Prediction of severity and treatment outcome for ASD from fMRI

10/28/2018
by   Juntang Zhuang, et al.
0

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many analytical models, there has been limited progress in accurate predictive models for early diagnosis. In this project, we aim to build an accurate model to predict treatment outcome and ASD severity from early stage functional magnetic resonance imaging (fMRI) scans. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis problems are challenges in this work. We propose a generic and accurate two-level approach for high-dimensional regression problems in medical image analysis. First, we perform region-level feature selection using a predefined brain parcellation. Based on the assumption that voxels within one region in the brain have similar values, for each region we use the bootstrapped mean of voxels within it as a feature. In this way, the dimension of data is reduced from number of voxels to number of regions. Then we detect predictive regions by various feature selection methods. Second, we extract voxels within selected regions, and perform voxel-level feature selection. To use this model in both linear and non-linear cases with limited training examples, we apply two-level elastic net regression and random forest (RF) models respectively. To validate accuracy and robustness of this approach, we perform experiments on both task-fMRI and resting state fMRI datasets. Furthermore, we visualize the influence of each region, and show that the results match well with other findings.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

10/17/2018

Prediction of treatment outcome for autism from structure of the brain based on sure independence screening

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder,...
08/17/2019

Locally Linear Embedding and fMRI feature selection in psychiatric classification

Background: Functional magnetic resonance imaging (fMRI) provides non-in...
07/16/2012

Learning to rank from medical imaging data

Medical images can be used to predict a clinical score coding for the se...
04/28/2011

A supervised clustering approach for fMRI-based inference of brain states

We propose a method that combines signals from many brain regions observ...
04/07/2022

Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder

The development of noninvasive brain imaging such as resting-state funct...
11/25/2019

Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder

Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent ...
02/03/2018

On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data

In contrast to conventional, univariate analysis, various types of multi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.