Combined functional and causal connectivity analyses of language networks in children: a feasibility study

Instead of assessing activation in distinct brain regions, approaches to investigating the networks underlying distinct brain functions have come into the focus of neuroscience research. Here, we provide a completely data-driven framework for assessing functional and causal connectivity in functional magnetic resonance imaging (fMRI) data, employing Granger's causality. We investigate the networks underlying story processing in 17 healthy children (8f, 9m, 10.4+/-2.8 years, 6.5-15.4 years). Extensive functional connectivity exists between brain regions, including some not detected in standard random effects analyses. Causal connectivity analyses demonstrate a clear dominance of left-sided language regions for both forward and backward interactions with other network nodes. We believe our approach to be useful in helping to assess language networks in the normal or pathological setting; it may also aid in providing better starting estimates for the more hypothesis-driven approaches like structural equation or dynamic causal modeling.     

Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.     

The covariation of independent and dependant variables in neurofeedback: A proposal framework to identify cognitive processes and brain activity variables

This methodological article proposes a framework for analysing the relationship between cognitive processes and brain activity using variables measured by neurofeedback (NF) carried out by functional Magnetic Resonance Imagery (fMRI NF). Cognitive processes and brain activity variables can be analysed as either the dependant variable or the independent variable. Firstly, we propose two traditional approaches, defined in the article as the “neuropsychological” approach (NP) and the “psychophysiology” approach (PP), to extract dependent and independent variables in NF protocols. Secondly, we suggest that NF can be inspired by the style of inquiry used in neurophenomenology. fMRI NF allows participants to experiment with his or her own cognitive processes and their effects on brain region of interest (ROI) activations simultaneously. Thus, we suggest that fMRI NF could be improved by implementing “the elicitation interview method”, which allows the investigator to gather relevant verbatim from participants’ introspection on subjective experiences.     

An independent component analysis of fMRI data of grapheme-colour synaesthesia

The independent component analysis (ICA) method was applied to fMRI data from two synaesthetes and their matched controls while they performed the coloured-word Stroop task and the single-letter (synaesthetic) Stroop task. ICA identified an 'attention' network, a 'perceptual' network as well as a 'conflict monitoring' network. Increased activity was observed in right V4 during the single-letter Stroop task for synaesthetes only. The finding confirms that the same neural substrate that is known to support the experience of physical colours also supports the experience of synaesthetic colours.     

观察因果学习和自主探索对学前儿童因果推理的影响

本研究采取单因素完全随机实验设计,以94名学前末期儿童(66-74月龄)为被试,在控制证据顺序的条件下探究观察因果学习结果和自主探索结果对儿童因果推理的影响,结果发现:(1)在只获得观察学习结果或自主探索结果一种证据条件下,绝大多数儿童依据所获证据推断因果关系;(2)在获得观察学习结果和自主探索结果两种证据条件下,儿童能综合两类证据推断因果关系,其中自主探索结果对儿童因果推理的影响力大于观察学习结果。     

Decomposing the brain: components and modes, networks and nodes

Smith and colleagues recently presented a temporal independent component analysis (tICA) decomposition of resting-state functional MRI data. Compared to the widely used spatial ICA (sICA), tICA better allows for a brain region to engage in multiple, independent interactions with other regions and will potentially offer new insights into brain function.     

适于自然情境脑成像研究的分析方法:被试间相关及其应用

被试间相关分析是一种基于大脑活动的时间模式的数据分析方法。该方法通过计算接收相同刺激时被试间脑区活动的一致性,探讨认知加工与脑区功能的关系。与传统的基于激活水平的数据分析方法相比,该方法不需要设置严格的实验条件,能更好地应用于自然情境下的脑成像研究。文章介绍了被试间相关分析的基本原理和方法,分析了该方法如何识别认知功能脑区及其可靠性,并结合其在自然情境脑成像以及特定研究领域的应用,阐明被试间相关在自然情境脑成像研究中的优势,以及该方法在多个研究领域的广泛应用扩展了认知神经科学研究的深度和广度。     

Mining event-related brain dynamics

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.     

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.     

On the origin of personal causal theories

Detecting the causal relations among environmental events is an important facet of learning. Certain variables have been identified which influence both human causal attribution and animal learning: temporal priority, temporal and spatial contiguity, covariation and contingency, and prior experience. Recent research has continued to find distinct commonalities between the influence these variables have in the two domains, supporting a neo-Humean analysis of the origins of personal causal theories. The cues to causality determine which event relationships will be judged as causal; personal causal theories emerge as a result of these judgments and in turn affect future attributions. An examination of animal learning research motivates further extensions of the analogy. Researchers are encouraged to study real-time causal attributions, to study additional methodological analogies to conditioning paradigms, and to develop rich learning accounts of the acquisition of causal theories.     

Independent component analysis: an introduction

Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides an informal introduction to ICA.     

