scholarly journals Comparative Analysis of the Permutation and Multiscale Entropies for Quantification of the Brain Signal Variability in Naturalistic Scenarios

2020 ◽  
Vol 10 (8) ◽  
pp. 527
Author(s):  
Soheil Keshmiri
Keyword(s):  
Brain Function ◽  
Human Subjects ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Affective States ◽  
Affective Stimuli ◽  
Eeg Recordings ◽  
The One ◽  
The Brain ◽  
Signal Variability

As alternative entropy estimators, multiscale entropy (MSE) and permutation entropy (PE) are utilized for quantification of the brain function and its signal variability. In this context, their applications are primarily focused on two specific domains: (1) the effect of brain pathology on its function (2) the study of altered states of consciousness. As a result, there is a paucity of research on applicability of these measures in more naturalistic scenarios. In addition, the utility of these measures for quantification of the brain function and with respect to its signal entropy is not well studied. These shortcomings limit the interpretability of the measures when used for quantification of the brain signal entropy. The present study addresses these limitations by comparing MSE and PE with entropy of human subjects’ EEG recordings, who watched short movie clips with negative, neutral, and positive content. The contribution of the present study is threefold. First, it identifies a significant anti-correlation between MSE and entropy. In this regard, it also verifies that such an anti-correlation is stronger in the case of negative rather than positive or neutral affects. Second, it finds that MSE significantly differentiates between these three affective states. Third, it observes that the use of PE does not warrant such significant differences. These results highlight the level of association between brain’s entropy in response to affective stimuli on the one hand and its quantification in terms of MSE and PE on the other hand. This, in turn, allows for more informed conclusions on the utility of MSE and PE for the study and analysis of the brain signal variability in naturalistic scenarios.

scholarly journals Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

SLEEP ◽  
2020 ◽  
Author(s):  
Fengzhen Hou ◽  
Lulu Zhang ◽  
Baokun Qin ◽  
Giulia Gaggioni ◽  
Xinyu Liu ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Sleep Time ◽  
Large Datasets ◽  
Permutation Entropy ◽  
Brain State ◽  
Eeg Signal ◽  
Spectral Measures ◽  
Progressive Change ◽  
Eeg Recordings ◽  
The Brain

Abstract Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power—Ptheta: 4–8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25–101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

scholarly journals Parents still matter! Parental warmth predicts adolescent brain function and anxiety and depressive symptoms 2 years later

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Rosalind D. Butterfield ◽  
Jennifer S. Silk ◽  
Kyung Hwa Lee ◽  
Greg S. Siegle ◽  
Ronald E. Dahl ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Brain Function ◽  
Functional Neuroimaging ◽  
Emotion Processing ◽  
Psychological Disorder ◽  
Anterior Cingulate ◽  
Parental Warmth ◽  
Maternal Warmth ◽  
Affective Stimuli ◽  
The Brain

Abstract Anxiety is the most prevalent psychological disorder among youth, and even following treatment, it confers risk for anxiety relapse and the development of depression. Anxiety disorders are associated with heightened response to negative affective stimuli in the brain networks that underlie emotion processing. One factor that can attenuate the symptoms of anxiety and depression in high-risk youth is parental warmth. The current study investigates whether parental warmth helps to protect against future anxiety and depressive symptoms in adolescents with histories of anxiety and whether neural functioning in the brain regions that are implicated in emotion processing and regulation can account for this link. Following treatment for anxiety disorder (Time 1), 30 adolescents (M age = 11.58, SD = 1.26) reported on maternal warmth, and 2 years later (Time 2) they participated in a functional neuroimaging task where they listened to prerecorded criticism and neutral statements from a parent. Higher maternal warmth predicted lower neural activation during criticism, compared with the response during neutral statements, in the left amygdala, bilateral insula, subgenual anterior cingulate (sgACC), right ventrolateral prefrontal cortex, and anterior cingulate cortex. Maternal warmth was associated with adolescents’ anxiety and depressive symptoms due to the indirect effects of sgACC activation, suggesting that parenting may attenuate risk for internalizing through its effects on brain function.

