Human Brain Activity Research Articles

How attitudes generated by humanoid robots shape human brain activity

During interpersonal interactions, people perform actions with different forms of vitality, communicating their positive or negative attitude toward others. For example, a handshake can be “soft” or “vigorous”, a caress can be ‘kind’ or ‘rushed’. While previous studies have shown that the dorso-central insula is a key area for the processing of human vitality forms, there is no information on the perception of vitality forms generated by a humanoid robot. In this study, two fMRI experiments were conducted in order to investigate whether and how the observation of actions generated by a humanoid robot (iCub) with low and fast velocities (Study 1) or replicating gentle and rude human forms (Study 2) may convey vitality forms eliciting the activation of the dorso-central insula. These studies showed that the observation of robotic actions, generated with low and high velocities, resulted in activation of the parieto-frontal circuit typically involved in the recognition and the execution of human actions but not of the insula (Study 1). Most interestingly, the observation of robotic actions, generated by replicating gentle and rude human vitality forms, produced a BOLD signal increase in the dorso-central insula (Study 2). In conclusion, these data highlight the selective role of dorso-central insula in the processing of vitality forms opening future perspectives on the perception and understanding of actions performed by humanoid robots.

Waves of Change: Brain Sensitivity to Differential, not Absolute, Stimulus Intensity is Conserved Across Humans and Rats.

Living in rapidly changing environments has shaped the mammalian brain toward high sensitivity to abrupt and intense sensory events—often signaling threats or affordances requiring swift reactions. Unsurprisingly, such events elicit a widespread electrocortical response (the vertex potential, VP), likely related to the preparation of appropriate behavioral reactions. Although the VP magnitude is largely determined by stimulus intensity, the relative contribution of the differential and absolute components of intensity remains unknown. Here, we dissociated the effects of these two components. We systematically varied the size of abrupt intensity increases embedded within continuous stimulation at different absolute intensities, while recording brain activity in humans (with scalp electroencephalography) and rats (with epidural electrocorticography). We obtained three main results. 1) VP magnitude largely depends on differential, and not absolute, stimulus intensity. This result held true, 2) for both auditory and somatosensory stimuli, indicating that sensitivity to differential intensity is supramodal, and 3) in both humans and rats, suggesting that sensitivity to abrupt intensity differentials is phylogenetically well-conserved. Altogether, the current results show that these large electrocortical responses are most sensitive to the detection of sensory changes that more likely signal the sudden appearance of novel objects or events in the environment.

β-lactolin increases cerebral blood flow in dorsolateral prefrontal cortex in healthy adults: a randomized controlled trial.

The number of elderly individuals with age-related cognitive decline or dementia is rapidly increasing. Dairy product consumption, including β-lactolin, is beneficial for their cognitive function. The underlying mechanism of β-lactolin’s effects on human brain activity is yet to be investigated. We examined the β-lactolin effects on human cerebral blood flow (CBF) using near-infrared spectroscopy (NIRS) in a placebo-controlled randomized double-blind study, which reported according to the CONSORT guidelines. Fifty healthy participants (aged 45–60 years) were randomly allocated into the β-lactolin or the placebo group (n = 25 each) and supplemented for 6 weeks. During the 6th week, oxy-hemoglobin during the working memory tasks was measured using 34-channels (CHs) NIRS. The changes of oxy-hemoglobin, which represents the CBF, in CH 23 located at the left dorsolateral prefrontal cortex (DLPFC) during the spatial working memory task showed higher statistical significance (false discovery rate (q) = 0.045) in the β-lactolin than in the placebo group. The oxy-Hb changes in CH23 have a co-relationship with the working memory task reaction time. This clinical trial showed an increase in the CBF in the left DLPFC area during the 6-week β-lactolin supplementation. This study contributes to elucidating the underlying mechanisms of β-lactolin on cognitive performance.

The impact of parental contact upon cortical noxious-related activity in human neonates.

