Understanding Cognition Through Large-Scale Cortical Networks

An emerging body of evidence from a number of fields is beginning to reveal general neural principles underlying cognition. The characteristic adaptability of cognitive function is seen to derive from large-scale networks in the cerebral cortex that are able to repeatedly change the state of coordination among their constituent areas on a subsecond time scale. Experimental and theoretical studies suggest that large-scale network dynamics operate in a metastable regime in which the interdependence of cortical areas is balanced between integrating and segregating activities. Cortical areas, through their coordination dynamics, are thought to rapidly resolve a large number of mutually imposed constraints, leading to consistent local states and a globally coherent state of cognition.     

Organization, development and function of complex brain networks

Recent research has revealed general principles in the structural and functional organization of complex networks which are shared by various natural, social and technological systems. This review examines these principles as applied to the organization, development and function of complex brain networks. Specifically, we examine the structural properties of large-scale anatomical and functional brain networks and discuss how they might arise in the course of network growth and rewiring. Moreover, we examine the relationship between the structural substrate of neuroanatomy and more dynamic functional and effective connectivity patterns that underlie human cognition. We suggest that network analysis offers new fundamental insights into global and integrative aspects of brain function, including the origin of flexible and coherent cognitive states within the neural architecture.     

Comparison of magneto-and electroencephalographic techniques in event-related response research—a brief survey

Event-related brain potentials are widely used in psychophysiological and neurophysiological research. Recently it has become possible to record weak magnetic fields associated with the electric events of the human brain. In this short survey the magnetoencephalo-graphic (MEG) techniques are compared with the conventional electric measures. MEG and EEG are sensitive to current sources of different orientation. Extracerebral tissues smear and damp the electric potentials but they do not have any significant effect on the magnetic fields. EEG measures potential differences whereas the actual current paths determine the magnetic fields measured outside the skull. In general MEG provides better spatial resolution than the electric recordings, as far as the cortical sources are concerned. It is concluded that EEG and MEG are complementary noninvasive techniques in brain research. As an example slow EEG and MEG shifts preceding voluntary foot movements are compared.     

Extending the global workspace theory to emotion: phenomenality without access

Recent accounts on the global workspace theory suggest that consciousness involves transient formations of functional connections in thalamo-cortico-cortical networks. The level of connectivity in these networks is argued to determine the state of consciousness. Emotions are suggested to play a role in shaping consciousness, but their involvement in the global workspace theory remains elusive. In the present study, the role of emotion in the neural workspace theory of consciousness was scrutinized by investigating, whether unconscious (masked) and conscious (unmasked) display of emotional compared to neutral facial expressions would differentially modulate EEG coherence. EEG coherence was measured by means of computing an average EEG coherence value between the frontal, parietal, and midline scalp sites. Objective awareness checks evidenced that conscious identification of the masked facial expressions was precluded. Analyses revealed reductions in EEG coherence in the lower frequency range for the masked as compared to unmasked neutral facial expressions. Crucially, a decline in EEG coherence was not observed for the emotional facial expressions. In other words, the level of EEG coherence did apparently vary as a function of awareness, but not when emotion was involved. The current finding suggests that EEG coherence is modulated by unconscious emotional processes, which extends common views on the global workspace architecture of consciousness.     

Changes of chaoticness in spontaneous EEG/MEG

Depending on the task being investigated in EEG/MEG experiments, the corresponding signal is more or less ordered. The question still open is how can one detect the changes of this order while the tasks performed by the brain vary continuously. By applying a static measurement of the fractal dimension or Lyapunov exponent, different brain states could be characterized. However, transitions between different states may not be detected, especially if the moments of transitions are not strictly defined. Here we show how the dynamical measure based on the largest local Lyapunov exponent can be applied for the detection of the changes of the chaoticity of the brain processes measured in EEG and MEG experiments. In this article, we demonstrate an algorithm for computation of chaoticity that is especially useful for nonstationary signals. Moreover, we introduce the idea that chaoticity is able to detect, locally in time, critical jumps (phase-transition-like phenomena) in the human brain, as well as the information flow through the cortex.     

