Electroencephalogram-detected stress levels in the frontal lobe region of patients with dry eye.

PURPOSE: To evaluate stress levels extracted from prefrontal electroencephalogram (EEG) signals and investigate their relationship with dry eye symptoms. METHODS: This prospective, cross-sectional, comparative study included 25 eyes of 25 patients with aqueous tear-deficient dry eye (low Schirmer group), 25 eyes of 25 patients with short tear breakup time dry eye (short breakup time group), and 24 eyes of 24 individuals without dry eye. An EEG test, the Japanese version of the Ocular Surface Disease Index (OSDI), and a stress questionnaire were administered. EEG-detected stress levels were assessed under three conditions: eyes closed, eyes open, and eyes open under ocular surface anesthesia. RESULTS: Stress levels were significantly lower when the eyes were closed than when they were open in all groups (all P < 0.05). Stress levels during eyes open under ocular surface anesthesia were significantly lower than those during eyes open without anesthesia only in the low Schirmer group; no differences were found between the short breakup time and control groups. OSDI scores were associated with EEG-detected stress levels (P = 0.06) and vital staining score (P < 0.05) in the low Schirmer group; they were not associated with EEG-detected stress (P > 0.05), but with subjective stress questionnaire scores and breakup time values in the short breakup time group (P < 0.05). CONCLUSIONS: In the low Schirmer group, peripheral nerve stimulation caused by ocular surface damage induced stress reactions in the frontal lobe, resulting in dry eye symptoms. Conversely, in the short breakup time group, the stress response in the frontal lobe was not related to symptom development.

Unraveling the Influence of Age, IQ, Education, and Negative Symptoms on Neurocognitive Performance in Schizophrenia: A Conditional Inference Tree Analysis.

INTRODUCTION: The complex nature of neurocognitive impairment in schizophrenia has been discussed in light of the mixed effects of antipsychotic drugs, psychotic symptoms, dopamine D2 receptor blockade, and intelligence quotient (IQ). These factors have not been thoroughly examined before. METHODS: This study conducted a comprehensive re-analysis of the CATIE data using machine learning techniques, in particular Conditional Inference Tree (CTREE) analysis, to investigate associations between neurocognitive functions and moderating factors such as estimated trough dopamine D2 receptor blockade with risperidone, olanzapine, or ziprasidone, Positive and Negative Syndrome Scale (PANSS), and baseline IQ in 573 patients with schizophrenia. RESULTS: The study reveals that IQ, age, and education consistently emerge as significant predictors across all neurocognitive domains. Furthermore, higher severity of PANSS-negative symptoms was associated with lower cognitive performance scores in several domains. CTREE analysis, in combination with a genetic algorithm approach, has been identified as particularly insightful for illustrating complex interactions between variables. Lower neurocognitive function was associated with factors such as age>52 years, IQ<94/95,<12/13 education years, and more pronounced negative symptoms (score<26). CONCLUSIONS: These findings emphasize the multifaceted nature of neurocognitive functioning in patients with schizophrenia, with the PANSS-negative score being an important predictor. This gives rise to a role in addressing negative symptoms as a therapeutic objective for enhancing cognitive impairments in these patients. Further research must examine nonlinear relationships among various moderating factors identified in this work, especially the role of D2 occupancy.

Evaluation of a new portable corneal topography system for self-measurement using smartphones: a pilot study.

