A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI.

Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, for example, the autism diagnosis observation schedule, the Autism Diagnostic Interview-Revised. In this study we introduce a computer-aided diagnosis system that uses features from structural MRI (sMRI) and resting state functional MRI (fMRI) to help predict an autism diagnosis by clinicians. The proposed system is capable of parcellating brain regions to show which areas are most likely affected by autism related abnormalities and thus help in targeting potential therapeutic interventions. When tested on 18 data sets (n = 1060) from the ABIDE consortium, our system was able to achieve high accuracy (sMRI 0.75-1.00; fMRI 0.79-1.00), sensitivity (sMRI 0.73-1.00; fMRI 0.78-1.00), and specificity (sMRI 0.78-1.00; fMRI 0.79-1.00). The proposed system could be considered an important step toward helping physicians interpret results of neuroimaging studies and personalize treatment options. To the best of our knowledge, this work is the first to combine features from structural and functional MRI, use them for personalized diagnosis and achieve high accuracies on a relatively large population.

A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data.

Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.

Using resting state functional MRI to build a personalized autism diagnosis system.

Autism spectrum disorder (ASD) is a neuro-developmental disorder associated with social impairments, communication difficulties, and restricted and repetitive behaviors. Yet, there is no confirmed cause identified for ASD. Studying the functional connectivity of the brain is an emerging technique used in diagnosing and understanding ASD. In this study, we obtained the resting state functional MRI data of 283 subjects from the National Database of Autism Research (NDAR). An automated autism diagnosis system was built using the data from NDAR. The proposed system is machine learning based. Power spectral densities (PSDs) of time courses corresponding to the spatial activation areas are used as input features, feeds them to a stacked autoencoder then builds a classifier using probabilistic support vector machines. Over the used dataset, around 90% of sensitivity, specificity and accuracy was achieved by our machine learning system. Moreover, the system generalization ability was checked over two different prevalence values, one for the general population and the other for the of high risk population, and the system proved to be very generalizable, especially among the population of high risk. The proposed system generates a full personalized report for each subject, along with identifying the global differences between ASD and typically developed (TD) subjects and its ability to diagnose autism. It shows the impacted areas and the severity of implications. From the clinical aspect, this report is considered very valuable as it helps in both predicting and understanding behavior of autistic subjects. Moreover, it helps in designing a plan for personalized treatment per each individual subject. The proposed work is taking a step towards achieving personalized medicine in autism which is the ultimate goal of our group's research efforts in this area.

Medical imaging diagnosis of early Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that influences the central nervous system, often leading to dire consequences for quality of life. The disease goes through some stages mainly divided into early, moderate, and severe. Among them, the early stage is the most important as medical intervention has the potential to alter the natural progression of the condition. In practice, the early diagnosis is a challenge since the neurodegenerative changes can precede the onset of clinical symptoms by 10-15 years. This factor along with other known and unknown ones, hinder the ability for the early diagnosis and treatment of AD. Numerous research efforts have been proposed to address the complex characteristics of AD exploiting various tests including brain imaging that is massively utilized due to its powerful features. This paper aims to highlight our present knowledge on the clinical and computer-based attempts at early diagnosis of AD. We concluded that the door is still open for further research especially with the rapid advances in scanning and computer-based technologies.