Recorded Brain Activity Research Articles

"Exploring the Potential of Brain-Computer Interfaces in Managing Alzheimer\'s disease: A Review"

Brain-computer interfaces (BCIs) have been proposed as a potential therapeutic tool for Alzheimer's disease patients. BCIs use electrodes placed on the scalp to record brain activity and translate it into control signals for a computer or other device. In Alzheimer's disease, BCIs have been shown to improve cognitive function and quality of life, particularly in the areas of memory, attention, and executive function. However, more research is needed to fully understand the potential benefits and limitations of BCIs for Alzheimer's patients.

Recording electrical activity from the brain of behaving octopus

Octopuses, which are among the most intelligent invertebrates,1,2,3,4 have no skeleton and eight flexible arms whose sensory and motor activities are at once autonomous and coordinated by a complex central nervous system.5,6,7,8 The octopus brain contains a very large number of neurons, organized into numerous distinct lobes, the functions of which have been proposed based largely on the results of lesioning experiments.9,10,11,12,13 In other species, linking brain activity to behavior is done by implanting electrodes and directly correlating electrical activity with observed animal behavior. However, because the octopus lacks any hard structure to which recording equipment can be anchored, and because it uses its eight flexible arms to remove any foreign object attached to the outside of its body, invivo recording of electrical activity from untethered, behaving octopuses has thus far not been possible. Here, we describe a novel technique for inserting a portable data logger into the octopus and implanting electrodes into the vertical lobe system, such that brain activity can be recorded for up to 12h from unanesthetized, untethered octopuses and can be synchronized with simultaneous video recordings of behavior. In the brain activity, we identified several distinct patterns that appeared consistently in all animals. While some resemble activity patterns in mammalian neural tissue, others, such as episodes of 2Hz, large amplitude oscillations, have not been reported. By providing an experimental platform for recording brain activity in behaving octopuses, our study is a critical step toward understanding how the brain controls behavior in these remarkable animals.

Contact Heat in Magnetoencephalography: A Systematic Review.

Contact heat is commonly used in experimental research to evoke brain activity, most frequently acquired with electroencephalography (EEG). Although magnetoencephalography (MEG) improves spatial resolution, using some contact heat stimulators with MEG can present methodological challenges. This systematic review assesses studies that utilise contact heat in MEG, their findings and possible directions for further research. Eight electronic databases were searched for relevant studies, in addition to the selected papers' reference lists, citations and ConnectedPapers maps. Best practice recommendations for systematic reviews were followed. Papers met inclusion criteria if they used MEG to record brain activity in conjunction with contact heat, regardless of stimulator equipment or paradigm. Of 646 search results, seven studies met the inclusion criteria. Studies demonstrated effective electromagnetic artefact removal from MEG data, the ability to elicit affective anticipation and differences in deep brain stimulation responders. We identify contact heat stimulus parameters that should be reported in publications to ensure comparisons between data outcomes are consistent. Contact heat is a viable alternative to laser or electrical stimulation in experimental research, and methods exist to successfully mitigate any electromagnetic noise generated by PATHWAY CHEPS equipment - though there is a dearth of literature exploring the post-stimulus time window.

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.

Entropy production of multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain.

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible nonequilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to Functional magnetic resonance imaging brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.

Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures.

The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.

Can MRI Be Used as a Sensor to Record Neural Activity?

Magnetic resonance provides exquisite anatomical images and functional MRI monitors physiological activity by recording blood oxygenation. This review attempts to answer the following question: Can MRI be used as a sensor to directly record neural behavior? It considers MRI sensing of electrical activity in the heart and in peripheral nerves before turning to the central topic: recording of brain activity. The primary hypothesis is that bioelectric current produced by a nerve or muscle creates a magnetic field that influences the magnetic resonance signal, although other mechanisms for detection are also considered. Recent studies have provided evidence that using MRI to sense neural activity is possible under ideal conditions. Whether it can be used routinely to provide functional information about brain processes in people remains an open question. The review concludes with a survey of artificial intelligence techniques that have been applied to functional MRI and may be appropriate for MRI sensing of neural activity.

