Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder

Abstract

The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more. Treatment of patients with MDD without predictors of treatment response and future recurrence presents challenges and clinical problems to patients and physicians. Recently, many neuroimaging studies have been published on biomarkers for treatment response and recurrence of MDD using various methods such as brain volumetric magnetic resonance imaging (MRI), functional MRI (resting-state and affective tasks), diffusion tensor imaging, magnetic resonance spectroscopy, near-infrared spectroscopy, and molecular imaging (i.e., positron emission tomography and single photon emission computed tomography). The results have been inconsistent, and we hypothesize that this could be due to small sample size; different study design, including eligibility criteria; and differences in the imaging and analysis techniques. In the future, we suggest a more sophisticated research design, larger sample size, and a more comprehensive integration including genetics to establish biomarkers for the prediction of treatment response and recurrence of MDD.

Left-right asymmetric and smaller right habenula volume in major depressive disorder on high-resolution 7-T magnetic resonance imaging

“…Although the results of this study are thought to contribute to clarifying the etiology of MDD patients in the future, it is not likely that brain imaging studies can be applied to biomarker for the diagnosis, treatment response, and recurrence of MDD yet [ 72 ]. Due to using self-reported measures, not structured interviews, to screen for personality disorders, there is a possibility of self-report bias.…”

The Usefulness of Functional Near-Infrared Spectroscopy for the Assessment of Post-Stroke Depression

“…In recent years, some studies have been conducted using new analysis methods such as machine learning, and it is necessary to further study the methods of analysis (Kang and Cho, 2020 ).…”

Impacts of Drug Interactions on Pharmacokinetics and the Brain Transporters: A Recent Review of Natural Compound-Drug Interactions in Brain Disorders

“…Moreover, considering that, on occasion, drug concentration in blood, plasma, or CSF does not represent the concentration into the brain tissues or ISF, NDIs in the BBB and BCSFB may not significantly change its plasma concentration, whereas NDIs can affect the drug concentration in the brain and its efficacy against brain disorders [ 4 , 112 ]. Therefore, for the exact determination and prediction of NDIs, other kinds of measuring methods like brain imaging by using positron emission tomography and magnetic resonance spectroscopy are considered as a more adequate and relevant tool instead of drug quantification through blood or CSF collection, in recent times [ 16 , 113 ]. Moreover, several recent studies reported that deep learning models, kinds of in silico method for prediction of NDIs, can exhibit improved accuracy and more efficient performance, insisting that these models may play a key role in future research, drug discovery, and development processes to estimate possible interactions between natural compounds and new drugs for brain disorders [ 114 , 115 , 116 ].…”