Distributed neural system for general intelligence revealed by lesion mapping

General intelligence (g) captures the performance variance shared across cognitive tasks and correlates with real-world success. Yet it remains debated whether g reflects the combined performance of brain systems involved in these tasks or draws on specialized systems mediating their interactions. Here we investigated the neural substrates of g in 241 patients with focal brain damage using voxel-based lesion–symptom mapping. A hierarchical factor analysis across multiple cognitive tasks was used to derive a robust measure of g. Statistically significant associations were found between g and damage to a remarkably circumscribed albeit distributed network in frontal and parietal cortex, critically including white matter association tracts and frontopolar cortex. We suggest that general intelligence draws on connections between regions that integrate verbal, visuospatial, working memory, and executive processes

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Temporal isolation of neural processes underlying face preference decisions

Decisions about whether we like someone are often made so rapidly from first impressions that it is difficult to examine the engagement of neural structures at specific points in time. Here, we used a temporally extended decision-making paradigm to examine brain activation with functional MRI (fMRI) at sequential stages of the decision-making process. Activity in reward-related brain structures—the nucleus accumbens (NAC) and orbitofrontal cortex (OFC)—was found to occur at temporally dissociable phases while subjects decided which of two unfamiliar faces they preferred. Increases in activation in the OFC occurred late in the trial, consistent with a role for this area in computing the decision of which face to choose. Signal increases in the NAC occurred early in the trial, consistent with a role for this area in initial preference formation. Moreover, early signal increases in the NAC also occurred while subjects performed a control task (judging face roundness) when these data were analyzed on the basis of which of those faces were subsequently chosen as preferred in a later task. The findings support a model in which rapid, automatic engagement of the NAC conveys a preference signal to the OFC, which in turn is used to guide choice

The gray matter volume of the amygdala is correlated with the perception of melodic intervals: a voxel-based morphometry study

Music is not simply a series of organized pitches, rhythms, and timbres, it is capable of evoking emotions. In the present study, voxel-based morphometry (VBM) was employed to explore the neural basis that may link music to emotion. To do this, we identified the neuroanatomical correlates of the ability to extract pitch interval size in a music segment (i.e., interval perception) in a large population of healthy young adults (N = 264). Behaviorally, we found that interval perception was correlated with daily emotional experiences, indicating the intrinsic link between music and emotion. Neurally, and as expected, we found that interval perception was positively correlated with the gray matter volume (GMV) of the bilateral temporal cortex. More important, a larger GMV of the bilateral amygdala was associated with better interval perception, suggesting that the amygdala, which is the neural substrate of emotional processing, is also involved in music processing. In sum, our study provides one of first neuroanatomical evidence on the association between the amygdala and music, which contributes to our understanding of exactly how music evokes emotional responses

What we observe is biased by what other people tell us: beliefs about the reliability of gaze behavior modulate attentional orienting to gaze cues

For effective social interactions with other people, information about the physical environment must be integrated with information about the interaction partner. In order to achieve this, processing of social information is guided by two components: a bottom-up mechanism reflexively triggered by stimulus-related information in the social scene and a top-down mechanism activated by task-related context information. In the present study, we investigated whether these components interact during attentional orienting to gaze direction. In particular, we examined whether the spatial specificity of gaze cueing is modulated by expectations about the reliability of gaze behavior. Expectations were either induced by instruction or could be derived from experience with displayed gaze behavior. Spatially specific cueing effects were observed with highly predictive gaze cues, but also when participants merely believed that actually non-predictive cues were highly predictive. Conversely, cueing effects for the whole gazed-at hemifield were observed with non-predictive gaze cues, and spatially specific cueing effects were attenuated when actually predictive gaze cues were believed to be non-predictive. This pattern indicates that (i) information about cue predictivity gained from sampling gaze behavior across social episodes can be incorporated in the attentional orienting to social cues, and that (ii) beliefs about gaze behavior modulate attentional orienting to gaze direction even when they contradict information available from social episodes

Comparison of social cognitive functioning in schizophrenia and high functioning autism: more convergence than divergence

Background: Individuals with schizophrenia and individuals with high-functioning autism (HFA) seem to share some social, behavioral and biological features. Although marked impairments in social cognition have been documented in both groups, little empirical work has compared the social cognitive functioning of these two clinical groups. Method: Forty-four individuals with schizophrenia, 36 with HFA and 41 non-clinical controls completed a battery of social cognitive measures that have been linked previously to specific brain regions. Results: The results indicate that the individuals with schizophrenia and HFA were both impaired on a variety of social cognitive tasks relative to the non-clinical controls, but did not differ from one another. When individuals with schizophrenia were divided into negative symptom and paranoid subgroups, exploratory analyses revealed that individuals with HFA may be more similar, in terms of the pattern of social cognition impairments, to the negative symptom group than to the paranoia group. Conclusions: Our findings provide further support for similarities in social cognition deficits between HFA and schizophrenia, which have a variety of implications for future work on gene-brain-behavior relationship

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities

Quantum entanglement in photosynthetic light harvesting complexes

Light harvesting components of photosynthetic organisms are complex, coupled, many-body quantum systems, in which electronic coherence has recently been shown to survive for relatively long time scales despite the decohering effects of their environments. Within this context, we analyze entanglement in multi-chromophoric light harvesting complexes, and establish methods for quantification of entanglement by presenting necessary and sufficient conditions for entanglement and by deriving a measure of global entanglement. These methods are then applied to the Fenna-Matthews-Olson (FMO) protein to extract the initial state and temperature dependencies of entanglement. We show that while FMO in natural conditions largely contains bipartite entanglement between dimerized chromophores, a small amount of long-range and multipartite entanglement exists even at physiological temperatures. This constitutes the first rigorous quantification of entanglement in a biological system. Finally, we discuss the practical utilization of entanglement in densely packed molecular aggregates such as light harvesting complexes.Comment: 14 pages, 7 figures. Improved presentation, published versio