Advanced feature selection methods in multinominal dementia classification from structural MRI data

Recent studies showed that features extracted from brain MRIs can well discriminate Alzheimer’s disease from Mild Cognitive Impairment. This study provides an algorithm that sequentially applies advanced feature selection methods for findings the best subset of features in terms of binary classification accuracy. The classifiers that provided the highest accuracies, have been then used for solving a multi-class problem by the one-versus-one strategy. Although several approaches based on Regions of Interest (ROIs) extraction exist, the prediction power of features has not yet investigated by comparing filter and wrapper techniques. The findings of this work suggest that (i) the IntraCranial Volume (ICV) normalization can lead to overfitting and worst the accuracy prediction of test set and (ii) the combined use of a Random Forest-based filter with a Support Vector Machines-based wrapper, improves accuracy of binary classification.

Towards the computer-aided diagnosis of dementia based on the geometric and network connectivity of structural MRI data

We present an intuitive geometric approach for analysing the structure and fragility of T1-weighted structural MRI scans of human brains. Apart from computing characteristics like the surface area and volume of regions of the brain that consist of highly active voxels, we also employ Network Theory in order to test how close these regions are to breaking apart. This analysis is used in an attempt to automatically classify subjects into three categories: Alzheimer’s disease, mild cognitive impairment and healthy controls, for the CADDementia Challenge.

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

K-Surfer: a KNIME extension for the management and analysis of Human brain MRI FreeSurfer/FSL data

Human brain imaging techniques, such as Magnetic Resonance Imaging (MRI) or Diffusion Tensor Imaging (DTI), have been established as scientific and diagnostic tools and their adoption is growing in popularity. Statistical methods, machine learning and data mining algorithms have successfully been adopted to extract predictive and descriptive models from neuroimage data. However, the knowledge discovery process typically requires also the adoption of pre-processing, post-processing and visualisation techniques in complex data workflows. Currently, a main problem for the integrated preprocessing and mining of MRI data is the lack of comprehensive platforms able to avoid the manual invocation of preprocessing and mining tools, that yields to an error-prone and inefficient process. In this work we present K-Surfer, a novel plug-in of the Konstanz Information Miner (KNIME) workbench, that automatizes the preprocessing of brain images and leverages the mining capabilities of KNIME in an integrated way. K-Surfer supports the importing, filtering, merging and pre-processing of neuroimage data from FreeSurfer, a tool for human brain MRI feature extraction and interpretation. K-Surfer automatizes the steps for importing FreeSurfer data, reducing time costs, eliminating human errors and enabling the design of complex analytics workflow for neuroimage data by leveraging the rich functionalities available in the KNIME workbench.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.

Overcoming your child’s fears and worries: guided self-help manual for therapists working with groups

This manual for therapists accompanies ‘Overcoming your child’s fears and worries’ (Creswell & Willetts, 2007), a guide for parents to help their children overcome difficulties with anxiety

Overcoming your child’s fears and worries: guided parent delivered CBT treatment manual for therapists

This manual for therapists accompanies ‘Overcoming your child’s fears and worries’ (Creswell & Willetts, 2007), a guide for parents to help their children overcome difficulties with anxiety

Acellular dermal matrix as a barrier in guided bone regeneration: a clinical, radiographic and histomorphometric study in dogs

Objectives The purpose of this study was to evaluate the effectiveness of the acellular dermal matrix (ADM) as a membrane for guided bone regeneration (GBR), in comparison with a bioabsorbable membrane. Material and methods In seven dogs, the mandibular pre-molars were extracted. After 8 weeks, one bone defect was surgically created bilaterally and the GBR was performed. Each side was randomly assigned to the control group (CG: bioabsorbable membrane made of glycolide and lactide copolymer) or the test group (TG: ADM as a membrane). Immediately following GBR, standardized digital X-ray radiographs were taken, and were repeated at 8 and 16 weeks post-operatively. Before the GBR and euthanasia, clinical measurements of the width and thickness of the keratinized tissue (WKT and TKT, respectively) were performed. One animal was excluded from the study due to complications in the TG during wound healing; therefore, six dogs remained in the sample. The dogs were sacrificed 16 weeks following GBR, and a histomorphometric analysis was performed. Area measurements of new tissue and new bone, and linear measurements of bone height were performed. Results Post-operative healing of the CG was uneventful. In the TG membrane was exposed in two animals, and one of them was excluded from the sample. There were no statistically significant differences between the groups for any histomorphometric measurement. Clinically, both groups showed an increase in the TKT and a reduction in the WKT. Radiographically, an image suggestive of new bone formation could be observed in both groups at 8 and 16 weeks following GBR. Conclusion ADM acted as a barrier in GBR, with clinical, radiographic and histomorphometric results similar to those obtained with the bioabsorbable membrane. To cite this article:Borges GJ, Novaes AB Jr, de Moraes Grisi MF, Palioto DB, Taba M Jr, de Souza SLS. Acellular dermal matrix as a barrier in guided bone regeneration: a clinical, radiographic and histomorphometric study in dogs.Clin. Oral Impl. Res. 20, 2009; 1105-1115.

Sympathetic nerve activity is decreased during device-guided slow breathing

It is known that slow breathing (<10 breaths min(-1)) reduces blood pressure ( BP), but the mechanisms involved in this phenomenon are not completely clear. The aim of this study was to evaluate the acute responses of the muscle sympathetic nerve activity, BP and heart rate (HR), using device-guided slow breathing ( breathe with interactive music (BIM)) or calm music. In all, 27 treated mild hypertensives were enrolled. Muscle sympathetic nerve activity, BP and HR were measured for 5min before the use of the device (n=14) or while subjects listened to calm music (n=13), it was measured again for 15 min while in use and finally, 5min after the interventions. BIM device reduced respiratory rate from 16 +/- 3 beats per minute (b.p.m) to 5.5 +/- 1.8 b.p.m (P<0.05), calm music did not affect this variable. Both interventions reduced systolic (-6 and -4mmHg for both) and diastolic BPs (-4mmHg and -3mmHg, respectively) and did not affect the HR (-1 and -2 b.p.m respectively). Only the BIM device reduced the sympathetic nerve activity of the sample (-8bursts min(-1)). In conclusion, both device-guided slow breathing and listening to calm music have decreased BP but only the device-guided slow breathing was able to reduce the peripheral sympathetic nerve activity. Hypertension Research ( 2010) 33, 708-712; doi: 10.1038/hr.2010.74; published online 3 June 2010