Magnetoencephalography in pediatric neuroimaging

Neural currents give rise to electroencephalogram (EEG) and magnetoencephalogram (MEG). MEG has selective sensitivity to tangential currents (from fissural cortex), and less distorted signals compared with EEG. A major goal of MEG is to determine the location and timing of cortical generators for event‐related responses, spontaneous brain oscillations or epileptiform activity. MEG provides a spatial accuracy of a few mm under optimal conditions, combined with an excellent submillisecond temporal resolution, which together enable spatiotemporal tracking of distributed neural activities, e.g. during cognitive tasks or epileptic discharges. While the present focus of pediatric MEG is on tailored epilepsy surgery, the complete noninvasiveness of MEG also provides unlimited possibilities to study the brain functions of healthy and developmentally deviant children.     

探究事件相关脑电/脑磁信号中的神经表征模式：基于分类解码和表征相似性分析的方法

探究不同心智活动下的神经表征差异, 是认知神经科学关注的核心问题之一。早期的脑电/脑磁分析方法主要关注组平均后的神经响应水平, 这要求在关注的时间进程上, 各个被试在相同刺激条件下事件相关电位/事件相关磁场的振幅大小和方向、以及地形图分布和极性均要有较高的一致性。近些年来, 研究者们将功能性磁共振成像研究中常用到的两种技术——机器学习中的分类算法(即基于分类的解码)和表征相似性分析——引入到了脑电/脑磁数据分析中。这两种新技术可以克服传统脑电/脑磁数据基于具体电压/磁感应强度波形平均分析的缺点, 具有在个体水平上探究神经表征编码的特点, 为人们探究大脑在不同时间进程上如何对特定的神经表征信息进行动态编码提供了新的思路。两种技术基于不同的方法学原理来抽提个体间一致的脑认知加工机制, 还为脑电/脑磁研究开展跨时域、跨任务、跨模态、跨群体比较不同认知过程中的表征差异提供了更多新颖的途径。我们首先通过与传统的脑电/脑磁分析方法进行比较, 系统性介绍了基于分类的解码和表征相似性分析的原理和操作流程, 之后对两种方法的应用场景进行了梳理, 并在最后对未来可供研究的方向提出了我们的见解。     

Simulating bistable perception with interrupted ambiguous stimulus using self-oscillator dynamics with percept choice bifurcation

A behavioral stochastic self-oscillator model is used for simulating interrupted ambiguous stimulus-induced percept reversals. The results provide further support for a dynamical systems foundation of cognitive and psychological problems as discussed in detail within the context of Gestalt psychology by Wagemans et al. (Concept Theor Found Psychol Bull 138(6):1218–1252, 2012), and for coordination dynamics of the brain (Kelso in Philos Trans R Soc B 367:906–918, 2012). Statistical evaluation of simulated reversal time series predicts a maximum of the percept reversal rate that conforms with a number of results in the literature. The macroscopic model is based on two inhibitorily coupled sets of three coupled nonlinear equations, one triplet for each percept. The derivation of our specific dynamics equations is based on a drastically simplified field theoretical approach using well-known phase synchronization for explaining brain dynamics on the macroscopic EEG level. The degree of coherence (contrast μ, 0 ≤ μ ≤ 1) of the superimposed fields required for onset of bistable dynamics is related to a phase synchronization index of EEG fields, and it is used in the present context as ambiguity control parameter. For quantitative agreement with the experimental data, the addition of a stochastic Langevin force term in the attention equation proved essential. Formal analysis leads to a quantification of well-known “cognitive inertia” and supports the interplay between percept choice (bifurcation) dynamics during stimulus onset and adaptive gain (attention fatigue) driven quasiperiodic percept reversals.     

