Where and how does grammatically geared processing take place-and why is Broca's area often involved. A coordinated fMRI/ERBP study of language processing

We address the possibility of combining the results from hemodynamic and electrophysiological methods for the study of cognitive processing of language. The hemodynamic method we use is Event-Related fMRI, and the electrophysiological method measures Event-Related Band Power (ERBP) of the EEG signal. The experimental technique allows us to approach the relation between cortical structure and cognitive function in a sophisticated way. In particular, we can formulate original working hypotheses about the language-induced changes in the ongoing brain dynamics. We show, on the basis of electrophysiological data collected in an experiment on language production, that synchronized cortical networks code cognitive processes induced by language in form of power modulations of specific frequency bands. The hemodynamic (fMRI) data collected in the same task point to the existence of a central processor for the phrase structure assignment. We conceptualize such a central processor as a frequency scanner, a cortical device designed to pick up synchronized brain activity over a specific range of frequencies. We discuss the experimental designs which result from this set of hypotheses and show their relevance for the models of language processing.     

Spatial and temporal limits in cognitive neuroimaging with fMRI

A large body of research in human perception and cognition has been concerned with the segregation of mental events into their presumed hierarchical processing stages, the temporal aspect of such processing being termed ‘mental chronometry’. Advances in single-event functional magnetic resonance imaging (fMRI) have allowed the extraction of relative timing information between the onset of activity in different neural substrates as well as the duration of cognitive processing during a task, offering new opportunities in the study of human perception and cognition. Single-event fMRI studies have also facilitated increased spatial resolution in fMRI, allowing studies of columnar organization in humans. Important processes such as object recognition, binocular vision and other processes are thought to be organized at the columnar level; thus, these advances in the spatial and temporal capabilities of fMRI allow a new generation of cognitive and basic neuroscience studies to be performed, investigating the temporal and spatial relationships between these cortical sub-units. Such experiments bear a closer resemblance to single-unit or evoked-potential studies than to classical static brain activation maps and might serve as a bridge between primate electrophysiology and human studies. These advances are initially demonstrated only in simple visual and motor system tasks and it is likely to be several years before the techniques we describe are robust enough for general use.     

面孔加工的认知神经科学研究：回顾与展望

面孔加工的认知神经科学研究中的核心问题是,是否存在功能和神经机制上独立的面孔加工模块以及面孔加工系统的组织形式。使用电生理、脑成像以及对脑损伤病人进行神经心理学检查等手段,研究者已经找到选择性地对面孔反应的脑区,即梭状回面孔区（FFA）。文章从面孔加工系统的特异性与多成分性以及面孔识别模型等方面,系统回顾了该领域的主要研究成果。文章最后还简单展望了今后的研究方向。     

脑成像技术及其在决策研究中的应用

先进的无创神经影像技术(如EEG和fMRI)允许研究者直接观察被试在完成多种知觉、运动和认知任务时的大脑活动。将脑功能成像与严密的实验设计和数据分析方法结合起来, 我们可以考察大脑不同脑区的功能以及它们之间的交互作用。随着脑功能成像技术在研究人类决策行为中的日益成功运用, 一个被称为神经经济学的新兴领域正在逐渐形成和发展起来。本文中首先对脑成像技术进行一个总体介绍, 重点在于探讨近年来在多体素分析和多模态数据整合的最新进展。接下来, 我们以风险决策、跨时间选择以及社会决策领域的几个研究为例, 阐述神经影像技术如何能加深和拓展我们对人类决策的认识。最后, 我们讨论了神经经济学中研究中面临的一些挑战以及未来的研究方向。     

视觉词汇加工的动态神经网络及其形成

揭示大脑加工的神经网络机制成为认知神经科学研究的最新取向.本研究以视觉词汇加工脑区(VWFA)的神经功能作为切入点,探讨视觉词汇加工神经网络的动态机制及其形成.研究一考察VWFA在刺激驱动和任务调节下的动态激活,及其与语音、语义脑区所组成神经网络的动态机制.研究二通过跨文化对比以及儿童阅读发展研究,阐明语言经验对视觉词汇加工网络的塑造作用.研究三对比功能网络、静息网络以及白质纤维束联结,探讨视觉词汇加工网络的动态联结及其形成.研究结果有助于建构视觉词汇加工的神经生理模型,为基于脑科学的阅读教学和阅读障碍矫治奠定理论基础,为认知神经科学研究提供了新的思路.     