PURPOSE: Herein, we propose the use of the "KeraVio Ring", which is a portable, selfie-based, smartphone-attached corneal topography system that is based on the Placido ring videokeratoscope. The goal of this study was to evaluate and compare corneal parameters between KeraVio Ring and conventional corneal tomography images. METHODS: We designed the KeraVio Ring as a device comprising 3D-printed LED rings for generating Placido rings that can be attached to a smartphone. Two LED rings are attached to a cone-shaped device, and both corneas are illuminated. Selfies were taken using the KeraVio Ring attached to the smartphone without assistance from any of the examiners. Captured Placido rings on the cornea were analysed by intelligent software to calculate corneal parameters. Patients with normal, keratoconus, or LASIK-treated eyes were included. Anterior segment optical coherence tomography (AS-OCT) was also performed for each subject. RESULTS: We found highly significant correlations between the steepest and flattest keratometry, corneal astigmatism, and vector components obtained with the KeraVio Ring and AS-OCT. In subjects with normal, keratoconus, and LASIK-treated eyes, the mean difference in corneal astigmatism between the two devices was -0.8 ± 1.4 diopters (D) (95% limits of agreement (LoA), -3.6 to 2.0), -1.8 ± 3.7 D (95% LoA, -9.1 to 5.5), and -1.5 ± 1.3 D (95% LoA, -4.0 to 1.1), respectively. CONCLUSIONS: The experimental results showed that the corneal parameters obtained by the KeraVio Ring were correlated with those obtained with AS-OCT. The KeraVio Ring has the potential to address an unmet need by providing a tool for portable selfie-based corneal topography.

Dexamethasone induces sleep disturbance in a dose-dependent manner in mice.

Synthetic corticosteroids, the most well-known anti-inflammatory drugs globally, are effective against inflammatory diseases despite their adverse effects that decrease a patient's quality of life (QOL). One of these effects is sleep disturbance, which causes other health issues and further diminishes the QOL. However, the acute effects of steroid drugs on sleep-wake issues are not fully understood and must be clarified in detail using experimental animals. Therefore, this study examines the dose-dependent effect of dexamethasone (DXM), one of the strongest steroid drugs, on the sleep-wake architecture of mice. We conducted acute DXM administration at multiple doses and 24-hour EEG/EMG recordings. Our results revealed that DXM increased the time spent in arousal and decreased that of NREM sleep, even at very low doses. These results imply that steroid-induced sleep disturbance must be addressed at any dosage.

Autonomic Nerve Activity Features According to Dry Eye Type.

Purpose: The purpose of this study was to investigate the association between autonomic nerve activity and symptom intensity according to the type of dry eye (DE). Methods: This prospective, cross-sectional, comparative study included 25 eyes of 25 patients with short tear break-up time DE (sBUTDE; mean age = 57.4 ± 11.4 years, range = 30-74 years) and 24 eyes of 24 patients with aqueous tear-deficient DE (ADDE; mean age = 62.3 ± 10.7 years, range = 29-76 years) were studied. Autonomic nerve activity was examined, and the Japanese version of the Ocular Surface Disease Index (J-OSDI) and a stress check questionnaire were administered. Autonomic nerve activity was continuously measured for 10 minutes. The parameters were low-frequency (LF) and high-frequency (HF) components of heart rate variability, reflecting cardiac sympathetic and parasympathetic nerve activity, and parasympathetic nerve activity alone, respectively; and the coefficient of variation of R wave-to-R wave (RR) interval (cvRR), component coefficient of variation of LF (ccvLF), and component coefficient of variation of HF (ccvHF), reflecting fluctuation of RR interval, LF, and HF, respectively. Results: Higher J-OSDI scores were associated with higher HF, ccvHF, and subjective stress in sBUTDE, showing a significant correlation (r = 0.53, P < 0.01; r = 0.55, P = 0.01; and r = -0.66, P = 0.01); no correlations were observed between the J-OSDI score and autonomic parameters and stress in ADDE. Conclusions: DE symptoms were significantly associated with the magnitude and fluctuation of parasympathetic activity in sBUTDE. Thus, among the autonomic parameters, parasympathetic activity is involved in the development of symptoms in sBUTDE, whereas the involvement of the autonomic nervous system may be minimal in ADDE.

Variability of autonomic nerve activity in dry eye with decreased tear stability.

