Dynamic network mechanisms of relational integration

Causation in neuroscience: keeping mechanism meaningful

A fundamental goal of research in neuroscience is to uncover the causal structure of the brain. This focus on causation makes sense, because causal information can provide explanations of brain function and identify reliable targets with which to understand cognitive function and prevent or change neurological conditions and psychiatric disorders. In this research, one of the most frequently used causal concepts is 'mechanism' - this is seen in the literature and language of the field, in grant and funding inquiries that specify what research is supported, and in journal guidelines on which contributions are considered for publication. In these contexts, mechanisms are commonly tied to expressions of the main aims of the field and cited as the 'fundamental', 'foundational' and/or 'basic' unit for understanding the brain. Despite its common usage and perceived importance, mechanism is used in different ways that are rarely distinguished. Given that this concept is defined in different ways throughout the field - and that there is often no clarification of which definition is intended - there remains a marked ambiguity about the fundamental goals, orientation and principles of the field. Here we provide an overview of causation and mechanism from the perspectives of neuroscience and philosophy of science, in order to address these challenges.

Dual-site beta tACS over rIFG and M1 enhances response inhibition: A parallel multiple control and replication study

Response inhibition is a core component of cognitive control. Past electrophysiology and neuroimaging studies have identified beta oscillations and inhibitory control cortical regions correlated with response inhibition, including the right inferior frontal gyrus (rIFG) and primary motor cortex (M1). Hence, increasing beta activity in multiple brain regions is a potential way to enhance response inhibition. Here, a novel dual-site transcranial alternating current stimulation (tACS) method was used to modulate beta activity over the rIFG-M1 network in a sample of 115 (excluding 2 participants) with multiple control groups and a replicated experimental design. In Experiment 1, 70 healthy participants were randomly assigned to three dual-site beta-tACS groups, including in-phase, anti-phase or sham stimulation. During and after stimulation, participants were required to complete the stop-signal task, and electroencephalography (EEG) was collected before and after stimulation. The Barratt Impulsiveness Scale was completed before the experiment to evaluate participants' impulsiveness. In addition, we conducted an active control experiment with a sample size of 20 to exclude the potential effects of the dual-site tACS "return" electrode. To validate the behavioural findings of Experiment 1, 25 healthy participants took part in Experiment 2 and were randomized into two groups, including in-phase and sham stimulation groups. We found that compared to the sham group, in-phase but not anti-phase beta-tACS significantly improved both response inhibition performance and beta synchronization of the inhibitory control network in Experiment 1. Furthermore, the increased beta synchronization was correlated with enhanced response inhibition. In an independent sample of Experiment 2, the enhanced response inhibition performance observed in the in-phase group was replicated. After combining the data from the above two experiments, the time dynamics analysis revealed that the in-phase beta-tACS effect occurred in the post-stimulation period but not the stimulation period. The state-dependence analysis showed that individuals with poorer baseline response inhibition or higher attentional impulsiveness had greater improvement in response inhibition for the in-phase group. These findings strongly support that response inhibition in healthy adults can be improved by in-phase dual-site beta-tACS of the rIFG-M1 network, and provide a new potential treatment targets of synchronized cortical network activity for patients with clinically deficient response inhibition.

Evaluating the reliability, validity, and utility of overlapping networks: Implications for network theories of cognition

Brain network definitions typically assume nonoverlap or minimal overlap, ignoring regions' connections to multiple networks. However, new methods are emerging that emphasize network overlap. Here, we investigated the reliability and validity of one assignment method, the mixed membership algorithm, and explored its potential utility for identifying gaps in existing network models of cognition. We first assessed between-sample reliability of overlapping assignments with a split-half design; a bootstrapped Dice similarity analysis demonstrated good agreement between the networks from the two subgroups. Next, we assessed whether overlapping networks captured expected nonoverlapping topographies; overlapping networks captured portions of one to three nonoverlapping topographies, which aligned with canonical network definitions. Following this, a relative entropy analysis showed that a majority of regions participated in more than one network, as is seen biologically, and many regions did not show preferential connection to any one network. Finally, we explored overlapping network membership in regions of the dual-networks model of cognitive control, showing that almost every region was a member of multiple networks. Thus, the mixed membership algorithm produces consistent and biologically plausible networks, which presumably will allow for the development of more complete network models of cognition.