BackgroundNeonates display strong behavioural, physiological and cortical responses to tissue‐damaging procedures. Parental contact can successfully regulate general behavioural and physiological reactivity of the infant, but it is not known whether it can influence noxious‐related activity in the brain. Brain activity is highly dependent upon maternal presence in animal models, and therefore this could be an important contextual factor in human infant pain‐related brain activity.MethodsGlobal topographic analysis was used to identify the presence and inter‐group differences in noxious‐related activity in three separate parental contexts. EEG was recorded during a clinically required heel lance in three age and sex‐matched groups of neonates (a) while held by a parent in skin‐to‐skin (n = 9), (b) while held by a parent with clothing (n = 9) or (c) not held at all, but in individualized care (n = 9).ResultsThe lance elicited a sequence of 4–5 event‐related potentials (ERPs), including the noxious ERP (nERP), which was smallest for infants held skin‐to‐skin and largest for infants held with clothing (p=0.016). The nERP was then followed by additional and divergent long‐latency ERPs (> 750 ms post‐lance), not previously described, in each of the groups, suggesting the engagement of different higher level cortical processes depending on parental contact.ConclusionsThese results show the importance of considering contextual factors in determining infant brain activity and reveal the powerful influence of parental contact upon noxious‐related activity across the developing human brain.SignificanceThis observational study found that the way in which the neonatal brain processes a noxious stimulus is altered by the type of contact the infant has with their mother. Specifically, being held in skin‐to‐skin reduces the magnitude of noxious‐related cortical activity. This work has also shown that different neural mechanisms are engaged depending on the mother/infant context, suggesting maternal contact can change how a baby's brain processes a noxious stimulus.

Wireless Programmable Recording and Stimulation of Deep Brain Activity in Freely Moving Humans

Uncovering the neural mechanisms underlying human natural ambulatory behavior is a major challenge for neuroscience. Current commercially available implantable devices that allow for recording and stimulation of deep brain activity in humans can provide invaluable intrinsic brain signals but are not inherently designed for research and thus lack flexible control and integration with wearable sensors. We developed a mobile deep brain recording and stimulation (Mo-DBRS) platform that enables wireless and programmable intracranial electroencephalographic recording and electrical stimulation integrated and synchronized with virtual reality/augmented reality (VR/AR) and wearables capable of external measurements (e.g., motion capture, heart rate, skin conductance, respiration, eye tracking, and scalp EEG). When used in freely moving humans with implanted neural devices, this platform is adaptable to ecologically valid environments conducive to elucidating the neural mechanisms underlying naturalistic behaviors and to the development of viable therapies for neurologic and psychiatric disorders.

An Attention-based Bi-LSTM Method for Visual Object Classification via EEG

Background and ObjectiveDespite many models have been proposed for brain visual perception and content understanding via electroencephalograms (EEGs), due to the lack of research on the inherent temporal relationship, EEG-based visual object classification still demands the improvement on its accuracy and computation complexity. MethodsTo take full advantage of the uneven visual feature saturation between time segments, an end-to-end attention-based Bi-LSTM Method is proposed, named Bi-LSTM-AttGW. Two attention strategies are introduced to Bi-LSTM framework. The attention gate replaces the forget gate in traditional LSTM. It is only relevant to the historical cell state, and not related to the current input. Hence, the attention gate can greatly reduce the number of training parameters. Moreover, the attention weighting method is applied to Bi-LSTM output, and it can explore the most decisive information. ResultsThe best classification accuracy achieved by Bi-LSTM-AttGW model is 99.50%. Compared with the state-of-art algorithms and baseline models, the proposed method has great advantages in classification performance and computational complexity. Considering brain region level contribution on visual cognition task, we also verify our method using EEG signals collected from the frontal and occipital regions, that are highly correlated with visual perception tasks. ConclusionsThe results show promise towards the idea that human brain activity related to visual recognition can be more effectively decoded by neural networks with neural mechanism. The experimental results not only could provide strong support for the modularity theory about the brain cognitive function, but show the superiority of the proposed Bi-LSTM model with attention mechanism again.

Resting EEG in alpha band predicts individual differences in visual size perception

Human visual size perception results from an interaction of external sensory information and internal state. The cognitive mechanisms involved in the processing of context-dependent visual size perception have been found to be innate in nature to some extent, suggesting that visual size perception might correlate with human intrinsic brain activity. Here we recorded human resting alpha activity (8–12 Hz), which is an inverse indicator of sustained alertness. Moreover, we measured an object’s perceived size in a two-alternative forced-choice manner and the Ebbinghaus illusion magnitude which is a classic illustration of context-dependent visual size perception. The results showed that alpha activity along the ventral visual pathway, including left V1, right LOC and bilateral inferior temporal gyrus, negatively correlated with an object’s perceived size. Moreover, alpha activity in the left superior temporal gyrus positively correlated with size discrimination threshold and size illusion magnitude. The findings provide clear evidence that human visual size perception scales as a function of intrinsic alertness, with higher alertness linking to larger perceived size of objects and better performance in size discrimination and size illusion tasks, and suggest that individual variation in resting-state brain activity provides a neural explanation for individual variation in cognitive performance of normal participants.

