Evaluation of the Brain Activity Using the Functional Near-Infared Spectroscopy while Having Stimulated by Pleasant and Unpleasant Music

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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Relation Between Level of Prefrontal Activity and Subject's Performance

Journal of Psychophysiology ◽

10.1027//0269-8803.13.2.117 ◽

1999 ◽

Vol 13 (2) ◽

pp. 117-125 ◽

Cited By ~ 14

Author(s):

Laurence Casini ◽

Françoise Macar ◽

Marie-Hélène Giard

Keyword(s):

Brain Activity ◽

Sensory Information ◽

Practice Phase ◽

Trained Subjects ◽

Anagram Task ◽

Economic Information ◽

Long Duration ◽

Level Of Activity ◽

The Brain ◽

Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

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Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

Information and Control Systems ◽

10.31799/1684-8853-2018-4-115-122 ◽

2018 ◽

pp. 115-122 ◽

Cited By ~ 1

Author(s):

V. A. Maksimenko ◽

A. A. Harchenko ◽

A. Lüttjohann

Keyword(s):

Epileptic Seizure ◽

Brain Activity ◽

Epileptic Seizures ◽

Automated System ◽

Brain Computer Interfaces ◽

Electrical Brain Activity ◽

Computer Interfaces ◽

Prediction And Prevention ◽

The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans.

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Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽

10.3390/e23030286 ◽

2021 ◽

Vol 23 (3) ◽

pp. 286

Author(s):

Soheil Keshmiri

Keyword(s):

Resting State ◽

Nearest Neighbor ◽

Brain Activity ◽

Solution Concept ◽

Conditional Entropy ◽

K Nearest Neighbor ◽

Brain Response ◽

Substantial Progress ◽

Eeg Recordings ◽

The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

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Consciousness, decision making, and volition: freedom beyond chance and necessity

Theory in Biosciences ◽

10.1007/s12064-021-00346-6 ◽

2021 ◽

Author(s):

Hans Liljenström

Keyword(s):

Decision Making ◽

Free Will ◽

Brain Activity ◽

Brain Structures ◽

Complex Process ◽

Neural Information ◽

Neural Information Processing ◽

Stochastic Population Model ◽

The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

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Information-based decoding of the coupling among human brain activity and movement paths

Technology and Health Care ◽

10.3233/thc-202744 ◽

2021 ◽

pp. 1-10

Author(s):

Shahul Mujib Kamal ◽

Norazryana Mat Dawi ◽

Hamidreza Namazi

Keyword(s):

Brain Activity ◽

Point Of View ◽

Physiological Signals ◽

Human Walking ◽

Eeg Signals ◽

Rehabilitation Science ◽

Information Contents ◽

First Time ◽

The Brain ◽

Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

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Neurodevelopment in the first three years: implications for child development, professional practice and policy

Journal of Children s Services ◽

10.1108/jcs-01-2014-0005 ◽

2014 ◽

Vol 9 (2) ◽

pp. 154-164 ◽

Cited By ~ 4

Author(s):

Danya Glaser

Keyword(s):

Brain Development ◽

Brain Activity ◽

Brain Maturation ◽

Policy And Practice ◽

Content Type ◽

Key Issues ◽

And Function ◽

The Relationship ◽

The Brain ◽

Brain Connections

Purpose – The purpose of this paper is to outline brain structure and development, the relationship between environment and brain development and implications for practice. Design/methodology/approach – The paper is based on a selected review of the literature and clinical experience. Findings – While genetics determine the sequence of brain maturation, the nature of brain development and functioning is determined by the young child's caregiving environment, to which the developing brain constantly adapts. The absence of input during sensitive periods may lead to later reduced functioning. There is an undoubted immediate equivalence between every mind function – emotion, cognition, behaviour and brain activity, although the precise location of this in the brain is only very partially determinable, since brain connections and function are extremely complex. Originality/value – This paper provides an overview of key issues in neurodevelopment relating to the development of young children, and implications for policy and practice.

