Compact stacked planar inverted-F antenna for passive deep brain stimulation implants

A compact meandered three-layer stacked circular planar inverted-F antenna is designed and simulated at the UHF band (902.75 – 927.25 MHz) for passive deep brain stimulation implants. The UHF band is used because it offers small antenna size, and high data rate. The top and middle radiating layers are meandered, and low cost substrate and superstrate materials are used to limit the radius and height of the antenna to 5 mm and 1.64 mm, respectively. A dielectric substrate of FR-4 of εr= 4.7 and δ= 0.018, and a biocompatible superstrate of silicone of er= 3.7 and d= 0.003 with thickness of 0.2 mm are used in the design. The resonance frequency of the proposed antenna is 918 MHz with a bandwidth of 24 MHz at return loss of −10 dB in free space. The antenna parameter such as 3D gain pattern of the designed antenna within a skin-tissue model is evaluated by using the finite element method. The compactness, wide bandwidth, round shape, and stable characteristics in skin make this antenna suitable for DBS. The feasibility of the wireless power transmission to the implant in the human head is also examined.

Application of growing self-organizing map to distinguish between finger tapping and non tapping from brain images

Growing self-organizing map (GSOM) has been characterized as a knowledge discovery visualization application which outshines the traditional self-organizing map (SOM) due to its dynamic structure in which nodes can grow based on the input data. GSOM is utilized as a visualization tool in this paper to cluster fMRI finger tapping and non- tapping data, demonstrating the visualization capability to distinguish between tapping or non-tapping. A unique feature of GSOM is a parameter called the spread factor whose functionality is to control the spread of the GSOM map. By setting different levels of spread factor, different granularities of region of interests within tapping or non-tapping images can be visualized and analyzed. Euclidean distance based similarity calculation is used to quantify the visualized difference between tapping and non tapping images. Once the differences are identified, the spread factor is used to generate a more detailed view of those regions to provide a better visualization of the brain regions.

Multi-layer implantable antenna for closed loop deep brain stimulation system

A multi-layer circular planar inverted-F antenna is designed and simulated at the industrial, scientific, and medical (ISM) band of 915 MHz for closed loop deep brain stimulation implant. The ISM band is considered due to the capabilities of small antenna size, high data rate, and long transmission range. In the proposed four-layer antenna, the top three radiating layers are meandered, and a high permittivity substrate and superstrate materials are used to limit the radius and the height of the antenna to 3.5 mm and 2.2 mm, respectively. The bottom layer works as a ground plate. The Roger RO3210 of εr = 10.2 and δ = 0.003 is used as a dielectric substrate and superstrate. The resonance frequency of the proposed antenna is 915 MHz with a bandwidth of 12 MHz at the return loss of -10 dB in free space. The stacked layered structure reduces the antenna size, and the circular shape makes it easily implantable into the human head. The antenna parameters (e.g. 3D gain pattern), SAR value, and electric field distribution within a six layers spherical head model are evaluated by using the finite element method (FEM). The feasibility of the wireless transmission of power, control and command signal to the implant in the human head is also examined. © 2012 IEEE.

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

Modelling skill acquisition in acquired brain injury

Acquired brain injury (ABI) is a debilitating condition often requiring extensive rehabilitation. Although cognitive rehabilitation is concerned with overcoming a skills deficit, the application of skill acquisition research in this context has been non-existent. Examining post-injury learning in terms of the qualitative variables associated with different phases of skill acquisition is likely to be beneficial in assessing patient status and monitoring progress, as well as identifying changing needs over the course of learning. However, current models of skill acquisition overlook the potential impact of variables such as emotion, implicit learning, metacognition, motivation, and strategies that can be leveraged to improve skill acquisition. The current paper attempts to lay the groundwork for modelling and improving skill acquisition in ABI.

