Altered beta band spatial-temporal interactions during negative emotional processing in major depressive disorder: An MEG study

BACKGROUND

The mood-concordance bias is a key feature of major depressive disorder (MDD), but the spatiotemporal neural activity associated with emotional processing in MDD remains unclear. Understanding the dysregulated connectivity patterns during emotional processing and their relationship with clinical symptoms could provide insights into MDD neuropathology.

METHODS

We enrolled 108 MDD patients and 64 healthy controls (HCs) who performed an emotion recognition task during magnetoencephalography recording. Network-based statistics (NBS) was used to analyze whole-brain functional connectivity (FC) across different frequency ranges during distinct temporal periods. The relationship between the aberrant FC and affective symptoms was explored.

RESULTS

MDD patients exhibited decreased FC strength in the beta band (13-30 Hz) compared to HCs. During the early stage of emotional processing (0-100 ms), reduced FC was observed between the left parahippocampal gyrus and the left cuneus. In the late stage (250-400 ms), aberrant FC was primarily found in the cortex-limbic-striatum systems. Moreover, the FC strength between the right fusiform gyrus and left thalamus, and between the left calcarine fissure and left inferior temporal gyrus were negatively associated with Hamilton Depression Rating Scale (HAMD) scores.

LIMITATIONS

Medication information was not involved.

CONCLUSION

MDD patients exhibited abnormal temporal-spatial neural interactions in the beta band, ranging from early sensory to later cognitive processing stages. These aberrant interactions involve the cortex-limbic-striatum circuit. Notably, aberrant FC in may serve as a potential biomarker for assessing depression severity.

The Mandarin Chinese auditory emotions stimulus database: A validated set of Chinese pseudo-sentences

Emotional prosody is fully embedded in language and can be influenced by the linguistic properties of a specific language. Considering the limitations of existing Chinese auditory stimulus database studies, we developed and validated an emotional auditory stimuli database composed of Chinese pseudo-sentences, recorded by six professional actors in Mandarin Chinese. Emotional expressions included happiness, sadness, anger, fear, disgust, pleasant surprise, and neutrality. All emotional categories were vocalized into two types of sentence patterns, declarative and interrogative. In addition, all emotional pseudo-sentences, except for neutral, were vocalized at two levels of emotional intensity: normal and strong. Each recording was validated with 40 native Chinese listeners in terms of the recognition accuracy of the intended emotion portrayal; finally, 4361 pseudo-sentence stimuli were included in the database. Validation of the database using a forced-choice recognition paradigm revealed high rates of emotional recognition accuracy. The detailed acoustic attributes of vocalization were provided and connected to the emotion recognition rates. This corpus could be a valuable resource for researchers and clinicians to explore the behavioral and neural mechanisms underlying emotion processing of the general population and emotional disturbances in neurological, psychiatric, and developmental disorders. The Mandarin Chinese auditory emotion stimulus database is available at the Open Science Framework ( https://osf.io/sfbm6/?view_only=e22a521e2a7d44c6b3343e11b88f39e3 ).

Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

A novel deep learning model to detect COVID-19 based on wavelet features extracted from Mel-scale spectrogram of patients' cough and breathing sounds

The goal of this paper is to classify the various cough and breath sounds of COVID-19 artefacts in the signals from dynamic real-life environments. The main reason for choosing cough and breath sounds than other common symptoms to detect COVID-19 patients from the comfort of their homes, so that they do not overload the Medicare system and therefore do not unwittingly spread the disease by regularly monitoring themselves. The presented model includes two main phases. The first phase is the sound-to-image transformation, which is improved by the Mel-scale spectrogram approach. The second phase consists of extraction of features and classification using nine deep transfer models (ResNet18/34/50/100/101, GoogLeNet, SqueezeNet, MobileNetv2, and NasNetmobile). The dataset contains information data from almost 1600 people (1185 Male and 415 Female) from all over the world. Our classification model is the most accurate, its accuracy is 99.2% according to the SGDM optimizer. The accuracy is good enough that a large set of labelled cough and breath data may be used to check the possibility for generalization. The results demonstrate that ResNet18 is the best stable model for classifying cough and breath tones from a restricted dataset, with a sensitivity of 98.3% and a specificity of 97.8%. Finally, the presented model is shown to be more trustworthy and accurate than any other present model. Cough and breath study accuracy is promising enough to put extrapolation and generalization to the test.

