Marcelo G. Mattar

A Functional Cartography of Cognitive Systems

One of the most remarkable features of the human brain is its ability to adapt rapidly and efficiently to external task demands. Novel and non-routine tasks, for example, are implemented faster than structural connections can be formed. The neural underpinnings of these dynamics are far from understood. Here we develop and apply novel methods in network science to quantify how patterns of functional connectivity between brain regions reconfigure as human subjects perform 64 different tasks. By applying dynamic community detection algorithms, we identify groups of brain regions that form putative functional communities, and we uncover changes in these groups across the 64-task battery. We summarize these reconfiguration patterns by quantifying the probability that two brain regions engage in the same network community (or putative functional module) across tasks. These tools enable us to demonstrate that classically defined cognitive systems—including visual, sensorimotor, auditory, default mode, fronto-parietal, cingulo-opercular and salience systems—engage dynamically in cohesive network communities across tasks. We define the network role that a cognitive system plays in these dynamics along the following two dimensions: (i) stability vs. flexibility and (ii) connected vs. isolated. The role of each system is therefore summarized by how stably that system is recruited over the 64 tasks, and how consistently that system interacts with other systems. Using this cartography, classically defined cognitive systems can be categorized as ephemeral integrators, stable loners, and anything in between. Our results provide a new conceptual framework for understanding the dynamic integration and recruitment of cognitive systems in enabling behavioral adaptability across both task and rest conditions. This work has important implications for understanding cognitive network reconfiguration during different task sets and its relationship to cognitive effort, individual variation in cognitive performance, and fatigue.

Functional Network Dynamics of the Language System

During linguistic processing, a set of brain regions on the lateral surfaces of the left frontal, temporal, and parietal cortices exhibit robust responses. These areas display highly correlated activity while a subject rests or performs a naturalistic language comprehension task, suggesting that they form an integrated functional system. Evidence suggests that this system is spatially and functionally distinct from other systems that support high-level cognition in humans. Yet, how different regions within this system might be recruited dynamically during task performance is not well understood. Here we use network methods, applied to fMRI data collected from 22 human subjects performing a language comprehension task, to reveal the dynamic nature of the language system. We observe the presence of a stable core of brain regions, predominantly located in the left hemisphere, that consistently coactivate with one another. We also observe the presence of a more flexible periphery of brain regions, predominantly located in the right hemisphere, that coactivate with different regions at different times. However, the language functional ROIs in the angular gyrus and the anterior temporal lobe were notable exceptions to this trend. By highlighting the temporal dimension of language processing, these results suggest a trade-off between a regions specialization and its capacity for flexible network reconfiguration.

Optimal trajectories of brain state transitions

Abstract The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering‐based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury. HighlightsWe use network control theory to model mechanisms of brain state transitions.Attention and executive areas are poised to affect an array of state transitions.Patients with mild traumatic injury display less specificity in control processes.

Simultaneous perceptual and response biases on sequential face attractiveness judgments.

Face attractiveness is a social characteristic that we often use to make first-pass judgments about the people around us. However, these judgments are highly influenced by our surrounding social world, and researchers still understand little about the mechanisms underlying these influences. In a series of 3 experiments, we use a novel sequential rating paradigm that enables us to measure biases in attractiveness judgments from the previous face and the previous rating. Our results reveal 2 simultaneous and opposing influences on face attractiveness judgments that arise from past experience of faces: a response bias in which attractiveness ratings shift toward a previously given rating and a stimulus bias in which attractiveness ratings shift away from the mean attractiveness of the previous face. Further, we provide evidence that the contrastive stimulus bias (but not the assimilative response bias) is strengthened by increasing the duration of the previous stimulus, suggesting an underlying perceptual mechanism. These results demonstrate that judgments of face attractiveness are influenced by information from our evaluative and perceptual history and that these influences have measurable behavioral effects over the course of just a few seconds.

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

Humans adapt their behavior to their external environment in a process often facilitated by learning. Efforts to describe learning empirically can be complemented by quantitative theories that map changes in neurophysiology to changes in behavior. In this review we highlight recent advances in network science that offer a sets of tools and a general perspective that may be particularly useful in understanding types of learning that are supported by distributed neural circuits. We describe recent applications of these tools to neuroimaging data that provide unique insights into adaptive neural processes, the attainment of knowledge, and the acquisition of new skills, forming a network neuroscience of human learning. While promising, the tools have yet to be linked to the well-formulated models of behavior that are commonly utilized in cognitive psychology. We argue that continued progress will require the explicit marriage of network approaches to neuroimaging data and quantitative models of behavior.

