Original Research ARTICLE

Front. Syst. Neurosci., 21 December 2012 | doi: 10.3389/fnsys.2012.00078

Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects

Jason W. Bohland1*, Sara Saperstein2, Francisco Pereira3, Jérémy Rapin3 and Leo Grady3
  • 1Department of Health Sciences, Boston University, Boston, MA, USA
  • 2Graduate Program for Neuroscience, Boston University, Boston, MA, USA
  • 3Siemens Corporation, Corporate Research and Technology, Princeton, NJ, USA

Brain imaging methods have long held promise as diagnostic aids for neuropsychiatric conditions with complex behavioral phenotypes such as Attention-Deficit/Hyperactivity Disorder. This promise has largely been unrealized, at least partly due to the heterogeneity of clinical populations and the small sample size of many studies. A large, multi-center dataset provided by the ADHD-200 Consortium affords new opportunities to test methods for individual diagnosis based on MRI-observable structural brain attributes and functional interactions observable from resting-state fMRI. In this study, we systematically calculated a large set of standard and new quantitative markers from individual subject datasets. These features (>12,000 per subject) consisted of local anatomical attributes such as cortical thickness and structure volumes, and both local and global resting-state network measures. Three methods were used to compute graphs representing interdependencies between activations in different brain areas, and a full set of network features was derived from each. Of these, features derived from the inverse of the time series covariance matrix, under an L1-norm regularization penalty, proved most powerful. Anatomical and network feature sets were used individually, and combined with non-imaging phenotypic features from each subject. Machine learning algorithms were used to rank attributes, and performance was assessed under cross-validation and on a separate test set of 168 subjects for a variety of feature set combinations. While non-imaging features gave highest performance in cross-validation, the addition of imaging features in sufficient numbers led to improved generalization to new data. Stratification by gender also proved to be a fruitful strategy to improve classifier performance. We describe the overall approach used, compare the predictive power of different classes of features, and describe the most impactful features in relation to the current literature.

Keywords: ADHD, fMRI, network analysis, functional connectivity, resting state, machine learning

Citation: Bohland JW, Saperstein S, Pereira F, Rapin J and Grady L (2012) Network, anatomical, and non-imaging measures for the prediction of ADHD diagnosis in individual subjects. Front. Syst. Neurosci. 6:78. doi: 10.3389/fnsys.2012.00078

Received: 16 May 2012; Accepted: 19 November 2012;
Published online: 21 December 2012.

Edited by:

Damien Fair, Oregon Health and Science University, USA

Reviewed by:

Alex Fornito, University of Melbourne, Australia
Matthew R. G. Brown, University of Alberta, Canada
Binyam Nardos, Washington University in Saint Louis, USA

Copyright: © 2012 Bohland, Saperstein, Pereira, Rapin and Grady. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: Jason W. Bohland, Health Sciences Department, Sargent College of Health and Rehabilitation Sciences, Boston University, 635 Commonwealth Avenue, Room 403, Boston, MA 02215, USA. e-mail: jbohland@bu.edu

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