Decoding Humor Experiences from Brain Activity of People Viewing Comedy Movies

Humans naturally have a sense of humor. Experiencing humor not only encourages social interactions, but also produces positive physiological effects on the human body, such as lowering blood pressure. Recent neuro-imaging studies have shown evidence for distinct mental state changes at work in people experiencing humor. However, the temporal characteristics of these changes remain elusive. In this paper, we objectively measured humor-related mental states from single-trial functional magnetic resonance imaging (fMRI) data obtained while subjects viewed comedy TV programs. Measured fMRI data were labeled on the basis of the lag before or after the viewer’s perception of humor (humor onset) determined by the viewer-reported humor experiences during the fMRI scans. We trained multiple binary classifiers, or decoders, to distinguish between fMRI data obtained at each lag from ones obtained during a neutral state in which subjects were not experiencing humor. As a result, in the right dorsolateral prefrontal cortex and the right temporal area, the decoders showed significant classification accuracies even at two seconds ahead of the humor onsets. Furthermore, given a time series of fMRI data obtained during movie viewing, we found that the decoders with significant performance were also able to predict the upcoming humor events on a volume-by-volume basis. Taking into account the hemodynamic delay, our results suggest that the upcoming humor events are encoded in specific brain areas up to about five seconds before the awareness of experiencing humor. Our results provide evidence that there exists a mental state lasting for a few seconds before actual humor perception, as if a viewer is expecting the future humorous events.     

基于优化核参数支持向量机的意识任务分类

根据支持向量机的基本原理,给出一种推广误差上界估计判据,并利用该判据进行最优核参数的自动选取。对三种不同意识任务的脑电信号进行多变量自回归模型参数估计,作为意识任务的特征向量,利用支持向量机进行训练和分类测试。分类结果表明,优化核参数的支持向量机分类器取得了最佳的分类效果,分类正确率明显高于径向基函数神经网络。     

Evaluation of mental task strain based on occipital beta activity and subjective rating of task difficulty

Occipital midline beta-2 (Oz beta 2) amplitudes and subjective ratings of task difficulty were measured in 24 healthy male students, aged 19-23 years, performing four kinds of mental task (transcribing, cancelling, adding, and calculating) at two grades of self-pace (maximum workload and moderate workload) for 5 min each. Significant differences in Oz beta 2 amplitudes were observed between the tasks at either workload, except between the adding task and the calculating task. However no significant difference in the subjective ratings was observed between the tasks at either workload, except between the adding task and the calculating task or the cancelling task at maximum workload. Thus the Oz beta 2 amplitude was more sensitive to differentiation of task strain between the mental tasks, than the subjective rating. In comparison with the critical levels deduced previously, it was estimated from the Oz beta 2 amplitudes that all the tasks at either workload could induce heavy task strain in the subject, except for the cancelling task at moderate workload. From the subjective ratings, however, it was estimated that no task could induce heavy task strain in the subject. It was, moreover, suggested that the Oz beta 2 amplitudes reflected the difference in the quality of cortical functions and that the subjective ratings of task difficulty were associated with time pressure rather than the quality of cortical functions.     

Changes in some indices of the cardiovascular system in different mental tasks

In order to study the effects of the type of mental activity on the function of the cardiovascular system in humans, the following indices were used: the heart rate (HR), RR interval, variation magnitude (VM), systolic wave amplitude (SWA), and pulse wave transit time (PWTT). These indices were recorded when the subjects solved verbal-logical or spatial mental tasks. The HR was substantially increased during solving of the spatial tasks as compared to solving of the verbal-logical tasks, whereas the SWA showed the opposite changes. The latencies of the performance of the tasks of various types were similar in the subjects; therefore, the changes in the autonomic indices did not depend on the difficulties of the mental tasks. They rather depended on specific features of the mental processes involved in the performance of the tasks of different types. Thus, changes in the HR and the decreased SWA, which was related to elevated blood pressure, represent the effect of the sympathetic nervous system on the heart function during solving verbal-logical tasks. Our data demonstrate that the HR and blood pressure can be used as additional indices for the development of new techniques for assessment of different types of mental processes together with the indices of electrical brain activity.     

Neural coupling between upper and lower limbs during recumbent stepping.