Modeling an auditory stimulated brain under altered states of consciousness using the generalized Ising model

Propofol is a short-acting medication that results in decreased levels of consciousness and is used for general anesthesia. Although it is the most commonly used anesthetic in the world, much remains unknown about the mechanisms by which it induces a loss of consciousness. Characterizing anesthesia-induced alterations to brain network activity might provide a powerful framework for understanding the neural mechanisms of unconsciousness.The aim of this work was to model brain activity in healthy brains during various stages of consciousness, as induced by propofol, in the auditory paradigm. We used the generalized Ising model (GIM) to fit the empirical fMRI data of healthy subjects while they listened to an audio clip from a movie. The external stimulus (audio clip) is believed to be at least partially driving a synchronization process of the brain activity and provides a similar conscious experience in different subjects. In order to observe the common synchronization among the subjects, a novel technique called the inter subject correlation (ISC) was implemented.We showed that the GIM—modified to incorporate the naturalistic external field—was able to fit the empirical task fMRI data in the awake state, in mild sedation, in deep sedation, and in recovery, at a temperature T* which is well above the critical temperature. To our knowledge this is the first study that captures human brain activity in response to real-life external stimuli at different levels of conscious awareness using mathematical modeling. This study might be helpful in the future to assess the level of consciousness of patients with disorders of consciousness and help in regaining their consciousness.

Stroking a Real Horse Versus Stroking a Toy Horse: Effects on the Frontopolar Area of the Human Brain

ABSTRACT The effects of animal-assisted therapy (AAT) on human cerebral activity are not clearly understood, although many studies have reported psychological and physiotherapeutic benefits associated with it. Any benefits of AAT are likely to be closely related to brain activity. The aim of the present study was to investigate the effects of stroking a horse on brain activity in humans, specifically the frontopolar area, to evaluate the significance of using real animals in AAT. In a randomized cross-over trial, 30 healthy adults saw and stroked a stuffed toy (Session T) followed by a miniature horse (Session A), or vice versa. Participants saw and stroked the object (the stuffed toy or the real animal) for 10 minutes, followed by seeing and stroking another object (the real animal or the stuffed toy) for 10 minutes, with 20 minutes for measurements and resting. We measured changes in oxygenated hemoglobin (oxy-Hb) in the frontopolar area using near-infrared spectroscopy (NIRS). Three-way repeated measures ANOVA was used to compare oxy-Hb changes between the object used (stuffed toy versus real animal), contact method (see versus stroke), and laterality (left versus right). Oxy-Hb changes were larger in the right frontopolar area compared with those in the left frontopolar area when participants stroked a real horse (p < 0.05). Binominal tests showed that, in participants who reported that they “loved horses,” the laterality of oxy-Hb changes when participants stroked a real horse was significant (p = 0.031), but this was not seen in those who reported that they only “kind of liked” horses. These findings suggest that stroking a real horse activates the right frontopolar area, but this effect is not achieved when stroking a stuffed toy. Using horses in AAT that aims to activate human brain activity could be useful.

Brain Hierarchy Score: Which Deep Neural Networks are Hierarchically Brain-Like?

Achievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on the decoding of individual DNN unit activations from human brain activity. We find that BH scores for 29 pre-trained DNNs with varying architectures are negatively correlated with image recognition performance, indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that relatively simple feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method provides new ways for designing DNNs and understanding the brain in consideration of their representational homology.

More than just statics: Abnormal dynamic amplitude of low-frequency fluctuation in adolescent patients with pure conduct disorder

BackgroundThe human brain activity is inherently dynamic over time. Conventional neuroimaging studies have reported abnormalities of static intrinsic brain activity or connectivity in adolescent patients with conduct disorder (CD). Little is known, however, regarding the temporal dynamics alterations of brain activity in CD. MethodsIn this study, resting-state functional magnetic resonance imaging examinations were performed on adolescent patients with pure CD and age-matched typically developing (TD) controls. The dynamic amplitude of low-frequency fluctuation (dALFF) was first measured using a sliding-window method. The temporal variability (TV) was then quantified as the variance of dALFF over time and compared between the two groups. Further, the relationships between aberrant TV of dALFF and clinical features were evaluated. ResultsCD patients showed reduced brain dynamics (less temporal variability) in the default-mode network, frontal-limbic cortices, sensorimotor areas, and visual regions which are involved in cognitive, emotional and perceptional processes. Importantly, receiver operating characteristic curve analysis revealed that regions with altered TV of dALFF exhibited a better ability to distinguish CD patients than the results from static ALFF in the current data set. ConclusionsOur findings extended previous work by providing a novel perspective on the neural mechanisms underlying adolescent patients with CD and demonstrated that the altered dynamic local brain activity may be a potential biomarker for CD diagnosis.

