Human Brain Activity Research Articles

Deep learning models to study sentence comprehension in the human brain

ABSTRACT Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data.

Eye tracking method based on mobile big data in computer environment

The human brain needs to rely on the information obtained by the eyes to process information. To a certain extent, the eyes are the reflection of the human brain activity. Based on this, many people pursue eyeball changes to judge a person’s psychological state, and eye tracking technology has also been proposed, playing an increasingly important role in different fields. The purpose is to study the human attention distribution system in the computer environment. We use the eye tracking technology of the eye tracker to record any actions of each learner during learning, use the function of the eye tracker to obtain various data, and use statistical analysis to analyze it. The experimental results show that the eyes can reflect human behavior information well. Analyzing eye movement data can deepen people’s understanding of the mechanism of attention distribution and improve learning efficiency by 20%.

Changes in behavioral priority influence the accessibility of working memory content

Evolving behavioral goals require the existence of selection mechanisms that prioritize task-relevant working memory (WM) content for action. Selecting an item stored in WM is known to blunt and/or reverse information loss in stimulus-specific representations of that item reconstructed from human brain activity, but extant studies have focused on all-or-none circumstances that allow or disallow an agent to select one of several items stored in WM. Conversely, behavioral studies suggest that humans can flexibly assign different levels of priority to different items stored in WM, but how doing so influences neural representations of WM content is unclear. One possibility is that assigning different levels of priority to items in WM influences the quality of those representations, resulting in more robust neural representations of high- vs. low-priority WM content. A second – and non-exclusive – possibility is that asymmetries in behavioral priority influence how rapidly neural representations of high- vs. low-priority WM content can be selected and reported. We tested these possibilities in two experiments by decoding high- and low-priority WM content from EEG recordings obtained while human volunteers performed a retrospectively cued WM task. Probabilistic changes in the behavioral relevance of a remembered item had no effect on our ability to decode it from EEG signals; instead, these changes influenced the latency at which above-chance decoding performance was reached. Thus, our results indicate that probabilistic changes in the behavioral relevance of WM content influence the ease with which memories can be selected independently of their strength.

Semantic Representations during Language Comprehension Are Affected by Context.

The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. fMRI was used to record human brain activity while four subjects (two female) read words in four conditions that vary in context: narratives, isolated sentences, blocks of semantically similar words, and isolated words. We then compared the signal-to-noise ratio (SNR) of evoked brain responses, and we used a voxelwise encoding modeling approach to compare the representation of semantic information across the four conditions. We find four consistent effects of varying context. First, stimuli with more context evoke brain responses with higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared with stimuli with little context. Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. In individual subjects, only natural language stimuli consistently evoke widespread representation of semantic information. Third, context affects voxel semantic tuning. Finally, models estimated using stimuli with little context do not generalize well to natural language. These results show that context has large effects on the quality of neuroimaging data and on the representation of meaning in the brain. Thus, neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.SIGNIFICANCE STATEMENT Context is an important part of understanding the meaning of natural language, but most neuroimaging studies of meaning use isolated words and isolated sentences with little context. Here, we examined whether the results of neuroimaging studies that use out-of-context stimuli generalize to natural language. We find that increasing context improves the quality of neuro-imaging data and changes where and how semantic information is represented in the brain. These results suggest that findings from studies using out-of-context stimuli may not generalize to natural language used in daily life.

A systematic review of the effects of transcranial photobiomodulation on brain activity in humans.

In recent years, transcranial photobiomodulation (tPBM) has been developing as a promising method to protect and repair brain tissues against damages. The aim of our systematic review is to examine the results available in the literature concerning the efficacy of tPBM in changing brain activity in humans, either in healthy individuals, or in patients with neurological diseases. Four databases were screened for references containing terms encompassing photobiomodulation, brain activity, brain imaging, and human. We also analysed the quality of the included studies using validated tools. Results in healthy subjects showed that even after a single session, tPBM can be effective in influencing brain activity. In particular, the different transcranial approaches- using a focal stimulation or helmet for global brain stimulation- seemed to act at both the vascular level by increasing regional cerebral blood flow (rCBF) and at the neural level by changing the activity of the neurons. In addition, studies also showed that even a focal stimulation was sufficient to induce a global change in functional connectivity across brain networks. Results in patients with neurological disease were sparser; nevertheless, they indicated that tPBM could improve rCBF and functional connectivity in several regions. Our systematic review also highlighted the heterogeneity in the methods and results generated, together with the need for more randomised controlled trials in patients with neurological diseases. In summary, tPBM could be a promising method to act on brain function, but more consistency is needed in order appreciate fully the underlying mechanisms and the precise outcomes.

