Human Brain Activity Research Articles

The Impact of Dynamic Fractal Laser Images on Human Functional State

The subject matter of the article is the effect of a visual stimulation in the form of visual environment formed by dynamic laser images with preset fractality parameters on human. The structural and restorative characteristics of this environment are similar to those of the natural visual environment known for its restorative properties: it reduces the level of loading of sensory systems, recovers functional (general) state of humans, etc. Such analogue environment is formed using a laser optoelectronic RGB device designed with a non-conventional optical element, which transforms the structure of light beams from sources of radiation into an abstract light field with fractal dimensions of D = 1.3–1.5 optimal for perception, which is a positive criterion of evaluation of correspondence between this field and the natural visual environment. To estimate the level of the effect of the visual analogue environment, the electroencephalography (EEG) method was used which is based on analysis of activity of human brain α rhythm related to relaxed wakeful state. The estimation demonstrated that the proposed visual stimulation causes a valid positive effect as a light therapy instrument. The obtained percentage data on the difference between median values of α rhythm power in 18 derivations before and after the visual stimulation has demonstrated increase of α rhythm power by 40 % as compared to the baseline level. Minimal growth was equal to 17.8 % which corresponds to parietal derivation P3-A1 whereas the maximum growth was 51.4 % and corresponded to the mid-temporal derivation T4-A2. Also valid reductions of systolic and diastolic blood pressure (BP) and cardiac rate (CR) by 8.5 % and 10 % respectively were observed. The obtained data are indicative of reduction of anxiety level, unloading of sensory systems and positive dynamics of the effect of dynamic laser images on human functional state. The statistical analysis confirms validity of the positive dynamics of α rhythm power, BP and CR changes.

Impact of inspiratory threshold loading on brain activity and cognitive performances in healthy humans.

In healthy humans, inspiratory threshold loading deteriorates cognitive performances. This can result from motor-cognitive interference (activation of motor respiratory-related cortical networks vs. executive resources allocation), sensory-cognitive interference (dyspnea vs. shift in attentional focus), or both. We hypothesized that inspiratory loading would concomitantly induce dyspnea, activate motor respiratory-related cortical networks, and deteriorate cognitive performance. We reasoned that a concomitant activation of cortical networks and cognitive deterioration would be compatible with motor-cognitive interference, particularly in case of a predominant alteration of executive cognitive performances. Symmetrically, we reasoned that a predominant alteration of attention-depending performances would suggest sensory-cognitive interference. Twenty-five volunteers (12 men; 19.5-51.5 yr) performed the Paced Auditory Serial Addition Test (PASAT-A and B; calculation capacity, working memory, attention), the Trail Making Test (TMT-A, visuospatial exploration capacity; TMT-B, visuospatial exploration capacity, and attention), and the Corsi block-tapping test (visuospatial memory, short-term, and working memory) during unloaded breathing and inspiratory threshold loading in random order. Loading consistently induced dyspnea and respiratory-related brain activation. It was associated with deteriorations in PASAT-A [52 [45.5;55.5]; (median [interquartile range]) to 48 [41;54.5], P = 0.01], PASAT-B (55 [47.5;58] to 51 [44.5;57.5], P = 0.01), and TMT-B (44 s [36;54.5] to 53 s [42;64], P = 0.01), but did not affect TMT-A and Corsi. The concomitance of cortical activation and cognitive performance deterioration is compatible with competition for cortical resources (motor-cognitive interference), whereas the profile of cognitive impairment (PASAT and TMT-B but not TMT-A and Corsi) is compatible with a contribution of attentional distraction (sensory-cognitive interference). Both mechanisms are therefore likely at play.NEW & NOTEWORTHY To our knowledge, this is the first study exploring the interferences between inspiratory loading and cognition in healthy subjects with the concomitant use of neuropsychological tests and electroencephalographic recordings. Inspiratory loading was associated with dyspnea, respiratory-related changes in brain activation, and a pattern of deterioration of neuropsychological tests suggestive of attentional disruption. Inspiratory loading is therefore likely to impact cognitive performances through both motor-cognitive interference (engagement of cortical networks) and sensory-cognitive interference (dyspnea-related shift in attentional focus).

