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.

A low power micro deep brain stimulation device for murine preclinical research

Deep brain stimulation has emerged as an effective medical procedure that has therapeutic efficacy in a number of neuropsychiatric disorders. Preclinical research involving laboratory animals is being conducted to study the principles, mechanisms, and therapeutic effects of deep brain stimulation. A bottleneck is, however, the lack of deep brain stimulation devices that enable long term brain stimulation in freely moving laboratory animals. Most of the existing devices employ complex circuitry, and are thus bulky. These devices are usually connected to the electrode that is implanted into the animal brain using long fixed wires. In long term behavioral trials, however, laboratory animals often need to continuously receive brain stimulation for days without interruption, which is difficult with existing technology. This paper presents a low power and lightweight portable microdeep brain stimulation device for laboratory animals. Three different configurations of the device are presented as follows: 1) single piece head mountable; 2) single piece back mountable; and 3) two piece back mountable. The device can be easily carried by the animal during the course of a clinical trial, and that it can produce non-stop stimulation current pulses of desired characteristics for over 12 days on a single battery. It employs passive charge balancing to minimize undesirable effects on the target tissue. The results of bench, in-vitro, and in-vivo tests to evaluate the performance of the device are presented.

Brain metastases in breast cancer

Despite therapeutic advances, the development of breast cancer brain metastases (BCBM) is still the harbinger of a dismal prognosis. Patient outcomes vary depending on factors, including tumor phenotype, extent of disease within and outside the brain, as well as patient performance status. Treatment includes surgery, radiation therapy and systemic therapy determined by patient and tumor characteristics. Despite these approaches, novel treatments are needed and there is growing interest in systemic therapies. However, the efficacy of pharmacologic agents is hampered by poor penetration of drugs across the blood–brain barrier. Therefore, there is a pressing need for a greater understanding of the natural history of BCBM to guide the development of further therapies. This review analyzes prognosis and treatment of BCBM by tumor phenotype and discusses ongoing research into new therapies.

Informing brain health behaviour choices: the efficacy of a high-school brain awareness pilot study

Aims:
Lifestyle choices such as diet and exercise significantly impact mental wellbeing and this is particularly so during the period of adolescence. The aim of the current study was to determine whether neuroscience concepts could be introduced to the classroom in a manner that improved high school student awareness of how health behaviour choices impact brain health. 

Study Design:
This study was a quantitative study that measured 47 assertions relating to brain health and neuroscience pre and post an interactive seminar.

Place and Duration of Study:
A Victorian high school in Geelong, Australia. Participation in the seminar took approximately 100 minutes, including time to complete the questionnaires.

Methodology:
The current study trialed a ‘Brain Basics’ educational program in a Victorian high-school. The neuro-educative interactive seminar was presented to 48female year 11 students. The level of student understanding, interest and enjoyment was assessed prior to and following an interactive seminar.

Results:
Student understanding of brain health significantly improved in 31 out of 47 questionnaire items and interest and enjoyment were highly rated.

Conclusion:
This supports the notion that basic neuroscience concepts can be introduced into Victorian schools to increase brain health awareness of our youth during this criticaltime of brain development. - See more at: http://www.sciencedomain.org/abstract.php?iid=431&id=21&aid=3887#.UykK5oXAwZm

An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. 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. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.