Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging

BackgroundPrevious morphology and diffusion-imaging studies have suggested that structural changes in white matter is an important part of the pathophysiology of obsessive–compulsive disorder (OCD). However, different methodological approaches and the heterogeneity of patient samples question the validity of the findings.Materials and methodsIn total, 30 patients were matched for age and sex with 30 healthy controls. All participants underwent T1-weighted magnetic resonance imaging, diffusion tensor imaging and T2 fluid-attenuated inversion recovery. Voxel-based morphometry and tract-based spatial statistics were used to compare white matter volumes and diffusion tensor imaging between groups. These data were analysed correcting for the effects of multiple comparisons, age, sex, severity and duration of illness as nuisance covariates. White matter hyperintensities were manually identified.ResultsIncrease in fractional anisotropy in cerebellum was the most prominent result. A decrease in fractional anisotrophy in patients comparable with previous studies was located in forceps minor. There were no differences in the white matter morphology or in the white matter hyperintensities between patients and healthy controls.ConclusionDecrease in fractional anisotrophy in forceps minor and increase in cerebellum were found, and they were not due to neither white matter hyperintensities nor morphology of the white matter. Cerebellar hyperconnectivity could be an important part of OCD pathophysiology.

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Classification of Obsessive Compulsive Disorder by EEG Complexity and Hemispheric Dependency Measurements

International Journal of Neural Systems ◽

10.1142/s0129065715500100 ◽

2015 ◽

Vol 25 (03) ◽

pp. 1550010 ◽

Cited By ~ 30

Author(s):

Serap Aydin ◽

Nafiz Arica ◽

Emrah Ergul ◽

Oğuz Tan

Keyword(s):

Obsessive Compulsive Disorder ◽

Single Channel ◽

Electrode Placement ◽

Permutation Entropy ◽

Support Vector ◽

Obsessive Compulsive ◽

Validation Data ◽

Compulsive Disorder ◽

Eyes Closed

In the present study, both single channel electroencephalography (EEG) complexity and two channel interhemispheric dependency measurements have newly been examined for classification of patients with obsessive–compulsive disorder (OCD) and controls by using support vector machine classifiers. Three embedding entropy measurements (approximate entropy, sample entropy, permutation entropy (PermEn)) are used to estimate single channel EEG complexity for 19-channel eyes closed cortical measurements. Mean coherence and mutual information are examined to measure the level of interhemispheric dependency in frequency and statistical domain, respectively for eight distinct electrode pairs placed on the scalp with respect to the international 10–20 electrode placement system. All methods are applied to short EEG segments of 2 s. The classification performance is measured 20 times with different 2-fold cross-validation data for both single channel complexity features (19 features) and interhemispheric dependency features (eight features). The highest classification accuracy of 85 ±5.2% is provided by PermEn at prefrontal regions of the brain. Even if the classification success do not provided by other methods as high as PermEn, the clear differences between patients and controls at prefrontal regions can also be obtained by using other methods except coherence. In conclusion, OCD, defined as illness of orbitofronto-striatal structures [Beucke et al., JAMA Psychiatry 70 (2013) 619–629; Cavedini et al., Psychiatry Res. 78 (1998) 21–28; Menzies et al., Neurosci. Biobehav. Rev. 32(3) (2008) 525–549], is caused by functional abnormalities in the pre-frontal regions. Particularly, patients are characterized by lower EEG complexity at both pre-frontal regions and right fronto-temporal locations. Our results are compatible with imaging studies that define OCD as a sub group of anxiety disorders exhibited a decreased complexity (such as anorexia nervosa [Toth et al., Int. J. Psychophysiol. 51(3) (2004) 253–260] and panic disorder [Bob et al., Physiol. Res. 55 (2006) S113–S119]).

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