Instruction-based learning: A review

Humans are able to learn to implement novel rules from instructions rapidly, which is termed "instruction-based learning" (IBL). This remarkable ability is very important in our daily life in both learning individually or working as a team, and almost every psychology experiment starts with instructing participants. Many recent progresses have been made in IBL research both psychologically and neuroscientifically. In this review, we discuss the role of language in IBL, the importance of the first trial performance in IBL, why IBL should be considered as a goal-directed behavior, intelligence and IBL, cognitive flexibility and IBL, how behaviorally relevant information is processed in the lateral prefrontal cortex (LPFC), how the lateral frontal cortex (LFC) networks work as a functional hierarchy during IBL, and the cortical and subcortical contributions to IBL. Finally, we develop a neural working model for IBL and provide some sensible directions for future research.

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.

Patterns of Focal- and Large-Scale Synchronization in Cognitive Control and Inhibition: A Review

Neural synchronization patterns are involved in several complex cognitive functions and constitute a growing trend in neuroscience research. While synchrony patterns in working memory have been extensively discussed, a complete understanding of their role in cognitive control and inhibition is still elusive. Here, we provide an up-to-date review on synchronization patterns underlying behavioral inhibition, extrapolating common grounds, and dissociating features with other inhibitory functions. Moreover, we suggest a schematic conceptual framework and highlight existing gaps in the literature, current methodological challenges, and compelling research questions for future studies.

Interference Control in Working Memory Is Associated with Ventrolateral Prefrontal Cortex Volume

Goal-irrelevant information may interfere with ongoing task activities if not controlled properly. Evidence suggests that the ability to control interference is connected mainly to the prefrontal cortex (pFC). However, it remains unclear whether gray matter (GM) volume in prefrontal regions influences individual differences in interference control (IC) and if these relationships are affected by aging. Using cross-sectional and longitudinal estimates over a 4- to 5-year period, we examined the relationship between relative IC scores, obtained from a 2-back working memory task, GM volumes, and performance in different cognitive domains. By identifying individuals with either no or high levels of interference, we demonstrated that participants with superior IC had larger volume of the ventrolateral pFC, regardless of participant demographics. The same pattern was observed both at baseline and follow-up. Cross-sectional estimates further showed that interference increased as a function of age, but interference did not change between baseline and follow-up. Similarly, across-sample associations between IC and pFC volume were found in the cross-sectional data, along with no longitudinal change-change relationships. Moreover, relative IC scores could be linked to composite scores of fluid intelligence, indicating that control of interference may relate to performance in expected cognitive domains. These results provide new evidence that a relative IC score can be related to volume of specific and relevant regions within pFC and that this relationship is not modulated by age. This supports a view that the GM volume in these regions plays a role in resisting interference during a working memory task.

Two critical brain networks for generation and combination of remote associations

Recent functional imaging findings in humans indicate that creativity relies on spontaneous and controlled processes, possibly supported by the default mode and the fronto-parietal control networks, respectively. Here, we examined the ability to generate and combine remote semantic associations, in relation to creative abilities, in patients with focal frontal lesions. Voxel-based lesion-deficit mapping, disconnection-deficit mapping and network-based lesion-deficit approaches revealed critical prefrontal nodes and connections for distinct mechanisms related to creative cognition. Damage to the right medial prefrontal region, or its potential disrupting effect on the default mode network, affected the ability to generate remote ideas, likely by altering the organization of semantic associations. Damage to the left rostrolateral prefrontal region and its connections, or its potential disrupting effect on the left fronto-parietal control network, spared the ability to generate remote ideas but impaired the ability to appropriately combine remote ideas. Hence, the current findings suggest that damage to specific nodes within the default mode and fronto-parietal control networks led to a critical loss of verbal creative abilities by altering distinct cognitive mechanisms.

