Diagnostic neuroimaging markers of obsessive-compulsive disorder: Initial evidence from structural and functional MRI studies

&NA; As of yet, no diagnostic biomarkers are available for obsessive‐compulsive disorder (OCD), and its diagnosis relies entirely upon the recognition of behavioural features assessed through clinical interview. Neuroimaging studies have shown that various brain structures are abnormal in OCD patients compared to healthy controls. However, the majority of these results are based on average differences between groups, which limits diagnostic usage in clinical practice. In recent years, a growing number of studies have applied multivariate pattern analysis (MVPA) techniques on neuroimaging data to extract patterns of altered brain structure, function and connectivity typical for OCD. MVPA techniques can be used to develop predictive models that extract regularities in data to classify individual subjects based on their diagnosis. In the present paper, we reviewed the literature of MVPA studies using data from different imaging modalities to distinguish OCD patients from controls. A systematic search retrieved twelve articles that fulfilled the inclusion and exclusion criteria. Reviewed studies have been able to classify OCD diagnosis with accuracies ranging from 66% up to 100%. Features important for classification were different across imaging modalities and widespread throughout the brain. Although studies have shown promising results, sample sizes used are typically small which can lead to high variance of the estimated model accuracy, cohort‐specific solutions and lack of generalizability of findings. Some of the challenges are discussed that need to be overcome in order to move forward toward clinical applications. HighlightsMultivariate techniques are increasingly used to predict diagnosis using neuroimaging data.Classification accuracies for OCD diagnosis range between 66% up to 100%.Features reported as important are widespread throughout the brain.Small sample sizes lead to cohort‐specific solutions and low generalizability.Sharing of code and data can help the field advance to clinical applicability.