Brain, Mind and Behaviour

1993 ◽  
Vol 163 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Peter Fenwick
Keyword(s):  
Brain Function ◽  
Imaging Techniques ◽  
Psychiatric Illnesses ◽  
The Mind ◽  
The Brain ◽  
Apparent Conflict

The advent of new imaging techniques is broadening our understanding of the major psychiatric illnesses. The increased knowledge of brain function will have consequences for the expert medical witness who has to give evidence in court. Both the insanity defence and the defence of automatism depend on disorders of the mind. Psychiatry is now able in many cases to produce evidence that these are consequent upon disorders of the brain. In presenting evidence in court there is an apparent conflict between ‘brain words' and ‘mind words'.

Introduction

2018 ◽  
Author(s):  
Jack M. Gorman
Keyword(s):  
World War Ii ◽  
Brain Function ◽  
Psychiatric Illness ◽  
Life Experiences ◽  
Human Mind ◽  
World War ◽  
Psychiatric Illnesses ◽  
The Past ◽  
Neuroscience Research ◽  
The Mind

After World War II, mental health turned toward psychopharmacology, the use of medications to treat psychiatric illnesses, as its mainstay. The success of medications led some to insist that all mental illness is due to the inheritance of abnormal genes and that life’s experiences play a diminished role. This alienated many who believe that psychotherapy is also an effective way of treating these disorders and led to a mistrust of neuroscience research. Some insisted that neuroscience ignores the human “mind.” In fact, neuroscience research in the past 50 years has clearly shown that adverse life experiences have profound effects on brain function and are involved in every psychiatric illness. By accepting this view of neuroscience, we can also accept the idea that the “mind” is in reality the work of the physical brain.

Enactivist Interventions

2017 ◽  
Author(s):  
Shaun Gallagher
Keyword(s):  
Cognitive Processes ◽  
Brain Function ◽  
Cultural Practices ◽  
Mathematical Reasoning ◽  
Relational Dynamics ◽  
Close Coupling ◽  
Affective Processes ◽  
The Mind ◽  
The Brain

Enactivist Interventions explores central issues in the contemporary debates about embodied cognition, addressing interdisciplinary questions about intentionality, representation, affordances, the role of affect, and the problems of perception and cognitive penetration, action and free will, higher-order cognition, and intersubjectivity. It argues for a rethinking of the concept of mind, drawing on pragmatism, phenomenology, and cognitive science. It interprets enactivism as a philosophy of nature that has significant methodological and theoretical implications for the scientific investigation of the mind. Enactivist Interventions argues that, like the basic phenomena of perception and action, sophisticated cognitive phenomena like reflection, imagining, and mathematical reasoning are best explained in terms of an affordance-based skilled coping. It thus argues for a continuity that runs between basic action, affectivity, and a rationality that in every case remains embodied. It also discusses recent predictive models of brain function and outlines an alternative, enactivist interpretation that emphasizes the close coupling of brain, body, and environment rather than a strong boundary that isolates the brain in its internal processes. The extensive relational dynamics that integrates the brain with the extra-neural body opens into an environment that is physical, social, and cultural and that recycles back into the enactive process. Cognitive processes are in the world, situated in affordance spaces defined across evolutionary, developmental, and individual histories, and are constrained by affective processes and normative dimensions of social and cultural practices.

scholarly journals The gut microbiome in Huntington disease: A review

2021 ◽  
Vol 15 (3) ◽  
pp. 317-326
Author(s):  
Rhutik S Patil ◽  
Sanjoli G Vyas ◽  
Wasiyoddin T Quazi ◽  
Harshwardhan J Tembhurnikar ◽  
Priya S Milmile ◽  
...  
Keyword(s):  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Brain Function ◽  
Trinucleotide Repeat ◽  
Gastrointestinal Microbiota ◽  
Peripheral Tissues ◽  
Psychiatric Illnesses ◽  
Gut Dysbiosis ◽  
Weight Growth ◽  
The Brain

Huntington's disease (HD) is a neurological disease caused by a trinucleotide repeat increase in the huntingtin (htt) gene, which is widely expressed in the brain and peripheral tissues. While many studies have focused on the cognitive, psychological, and motor symptoms of HD, however, the scope of peripheral pathology and its possible impact on central symptoms has received less attention. We hypothesised that because disruption of the gastrointestinal microbiota (gut dysbiosis) has lately been identified in a number of neurological and psychiatric illnesses, it might also occur in HD. In the HD gut microbiota, bacteriodetes increased whereas firmicutes decreased proportionally. Despite a larger food consumption, the gut dysbiosis was associated with a reduction in body weight growth. The presence of greater faecal water content in hd was also linked to a shift in the gut microenvironment. In this review, we present an update on the association between microbiome and brain function as it relates to huntington's disease.

Variations on Embodied Cognition

2017 ◽  
Author(s):  
Shaun Gallagher
Keyword(s):  
Embodied Cognition ◽  
Empirical Evidence ◽  
Brain Function ◽  
Extended Mind ◽  
Biological Model ◽  
System P ◽  
Neural Reuse ◽  
The Mind ◽  
The Brain

This chapter maps out a range of embodied cognition (EC) theories, starting with ‘weak EC’, which focuses on body-formatted representations and the neural reuse hypothesis, and remains close to traditional cognitivist conceptions of the mind. This approach to EC is then contrasted to functionalist proposals for extended mind, to a biological model of EC, and finally to enactivist proposals. Each section includes discussions of the empirical evidence for these approaches. The chapter concludes by arguing that weak EC’s representationalist conception of brain function is not compatible with the more radical conceptions of EC, which suggest that we rethink how the brain works within a dynamical brain–body–environment system.

Lighting Up the Brain to Illuminate the Mind

2004 ◽  
Vol 49 (6) ◽  
pp. 713-716
Author(s):  
Ellen S. Berscheid
Keyword(s):  
The Mind ◽  
The Brain

How Does the Brain Make the Mind? We Do Not Know.

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (32) ◽  
Author(s):  
Christopher A. Was
Keyword(s):  
The Mind ◽  
The Brain

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.

Big Heads title page

2019 ◽  
Author(s):  
vernon thornton
Keyword(s):  
Title Page ◽  
Language Of Thought ◽  
Correct Version ◽  
Evolutionary Context ◽  
The Mind ◽  
The Brain

A description of of the mind and its relationship to the brain, set in an evolutionary context. Introduction of a correct version of 'language-of-thought' called 'thinkish'.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

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.

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