Deep learning for brain disorders: from data processing to disease treatment

International audienceIn order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine

Treatable brain network biomarkers in children in coma using task and resting-state functional MRI: a case series

The withdrawal of life-sustaining therapies is frequently considered for pediatric patients with severe acute brain injuries who are admitted to the intensive care unit. However, it is worth noting that some children with a resultant poor neurological status may ultimately survive and achieve a positive neurological outcome. Evidence suggests that adults with hidden consciousness may have a more favorable prognosis compared to those without it. Currently, no treatable network disorders have been identified in cases of severe acute brain injury, aside from seizures detectable through an electroencephalogram (EEG) and neurostimulation via amantadine. In this report, we present three cases in which multimodal brain network evaluation played a helpful role in patient care. This evaluation encompassed various assessments such as continuous video EEG, visual-evoked potentials, somatosensory-evoked potentials, auditory brainstem-evoked responses, resting-state functional MRI (rs-fMRI), and passive-based and command-based task-based fMRI. It is worth noting that the latter three evaluations are unique as they have not yet been established as part of the standard care protocol for assessing acute brain injuries in children with suppressed consciousness. The first patient underwent serial fMRIs after experiencing a coma induced by trauma. Subsequently, the patient displayed improvement following the administration of antiseizure medication to address abnormal signals. In the second case, a multimodal brain network evaluation uncovered covert consciousness, a previously undetected condition in a pediatric patient with acute brain injury. In both patients, this discovery potentially influenced decisions concerning the withdrawal of life support. Finally, the third patient serves as a comparative control case, demonstrating the absence of detectable networks. Notably, this patient underwent the first fMRI prior to experiencing brain death as a pediatric patient. Consequently, this case series illustrates the clinical feasibility of employing multimodal brain network evaluation in pediatric patients. This approach holds potential for clinical interventions and may significantly enhance prognostic capabilities beyond what can be achieved through standard testing methods alone

Advanced Sensing and Image Processing Techniques for Healthcare Applications

This Special Issue aims to attract the latest research and findings in the design, development and experimentation of healthcare-related technologies. This includes, but is not limited to, using novel sensing, imaging, data processing, machine learning, and artificially intelligent devices and algorithms to assist/monitor the elderly, patients, and the disabled population

The Lived Experience of Fathers whose Children are Diagnosed with a Genetic Disorder

Parenting a child with a genetic disorder requires overcoming numerous obstacles. The purpose of this study was to understand the experience of fathers whose children have been diagnosed with a genetic disorder. By gaining insight into a father\u27s reactions and needs, nurses will be better able to provide anticipatory guidance, emotional support, assistance with stressor identification, development of coping mechanisms, and problem solving strategies to assist him in coping with his child\u27s condition

Data Descriptor: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eyetracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n =664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis

Criação de uma ferramenta para geração de impressões digitais de áudio

A utilização de aplicações com o intuito de realizar um reconhecimento de áudio tem crescido nos últimos tempos. Uma das aplicações mais reconhecidas, neste momento, é o Shazam que tem como objetivo o reconhecimento de músicas. Nesta é possível, ao realizar uma captura de áudio ficar-se a saber qual o nome da banda e da música em questão. Assim sendo, neste projeto são apresentados dois métodos capazes de construir um audio fingerprint e a sua classificação. Aqui, serão descritos todos os procedimentos realizados bem como realizada uma discussão dos testes e resultados obtidos pelo sistema desenvolvido. Está presente uma comparação relativamente ao desempenho observado pelos dois métodos desenvolvidos. Adicionalmente, é realizado um estudo relativo ao estado desta área

2018 Symposium Brochure

This dissertation explores the mean field Heisenberg spin system and its evolution in time. We first study the system in equilibrium, where we explore the tool known as Stein's method, used for determining convergence rates to thermodynamic limits, both in an example proof for a mean field Ising system and in tightening a previous result for the equilibrium mean field Heisenberg system. We then model the evolution of the mean field Heisenberg model using Glauber dynamics and use this method to test the equilibrium results of two previous papers, uncovering a typographical error in one. Agreement in other aspects between theory and our simulations validates our approach in the equilibrium case. Next, we compare the evolution of the Heisenberg system under Glauber dynamics to a number of forms of Brownian motion and determine that Brownian motion is a poor match in most situations. Turning back to Stein's method, we consider what sort of proof regarding the behavior of the mean field Heisenberg model over time is obtainable and look at several possible routes to that path. We finish up by offering a Stein's method approach to understanding the evolution of the mean field Heisenberg model and offer some insight into its convergence in time to a thermodynamic limit. This demonstrates the potential usefulness of Stein's method in understanding the finite time behavior of evolving systems. In our efforts, we encounter several holes in current mathematical and physical knowledge. In particular, we suggest the development of tools for Markov chains currently unavailable and the development of a more physically based algorithm for the evolution of Heisenberg systems. These projects lie beyond the scope of this dissertation but it is our hope that these ideas may be useful to future research

Wireless sensor networks for medical care.

Chen, Xijun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 72-77).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Design Challenges --- p.2Chapter 1.2 --- Wireless Sensor Network Applications --- p.6Chapter 1.2.1 --- Military Applications --- p.7Chapter 1.2.2 --- Environmental Applications --- p.9Chapter 1.2.3 --- Health Applications --- p.11Chapter 1.3 --- Wireless Biomedical Sensor Networks (WBSN) --- p.12Chapter 1.4 --- Text Organization --- p.13Chapter Chapter 2 --- Design a Wearable Platform for Wireless Biomedical Sensor Networks --- p.15Chapter 2.1 --- Objective --- p.17Chapter 2.2 --- Requirements for Wireless Medical Sensors --- p.19Chapter 2.3 --- Hardware design --- p.21Chapter 2.3.1 --- Materials and Methods --- p.21Chapter 2.3.2 --- Results --- p.24Chapter 2.3.3 --- Conclusion --- p.27Chapter 2.4 --- Software design --- p.28Chapter 2.4.1 --- TinyOS --- p.28Chapter 2.4.2 --- Software Organization --- p.28Chapter Chapter 3 --- Wireless Medical Sensors --- p.32Chapter 3.1 --- Sensing Physiological Information --- p.32Chapter 3.1.1 --- Pulse Oximetry --- p.32Chapter 3.1.2 --- Electrocardiograph --- p.36Chapter 3.1.3 --- Galvanic Skin Response --- p.41Chapter 3.2 --- Location Tracking --- p.43Chapter 3.2.1 --- Outdoor Location Tracking --- p.43Chapter 3.2.2 --- Indoor Location Tracking --- p.44Chapter 3.3 --- Motion Tracking --- p.49Chapter 3.3.1 --- Technology --- p.50Chapter 3.3.2 --- Motion Analysis Sensor Board --- p.51Chapter 3.4 --- Discussions --- p.52Chapter Chapter 4 --- Applications in Medical Care --- p.54Chapter 4.1 --- Introduction --- p.54Chapter 4.2 --- Wearable Wireless Body Area Network --- p.56Chapter 4.2.1 --- Architecture --- p.58Chapter 4.2.2 --- Deployment Scenarios --- p.62Chapter 4.3 --- Application in Ambulatory Setting --- p.63Chapter 4.3.1 --- Method --- p.64Chapter 4.3.2 --- The Software Architecture --- p.66Chapter Chapter 5 --- Conclusions and Future Work --- p.69References --- p.72Appendix --- p.7