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。     

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.     

Distinguishing multi-voxel patterns and mean activation: Why, how, and what does it tell us?

The introduction of multi-voxel pattern analysis (MVPA) to the functional magnetic resonance imaging (fMRI) community has brought a deeper appreciation for the diverse forms of information that can be present within fMRI activity. The conclusions drawn from MVPA investigations are frequently influenced by both the ability to decode information from multi-voxel patterns and mean activation levels. In practice, MVPA studies vary widely in why and how they test for differing overall response levels. In this article, I examine the place of univariate information in MVPA investigations. I first discuss the variety of interpretations given to finding univariate response differences. I go on to discuss some of the analysis approaches used to investigate and compare univariate and multivariate sources of information, which can illuminate their respective contributions. It will be important for the MVPA and general fMRI community to continue to discuss and debate these important issues.     

Multidimensional scale types and invariant multivariate statistics

This paper proposes an extension of the definition of one‐dimensional ordinal, interval, ratio, difference and absolute scales to the multidimensional case. The connection between measurement structures and statistical structures suggested in a previous paper is also extended to the multivariate case, and examples of invariance properties of multivariate functions and statistics are discussed.     

Connectivity among semantic associates: an fMRI study of semantic priming

Semantic priming refers to a reduction in the reaction time to identify or make a judgment about a stimulus that has been immediately preceded by a semantically related word or picture and is thought to result from a partial overlap in the semantic associates of the two words. A semantic priming lexical decision task using spoken words was presented in event-related fMRI and behavioral paradigms. Word pairs varied in terms of semantic relatedness and the connectivity between associates. Thirteen right-handed subjects underwent fMRI imaging and 10 additional subjects were tested in a behavioral version of the semantic priming task. It was hypothesized priming would be greatest, reaction time fastest, and cortical activation reduced the most for related word pairs of high connectivity, followed by related word pairs of low connectivity, and then by unrelated word pairs. Behavioral and fMRI results confirmed these predictions. fMRI activity located primarily in bilateral posterior superior and middle temporal regions showed modulation by connectivity and relatedness. The results suggest that these regions are involved in semantic processing.     

Empirical Bayes Approaches to Multivariate Fuzzy Partitions

In describing high dimensional discrete response data, mathematical and statistical issues arise that require multivariate procedures that are not based on normal distributions, that is, the mathematical representation of high dimensional discrete response data (Event Spaces) requires a representation in lower dimensional parameter spaces consistent with the discrete properties of the Event Space. Mapping discrete responses to latent discrete classes has the limitation of not representing real individual variation within the categories. The use of a fuzzy partition model is proposed which describes individuals in terms of partial membership in multiple latent categories which represents bounded discrete event spaces with significant third and higher order moments. We discuss statistical issues arising in identifying both the deterministic and the stochastic variation of data when applications involve systematic variation due to observed and unobserved variables. We present an empirical Bayes-maximum likelihood estimation scheme for the application of the fuzzy partition models.     

Parametrically Dissociating Speech and Nonspeech Perception in the Brain Using fMRI

Candidate brain regions constituting a neural network for preattentive phonetic perception were identified with fMRI and multivariate multiple regression of imaging data. Stimuli contrasted along speech/nonspeech, acoustic, or phonetic complexity (three levels each) and natural/synthetic dimensions. Seven distributed brain regions' activity correlated with speech and speech complexity dimensions, including five left-sided foci [posterior superior temporal gyrus (STG), angular gyrus, ventral occipitotemporal cortex, inferior/posterior supramarginal gyrus, and middle frontal gyrus (MFG)] and two right-sided foci (posterior STG and anterior insula). Only the left MFG discriminated natural and synthetic speech. The data also supported a parallel rather than serial model of auditory speech and nonspeech perception.     

Treatment of chronic stress in employees: subjective, cognitive and neural correlates

This study reports the effect of an affect-focused intervention program, the Affect School, on stress, psychological symptoms, cognitive functioning and neural activity. Fifty employees in social service and education, with high levels of chronic stress, were randomly divided into a treatment (N=27) and control (N=23) group. Complete sets of data were available in 20 participants in the treatment group and 17 in the control group. The Perceived Stress Questionnaire assessed stress and the Symptom Check List-90 psychological symptoms before and after treatment. Episodic-memory functioning under focused and divided attention conditions was also assessed. Prior and after the Affect School, seven participants in the treatment group were studied with functional magnetic resonance imaging (fMRI) during episodic memory processing. After the Affect School there was a reduction in stress and psychological symptoms for the treatment group but not in the control group. The controls showed a reduction in episodic memory functioning whereas the performance of the treatment group remained intact. The fMRI scanning indicated a qualitative change in the neural network subserving episodic memory. These preliminary results suggest that the Affect School is effective on individuals with high stress.     