NF-κB/c-Rel DNA-binding is reduced in substantia nigra and peripheral blood mononuclear cells of Parkinson's disease patients

Although Parkinson's disease (PD) key neuropathological hallmarks are well known, the underlying pathogenic mechanisms of the disease still need to be elucidated to identify innovative disease-modifying drugs and specific biomarkers. NF-κB transcription factors are involved in regulating several processes associated with neurodegeneration, such as neuroinflammation and cell death, that could be related to PD pathology. NF-κB/c-Rel deficient (c-rel−/−) mice develop a progressive PD-like phenotype. The c-rel−/− mice present both prodromal and motor symptoms as well as key neuropathological features, including nigrostriatal dopaminergic neurons degeneration, accumulation of pro-apoptotic NF-κB/RelA acetylated at the lysine 310 residue (Ac-RelA(lys310)) and progressive caudo-rostral brain deposition of alpha-synuclein. c-Rel inhibition can exacerbate MPTP-induced neurotoxicity in mice. These findings support the claim that misregulation of c-Rel protein may be implicated in PD pathophysiology. In this study, we aimed at evaluating c-Rel levels and DNA-binding activity in human brains and peripheral blood mononuclear cells (PBMCs) of sporadic PD patients. We analyzed c-Rel protein content and activity in frozen substantia nigra (SN) samples from post-mortem brains of 10 PD patients and 9 age-matched controls as well as in PBMCs from 72 PD patients and 40 age-matched controls. c-Rel DNA-binding was significantly lower and inversely correlated with Ac-RelA(lys310) content in post-mortem SN of sporadic PD cases, when compared to healthy controls. c-Rel DNA-binding activity was also reduced in PBMCs of followed-up PD subjects. The decrease of c-Rel activity in PBMCs from PD patients appeared to be independent from dopaminergic medication or disease progression, as it was evident even in early stage, drug-naïve patients. Remarkably, the levels of c-Rel protein were comparable in PD and control subjects, pointing out a putative role for post-translational modifications of the protein in c-Rel dysfunctions.These findings support that PD is characterized by the loss of NF-κB/c-Rel activity that potentially has a role in PD pathophysiology. Future studies will be aimed at addressing whether the reduction of c-Rel DNA-binding could constitute a novel biomarker for PD.

A systematic review of fMRI studies of healthy adult human brain activation on learning and memory patterns for numerals

This systematic review examines the empirical evidence from previous fMRI studies on brain activation areas for learning and memory patterns for numerals to understand the question of the brain activation areas found from previous fMRI studies on learning and memory patterns for numerals. Relevant articles in the fields of psychology, neuroscience, paediatrics, and education were extracted from the PubMed database. Keyword searches included "learning", "memory", "brain", "digits", "numerals", and "fMRI". The database search was conducted from July 2022 to August 2022. The data collected was limited to English texts, and studies involving animal experiments were excluded. The remaining articles were screened based on the inclusion and exclusion criteria. Articles included in this review were selected from the database search between 1999 to 2022. Ten full-text articles were identified and evaluated. Examination of brain activation regions in learning and memory patterns for numerals revealed that 30% of studies (n=3) found activation in the hippocampus, inferior parietal lobe and parietal lobe. Subsequently, 20% of the studies (n=2) found activation in the anterior cingulate, caudate nucleus, cerebellum, frontal lobe, inferior frontal gyrus, inferior temporal gyrus, medial temporal lobe, occipital lobe, prefrontal cortex, striatum, and thalamus. The frontoparietal network is considered important for working memory, while the limbic areas are essential for learning and memory. Therefore, future studies should look more closely at the brain's functional and structural regions involved in learning and a more specific memory system.