ALCOHOLIC INDEX USING NON-LINEAR FEATURES EXTRACTED FROM DIFFERENT FREQUENCY BANDS

2017 ◽  
Vol 17 (07) ◽  
pp. 1740009
Author(s):  
G. MURALIDHAR BAIRY ◽  
U. C. NIRANJAN ◽  
SHU LIH OH ◽  
JOEL E. W. KOH ◽  
VIDYA K. SUDARSHAN ◽  
...  
Keyword(s):  
Fuzzy Entropy ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Linear Features ◽  
Entropy Formula ◽  
Non Linear ◽  
The Brain

Alcoholism is a complex condition that mainly disturbs the neuronal networks in Central Nervous System (CNS). This disorder not only disturbs the brain, but also affects the behavior, emotions, and cognitive judgements. Electroencephalography (EEG) is a valuable tool to examine the neuropsychiatric disorders like alcoholism. The EEG is a well-established modality to diagnose the electrical activity produced by the populations of neurons in cerebral cortex. However, EEG signals are non-linear in nature; hence very challenging to interpret the valuable information from them using linear methods. Thus, using non-linear methods to analyze EEG signals can be beneficial in order to predict the brain signals condition. This paper presents a computer-aided diagnostic method for the detection of alcoholic EEG signals from normal by employing the non-linear techniques. First, the EEG signals are subjected to six levels of Wavelet Packet Decomposition (WPD) to obtain seven wavebands (delta ([Formula: see text]), theta ([Formula: see text]), lower alpha (la), upper alpha (ua), lower beta (lb), upper beta (ub), lower gamma (lg)). From each wavebands (activity bands), 19 non-linear features such as Recurrence Quantification Analysis (RQA) ([Formula: see text]), Approximate Entropy ([Formula: see text]), Energy ([Formula: see text]), Fractal Dimension (FD) ([Formula: see text]), Permutation Entropy ([Formula: see text]), Detrended Fluctuation Analysis ([Formula: see text]), Hurst Exponent ([Formula: see text]), Largest Lyapunov Exponent ([Formula: see text]), Sample Entropy ([Formula: see text]), Shannon’s Entropy ([Formula: see text]), Renyi’s entropy ([Formula: see text]), Tsalli’s entropy ([Formula: see text]), Fuzzy entropy ([Formula: see text]), Wavelet entropy ([Formula: see text]), Kolmogorov–Sinai entropy ([Formula: see text]), Modified Multiscale Entropy ([Formula: see text]), Hjorth’s parameters (activity ([Formula: see text]), mobility ([Formula: see text]), and complexity ([Formula: see text])) are extracted. The extracted features are then ranked using Bhattacharyya, Entropy, Fuzzy entropy-based Max-Relevancy and Min-Redundancy (mRMR), Receiver Operating Characteristic (ROC), [Formula: see text]-test, and Wilcoxon. These ranked features are given to train Support Vector Machine (SVM) classifier. The SVM classifier with radial basis function (RBF) achieved 95.41% accuracy, 93.33% sensitivity and 97.50% specificity using four non-linear features ranked by Wilcoxon method. In addition, an integrated index called Alcoholic Index (ALCOHOLI) is developed using highly ranked two features for identification of normal and alcoholic EEG signals using a single number. This system is rapid, efficient, and inexpensive and can be employed as an EEG analysis assisting system by clinicians in the detection of alcoholism. In addition, the proposed system can be used in rehabilitation centers to evaluate person with alcoholism over time and observe the outcome of treatment provided for reducing or reversing the impact of the condition on the brain.

scholarly journals PhenoWorld: addressing animal welfare in a new paradigm to house and assess rat behaviour

