ADHD-200 results summary.

<p>ADHD-200 results summary.</p

Summary of ABIDE dataset classification results.

<p>Conventions are the same as for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166934#pone.0166934.g007" target="_blank">Fig 7</a>. This figure is best viewed in color.</p

Details of Holdout Dataset participants, ABIDE.

<p>Details of Holdout Dataset participants, ABIDE.</p

ADHD-200, personal characteristic data.

<p>ADHD-200, personal characteristic data.</p

Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism

<div><p>A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset (769 participants in training set, 171 in test set). It is able to distinguish autism from control with hold-out accuracy of 65.0% (over baseline 51.6%) using functional images with personal characteristic data when trained on the Autism Brain Imaging Data Exchange (ABIDE) dataset (889 participants in training set, 222 in test set). These results outperform all previously presented methods on both datasets. To our knowledge, this is the first demonstration of a single automated learning process that can produce classifiers for distinguishing patients vs. controls from brain imaging data with above-chance accuracy on large datasets for two different psychiatric illnesses (ADHD and autism). Working toward clinical applications requires robustness against real-world conditions, including the substantial variability that often exists among data collected at different institutions. It is therefore important that our algorithm was successful with the large ADHD-200 and ABIDE datasets, which include data from hundreds of participants collected at multiple institutions. While the resulting classifiers are not yet clinically relevant, this work shows that there is a signal in the (f)MRI data that a learning algorithm is able to find. We anticipate this will lead to yet more accurate classifiers, over these and other psychiatric disorders, working toward the goal of a clinical tool for high accuracy differential diagnosis.</p></div

ADHD-200, functional images.

<p>ADHD-200, functional images.</p

Extracted block regions from ABIDE fMRI dataset.

<p>See text for details.</p

Summary of ADHD-200 dataset classification results.

<p>The black horizontal dotted line shows the baseline chance accuracy of the test set. Each vertical bar shows the mean and range of the cross validation results for the selected base learner (L) and feature set (FS*(L)) on the training set, as produced with MHPC (Algorithm 1). The blue asterisks * show the accuracy of each classifier on the hold-out set. The classifiers on the x-axis are ordered by the types of features they used, including various combinations of structural MRI, functional MRI, and personal characteristic data. The legend also identifies the actual classifier used. This figure is best viewed in color.</p

Summary of the learning pipeline.

<p>1) Each image in the datasets is preprocessed (see section Preprocessing and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166934#pone.0166934.g002" target="_blank">Fig 2</a>), reducing the dimensions from about 100,000,000 (79 × 95 × 68 × 200) to about 500,000. 2) The MHPC system then extracts the 3D-HOG features of each image reducing the number of dimensions to about 100,000; see section Histogram of oriented gradients (HOG) features. 3) The last step tries to select the best learner (from the initial set of base learners) and feature set, based on running 5-fold cross validation over the training set, using different combinations of the number of features and base learners. This step reduces the number of dimensions to a number under 1000; see section Results. HOG feature extraction, minimum redundancy maximum relevance (MRMR) feature selection and base learner selection are all parts of the MHPC algorithm (shown in the red box above). See Algorithm 1 for details. This figure is best viewed in color.</p

ADHD-200, personal characteristic data with functional images.

<p>ADHD-200, personal characteristic data with functional images.</p