Coherent conditional previsions with respect to inner and outer Hausdorff measures to represent conscious and unconscious human brain activity

Coherent upper and lower conditional probabilities defined by Hausdorff outer and inner measures are proposed to represent respectively the unconscious and conscious activities of the human brain when information is given. In the model uncertainty measures are defined according to the complexity of the conditioning event that represents the given information. The model is applied to explain mathematically the conjunction fallacy in Linda's Problem and the bias of selective attention described in the so-called invisible Gorilla experiment, that is often taken as a characteristic example of the inescapable limitations of human perception. Once people are concentrated on doing a specific action, they do not notice unexpected events (having 0 probability) occurring in the meantime. When applying the model, selective attention is no longer a bias since it is able to explain this function of the human brain mathematically. Moreover different reactions of people to unexpected events can be represented in different metric spaces with metrics which are not bi-Lipschitz. In these metric spaces coherent upper conditional probabilities defined by Hausdorff outer measures are not mutually absolutely continuous and so they do not share the same null events.

Fortifying Brain Signals for Robust Interpretation

Brain-Media, is the discipline of decoding sophisticated human brain activity such as imagination, memories, colours, textures, patterns, etc. Existing efforts either classify brain signals or map them to an image of the same class. The second technique has only been investigated in a few papers using Electro Encephalography (EEG) datasets based on ImageNet and MNIST images. The role of Deep Neural Networks (DNN) must be researched to make them robust against malicious noise. Existing frameworks ignore the existence of such disruptive noises. The current research provides a multimodality time-series and spatial-domain hybrid framework and a unique ResilientNet Generator to classify signals robustly. In the first stage, two teachers are trained using a time series and an image dataset that shares the class. The second phase trains a ResilientNet-Generator with a new penalized-reconstruction loss-function apart from the adversarial loss. Finally, the trained ResilientNet-Generator is used as a pre-processing module for training a DNN-classifier in the third phase. It is noted that representing time-series data by the spatial domain can significantly improve accuracy compared to existing approaches. An ablation study on the resilience of trained classifiers against attacked test samples shows that DNNs can be fortified using the proposed framework.

Real-time emotion recognition using end-to-end attention-based fusion network

Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

Food Hardness Modulates Behavior, Cognition, and Brain Activation: A Systematic Review of Animal and Human Studies.

Food hardness is one of the dietary features that may impact brain functions. We performed a systematic review to evaluate the effect of food hardness (hard food versus soft food diet) on behavior, cognition, and brain activation in animals and humans (PROSPERO ID: CRD42021254204). The search was conducted on 29 June 2022 using Medline (Ovid), Embase, and Web of Science databases. Data were extracted, tabulated by food hardness as an intervention, and summarized by qualitative synthesis. The SYRCLE and JBI tools were used to assess the risk of bias (RoB) of individual studies. Of the 5427 studies identified, 18 animal studies and 6 human studies met the inclusion criteria and were included. The RoB assessment indicated that 61% of animal studies had unclear risks, 11% had moderate risks, and 28% had low risks. All human studies were deemed to have a low risk of bias. The majority (48%) of the animal studies showed that a hard food diet improved behavioral task performance compared to soft food diets (8%). However, 44% of studies also showed no differential effects of food hardness on behavioral tests. It was also evident that certain regions of the brain were activated in response to changes in food hardness in humans, with a positive association between chewing hard food, cognition performance, and brain function. However, variations in the methodologies of the included studies hindered the meta-analysis execution. In conclusion, our findings highlight the beneficial effects of dietary food hardness on behavior, cognition, and brain function in both animals and humans, however, this effect may depend on several factors that require further understanding of the causality.

