ABSTRACT
BACKGROUND: Research into the shared and distinct brain dysfunctions in patients with schizophrenia (SCZ) and major depressive disorder (MDD) has been increasing. However, few studies have explored the application of functional near-infrared spectroscopy (fNIRS) in investigating brain dysfunction and enhancing diagnostic methodologies in these two conditions. METHODS: A general linear model was used for analysis of brain activation following task-state fNIRS from 131 patients with SCZ, 132 patients with MDD and 130 healthy controls (HCs). Subsequently, seventy-seven time-frequency analysis methods were used to construct new features of fNIRS, followed by the implementation of five machine learning algorithms to develop a differential diagnosis model for the three groups. This model was evaluated by comparing it to both a diagnostic model relying on traditional fNIRS features and assessments made by two psychiatrists. RESULTS: Brain activation analysis revealed significantly lower activation in Broca's area, the dorsolateral prefrontal cortex, and the middle temporal gyrus for both the SCZ and MDD groups compared to HCs. Additionally, the SCZ group exhibited notably lower activation in the superior temporal gyrus and the subcentral gyrus compared to the MDD group. When distinguishing among the three groups using independent validation datasets, the models utilizing new fNIRS features achieved an accuracy of 85.90 % (AUC = 0.95). In contrast, models based on traditional fNIRS features reached an accuracy of 52.56 % (AUC = 0.66). The accuracies of the two psychiatrists were 42.00 % (AUC = 0.60) and 38.00 % (AUC = 0.50), respectively. CONCLUSION: This investigation brings to light the shared and distinct neurobiological abnormalities present in SCZ and MDD, offering potential enhancements for extant diagnostic systems.
ABSTRACT
BACKGROUND: Exploratory eye movements (EEMs) and P300 are often used to facilitate the clinical diagnosis of depression. However, There were few studies using the combination of EEMs and P300 to build a model for detecting depression and predicting a curative effect. METHODS: Sixty patients were recruited for 2 groups: high frequency repetitive transcranial magnetic stimulation(rTMS) combined with paroxetine group and simple paroxetine group. Clinical efficacy was evaluated by the Hamilton Depression scale-24(HAMD-24), EEMs and P300. The classification model of the auxiliary diagnosis of depression and the prediction model of the two treatments were developed based on a machine learning algorithm. RESULTS: The classification model with the greatest accuracy for patients with depression and healthy controls was 95.24% (AUC = 0.75, recall = 1.00, precision = 0.95, F1-score = 0.97). The root mean square error (RMSE) of the model for predicting the efficacy of high frequency rTMS combined with paroxetine was 3.54 (MAE [mean absolute error] = 2.56, R2 = -0.53). The RMSE of the model for predicting the efficacy of paroxetine was 4.97 (MAE = 4.00, R2 = -0.91). CONCLUSION: Based on the machine learning algorithm, P300 and EEMs data was suitable for modeling to distinguish depression patients and healthy individuals. However, it was not suitable for predicting the efficacy of high frequency rTMS combined with paroxetine or to predict the efficacy of paroxetine.
