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Register a new userDepressive patients are more impulsive and inconsistent in intertemporal choice behavior for monetary gain and loss than healthy subjects- an analysis based on Tsallis statistics
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Taiki Takahashi
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Depression has been associated with impaired neural processing of reward and punishment. However, to date, little is known regarding the relationship between depression and intertemporal choice for gain and loss. We compared impulsivity and inconsistency in intertemporal choice for monetary gain and loss (quantified with parameters in the q-exponential discount function based on Tsallis statistics) between depressive patients and healthy control subjects. This examination is potentially important for advances in neuroeconomics of intertemporal choice, because depression is associated with reduced serotonergic activities in the brain. We observed that depressive patients were more impulsive and time-inconsistent in intertemporal choice action for gain and loss, in comparison to healthy controls. The usefulness of the q-exponential discount function for assessing the impaired decision-making by depressive patients was demonstrated. Furthermore, biophysical mechanisms underlying the altered intertemporal choice by depressive patients are discussed in relation to impaired serotonergic neural systems. Keywords: Depression, Discounting, Neuroeconomics, Impulsivity, Inconsistency, Tsallis statistics
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What happen in the brain when human beings play games with computers? Here a simple zero-sum game was conducted to investigate how people make decision via their brain even they know that their opponent is a computer. There are two choices (a low or high number) for people and also two strategies for the computer (red color or green color). When the number selected by the human subject meet the red color, the person loses the score which is equal to the number. On the contrary, the person gains the number of score if the computer chooses a green color for the number selected by the human being. Both the human subject and the computer give their choice at the same time, and subjects have been told that the computer make its decision randomly on the red color or green color. During the experiments, the signal of electroencephalograph (EEG) obtained from brain of subjects was recorded. From the analysis of EEG, we find that people mind the loss more than the gain, and the phenomenon becoming obvious when the gap between loss and gain grows. In addition, the signal of EEG is clearly distinguishable before making different decisions. It is observed that significant negative waves in the entire brain region when the participant has a greater expectation for the outcome, and these negative waves are mainly concentrated in the forebrain region in the brain of human beings.
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Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.
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