-symptom mapping. Stepwise regression analyses revealed that working memory, processing speed, and emotional intelligence predict individual differences in everyday problem solving. A targeted analysis of specific everyday problem solving domains (involving friends, home management, consumerism, work, information management, and family) revealed psychological variables that selectively contribute to each. Lesion mapping results indicated that social problem solving, psychometric intelligence, and emotional intelligence are supported by a shared network of frontal, temporal, and parietal regions, including white matter association tracts that bind these areas into a coordinated system. The results support an integrative framework for understanding social intelligence and make specific recommendations for the application of the Everyday Problem Solving Inventory to the study of social problem solving in health and disease.

Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa.

OBJECTIVE: To examine set shifting in a group of women previously diagnosed with anorexia nervosa who are now weight-restored (AN-WR) and then apply a biologically based computational model (Competition between Verbal and Implicit Systems [COVIS]) to simulate the pattern of category learning and set shifting performances observed. METHOD: Nineteen AN-WR women and 35 control women (CW) were administered an explicit category learning task that required rule acquisition and then a set shift following a rule change. COVIS was first fit to the behavioral results of the controls and then parameters of the model theoretically relevant to AN were altered to mimic the behavioral results. RESULTS: Relative to CW, the AN-WR group displayed steeper learning curves (i.e., hyper learning) before the rule shift, but greater difficulty in learning the new categories after the rule shift (i.e., a deficit in set shifting). Hyper learning and set shifting deficits in the AN-WR group were not associated and differentially correlated with clinical measures. Hyper learning in the AN-WR group was simulated by increasing the model parameter that represents sensitivity to negative feedback (δ parameter), whereas the deficit in set shifting was simulated by altering the parameters that represent changes in rule selection and flexibility (λ and γ parameters, respectively). CONCLUSIONS: These simulations suggest that multiple factors can impact category learning and set shifting in AN-WR individuals (e.g., alterations in sensitivity to negative feedback, rule selection deficits, and inflexibility) and provide an important starting point to further investigate this pervasive deficit in adult AN.

Architecture of fluid intelligence and working memory revealed by lesion mapping.

Although cognitive neuroscience has made valuable progress in understanding the role of the prefrontal cortex in human intelligence, the functional networks that support adaptive behavior and novel problem solving remain to be well characterized. Here, we studied 158 human brain lesion patients to investigate the cognitive and neural foundations of key competencies for fluid intelligence and working memory. We administered a battery of neuropsychological tests, including the Wechsler Adult Intelligence Scale (WAIS) and the N-Back task. Latent variable modeling was applied to obtain error-free scores of fluid intelligence and working memory, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. The observed latent variable modeling and lesion results support an integrative framework for understanding the architecture of fluid intelligence and working memory and make specific recommendations for the interpretation and application of the WAIS and N-Back task to the study of fluid intelligence in health and disease.

A neurocomputational theory of how explicit learning bootstraps early procedural learning.

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

A neurocomputational account of cognitive deficits in Parkinson's disease.

Parkinson's disease (PD) is caused by the accelerated death of dopamine (DA) producing neurons. Numerous studies documenting cognitive deficits of PD patients have revealed impairments in a variety of tasks related to memory, learning, visuospatial skills, and attention. While there have been several studies documenting cognitive deficits of PD patients, very few computational models have been proposed. In this article, we use the COVIS model of category learning to simulate DA depletion and show that the model suffers from cognitive symptoms similar to those of human participants affected by PD. Specifically, DA depletion in COVIS produced deficits in rule-based categorization, non-linear information-integration categorization, probabilistic classification, rule maintenance, and rule switching. These were observed by simulating results from younger controls, older controls, PD patients, and severe PD patients in five well-known tasks. Differential performance among the different age groups and clinical populations was modeled simply by changing the amount of DA available in the model. This suggests that COVIS may not only be an adequate model of the simulated tasks and phenomena but also more generally of the role of DA in these tasks and phenomena.

Simulating the effects of dopamine imbalance on cognition: from positive affect to Parkinson's disease.

Cools (2006) suggested that prefrontal dopamine levels are related to cognitive stability whereas striatal dopamine levels are related to cognitive plasticity. With such a wide ranging role, almost all cognitive activities should be affected by dopamine levels in the brain. Not surprisingly, factors influencing brain dopamine levels have been shown to improve/worsen performance in many behavioral experiments. On the one hand, Nadler, Rabi, and Minda (2010) showed that positive affect (which is thought to increase cortical dopamine levels) improves a type of categorization that depends on explicit reasoning (rule-based) but not another type that depends on procedural learning (information-integration). On the other hand, Parkinson's disease (which is known to decrease dopamine levels in both the striatum and cortex) produces proactive interference in the odd-man-out task (Flowers & Robertson, 1985) and renders subjects insensitive to negative feedback during reversal learning (Cools, Altamirano, & D'Esposito, 2006). This article uses the COVIS model of categorization to simulate the effects of different dopamine levels in categorization, reversal learning, and the odd-man-out task. The results show a good match between the simulated and human data, which suggests that the role of dopamine in COVIS can account for several cognitive enhancements and deficits related to dopamine levels in healthy and patient populations.