scholarly journals Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

2017 ◽  
Author(s):  
James J Bonaiuto ◽  
Holly E Rossiter ◽  
Sofie S Meyer ◽  
Natalie Adams ◽  
Simon Little ◽  
...  
Keyword(s):  
Free Energy ◽  
Data Quality ◽  
Patch Size ◽  
Human Cognition ◽  
Source Inversion ◽  
Non Invasive ◽  
Recent Developments ◽  
Neuronal Current ◽  
Size Estimates ◽  
Sparse Priors

AbstractMagnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency- specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.

scholarly journals Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2469
Author(s):  
Chen-Yi Xie ◽  
Chun-Lap Pang ◽  
Benjamin Chan ◽  
Emily Yuen-Yuen Wong ◽  
Qi Dou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Imaging ◽  
Risk Groups ◽  
Review Of Literature ◽  
Primary Tumors ◽  
Non Invasive ◽  
Significant Difference ◽  
Recent Developments ◽  
Health Significance ◽  
Imaging Markers

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

scholarly journals Nanoparticles as Adjuvants and Nanodelivery Systems for mRNA-Based Vaccines

Pharmaceutics ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 45
Author(s):  
Iman M. Alfagih ◽  
Basmah Aldosari ◽  
Bushra AlQuadeib ◽  
Alanood Almurshedi ◽  
Mariyam M. Alfagih
Keyword(s):  
Messenger Rna ◽  
Immune Cell ◽  
Natural Infection ◽  
Immunological Response ◽  
Infection Process ◽  
Dual Function ◽  
Therapeutic Vaccines ◽  
Non Invasive ◽  
Recent Developments

Messenger RNA (mRNA)-based vaccines have shown promise against infectious diseases and several types of cancer in the last two decades. Their promise can be attributed to their safety profiles, high potency, and ability to be rapidly and affordably manufactured. Now, many RNA-based vaccines are being evaluated in clinical trials as prophylactic and therapeutic vaccines. However, until recently, their development has been limited by their instability and inefficient in vivo transfection. The nanodelivery system plays a dual function in RNA-based vaccination by acting as a carrier system and as an adjuvant. That is due to its similarity to microorganisms structurally and size-wise; the nanodelivery system can augment the response by the immune system via simulating the natural infection process. Nanodelivery systems allow non-invasive mucosal administration, targeted immune cell delivery, and controlled delivery, reducing the need for multiple administrations. They also allow co-encapsulating with immunostimulators to improve the overall adjuvant capacity. The aim of this review is to discuss the recent developments and applications of biodegradable nanodelivery systems that improve RNA-based vaccine delivery and enhance the immunological response against targeted diseases.

scholarly journals Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

NeuroImage ◽  
2018 ◽  
Vol 167 ◽  
pp. 372-383 ◽  
Author(s):  
James J. Bonaiuto ◽  
Holly E. Rossiter ◽  
Sofie S. Meyer ◽  
Natalie Adams ◽  
Simon Little ◽  
...  
Keyword(s):  
Comparison Of Methods ◽  
Source Inversion ◽  
Non Invasive

Non-Invasive Monitoring of Glucose Level Changes Utilizing a mm-Wave Radar System

2018 ◽  
Vol 10 (3) ◽  
pp. 10-29 ◽  
Author(s):  
George Shaker ◽  
Karly Smith ◽  
Ala Eldin Omer ◽  
Shuo Liu ◽  
Clement Csech ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Detection System ◽  
Diabetic Patients ◽  
Glucose Detection ◽  
60 Ghz ◽  
Non Invasive ◽  
Wave Radar ◽  
Recent Developments ◽  
Blood Glucose Concentrations ◽  
Different Levels

This article discusses recent developments in the authors' experiments using Google's Soli alpha kit to develop a non-invasive blood glucose detection system. The Soli system (co-developed by Google and Infineon) is a 60 GHz mm-wave radar that promises a small, mobile, and wearable platform intended for gesture recognition. They have retrofitted the setup for the system and their experiments outline a proof-of-concept prototype to detect changes of the dielectric properties of solutions with different levels of glucose and distinguish between different concentrations. Preliminary results indicated that mm-waves are suitable for glucose detection among biological mediums at concentrations similar to blood glucose concentrations of diabetic patients. The authors discuss improving the repeatability and scalability of the system, other systems of glucose detection, and potential user constraints of implementation.