Phases of learning: How skill acquisition impacts cognitive processing

This fMRI study examines the changes in participants’ information processing as they repeatedly solve the same mathematical problem. We show that the majority of practice-related speedup is produced by discrete changes in cognitive processing. Because the points at which these changes take place vary from problem to problem, and the underlying information processing steps vary in duration, the existence of such discrete changes can be hard to detect. Using two converging approaches, we establish the existence of three learning phases. When solving a problem in one of these learning phases, participants can go through three cognitive stages: Encoding, Solving, and Responding. Each cognitive stage is associated with a unique brain signature. Using a bottom-up approach combining multi-voxel pattern analysis and hidden semi-Markov modeling, we identify the duration of that stage on any particular trial from participants brain activation patterns. For our top-down approach we developed an ACT-R model of these cognitive stages and simulated how they change over the course of learning. The Solving stage of the first learning phase is long and involves a sequence of arithmetic computations. Participants transition to the second learning phase when they can retrieve the answer, thereby drastically reducing the duration of the Solving stage. With continued practice, participants then transition to the third learning phase when they recognize the problem as a single unit and produce the answer as an automatic response. The duration of this third learning phase is dominated by the Responding stage.     

应用动态因果模型研究阅读神经网络背、腹侧通路的协作机制

在神经网络的最新取向下, 探讨阅读脑机制中背侧和腹侧通路的协作机制, 是解决语言认知神经科学多个理论问题共同面临的焦点。本项目拟通过两个脑功能成像实验, 建构汉字阅读的动态因果模型, 系统地考察汉字阅读的神经网络, 以及阅读网络中背、腹侧通路的协作机制。实验一利用快速适应实验范式的优点, 识别和考察汉字阅读涉及的认知成分所对应的功能脑区, 以及脑区联结形成的神经回路, 并建构汉字阅读的动态因果模型; 实验二进一步考察在刺激属性(语音和语义信息)和任务要求下阅读脑区的动态激活及相互作用。通过不同任务下的模型对比, 重点探讨阅读网络的脑区联结模式变化, 尤其是背、腹侧通路受刺激和任务影响时的协作机制。研究结果将为揭示阅读的神经生理模型、解决语言特异性脑区激活的争论等理论问题提供直接的证据, 还能为语言教学、阅读障碍矫治、以及临床应用提供理论基础与指导。     

脑的感知觉无意识加工及其研究进展

该文从无意识加工与认知、脑结构,神经网络的关系等三个层面,阐述了近年来认知神经科学在无意识研究方面的进展。主要就无意识与意识的相互转化、无意识加工与认知层次、脑结构之间的关联性、突触的联结方式等问题进行了讨论,并对认知神经科学如何研究无意识与意识问题提出展望,     

Brain activation and the phonological loop: the impact of rehearsal

Brain activation studies offer valuable techniques for exploring human cognition to complement behavioral measures and several studies report a wide range of neuroanatomical networks activated during verbal immediate memory. Behavioral investigations have shown use of multiple cognitive strategies across and within individuals, although aggregate data appear to reflect a common cognitive function. Variation in cognitive strategies could result in aggregate activation patterns that are relatively widespread and difficult to interpret. Imaging data (fMRI) from six participants instructed to use subvocal rehearsal showed significant left hemisphere activation in the inferior parietal gyrus and inferior and middle frontal gyri, a pattern of activation more clearly focused than in previous brain activation studies of immediate verbal serial ordered recall. Our results should be relatively free of the influence of other mental operations, and emphasise the importance of considering which cognitive strategies might give rise to focused or to diverse patterns of brain activation.     

