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Functional neuroimaging studies have implicated the fusiform gyri (FG) in structural encoding of faces, while event-related potential (ERP) and magnetoencephalography studies have shown that such encoding occurs approximately 170 ms poststimulus. Behavioral and functional neuroimaging studies suggest that processes involved in face recognition may be strongly modulated by socially relevant information conveyed by faces. To test the hypothesis that affective information indeed modulates early stages of face processing, ERPs were recorded to individually assessed liked, neutral, and disliked faces and checkerboard-reversal stimuli. At the N170 latency, the cortical three-dimensional distribution of current density was computed in stereotactic space using a tomographic source localization technique. Mean activity was extracted from the FG, defined by structure-probability maps, and a meta-cluster delineated by the coordinates of the voxel with the strongest face-sensitive response from five published functional magnetic resonance imaging studies. In the FG, approximately 160 ms poststimulus, liked faces elicited stronger activation than disliked and neutral faces and checkerboard-reversal stimuli. Further, confirming recent results, affect-modulated brain electrical activity started very early in the human brain (approximately 112 ms). These findings suggest that affective features conveyed by faces modulate structural face encoding. Behavioral results from an independent study revealed that the stimuli were not biased toward particular facial expressions and confirmed that liked faces were rated as more attractive. Increased FG activation for liked faces may thus be interpreted as reflecting enhanced attention due to their saliency.
Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion. Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The "accurate" parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the "fast" parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either. The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement.
OBJECTIVE: High-density EEG recording offers increased spatial resolution but requires careful consideration of how the density of electrodes affects the potentials being measured. Power differences as a function of electrode density and electrolyte spreading were examined and a method for correcting these differences was tested. METHODS: Separate EEG recordings from 8 participants were made using a high-density electrode net, first with 6 of 128 electrodes active followed by recordings with all electrodes active. For a subset of 4 participants measurements were counterbalanced with recordings made in the reversed order by drying the hair after the high-density recordings and using a fresh dry electrode net of the same size for the low-density recordings. Mean power values over 6 resting eyes open/closed EEG recordings at the 6 active electrodes common to both recording conditions were compared. Evidence for possible electrolyte spreading or bridging between electrodes was acquired by computing Hjorth electrical distances. Spherical spline interpolation was tested for correcting power values at electrodes affected by electrolyte spreading for these participants and for a subset of participants from a larger previous study. RESULTS: For both the complete set and the counterbalanced subset, significant decreases in power at the 6 common electrodes for the high-density recordings were observed across the range of the standard EEG bands (1-44 Hz). The number of bridges or amount of electrolyte spreading towards the reference electrode as evidenced by small Hjorth electrical distances served as a predictor of this power decrease. Spherical spline interpolation increased the power values at electrodes affected by electrolyte spreading and by a significant amount for the larger number of participants in the second group. CONCLUSIONS: Understanding signal effects caused by closely spaced electrodes, detecting electrolyte spreading and correcting its effects are important considerations for high-density EEG recordings. A combination of scalp maps of power density and plots of small Hjorth electrical distances can be used to identify electrodes affected by electrolyte spreading. Interpolation using spherical splines offers a method for correcting the potentials measured at these electrodes.
Muscle or electromyogenic (EMG) artifact poses a serious risk to inferential validity for any electroencephalography (EEG) investigation in the frequency-domain owing to its high amplitude, broad spectrum, and sensitivity to psychological processes of interest. Even weak EMG is detectable across the scalp in frequencies as low as the alpha band. Given these hazards, there is substantial interest in developing EMG correction tools. Unfortunately, most published techniques are subjected to only modest validation attempts, rendering their utility questionable. We review recent work by our laboratory quantitatively investigating the validity of two popular EMG correction techniques, one using the general linear model (GLM), the other using temporal independent component analysis (ICA). We show that intra-individual GLM-based methods represent a sensitive and specific tool for correcting on-going or induced, but not evoked (phase-locked) or source-localized, spectral changes. Preliminary work with ICA shows that it may not represent a panacea for EMG contamination, although further scrutiny is strongly warranted. We conclude by describing emerging methodological trends in this area that are likely to have substantial benefits for basic and applied EEG research.
Developments in technologic and analytical procedures applied to the study of brain electrical activity have intensified interest in this modality as a means of examining brain function. The impact of these new developments on traditional methods of acquiring and analyzing electroencephalographic activity requires evaluation. Ultimately, the integration of the old with the new must result in an accepted standardized methodology to be used in these investigations. In this paper, basic procedures and recent developments involved in the recording and analysis of brain electrical activity are discussed and recommendations are made, with emphasis on psychophysiological applications of these procedures.
Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic ("resting" or "spontaneous") electroencephalogram (EEG) into five bands: delta (1-5Hz), alpha-low (6-9Hz), alpha-high (10-11Hz), beta (12-19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization/desynchronization) domains.
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.
Three experiments demonstrated that situational information contributes to the categorization of functional object categories, as well as to inferences about these categories. When an object was presented in the context of setting and event information, categorization was more accurate than when the object was presented in isolation. Inferences about the object similarly became more accurate as the amount of situational information present during categorization increased. The benefits of situational information were higher when both setting and event information were available than when only setting information was available. These findings indicate that situational information about settings and events is stored with functional object categories in memory. Categorization and inference become increasingly accurate as the information available during categorization matches situational information stored with the category.
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.