Dynamic network coding of working-memory domains and working-memory processes

The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes (encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity that they evoke. This is the case even when focusing on 'multiple demands' brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.

Hierarchical cognitive control and the frontal lobes

Cognitive control refers to our ability to choose courses of thought and action that achieve our goals over habitual but contextually inappropriate ones. Hierarchical control problems are those in which multiple goals or contextual contingency must be managed at once and related to one another. In the open-ended complexity of the real world, hierarchical control arguably characterizes most of the problems faced by our control systems. And, it is these cases of hierarchical control where patients with damage to executive systems are most apt to fail, even those that perform well on simplified laboratory tasks. In this chapter, we consider the functional organization of frontal brain systems that support hierarchical cognitive control. We focus on two particular cases of hierarchical control. First, we discuss a line of work testing how managing multiple contingencies en route to a response relates to processing along the rostrocaudal axis of frontal cortex. Second, we consider cases of sequential tasks that require monitoring and behaving according to a series of tasks performed in time. In this latter case, we focus on the particular role of rostrolateral prefrontal cortex. We conclude with considerations of future directions of basic and clinically relevant research in this domain.

Sequential Control Underlies Robust Ramping Dynamics in the Rostrolateral Prefrontal Cortex

An essential human skill is our capacity to monitor and execute a sequence of tasks in the service of an overarching goal. Such a sequence can be as mundane as making a cup of coffee or as complex as flying a fighter plane. Previously, we showed that, during sequential control, the rostrolateral prefrontal cortex (RLPFC) exhibits activation that ramps steadily through the sequence and is necessary for sequential task execution using fMRI in humans (Desrochers et al., 2015). It remains unknown what computations may underlie this ramping dynamic. Across two independent fMRI experiments, we manipulated three features that were unique to the sequential control task to determine whether and how they modulated ramping activity in the RLPFC: (1) sequence position uncertainty, (2) sequential monitoring without external position cues (i.e., from memory), and (3) sequential monitoring without multilevel decision making (i.e., task execution). We replicated the ramping activation in RLPFC and found it to be remarkably robust regardless of the level of task abstraction or engagement of memory functions. Therefore, these results both replicate and extend previous findings regarding the function of the RLPFC. They suggest that sequential control processes are integral to the dynamics of RLPFC activity. Advancing knowledge of the neural bases of sequential control is crucial for our understanding of the sequential processes that are necessary for daily living.SIGNIFICANCE STATEMENT We perform sequences of tasks every day, but little is known about how they are controlled in the brain. Previously we found that ramping activity in the rostrolateral prefrontal cortex (RLPFC) was necessary to perform a sequence of tasks. We designed two independent fMRI experiments in human participants to determine which features of the previous sequential task potentially engaged ramping in the RLPFC. We found that any demand to monitor a sequence of state transitions consistently elicited ramping in the RLPFC, regardless of the level of the decisions made at each step in the sequence or engagement of memory functions. These results provide a framework for understanding RLPFC function during sequential control, and consequently, daily life.

The Neural Correlates of Relational Reasoning: A Meta-analysis of 47 Functional Magnetic Resonance Studies

It is a core cognitive ability of humans to represent and reason about relational information, such as “the train station is north of the hotel” or “Charles is richer than Jim.” However, the neural processes underlying the ability to draw conclusions about relations are still not sufficiently understood. Central open questions are as follows: (1) What are the neural correlates of relational reasoning? (2) Where can deductive and inductive reasoning be localized? (3) What is the impact of different informational types on cerebral activity? For that, we conducted a meta-analysis of 47 neuroimaging studies. We found activation of the frontoparietal network during both deductive and inductive reasoning, with additional activation in an extended network during inductive reasoning in the basal ganglia and the inferior parietal cortex. Analyses revealed a double dissociation concerning the lateral and medial Brodmann's area 6 during deductive and inductive reasoning, indicating differences in terms of processing verbal information in deductive and spatial information in inductive tasks. During semantic and symbolic tasks, the frontoparietal network was found active, whereas geometric tasks only elicited prefrontal activation, which can be explained by the reduced demand for the construction of a mental representation in geometric tasks. Our study provides new insights into the cognitive mechanisms underlying relational reasoning and clarifies previous controversies concerning involved brain areas.