Localizing complex neural circuits with MEG data

During cognitive processing, the various cortical areas, with specialized functions, supply for different tasks. In most cases then, the information flows are processed in a parallel way by brain networks which work together integrating the single performances for a common goal. Such a step is generally performed at higher processing levels in the associative areas. The frequency range at which neuronal pools oscillate is generally wider than the one which is detectable by bold changes in fMRI studies. A high time resolution technique like magnetoencephalography or electroencephalography is therefore required as well as new data processing algorithms for detecting different coherent brain areas cooperating for one cognitive task. Our experiments show that no algorithm for the inverse problem solution is immune from bias. We propose therefore, as a possible solution, our software LOCANTO (LOcalization and Coherence ANalysis TOol). This new package features a set of tools for the detection of coherent areas. For such a task, as a default, it employs the algorithm with best performances for the neural landscape to be detected. If the neural landscape under attention involves more than two interacting areas the SLoreta algorithm is used. Our study shows in fact that SLoreta performance is not biased when the correlation among multiple sources is high. On the other hand, the Beamforming algorithm is more precise than SLoreta at localizing single or double sources but it gets a relevant localization bias when the sources are more than three and are highly correlated.     

Microstate analysis in infancy

BackgroundMicrostate analysis is an emerging method for investigating global brain connections using electroencephalography (EEG). Microstates have been colloquially referred to as the “atom of thought,” meaning that from these underlying networks comes coordinated neural processing and cognition. The present study examined microstates at 6-, 8-, and 10-months of age. It was hypothesized that infants would demonstrate distinct microstates comparable to those identified in adults that also parallel resting-state networks using fMRI. An additional exploratory aim was to examine the relationship between microstates and temperament, assessed via parent reports, to further demonstrate microstate analysis as a viable tool for examining the relationship between neural networks, cognitive processes as well as emotional expression embodied in temperament attributes.MethodsThe microstates analysis was performed with infant EEG data when the infant was either 6- (n = 12), 8- (n = 16), or 10-months (n = 6) old. The resting-state task involved watching a 1-minute video segment of Baby Einstein while listening to the accompanying music. Parents completed the IBQ-R to assess infant temperament.ResultsFour microstate topographies were extracted. Microstate 1 had an isolated posterior activation; Microstate 2 had a symmetric occipital to prefrontal orientation; Microstate 3 had a left occipital to right frontal orientation; and Microstate 4 had a right occipital to left frontal orientation. At 10-months old, Microstate 3, thought to reflect auditory/language processing, became activated more often, for longer periods of time, covering significantly more time across the task and was more likely to be transitioned into. This finding is interpreted as consistent with language acquisition and phonological processing that emerges around 10-months. Microstate topographies and parameters were also correlated with differing temperament broadband and narrowband scales on the IBQ-R.ConclusionThree microstates emerged that appear comparable to underlying networks identified in adult and infant microstate literature and fMRI studies. Each of the temperament domains was related to specific microstates and their parameters. These networks also correspond with auditory and visual processing as well as the default mode network found in prior research and can lead to new investigations examining differences across stimulus presentations to further explain how infants begin to recognize, respond to, and engage with the world around them.     

Merging familiar and new senses to perceive and act in space

Our experience of the world seems to unfold seamlessly in a unitary 3D space. For this to be possible, the brain has to merge many disparate cognitive representations and sensory inputs. How does it do so? I discuss work on two key combination problems: coordinating multiple frames of reference (e.g. egocentric and allocentric), and coordinating multiple sensory signals (e.g. visual and proprioceptive). I focus on two populations whose spatial processing we can observe at a crucial stage of being configured and optimised: children, whose spatial abilities are still developing significantly, and naïve adults learning new spatial skills, such as sensing distance using auditory cues. The work uses a model-based approach to compare participants’ behaviour with the predictions of alternative information processing models. This lets us see when and how—during development, and with experience—the perceptual-cognitive computations underpinning our experiences in space change. I discuss progress on understanding the limits of effective spatial computation for perception and action, and how lessons from the developing spatial cognitive system can inform approaches to augmenting human abilities with new sensory signals provided by technology.