The role of social support in families coping with childhood brain tumor

Previous studies suggest that support from social networks is a protective factor buffering the negative effects of stressful events, such as having a child with a chronic illness. The literature highlights the need for more systematic examination of parents’ social support networks across the disease trajectory, to obtain a more complete understanding of how a family's support system affects adjustment over time. This was attempted in this study of 88 parents of children with brain tumors, recruited from hospitals in Australia, Singapore, and New Zealand. It employed a longitudinal design, tracking families for 2 years postdiagnosis to examine the relationship between social support and coping. As in previous research this study showed that different types of support are needed at different stages in the illness trajectory. The study also identified the use of various coping strategies by families, directed at the maintenance and enhancement of existing supports and the securing of new supports. The study failed to establish a statistically significant relationship between level of coping and social support, however, suggesting that parents were using primarily “internal” familial modes of coping, including preexisting patterns of coping, with external social support being an adjunct to their coping rather than being a major contributor.

Oiling the brain : a review of randomized controlled trials of omega-3 fatty acids in psychopathology across the lifespan

Around one in four people suffer from mental illness at some stage in their lifetime. There is increasing awareness of the importance of nutrition, particularly omega-3 polyunsaturated fatty acids (n-3 PUFA), for optimal brain development and function. Hence in recent decades, researchers have explored effects of n-3 PUFA on mental health problems over the lifespan, from developmental disorders in childhood, to depression, aggression, and schizophrenia in adulthood, and cognitive decline, dementia and Alzheimer’s disease in late adulthood. This review provides an updated overview of the published and the registered clinical trials that investigate effects of n-3 PUFA supplementation on mental health and behavior, highlighting methodological differences and issues.

Localization of copper and copper transporters in the human brain

Disturbances in brain copper result in rare and severe neurological disorders and may play a role in the pathogenesis and progression of multiple neurodegenerative diseases. Our current understanding of mammalian brain copper transport is based on model systems outside the central nervous system and no data are available regarding copper transport systems in the human brain. To address this deficit, we quantified regional copper concentrations and examined the distribution and cellular localization of the copper transport proteins Copper transporter 1, Atox1, ATP7A, and ATP7B in multiple regions of the human brain using inductively coupled plasma-mass spectrometry, Western blot and immunohistochemistry. We identified significant relationships between copper transporter levels and brain copper concentrations, supporting a role for these proteins in copper transport in the human brain. Interestingly, the substantia nigra contained twice as much copper than that in other brain regions, suggesting an important role for copper in this brain region. Furthermore, ATP7A levels were significantly greater in the cerebellum, compared with other brain regions, supporting an important role for ATP7A in cerebellar neuronal health. This study provides novel data regarding copper regulation in the human brain, critical to understand the mechanisms by which brain copper levels can be altered, leading to neurological disease.

An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.

Evaluation of feature selection algorithms for detection of depression from brain sMRI scans

Detection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.

Thai emergency nurses’ management of patients with severe traumatic brain injury: comparison of knowledge and clinical management with best available evidence

Background:
In Thailand, the rate of TBI-related hospitalisation is increasing, however, little is known about the evidence-based management of severe TBI in the developing world. The aim of this study was to explore Thai emergency nurses’ management of patients with severe TBI.

Methods:
An exploratory descriptive mixed method design was used to conduct this two stage study: survey methods were used to examine emergency nurses’ knowledge regarding management of patients with severe TBI (Stage 1) and observational methods were used to examine emergency nurses’ clinical management of patients with severe TBI (Stage 2). The study setting was the emergency department (ED) at a regional hospital in Southern Thailand.

Results:
34 nurses participated in Stage 1 (response rate 91.9%) and the number of correct responses ranged from 33.3% to 95.2%. In Stage 2, a total of 160 points of measurement were observed in 20 patients with severe TBI over 40 h. In this study there were five major areas identified for the improvement of care of patients with severe TBI: (i) end-tidal carbon dioxide (ETCO2) monitoring and targets; (ii) use of analgesia and sedation; (iii) patient positioning; (iv) frequency of nursing assessment; and (v) dose of Mannitol diuretic.

Conclusions:
There is variation in Thai nurses’ knowledge and care practices for patients with severe TBI. To increase consistency of evidence-based TBI care in the Thai context, a knowledge translation intervention that is ecologically valid, appropriate to the Thai healthcare context and acceptable to the multidisciplinary care team is needed.