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      A Bayesian Algorithm for Anxiety Index Prediction Based on Cerebral Blood Oxygenation in the Prefrontal Cortex Measured by Near Infrared Spectroscopy

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          Abstract

          Stress-induced psychological and somatic diseases are virtually endemic nowadays. Written self-report anxiety measures are available; however, these indices tend to be time consuming to acquire. For medical patients, completing written reports can be burdensome if they are weak, in pain, or in acute anxiety states. Consequently, simple and fast non-invasive methods for assessing stress response from neurophysiological data are essential. In this paper, we report on a study that makes predictions of the state-trait anxiety inventory (STAI) index from oxyhemoglobin and deoxyhemoglobin concentration changes of the prefrontal cortex using a two-channel portable near-infrared spectroscopy device. Predictions are achieved by constructing machine learning algorithms within a Bayesian framework with nonlinear basis function together with Markov Chain Monte Carlo implementation. In this paper, prediction experiments were performed against four different data sets, i.e., two comprising young subjects, and the remaining two comprising elderly subjects. The number of subjects in each data set varied between 17 and 20 and each subject participated only once. They were not asked to perform any task; instead, they were at rest. The root mean square errors for the four groups were 6.20, 6.62, 4.50, and 6.38, respectively. There appeared to be no significant distinctions of prediction accuracies between age groups and since the STAI are defined between 20 and 80, the predictions appeared reasonably accurate. The results indicate potential applications to practical situations such as stress management and medical practice.

          Abstract

          This paper reports the development and validation of a simple and fast non-invasive method for assessing stress response from neurophysiological data. Predictions of the state-trait anxiety inventory (STAI) index are made from oxy and deoxy hemoglobin concentration changes of the prefrontal cortex (PFC) using a two-channel portable near-infrared spectroscopy (NIRS) device. Predictions are achieved by constructing machine learning algorithms within a Bayesian framework with nonlinear basis function together with Markov Chain Monte Carlo (MCMC) implementation. In validation studies in four human subjects databases the predictions appeared reasonably accurate. The results indicate potential applications to practical situations such as stress management and medical practice.

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          Most cited references13

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          Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model.

          Using a newly developed perfused rat brain model, we examined direct effects of each change in cerebral blood flow (CBF) and oxygen metabolic rate on cerebral hemoglobin oxygenation to interpret near-infrared spectroscopy signals. Changes in CBF and total hemoglobin (tHb) were in parallel, although tHb showed no change when changes in CBF were small (< or =10%). Increasing CBF caused an increase in oxygenated hemoglobin (HbO(2)) and a decrease in deoxygenated hemoglobin (deoxy-Hb). Decreasing CBF was accompanied by a decrease in HbO(2), whereas changes in direction of deoxy-Hb were various. Cerebral blood congestion caused increases in HbO(2), deoxy-Hb, and tHb. Administration of pentylenetetrazole without increasing the flow rate caused increases in HbO(2) and tHb with a decrease in deoxy-Hb. There were no significant differences in venous oxygen saturation before vs. during seizure. These results suggest that, in activation studies with near-infrared spectroscopy, HbO(2) is the most sensitive indicator of changes in CBF, and the direction of changes in deoxy-Hb is determined by the degree of changes in venous blood oxygenation and volume.
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            The visual analog scale allows effective measurement of preoperative anxiety and detection of patients' anesthetic concerns.

