A CNN Deep Local and Global ASD Classification Approach with Continuous Wavelet Transform Using Task-Based FMRI.

Autism spectrum disorder (ASD) is a neurodegenerative disorder characterized by lingual and social disabilities. The autism diagnostic observation schedule is the current gold standard for ASD diagnosis. Developing objective computer aided technologies for ASD diagnosis with the utilization of brain imaging modalities and machine learning is one of main tracks in current studies to understand autism. Task-based fMRI demonstrates the functional activation in the brain by measuring blood oxygen level-dependent (BOLD) variations in response to certain tasks. It is believed to hold discriminant features for autism. A novel computer aided diagnosis (CAD) framework is proposed to classify 50 ASD and 50 typically developed toddlers with the adoption of CNN deep networks. The CAD system includes both local and global diagnosis in a response to speech task. Spatial dimensionality reduction with region of interest selection and clustering has been utilized. In addition, the proposed framework performs discriminant feature extraction with continuous wavelet transform. Local diagnosis on cingulate gyri, superior temporal gyrus, primary auditory cortex and angular gyrus achieves accuracies ranging between 71% and 80% with a four-fold cross validation technique. The fused global diagnosis achieves an accuracy of 86% with 82% sensitivity, 92% specificity. A brain map indicating ASD severity level for each brain area is created, which contributes to personalized diagnosis and treatment plans.

A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI.

PURPOSE: Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment. METHODS: To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step. RESULTS: Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%). CONCLUSION: The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

Biological and proteomic studies of Schistosoma mansoni with decreased sensitivity to praziquantel.

The impact of the laboratory induced Schistosoma mansoni with decreased PZQ sensitivity on the biological performance of its different developmental stages and the concomitant structural changes of adult worms' total proteins were investigated. PZQ exposed snails showed stoppage of cercarial shedding for eight weeks followed by progressive significant reduction of cercarial production along four successive weeks. In the vertebrate host, in comparison to Schistosoma mansoni susceptible isolate, inoculated cercariae with decreased PZQ sensitivity led to an evident decrease in male to female ratio associated with significant reduction in tissue egg counts and significant increase in dead egg percentage. Significant reduction in the fecundity was also determined. Interestingly, eggs from adult worms with decreased PZQ sensitivity showed two unique features as they found to be smaller and more spherical in addition to the observation of hourglass shaped miracidium in about 10% of the detected mature eggs. Proteomic analysis of adult worms with decreased sensitivity to PZQ using mass spectrometry revealed up-regulation of Ca2+ ATPase 2 and Hsp70. This study can point to the increase incidence of the neuroschistosomiasis due to the small size eggs of Schistosoma mansoni with reduced PZQ sensitivity. These worms can also impact the epidemiology in the field. The study can also provide help to elucidate underlying potential molecular mechanisms of resistance that could lead to possible strategies to reverse drug resistance.