The effect of feature selection on multivariate pattern analysis of structural brain MR images.

Clinical predictions performed using structural magnetic resonance (MR) images are crucial in neuroimaging studies and can be used as a successful complementary method for clinical decision making. Multivariate pattern analysis (MVPA) is a significant tool that helps correct predictions by exhibiting a compound relationship between disease-related features. In this study, the effectiveness of determining the most relevant features for MVPA of the brain MR images are examined using ReliefF and minimum Redundancy Maximum Relevance (mRMR) algorithms to predict the Alzheimer's disease (AD), schizophrenia, autism, and attention deficit and hyperactivity disorder (ADHD). Three state-of-the-art MVPA algorithms namely support vector machines (SVM), k-nearest neighbor (kNN) and backpropagation neural network (BP-NN) are employed to analyze the images from five different datasets that include 1390 subjects in total. Feature selection is performed on structural brain features such as volumes and thickness of anatomical structures and selected features are used to compare the effect of feature selection on different MVPA algorithms. Selecting the most relevant features for differentiating images of healthy controls from the diseased subjects using both ReliefF and mRMR methods significantly increased the performance. The most successful MVPA method was SVM for all classification tasks.

FEATURE SELECTION IMPROVES THE ACCURACY OF CLASSIFYING ALZHEIMER DISEASE USING DIFFUSION TENSOR IMAGES.

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.

Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.

Performance evaluation of the machine learning algorithms used in inference mechanism of a medical decision support system.

The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select optimal method during the treatment of the patients. Decision support systems are intelligent software systems that support decision makers on their decisions. The design of decision support systems consists of four main subjects called inference mechanism, knowledge-base, explanation module, and active memory. Inference mechanism constitutes the basis of decision support systems. There are various methods that can be used in these mechanisms approaches. Some of these methods are decision trees, artificial neural networks, statistical methods, rule-based methods, and so forth. In decision support systems, those methods can be used separately or a hybrid system, and also combination of those methods. In this study, synthetic data with 10, 100, 1000, and 2000 records have been produced to reflect the probabilities on the ALARM network. The accuracy of 11 machine learning methods for the inference mechanism of medical decision support system is compared on various data sets.

A survey on application of quantitative methods on analysis of brain parameters changing with temperature.

Brain temperature fluctuations occur in consequence of physiological and pathophysiological conditions and indicate changes in brain metabolism, cerebral blood flow (CBF), brain functions and neural damage. Lowering the brain temperature of patients with traumatic brain injuries achieves considerable improvements. When the human brain is cooled down to 30°C, it switches to a sub functional regime where it can live longer with less oxygen, glucose and other supplies. Fluctuations in brain temperature cause changes in brain parameters which can be measured by electroencephalogram (EEG) and transcranial Doppler (TCD). It is very important to understand the temperature dependencies of brain's electrical activity and blood flow and their interrelations considering the good clinical results achieved by lowering the brain temperature of neurologically injured patients. Since protecting the patient's brain is of primary importance in many fields including cardiology, neurology, traumatology and anesthesia it can be clearly seen that this subject is very important. In this study, we survey the "state-of-the-art" in analysis of EEG and TCD brain parameters changing with temperature and present further research opportunities.