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Science (New York, N.Y.) Oct 2021Sleep is crucial for healthy cognition, including memory. The two main phases of sleep, REM (rapid eye movement) and non-REM sleep, are associated with characteristic... (Review)
Review
Sleep is crucial for healthy cognition, including memory. The two main phases of sleep, REM (rapid eye movement) and non-REM sleep, are associated with characteristic electrophysiological patterns that are recorded using surface and intracranial electrodes. These patterns include sharp-wave ripples, cortical slow oscillations, delta waves, and spindles during non-REM sleep and theta oscillations during REM sleep. They reflect the precisely timed activity of underlying neural circuits. Here, we review how these electrical signatures have been guiding our understanding of the circuits and processes sustaining memory consolidation during sleep, focusing on hippocampal theta oscillations and sharp-wave ripples and how they coordinate with cortical patterns. Finally, we highlight how these brain patterns could also sustain sleep-dependent homeostatic processes and evoke several potential future directions for research on the memory function of sleep.
Topics: Animals; Brain Waves; Cerebral Cortex; Hippocampus; Homeostasis; Humans; Memory Consolidation; Neural Pathways; Sleep Stages; Sleep, REM; Theta Rhythm
PubMed: 34709916
DOI: 10.1126/science.abi8370 -
The European Journal of Neuroscience Jun 2022Brain waves, determined by electrical and magnetic brain recordings (e.g., EEG and MEG), and fluctuating behavioral responses, determined by response time or accuracy... (Review)
Review
Brain waves, determined by electrical and magnetic brain recordings (e.g., EEG and MEG), and fluctuating behavioral responses, determined by response time or accuracy measures, are frequently taken to support discrete perception. For example, it has been proposed that humans experience only one conscious percept per brain wave (e.g., during one alpha cycle). However, the proposed link between brain waves and discrete perception is typically rather vague. More importantly, there are many models and aspects of discrete perception and it is often not apparent in what theoretical framework brain wave findings are interpreted and to what specific aspects of discrete perception they relate. Here, we review different approaches to discrete perception and highlight issues with particular interpretations. We then discuss how certain findings on brain waves may relate to certain aspects of discrete perception. The main purpose of this meta-contribution is to give a short overview of discrete models of perception and to illustrate the need to make explicit what aspects of discrete theories are addressed by what aspects of brain wave findings.
Topics: Brain; Brain Waves; Consciousness; Humans; Perception; Reaction Time
PubMed: 34125452
DOI: 10.1111/ejn.15349 -
Hippocampus Oct 2015Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and... (Review)
Review
Sharp wave ripples (SPW-Rs) represent the most synchronous population pattern in the mammalian brain. Their excitatory output affects a wide area of the cortex and several subcortical nuclei. SPW-Rs occur during "off-line" states of the brain, associated with consummatory behaviors and non-REM sleep, and are influenced by numerous neurotransmitters and neuromodulators. They arise from the excitatory recurrent system of the CA3 region and the SPW-induced excitation brings about a fast network oscillation (ripple) in CA1. The spike content of SPW-Rs is temporally and spatially coordinated by a consortium of interneurons to replay fragments of waking neuronal sequences in a compressed format. SPW-Rs assist in transferring this compressed hippocampal representation to distributed circuits to support memory consolidation; selective disruption of SPW-Rs interferes with memory. Recently acquired and pre-existing information are combined during SPW-R replay to influence decisions, plan actions and, potentially, allow for creative thoughts. In addition to the widely studied contribution to memory, SPW-Rs may also affect endocrine function via activation of hypothalamic circuits. Alteration of the physiological mechanisms supporting SPW-Rs leads to their pathological conversion, "p-ripples," which are a marker of epileptogenic tissue and can be observed in rodent models of schizophrenia and Alzheimer's Disease. Mechanisms for SPW-R genesis and function are discussed in this review.
Topics: Animals; Biomarkers; Brain Waves; Executive Function; Hippocampus; Humans; Memory, Episodic
PubMed: 26135716
DOI: 10.1002/hipo.22488 -
Small (Weinheim An Der Bergstrasse,... Sep 2022Smart modulation of bioelectric signals is of great significance for the development of brain-computer interfaces, bio-computers, and other technologies. The regulation...
Smart modulation of bioelectric signals is of great significance for the development of brain-computer interfaces, bio-computers, and other technologies. The regulation and transmission of bioelectrical signals are realized through the synergistic action of various ion channels in organisms. The bionic nanochannels, which have similar physiological working environment and ion rectification as their biological counterparts, can be used to construct ion rectifier bridges to modulate the bioelectric signals. Here, the artificial smart ionic rectifier bridge with light response is constructed by anodic aluminum oxide (AAO)/poly (spiropyran acrylate) (PSP) nanochannels. The output ion current of the rectifier bridge can be switched between "ON" and "OFF" states by irradiation with UV and visible (Vis) light, and the conversion efficiency (η) of the system in "ON" state is ≈70.5%. The controllable modulation of brain wave-like signal can be realized by ionic rectifier bridge. The ion transport properties and processes of ion rectifier bridges are explained using theoretical calculations based on Poisson-Nernst-Planck (PNP) equations. These findings have significant implications for the understanding of the intelligent ionic circuit and combination of artificial smart ionic channels to organisms, which provide new avenues for development of intelligent ion devices.
