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.