Characterization of state transitions in spatially distributed,chaotic, nonlinear,dynamical systems in cerebral cortex

The neurons of cerebral cortex are largely autonomous and generate activity that is manifested in trains of microscopic axonal action potentials. The neurons interact by sparse but numerous synaptic connections to generate macroscopic dendritic activity patterns that are observed in electroencephalographic (EEG) waves. The macroscopic patterns are constructed by the populations and they shape the output of cortical neurons in parallel arrays. Sensory cortexes receive sensory information in the form of microscopic action potentials, which induce state transitions in population dynamics. Each state transition transforms sensory information to perceptual meaning. The EEG reflects both kinds of activity. The sensory input is accessed by time ensemble averaging, whereas the perceptual output is found by spatial ensemble averaging. Spatial phase gradients in the EEG are useful for identifying EEG segments in a sequence of state transitions in response to sensory input. The rapidity and flexibility with which they take place give strong reason to postulate that the mechanism for the construction of these sequences of patterns is a dynamical system operating in a chaotic domain.     

The functional role of cross-frequency coupling

Recent studies suggest that cross-frequency coupling (CFC) might play a functional role in neuronal computation, communication and learning. In particular, the strength of phase-amplitude CFC differs across brain areas in a task-relevant manner, changes quickly in response to sensory, motor and cognitive events, and correlates with performance in learning tasks. Importantly, whereas high-frequency brain activity reflects local domains of cortical processing, low-frequency brain rhythms are dynamically entrained across distributed brain regions by both external sensory input and internal cognitive events. CFC might thus serve as a mechanism to transfer information from large-scale brain networks operating at behavioral timescales to the fast, local cortical processing required for effective computation and synaptic modification, thus integrating functional systems across multiple spatiotemporal scales.     

Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.     

Précis of Neural organization: structure, function, and dynamics

NEURAL ORGANIZATION: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) STRUCTURE: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) FUNCTION: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) DYNAMICS: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.     

Bridging emotion theory and neurobiology through dynamic systems modeling

Efforts to bridge emotion theory with neurobiology can be facilitated by dynamic systems (DS) modeling. DS principles stipulate higher-order wholes emerging from lower-order constituents through bidirectional causal processes--offering a common language for psychological and neurobiological models. After identifying some limitations of mainstream emotion theory, I apply DS principles to emotion-cognition relations. I then present a psychological model based on this reconceptualization, identifying trigger, self-amplification, and self-stabilization phases of emotion-appraisal states, leading to consolidating traits. The article goes on to describe neural structures and functions involved in appraisal and emotion, as well as DS mechanisms of integration by which they interact. These mechanisms include nested feedback interactions, global effects of neuromodulation, vertical integration, action-monitoring, and synaptic plasticity, and they are modeled in terms of both functional integration and temporal synchronization. I end by elaborating the psychological model of emotion-appraisal states with reference to neural processes.     

Sleep EEG changes during adolescence: An index of a fundamental brain reorganization

Delta (1–4 Hz) EEG power in non-rapid eye movement (NREM) sleep declines massively during adolescence. This observation stimulated the hypothesis that during adolescence the human brain undergoes an extensive reorganization driven by synaptic elimination. The parallel declines in synaptic density, delta wave amplitude and cortical metabolic rate during adolescence further support this model. These late brain changes probably represent the final ontogenetic manifestation of nature’s strategy for constructing nervous systems: an initial overproduction of neural elements followed by elimination. Errors in adolescent brain reorganization may cause mental illness; this could explain the typical age of onset of schizophrenia. Longitudinal studies of sleep EEG are enhancing our knowledge of adolescent brain maturation. Our longitudinal study of sleep EEG changes in adolescence showed that delta power, which may reflect frontal cortex maturation, begins its decline between ages 11 and 12 years and falls by 65% by age 17 years. In contrast, NREM theta power begins its decline much earlier. Delta and theta EEG frequencies are important to sleep theory because they behave homeostatically. Surprisingly, these brain changes are unrelated to pubertal maturation but are strongly linked to age. In addition to these (and other) maturational EEG changes, sleep schedules in adolescence change in response to a complex interaction of circadian, social and other influences. Our data demonstrate that the daytime sleepiness that emerges in adolescence is related to the decline in NREM delta as well as to altered sleep schedules. These longitudinal sleep data provide guideposts for studying cognitive and behavioral correlates of adolescent brain reorganization.     

