Hervé Abdi

Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging.

Connectionist models of face processing : a survey

Abstract Connectionist models of face recognition, identification, and categorization have appeared recently in several disciplines, including psychology, computer science, and engineering. We present a review of these models with the goal of complementing a recent survey by Samal and Iyengar [Pattern Recognition25, 65–77 (1992)] of nonconnectionist approaches to the problem of the automatic face recognition. We concentrate on models that use linear autoassociative networks, nonlinear autoassociative (or compression) and/or heteroassociative backpropagation networks. One advantage of these models over some nonconnectionist approaches is that analyzable features emerge naturally from image-based codes, and hence the problem of feature selection and segmentation from faces can be avoided.

Recognizing moving faces: a psychological and neural synthesis

Information for identifying a human face can be found both in the invariant structure of features and in idiosyncratic movements and gestures. When both kinds of information are available, psychological evidence indicates that: (1) dynamic information contributes more to recognition under non-optimal viewing conditions, e.g. poor illumination, low image resolution, recognition from a distance; (2) dynamic information contributes more as a viewers experience with the face increases; and (3) a structure-from-motion analysis can make a perceptually based contribution to face recognition. A recently proposed distributed neural system for face perception, with minor modifications, can accommodate the psychological findings with moving faces.

Partially Distributed Representations of Objects and Faces in Ventral Temporal Cortex

Object and face representations in ventral temporal (VT) cortex were investigated by combining object confusability data from a computational model of object classification with neural response confusability data from a functional neuroimaging experiment. A pattern-based classification algorithm learned to categorize individual brain maps according to the object category being viewed by the subject. An identical algorithm learned to classify an image-based, view-dependent representation of the stimuli. High correlations were found between the confusability of object categories and the confusability of brain activity maps. This occurred even with the inclusion of multiple views of objects, and when the object classification model was tested with high spatial frequency line drawings of the stimuli. Consistent with a distributed representation of objects in VT cortex, the data indicate that object categories with shared image-based attributes have shared neural structure.

Structural aspects of face recognition and the other-race effect

The other-race effect was examined in a series of experiments and simulations that looked at the relationships among observer ratings of typicality, familiarity, attractiveness, memorability, and the performance variables ofd’ and criterion. Experiment 1 replicated the other-race effect with our Caucasian and Japanese stimuli for both Caucasian and Asian observers. In Experiment 2, we collected ratings from Caucasian observers on the faces used in the recognition task. A Varimax-rotated principal components analysis on the rating and performance data for the Caucasian faces replicated Vokey and Read’s (1992) finding that typicality is composed of two orthogonal components, dissociable via their independent relationships to: (1) attractiveness and familiarity ratings and (2) memorahility ratings. For Japanese faces, however, we fond that typicality was related only to memorahility. Where performance measures were concerned, two additional principal components dominated by criterion and byd’ emerged for Caucasian faces. For the Japanese faces, however, the performance measures ofd’ and criterion merged into a single component that represented a second component of typicality, one orthogonal to thememorability-dominated component. A measure offace representation quality extracted from an autoassociative neural network trained with a majority of Caucasian faces and a minority of Japanese faces was incorporated into the principal components analysis. For both Caucasian and Japanese faces, the neural network measure related both to memorability ratings and to human accuracy measures. Combined, the human data and simulation results indicate that the memorahility component of typicality may be related to small, local, distinctive features, whereas the attractiveness/familiarity component may be more related to the global, shape-based properties of the face.

Processing Faces and Facial Expressions

This paper reviews processing of facial identity and expressions. The issue of independence of these two systems for these tasks has been addressed from different approaches over the past 25 years. More recently, neuroimaging techniques have provided researchers with new tools to investigate how facial information is processed in the brain. First, findings from “traditional” approaches to identity and expression processing are summarized. The review then covers findings from neuroimaging studies on face perception, recognition, and encoding. Processing of the basic facial expressions is detailed in light of behavioral and neuroimaging data. Whereas data from experimental and neuropsychological studies support the existence of two systems, the neuroimaging literature yields a less clear picture because it shows considerable overlap in activation patterns in response to the different face-processing tasks. Further, activation patterns in response to facial expressions support the notion of involved neural substrates for processing different facial expressions.

Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These brain-reading analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.

The perception of face gender: the role of stimulus structure in recognition and classification.

The perception of face gender was examined in the context of extending “face space” models of human face representations to include the perceptual categories defined by male and female faces. We collected data on the recognizability, gender classifiability (reaction time to classify a face as male/female), attractiveness, and masculinity/femininity of individual male and female faces. Factor analyses applied separately to the data for male and female faces yielded the following results. First, for both male and female faces, the recognizability and gender classifiability of faces were independent—a result inconsistent with the hypothesis that both recognizability and gender classifiability depend on a face’s “distance” from the subcategory gender prototype. Instead, caricatured aspects of gender (femininity/masculinity ratings) related to the gender classifiability of the faces. Second, facial attractiveness related inversely to face recognizability for male, but not for female, faces—a result that resolves inconsistencies in previous studies. Third, attractiveness and femininity for female faces were nearly equivalent, but attractiveness and masculinity for male faces were not equivalent. Finally, we applied principal component analysis to the pixel-coded face images with the aim of extracting measures related to the gender classifiability and recognizability of individual faces. We incorporated these model-derived measures into the factor analysis with the human rating and performance measures.

The Representation of Biological Classes in the Human Brain

Evidence of category specificity from neuroimaging in the human visual system is generally limited to a few relatively coarse categorical distinctions—e.g., faces versus bodies, or animals versus artifacts—leaving unknown the neural underpinnings of fine-grained category structure within these large domains. Here we use fMRI to explore brain activity for a set of categories within the animate domain, including six animal species—two each from three very different biological classes: primates, birds, and insects. Patterns of activity throughout ventral object vision cortex reflected the biological classes of the stimuli. Specifically, the abstract representational space—measured as dissimilarity matrices defined between species-specific multivariate patterns of brain activity—correlated strongly with behavioral judgments of biological similarity of the same stimuli. This biological class structure was uncorrelated with structure measured in retinotopic visual cortex, which correlated instead with a dissimilarity matrix defined by a model of V1 cortex for the same stimuli. Additionally, analysis of the shape of the similarity space in ventral regions provides evidence for a continuum in the abstract representational space—with primates at one end and insects at the other. Further investigation into the cortical topography of activity that contributes to this category structure reveals the partial engagement of brain systems active normally for inanimate objects in addition to animate regions.

Diffusion tensor tractography of traumatic diffuse axonal injury.

BACKGROUND Diffuse axonal injury is a common consequence of traumatic brain injury that frequently involves the parasagittal white matter, corpus callosum, and brainstem. OBJECTIVE To examine the potential of diffusion tensor tractography in detecting diffuse axonal injury at the acute stage of injury and predicting long-term functional outcome. DESIGN Tract-derived fiber variables were analyzed to distinguish patients from control subjects and to determine their relationship to outcome. SETTING Inpatient traumatic brain injury unit. PATIENTS From 2005 to 2006, magnetic resonance images were acquired in 12 patients approximately 7 days after injury and in 12 age- and sex-matched controls. MAIN OUTCOME MEASURES Six fiber variables of the corpus callosum, fornix, and peduncular projections were obtained. Glasgow Outcome Scale-Extended scores were assessed approximately 9 months after injury in 11 of the 12 patients. RESULTS At least 1 fiber variable of each region showed diffuse axonal injury-associated alterations. At least 1 fiber variable of the anterior body and splenium of the corpus callosum correlated significantly with the Glasgow Outcome Scale-Extended scores. The predicted outcome scores correlated significantly with actual scores in a mixed-effects model. CONCLUSION Diffusion tensor tractography-based quantitative analysis at the acute stage of injury has the potential to serve as a valuable biomarker of diffuse axonal injury and predict long-term outcome.