During gait rehabilitation, therapists or robotic devices often supply physical assistance to a patient's lower limbs to aid stepping. The expensive equipment and intensive manual labor required for these therapies limit their availability to patients. One alternative solution is to design devices where patients could use their upper limbs to provide physical assistance to their lower limbs (i.e., self-assistance). To explore potential neural effects of coupling upper and lower limbs, we investigated neuromuscular recruitment during self-driven and externally driven lower limb motion. Healthy subjects exercised on a recumbent stepper using different combinations of upper and lower limb exertions. The recumbent stepper mechanically coupled the upper and lower limbs, allowing users to drive the stepping motion with upper and/or lower limbs. We instructed subjects to step with 1) active upper and lower limbs at an easy resistance level (active arms and legs); 2) active upper limbs and relaxed lower limbs at easy, medium, and hard resistance levels (self-driven); and 3) relaxed upper and lower limbs while another person drove the stepping motion (externally driven). We recorded surface electromyography (EMG) from six lower limb muscles. Self-driven EMG amplitudes were always higher than externally driven EMG amplitudes (P < 0.05). As resistance and upper limb exertion increased, self-driven EMG amplitudes also increased. EMG bursts during self-driven and active arms and legs stepping occurred at similar times. These results indicate that active upper limb movement increases neuromuscular activation of the lower limbs during cyclic stepping motions. Neurologically impaired humans that actively engage their upper limbs during gait rehabilitation may increase neuromuscular activation and enhance activity-dependent plasticity.     

Changes in indexes of cardiovascular system in different mental tasks

The influence of different types of mental activity on the cardiovascular system was analyzed by following indexes: the heart rate (HR), the variation magnitude (VM), the amplitude of systolic wave (SWA) and the pulse wave transit time (PWTT). A registration of these indexes was conducted when the subjects solved verbal-logical and spatial mental tasks. HR was significantly greater in the spatial tasks than in the verbal-logical tasks, while SWA had an inverse relationship. The time for solving of tasks of the both types did not differ significantly. Therefore, the observed changes in autonomic indexes were not related to a difficulty of the mental tasks, but depended on the specific cognitive processes involved in the mental tasks of different types. Thus, the observed change in HR and the decrease in SWA correlating with the blood pressure rise reflect an influence of the sympathetic nervous system on the heart work during mental tasks of the verbal-logic type. The obtained results suggest that the indexes of HR and a blood pressure may also serve as supporting criteria in a development of new technologies for a determination of different types of mental operations in addition to the rhythms of the brain electrical activity.     

Does creatine supplementation improve functional capacity in elderly women?

The purpose of this study was to determine the effects of short-term (7 days) oral creatine supplementation (0.3 g.kg(-1)) in elderly women during exercise tests that reflect functional capacity during daily living tasks. We assessed several indices of endurance capacity (1-mile walk test, gross mechanical efficiency, ventilatory threshold, and peak oxygen intake determined during cycle-ergometry) and lower-extremity functional performance (time to complete sit-stand test). Subjects were assigned to a creatine (n = 10; age 67 +/- 6 years) or placebo (n = 6; age 68 +/- 4 years) group. We found a significant improvement only after creatine loading in the sit-stand test (placebo: 9.7 +/- 0.9 seconds for pretest and 9.3 +/- 0.7 seconds for posttest, p > 0.05; creatine: 10.0 +/- 0.7 seconds for pretest and 8.8 +/- 1.1 seconds for posttest). Significance was recorded at p < 0.05 for the interaction effect (group [creatine, placebo] x time [pretest, posttest]). In elderly women, short-term oral creatine supplementation does not improve endurance capacity but increases the ability to perform lower-body functional living tasks involving rapid movements.     

EEG classification of driver mental states by deep learning

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .     

Changes in the space-time organization of the bioelectrical activity of the human brain on completing the solution of a mental task

Changes in spatial-temporal organization of human bioelectrical brain activity were studied following formal completion of the process of solution of mental tasks of various complexity. It was shown that in the first seconds after the response, desynchronization took place of cortical potentials, identical to the one observed in the process of solution; at frequencies of alpha- and beta-2-rhythms, predominant decrease took place of parameters of spatial-temporal biopotentials conjunction as compared to the level of calm alertness. Dynamics of recovery of calm alertness EEG characteristics is determined by subjective complexity for the subject of previous activity and by the degree of his confidence in the correctness of the obtained result. The latter is determined by individual-typological properties of the subject.     

Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis

ABSTRACT: BACKGROUND: Schizophrenia is a severe mental illness associated with the symptoms such as hallucination and delusion. The objective of this study was to investigate the abnormal resting-state functional connectivity patterns of schizophrenic patients which could identify furthest patients from healthy controls. METHODS: The whole-brain resting-state fMRI was performed on patients diagnosed with schizophrenia (n=22) and on age- and gender-matched, healthy control subjects (n=22). To differentiate schizophrenic individuals from healthy controls, the multivariate classification analysis was employed. The weighted brain regions were got by reconstruction arithmetic to extract highly discriminative functional connectivity information. RESULTS: The results showed that 93.2% (p<0.001) of the subjects were correctly classified via the leave-one-out cross-validation method. And most of the altered functional connections identified located within the visual cortical-, default-mode-, and sensorimotor network. Furthermore, in reconstruction arithmetic, the fusiform gyrus exhibited the greatest amount of weight. CONCLUSIONS: This study demonstrates that schizophrenic patients may be successfully differentiated from healthy subjects by using whole-brain resting-state fMRI, and the fusiform gyrus may play an important functional role in the physiological symptoms manifested by schizophrenic patients. The brain region of great weight may be the problematic region of information exchange in schizophrenia. Thus, our result may provide insights into the identification of potentially effective biomarkers for the clinical diagnosis of schizophrenia.     

Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation

Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.     

Adjustment Disorders as a Stress-Related Disorder: A Longitudinal Study of the Associations among Stress,Resources, and Mental Health

Objective

Adjustment disorders are re-conceptualized in the DSM-5 as a stress-related disorder; however, besides the impact of an identifiable stressor, the specification of a stress concept, remains unclear. This study is the first to examine an existing stress-model from the general population, in patients diagnosed with adjustment disorders, using a longitudinal design.

Methods

The study sample consisted of 108 patients consecutively admitted for adjustment disorders. Associations of stress perception, emotional distress, resources, and mental health were measured at three time points: the outpatients’ presentation, admission for inpatient treatment, and discharge from the hospital. To evaluate a longitudinal stress model of ADs, we examined whether stress at admission predicted mental health at each of the three time points using multiple linear regressions and structural equation modeling. A series of repeated-measures one-way analyses of variance (rANOVAs) was performed to assess change over time.

Results

Significant within-participant changes from baseline were observed between hospital admission and discharge with regard to mental health, stress perception, and emotional distress (p<0.001). Stress perception explained nearly half of the total variance (44%) of mental health at baseline; the adjusted R² increased (0.48), taking emotional distress (i.e., depressive symptoms) into account. The best predictor of mental health at discharge was the level of emotional distress (i.e., anxiety level) at baseline (β = −0.23, R² _corr = 0.56, p<0.001). With a CFI of 0.86 and an NFI of 0.86, the fit indices did not allow for acceptance of the stress-model (C_min/df = 15.26; RMSEA = 0.21).

Conclusions

Stress perception is an important predictor in adjustment disorders, and mental health-related treatment goals are dependent on and significantly impacted by stress perception and emotional distress.     

Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang,China

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern.     

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (N_sz = 151) and healthy controls (N_hc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.     

Time-dependent changes in brain biogenic amine dynamics following castration in male rats

—Alterations in whole-brain and hypothalamic levels of serotonin (5-HT), 5-hydroxyindoleacetic acid (5-HIAA), norepinephrine (NE), dopamine (DA) as well as the turnover rates of NE and DA of adult male rats were analysed fluorometrically at either 3 weeks or 6 weeks following castration. Significant increases were observed in whole-brain (minus hypothalamus) 5-HIAA levels and hypothalamic DA levels, fractional rate constants and utilization rates at the 3 but not the 6 week intervals. Elevated levels of 5-HT were observed at both time intervals while an increase in whole-brain DA was seen only at the 6 week interval. Whole brain NE turnover rates of castrated animals did not differ significantly from those of sham-castrate control animals at either test interval. However, a tendency toward increased hypothalamic NE turnover rates was seen in the castrated animals. These biochemical changes resulted in decreased NE/5-HT and DA/5-HT ratios for the castrate rats as compared to controls. The results are discussed in relation to emotional and aggressive behavior and are interpreted as being consistent with the hypothesis purporting an inhibitory role for 5-HT and excitatory role for NE and DA in sex-specific behavior patterns including aggression.     

Cognitive brain–Computer interface and probable aspects of its practical application

A new type of brain-computer interface was elaborated. It considers a variety of brain activity parameters to determine the type of mental operation being performed at the moment. The corresponding algorithm previously developed in the lab was modified for real-time application. The possibility of interface application for cognitive skills training was investigated. In the proposed paradigm, as soon as the EEG spectral pattern was adequate for the current task, some clue to the solution was presented. As we supposed, such positive biofeedback should facilitate memorization of the current brain state. After just one learning session, the differences in EEG spectra, corresponding to two types of tasks, were concentrated in more narrow frequency ranges. It indicates a decrease in mental effort. Moreover, the majority of subjects succeeded in solving the tasks faster, which is evidence of increased efficiency. The developed interface could be used for the new type of training, based on objective features of brain activity.     