Brain Networks in Schizophrenia

Schizophrenia—a severe psychiatric condition characterized by hallucinations, delusions, loss of initiative and cognitive function—is hypothesized to result from abnormal anatomical neural connectivity and a consequent decoupling of the brain’s integrative thought processes. The rise of in vivo neuroimaging techniques has refueled the formulation of dysconnectivity hypotheses, linking schizophrenia to abnormal structural and functional connectivity in the brain at both microscopic and macroscopic levels. Over the past few years, advances in high-field structural and functional neuroimaging techniques have made it increasingly feasible to reconstruct comprehensive maps of the macroscopic neural wiring system of the human brain, know as the connectome. In parallel, advances in network science and graph theory have improved our ability to study the spatial and topological organizational layout of such neural connectivity maps in detail. Combined, the field of neural connectomics has created a novel platform that provides a deeper understanding of the overall organization of brain wiring, its relation to healthy brain function and human cognition, and conversely, how brain disorders such as schizophrenia arise from abnormal brain network wiring and dynamics. In this review we discuss recent findings of connectomic studies in schizophrenia that examine how the disorder relates to disruptions of brain connectivity.     

Single-trial EEG-fMRI reveals the dynamics of cognitive function

Two major non-invasive techniques in cognitive neuroscience, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. Recent hardware and software developments have made it feasible to acquire EEG and fMRI data simultaneously. We emphasize the potential of simultaneous EEG and fMRI recordings to pursue new strategies in cognitive neuroimaging. Specifically, we propose that, by exploiting the combined spatiotemporal resolution of the methods, the integration of EEG and fMRI recordings on a single-trial level enables the rich temporal dynamics of information processing to be characterized within spatially well-defined neural networks.     

The Aha! Moment: The Cognitive Neuroscience of Insight

ABSTRACT— A sudden comprehension that solves a problem, reinterprets a situation, explains a joke, or resolves an ambiguous percept is called an insight (i.e., the " Aha! moment"). Psychologists have studied insight using behavioral methods for nearly a century. Recently, the tools of cognitive neuroscience have been applied to this phenomenon. A series of studies have used electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to study the neural correlates of the " Aha! moment" and its antecedents. Although the experience of insight is sudden and can seem disconnected from the immediately preceding thought, these studies show that insight is the culmination of a series of brain states and processes operating at different time scales. Elucidation of these precursors suggests interventional opportunities for the facilitation of insight.     

If we compute faster, do we understand better?

Practitioners of cognitive science, “theoretical” neuroscience, and psychology have made less use of high-performance computing for testing theories than have those in many other areas of science. Why is this? In high-performance scientific computation, potentially billions of operations must lead to a trustable conclusion. Technical problems with the stability of algorithms aside, this requirement also places extremely rigorous constraints on the accuracy of the underlying theory. For example, electromagnetic interactions seem to hold accurately from atomic to galactic scales. Large-scale computations using elementary principles are possible and useful. Many have commented that the behavioral and neural sciences are largely pretheoretical. One consequence is that we cannot trust our few theories to scale well for a very good reason: They don’t. We have some quite good computational theories for single neurons and some large-scale aspects of behavior seem to be surprisingly lawful. However, we have little idea about how to go from the behavior of a single neuron to the behavior of the 10¹¹ neurons involved when the brain actually does something. Neural networks have offered one potential way to leap this enormous gap in scale, since many elementary units cooperate in a neural network computation. As currently formulated, however, neural networks seem to lack essential mechanisms that are required for flexible control of the computation, and they also neglect structure at intermediate scales of organization. We will present some speculations related to controllability and scaling in neural networks.     

Global precedence effect in audition and vision: evidence for similar cognitive styles across modalities

This study aimed to provide evidence for a Global Precedence Effect (GPE) in both vision and audition modalities. In order to parallel Navon's paradigm, a novel auditory task was designed in which hierarchical auditory stimuli were used to involve local and global processing. Participants were asked to process auditory and visual hierarchical patterns at the local or global level. In both modalities, a global-over-local advantage and a global interference on local processing were found. The other compelling result is a significant correlation between these effects across modalities. Evidence that the same participants exhibit similar processing style across modalities strongly supports the idea of a cognitive style to process information and common processing principle in perception.