scholarly journals Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability

2019 ◽  
Author(s):  
Julia Moser ◽  
Siouar Bensaid ◽  
Eleni Kroupi ◽  
Franziska Schleger ◽  
Fabrice Wendling ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Reference Population ◽  
Computational Effort ◽  
Ease Of Use ◽  
Control Channel ◽  
Complexity Metrics ◽  
Entropy Measures ◽  
And Control ◽  
Future Work

AbstractIn this work, we aim to investigate whether information based metrics of neural activity are a useful tool for the search for consciousness before and shortly after birth. Neural activity is measured using fetal magnetoencephalography (fMEG) in human fetuses and neonates. Based on recent theories on consciousness, information-based metrics are established to measure brain complexity and to assess different levels of consciousness. Different metrics (measures of entropy, compressibility and fractality) are, thus, explored in a reference population and their usability is evaluated. For comparative analysis, two fMEG channels were selected: one where brain activity was previously detected and one at least 15cm away, that represented a control channel. The usability of each metric was evaluated and results from the brain and control channel were compared. Concerning the ease of use with fMEG data, Lempel-Ziv-Welch (LZW) compression was evaluated as best, as it is unequivocal and needs low computational effort. The fractality measures have a high parameter space and therefore forfeit comparability, while entropy measures require a higher computational effort and more parameters to adjust compared to LZW. Comparison of a channel with brain activity and a control channel in neonatal recordings showed significant differences in most complexity metrics. This clear difference can be seen as proof of concept for the usability of complexity metrics in fMEG. For fetal data, this comparison produced less clear results which can be related to leftover maternal signals included in the control channel. Further work is necessary to conclusively interpret results from the analysis of fetal recordings. Yet this study shows that complexity metrics can be used for fMEG data on early consciousness and the evaluation gives a guidance for future work. The inconsistency of results from different metrics highlights the challenges of working with complexity metrics as neural correlates of consciousness, as well as the caution one should apply to interpret them.

scholarly journals A novel automated autism spectrum disorder detection system

2021 ◽  
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Detection System ◽  
Brain Activity ◽  
Limited Range ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nonlinear Features ◽  
Genetic And Environmental Factors ◽  
Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

Asymptotic Behavior in Slumping From a Cylindrical Tank

1984 ◽  
Vol 106 (3) ◽  
pp. 279-284 ◽  
Author(s):  
Y. Bai ◽  
W. Johnson ◽  
R. G. M. Low ◽  
S. K. Ghosh
Keyword(s):  
Transition Period ◽  
Health And Safety ◽  
Local Environment ◽  
Theoretical Background ◽  
Hazardous Substance ◽  
Substance Flows ◽  
Linear Relationships ◽  
Characteristic Features ◽  
And Control ◽  
Future Work

When an oil tank collapses or ruptures any contained hazardous substance flows outwards and can damage nearby plant or people as well as lead to pollution of the local environment. In recent years, this and similar subjects have given rise to a new kind of engineering—spill prevention and control. However, theoretical background, backed by experiment, is lacking to work out reliable regulations. An intermediate-asymptotic analysis for late-stage spreading is carried out in this paper. This analysis reveals several characteristic features of the spill wave such as transition period and linear relationships between spreading area and time, and wave front velocity and the inverse of zone radius. Most of the latter results have been verified by model experiment. This paper also discusses the discrepancies between observations and the theory suggested in a recent UK Health and Safety Executive report. Finally, the present paper puts forward proper modeling rules for future work.

Pre-service music teachers' satisfaction: person–environment fit approach

2012 ◽  
Vol 29 (3) ◽  
pp. 371-385 ◽  
Author(s):  
Serkan Perkmen ◽  
Beste Cevik ◽  
Mahir Alkan
Keyword(s):  
Music Education ◽  
Music Teachers ◽  
Music Teacher ◽  
Theoretical Frameworks ◽  
Vocational Satisfaction ◽  
Highly Correlated ◽  
Service Music ◽  
And Control ◽  
Future Work