Restoring drinking water acceptance following a waterborne disease outbreak: the role of trust,risk perception,and communication

Although research shows that acceptance, trust, and risk perception are often related, little is known about the underlying patterns of causality among the three constructs. In the context of a waterborne disease outbreak, we explored via zero‐order/partial correlation analysis whether acceptance predicts both trust and risk perception (associationist model), or whether trust influences risk perception and acceptance (causal chain model). The results supported the causal chain model suggesting a causal role for trust. A subsequent path analysis confirmed that the effect of trust on acceptance is fully mediated by risk perception. It also revealed that trust is positively predicted by prior institutional trust and communication with the public. Implications of the findings for response strategies to contamination events are discussed.     

人脑自适应多尺度功能连接的性别差异

已有脑成像研究展示了男女脑功能差异, 但功能磁共振信号的频率划分通常基于主观经验, 使脑功能性别差异的生物学解释遭遇瓶颈。本文提出人脑自适应多尺度功能连接算法, 刻画功能连接的时空多尺度特性, 揭示出0.06~0.10 Hz的性别差异：男性较强的连接主要与边缘网络和腹侧注意网络有关, 女性较强的连接主要与腹侧注意网络、视觉网络和额顶网络有关。     

Disentangling narrow and coarse semantic networks in the brain: The role of computational models of word meaning

There has been a recent boom in research relating semantic space computational models to fMRI data, in an effort to better understand how the brain represents semantic information. In the first study reported here, we expanded on a previous study to examine how different semantic space models and modeling parameters affect the abilities of these computational models to predict brain activation in a data-driven set of 500 selected voxels. The findings suggest that these computational models may contain distinct types of semantic information that relate to different brain areas in different ways. On the basis of these findings, in a second study we conducted an additional exploratory analysis of theoretically motivated brain regions in the language network. We demonstrated that data-driven computational models can be successfully integrated into theoretical frameworks to inform and test theories of semantic representation and processing. The findings from our work are discussed in light of future directions for neuroimaging and computational research.     

Launching at a distance: The effect of spatial markers

It has been widely reported that spatial contiguity is important to judgements of causality involving one object launching another [Michotte's “launching effect” (1963, 1991)]. The present study examined the impact of different types of spatial markers on causal judgements of a distal launch (one object approaching other, stopping short of it, and the second object subsequently moving along the same trajectory). The spatial markers were objects that either partially or completely bridged the spatial gap between two objects (Experiment 1), or they were dashed lines that marked the stopping location of the first object or the starting location of the second object (Experiment 2). The presence of either objects or dashed lines could produce higher causal ratings, but the location of the marker mattered. The results suggest that altering a cause's ability to predict when the effect would occur (via a spatial marker) and the presence of a conduit for energy transmission have independent effects on causal judgements of object interaction.     

A rigorous approach for testing the constructionist hypotheses of brain function

Although the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.     

Brain activation detection by neighborhood one-class SVM

Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). Based on the probability distribution assumption of the one-class SVM algorithm and the neighborhood consistency hypothesis, NOC-SVM identifies a voxel as either an activated or non-activated voxel by a weighted distance between its near neighbors and a hyperplane in a high-dimensional kernel space. The proposed NOC-SVM are evaluated by using both synthetic and real datasets. On two synthetic datasets with different SNRs, NOC-SVM performs better than K-means and fuzzy K-means clustering and is comparable to POM. On a real fMRI dataset, NOC-SVM can discover activated regions similar to K-means and fuzzy K-means. These results show that the proposed algorithm is an effective activation detection method for fMRI data analysis. Furthermore, it is stabler than K-means and fuzzy K-means clustering.     

Introducing Transcranial Magnetic Stimulation (TMS) and its Property of Causal Inference in Investigating Brain-Function Relationships

Transcranial magnetic stimulation (TMS) is a method capable of transiently modulating neural excitability. Depending on the stimulation parameters information processing in the brain can be either enhanced or disrupted. This way the contribution of different brain areas involved in mental processes can be studied, allowing a functional decomposition of cognitive behavior both in the temporal and spatial domain, hence providing a functional resolution of brain/mind processes. The aim of the present paper is to argue that TMS with its ability to draw causal inferences on function and its neural representations is a valuable neurophysiological tool for investigating the causal basis of neuronal functions and can provide substantive insight into the modern interdisciplinary and (anti)reductionist neurophilosophical debates concerning the relationships between brain functions and mental abilities. Thus, TMS can serve as a heuristic method for resolving causal issues in an arena where only correlative tools have traditionally been available.     

视觉表象可视化——视觉表象研究的新途径

功能磁共振技术在表象研究中得到广泛应用是表象研究追求客观化精确性的必然趋势。本文介绍了功能磁共振多变量模式分析方法及其演变历程,探讨了借助该方法实现“视觉表象可视化”的理论依据与亟待解决的关键问题。分析指出“视觉表象可视化”将为表象研究提供全新的研究视角与方法途径。