Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future

Abstract

Purpose of Review This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression. Recent Findings Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures. Summary Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.

Acute effects of ketamine on the pregenual anterior cingulate: linking spontaneous activation, functional connectivity, and glutamate metabolism

“…Along that line, a recent study by Milak et al [ 36 ] demonstrated that the lower the Glx response to ketamine, the better the antidepressant response. Activity in pgACC during emotional and cognitive tasks not only predicts antidepressant response to ketamine [ 79 , 80 ], but the pgACC is currently the best supported candidate for a general neuroimaging biomarker for antidepressant response [ 81 ]. It has been proposed that an increased activity state of the pgACC may represent its treatment-responsive mode and be specifically important for clinical effects of rapid-acting glutamatergic drugs such as ketamine [ 61 , 81 , 82 ].…”

“…Activity in pgACC during emotional and cognitive tasks not only predicts antidepressant response to ketamine [ 79 , 80 ], but the pgACC is currently the best supported candidate for a general neuroimaging biomarker for antidepressant response [ 81 ]. It has been proposed that an increased activity state of the pgACC may represent its treatment-responsive mode and be specifically important for clinical effects of rapid-acting glutamatergic drugs such as ketamine [ 61 , 81 , 82 ]. Therefore, our findings might indicate that acute ketamine administration increases pgACC activity, which would correspond to a treatment-responsive mode in depressed subjects.…”

Predicting Antidepressant Effects of Ketamine: the Role of the Pregenual Anterior Cingulate Cortex as a Multimodal Neuroimaging Biomarker

“…Our fMRI findings are in line with previous studies demonstrating an association of increased pgACC activity prior to treatment with positive antidepressant response across a variety of antidepressant treatments, neuroimaging modalities, and analytical approaches ( Pizzagalli, 2011 ; Fu et al, 2013 ; Godlewska et al, 2018a ; Pizzagalli et al, 2018 ). Based on these findings, the pgACC is currently the best supported candidate for a general neuroimaging biomarker for antidepressant response ( Godlewska, 2020 ). Similarly, with regard to ketamine it was shown that pgACC activity during emotional and cognitive tasks predicted antidepressant response to ketamine ( Salvadore et al, 2009 , 2010 ).…”

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

“…Predicting antidepressant treatment outcome with neuroimaging data has been a focus of attention for at least two decades, from employing simple statistical approaches to using machine learning and deep learning methods [ 24 , 25 ]. There are a wide variety of neuroimaging modalities for examining predictive variables of antidepressant treatment response, including structural magnetic resonance imaging (MRI), diffusion tensor imaging, electroencephalography, functional MRI, positron emission tomography, single-photon emission computed tomography, near-infrared spectroscopy, and proton magnetic resonance spectroscopy [ 46 , 47 ].…”

“…In addition, the usage of machine learning and deep learning approaches may play a crucial role in integrating pharmacogenomics with neuroimaging [ 23 ]. In the recent past, there were a wide range of relevant research studies for various antidepressant treatments of significance for neuroimaging with consideration of machine learning approaches [ 24 , 25 ]. Hence, it would be remarkably intriguing to develop machine learning and deep learning models that can forecast antidepressant treatment response and remission for MDD patients by incorporating pharmacogenomics with neuro-imaging.…”