Hierarchical Classification of Event-Related Potentials for the Recognition of Gender Differences in the Attention Task.

Research on the functioning of human cognition has been a crucial problem studied for years. Electroencephalography (EEG) classification methods may serve as a precious tool for understanding the temporal dynamics of human brain activity, and the purpose of such an approach is to increase the statistical power of the differences between conditions that are too weak to be detected using standard EEG methods. Following that line of research, in this paper, we focus on recognizing gender differences in the functioning of the human brain in the attention task. For that purpose, we gathered, analyzed, and finally classified event-related potentials (ERPs). We propose a hierarchical approach, in which the electrophysiological signal preprocessing is combined with the classification method, enriched with a segmentation step, which creates a full line of electrophysiological signal classification during an attention task. This approach allowed us to detect differences between men and women in the P3 waveform, an ERP component related to attention, which were not observed using standard ERP analysis. The results provide evidence for the high effectiveness of the proposed method, which outperformed a traditional statistical analysis approach. This is a step towards understanding neuronal differences between men’s and women’s brains during cognition, aiming to reduce the misdiagnosis and adverse side effects in underrepresented women groups in health and biomedical research.

Whole-brain propagating patterns in human resting-state brain activities

Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. “Whole-brain” propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.

A canonical visualization tool for SEEG electrodes.

Stereoencephalographic (SEEG) electrodes are clinically implanted into the brains of patients with refractory epilepsy to locate foci of seizure onset. They are increasingly used in neurophysiology research to determine focal human brain activity in response to tasks or stimuli. Clear visualization of SEEG electrode location with respect to patient anatomy on magnetic resonance image (MRI) scan is vital to neuroscientific understanding. An intuitive way to accomplish this is to plot brain activity and labels at electrode locations on closest MRI slices along the canonical axial, coronal, and sagittal planes. Therefore, we've developed an open-source software tool in Matlab for visualizing SEEG electrode positions, determined from computed tomography (CT), onto canonical planes of resliced brain MRI. The code and graphical user interface are available at: https://github.com/MultimodalNeuroimagingLab/mnl_seegviewClinical Relevance- This tool enables precise communication of SEEG electrode activity and location by visualization on slices of MRI in canonical axial, coronal, and sagittal planes.

Gated Transformer for Decoding Human Brain EEG Signals.

In this work, we propose to use a deep learning framework for decoding the electroencephalogram (EEG) signals of human brain activities. More specifically, we learn an end-to-end model that recognizes natural images or motor imagery by the EEG data that is collected from the corresponding human neural activities. In order to capture the temporal information encoded in the long EEG sequences, we first employ an enhanced version of Transformer, i.e., gated Transformer, on EEG signals to learn the feature representation along a sequence of embeddings. Then a fully-connected Softmax layer is used to predict the classification results of the decoded representations. To demonstrate the effectiveness of the gated Transformer approach, we conduct experiments on the image classification task for a human brain-visual dataset and the classification task for a motor imagery dataset. The experimental results show that our method achieves new state-of-the-art performance compared to multiple existing methods that are widely used for EEG classification.

Dynamical regional activity in putamen distinguishes bipolar type I depression and unipolar depression

ObjectivesIntrinsic human brain activity is time-varying and dynamic. However, there is still a lack of knowledge about the dynamic regional activity differences between unipolar depression (UD) and bipolar type I depression (BD-I), and whether their differential pattern can help to distinguish these two patient groups who are prone to misdiagnosis in clinical practice. MethodIn this study, we used the dynamical fractional amplitude of low-frequency fluctuations (dfALFF) to examine the resting-state dynamical regional activity in 40 BD-I, 42 UD, and 44 healthy controls (HCs). Analysis of covariance was applied to explore the shared and distinct dfALFF pattern among three groups, and machine-learning methods were conducted to classify BD-I from UD by using the detected distinct dfALFF pattern. ResultsCompared with HCs, both BD-I and UD exhibited decreased dfALFF temporal variability in the left inferior temporal gyrus. The BD-I showed significantly decreased dfALFF temporal variability in the left putamen compared to UD. By using the dfALFF variability pattern of the left putamen as features, we achieved the 75.61% accuracy and 0.756 area under curve in classifying BD-I from UD. LimitationsThe small sample size of the current study may limit the generalizability of the findings. ConclusionsThe current study demonstrated that the dfALFF temporal variability pattern in the putamen may show a promise as future diagnostic aids for BD-I and UD.