Predicting future learning from baseline network architecture

&NA; Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brains baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual‐motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual‐motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution. HighlightsSensorimotor autonomy at rest predicts faster motor learning in the future.Connection between calcarine and superior precentral sulci form strongest predictor.Sensorimotor autonomy is a relatively stable individual trait.

The flexible brain.

This scientific commentary refers to ‘Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders’ by Zhang et al. (doi:10.1093/aww143). The vastness of the brain’s dynamic repertoire is one of the remarkable features of brain function, making it possible to adapt rapidly and efficiently to external task demands, implement novel behaviours, and switch from one task to another. Variability in the neural dynamics is, nonetheless, constrained and displays heterogeneous topography—specific regions appear more or less variable over time, both in terms of their activity time courses (Garrett et al. , 2011) and also in terms of their interactions with other brain regions (their functional connectivity). In this issue of Brain , Zhang and co-workers present a novel method for characterizing the temporal variability of a region’s functional connectivity profile [estimated from blood oxygen level-dependent (BOLD) functional MRI], relating this variability to the region’s electrophysiology (measured with electroencephalography; EEG) and to its macroscale structural connectivity (white-matter pathways), and further demonstrating its potential utility as a neural marker for mental disorders (Zhang et al. , 2016). The past decade has witnessed a burgeoning interest in the functional network architecture of the human brain. Most of these earlier studies have adopted a ‘static’ point of view, wherein functional connections between regions are characterized over long time scales, obscuring faster dynamics. More recently, however, it has become apparent that over the course of seconds to minutes, the human brain displays network-wide reconfigurations both at rest (Zalesky et al. , 2014) and during task performance (Braun et al. , 2015). These findings …

Structural Pathways Supporting Swift Acquisition of New Visuomotor Skills

Abstract Human skill learning requires fine‐scale coordination of distributed networks of brain regions linked by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact individual differences in learning remains far from understood. Here, we test the hypothesis that individual differences in structural organization of networks supporting task performance predict individual differences in the rate at which humans learn a visuomotor skill. Over the course of 6 weeks, 20 healthy adult subjects practiced a discrete sequence production task, learning a sequence of finger movements based on discrete visual cues. We collected structural imaging data, and using deterministic tractography generated structural networks for each participant to identify streamlines connecting cortical and subcortical brain regions. We observed that increased white matter connectivity linking early visual regions was associated with a faster learning rate. Moreover, the strength of multiedge paths between motor and visual modules was also correlated with learning rate, supporting the potential role of extended sets of polysynaptic connections in successful skill acquisition. Our results demonstrate that estimates of anatomical connectivity from white matter microstructure can be used to predict future individual differences in the capacity to learn a new motor‐visual skill, and that these predictions are supported both by direct connectivity in visual cortex and indirect connectivity between visual cortex and motor cortex.

The network architecture of value learning

Value guides behavior. With knowledge of stimulus values and action consequences, behaviors that maximize expected reward can be selected. Prior work has identified several brain structures critical for representing both stimuli and their values. Yet, it remains unclear how these structures interact with one another and with other regions of the brain to support the dynamic acquisition of value-related knowledge. Here, we use a network neuroscience approach to examine how BOLD functional networks change as 20 healthy human subjects learn the values of novel visual stimuli over the course of four consecutive days. We show that connections between regions of the visual, frontal, and cingulate cortices become stronger as learning progresses, with some of these changes being specific to the type of feedback received during learning. These results demonstrate that functional networks dynamically track behavioral improvement in value judgments, and that interactions between network communities form predictive biomarkers of learning. Author Summary Rational human behavior is the pursuit of actions that maximize expected reward. These rewards can be understood as stimulus-value contingencies, learned by experience throughout our lives. Various structures have been recognized to participate in these learning processes. Yet, an understanding of how these structures interact with one another and with other brain regions remains vastly unexplored. Here, we propose a novel analytical framework utilizing and extending techniques from the dynamic network neuroscience to ask “How do our brains change when we learn values?” We find that interactions between sensory and fronto-cingulate structures grow stronger as learning progresses, bringing together several isolated findings in the cognitive neuroscience of value-based behavior and extending our understanding of human learning in general.

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration

&NA; The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germane to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture – which brain regions may functionally interact – and their temporal architecture – how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called non‐negative matrix factorization to time‐evolving, resting state functional networks in 20 healthy subjects. This machine learning approach automatically groups temporally co‐varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time‐varying subgraph expression to explain inter‐individual differences in module reorganization. Collectively, these results point to the critical role that subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.