Neural decoding of music from the EEG

Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder. Specifically, we first used a joint EEG-fMRI paradigm to record brain activity while participants listened to music. We then used fMRI-informed EEG source localisation and a bi-directional long-term short term deep learning network to first extract neural information from the EEG related to music listening and then to decode and reconstruct the individual pieces of music an individual was listening to. We further validated our decoding model by evaluating its performance on a separate dataset of EEG-only recordings. We were able to reconstruct music, via our fMRI-informed EEG source analysis approach, with a mean rank accuracy of 71.8% (n~=~18, p~<~0.05). Using only EEG data, without participant specific fMRI-informed source analysis, we were able to identify the music a participant was listening to with a mean rank accuracy of 59.2% (n~=~19, p~<~0.05). This demonstrates that our decoding model may use fMRI-informed source analysis to aid EEG based decoding and reconstruction of acoustic information from brain activity and makes a step towards building EEG-based neural decoders for other complex information domains such as other acoustic, visual, or semantic information.

Sleep Track: Automated Detection and Classification of Sleep Stages

Sleep is vital for our body’s physical restoration, but sleep disorders can cause various problems. Determining sleep stages is essential for diagnosing and curing such disorders. Polysomnography (PSG) signals are recordings of brain activity, eye movements, muscle activity and other physiological signals that are collected during a sleep study. Insomnia, Sleep Apnea, and Restless Legs Syndrome are some of the sleep problems that can be identified using these signals. However, analysing PSG signals manually can be time-consuming and prone to errors. Deep Learning Models such as Convolutional Neural Networks (CNN), can be used to automate the analysis of PSG signals. CNN is followed by Long-Short Term Memory (LSTM) and CNN are used as a stack ensemble method to recognize patterns in the signals that correspond to different sleep stages and events. By training these models on large datasets of PSG signals, they can detect the disorders. The dataset is collected from PhysioNet Sleep-EDF dataset that consists of PSG signals. The accuracies obtained using different training and testing data using CNN and CNN-LSTM are 95.15% and 83.9% respectively, and using metadata classifier the overall accuracy is increased by 1%. The future enhancement of the paper can be done by considering Heart rate, EEG Pz-oz signals and EEG Pz-oz along with EEG Fpz-cz.

Biocompatibility and electrochemical characterization of new thin-film electrode arrays for monitoring, recording and stimulation of brain activity

Biocompatibility and electrochemical characterization of new thin-film electrode arrays for monitoring, recording and stimulation of brain activity

Robotic headgear records brain activity in moving mice

Robotic headgear records brain activity in moving mice

EEG Sinyallerinden Çıkarılan Poincare Ölçümlerini Kullanarak Çok Sınıflı Motor Hayali Görevlerin Sınıflandırılması

Motor Imaginary (MI) electroencephalography (EEG) signals are generated with the recording of brain activities when a participant imagines a movement without physically performing it. The correct decoding of MI signals have been became an important task due to the application of these signals in the rehabilitation process of paralyzed patients in recent studies. However, the decoding of the these signals is still an evolving challenge in the design of a brain-computer interface (BCI) system. In this study, a machine learning based approach using Poincare measurements from non-linear measurements of MI EEG signals is proposed for classification of four-class MI tasks. The m-lagged Poincare plots were used to extract non-linear features and m is set to be values from 1 to 10. The performances of feature vectors which are extracted from 10 lag values and feature vector which is the combinations of these vectors were investigated separately in experimental evaluation section. The 24 different typical classification algorithms were tested in differentiating MI tasks using 5-fold cross-validation. Each of the these algorithms tested 10 times to analyzed the repeatability of the experimental results. The highest classifier performance of 47.08% among these 11 feature vectors was achieved over the combination feature vector that includes all lag values features using Quadratic Support Vector Machine (SVM). According to average accuracy value of 24 classifiers in 11 feature vector, the most discriminative feature set is 9th vector that consists of features extracted when lag value defined as 9. As a result, the innovative aspect of this study is the application of Poincare plots, one of the nonlinear feature extraction methods, in motor imaginary task classification.

Neurophysiological evidence against attentional suppression as the source of the same-location cost in spatial cueing.

