Commentary: Targeting the MRI-mapped psychopathology of major psychiatric disorders with neurostimulation

A group of European experts reappraised the guidelines on the therapeutic efficacy of repetitive transcranial magnetic stimulation (rTMS) previously published in 2014 [Lefaucheur et al., Clin Neurophysiol 2014;125:2150-206]. These updated recommendations take into account all rTMS publications, including data prior to 2014, as well as currently reviewed literature until the end of 2018. Level A evidence (definite efficacy) was reached for: high-frequency (HF) rTMS of the primary motor cortex (M1) contralateral to the painful side for neuropathic pain; HF-rTMS of the left dorsolateral prefrontal cortex (DLPFC) using a figure-of-8 or a H1-coil for depression; low-frequency (LF) rTMS of contralesional M1 for hand motor recovery in the post-acute stage of stroke. Level B evidence (probable efficacy) was reached for: HF-rTMS of the left M1 or DLPFC for improving quality of life or pain, respectively, in fibromyalgia; HF-rTMS of bilateral M1 regions or the left DLPFC for improving motor impairment or depression, respectively, in Parkinson's disease; HF-rTMS of ipsilesional M1 for promoting motor recovery at the post-acute stage of stroke; intermittent theta burst stimulation targeted to the leg motor cortex for lower limb spasticity in multiple sclerosis; HF-rTMS of the right DLPFC in posttraumatic stress disorder; LF-rTMS of the right inferior frontal gyrus in chronic post-stroke non-fluent aphasia; LF-rTMS of the right DLPFC in depression; and bihemispheric stimulation of the DLPFC combining right-sided LF-rTMS (or continuous theta burst stimulation) and left-sided HF-rTMS (or intermittent theta burst stimulation) in depression. Level A/B evidence is not reached concerning efficacy of rTMS in any other condition. The current recommendations are based on the differences reached in therapeutic efficacy of real vs. sham rTMS protocols, replicated in a sufficient number of independent studies. This does not mean that the benefit produced by rTMS inevitably reaches a level of clinical relevance.

The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals.

Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.