Integration of the Cortical Haemodynamic Response Measured by Functional Near-Infrared Spectroscopy and Amino Acid Analysis to Aid in the Diagnosis of Major Depressive Disorder

Abstract

Background: Major depressive disorder (MDD) is a debilitating condition with a high disease burden and medical comorbidities. There are currently few to no validated biomarkers to guide the diagnosis and treatment of MDD. In the present study, we evaluated the differences between MDD patients and healthy controls (HCs) in terms of cortical haemodynamic responses during a verbal fluency test (VFT) using functional near-infrared spectroscopy (fNIRS) and serum amino acid profiles, and ascertained if these parameters were correlated with clinical characteristics. Methods: Twenty-five (25) patients with MDD and 25 age-, gender-, and ethnicity-matched HCs were recruited for the study. Real-time monitoring of the haemodynamic response during completion of a VFT was quantified using a 52-channel NIRS system. Serum samples were analysed and quantified by liquid chromatography-mass spectrometry for amino acid profiling. Receiver-operating characteristic (ROC) curves were used to classify potential candidate biomarkers. Results: The MDD patients had lower prefrontal and temporal activation during completion of the VFT than HCs. The MDD patients had lower mean concentrations of oxy-Hb in the left orbitofrontal cortex (OFC), and lower serum histidine levels. When the oxy-haemoglobin response was combined with the histidine concentration, the sensitivity and specificity of results improved significantly from 66.7% to 73.3% and from 65.0% to 90.0% respectively, as compared to results based only on the NIRS response. Conclusions: These findings demonstrate the use of combination biomarkers to aid in the diagnosis of MDD. This technique could be a useful approach to detect MDD with greater precision, but additional studies are required to validate the methodology.

Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder

“…While many relevant works implement traditional statistical techniques to differentiate MDD and healthy control subjects [ 44 , 45 , 46 , 47 , 48 ], there are substantially fewer studies on using amino acid data in machine learning for the predictive classification of MDD and HCs. Despite many machine learning studies aimed at distinguishing between individuals with MDD and HCs, effectively applying machine learning techniques to diagnose MDD in the clinical setting still poses significant challenges.…”

Using an interpretable amino acid-based machine learning method to enhance the diagnosis of major depressive disorder

“…While many relevant works implement traditional statistical techniques to differentiate MDD and healthy control subjects (39)(40)(41)(42)(43), there are substantially fewer studies on using amino acid data in machine learning for the predictive classi cation of MDD and HCs. Despite many machine learning studies aimed at distinguishing between individuals with MDD and HCs, effectively applying machine learning techniques to diagnose MDD in the clinical setting still poses signi cant challenges.…”

Investigation of neuromodulatory effect of anodal cerebellar transcranial direct current stimulation on the primary motor cortex using functional near-infrared spectroscopy

“…fNIRS provides metabolic information non-invasively and with adequate sensitivity to detect even small changes in the cerebral hemodynamic response. It allows estimation of metabolic-based specificity [25], and robustness to various artifacts [26]. Furthermore, fNIRS can be used simultaneously with tDCS to measure brain activity even during the stimulation period without significant electro-optic interference.…”