Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model.

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models.

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing.

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress.

Default mode network alterations after intermittent theta burst stimulation in healthy subjects.

Understanding the mechanisms by which intermittent theta burst stimulation (iTBS) protocols exert changes in the default-mode network (DMN) is paramount to develop therapeutically more effective approaches in the future. While a full session (3000 pulses) of 10 Hz repetitive transcranial magnetic stimulation (HF-rTMS) reduces the functional connectivity (FC) of the DMN and the subgenual anterior cingulate cortex, the current understanding of the effects of a single session of iTBS on the DMN in healthy subjects is limited. Here, we use a previously validated target selection approach for an unprecedented investigation into the effects of a single session (1800 pulses) of iTBS over the DMN in healthy controls.

Transcranial Magnetic and Direct Current Stimulation in the Treatment of Depression: Basic Mechanisms and Challenges of Two Commonly Used Brain Stimulation Methods in Interventional Psychiatry.

Noninvasive neuromodulation, including repetitive trans-cranial magnetic stimulation (rTMS) and direct current stimulation (tDCS), provides researchers and health care professionals with the ability to gain unique insights into brain functions and treat several neurological and psychiatric conditions. Undeniably, the number of published research and clinical papers on this topic is increasing exponentially. In parallel, several methodological and scientific caveats have emerged in the transcranial stimulation field; these include less robust and reliable effects as well as contradictory clinical findings.

No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium Analysis.

Objective: Asymmetry is a subtle but pervasive aspect of the human brain, and it may be altered in several psychiatric conditions. MRI studies have shown subtle differences of brain anatomy between people with major depressive disorder and healthy control subjects, but few studies have specifically examined brain anatomical asymmetry in relation to this disorder, and results from those studies have remained inconclusive. At the functional level, some electroencephalography studies have indicated left fronto-cortical hypoactivity and right parietal hypoactivity in depressive disorders, so aspects of lateralized anatomy may also be affected.

Personalized repetitive transcranial magnetic stimulation temporarily alters default mode network in healthy subjects.

High frequency repetitive transcranial magnetic stimulation (HF-rTMS) delivered to the left dorsolateral prefrontal cortex (DLPFC) is an effective treatment option for treatment resistant depression. However, the underlying mechanisms of a full session of HF-rTMS in healthy volunteers have not yet been described. Here we investigated, with a personalized selection of DLPFC stimulation sites, the effects driven by HF-rTMS in healthy volunteers (n = 23) over the default mode network (DMN) in multiple time windows.