Portfolio Decisions and Brain Reactions via the CEAD method

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.     

Functional Magnetic Resonance Imaging of Language in Epilepsy

Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of functional networks and cerebral organization in both normal and pathological brains. In the present review, we describe the use of fMRI for mapping language in epilepsy patients prior to surgical intervention including a discussion of methodological issues and task design, comparisons between fMRI and the intracarotid sodium amobarbital test, fMRI studies of language reorganization, and the use of fMRI laterality indexes to predict outcome after anterior temporal lobectomy.     

Regional Analysis of Self‐Reported Personality Disorder Criteria

ABSTRACT Building on the theoretical work of Louis Guttman, we propose that the core problem facing research into the multidimensional structure of the personality disorders is not the identification of factorial simple structure but rather detailed characterization of the multivariate configuration of the diagnostic criteria. Dimensions rotated to orthogonal or oblique simple structure are but one way out of many to characterize a multivariate map, and their current near universal application represents a choice for a very particular set of interpretive advantages and disadvantages. We use multidimensional scaling and regional interpretation to investigate the structure of 78 self‐reported personality disorder criteria from a large sample of military recruits and college students. Results suggest that the criteria have a three‐dimensional radex structure that conforms only loosely to the 10 existing personality disorder (PD) categories. Regional interpretation in three dimensions elucidates several important aspects of PDs and their interrelationships.     

Brain activity patterns of phonemic representations are atypical in beginning readers with family risk for dyslexia

There is an ongoing debate whether phonological deficits in dyslexics should be attributed to (a) less specified representations of speech sounds, like suggested by studies in young children with a familial risk for dyslexia, or (b) to an impaired access to these phonemic representations, as suggested by studies in adults with dyslexia. These conflicting findings are rooted in between study differences in sample characteristics and/or testing techniques. The current study uses the same multivariate functional MRI (fMRI) approach as previously used in adults with dyslexia to investigate phonemic representations in 30 beginning readers with a familial risk and 24 beginning readers without a familial risk of dyslexia, of whom 20 were later retrospectively classified as dyslexic. Based on fMRI response patterns evoked by listening to different utterances of /bA/ and /dA/ sounds, multivoxel analyses indicate that the underlying activation patterns of the two phonemes were distinct in children with a low family risk but not in children with high family risk. However, no group differences were observed between children that were later classified as typical versus dyslexic readers, regardless of their family risk status, indicating that poor phonemic representations constitute a risk for dyslexia but are not sufficient to result in reading problems. We hypothesize that poor phonemic representations are trait (family risk) and not state (dyslexia) dependent, and that representational deficits only lead to reading difficulties when they are present in conjunction with other neuroanatomical or—functional deficits.     

Comparing the Predictive Accuracy of Case Linkage Methods in Serious Sexual Assaults

The empirical support for linkage analysis is steadily increasing, but the question remains as to what method of linking is the most effective. We compared a more theory‐based, dimensional behavioural approach with a rather pragmatic, multivariate behavioural approach with regard to their accuracy in linking serial sexual assaults in a UK sample of serial sexual assaults (n = 90) and one‐off sexual assaults (n = 129). Their respective linkage accuracy was assessed by (1) using seven dimensions derived by non‐parametric Mokken scale analysis (MSA) as predictors in discriminant function analysis (DFA) and (2) 46 crime scene characteristics simultaneously in a naive Bayesian classifier (NBC). The dimensional scales predicted 28.9% of the series correctly, whereas the NBC correctly identified 34.5% of the series. However, a subsequent inclusion of non‐serial offences in the target group decreased the amount of correct links in the dimensional approach (MSA–DFA: 8.9%; NBC: 32.2%). Receiver operating characteristic analysis was used as a more objective comparison of the two methods under both conditions, confirming that each achieved good accuracies (AUCs = .74–.89), but the NBC performed significantly better than the dimensional approach. The consequences for the practical implementation in behavioural case linkage are discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

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。     

A direct approach to individual differences scaling using increasingly complex transformations

A family of models for the representation and assessment of individual differences for multivariate data is embodied in a hierarchically organized and sequentially applied procedure called PINDIS. The two principal models used for directly fitting individual configurations to some common or hypothesized space are the dimensional salience and perspective models. By systematically increasing the complexity of transformations one can better determine the validities of the various models and assess the patterns and commonalities of individual differences. PINDIS sheds some new light on the interpretability and general applicability of the dimension weighting approach implemented by the commonly used INDSCAL procedure.     