2016 ◽  
Vol 51 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Magda J Castelhano-Carlos ◽  
Vera Baumans ◽  
Nuno Sousa
Keyword(s):  
Animal Welfare ◽  
Brain Function ◽  
Animal Experiments ◽  
Well Being ◽  
Housing Conditions ◽  
Affective States ◽  
New Paradigm ◽  
Social Challenges ◽  
International Regulations ◽  
The Brain

The use of animals is essential in biomedical research. The laboratory environment where the animals are housed has a major impact on them throughout their lives and influences the outcome of animal experiments. Therefore, there has been an increased effort in the refinement of laboratory housing conditions which is explicitly reflected in international regulations and recommendations. Since housing conditions affect behaviour and brain function as well as well-being, the validation of an animal model or paradigm to study the brain and central nervous system disorders is not complete without an evaluation of its implication on animal welfare. Here we discuss several aspects of animal welfare, comparing groups of six rats living in the PhenoWorld (PhW), a recently developed and validated paradigm for studying rodent behaviour, with standard-housed animals (in cages of six rats or pair-housed). In this study we present new data on home-cage behaviour showing that PhW animals have a clearer circadian pattern of sleep and social interaction. We conclude that, by promoting good basic health and functioning, together with the performance of natural behaviours, and maintaining animals’ control over some of their environment but still keeping some physical and social challenges, the PhW stimulates positive affective states and higher motivation in rats, which might contribute to an increased welfare for animals living in the PhW.

Multiscale Entropy Analysis for Recognition of Visually Elicited Negative Stress From EEG Recordings

2019 ◽  
Vol 29 (02) ◽  
pp. 1850038 ◽  
Author(s):  
Arturo Martínez-Rodrigo ◽  
Beatriz García-Martínez ◽  
Raúl Alcaraz ◽  
Pascual González ◽  
Antonio Fernández-Caballero
Keyword(s):  
Multiscale Entropy ◽  
Brain Dynamics ◽  
Automatic Identification ◽  
Emotional States ◽  
Discriminant Ability ◽  
Short And Long Term ◽  
Eeg Recordings ◽  
First Time ◽  
The Brain

Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas.

scholarly journals A surface metric and software toolbox for EEG electrode grids in the macaque

2020 ◽  
Author(s):  
Fan Li ◽  
Tobias Teichert
Keyword(s):  
Current Approach ◽  
Non Invasive ◽  
Brain Surface ◽  
Manual Intervention ◽  
Planning Optimization ◽  
Efficient Planning ◽  
Eeg Recordings ◽  
The One ◽  
High Level ◽  
The Brain

AbstractBackgroundThe past years have seen increased appreciation of non-invasive extracranial electroencephalographic (EEG) recordings in non-human primates (NHP) as a tool for translational research. In humans, the international 10-20 system or extensions thereof provide standardized electrode positions that enable easy comparison of data between subjects and laboratories. In the NHP, no such generally accepted, standardized placement system is available.New MethodHere we introduce a surface metric and software package (NHP1020) that automates the planning of large, approximately evenly spaced electrode grids on the NHP skull.ResultsThe system is based on one CT and one MRI image and requires the user to specify two intracranial markers. Based on this, the software defines electrode positions on the brain surface using a surface-based spherical metric similar to the one used by the international 10-20 system. The electrode positions are then projected to the surface of the skull. Standardized electrode grids can be shared, imported or defined with few high-level commands.Existing MethodsNHP EEG electrodes are often placed on an individual basis relative to extracranial markers, or relative to underlying neural structures. Both approaches are time-consuming and require manual intervention. Furthermore, the use of extracranial markers in this species may be more problematic than in humans, because cranial muscles and ridges are larger and keep maturing long into adulthood thus potentially affecting electrode positions.ConclusionThe key advantage of the current approach is the automated and objective identification of corresponding electrode positions in different animals. Automation was made possible by the use of a two-dimensional metric on the brain surface which has a simpler, i.e., more convex and sphere-like anatomy than the skull. This enables fast and efficient planning, optimization and calculation of large electrode grids.

