Going beyond the linear approximation in describing electron- phonon coupling: relevance for the Holstein model

Using the momentum average approximation we study the importance of adding higher-than-linear terms in the electron-phonon coupling on the properties of single polarons described by a generalized Holstein model. For medium and strong linear coupling, even small quadratic electron-phonon coupling terms are found to lead to very significant quantitative changes in the properties of the polaron, which cannot be captured by a linear Holstein Hamiltonian with renormalized parameters. We argue that the bi-polaron phase diagram is equally sensitive to addition of quadratic coupling terms if the linear coupling is large. These results suggest that the linear approximation is likely to be inappropriate to model systems with strong electron-phonon coupling, at least for low carrier concentrations.Comment: 6 pages, 4 figures Final version accepted into EP

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

Subthalamic nucleus stimulation affects orbitofrontal cortex in facial emotion recognition: a pet study

Deep brain stimulation (DBS) of the bilateral subthalamic nucleus (STN) in Parkinson's disease is thought to produce adverse events such as emotional disorders, and in a recent study, we found fear recognition to be impaired as a result. These changes have been attributed to disturbance of the STN's limbic territory and would appear to confirm that the negative emotion recognition network passes through the STN. In addition, it is now widely acknowledged that damage to the orbitofrontal cortex (OFC), especially the right side, can result in impaired recognition of facial emotions (RFE). In this context, we hypothesized that this reduced recognition of fear is correlated with modifications in the cerebral glucose metabolism of the right OFC. The objective of the present study was first, to reinforce our previous results by demonstrating reduced fear recognition in our Parkinson's disease patient group following STN DBS and, second, to correlate these emotional performances with glucose metabolism using 18FDG-PET. The 18FDG-PET and RFE tasks were both performed by a cohort of 13 Parkinson's disease patients 3 months before and 3 months after surgery for STN DBS. As predicted, we observed a significant reduction in fear recognition following surgery and obtained a positive correlation between these neuropsychological results and changes in glucose metabolism, especially in the right OFC. These results confirm the role of the STN as a key basal ganglia structure in limbic circuits

The Physical Basis for Long-lived Electronic Coherence in Photosynthetic Light Harvesting Systems

The physical basis for observed long-lived electronic coherence in photosynthetic light-harvesting systems is identified using an analytically soluble model. Three physical features are found to be responsible for their long coherence lifetimes: i) the small energy gap between excitonic states, ii) the small ratio of the energy gap to the coupling between excitonic states, and iii) the fact that the molecular characteristics place the system in an effective low temperature regime, even at ambient conditions. Using this approach, we obtain decoherence times for a dimer model with FMO parameters of $\approx$ 160 fs at 77 K and $\approx$ 80 fs at 277 K. As such, significant oscillations are found to persist for 600 fs and 300 fs, respectively, in accord with the experiment and with previous computations. Similar good agreement is found for PC645 at room temperature, with oscillations persisting for 400 fs. The analytic expressions obtained provide direct insight into the parameter dependence of the decoherence time scales.Comment: 5 figures; J. Phys. Chem. Lett. (2011

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

A specific hypoactivation of right temporo-parietal junction/posterior superior temporal sulcus in response to socially awkward situations in autism

People with autism spectrum disorders (ASD) often have difficulty comprehending social situations in the complex, dynamic contexts encountered in the real world. To study the social brain under conditions which approximate naturalistic situations, we measured brain activity with fMRI while participants watched a full-length episode of the sitcom The Office. Having quantified the degree of social awkwardness at each moment of the episode, as judged by an independent sample of controls, we found that both individuals with ASD and control participants showed reliable activation of several brain regions commonly associated with social perception and cognition (e.g., those comprising the “mentalizing network”) during the more awkward moments. However, individuals with ASD showed less activity than controls in a region near right temporo-parietal junction (RTPJ) extending into the posterior end of the right superior temporal sulcus (RSTS). Further analyses suggested that, despite the free-form nature of the experimental design, this group difference was specific to this RTPJ/RSTS area of the mentalizing network; other regions of interest showed similar activity across groups with respect to both location and magnitude. These findings add support to a body of evidence suggesting that RTPJ/RSTS plays a special role in social processes across modalities and may function atypically in individuals with ASD navigating the social world

Emergence of qualia from brain activity or from an interaction of proto-consciousness with the brain: which one is the weirder? Available evidence and a research agenda

This contribution to the science of consciousness aims at comparing how two different theories can explain the emergence of different qualia experiences, meta-awareness, meta-cognition, the placebo effect, out-of-body experiences, cognitive therapy and meditation-induced brain changes, etc. The first theory postulates that qualia experiences derive from specific neural patterns, the second one, that qualia experiences derive from the interaction of a proto-consciousness with the brain\u2019s neural activity. From this comparison it will be possible to judge which one seems to better explain the different qualia experiences and to offer a more promising research agenda