A Survey of the Sources of Noise in fMRI

Functional magnetic resonance imaging (fMRI) is a noninvasive method for measuring brain function by correlating temporal changes in local cerebral blood oxygenation with behavioral measures. fMRI is used to study individuals at single time points, across multiple time points (with or without intervention), as well as to examine the variation of brain function across normal and ill populations. fMRI may be collected at multiple sites and then pooled into a single analysis. This paper describes how fMRI data is analyzed at each of these levels and describes the noise sources introduced at each level.     

Ethical issues in the clinical application of fMRI: factors affecting the validity and interpretation of activations

The ability of functional magnetic resonance imaging (fMRI) to localize activations in a single patient, along with the safety and widespread availability of this methodology, has lead to an increasing use of fMRI for clinical purposes such as pre-surgical planning. As methodology continues to improve and more experience with fMRI in the clinical setting is acquired, clinical functional neuroimaging will likely have an increasing influence over patient care. Therefore, ethical use of fMRI, as with other medical techniques, requires understanding the factors impacting the interpretation of the methodology. Issues affecting the validity and interpretation of clinical functional neuroimaging, including effects of altered hemodynamic response function, head motion, and structural changes in the brain, are reviewed. The distinction between correlated and necessary activation in a clinical context is discussed. Different types of statistical errors in fMRI analysis are described, along with their consequences to the patient. Finally, for the future of clinical fMRI development, the need for normative patient data, as well as standardized tasks, scan protocols, and data analyses, is discussed.     

A Social Constructivism Decision-Making Approach to Managing Incidental Findings in Neuroimaging Research

Functional magnetic resonance imaging (fMRI) is a powerful tool used in cognitive neuroscientific research. fMRI is noninvasive, safe, and relatively accessible, making it an ideal method to draw inferences about the brain–behavior relationship. When conducting fMRI research, scientists must consider risks associated with brain imaging. In particular, the risk of potentially identifying an abnormal brain finding in an fMRI research scan poses a complex problem that researchers should be prepared to address. This article illustrates how a social constructivism decision-making model can be used as a framework to guide researchers as they develop protocols to address this issue.     

The brain basis of the phonological deficit in dyslexia is independent of IQ

Although the role of IQ in developmental dyslexia remains ambiguous, the dominant clinical and research approaches rely on a definition of dyslexia that requires reading skill to be significantly below the level expected given an individual's IQ. In the study reported here, we used functional MRI (fMRI) to examine whether differences in brain activation during phonological processing that are characteristic of dyslexia were similar or dissimilar in children with poor reading ability who had high IQ scores (discrepant readers) and in children with poor reading ability who had low IQ scores (nondiscrepant readers). In two independent samples including a total of 131 children, using univariate and multivariate pattern analyses, we found that discrepant and nondiscrepant poor readers exhibited similar patterns of reduced activation in brain areas such as left parietotemporal and occipitotemporal regions. These results converge with behavioral evidence indicating that, regardless of IQ, poor readers have similar kinds of reading difficulties in relation to phonological processing.     

The role of regional heterogeneity in age-related differences in functional hemispheric asymmetry: an fMRI study

Neuroimaging literature has documented age-related hemispheric asymmetry reduction in frontal regions during task performances. As most studies employed working memory paradigms, it is therefore less clear if this pattern of neural reorganization is constrained by working memory processes or it would also emerge in other cognitive domains which are predominantly lateralized. Using blocked functional magnetic resonance imaging (fMRI), the present study used a homophone judgment task and a line judgment task to investigate age-related differences in functional hemispheric asymmetry in language and visuospatial processing respectively. Young and older adults achieved similar task accuracy although older adults required a significantly longer time. Age-related functional hemispheric asymmetry reduction was found only in dorsal inferior frontal gyrus and was associated with better performance when the homophone condition was contrasted against fixation, and not line condition. Our data thus highlights the importance of considering regional heterogeneity of aging effects together with general age-related cognitive processes.