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Inferring brain-computational mechanisms with models of activity measurements

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2016.0278 ◽

2016 ◽

Vol 371 (1705) ◽

pp. 20160278 ◽

Cited By ~ 30

Author(s):

Nikolaus Kriegeskorte ◽

Jörn Diedrichsen

Keyword(s):

Computational Models ◽

Brain Activity ◽

Visual Object ◽

Visual Object Recognition ◽

Summary Statistics ◽

Activity Data ◽

Individual Measurement ◽

Activity Measurements ◽

The Brain ◽

The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

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Adaptive brain activity changes during tongue movement with palatal coverage from fMRI data

Scientific Reports ◽

10.1038/s41598-021-93332-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yuka Inamochi ◽

Kenji Fueki ◽

Nobuo Usui ◽

Masato Taira ◽

Noriyuki Wakabayashi

Keyword(s):

Spatial Information ◽

Brain Activity ◽

Motor Impairment ◽

Angular Gyrus ◽

Tongue Movement ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Oral Environment ◽

Motor Dexterity ◽

The Brain

AbstractSuccessful adaptation to wearing dentures with palatal coverage may be associated with cortical activity changes related to tongue motor control. The purpose was to investigate the brain activity changes during tongue movement in response to a new oral environment. Twenty-eight fully dentate subjects (mean age: 28.6-years-old) who had no experience with removable dentures wore experimental palatal plates for 7 days. We measured tongue motor dexterity, difficulty with tongue movement, and brain activity using functional magnetic resonance imaging during tongue movement at pre-insertion (Day 0), as well as immediately (Day 1), 3 days (Day 3), and 7 days (Day 7) post-insertion. Difficulty with tongue movement was significantly higher on Day 1 than on Days 0, 3, and 7. In the subtraction analysis of brain activity across each day, activations in the angular gyrus and right precuneus on Day 1 were significantly higher than on Day 7. Tongue motor impairment induced activation of the angular gyrus, which was associated with monitoring of the tongue’s spatial information, as well as the activation of the precuneus, which was associated with constructing the tongue motor imagery. As the tongue regained the smoothness in its motor functions, the activation of the angular gyrus and precuneus decreased.

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Wrist and finger motor representations embedded in the cerebral and cerebellar resting-state activation

Brain Structure and Function ◽

10.1007/s00429-021-02330-8 ◽

2021 ◽

Author(s):

Toshiki Kusano ◽

Hiroki Kurashige ◽

Isao Nambu ◽

Yoshiya Moriguchi ◽

Takashi Hanakawa ◽

...

Keyword(s):

Resting State ◽

Brain Activity ◽

Body Part ◽

Resting State Fmri ◽

Body Parts ◽

Finger Movements ◽

Multiple Components ◽

Brain Activity Pattern ◽

Motor Representations ◽

The Brain

AbstractSeveral functional magnetic resonance imaging (fMRI) studies have demonstrated that resting-state brain activity consists of multiple components, each corresponding to the spatial pattern of brain activity induced by performing a task. Especially in a movement task, such components have been shown to correspond to the brain activity pattern of the relevant anatomical region, meaning that the voxels of pattern that are cooperatively activated while using a body part (e.g., foot, hand, and tongue) also behave cooperatively in the resting state. However, it is unclear whether the components involved in resting-state brain activity correspond to those induced by the movement of discrete body parts. To address this issue, in the present study, we focused on wrist and finger movements in the hand, and a cross-decoding technique trained to discriminate between the multi-voxel patterns induced by wrist and finger movement was applied to the resting-state fMRI. We found that the multi-voxel pattern in resting-state brain activity corresponds to either wrist or finger movements in the motor-related areas of each hemisphere of the cerebrum and cerebellum. These results suggest that resting-state brain activity in the motor-related areas consists of the components corresponding to the elementary movements of individual body parts. Therefore, the resting-state brain activity possibly has a finer structure than considered previously.

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