Topics: Brain Waves; Ion Channels; Ion Transport; Ions; Light
PubMed: 35931455
DOI: 10.1002/smll.202203104 -
Medical Hypotheses May 2019Cognitive impairment (CI) is a common morbidity after cardio-pulmonary resuscitation (CPR) with long time persistence. Brain hypoxia is believed to be the main but not...
Cognitive impairment (CI) is a common morbidity after cardio-pulmonary resuscitation (CPR) with long time persistence. Brain hypoxia is believed to be the main but not the single etiology of post CPR cognitive impairment. Theta and lower theta waves of the EEG have essential role in proper functioning of the memory performance. Both endotracheal intubation and atropine administration in CPR process can abolish these waves. We hypothesize that CI in CPR survivors can be caused by disturbance in aforementioned waves due to endotracheal intubation and atropine administration.
Topics: Atropine; Brain Waves; Cardiopulmonary Resuscitation; Cholinergic Antagonists; Cognitive Dysfunction; Electroencephalography; Heart Arrest; Humans; Hypoxia, Brain; Intubation, Intratracheal; Models, Theoretical; Neurons; Olfactory Bulb
PubMed: 31010488
DOI: 10.1016/j.mehy.2019.03.009 -
Molecules (Basel, Switzerland) Oct 2020Tangerine () is one of the most important crops of Thailand with a total harvest that exceeds 100,000 tons. Citrus essential oils are widely used as aromatherapy and...
Tangerine () is one of the most important crops of Thailand with a total harvest that exceeds 100,000 tons. Citrus essential oils are widely used as aromatherapy and medicinal agents. The effect of tangerine essential oil on human brain waves and sleep activity has not been reported. In the present study, we therefore evaluated these effects of tangerine essential oil by measurement of electroencephalography (EEG) activity with 32 channel platforms according to the international 10-20 system in 10 male and 10 female subjects. Then the sleep onset latency was studied to further confirm the effect on sleep activity. The results revealed that different concentrations, subthreshold to suprathreshold, of tangerine oil gave different brain responses. Undiluted tangerine oil inhalation reduced slow and fast alpha wave powers and elevated low and mid beta wave powers. The subthreshold and threshold dilution showed the opposite effect to the brain compared with suprathreshold concentration. Inhalation of threshold concentration showed effectively decreased alpha and beta wave powers and increased theta wave power, which emphasize its sedative effect. The reduction of sleep onset latency was confirmed with the implementation of the observed sedative effect of tangerine oil.
Topics: Adult; Brain Waves; Citrus; Electroencephalography; Female; Humans; Male; Oils, Volatile; Sleep Latency; Young Adult
PubMed: 33096890
DOI: 10.3390/molecules25204865 -
Journal of Physiology and Pharmacology... Jun 2022The paper primarily focuses on differences in electroencephalogram (EEG) brain wave frequencies in the presence of symptoms of severe, chronic stress. In the case of a...
The paper primarily focuses on differences in electroencephalogram (EEG) brain wave frequencies in the presence of symptoms of severe, chronic stress. In the case of a constant increase of stress triggers, it is important to quickly diagnose people who reveal difficulties coping with difficult situations in order to prevent the occurrence of mental disorders. One way to do this is to diagnose brainwave patterns. The study aimed to identify differences in the brainwave levels of participants reporting intense stress compared to the control group. Differences in brainwave frequency between the right and left hemisphere were also investigated in the study group. The study consisted of two stages. Initially, the study group was enrolled based on their level of stress intensity criterion determined by means of an interview (in which participants declared a sense of chronic stress) and high scores on the Perceived Stress Scale (PSS). The control group consisted of subjects with a low score. In the next stage brainwave frequencies were analyzed using quantitative analysis of EEG (electroencephalography, QEEG) recordings. QEEG is a quantitative analysis of the EEG record, in which the data is digitally coded and statistically analyzed using the Fourier transform algorithm. The results demonstrated that people reporting intense, chronic stress statistically significantly more often had higher frequencies of theta, alpha, and beta 2 waves, and a lower level of SMR. Significant differences in the frequencies of the waves in both hemispheres were also noted.
Topics: Humans; Electroencephalography; Brain Waves; Brain; Algorithms
PubMed: 36515629
DOI: 10.26402/jpp.2022.3.14 -
Nature Communications Mar 2019Traveling patterns of neuronal activity-brain waves-have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in...
Traveling patterns of neuronal activity-brain waves-have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.
Topics: Adolescent; Adult; Brain; Brain Waves; Computer Simulation; Diffusion Tensor Imaging; Electrocorticography; Female; Healthy Volunteers; Humans; Male; Models, Neurological; Nerve Net; Neurons; Nonlinear Dynamics; Young Adult
PubMed: 30837462
DOI: 10.1038/s41467-019-08999-0 -
Scientific Reports Jun 2019Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have...
Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL .
Topics: Benchmarking; Brain Waves; Electroencephalography; Humans; Memory, Short-Term; Neural Networks, Computer; Signal Processing, Computer-Assisted; Support Vector Machine
PubMed: 31235800
DOI: 10.1038/s41598-019-45605-1 -
BMC Bioinformatics Jun 2021Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain-computer interface (BCI)...
BACKGROUND
Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain-computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP).
RESULTS
The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively.
CONCLUSIONS
Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
Topics: Algorithms; Brain; Brain Waves; Brain-Computer Interfaces; Electroencephalography; Imagination; Signal Processing, Computer-Assisted
PubMed: 34078274
DOI: 10.1186/s12859-021-04091-x