            The advent of managed care, reduction of costs, and advances in medical technology place increasing demands on anesthesiologists. Preoperative anxiety may go unnoticed in an environment that stresses increased productivity. The present study compares different methods for measuring preoperative anxiety, identifies certain patient characteristics that predispose to high anxiety, and describes the quantity and quality of anxiety that patients experience preoperatively. Seven hundred thirty-four patients participated in the study. We assessed aspects of anxiety by means of visual analog scales (VAS) and the State Anxiety Score of the Spielberger State-Trait Anxiety Inventory (STAI). The mean STAI anxiety score was 39 +/- 1 (n = 486) and the mean VAS for fear of anesthesia was 29 +/- 1 (n = 539). Patients feared surgery significantly more than anesthesia (P < 0.001). The VAS measuring fear of anesthesia correlated well with the STAI score (r = 0.55; P < 0.01). Young patients, female patients, and patients with no previous anesthetic experience or a previous negative anesthetic experience had higher anxiety scores. Patients worried most about the waiting period preceding surgery and were least concerned about possible awareness intraoperatively. Factor analysis of various anxiety items showed three distinct dimensions of fear: 1) the fear of the unknown 2) the fear of feeling ill, and 3) the fear for one's life. Among these dimensions, fear of the unknown correlated highest with the anxiety measuring techniques STAI and VAS. The simple VAS proved to be a useful and valid measure of preoperative anxiety. The study of qualitative aspects of anxiety reveals three distinct dimensions of preoperative fear: fear of the unknown, fear of feeling ill, and fear for one's life. Groups of patients with a higher degree of preoperative anxiety and their specific anesthetic concerns can be identified using the visual analog scale.
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              Resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder and unipolar depression.

              This study for the first time investigated resting state corticolimbic connectivity abnormalities in unmedicated bipolar disorder (BD) and compared them with findings in healthy controls and unipolar major depressive disorder (MDD) patient groups. Resting state correlations of low frequency BOLD fluctuations (LFBF) in echoplanar functional magnetic resonance (fMRI) data were acquired from a priori defined regions of interests (ROIs) in the pregenual anterior cingulate cortex (pgACC), dorsomedial thalamus (DMTHAL), pallidostriatum (PST) and amygdala (AMYG), to investigate corticolimbic functional connectivity in unmedicated BD patients in comparison to healthy subjects and MDD patients. Data were acquired from 11 unmedicated BD patients [six manic (BDM) and five depressed (BDD)], and compared with data available from 15 unmedicated MDD and 15 healthy subjects. BD patients had significantly decreased pgACC connectivity to the left and right DMTHAL, similar to findings seen in MDD. Additionally, BD patients had decreased pgACC connectivity with the left and right AMYG as well as the left PST. An exploratory analysis revealed that both BDD and BDM patients had decreased connectivity between the pgACC and DMTHAL. The results of the study indicate a common finding of decreased corticolimbic functional connectivity in different types of mood disorders.
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                Author and article information

                Contributors
                Journal
                IEEE J Transl Eng Health Med
                IEEE J Transl Eng Health Med
                0063400
                JTEHM
                IJTEBN
                IEEE Journal of Translational Engineering in Health and Medicine
                IEEE
                2168-2372
                2014
                08 October 2014
                : 2
                : 2200110
                Affiliations
                [1]departmentDepartment of Electrical Engineering and Bioscience institutionWaseda University Tokyo169-8050Japan
                [2]divisionCollege of Engineering institutionNihon University Tokyo101-8308Japan
                Author notes
                Article
                2200110
                10.1109/JTEHM.2014.2361757
                4848070
                27170880
                6047e3c7-f3a6-4c95-9e20-9aa241948139
                2168-2372 © 2014 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
                History
                : 25 April 2014
                : 16 August 2014
                : 18 September 2014
                : 30 October 2014
                Page count
                Figures: 8, Tables: 2, Equations: 96, References: 33, Pages: 10
                Funding
                Funded by: Grant-in-Aid through the Ministry of Education, Culture, Sports, Sciences, and Technology, Japan
                Award ID: B23300247
                Funded by: Alpha Electron Company, Ltd., Fukushima, Japan
                Funded by: Iing Company, Ltd., Tokyo, Japan
                This work was supported in part by a Grant-in-Aid through the Ministry of Education, Culture, Sports, Sciences, and Technology, Japan, under Grant B23300247, Alpha Electron Company, Ltd., Fukushima, Japan, and Iing Company, Ltd., Tokyo, Japan.
                Categories
                Article
                Behavioral Science

                anxiety index,blood oxygenation,health and safety,near infrared spectroscopy,neuronal activity,oxyhemoglobin,prediction methods,prevention medicine,regional cerebral blood flow,translational engineering

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