scholarly journals Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review

2017 ◽  
Vol 2 (3) ◽  
pp. 225-234 ◽  
Author(s):  
James A. Roberts ◽  
Karl J. Friston ◽  
Michael Breakspear
Keyword(s):  
Dynamic Models ◽  
Clinical Applications ◽  
Stochastic Dynamic ◽  
The Brain ◽  
Brain Part

scholarly journals Clinical Applications of Stochastic Dynamic Models of the Brain, Part I: A Primer

2017 ◽  
Vol 2 (3) ◽  
pp. 216-224 ◽  
Author(s):  
James A. Roberts ◽  
Karl J. Friston ◽  
Michael Breakspear
Keyword(s):  
Dynamic Models ◽  
Clinical Applications ◽  
Stochastic Dynamic ◽  
The Brain ◽  
Brain Part

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

Event-Related Potentials in Psychiatry: Approaches to Research and Clinical Applications

1983 ◽  
Vol 17 (4) ◽  
pp. 307-318 ◽  
Author(s):  
H. G. Stampfer
Keyword(s):  
Information Processing ◽  
Psychiatric Disorders ◽  
Rapid Development ◽  
Event Related Potentials ◽  
Clinical Applications ◽  
Direct Access ◽  
Practical Application ◽  
Clinical Methods ◽  
Related Potentials ◽  
The Brain

This article suggests that the potential usefulness of event-related potentials in psychiatry has not been fully explored because of the limitations of various approaches to research adopted to date, and because the field is still undergoing rapid development. Newer approaches to data acquisition and methods of analysis, combined with closer co-operation between medical and physical scientists, will help to establish the practical application of these signals in psychiatric disorders and assist our understanding of psychophysiological information processing in the brain. Finally, it is suggested that psychiatrists should seek to understand these techniques and the data they generate, since they provide more direct access to measures of complex cerebral processes than current clinical methods.

Diagnosis as a Detriment: A Cognitive Approach to Understanding the Depressive Experience

2015 ◽  
Vol 17 (2) ◽  
pp. 125-134 ◽  
Author(s):  
Evan Hy Einstein
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Theoretical Model ◽  
Mental Health Care ◽  
Cognitive Model ◽  
Clinical Applications ◽  
Psychotropic Medications ◽  
Cognitive Mechanisms ◽  
Cognitive Approach ◽  
The Brain

Depression is currently understood within a biomedical paradigm. This paradigm is an example of reductionism; people are clinically diagnosed and categorized based on behavior and affect, while they are then prescribed psychotropic medications based on an inconclusively correlated neurotransmitter imbalance in the brain. In this article, clinical diagnosis and labeling are explored with respect to their detrimental potential. A framework of embodied cognition is used to conceptualize a cognitive model of depressive experience. This theoretical model explores the potentially self-reinforcing cognitive mechanisms behind a depressive experience, with the goal of highlighting the possibility of diagnosis as a detrimental influence on these mechanisms. The aim of this article is to further a discussion about our current mental health care paradigm and provide an explanation as to how it could cause harm to some. Clinical applications of the model are also discussed pertaining to the potential of rendering formal dichotomist diagnoses irrelevant to the ultimate goal of helping people feel better.

Burster Reconstruction Considering Unmeasurable Variables in the Epileptor Model

2021 ◽  
pp. 1-46
Author(s):  
João Angelo Ferres Brogin ◽  
Jean Faber ◽  
Douglas Domingues Bueno
Keyword(s):  
Computational Neuroscience ◽  
Dynamic Models ◽  
Epileptiform Activity ◽  
Convergence Speed ◽  
Brain Disorders ◽  
Feedback Controllers ◽  
Control Techniques ◽  
The Brain ◽  
Significant Effort ◽  
Theoretical Standpoint

Abstract Epilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Although significant effort has been put into better understanding it and mitigating its effects, the conventional treatments are not fully effective. Advances in computational neuroscience, using mathematical dynamic models that represent brain activities at different scales, have enabled addressing epilepsy from a more theoretical standpoint. In particular, the recently proposed Epileptor model stands out among these models, because it represents well the main features of seizures, and the results from its simulations have been consistent with experimental observations. In addition, there has been an increasing interest in designing control techniques for Epileptor that might lead to possible realistic feedback controllers in the future. However, such approaches rely on knowing all of the states of the model, which is not the case in practice. The work explored in this letter aims to develop a state observer to estimate Epileptor's unmeasurable variables, as well as reconstruct the respective so-called bursters. Furthermore, an alternative modeling is presented for enhancing the convergence speed of an observer. The results show that the proposed approach is efficient under two main conditions: when the brain is undergoing a seizure and when a transition from the healthy to the epileptiform activity occurs.

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