The Global Configuration of Visual Stimuli Alters Co-Fluctuations of Cross-Hemispheric Human Brain Activity.

We tested how a stimulus gestalt, defined by the neuronal interaction between local and global features of a stimulus, is represented within human primary visual cortex (V1). We used high-resolution fMRI, which serves as a surrogate of neuronal activation, to measure co-fluctuations within subregions of V1 as (male and female) subjects were presented with peripheral stimuli, each with different global configurations. We found stronger cross-hemisphere correlations when fine-scale V1 cortical subregions represented parts of the same object compared with different objects. This result was consistent with the vertical bias in global processing and, critically, was independent of the task and local discontinuities within objects. Thus, despite the relatively small receptive fields of neurons within V1, global stimulus configuration affects neuronal processing via correlated fluctuations between regions that represent different sectors of the visual field.SIGNIFICANCE STATEMENT We provide the first evidence for the impact of global stimulus configuration on cross-hemispheric fMRI fluctuations, measured in human primary visual cortex. Our results are consistent with changes in the level of γ-band synchrony, which has been shown to be affected by global stimulus configuration, being reflected in the level fMRI co-fluctuations. These data help narrow the gap between knowledge of global stimulus configuration encoding at the single-neuron level versus at the behavioral level.

When makes you unique: Temporality of the human brain fingerprint.

The extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability are still largely unexplored. We here investigate the dynamics of brain fingerprints along two complementary axes: (i) What is the optimal time scale at which brain fingerprints integrate information and (ii) when best identification happens. Using dynamic identifiability, we show that the best identification emerges at longer time scales; however, short transient “bursts of identifiability,” associated with neuronal activity, persist even when looking at shorter functional interactions. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales, i.e., more visual-somatomotor at short temporal windows and more frontoparietal-DMN driven at increasing temporal windows. Last, different cognitive functions appear to be meta-analytically implicated in dynamic fingerprints across time scales. We hope that this investigation will advance our understanding of what makes our brains unique.

Exploring\ufeff electroencephalography with a model inspired by quantum mechanics

An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.

Functional Brain Imaging During Extra-Ocular Light Stimulation in Anophthalmic and Sighted Participants: No Evidence for Extra-Ocular Photosensitive Receptors.

Light plays a critical role in regulating physiology and behavior, including both visual and non-visual responses. In mammals, loss of both eyes abolishes all of these responses, demonstrating that the photoreceptors involved are exclusively ocular. By contrast, many non-mammalian species possess extra-ocular photoreceptors located in the pineal complex and deep brain. Whilst there have been suggestions of extra-ocular photoreception in mammals, including man, evidence for these photoreceptors is limited. One approach to objectively determine the presence of such receptors is to measure brain responses to light using functional magnetic resonance imaging (fMRI). Moreover, by using participants who are clinically anophthalmic (congenital and acquired), it is possible to investigate potential light detection in the absence of the retina. Here we scanned participants with anophthalmia and sighted participants in 4 different conditions; the first 3 conditions had a bright light source applied to the following locations: behind the right ear (“ear”), just below the nasal bridge and between the eyes (“head”), and at the right popliteal fossa (“knee”). In the fourth and final scan, the light source was switched off so that there was no light stimulus. All participants were scanned in a completely dark room. No consistent brain activity was detected during any of the light conditions in either sighted controls or anophthalmic participants. Thus, we do not provide any evidence for the presence of extraocular photoreceptors modulating human brain activity, despite recent evidence for gene transcription that may occur as a result of these photoreceptors.