A Hybrid Time-Distributed Deep Neural Architecture for Speech Emotion Recognition

In recent years, speech emotion recognition (SER) has emerged as one of the most active human–machine interaction research areas. Innovative electronic devices, services and applications are increasingly aiming to check the user emotional state either to issue alerts under some predefined conditions or to adapt the system responses to the user emotions. Voice expression is a very rich and noninvasive source of information for emotion assessment. This paper presents a novel SER approach based on that is a hybrid of a time-distributed convolutional neural network (TD-CNN) and a long short-term memory (LSTM) network. Mel-frequency log-power spectrograms (MFLPSs) extracted from audio recordings are parsed by a sliding window that selects the input for the TD-CNN. The TD-CNN transforms the input image data into a sequence of high-level features that are feed to the LSTM, which carries out the overall signal interpretation. In order to reduce overfitting, the MFLPS representation allows innovative image data augmentation techniques that have no immediate equivalent on the original audio signal. Validation of the proposed hybrid architecture achieves an average recognition accuracy of 73.98% on the most widely and hardest publicly distributed database for SER benchmarking. A permutation test confirms that this result is significantly different from random classification ([Formula: see text]). The proposed architecture outperforms state-of-the-art deep learning models as well as conventional machine learning techniques evaluated on the same database trying to identify the same number of emotions.

Using a new phase-locked visual feedback protocol to affirm simpler models for alpha dynamics

Alpha band oscillations are the most prominent rhythmic oscillations in EEG, which are related to various types of mental diseases, such as attention deficit hyperactivity disorder, anxiety, and depression. However, the dynamics of alpha oscillations, especially how the endogenous alpha oscillations be entrained by exogenous stimulus, are still unclear. Recently, a newly-developed phase-locked visual feedback (PLVF) protocol has shown effectiveness in modulating alpha rhythm, which provides empirical evidence for the further investigation of the neural mechanism of alpha dynamics. In this work, extensive numerical simulations based on four well-studied models were used to investigate the questions that (1) What kind of dynamic model exhibits a modulation phenomenon of PLVF? (2) What is the dynamic mechanism of PLVF for alpha modulation? (3) Which factors affect the modulation effects in PLVF? The result indicates that the dynamics of endogenous alpha oscillations are close to a simpler dynamic structure, like fixed-point attractor or limit-cycle attractor, which shows a global consistent dynamic behavior at different phases of the alpha oscillation. The further analysis explains the dynamic mechanism of PLVF for amplitude and frequency modulation of the alpha rhythm, as well as the influence of parameter settings in the modulation. All these findings provide a deeper understanding of the endogenous alpha oscillations entrained by exogenous phased locked visual stimulus and lead in turn to the refinement of a control strategy for alpha modulation, which could potentially be used in developing new neural modulation methods for cognitive enhancement and mental diseases treatment.

Real-Time Multi-Modal Estimation of Dynamically Evoked Emotions Using EEG, Heart Rate and Galvanic Skin Response

Emotion estimation systems based on brain and physiological signals such as electro encephalography (EEG), blood-volume pressure (BVP), and galvanic skin response (GSR) are gaining special attention in recent years due to the possibilities they offer. The field of human–robot interactions (HRIs) could benefit from a broadened understanding of the brain and physiological emotion encoding, together with the use of lightweight software and cheap wearable devices, and thus improve the capabilities of robots to fully engage with the users emotional reactions. In this paper, a previously developed methodology for real-time emotion estimation aimed for its use in the field of HRI is tested under realistic circumstances using a self-generated database created using dynamically evoked emotions. Other state-of-the-art, real-time approaches address emotion estimation using constant stimuli to facilitate the analysis of the evoked responses, remaining far from real scenarios since emotions are dynamically evoked. The proposed approach studies the feasibility of the emotion estimation methodology previously developed, under an experimentation paradigm that imitates a more realistic scenario involving dynamically evoked emotions by using a dramatic film as the experimental paradigm. The emotion estimation methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation when using the self-produced dynamically evoked emotions multi-signal database.