Spatial cues that mismatch the colour of a subsequent target have been shown to slow responses to targets that share their location. The source of this 'same location cost' (SLC) is currently unknown. Two potential sources are attentional signal suppression and object-file updating. Here, we tested a direct prediction of the suppression account using data from a spatial-cueing study in which we recorded brain activity using electroencephalography (EEG), and focusing on the event-related PD component, which is thought to index attentional signal suppression. Correlating PD amplitude with SLC magnitude, we tested the prediction that if attentional signal suppression is the source of the SLC, then the SLC should be positively correlated with PD amplitude. Across 48 participants, SLC and PD magnitudes were negatively correlated, in direct contradiction to a suppression account of the SLC. These results are compatible with an object-file updating account of the SLC in which updating is facilitated by reactive suppression of the to-be-updated stimulus information.

Commonalities between the Berger Rhythm and spectra differences driven by cross-modal attention and imagination

When people close their eyes, the power of alpha-band oscillatory brain activity increases. We explored the possibility that this could be related to a suppression of visual processing, rather than being a default dynamic of the visual brain. We recorded brain activity while people meditated with their eyes open or closed, and when people attended to or imagined having auditory or visual experiences. We could decode the attended or imagined modality of experiences based on the spectra of brain activity that prevailed while meditating with open or closed eyes. We also found anecdotal evidence suggesting the strength of imagined sensory experiences may be predicted by the dynamics of neural networks that are responsive to inputs. Overall, our data suggest spectra changes when people close their eyes might relate to a targeted suppression of visual processing, as opposed to being a default state of idle visual brains.

The neural correlates of value representation: From single items to bundles.

One of the core questions in Neuro-economics is to determine where value is represented. To date, most studies have focused on simple options and identified the ventromedial prefrontal cortex (VMPFC) as the common value region. We report the findings of an fMRI study in which we asked participants to make pairwise comparisons involving options of varying complexity: single items (Control condition), bundles made of the same two single items (Scaling condition) and bundles made of two different single items (Bundling condition). We construct a measure of choice consistency to capture how coherent the choices of a participant are with one another. We also record brain activity while participants make these choices. We find that a common core of regions involving the left VMPFC, the left dorsolateral prefrontal cortex (DLPFC), regions associated with complex visual processing and the left cerebellum track value across all conditions. Also, regions in the DLPFC, the ventrolateral prefrontal cortex (VLPFC) and the cerebellum are differentially recruited across conditions. Last, variations in activity in VMPFC and DLPFC value-tracking regions are associated with variations in choice consistency. This suggests that value based decision-making recruits a core set of regions as well as specific regions based on task demands. Further, correlations between consistency and the magnitude of signal change with lateral portions of the PFC suggest the possibility that activity in these regions may play a causal role in decision quality.

Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.

Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9dB compared to the Au grid (13.2dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.

ERP differences between monolinguals and bilinguals: The role of linguistic distance

AbstractA growing body of research suggests that bilingualism may afford benefits to certain aspects of cognitive functioning. Inconsistent findings may arise because of methodological differences within and across studies. One limitation is that studies often compare linguistically similar languages. The present study recorded brain activity (event-related potentials; ERPs) while English monolinguals, English–French bilinguals, and Arabic–English bilinguals completed an n-back task and a delayed matching-to-sample task. Group ERP differences were observed in the absence of behavioral differences. In the delayed matching-to-sample task, monolinguals exhibited smaller N2 amplitude compared to both bilingual groups, and smaller P3b amplitude compared to English–French bilinguals. In the n-back, English–French bilinguals displayed larger P3b amplitudes than monolinguals and Arabic–English bilinguals. P3b amplitude did not differ between Arabic–English bilinguals and monolinguals in either task. These results suggest that conflicting findings across studies may be due in part to the linguistic distance between the languages under study.