Sequential neural processes in abacus mental addition: an EEG and FMRI case study

Abacus experts are able to mentally calculate multi-digit numbers rapidly. Some behavioral and neuroimaging studies have suggested a visuospatial and visuomotor strategy during abacus mental calculation. However, no study up to now has attempted to dissociate temporally the visuospatial neural process from the visuomotor neural process during abacus mental calculation. In the present study, an abacus expert performed the mental addition tasks (8-digit and 4-digit addends presented in visual or auditory modes) swiftly and accurately. The 100% correct rates in this expert's task performance were significantly higher than those of ordinary subjects performing 1-digit and 2-digit addition tasks. ERPs, EEG source localizations, and fMRI results taken together suggested visuospatial and visuomotor processes were sequentially arranged during the abacus mental addition with visual addends and could be dissociated from each other temporally. The visuospatial transformation of the numbers, in which the superior parietal lobule was most likely involved, might occur first (around 380 ms) after the onset of the stimuli. The visuomotor processing, in which the superior/middle frontal gyri were most likely involved, might occur later (around 440 ms). Meanwhile, fMRI results suggested that neural networks involved in the abacus mental addition with auditory stimuli were similar to those in the visual abacus mental addition. The most prominently activated brain areas in both conditions included the bilateral superior parietal lobules (BA 7) and bilateral middle frontal gyri (BA 6). These results suggest a supra-modal brain network in abacus mental addition, which may develop from normal mental calculation networks.     

The effects of various mental tasks on appearance of frontal midline theta activity in EEG.

A distinct theta rhythm of EEG in the frontal midline area during performance of mental tasks has been called Fm theta. One of the characteristics of Fm theta is individual differences in its appearance. The effects of various mental tasks and its repetition on appearance of Fm theta were investigated. Adding, correcting wrong words, short-term memory (STM) and counting cubes were imposed 6 times on 7 male students who didn't generate Fm theta at the previous experiment. Counting cubes evoked more Fm theta than the other three mental tasks. In every task the first trial showed little Fm theta, and the appearance time of Fm theta was enhanced by repetition. Type I of Spielberger's State-Trait Anxiety Inventory (STAI-I) demonstrated a higher state anxiety level of the subjects at the first trial. It may be concluded that the amount of Fm theta corresponds to the level in concentration of attention.     

参与不同难度数字计算的脑区——脑功能磁共振成像研究

本研究用功能磁共振成像技术观察了人脑进行不同难度数字加减计算时的脑区激活情况,并探讨大脑皮层和皮层下结构在数字计算中的作用.用Siemens 1.5 Tesla磁共振机对16名右利手健康志愿者进行简单及复杂数字加减任务的fMRI扫描.实验采用组块设计.刺激任务分为简单加减计算任务、复杂加减计算任务和基线任务.用SPM99软件进行数据分析和脑功能区定位.分别比较同一任务各个脑区平均激活强度和同一脑区在两种任务中的激活强度.结果显示,简单及复杂加减计算激活的被试者的脑区基本相同,激活的皮层区主要见于额叶、顶叶、枕叶、扣带回、丘脑及小脑;简单及复杂加减计算激活的皮层下结构包括两侧尾状核、左纹状体边缘区等基底核结构和丘脑.在简单及复杂计算中,纹状体与皮质结构(额叶、顶叶)间激活强度均无显著性差异.简单计算与复杂计算比较,右顶叶,在复杂任务时出现激活,在简单任务时未出现激活.上述结果提示,完成数字计算任务的脑区除了额叶、顶叶、扣带回等皮层结构外,大脑皮层下的一些结构如纹状体、纹状体边缘区,也是参与数字计算的重要部位.皮层下结构纹状体和优势半球的纹状体边缘区参与了数字工作记忆,可能是进行数字计算神经环路的重要组成部位.右项叶(缘上回)只在复杂任务出现激活,该区可能是视空间记忆和加工的重要部位.     

Cardiorespiratory Information Dynamics during Mental Arithmetic and Sustained Attention

An analysis of cardiorespiratory dynamics during mental arithmetic, which induces stress, and sustained attention was conducted using information theory. The information storage and internal information of heart rate variability (HRV) were determined respectively as the self-entropy of the tachogram, and the self-entropy of the tachogram conditioned to the knowledge of respiration. The information transfer and cross information from respiration to HRV were assessed as the transfer and cross-entropy, both measures of cardiorespiratory coupling. These information-theoretic measures identified significant nonlinearities in the cardiorespiratory time series. Additionally, it was shown that, although mental stress is related to a reduction in vagal activity, no difference in cardiorespiratory coupling was found when several mental states (rest, mental stress, sustained attention) are compared. However, the self-entropy of HRV conditioned to respiration was very informative to study the predictability of RR interval series during mental tasks, and showed higher predictability during mental arithmetic compared to sustained attention or rest.