Neurocognitive reorganization of emotional processing following a socio-cognitive intervention in Colombian ex-combatants

Ex-combatants often exhibit atypical Emotional Processing (EP) such as reduced emphatic levels and higher aggressive attitudes. Social Cognitive Training (SCT) addressing socio-emotional components powerfully improve social interaction among Colombian ex-combatants. However, with narrow neural evidence, this study offers a new testimony. A sample of 28 ex-combatants from Colombian illegal armed groups took part in this study, split into 15 for SCT and 13 for the conventional program offered by the Governmental Reintegration Route. All of them were assessed before and after the intervention with a protocol that included an EP task synchronized with electroencephalographic recordings. We drew behavioral scores and brain connectivity (Coherency) metrics from task performance. Behavioral scores yielded no significant effects. Increased post-intervention connectivity in the delta band was observed during negative emotional processing only SCT group. Positive emotions exposed distinctive gamma band connectivity that differentiate groups. These results suggest that SCT can trigger covert neurofunctional reorganization in ex-combatants embarked on the reintegration process even when overt behavioral improvements are not yet apparent. Such covert functional changes may be the neural signature of compensatory mechanisms necessary to reshape behaviors adaptively. This novel framework may inspire cutting-edge translational research at the crossing of neuroscience, sociology, and public policy-making.

Segmentation and clustering in brain MRI imaging

Clustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.

Identifying Suitable Brain Regions and Trial Size Segmentation for Positive/Negative Emotion Recognition

The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1[Formula: see text]s to 12[Formula: see text]s segments, was 12[Formula: see text]s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.

An fNIRS-based investigation of visual merchandising displays for fashion stores

This paper investigates a brain-based approach for visual merchandising display (VMD) in fashion stores. In marketing, VMD has become a research topic of interest. However, VMD research using brain activation information is rare. We examine the hemodynamic responses (HRs) in the prefrontal cortex (PFC) using functional near-infrared spectroscopy (fNIRS) while positive/negative displays of four stores (menswear, womenswear, underwear, and sportswear) are shown to 20 subjects. As features for classifying the HRs, the mean, variance, peak, skewness, kurtosis, t-value, and slope of the signals for a 20-sec time window for the activated channels are analyzed. Linear discriminant analysis is used for classifying two-class (positive and negative displays) and four-class (four fashion stores) models. PFC brain activation maps based on t-values depicting the data from the 16 channels are provided. In the two-class classification, the underwear store had the highest average classification result of 67.04%, whereas the menswear store had the lowest value of 64.15%. Men's classification accuracy for the underwear stores with positive and negative displays was 71.38%, whereas the highest classification accuracy obtained by women for womenswear stores was 73%. The average accuracy over the 20 subjects for positive displays was 50.68%, while that of negative displays was 51.07%. Therefore, these findings suggest that human brain activation is involved in the evaluation of the fashion store displays. It is concluded that fNIRS can be used as a brain-based tool in the evaluation of fashion stores in a daily life environment.