     

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.     

Using EEG to Study Cognitive Development: Issues and Practices

Developmental research is enhanced by use of multiple methodologies for examining psychological processes. The electroencephalogram (EEG) is an efficient and relatively inexpensive method for the study of developmental changes in brain–behavior relations. In this review, we highlight some of the challenges for using EEG in cognitive development research. We also list best practices for incorporating this methodology into the study of early cognitive processes. Consideration of these issues is critical for making an informed decision regarding implementation of EEG methodology.     

Modeling fMRI Data: Challenges and Opportunities

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data—the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.     

Transcranial magnetic stimulation as a tool for cognitive studies

Transcranial Magnetic Stimulation (TMS) is a tool for the non-invasive stimulation of the human brain. It allows the activation of arbitrary sites of the superficial cortex and, combined with other brain-imaging techniques such as EEG, PET, and fMRI, it can be used to evaluate cortical excitability and connectivity. This is of major importance in, for example, the study of cognitive processes such as language, learning, memory and self-representation, which are thought to be represented in multiple brain areas. The mechanisms of action of TMS are known on a basic level, but its effect on the activation state of brain tissue is still poorly understood. Clinical applications of TMS have also been proposed and guidelines for its safe use drafted.     

Alternative-based thresholding with application to presurgical fMRI

Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive—that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.     

Brain network interactions in auditory, visual and linguistic processing

In the paper, we discuss the importance of network interactions between brain regions in mediating performance of sensorimotor and cognitive tasks, including those associated with language processing. Functional neuroimaging, especially PET and fMRI, provide data that are obtained essentially simultaneously from much of the brain, and thus are ideal for enabling one to assess interregional functional interactions. Two ways to use these types of data to assess network interactions are presented. First, using PET, we demonstrate that anterior and posterior perisylvian language areas have stronger functional connectivity during spontaneous narrative production than during other less linguistically demanding production tasks. Second, we show how one can use large-scale neural network modeling to relate neural activity to the hemodynamically-based data generated by fMRI and PET. We review two versions of a model of object processing - one for visual and one for auditory objects. The regions comprising the models include primary and secondary sensory cortex, association cortex in the temporal lobe, and prefrontal cortex. Each model incorporates specific assumptions about how neurons in each of these areas function, and how neurons in the different areas are interconnected with each other. Each model is able to perform a delayed match-to-sample task for simple objects (simple shapes for the visual model; tonal contours for the auditory model). We find that the simulated electrical activities in each region are similar to those observed in nonhuman primates performing analogous tasks, and the absolute values of the simulated integrated synaptic activity in each brain region match human fMRI/PET data. Thus, this type of modeling provides a way to understand the neural bases for the sensorimotor and cognitive tasks of interest.     

Functional magnetic resonance imaging studies in bipolar disorder

Abnormalities in brain activation using functional magnetic resonance imaging (fMRI) during cognitive and emotional tasks have been identified in bipolar disorder patients, in frontal, subcortical and limbic regions. Several studies also indicate that mood state may be differentiated by lateralization of brain activation in fronto-limbic regions. The interpretation of fMRI studies in bipolar disorder is limited by the choice of regions of interest, medication effects, comorbidity, and task performance. These studies suggest that there is a complex alteration in regions important for neural networks underlying cognition and emotional processing in bipolar disorder. However, measuring changes in specific brain regions does not identify how these neural networks are affected. New analytical techniques of fMRI data are needed in order to resolve some of these issues and identify how changes in neural networks relate to cognitive and emotional processing in bipolar disorder.