Guided by three theoretical frameworks in vocational psychology, (i) theory of work adjustment, (ii) two factor theory, and (iii) value discrepancy theory, the purpose of this study was to investigate Turkish pre-service music teachers' values and the role of fit between person and environment in understanding vocational satisfaction. Participants were 85 students enrolled in the department of music education in a Turkish university. The Minnesota Importance Questionnaire (MIQ) was used to examine the participants’ values in six dimensions: achievement, comfort, status, altruism, safety and autonomy. Results revealed that the pre-service teachers value achievement most followed by autonomy, which suggests that they would like to have a sense of accomplishment and control in their future job. The degree to which their values fit their predictions about future work environment was found to be highly correlated with vocational satisfaction. These results provided evidence that the vocational theories used in the current study offers a helpful and different perspective to understand the pre-service teachers' satisfaction with becoming a music teacher in the future. We believe that researchers in the field of music education may use these theories and MIQ to examine the role of values in pre-service and in-service music teachers' job satisfaction.

On Measures of Coupling Between the Artifact and Controller Optimal Design Problems

2009 ◽  
Author(s):  
Diane L. Peters ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
System Optimization ◽  
Optimization Method ◽  
Ease Of Use ◽  
Control Problems ◽  
Design Problems ◽  
Sensitivity Equations ◽  
Smart Products ◽  
Two Measures ◽  
And Control ◽  
Artifact Design

Optimization of smart products requires optimizing both the artifact design and its controller. The presence of coupling between the design and control problems is an important consideration in choosing the system optimization method. Several measures of coupling have been proposed based on different viewpoints of the system. In this paper, two measures of coupling, a vector based on optimality conditions and a matrix derived from an extension of the global sensitivity equations, are shown to be related under certain conditions and to be consistent in their coupling determination. The measures’ physical interpretation and relative ease of use are discussed using the example of a positioning gantry. A further relation is derived between one measure and a modified sequential formulation that would give results sufficiently close to the true solutions.

Learning to See Biological Motion: Brain Activity Parallels Behavior

2004 ◽  
Vol 16 (9) ◽  
pp. 1669-1679 ◽  
Author(s):  
Emily D. Grossman ◽  
Randolph Blake ◽  
Chai-Youn Kim
Keyword(s):  
Neural Activity ◽  
Biological Motion ◽  
Brain Activity ◽  
Daily Practice ◽  
Superior Temporal Sulcus ◽  
Fusiform Face Area ◽  
Face Area ◽  
Perceptual Performance ◽  
Trained Observers ◽  
Posterior Superior Temporal Sulcus

Individuals improve with practice on a variety of perceptual tasks, presumably reflecting plasticity in underlying neural mechanisms. We trained observers to discriminate biological motion from scrambled (nonbiological) motion and examined whether the resulting improvement in perceptual performance was accompanied by changes in activation within the posterior superior temporal sulcus and the fusiform “face area,” brain areas involved in perception of biological events. With daily practice, initially naive observers became more proficient at discriminating biological from scrambled animations embedded in an array of dynamic “noise” dots, with the extent of improvement varying among observers. Learning generalized to animations never seen before, indicating that observers had not simply memorized specific exemplars. In the same observers, neural activity prior to and following training was measured using functional magnetic resonance imaging. Neural activity within the posterior superior temporal sulcus and the fusiform “face area” reflected the participants' learning: BOLD signals were significantly larger after training in response both to animations experienced during training and to novel animations. The degree of learning was positively correlated with the amplitude changes in BOLD signals.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

New suspension for ionization chambers and the control channel for the RBMK safety and control system

Atomic Energy ◽  
2000 ◽  
Vol 88 (3) ◽  
pp. 239-241
Author(s):  
I. N. Aristov ◽  
V. F. Danilov ◽  
N. R. Kuzelev ◽  
E. K. Malyshev ◽  
I. N. Demin ◽  
...  
Keyword(s):  
Control System ◽  
Control Channel ◽  
Ionization Chambers ◽  
And Control

scholarly journals Expectations and desires of palliative health care personnel concerning their future work culture