The impact of musical experience on neural sound encoding performance

In this study, 64-channel single trial auditory brain oscillations (STABO) have been firstly analyzed by using complexity metrics to observe the effect of musical experience on brain functions. Experimental data was recorded from eyes-opened volunteers during listening the musical chords by piano. Complexity estimation methods were compared to each other for classification of groups (professional musicians and non-musicians) by using both classifiers (support vector machine (SVM), Naive Bayes (NB)) and statistical tests (one-way ANOVA) with respect to electrode locations. Permutation entropy (PermEn) is found to be the best metric (p ≪ 0.0001, 98.37% and 98.41% accuracies for tonal and atonal ensembles) at fronto-temporal regions which are responsible for cognitive task evaluation and perception of sound. PermEn also provides the meaningful results at the whole cortex (p ≪ 0.0001, 99.81% accuracy for both tonal and atonal ensembles) through SVM with Radial Basis kernels superior to Gaussians. Almost the similar performance is also obtained for temporal features. Although, performance improvements are observed for spectral methods with NB, the considerable better results are obtained with SVM. The results indicate that musical stimuli cause pattern variations instead of spectral variations on STABO due to relatively higher neuronal activities around auditory cortex. In conclusion, temporal regions produce response to auditory stimuli, while frontal area integrates the auditory task at the same time. As well, the parietal cortex produces neural information according to the degree of attention generated by environmental changes such as atonal stimuli. It can be clearly stated that musical experience enhances the neural encoding performance of sound tonality at mostly fronto-temporal regions.

Measuring and monitoring emotional changes in children who stutter

The assessment of clients with speech disorders presents challenges for speech-language pathologists. For example, having a reliable way of measuring the severity of the case, determining which remedial program is aligned with a patient's needs, and measuring of treatment processes. There is potential for brain-computer interface (BCI) applications to enhance speech therapy sessions by providing objective insights and real-time visualization of brain activity during the sessions. This paper presents a study on emotional state detection during speech pathology. The goal of this study is to investigate affective-motivational brain responses to stimuli in children who stutter. To this end, we conducted an experiment that involved recording frontal electroencephalography (EEG) activity from fifteen children with stuttering whilst they looked at visual stimuli. The contribution of our study is to provide a comprehensive background and a framework for emotional state detection experiments as assessment and monitoring tool in speech pathology. It mainly discusses the feasibility and potential benefits of applying EEG-based emotion detection in speech-language therapy contexts of use. The findings of our research indicate that emotional recognition using non-invasive EEG-based BCI system is sufficient to differentiate between affective states of individuals in treatment contexts.

A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG

Constructing a reliable and stable emotion recognition system is a critical but challenging issue for realizing an intelligent human-machine interaction. In this study, we contribute a novel channel-frequency convolutional neural network (CFCNN), combined with recurrence quantification analysis (RQA), for the robust recognition of electroencephalogram (EEG) signals collected from different emotion states. We employ movie clips as the stimuli to induce happiness, sadness, and fear emotions and simultaneously measure the corresponding EEG signals. Then the entropy measures, obtained from the RQA operation on EEG signals of different frequency bands, are fed into the novel CFCNN. The results indicate that our system can provide a high emotion recognition accuracy of 92.24% and a relatively excellent stability as well as a satisfactory Kappa value of 0.884, rendering our system particularly useful for the emotion recognition task. Meanwhile, we compare the performance of the entropy measures, extracted from each frequency band, in distinguishing the three emotion states. We mainly find that emotional features extracted from the gamma band present a considerably higher classification accuracy of 90.51% and a Kappa value of 0.858, proving the high relation between emotional process and gamma frequency band.

Neural Correlates of Phrase Quadrature Perception in Harmonic Rhythm: An EEG Study Using a Brain-Computer Interface

For the sake of establishing the neural correlates of phrase quadrature perception in harmonic rhythm, a musical experiment has been designed to induce music-evoked stimuli related to one important aspect of harmonic rhythm, namely the phrase quadrature. Brain activity is translated to action through electroencephalography (EEG) by using a brain-computer interface. The power spectral value of each EEG channel is estimated to obtain how power variance distributes as a function of frequency. The results of processing the acquired signals are in line with previous studies that use different musical parameters to induce emotions. Indeed, our experiment shows statistical differences in theta and alpha bands between the fulfillment and break of phrase quadrature, an important cue of harmonic rhythm, in two classical sonatas.

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.