Binary Brain Trust Review Is Binary Brain Trust SCAM Or NOT?

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Assess the regional deformations in thebinary brain trust system and its low performing results to the atrophy of surrounding structures. Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases.

To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures, shape analysis is a promising approach. However, there are hurdles in modeling the third ventricle shape. First, it has topological variations across individuals due to the inter-thalamic adhesion. In addition, as an interhemispheric structure, it needs to be aligned to the midsagittal plane to assess its asymmetric and regional deformation.

To address these issues, we propose a model-based shape assessment. Our template model of the third ventricle consists of a thebinary brain trust system and its low performing results and a symmetric mesh of generic shape. To build the vertex-wise correspondence between the individual third ventricle and the template mesh, we employ a minimal-distortion surface deformation framework.

In addition, to account for topological variations, we implement geometric constraints guiding the template mesh to have zero width where the inter-thalamic adhesion passes thebinary brain trust system and its low performing results, preventing vertices crossing between left and right walls of the third ventricle. The individual shapes are compared using a vertex-wise deformity from the symmetric template.

Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume i. It also revealed that the proposed method can detect the regional and asymmetrical deformation unlike the conventional measures: We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle, providing high accuracy and inter-subject consistency in the shape quantification.

This shape modeling method with geometric constraints based on anatomical landmarks could be extended to other brain structures which require a consistent measurement basis in the morphometry.

Author links open overlay panel Jaeil Kim a Maria del C. Aribisala b c d g Alan J. Gow c f Mark E. Bastin b c d Ian J. Deary c d e Joanna M. Wardlaw b c d Jinah Park a. Under a Creative Commons license. Abstract Background Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases.

Method To address these issues, we propose a model-based shape assessment. Results Experiments on imaging and demographic data from a study of aging showed that our model was sensitive in assessing morphological differences between individuals in relation to brain volume i. Conclusions We have demonstrated that our approach is suitable to morphometrical analyses of the third ventricle, providing high accuracy and inter-subject consistency in the shape quantification.

Recommended articles Citing articles 0. Published by Elsevier Ireland Ltd.

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Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel.

The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established.

The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system.

The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.

His main research interests include kernel methods, signal processing, and multimodal interfaces. He has been involved in several EU and national government funded projects on speech and biomedical signal processing.

He is a senior member of IEEE. William Marnane received the B. Geraldine Boylan received the M. Much of her more recent work is of an interdisciplinary nature and aims to create a synergy between medicine and engineering by using the skills and techniques of engineering signal processing research to address important medical problems such as seizure detection in the neonate.

Gordon Lightbody graduated with the M. After completing a one year post-doctoral position funded by Du Pont, he was appointed by Queen's University as a lecturer in Modern Control Systems. In he was appointed as a lecturer in Control Engineering at University College Cork, and subsequently promoted to senior lecturer in Under a Creative Commons license.

Abstract Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. Keywords Neonatal seizure detection. Recommended articles Citing articles 0. Published by Elsevier B.