A large-scale causal analysis of gut microbiota and delirium: A Mendelian randomization study

BackgroundSeveral studies have linked gut microbiota to human brain activity. This study used Mendelian randomization (MR) to investigate the causal relationship between gut microbes and delirium. MethodsMR was used to select SNPs from large-scale GWAS summary data on 211 gut microbiota taxa and delirium. Inverse variance weighting (IVW), weighted median, and MR-Egger methods were used for statistical analyses. Outliers were assessed using the leave-one-out method. To avoid horizontal pleiotropy, we performed the MR-PRESSO and MR-Egger intercept tests. Cochran's Q and I2 values for IVW and MR-Egger were used to assess heterogeneity. ResultsIVW suggested that genetic prediction of the family Desulfovibrionaceae (1.784 (1.267–2.512), P = 0.001), order Desulfovibrionales (1.501 (1.058–2.128), P = 0.023), and genus Candidatus Soleaferrea (1.322 (1.052–1.659), P = 0.016) increased the risk of delirium, but the family Oxalobacteraceae (0.841 (0.722–0.981), P = 0.027), and genera Holdemania (0.766 (0.620–0.946), P = 0.013), Ruminococcus gnavus (0.806 (0.661–0.982), P = 0.033), and Eggerthella (0.815 (0.667–0.997), P = 0.047) reduced the risk of delirium. Limitations(1) Limited sample size, (2) inability to assess gut microbiota interactions, and (3) limited to European populations. ConclusionOur results suggest that presence of the microbial family Desulfovibrionaceae, order Desulfovibrionales, and genus Candidatus Soleaferrea increased the risk of delirium, whereas the Oxalobacteraceae family, and the genera Holdemania, Ruminococcus gnavus, and Eggerthella decreased the risk of delirium. However, the potential of gut probiotic interventions in the prevention of perioperative delirium should be emphasized.

A wearable platform for closed-loop stimulation and recording of single-neuron and local field potential activity in freely moving humans

Advances in technologies that can record and stimulate deep brain activity in humans have led to impactful discoveries within the field of neuroscience and contributed to the development of novel therapies for neurological and psychiatric disorders. Further progress, however, has been hindered by device limitations in that recording of single-neuron activity during freely moving behaviors in humans has not been possible. Additionally, implantable neurostimulation devices, currently approved for human use, have limited stimulation programmability and restricted full-duplex bidirectional capability. In this study, we developed a wearable bidirectional closed-loop neuromodulation system (Neuro-stack) and used it to record single-neuron and local field potential activity during stationary and ambulatory behavior in humans. Together with a highly flexible and customizable stimulation capability, the Neuro-stack provides an opportunity to investigate the neurophysiological basis of disease, develop improved responsive neuromodulation therapies, explore brain function during naturalistic behaviors in humans and, consequently, bridge decades of neuroscientific findings across species.

Hierarchically nested networks optimize the analysis of audiovisual speech

In conversational settings, seeing the speaker's face elicits internal predictions about the upcoming acoustic utterance. Understanding how the listener's cortical dynamics tune to the temporal statistics of audiovisual (AV) speech is thus essential. Using magnetoencephalography, we explored how large-scale frequency-specific dynamics of human brain activity adapt to AV speech delays. First, we show that the amplitude of phase-locked responses parametrically decreases with natural AV speech synchrony, a pattern that is consistent with predictive coding. Second, we show that the temporal statistics of AV speech affect large-scale oscillatory networks at multiple spatial and temporal resolutions. We demonstrate a spatial nestedness of oscillatory networks during the processing of AV speech: these oscillatory hierarchies are such that high-frequency activity (beta, gamma) is contingent on the phase response of low-frequency (delta, theta) networks. Our findings suggest that the endogenous temporal multiplexing of speech processing confers adaptability within the temporal regimes that are essential for speech comprehension.

Statistical Learning of Distractor Suppression Downregulates Prestimulus Neural Excitability in Early Visual Cortex.