2013 ◽  
Vol 2 (3) ◽  
pp. 46 ◽  
Author(s):  
Beate Andre ◽  
Endre Sjøvold ◽  
Marte Holmemo ◽  
Toril Rannestad ◽  
Gerd I. Ringdal
Keyword(s):  
Health Care ◽  
Reference Group ◽  
Health Care Personnel ◽  
Future Perspective ◽  
Work Culture ◽  
Care Workers ◽  
Positive Work Culture ◽  
And Control ◽  
Future Work ◽  
Positive Work

Introduction: Exploring the work culture of health care personnel is important in order to understand the challenges they face and the issues they experience. Believing in and shaping their futures indicates a working culture influenced by promoting factors. The aims of this study were to explore how health care workers at a Palliative Medicine Unit perceive their future work culture would be and whether they perceive that their expectations and desires will be fulfilled. Design: A correlational study. Methods: Health care personnel, physicians, nurses, physiotherapists, and others (N = 26) at a PMU in Norway completed a questionnaire according to the two perspectives, expectations (future) and desire (wish). The findings in these two perspectives were compared. The method seeks to explore what aspects dominate the particular work culture and identifying challenges, limitations, and opportunities. The findings were also compared with a reference group of 347 ratings of well-functioning Norwegian organizations, named the “Norwegian Norm”. Results: The findings for the wish perspective showed significant (p<0.05; p<0.01) higher rates for nurturing and synergy dimensions and significant lower rates (p>0.05; p>0.05) for opposition and control dimensions than the findings for the future perspective. Conclusions: It appears that the health care personnel wish for changes that they don’t believe they will achieve. The changes the respondents wish for are fewer negative work culture qualities, such as assertiveness and resignation, and more positive work culture qualities, such as engagement and empathy. Changes must be made to give the health care personnel improved working conditions and empowerment in order to change their situations to reflect what they wish for. The present findings can give an indication as to the direction that research ought to follow in subsequent studies.

scholarly journals Neural evidence for the prediction of animacy features during language comprehension: Evidence from MEG and EEG Representational Similarity Analysis

2019 ◽  
Author(s):  
Lin Wang ◽  
Edward Wlotko ◽  
Edward Alexander ◽  
Lotte Schoot ◽  
Minjae Kim ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Language Comprehension ◽  
Neural Activity ◽  
Semantic Processing ◽  
Sentence Comprehension ◽  
Brain Activity ◽  
Coarse Grained ◽  
Similarity Analysis ◽  
Semantic Features ◽  
Representational Similarity Analysis

AbstractIt has been proposed that people can generate probabilistic predictions at multiple levels of representation during language comprehension. We used Magnetoencephalography (MEG) and Electroencephalography (EEG), in combination with Representational Similarity Analysis (RSA), to seek neural evidence for the prediction of animacy features. In two studies, MEG and EEG activity was measured as human participants (both sexes) read three-sentence scenarios. Verbs in the final sentences constrained for either animate or inanimate semantic features of upcoming nouns, and the broader discourse context constrained for either a specific noun or for multiple nouns belonging to the same animacy category. We quantified the similarity between spatial patterns of brain activity following the verbs until just before the presentation of the nouns. The MEG and EEG datasets revealed converging evidence that the similarity between spatial patterns of neural activity following animate constraining verbs was greater than following inanimate constraining verbs. This effect could not be explained by lexical-semantic processing of the verbs themselves. We therefore suggest that it reflected the inherent difference in the semantic similarity structure of the predicted animate and inanimate nouns. Moreover, the effect was present regardless of whether a specific word could be predicted, providing strong evidence for the prediction of coarse-grained semantic features that goes beyond the prediction of individual words.Significance statementLanguage inputs unfold very quickly during real-time communication. By predicting ahead we can give our brains a “head-start”, so that language comprehension is faster and more efficient. While most contexts do not constrain strongly for a specific word, they do allow us to predict some upcoming information. For example, following the context, “they cautioned the…”, we can predict that the next word will be animate rather than inanimate (we can caution a person, but not an object). Here we used EEG and MEG techniques to show that the brain is able to use these contextual constraints to predict the animacy of upcoming words during sentence comprehension, and that these predictions are associated with specific spatial patterns of neural activity.

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