NeuroImage. Clinical
-
NeuroImage. Clinical · Jan 2019
ReviewAddressing heterogeneity (and homogeneity) in treatment mechanisms in depression and the potential to develop diagnostic and predictive biomarkers.
It has been 10 years since machine learning was first applied to neuroimaging data in psychiatric disorders to identify diagnostic and prognostic markers at the level of the individual. Proof of concept findings in major depression have since been extended in international samples and are beginning to include hundreds of samples from multisite data. Neuroimaging provides the unique capability to detect an acute depressive state in major depression, while we would not expect perfect classification with current diagnostic criteria which are based solely on clinical features. ⋯ Irrespective of the mechanism, the capacity for response will moderate the outcome, which includes inherent models of interpersonal relationships that could be associated with genetic risk load and represented by patterns of functional and structural neural correlates as a predictive biomarker. We propose that methods which directly address heterogeneity are essential and that a synergistic combination could bring together data-driven inductive and symptom-based deductive approaches. Through this iterative process, major depression can develop from being syndrome characterized by a collection of symptoms to a disease with an identifiable pathophysiology.
-
NeuroImage. Clinical · Jan 2019
Meta AnalysisAlterations in grey matter density and functional connectivity in trigeminal neuropathic pain and trigeminal neuralgia: A systematic review and meta-analysis.
Various studies reported changes in grey matter volumes and modifications in functional connectivity of cortical and subcortical structures in patients suffering from trigeminal neuralgia (TN) and trigeminal neuropathic pain (TNP). This study meta-analyzed the concordant structural and functional changes in foci and provide further understanding of the anatomy and biology of TN/TNP. ⋯ Structural and functional changes meta-analyzed in this paper may contribute to elucidating the central pathophysiological mechanisms involved in TN/TNP. These results may be used as biomarkers to predict the response to medication and, ideally, in the future to offer personalized treatments.