Technical feasibility of using auditory phase-targeted stimulation after pediatric severe traumatic brain injury in an intensive care setting

BackgroundSupplementary treatment options after pediatric severe traumatic brain injury (TBI) are needed to improve neurodevelopmental outcome. Evidence suggests enhancement of brain delta waves via auditory phase-targeted stimulation might support neuronal reorganization, however, this method has never been applied in analgosedated patients on the pediatric intensive care unit (PICU). Therefore, we conducted a feasibility study to investigate this approach: In a first recording phase, we examined feasibility of recording over time and in a second stimulation phase, we applied stimulation to address tolerability and efficacy.MethodsPediatric patients (> 12 months of age) with severe TBI were included between May 2019 and August 2021. An electroencephalography (EEG) device capable of automatic delta wave detection and sound delivery through headphones was used to record brain activity and for stimulation (MHSL-SleepBand version 2). Stimulation tolerability was evaluated based on report of nurses, visual inspection of EEG data and clinical signals (heart rate, intracranial pressure), and whether escalation of therapy to reduce intracranial pressure was needed. Stimulation efficacy was investigated by comparing EEG power spectra of active stimulation versus muted stimulation (unpaired t-tests).ResultsIn total, 4 out of 32 TBI patients admitted to the PICU (12.5%) between 4 and 15 years of age were enrolled in the study. All patients were enrolled in the recording phase and the last one also to the stimulation phase. Recordings started within 5 days after insult and lasted for 1–4 days. Overall, 23–88 h of EEG data per patient were collected. In patient 4, stimulation was enabled for 50 min: No signs of patient stress reactions were observed. Power spectrums between active and muted stimulation were not statistically different (all P > .05).ConclusionResults suggests good feasibility of continuously applying devices needed for auditory stimulation over multiple days in pediatric patients with TBI on PICU. Very preliminary evidence suggests good tolerability of auditory stimuli, but efficacy of auditory stimuli to enhance delta waves remains unclear and requires further investigation. However, only low numbers of severe TBI patients could be enrolled in the study and, thus, future studies should consider an international multicentre approach.

Electrochemical Testing of a New Polyimide Thin Film Electrode for Stimulation, Recording, and Monitoring of Brain Activity.

Subdural electrode arrays are used for monitoring cortical activity and functional brain mapping in patients with seizures. Until recently, the only commercially available arrays were silicone-based, whose thickness and lack of conformability could impact their performance. We designed, characterized, manufactured, and obtained FDA clearance for 29-day clinical use (510(k) K192764) of a new thin-film polyimide-based electrode array. This study describes the electrochemical characterization undertaken to evaluate the quality and reliability of electrical signal recordings and stimulation of these new arrays. Two testing paradigms were performed: a short-term active soak with electrical stimulation and a 29-day passive soak. Before and after each testing paradigm, the arrays were evaluated for their electrical performance using Electrochemical Impedance Spectroscopy (EIS), Cyclic Voltammetry (CV) and Voltage Transients (VT). In all tests, the impedance remained within an acceptable range across all frequencies. The different CV curves showed no significant changes in shape or area, which is indicative of stable electrode material. The electrode polarization remained within appropriate limits to avoid hydrolysis.

Verification of multi-structure targeting in chronic microelectrode brain recordings from CT scans

BackgroundLarge-scale microelectrode recordings offer a unique opportunity to study neurophysiological processes at the network level with single cell resolution. However, in the small brains of many experimental animals, it is often technically challenging to verify the correct targeting of the intended structures, which inherently limits the reproducibility of acquired data. New methodTo mitigate this problem, we have developed a method to programmatically segment the trajectory of electrodes arranged in larger arrays from acquired CT-images and thereby determine the position of individual recording tips with high spatial resolution, while also allowing for coregistration with an anatomical atlas, without pre-processing of the animal samples or post-imaging histological analyses. ResultsTesting the technical limitations of the developed method, we found that the choice of scanning angle influences the achievable spatial resolution due to shadowing effects caused by the electrodes. However, under optimal acquisition conditions, individual electrode tip locations within arrays with 250 µm inter-electrode spacing were possible to reliably determine. Comparison to existing methodsComparison to a histological verification method suggested that, under conditions where individual wires are possible to track in slices, a 90% correspondence could be achieved in terms of the number of electrodes groups that could be reliably assigned to the same anatomical structure. ConclusionsThe herein reported semi-automated procedure to verify anatomical targeting of brain structures in the rodent brain could help increasing the quality and reproducibility of acquired neurophysiological data by reducing the risk of assigning recorded brain activity to incorrectly identified anatomical locations. Data availabilityThe tools developed in this study are freely available as a software package at: https://github.com/NRC-Lund/ct-tools