scholarly journals Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity

2020 ◽  
Vol 376 (1815) ◽  
pp. 20190622
Author(s):  
Anusha Mishra ◽  
Catherine N. Hall ◽  
Clare Howarth ◽  
Ralph D. Freeman
Keyword(s):  
Blood Flow ◽  
Neuronal Activity ◽  
Vascular Function ◽  
Functional Neuroimaging ◽  
Cell Types ◽  
Human Cognition ◽  
Measured Signal ◽  
Bold Signal ◽  
Theme Issue ◽  
Non Invasive

Functional neuroimaging using MRI relies on measurements of blood oxygen level-dependent (BOLD) signals from which inferences are made about the underlying neuronal activity. This is possible because neuronal activity elicits increases in blood flow via neurovascular coupling, which gives rise to the BOLD signal. Hence, an accurate interpretation of what BOLD signals mean in terms of neural activity depends on a full understanding of the mechanisms that underlie the measured signal, including neurovascular and neurometabolic coupling, the contribution of different cell types to local signalling, and regional differences in these mechanisms. Furthermore, the contributions of systemic functions to cerebral blood flow may vary with ageing, disease and arousal states, with regard to both neuronal and vascular function. In addition, recent developments in non-invasive imaging technology, such as high-field fMRI, and comparative inter-species analysis, allow connections between non-invasive data and mechanistic knowledge gained from invasive cellular-level studies. Considered together, these factors have immense potential to improve BOLD signal interpretation and bring us closer to the ultimate purpose of decoding the mechanisms of human cognition. This theme issue covers a range of recent advances in these topics, providing a multidisciplinary scientific and technical framework for future work in the neurovascular and cognitive sciences. This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

scholarly journals From Homo Sapiens to Robo Sapiens: The Evolution of Intelligence

Information ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 2 ◽  
Author(s):  
Anat Ringel Raveh ◽  
Boaz Tamir
Keyword(s):  
Artificial Intelligence ◽  
Human Behavior ◽  
Social Intelligence ◽  
Deep Neural Network ◽  
Homo Sapiens ◽  
Human Cognition ◽  
Network Technology ◽  
Research Fields ◽  
Recent Developments ◽  
Neural Network Technology

In this paper, we present a review of recent developments in artificial intelligence (AI) towards the possibility of an artificial intelligence equal that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines that are flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can be viewed as an opportunity for further progress in AI research.

scholarly journals EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Nazmi Sofian Suhaimi ◽  
James Mountstephens ◽  
Jason Teo
Keyword(s):  
Emotion Recognition ◽  
State Of The Art ◽  
Research Community ◽  
Human Interaction ◽  
Human Cognition ◽  
Future Research ◽  
Emotional States ◽  
Human Beings ◽  
Eeg Signals ◽  
Recent Developments

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful “emotional” interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

scholarly journals Perturbative QCD at High Temperature

1997 ◽  
Vol 12 (08) ◽  
pp. 1431-1464 ◽  
Author(s):  
Agustin Nieto
Keyword(s):  
Field Theory ◽  
Free Energy ◽  
Perturbation Theory ◽  
High Temperature ◽  
Perturbative Qcd ◽  
Effective Field Theory ◽  
Quark Gluon Plasma ◽  
Gluon Plasma ◽  
Effective Field ◽  
Recent Developments

Recent developments of perturbation theory at finite temperature based on effective field theory methods are reviewed. These methods allow the contributions from the different scales to be separated and the perturbative series to be reorganized. The construction of the effective field theory is shown in detail for ϕ4 theory and QCD. It is applied to the evaluation of the free energy of QCD at order g5 and the calculation of the g6 term is outlined. Implications for the application of perturbative QCD to the quark–gluon plasma are also discussed.

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