Multivariate pattern analysis of DTI reveals differential white matter in individuals with obsessive‐compulsive disorder

Diffusion tensor imaging (DTI) studies have revealed group differences in white matter between patients with obsessive‐compulsive disorder (OCD) and healthy controls. However, the results of these studies were based on average differences between the two groups, and therefore had limited clinical applicability. The objective of this study was to investigate whether fractional anisotropy (FA) of white matter can be used to discriminate between patients with OCD and healthy controls at the level of the individual. DTI data were acquired from 28 OCD patients and 28 demographically matched healthy controls, scanned using a 3T MRI system. Differences in FA values of white matter between OCD and healthy controls were examined using a multivariate pattern classification technique known as support vector machine (SVM). SVM applied to FA images correctly identified OCD patients with a sensitivity of 86% and a specificity of 82% resulting in a statistically significant accuracy of 84% (P ≤ 0.001). This discrimination was based on a distributed network including bilateral prefrontal and temporal regions, inferior fronto‐occipital fasciculus, superior fronto‐parietal fasciculus, splenium of corpus callosum and left middle cingulum bundle. The present study demonstrates subtle and spatially distributed white matter abnormalities in individuals with OCD, and provides preliminary support for the suggestion that that these could be used to aid the identification of individuals with OCD in clinical practice. Hum Brain Mapp 35:2643–2651, 2014. © 2013 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc..

[1]  N. Lori,et al.  Obsessive-compulsive disorder as a visual processing impairment. , 2010, Medical hypotheses.

[2]  Christos Davatzikos,et al.  Why voxel-based morphometric analysis should be used with great caution when characterizing group differences , 2004, NeuroImage.

[3]  F. P. Mac Master,et al.  Translational neuroimaging research in pediatric obsessive-compulsive disorder , 2010, Dialogues in clinical neuroscience.

[4]  A. Gorini,et al.  Disorganization of anatomical connectivity in obsessive compulsive disorder: A multi-parameter diffusion tensor imaging study in a subpopulation of patients , 2010, Neurobiology of Disease.

[5]  Olga V. Demler,et al.  Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. , 2005, Archives of general psychiatry.

[6]  David E. J. Linden,et al.  Neuroimaging in Psychiatry: From Bench to Bedside , 2009, Front. Hum. Neurosci..

[7]  William Stafford Noble,et al.  Support vector machine , 2013 .

[8]  A. Chopra,et al.  Obsessive-compulsive disorder after right temporal-lobe hemorrhage. , 2011, The Journal of neuropsychiatry and clinical neurosciences.

[9]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Stephen C. Strother,et al.  Support vector machines for temporal classification of block design fMRI data , 2005, NeuroImage.

[11]  R. Delorme,et al.  A genetic family-based association study of OLIG2 in obsessive-compulsive disorder. , 2007, Archives of general psychiatry.

[12]  A. Besga,et al.  Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation , 2011, Neuroscience Letters.

[13]  Gemma C. Garriga,et al.  Permutation Tests for Studying Classifier Performance , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[14]  J. Helpern,et al.  Neuropsychiatric applications of DTI – a review , 2002, NMR in biomedicine.

[15]  E. Bullmore,et al.  Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: The orbitofronto-striatal model revisited , 2008, Neuroscience & Biobehavioral Reviews.

[16]  Christian Gaser,et al.  Identifying patients with obsessive–compulsive disorder using whole-brain anatomy , 2007, NeuroImage.

[17]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[18]  Manzar Ashtari,et al.  White matter abnormalities in obsessive-compulsive disorder: a diffusion tensor imaging study. , 2005, Archives of general psychiatry.

[19]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[20]  M. Catani,et al.  A diffusion tensor imaging tractography atlas for virtual in vivo dissections , 2008, Cortex.

[21]  Philip K. McGuire,et al.  Multivariate pattern classification reveals differential brain activation during emotional processing in individuals with psychosis proneness , 2012, NeuroImage.

[22]  S. Rauch,et al.  Use of factor-analyzed symptom dimensions to predict outcome with serotonin reuptake inhibitors and placebo in the treatment of obsessive-compulsive disorder. , 1999, The American journal of psychiatry.

[23]  Qichang Shi,et al.  Prevalence, treatment, and associated disability of mental disorders in four provinces in China during 2001–05: an epidemiological survey , 2009, The Lancet.