The psychoacoustics annoyance research based on EEG rhythms for passengers in high-speed railway

Passengers’ psychoacoustics annoyance in the rail system is an awareness factor. It would be associated with rail services development and optimization. At present, the interior acoustics only measured and considered under the A-weighted sound pressure level (dBA), and have less consideration on passengers’ feelings. Therefore, it is necessary to apply a method for passengers’ psychoacoustics annoyance evaluation. Zwicker’s model has put forward the evaluation of psychoacoustic annoyance based on human hearing systems. Moreover, researchers have indicated that human brain activities and mental states can be reflected by the variation of sub-bands in electroencephalograph (EEG) under acoustics exposure. Therefore, the EEG sub-bands can be introduced for evaluating psychoacoustics annoyance on passengers. This study compares the interior acoustics in rail vehicles by Zwicker’s model and proposes the new EEG psychoacoustic annoyance indices to calculate the passengers’ psychoacoustics annoyance with EEG sub-bands under rail acoustics exposure. The loudness, sharpness and roughness of Zwicker’s model are analysed. The validation of this index and the comparison existing features have been made from the experiment carried on the high-speed train. Moreover, the correlation coefficients have been made between Zwicker’s model parameters and EEG indices. The results suggested the proposed index has good performance and can reflect the passengers’ psychoacoustics annoyance in high-speed rail.

The effects of Michelia alba oil against mould on brown rice and assessing the brain response using electroencephalogram (EEG).

The objective of this study was to develop antifungal fragrant brown rice using the vapour phase of Michelia alba oil and to understand the effects of fragrant brown rice on consumer reactions using an electroencephalography (EEG) technique. The effect of M. alba oil vapour (300–900 µL/L) on the growth of moulds was studied in brown rice. Then, optimisation of the M. alba oil vapour (300–900 µL/L) was studied through sensory evaluation. Next, EEG was used to investigate the effect of fragrant cooked brown rice on human brain activity. In addition, the key components of M. alba oil on the sensory effects were determined. The results indicated that M. alba oil vapour ≥ 450 µL/L provided effective antifungal activity against natural moulds on brown rice for at least 90 days of storage at 25 °C at 100% RH. Furthermore, the optimal concentration of M. alba oil vapour for enhancing consumer preference and acceptance of cooked brown rice was 300–600 µL/L with a rejection threshold of 2,052 µL/L. Moreover, it was found that linalool was the main key component and caryophyllene and β-elemene were the minor components affecting the sensory quality enhancement. Interestingly, the EEG results showed that fragrant cooked brown rice could increase the power of alpha and beta waves in the human brain, indicating anti-stress effects and a relaxed mood. Therefore, M. alba oil vapour demonstrated good potential to enhance consumer acceptance and preference for cooked brown rice while controlling the significant growth of moulds in brown rice.

BigGAN-based Bayesian Reconstruction of Natural Images from Human Brain Activity

In the visual decoding domain, the most difficult task is the visual reconstruction aimed at reconstructing the presented visual stimuli given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI), especially when reconstructing viewed natural images. Recent research regarded the visual reconstruction as the conditional image generation on fMRI voxels and started to use the generative adversarial networks (GANs) to design computational models for this task. Despite the great improvement in previous GAN-based methods, the fidelity and naturalness of the reconstructed images are still unsatisfactory, the reasons include the small number of fMRI data samples and the instability of GAN training. In this study, we propose a new GAN-based Bayesian visual reconstruction model (GAN-BVRM) to avoid the contradiction between naturalness and fidelity in current GAN-based methods. GAN-BVRM is composed of a classifier to decode the categories from fMRI data, a pre-trained conditional generator of the distinguished BigGAN to generate natural images of the specified categories, and a set of encoding models and an evaluator to evaluate the generated images. Composed of neural networks, GAN-BVRM is fully differentiable and can directly generate the reconstructed images by iteratively updating the noise input vector through backpropagation to fit the fMRI voxels. In this process, the decoded categories and encoding models are responsible for the semantic and detailed contents of the reconstructed images, respectively. Experimental results revealed that GAN-BVRM improved the fidelity and naturalness, which validated the advantage of the combining of GANs and Bayesian manner for visual reconstruction.

Decoding of the relationship between human brain activity and walking paths.

The human brain controls all actions of the body. Walking is one of the most important actions that deals with the movement of the body. In fact, the brain controls and regulates human walking based on different conditions. One of the conditions that affects human walking is the complexity of path of movement. Therefore, the brain activity should change when a person walks on a path with different complexities. In this research we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction. For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity). The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement. The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.

