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Thirty-two participants were tested for both resting electroencephalography (EEG) and neuropsychological function. Eight one-minute trials of resting EEG were recorded from 14 channels referenced to linked ears, which was rederived to an average reference. Neuropsychological tasks included Verbal Fluency, the Tower of London, and Corsi's Recurring Blocks. Asymmetries in EEG alpha activity were correlated with performance on these tasks. Similar patterns were obtained for delta and theta bands. Factor analyses of resting EEG asymmetries over particular regions suggested that asymmetries over anterior scalp regions may be partly independent from those over posterior scalp regions. These results support the notions that resting EEG asymmetries are specified by multiple mechanisms along the rostral/caudal plane, and that these asymmetries predict task performance in a manner consistent with lesion and neuroimaging studies.
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In a recent neuroimaging study of macaque monkeys, Gil-da-Costa and colleagues reported that a distributed circuit of modality-specific properties represents macaques' conceptual knowledge of social situations. The circuit identified shows striking similarities to analogous circuits in humans that represent conceptual knowledge. This parallel suggests that a common architecture underlies the conceptual systems of different species, although with additional systems extending human conceptual abilities significantly.
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The influence of approach and avoidance tendencies on affect, reasoning, and behavior has attracted substantial interest from researchers across various areas of psychology. Currently, frontal electroencephalographic (EEG) asymmetry in favor of left prefrontal regions is assumed to reflect the propensity to respond with approach-related tendencies. To test this hypothesis, we recorded resting EEG in 18 subjects, who separately performed a verbal memory task under three incentive conditions (neutral, reward, and punishment). Using a source-localization technique, we found that higher task-independent alpha2 (10.5-12 Hz) activity within left dorsolateral prefrontal and medial orbitofrontal regions was associated with stronger bias to respond to reward-related cues. Left prefrontal resting activity accounted for 54.8% of the variance in reward bias. These findings not only confirm that frontal EEG asymmetry modulates the propensity to engage in appetitively motivated behavior, but also provide anatomical details about the underlying brain systems.
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The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.
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We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present the weighted-SPHARM and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects.
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Muscle electrical activity, or "electromyogenic" (EMG) artifact, poses a serious threat to the validity of electroencephalography (EEG) investigations in the frequency domain. EMG is sensitive to a variety of psychological processes and can mask genuine effects or masquerade as legitimate neurogenic effects across the scalp in frequencies at least as low as the alpha band (8-13 Hz). Although several techniques for correcting myogenic activity have been described, most are subjected to only limited validation attempts. Attempts to gauge the impact of EMG correction on intracerebral source models (source "localization" analyses) are rarer still. Accordingly, we assessed the sensitivity and specificity of one prominent correction tool, independent component analysis (ICA), on the scalp and in the source-space using high-resolution EEG. Data were collected from 17 participants while neurogenic and myogenic activity was independently varied. Several protocols for classifying and discarding components classified as myogenic and non-myogenic artifact (e.g., ocular) were systematically assessed, leading to the exclusion of one-third to as much as three-quarters of the variance in the EEG. Some, but not all, of these protocols showed adequate performance on the scalp. Indeed, performance was superior to previously validated regression-based techniques. Nevertheless, ICA-based EMG correction exhibited low validity in the intracerebral source-space, likely owing to incomplete separation of neurogenic from myogenic sources. Taken with prior work, this indicates that EMG artifact can substantially distort estimates of intracerebral spectral activity. Neither regression- nor ICA-based EMG correction techniques provide complete safeguards against such distortions. In light of these results, several practical suggestions and recommendations are made for intelligently using ICA to minimize EMG and other common artifacts.
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We present a novel weighted Fourier series (WFS) representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a self-adjoint partial differential equation and the traditional spherical harmonic (SPHARM) representation. To reduce steep computational requirements, a new iterative residual fitting (IRF) algorithm is developed. Its computational and numerical implementation issues are discussed in detail. The computer codes are also available at http://www.stat.wisc.edu/-mchung/softwares/weighted.SPHARM/weighted-SPHARM.html. As an illustration, the WFS is applied i n quantifying the amount ofgray matter in a group of high functioning autistic subjects. Within the WFS framework, cortical thickness and gray matter density are computed and compared.
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