[24]  B. Aouizerate,et al.  Meta-Analysis of Brain Volume Changes in Obsessive-Compulsive Disorder , 2009, Biological Psychiatry.

[25]  B. Aouizerate,et al.  Gray Matter Alterations in Obsessive–Compulsive Disorder: An Anatomic Likelihood Estimation Meta-Analysis , 2010, Neuropsychopharmacology.

[26]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[27]  Monique Ernst,et al.  Decision-making in a Risk-taking Task: A PET Study , 2002, Neuropsychopharmacology.

[28]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[29]  A. Malhotra,et al.  White Matter Abnormalities in Pediatric Obsessive-Compulsive Disorder , 2012, Neuropsychopharmacology.

[30]  A. Mechelli,et al.  Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review , 2012, Neuroscience & Biobehavioral Reviews.

[31]  Steven C. R. Williams,et al.  Describing the Brain in Autism in Five Dimensions—Magnetic Resonance Imaging-Assisted Diagnosis of Autism Spectrum Disorder Using a Multiparameter Classification Approach , 2010, The Journal of Neuroscience.

[32]  Lars Kai Hansen,et al.  Model sparsity and brain pattern interpretation of classification models in neuroimaging , 2012, Pattern Recognit..

[33]  A. Brody,et al.  Brain-behavior relationships in obsessive-compulsive disorder. , 2001, Seminars in clinical neuropsychiatry.

[34]  O. Devinsky,et al.  Obsessive–compulsive symptoms in patients with temporal lobe epilepsy , 2004, Epilepsy & Behavior.

[35]  V. Wedeen,et al.  Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo , 2003, Magnetic resonance in medicine.

[36]  Janaina Mourão Miranda,et al.  Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach , 2010, NeuroImage.

[37]  Stefan Klöppel,et al.  White matter connections reflect changes in voluntary-guided saccades in pre-symptomatic Huntington's disease. , 2008, Brain : a journal of neurology.

[38]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[39]  Karl J. Friston,et al.  Dynamic discrimination analysis: A spatial–temporal SVM , 2007, NeuroImage.

[40]  E. Katz,et al.  Cerebral glucose metabolism in obsessive-compulsive hoarding. , 2004, The American journal of psychiatry.

[41]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[42]  Qiyong Gong,et al.  Microstructural brain abnormalities in patients with obsessive-compulsive disorder: diffusion-tensor MR imaging study at 3.0 T. , 2011, Radiology.

[43]  David D. Cox,et al.  Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex , 2003, NeuroImage.

[44]  T. Brugha,et al.  Obsessive-compulsive disorder: prevalence, comorbidity, impact, and help-seeking in the British National Psychiatric Morbidity Survey of 2000. , 2006, The American journal of psychiatry.

[45]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[46]  J. Kwon,et al.  White matter abnormalities in drug‐naïve patients with obsessive–compulsive disorder: a Diffusion Tensor Study before and after citalopram treatment , 2007, Acta psychiatrica Scandinavica.

[47]  T. Robbins,et al.  Orbitofrontal Dysfunction in Patients with Obsessive-Compulsive Disorder and Their Unaffected Relatives , 2008, Science.

[48]  Dinggang Shen,et al.  Morphological classification of brains via high-dimensional shape transformations and machine learning methods , 2004, NeuroImage.

[49]  K. Lovblad,et al.  Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. , 2010, Journal of Alzheimer's disease : JAD.

[50]  Philip K. McGuire,et al.  Prognostic prediction of therapeutic response in depression using high-field MR imaging , 2011, NeuroImage.

[51]  Fernando Zelaya,et al.  Distinct roles of prefrontal cortical subregions in the Iowa Gambling Task. , 2009, Cerebral cortex.

[52]  Paul M. Matthews,et al.  Changes in Gray Matter Volume and White Matter Microstructure in Adolescents with Obsessive-Compulsive Disorder , 2011, Biological Psychiatry.

[53]  M. Jenike Obsessive compulsive disorder. , 1983, Comprehensive psychiatry.

[54]  John Suckling,et al.  White matter abnormalities in patients with obsessive-compulsive disorder and their first-degree relatives. , 2008, The American journal of psychiatry.

[55]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.