Influence of dental magnets on human brain activity: A quantitative EEG analysis.

The magnetic field is known to attenuate mental depression, but it is not clear on the effect of dental magnets on human brain activity. Therefore, this study was to determine the influence of magnetic field from intraoral magnets on the human brain activity by analyzing the changes in alpha waves. Twenty complete denture male patients aged between 50 and 60 years were included after their consent. The neodymium-iron-boron sintered magnet was incorporated into the existing mandibular denture bilaterally in canine region. The participants were subjected to electroencephalogram (EEG) and the output measured alpha waves at three phases: (i) without placement of magnets (WM0), (ii) 30 days post-magnet placement in the mandibular denture (WM), and (iii) after 6 months post-removal of magnets (WM6). The data obtained was skewed (non-parametric), and hence multivariate analysis using Friedman test, and bivariate analysis using Wilcoxon signed rank test and Mann-Whitney test were done to assess the significant difference among the groups. The comparison of alpha waves between different phases showed a significant difference in all the pairs (p<.05) except WM-WM6. Comparison of alpha waves between right and left hemisphere showed no statistically significant difference (p>.05). We concluded that dental magnets had an influential effect on the alpha waves in the brain on both the left and right hemispheres. The removal of dental magnets retained the changes in brain activity even after 6 months of removal, denoting the alternative therapy to management of mental depression.

What to Believe? Impact of Knowledge and Message Length on Neural Activity in Message Credibility Evaluation

Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Message credibility is among crucial aspects of credibility evaluations. One of the most direct ways to understand message credibility is to use measurements of brain activity of humans performing credibility evaluations. Nevertheless, message credibility has never been investigated using such a method before. This article reports the results of an experiment during which we have measured brain activity during message credibility evaluation, using EEG. The experiment allowed for identification of brain areas that were active when participant made positive or negative message credibility evaluations. Based on experimental data, we modeled and predicted human message credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7.

Hippocampal and non-hippocampal correlates of physically active lifestyle and their relation to episodic memory in older adults

Aging is associated with compromised neurocognition. While aerobic exercise has been linked with cognitive resilience, findings regarding its relationship with brain morphology are inconsistent. Furthermore, the biological underpinnings of the relationship between aerobic activity and memory in the aging human brain are unclear. To investigate these issues, we examined hippocampal and non-hippocampal structural correlates of aerobically active lifestyle and cardiorespiratory fitness in older adults. We then examined structural pathways which may potentially mediate the association between active lifestyle and memory. Fifty participants (aged 65–80) underwent structural and diffusion MRI, memory evaluation, were examined for active lifestyle and cardiorespiratory fitness. Morphological features of the hippocampus and fornix, white matter lesions, and brain atrophy were assessed. Active lifestyle and cardiorespiratory fitness correlated with all neurocognitive measures. An exploratory mediation analysis revealed hippocampal and white matter lesions pathways linking active lifestyle and cardiorespiratory fitness with memory. Our results support a neuroprotective role of aerobic exercise on the aging brain and suggest plausible morphological pathways that may underlie the relationship between aerobic exercise and memory.

Abundant Firing Patterns in a Memristive Morris–Lecar Neuron Model

Improving the neuron model and studying its electrical activities according to the real biophysical environment are significant in human cognitive brain activity and neural behavior. The complex transmembrane motion of ions on the neuronal cell membrane can establish time-varying electromagnetic fields and affect the transition firing patterns of neurons. In this paper, a threshold memristor is used to describe the electromagnetic induction and magnetic field effects of neuron cell membrane ion exchange to improve the neuron model, and a memristive Morris–Lecar (mM–L) neuron model is proposed. Numerical simulation confirms that different intensities of electromagnetic fields can produce distinct pattern transitions in electrical activities of the neuron, such as periodic bursting, periodic spiking, chaotic bursting. From the perspective of neuron’s interspike interval (ISI), the ISIs bifurcation in the multiparameter planes, ISIs firing periods, the variance of ISIs and other methods are used to find the trend of the mM–L neuron firing pattern transition. Finally, based on the 4D nonlinear differential equation of the mM–L neuron model, the complete electronic implementation of the model is designed. The output of the designed circuit is consistent with the theoretical prediction, which is extremely useful for studying the dynamics of a single neuron.

