Proto-ideas of the network theory: antibody self-regulation, idiotypy, the brain analogy, and cybernetics

2008 ◽  
pp. 72-81
Keyword(s):  
Network Theory ◽  
Self Regulation ◽  
The Brain

Review of Self-regulation of the Brain and Behavior.

1985 ◽  
Vol 30 (12) ◽  
pp. 999-999
Author(s):  
Gerald S. Wasserman
Keyword(s):  
Self Regulation ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain

scholarly journals The Learning Curve in neurofeedback of Peter Van Deusen: A review article

2016 ◽  
Vol 10 (2) ◽  
pp. 98-103 ◽  
Author(s):  
Valdenilson Ribeiro Ribas ◽  
Renata de Melo Guerra Ribas ◽  
Hugo André de Lima Martins
Keyword(s):  
Learning Curve ◽  
Web Of Science ◽  
Self Regulation ◽  
Review Article ◽  
Behavioral Activity ◽  
Patient Evaluation ◽  
Temporal Lobes ◽  
Behavioral Psychology ◽  
Choice Of Treatment ◽  
The Brain

ABSTRACT The Learning Curve (TLC) in neurofeedback concept emerged after Peter Van Deusen compiled the results of articles on the expected electrical activity of the brain. This concept was subsequently tested on patients at four clinics in Atlanta between 1994 and 2001. The aim of this paper was to report the historical aspects of TLC. Articles published on the electronic databases MEDLINE/PubMed and Web of Science were reviewed. During patient evaluation, TLC investigates categories called disconnected, hot temporal lobes, reversal of alpha and beta waves, blocking, locking, and filtering or processing. This enables neuroscientists to use their training designs and, by means of behavioral psychology, to work on neuroregulation, as self-regulation for patients. TLC shows the relationships between electrical, mental and behavioral activity in patients. It also identifies details of patterns that can assist physicians in their choice of treatment.

scholarly journals So What? Implications of Network Theory for Research and Practice

2021 ◽  
Author(s):  
Payton J. Jones ◽  
Donald Robinaugh
Keyword(s):  
Clinical Practice ◽  
Network Theory ◽  
Medical Model ◽  
Signs And Symptoms ◽  
Psychological Problems ◽  
Research And Practice ◽  
Behavioral Therapies ◽  
Cognitive Behavioral Therapies ◽  
Disease Entities ◽  
The Brain

Research and practice in psychiatry and clinical psychology have been guided by differing schools of thought over the years. Recently, the network theory of psychopathology has arisen as a framework for thinking about mental health. Network theory challenges three assumptions common in the field: (1) psychological problems are caused by disease entities that exist independently of their signs and symptoms, (2) classification and diagnosis of psychological problems should follow a medical model, and (3) psychological problems are caused by diseases or aberrations in the brain. Conversely, it embraces many other assumptions that are already well accepted in clinical practice (e.g., the interaction of thoughts, behaviors, and emotions, as posited in cognitive-behavioral therapies) and integrates those assumptions into a coherent framework for research and practice. We review developments in the network theory with a focus on anxiety-related conditions, discuss future areas for change, and outline implications of the theory for both research and clinical practice.

scholarly journals A Role for Emotional Granularity in Judging

2019 ◽  
Vol 9 (9(5)) ◽  
pp. 557-576 ◽  
Author(s):  
Maria Gendron ◽  
Lisa Feldman Barrett
Keyword(s):  
Decision Making ◽  
Self Regulation ◽  
Judicial Decision ◽  
Judicial Decision Making ◽  
Classical View ◽  
Empirically Supported ◽  
Recent Developments ◽  
Toma De Decisiones ◽  
The Mind ◽  
The Brain

Emotions are traditionally viewed as detrimental to judicial responsibility, a belief rooted in the classical view of the mind as a battle ground between reason and emotion. Drawing on recent developments in psychology and neuroscience we propose that the brain uses past experience, organized as concepts, to guide actions and give sensations meaning, constructing experiences such as “fear” or “anger”. Wisdom comes from skill at constructing emotions in a more precise and functional way, a skill called “emotional granularity”. Studies show that individuals who are more emotionally granular have better function across a range of domains, including self regulation and decision making. We propose that effective judicial decision-making does not require a dispassionate judge, but a judge who is high in emotional granularity. We lay out an empirical agenda for testing this idea and end by discussing empirically supported recommendations for increasing emotional granularity in the judiciary. Tradicionalmente, se ha considerado que las emociones son perjudiciales para el desempeño responsable de la labor judicial, una creencia enraizada en la concepción clásica de la mente como campo de batalla entre razón y emoción. Partiendo de nuevos descubrimientos en psicología y neurociencia, argumentamos que el cerebro usa la experiencia pasada, organizada como conceptos, para guiar las acciones y dar sentido a las sensaciones, construyendo experiencias como “miedo” o “ira”. La sabiduría proviene de la habilidad en construir emociones de un modo más preciso y funcional, habilidad denominada “granularidad emocional”. Los estudios muestran que los individuos más granulares emocionalmente funcionan mejor en varios dominios, incluyendo la autorregulación y la toma de decisiones. Argumentamos que la toma de decisiones eficaz en judicatura no requiere de un juez desapasionado, sino de un juez que tenga alta granularidad emocional. Proponemos un programa empírico para poner a prueba esa idea, y concluimos con un debate de recomendaciones de base empírica para aumentar la granularidad emocional en la judicatura.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Control of Transcranial Direct Current Stimulation Duration by Assessing Functional Connectivity of Near-Infrared Spectroscopy Signals

2021 ◽  
Author(s):  
M. Atif Yaqub ◽  
Keum-Shik Hong ◽  
Amad Zafar ◽  
Chang-Seok Kim
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Direct Current ◽  
Network Theory ◽  
Short Range ◽  
Near Infrared ◽  
Brain State ◽  
Stimulation Period ◽  
The Brain ◽  
Connection Density

Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.

scholarly journals Music listening as a potential aid in reducing emotional eating: An exploratory study

2018 ◽  
Vol 24 (1) ◽  
pp. 78-95 ◽  
Author(s):  
Annemieke J. M. van den Tol ◽  
Helen Coulthard ◽  
Waldie E. Hanser
Keyword(s):  
Emotional Eating ◽  
Self Regulation ◽  
Music Listening ◽  
Emotional Needs ◽  
Mental Work ◽  
Regulation Strategies ◽  
Low Levels ◽  
Listening Strategies ◽  
The Brain ◽  
Mood Depression

Emotional Eating (EE) is understood as a maladaptive self-regulation strategy to satisfy emotional needs instead of hunger. Consequently, EE has been associated with negative health consequences. Enjoyment of food and music share similar neural activations in the brain and are both used by people for regulating affect. This suggests that music listening could potentially be a healthier alternative to EE. The present study was designed to investigate associations between EE, disordered mood, and music-related mood regulation. A total of 571 participants completed measures of EE, music listening strategies, and disordered mood. Associations between seven different music listening strategies and EE were examined, and also whether these regulation strategies were associated with depression, anxiety, and stress. Finally, we explored associations between music listening and EE in people with low and high (non-clinical) levels of disordered mood (depression, anxiety, and stress). The findings of this research indicated that music listening for discharge (releasing anger or sadness through music that expresses these same emotions) and EE were positively associated with one another. In addition, EE and the music listening strategies of entertainment, diversion or mental work were associated in people with low levels of disordered mood. When disordered mood was high, EE was higher, but was not associated with music listening strategies. These associations point towards the possibility of some music listening strategies being useful as healthier alternatives for EE.

scholarly journals Alzheimer’s disease under the purview of Graph Theory Centric Genetic Networks

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Yegnanarayanan Venkatraman ◽  
◽  
Narayanaa Y Krithicaa ◽  
Valentina E. Balas ◽  
Marius M. Balas ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Network Theory ◽  
Memory Loss ◽  
Amyloid Plaque ◽  
Brain Regions ◽  
Protein Protein Interaction ◽  
Communication Demands ◽  
The Brain

Notice that the synapsis of brain is a form of communication. As communication demands connectivity, it is not a surprise that "graph theory" is a fastest growing area of research in the life sciences. It attempts to explain the connections and communication between networks of neurons. Alzheimer’s disease (AD) progression in brain is due to a deposition and development of amyloid plaque and the loss of communication between nerve cells. Graph/network theory can provide incredible insights into the incorrect wiring leading to memory loss in a progressive manner. Network in AD is slanted towards investigating the intricate patterns of interconnections found in the pathogenesis of brain. Here, we see how the notions of graph/network theory can be prudently exploited to comprehend the Alzheimer’s disease. We begin with introducing concepts of graph/network theory as a model for specific genetic hubs of the brain regions and cellular signalling. We begin with a brief introduction of prevalence and causes of AD followed by outlining its genetic and signalling pathogenesis. We then present some of the network-applied outcome in assessing the disease-signalling interactions, signal transduction of protein-protein interaction, disturbed genetics and signalling pathways as compelling targets of pathogenesis of the disease.

scholarly journals Attention Networks and the Intrinsic Network Structure of the Human Brain

2021 ◽  
Author(s):  
Sebastian Markett ◽  
David Nothdurfter ◽  
Antonia Focsa ◽  
Martin Reuter ◽  
Philippe Jawinski
Keyword(s):  
Network Structure ◽  
Network Theory ◽  
Large Scale ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Relevant Information ◽  
Attention Network ◽  
Attention Networks ◽  
Large Scale Networks ◽  
The Brain

Attention network theory states that attention is not a unified construct but consists of three independent systems that are supported by separable distributed networks: an alerting network to deploy attentional resources in anticipation of upcoming events, an orienting network to direct attention to a cued location, and a control network to select relevant information at the expense of concurrently available information. Ample behavioral and neuroimaging evidence supports the dissociation of the three attention domains. The strong assumption that each attentional system is realized through a separable network, however, raises the question how these networks relate to the intrinsic network structure of the brain. Our understanding of brain networks has advanced majorly in the past years due to the increasing focus on brain connectivity. It is well established that the brain is intrinsically organized into several large-scale networks whose modular structure persists across task states. Existing proposals on how the presumed attention networks relate to intrinsic networks rely mostly on anecdotal and partly contradictory arguments. We addressed this issue by mapping different attention networks with highest spatial precision at the level of cifti-grayordinates. Resulting group maps were compared to the group-level topology of 23 intrinsic networks which we reconstructed from the same participants' resting state fMRI data. We found that all attention domains recruited multiple and partly overlapping intrinsic networks and converged in the dorsal fronto-parietal and midcingulo-insular network. While we observed a preference of each attentional domain for its own set of intrinsic networks, implicated networks did not match well to those proposed in the literature. Our results indicate a necessary refinement of the attention network theory.

Self-Regulation of the Brain and Behavior

1985 ◽  
Vol 98 (2) ◽  
pp. 316
Author(s):  
Angela Willis ◽  
Thomas Elbert ◽  
Brigitte Rockstroh ◽  
Werner Lutzenberger ◽  
Niels Bribaumer
Keyword(s):  
Self Regulation ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain
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