Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts

A Model-Free Approach for Classification of fMRI Brain Images

83 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.This dissertation considers the problem of classifying subjects into predefined groups based on functional magnetic resonance imaging (fMRI) data. Classification of subjects into predefined groups, such as patient vs. control, based on their functional MRI data is a potentially useful procedure for ensuring homogeneous research samples and for clinical diagnostic purposes. Unlike other methods addressing the same question that are using either predefined regions of interest or statistical parametric maps, the proposed methodology uses preprocessed time series for the whole brain volume. Using a training set of two groups of subjects the presented methodology identifies spatio-temporal features that distinguish the groups and uses these features to categorize new subjects. The methodology is illustrated using simulations and in vivo data sets.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Examining Similarity Structure: Multidimensional Scaling and Related Approaches in Neuroimaging

This paper covers similarity analyses, a subset of multivariate pattern analysis techniques that are based on similarity spaces defined by multivariate patterns. These techniques offer several advantages and complement other methods for brain data analyses, as they allow for comparison of representational structure across individuals, brain regions, and data acquisition methods. Particular attention is paid to multidimensional scaling and related approaches that yield spatial representations or provide methods for characterizing individual differences. We highlight unique contributions of these methods by reviewing recent applications to functional magnetic resonance imaging data and emphasize areas of caution in applying and interpreting similarity analysis methods

Predicting cognitive state from eye movements.

In human vision, acuity and color sensitivity are greatest at the center of fixation and fall off rapidly as visual eccentricity increases. Humans exploit the high resolution of central vision by actively moving their eyes three to four times each second. Here we demonstrate that it is possible to classify the task that a person is engaged in from their eye movements using multivariate pattern classification. The results have important theoretical implications for computational and neural models of eye movement control. They also have important practical implications for using passively recorded eye movements to infer the cognitive state of a viewer, information that can be used as input for intelligent human-computer interfaces and related applications

Lower dimensional representation of affective videos based on behavioral data.

<p>A two-dimensional solution from a separate group of participants described the data well (stress = .282, R² = .543, <i>n</i> = 49).</p

Identifying Core Affect in Individuals from fMRI Responses to Dynamic Naturalistic Audiovisual Stimuli

<div><p>Recent research has demonstrated that affective states elicited by viewing pictures varying in valence and arousal are identifiable from whole brain activation patterns observed with functional magnetic resonance imaging (fMRI). Identification of affective states from more naturalistic stimuli has clinical relevance, but the feasibility of identifying these states on an individual trial basis from fMRI data elicited by dynamic multimodal stimuli is unclear. The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing. These findings extend previous results on the distributed representation of affect to multimodal dynamic stimuli.</p></div