Visual attention is highly influenced by past experiences. Recent behavioral research has shown that expectations about the spatial location of distractors within a search array are implicitly learned, with expected distractors becoming less interfering. Little is known about the neural mechanism supporting this form of statistical learning. Here, we used magnetoencephalography (MEG) to measure human brain activity to test whether proactive mechanisms are involved in the statistical learning of distractor locations. Specifically, we used a new technique called rapid invisible frequency tagging (RIFT) to assess neural excitability in early visual cortex during statistical learning of distractor suppression while concurrently investigating the modulation of posterior alpha band activity (8-12 Hz). Male and female human participants performed a visual search task in which a target was occasionally presented alongside a color-singleton distractor. Unbeknown to the participants, the distracting stimuli were presented with different probabilities across the two hemifields. RIFT analysis showed that early visual cortex exhibited reduced neural excitability in the prestimulus interval at retinotopic locations associated with higher distractor probabilities. In contrast, we did not find any evidence of expectation-driven distractor suppression in alpha band activity. These findings indicate that proactive mechanisms of attention are involved in predictive distractor suppression and that these mechanisms are associated with altered neural excitability in early visual cortex. Moreover, our findings indicate that RIFT and alpha band activity might subtend different and possibly independent attentional mechanisms.SIGNIFICANCE STATEMENT What we experienced in the past affects how we perceive the external world in the future. For example, an annoying flashing light might be better ignored if we know in advance where it usually appears. This ability of extracting regularities from the environment is called statistical learning. In this study, we explore the neuronal mechanisms allowing the attentional system to overlook items that are unequivocally distracting based on their spatial distribution. By recording brain activity using MEG while probing neural excitability with a novel technique called RIFT, we show that the neuronal excitability in early visual cortex is reduced in advance of stimulus presentation for locations where distracting items are more likely to occur.

On Logical Inference over Brains, Behaviour, and Artificial Neural Networks

In the cognitive, computational, and neuro-sciences, practitioners often reason about what computational models represent or learn, as well as what algorithm is instantiated. The putative goal of such reasoning is to generalize claims about the model in question, to claims about the mind and brain, and the neurocognitive capacities of those systems. Such inference is often based on a model’s performance on a task, and whether that performance approximates human behavior or brain activity. Here we demonstrate how such argumentation problematizes the relationship between models and their targets; we place emphasis on artificial neural networks (ANNs), though any theory-brain relationship that falls into the same schema of reasoning is at risk. In this paper, we model inferences from ANNs to brains and back within a formal framework — metatheoretical calculus — in order to initiate a dialogue on both how models are broadly understood and used, and on how to best formally characterize them and their functions. To these ends, we express claims from the published record about models’ successes and failures in first-order logic. Our proposed formalization describes the decision-making processes enacted by scientists to adjudicate over theories. We demonstrate that formalizing the argumentation in the literature can uncover potential deep issues about how theory is related to phenomena. We discuss what this means broadly for research in cognitive science, neuroscience, and psychology; what it means for models when they lose the ability to mediate between theory and data in a meaningful way; and what this means for the metatheoretical calculus our fields deploy when performing high-level scientific inference.

Research on the Polygraph: the CMOS Neural Amplifier

The polygraph is an effective piece of equipment to detect if the suspect is lying in order to track down the criminal. The paper simulates and sets up the host part of the polygraph in order to explain how the polygraph works and some fields to use it. And we successfully make a neural amplifier to show the way that the host amplifies neural signals, and has a low passband that can filter out high-frequency signals. The circuit has two parts the folded cascode amplifier DC gain is 64.56, the -3dB bandwidth is about 100kHz and the total current consumption is 4uA and the capacitive feedback amplifier passband gain is 40dB and the passband is from about 3Hz to 5kHz. It is of great value not only in the field of criminal investigation, but also in VR, artificial intelligence (AI), and mental health. Because it can record and let people observe human brain activity and emotional changes. What’s more, with its development of it, it can solve many small crimes automatically, which means police can have more time and energy to deal with difficult cases and society can be more stable.

Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.

A spatiotemporal complexity architecture of human brain activity.

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.

A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics.

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirically observed spontaneous and stimulus-evoked dynamics in space, time, phase, and frequency domains. Large-scale properties of cortical dynamics are shown to emerge from both microscopic-scale adaptation that control transitions between wake-like to sleep-like activity, and the organization of the human structural connectome; together, they shape the spatial extent of synchrony and phase coherence across brain regions consistent with the propagation of sleep-like spontaneous traveling waves at intermediate scales. Remarkably, the model also reproduces brain-wide, enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologies.