<p>As Titchener pointed out more than one hundred years ago, attention is at the center of the psychological enterprise. Attention research investigates how voluntary control and subjective experience arise from and regulate our behavior. In recent years, attention has been one of the fastest growing of all fields within cognitive psychology and cognitive neuroscience. This review examines attention as characterized by linking common neural networks with individual differences in their efficient utilization. The development of attentional networks is partly specified by genes, but is also open to specific experiences through the actions of caregivers and the culture. We believe that the connection between neural networks, genes, and socialization provides a common approach to all aspects of human cognition and emotion. Pursuit of this approach can provide a basis for psychology that unifies social, cultural, differential, experimental, and physiological areas, and allows normal development to serve as a baseline for understanding various forms of pathology. D.O. Hebb proposed this approach 50 years ago in his volume Organization of Behavior and continued with introductory textbooks that dealt with all of the topics of psychology in a common framework. Use of a common network approach to psychological science may allow a foundation for predicting and understanding human behavior in its varied forms.</p>
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Test-retest reliability of resting regional cerebral metabolic rate of glucose (rCMR) was examined in selected subcortical structures: the amygdala, hippocampus, thalamus, and anterior caudate nucleus. Findings from previous studies examining reliability of rCMR suggest that rCMR in small subcortical structures may be more variable than in larger cortical regions. We chose to study these subcortical regions because of their particular interest to our laboratory in its investigations of the neurocircuitry of emotion and depression. Twelve normal subjects (seven female, mean age = 32.42 years, range 21-48 years) underwent two FDG-PET scans separated by approximately 6 months (mean = 25 weeks, range 17-35 weeks). A region-of-interest approach with PET-MRI coregistration was used for analysis of rCMR reliability. Good test-retest reliability was found in the left amygdala, right and left hippocampus, right and left thalamus, and right and left anterior caudate nucleus. However, rCMR in the right amygdala did not show good test-retest reliability. The implications of these data and their import for studies that include a repeat-test design are considered.
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<p>Social cognition, including complex social judgments and attitudes, is shaped by individual learning experiences, where affect often plays a critical role. Aversive classical conditioning-a form of associative learning involving a relationship between a neutral event (conditioned stimulus, CS) and an aversive event (unconditioned stimulus, US)-represents a well-controlled paradigm to study how the acquisition of socially relevant knowledge influences behavior and the brain. Unraveling the temporal unfolding of brain mechanisms involved appears critical for an initial understanding about how social cognition operates. Here, 128-channel ERPs were recorded in 50 subjects during the acquisition phase of a differential aversive classical conditioning paradigm. The CS+ (two fearful faces) were paired 50% of the time with an aversive noise (CS upward arrow + /Paired), whereas in the remaining 50% they were not (CS upward arrow + /Unpaired); the CS- (two different fearful faces) were never paired with the noise. Scalp ERP analyses revealed differences between CS upward arrow + /Unpaired and CS- as early as approximately 120 ms post-stimulus. Tomographic source localization analyses revealed early activation modulated by the CS+ in the ventral visual pathway (e.g. fusiform gyrus, approximately 120 ms), right middle frontal gyrus (approximately 176 ms), and precuneus (approximately 240 ms). At approximately 120 ms, the CS- elicited increased activation in the left insula and left middle frontal gyrus. These findings not only confirm a critical role of prefrontal, insular, and precuneus regions in aversive conditioning, but they also suggest that biologically and socially salient information modulates activation at early stages of the information processing flow, and thus furnish initial insight about how affect and social judgments operate.</p>
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We used fMRI to examine amygdala activation in response to fearful facial expressions, measured over multiple scanning sessions. 15 human subjects underwent three scanning sessions, at 0, 2 and 8 weeks. During each session, functional brain images centered about the amygdala were acquired continuously while participants were shown alternating blocks of fearful, neutral and happy facial expressions. Intraclass correlation coefficients calculated across the sessions indicated stability of response in left amygdala to fearful faces (as a change from baseline), but considerably less left amygdala stability in responses to neutral expressions and for fear versus neutral contrasts. The results demonstrate that the measurement of fMRI BOLD responses in amygdala to fearful facial expressions might be usefully employed as an index of amygdala reactivity over extended periods. While signal change to fearful facial expressions appears robust, the experimental design employed here has yielded variable responsivity within baseline or comparison conditions. Future studies might manipulate the experimental design to either amplify or attenuate this variability, according to the goals of the research.
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The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called the e-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.
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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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In recent years, three attentional networks have been defined in anatomical and functional terms. These functions involve alerting, orienting, and executive attention. Reaction time measures can be used to quantify the processing efficiency within each of these three networks. The Attention Network Test (ANT) is designed to evaluate alerting, orienting, and executive attention within a single 30-min testing session that can be easily performed by children, patients, and monkeys. A study with 40 normal adult subjects indicates that the ANT produces reliable single subject estimates of alerting, orienting, and executive function, and further suggests that the efficiencies of these three networks are uncorrelated. There are, however, some interactions in which alerting and orienting can modulate the degree of interference from flankers. This procedure may prove to be convenient and useful in evaluating attentional abnormalities associated with cases of brain injury, stroke, schizophrenia, and attention-deficit disorder. The ANT may also serve as an activation task for neuroimaging studies and as a phenotype for the study of the influence of genes on attentional networks.
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BACKGROUND: EEG alpha power has been demonstrated to be inversely related to mental activity and has subsequently been used as an indirect measure of brain activation. The hypothesis that the thalamus serves as a neuronal oscillator of alpha rhythms has been supported by studies in animals, but only minimally by studies in humans.
METHODS: In the current study, PET-derived measures of regional glucose metabolism, EEG, and structural MRI were obtained from each participant to assess the relation between thalamic metabolic activity and alpha power in depressed patients and healthy controls. The thalamus was identified and drawn on each subject's MRI. The MRI was then co-registered to the corresponding PET scan and metabolic activity from the thalamus extracted. Thalamic activity was then correlated with a 30-min aggregated average of alpha EEG power.
RESULTS: Robust inverse correlations were observed in the control data, indicating that greater thalamic metabolism is correlated with decreased alpha power. No relation was found in the depressed patient data.
CONCLUSIONS: The results are discussed in the context of a possible abnormality in thalamocortical circuitry associated with depression.
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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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Recent neuroimaging and neuropsychological work has begun to shed light on how the brain responds to the viewing of facial expressions of emotion. However, one important category of facial expression that has not been studied on this level is the facial expression of pain. We investigated the neural response to pain expressions by performing functional magnetic resonance imaging (fMRI) as subjects viewed short video sequences showing faces expressing either moderate pain or, for comparison, no pain. In alternate blocks, the same subjects received both painful and non-painful thermal stimulation. Facial expressions of pain were found to engage cortical areas also engaged by the first-hand experience of pain, including anterior cingulate cortex and insula. The reported findings corroborate other work in which the neural response to witnessed pain has been examined from other perspectives. In addition, they lend support to the idea that common neural substrates are involved in representing one's own and others' affective states.
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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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