An fNIRS Study on the Effect of Music Style on Cognitive Activities.

Music is a medium for people to convey and express information, and different music styles show distinct effects on the emotion and cognitive activities of human brain. The construal level denotes the abstract level of the representation of objects, and as a useful indicator has been widely used to measure people's cognitive activities. Meanwhile, the neurophysiological responses in the prefrontal cortex (PFC) are associated with human cognitive activities. This work investigated the influence of music style on cognitive activities by measuring the construal level and PFC activation level. Two music styles (i.e., soothing and uplifting) were presented to listeners in the experiments. A behavioral experiment measured listeners' subjective construal levels, while the PFC activation levels were measured by a functional near-infrared spectroscopy. Results showed that compared with uplifting music, soothing music increased the subjective construal level; and the PFC activation level was higher in uplifting music than that in soothing music. These findings suggested that different music styles had distinct effects on the construal level and PFC activation level, providing evidences that music styles could impact people's cognitive activities.

Enhanced Support Vector Machine based on Metaheuristic Search for Multiclass Problems

Machine learning is an important field of artificial intelligent researches and it highly growing for real intelligent applications systems that relate brain computer interface to human brain activities. Support Vector Machine (SVM) is a popular machine learning approach, which can be used for pattern recognition, prediction and classification with many diverse applications. However, the SVM has many parameters, which have significant influences on the performance of SVM in terms of its prediction accuracy that is very important measure specifically with critical applications such that used in Medical applications. This paper proposed an enhanced SVM, which employs a meta-heuristic method, called scatter search to determine the optimal or near optimal values of the SVM parameters and its kernel parameters in multi-classification problem. Scatter search has the potential to determine the appropriate values of parameters for machine learning algorithms due its flexibility and sophistication. Therefore, the proposed method integrates the advantages of scatter search method with SVM to specify the appropriate setting of SVM parameters. The experimental results on lung cancer datasets and other standard datasets prove that the scatter search is practical method for tuning SVM parameters and enhance its performance, where the achieved results are better and comparable to other related methods.

Negative Air Ions in Neuropsychiatric Disorders.

Air ions (AIs) are clusters of ionized particles present in the atmosphere, carrying an electrical charge of negative or positive polarity. Past speculations suggested that exposure to positive air ions may be harmful, while exposure to negative air ions (NAIs) may be associated with beneficial health effects. Increasing attention has been directed towards investigating the potential effect of NAIs on human brain activities since initial observations of their beneficial effects on some cognitive processes and mood. Given the paucity and scattered literature, our paper aims to review the available studies on potential positive effects of NAIs exposure on cognitive performances and depression. The review of the literature seems to confirm the effects of NAIs on several brain functions. Indeed, a significant association between NAIs exposure and both well-being and high cognitive performances has been described. Furthermore, exposure to high concentrations of NAIs could be related to the improvement of depressive symptoms. A growing evidence of data, although not yet conclusive, would suggest that NAIs might improve cognitive processes. These findings require specific and urgent controlled trials adopting systems based on AIs release to possibly prevent and treat cognitive dysfunctions present in a broad range of neuropsychiatric conditions.

Estimation and validation of individualized dynamic brain models with resting state fMRI

A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1–3 ​min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

A novel attention-guided JND Model for improving robust image watermarking

Just noticeable distortion (JND) and visual attention (VA), which are two widely used mathematical models of human visual system (HVS) that aim to simulate the human brain mechanism, are sufficiently explored and applied to many researches including digital watermarking. The activity of human brain, however, is extremely complex and it can be more limited due to complicated fusion of spatial saliency for image domain. In this paper, we propose a novel VA guided JND model in which we fuse the final attention map from the low-level features by using two laws of Gestalt principle. Firstly, we demonstrate a classic JND model in DCT domain, which consists of spatial contrast sensitivity function (CSF), luminance adaptation (LA) and contrast masking (CM). The foveation effect and orientation feature are considered to obtain the CSF and CM factor. The foveation effect is affected by spatial attention, and the orientation features are modeled for CM effect together with traditional block texture strength through three direction-based AC coefficients in DCT domain. The attention features are integrated with a novel Gestalt principle-based weighting mechanism for the final block-based VA model, which is then used to modulate JND profiles with two non-linear functions. Finally, the proposed VA-guided JND model is incorporated into a logarithmic spread transform dither modulation (L-STDM) watermarking scheme. Experimental results show that the newly proposed algorithm can achieve good performance in term of robustness and get better visual quality.