Central Nerve System Impairment, AMA Guides, Sixth Edition: An Overview

2018 ◽  
Vol 23 (1) ◽  
pp. 10-13
Author(s):  
James B. Talmage ◽  
Jay Blaisdell
Keyword(s):  
Spinal Cord ◽  
Central Nervous System ◽  
Nervous System ◽  
Neurological Impairment ◽  
Sixth Edition ◽  
Central Nerve System ◽  
The Central Nervous System ◽  
The One ◽  
Nerve System ◽  
The Brain

Abstract Injuries that affect the central nervous system (CNS) can be catastrophic because they involve the brain or spinal cord, and determining the underlying clinical cause of impairment is essential in using the AMA Guides to the Evaluation of Permanent Impairment (AMA Guides), in part because the AMA Guides addresses neurological impairment in several chapters. Unlike the musculoskeletal chapters, Chapter 13, The Central and Peripheral Nervous System, does not use grades, grade modifiers, and a net adjustment formula; rather the chapter uses an approach that is similar to that in prior editions of the AMA Guides. The following steps can be used to perform a CNS rating: 1) evaluate all four major categories of cerebral impairment, and choose the one that is most severe; 2) rate the single most severe cerebral impairment of the four major categories; 3) rate all other impairments that are due to neurogenic problems; and 4) combine the rating of the single most severe category of cerebral impairment with the ratings of all other impairments. Because some neurological dysfunctions are rated elsewhere in the AMA Guides, Sixth Edition, the evaluator may consult Table 13-1 to verify the appropriate chapter to use.

Frontier concept of rabi chip for intelligent humanoid

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Preecha Yupapin ◽  
Amiri I. S. ◽  
Ali J. ◽  
Ponsuwancharoen N. ◽  
Youplao P.
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Rabi Oscillation ◽  
Liquid Core ◽  
Brain Surface ◽  
Dipole Oscillation ◽  
On Chip ◽  
The Brain ◽  
Robotic Applications ◽  
Atom System

The sequence of the human brain can be configured by the originated strongly coupling fields to a pair of the ionic substances(bio-cells) within the microtubules. From which the dipole oscillation begins and transports by the strong trapped force, which is known as a tweezer. The tweezers are the trapped polaritons, which are the electrical charges with information. They will be collected on the brain surface and transport via the liquid core guide wave, which is the mixture of blood content and water. The oscillation frequency is called the Rabi frequency, is formed by the two-level atom system. Our aim will manipulate the Rabi oscillation by an on-chip device, where the quantum outputs may help to form the realistic human brain function for humanoid robotic applications.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

Neuroethics

2017 ◽  
Keyword(s):  
Brain Function ◽  
Ethical Issues ◽  
Wearable Technologies ◽  
Definition Of Death ◽  
Populations At Risk ◽  
The Future ◽  
Aging Adults ◽  
Definition Of ◽  
The Brain ◽  
New Criteria

We have new answers to how the brain works and tools which can now monitor and manipulate brain function. Rapid advances in neuroscience raise critical questions with which society must grapple. What new balances must be struck between diagnosis and prediction, and invasive and noninvasive interventions? Are new criteria needed for the clinical definition of death in cases where individuals are eligible for organ donation? How will new mobile and wearable technologies affect the future of growing children and aging adults? To what extent is society responsible for protecting populations at risk from environmental neurotoxins? As data from emerging technologies converge and are made available on public databases, what frameworks and policies will maximize benefits while ensuring privacy of health information? And how can people and communities with different values and perspectives be maximally engaged in these important questions? Neuroethics: Anticipating the Future is written by scholars from diverse disciplines—neurology and neuroscience, ethics and law, public health, sociology, and philosophy. With its forward-looking insights and considerations for the future, the book examines the most pressing current ethical issues.

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