A Commentary on “Can Neuroscience Provide a New Foundation for the Rorschach Variables?” (Jimura et al., 2021)

A Commentary on “Can Neuroscience Provide a New Foundation for the Rorschach Variables?” (Jimura et al., 2021)

Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

SummaryAchievement 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 neural decoding and encoding analyses where DNN unit activations and human brain activity are predicted from each other. We find that BH scores for 29 pre-trained DNNs with various architectures are negatively correlated with image recognition performance, thus indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that single-path sequential feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method may provide new ways to design DNNs in light of their representational homology to the brain.

Emotion-movement relationship: A study using functional brain network and cortico-muscular coupling

BackgroundEmotions play a crucial role in human communication and affect all aspects of human life. However, to date, there have been few studies conducted on how movements under different emotions influence human brain activity and cortico-muscular coupling (CMC). New methodsIn this study, for the first time, electroencephalogram (EEG) and electromyogram physiological electrical signals were used to explore this relationship. We performed frequency domain and nonlinear dynamics analyses on EEG signals and used transfer entropy to explore the CMC associated with the emotion-movement relationship. To study the transmission of information between different brain regions, we also constructed a functional brain network and calculated various network metrics using graph theory. ResultsWe found that, compared with a neutral emotional state, movements made during happy and sad emotions had increased CMC strength and EEG power and complexity. The functional brain network metrics of these three emotional states were also different. Comparison with existing methodsMuch of the emotion-movement relationship research has been based on subjective expression and external performance. Our research method, however, focused on the processing of physiological electrical signals, which contain a wealth of information and can objectively reveal the inner mechanisms of the emotion-movement relationship. ConclusionsDifferent emotional states can have a significant influence on human movement. This study presents a detailed introduction to brain activity and CMC.

A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream.

Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning objective, but these are limited to the supervised learning method. From the view of unsupervised learning mechanisms, this paper utilized a pre-trained neural network to construct a visual encoding model based on contrastive self-supervised learning for the ventral visual stream measured by functional magnetic resonance imaging (fMRI). We first extracted features using the ResNet50 model pre-trained in contrastive self-supervised learning (ResNet50-CSL model), trained a linear regression model for each voxel, and finally calculated the prediction accuracy of different voxels. Compared with the ResNet50 model pre-trained in a supervised classification task, the ResNet50-CSL model achieved an equal or even relatively better encoding performance in multiple visual cortical areas. Moreover, the ResNet50-CSL model performs hierarchical representation of input visual stimuli, which is similar to the human visual cortex in its hierarchical information processing. Our experimental results suggest that the encoding model based on contrastive self-supervised learning is a strong computational model to compete with supervised models, and contrastive self-supervised learning proves an effective learning method to extract human brain-like representations.

IMPROVED ARTIFICIAL NEURAL NETWORK FOR EPILEPTIC SEIZURES DETECTION

Electroencephalogram (EEG) is a fundamental and unique tool for exploring human brain activity in general and epileptic mechanism in particular. It offers significant information about epileptic seizures source known as epileptogenic area. However, it is often complicated to detect critical changes in EEG signals by visual examination, since this signal aspect of epileptic persons seems to be normal out of the seizure. Thus, the challenge is to design such a robust and automatic system to detect these unseen changes and use them for diagnosis. In this research, we apply the Artificial Metaplasticity Multi-Layer Perceptron (AMMLP) together with discrete wavelet transform (DWT) to Bonn EEG signals for seizure detection goal. Significant features were then extracted from the well-known EEG brainwaves. Aiming to decrease the computational time and improve classification accuracy, we performed a features ranking and selection employing the Relief algorithm. The obtained AMMLP classification accuracy of 98.97% proved the effctiveness of the applied approach. Our results were compared to recent researches results on the same database, proving to be superior or at least an interesting alternative for seizures detection within EEG signals.