Distinguishing the Roles of Dorsolateral and Anterior PFC in Visual Metacognition

Visual metacognition depends on regions within the prefrontal cortex (PFC). Two areas in particular have been implicated repeatedly: the dorsolateral PFC (DLPFC) and the anterior PFC (aPFC). However, it is still unclear what the function of each of these areas is and how they differ from each other. To establish the specific roles of DLPFC and aPFC in metacognition, we used online transcranial magnetic stimulation (TMS) to interfere causally with their functioning during confidence generation. Human subjects from both sexes performed a perceptual decision making and provided confidence ratings. We found a clear dissociation between the two areas: DLPFC TMS lowered confidence ratings, whereas aPFC TMS increased metacognitive ability, but only for the second half of the experimental blocks. These results support a functional architecture in which DLPFC reads out the strength of the sensory evidence and relays it to aPFC, which makes the confidence judgment by potentially incorporating additional, nonperceptual information. Indeed, simulations from a model that incorporates these putative DLPFC and aPFC functions reproduced our behavioral results. These findings establish DLPFC and aPFC as distinct nodes in a metacognitive network and suggest specific contributions from each of these regions to confidence generation.SIGNIFICANCE STATEMENT The prefrontal cortex (PFC) is known to be critical for metacognition. Two of its subregions, the dorsolateral PFC (DLPFC) and the anterior PFC (aPFC), have been specifically implicated in confidence generation. However, it is unclear whether these regions have distinct functions related to the underlying metacognitive computation. Using a causal intervention with transcranial magnetic stimulation, we demonstrate that DLPFC and aPFC have dissociable contributions: targeting DLPFC decreased average confidence ratings, whereas targeting aPFC affected metacognitive scores specifically. Based on these results, we postulated specific functions for DLPFC and aPFC in metacognitive computation and corroborated them using a computational model that reproduced our results. Our causal results reveal the existence of a specialized modular organization in PFC for confidence generation.

Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.

Evidence for a Functional Hierarchy of Association Networks

Patient lesion and neuroimaging studies have identified a rostral-to-caudal functional gradient in the lateral frontal cortex (LFC) corresponding to higher-order (complex or abstract) to lower-order (simple or concrete) cognitive control. At the same time, monkey anatomical and human functional connectivity studies show that frontal regions are reciprocally connected with parietal and temporal regions, forming parallel and distributed association networks. Here, we investigated the link between the functional gradient of LFC regions observed during control tasks and the parallel, distributed organization of association networks. Whole-brain fMRI task activity corresponding to four orders of hierarchical control [Badre, D., & D'Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19, 2082-2099, 2007] was compared with a resting-state functional connectivity MRI estimate of cortical networks [Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125-1165, 2011]. Critically, at each order of control, activity in the LFC and parietal cortex overlapped onto a common association network that differed between orders. These results are consistent with a functional organization based on separable association networks that are recruited during hierarchical control. Furthermore, corticostriatal functional connectivity MRI showed that, consistent with their participation in functional networks, rostral-to-caudal LFC and caudal-to-rostral parietal regions had similar, order-specific corticostriatal connectivity that agreed with a striatal gating model of hierarchical rule use. Our results indicate that hierarchical cognitive control is subserved by parallel and distributed association networks, together forming multiple localized functional gradients in different parts of association cortex. As such, association networks, while connectionally organized in parallel, may be functionally organized in a hierarchy via dynamic interaction with the striatum.

Frontal Cortex and the Hierarchical Control of Behavior

The frontal lobes are important for cognitive control, yet their functional organization remains controversial. An influential class of theory proposes that the frontal lobes are organized along their rostrocaudal axis to support hierarchical cognitive control. Here, we take an updated look at the literature on hierarchical control, with particular focus on the functional organization of lateral frontal cortex. Our review of the evidence supports neither a unitary model of lateral frontal function nor a unidimensional abstraction gradient. Rather, separate frontal networks interact via local and global hierarchical structure to support diverse task demands.

Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning

Our capacity for higher cognitive reasoning has a measurable limit. This limit is thought to arise from the brain's capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic "resting-state" network architecture. Here we combined resting-state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting-state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting-state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 × 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting-state and null-task demand conditions were associated with more segregated brain-network topology, whereas increases in reasoning complexity resulted in merging of resting-state modules. Further increments in task complexity did not change the established modular architecture, but affected selective patterns of connectivity between frontoparietal, subcortical, cingulo-opercular, and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity.SIGNIFICANCE STATEMENT Humans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting-state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand.

Clinical Concepts Emerging from fMRI Functional Connectomics

Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.

Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance

Cognitive functions such as working memory (WM) are emergent properties of large-scale network interactions. Synchronisation of oscillatory activity might contribute to WM by enabling the coordination of long-range processes. However, causal evidence for the way oscillatory activity shapes network dynamics and behavior in humans is limited. Here we applied transcranial alternating current stimulation (tACS) to exogenously modulate oscillatory activity in a right frontoparietal network that supports WM. Externally induced synchronization improved performance when cognitive demands were high. Simultaneously collected fMRI data reveals tACS effects dependent on the relative phase of the stimulation and the internal cognitive processing state. Specifically, synchronous tACS during the verbal WM task increased parietal activity, which correlated with behavioral performance. Furthermore, functional connectivity results indicate that the relative phase of frontoparietal stimulation influences information flow within the WM network. Overall, our findings demonstrate a link between behavioral performance in a demanding WM task and large-scale brain synchronization.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels.

Network mechanisms of intentional learning

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus–response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.

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We examine lateral frontal cortex (LFC) network dynamics across the learning process.

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LFC networks activate en masse and in synchrony when learning from instruction.

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Directed connectivities between LFC regions vary asymmetrically and hierarchically.

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LFC networks sequentially disengage from the task along an anterior–posterior axis.

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Corticostriatal circuits are selectively engaged during trial-and-error learning.

Spectral properties of the temporal evolution of brain network structure

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Echoes of the Brain: Local-Scale Representation of Whole-Brain Functional Networks within Transmodal Cortex

Transmodal (nonsensory-specific) regions sit at the confluence of different information streams, and play an important role in cognition. These regions are thought to receive and integrate information from multiple functional networks. However, little is known about (1) how transmodal cortices are functionally organized and (2) how this organization might facilitate information processing. In this article, we discuss recent findings that transmodal cortices contain a detailed local functional architecture of adjacent and partially overlapping subregions. These subregions show relative specializations, and contain traces or "echoes" of the activity of different large-scale intrinsic connectivity networks. We propose that this finer-grained organization can (1) explain how the same transmodal region can play a role in multiple tasks and cognitive disorders, (2) provide a mechanism by which different types of signals can be simultaneously segregated and integrated within transmodal regions, and (3) enhance current network- and node-level models of brain function, by showing that non-stationary functional connectivity patterns may be a result of dynamic shifts in subnodal signals. Finally, we propose that LFA may have an important role in regulating neural dynamics and facilitating balanced activity across the cortex to enable efficient and flexible high-level cognition.

Contrasting network and modular perspectives on inhibitory control

A prominent theory proposes that the right inferior frontal cortex of the human brain houses a dedicated region for motor response inhibition. However, there is growing evidence to support the view that this inhibitory control hypothesis is incorrect. Here, we discuss evidence in favour of our alternative hypothesis, which states that response inhibition is one example of a broader class of control processes that are supported by the same set of frontoparietal networks. These domain-general networks exert control by modulating local lateral inhibition processes, which occur ubiquitously throughout the cortex. We propose that to fully understand the neural basis of behavioural control requires a more holistic approach that considers how common network mechanisms support diverse cognitive processes.