The autonomic nervous system plays a crucial role in the maintenance of homeostasis. Neurogenic inflammation due to decreased stability of tear film may induce changes in autonomic nerve activity, which could be associated with symptom expression.This study aimed to measure biological parameters that represent autonomic nerve activity in dry eye (DE)s caused by tear film dysfunction and investigate their relationship with symptom intensity. This prospective, cross-sectional, comparative study evaluated 34 eyes of 34 participants (mean age: 52.5 ± 13.4 years; range: 20-81 years) without keratoconjunctival damage. Nineteen eyes in the DE group showed DE symptoms and tear break-up time (TBUT) of ≤5 seconds (short TBUT DE); the 15 eyes in the non-DE group showed no DE symptoms. Autonomic nerve activity was measured for 10 minutes-starting and ending 5 minutes before and after instilling ophthalmic solution-and evaluated using the low-frequency component (LF) to the high-frequency component (HF) ratio of heart rate variability (autonomic balance). The pre-ophthalmic solution administration LF/HF ratio was not significantly different (P = 0.59) between the two groups, however, the standard deviation of the LF/HF ratio (LF/HF-SD) tended to be higher in the DE group than that in the non-DE group (P = 0.086). The DE symptom intensity was significantly related to LF/HF-SD (P = 0.005), which significantly decreased after ophthalmic solution administration in the DE group (P = 0.04). The large fluctuations in autonomic balance may be key for the understanding of the mechanism underlying DE symptoms.

Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning.

Objective.Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.Approach.For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).Main results.The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min-1, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min-1, respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.Significance.Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

Oscillatory population-level activity of dorsal raphe serotonergic neurons is inscribed in sleep structure.

Dorsal raphe (DR) 5-HT neurons regulate sleep-wake transitions. Previous studies demonstrated that single-unit activity of DR 5-HT neurons is high during wakefulness, decreases during non-rapid eye movement (NREM) sleep, and ceases during rapid eye movement (REM) sleep. However, characteristics of the population-level activity of DR 5-HT neurons, which influence the entire brain, are largely unknown. Here, we measured population activities of 5-HT neurons in the male and female mouse DR across the sleep-wake cycle by ratiometric fiber photometry. We found a slow oscillatory activity of compound intracellular Ca2+ signals during NREM sleep. The trough of the concave 5-HT activity increased across sleep progression, but 5-HT activity always returned to that seen during the wake period. When the trough reached a minimum and remained there, REM sleep was initiated. We also found a unique coupling of the oscillatory 5-HT activity and wide-band EEG power fluctuation. Furthermore, optogenetic activation of 5-HT neurons during NREM sleep triggered a high EMG power and induced wakefulness, demonstrating a causal role of 5-HT neuron activation. Optogenetic inhibition induced REM sleep or sustained NREM, with an EEG power increase and EEG fluctuation, and pharmacological silencing of 5-HT activity using a selective serotonin reuptake inhibitor led to sustained NREM, with an EEG power decrease and EEG fluctuation. These inhibitory manipulations supported the association between oscillatory 5-HT activity and EEG fluctuation. We propose that NREM sleep is not a monotonous state, but rather it contains dynamic changes that coincide with the oscillatory population-level activity of DR 5-HT neurons.SIGNIFICANT STATEMENTPrevious studies have demonstrated single-cell 5-HT neuronal activity across sleep-wake conditions. However, population-level activities of these neurons are not well understood. We monitored dorsal raphe (DR) 5-HT population activity using a fiber photometry system in mice and found that activity was highest during wakefulness and lowest during rapid eye movement (REM) sleep. Surprisingly, during non-REM sleep, the 5-HT population activity decreased with an oscillatory pattern, coinciding with EEG fluctuations. EEG fluctuations persisted when DR 5-HT neuron activity was silenced by either optogenetic or pharmacological interventions during non-REM sleep, suggesting an association between the two. Although oscillatory DR 5-HT neuron activity did not generate EEG fluctuations, it provides evidence that non-REM sleep exhibits at least binary states.

Contactless continuous heart rate monitoring system using ballistocardiography.

Cardiovascular disease is the number one cause of death in the world and is a serious problem. In the case of cardiopulmonary arrest due to myocardial infarction, the survival rate is as low as 13.3% one month after resuscitation, which birthed the need for continuous heart monitoring. In this study, we develop a Ballistocardiogram (BCG) measurement system using a load cell installed on a chair and a heart rate estimation algorithm that is robust to waveform changes, with the aim of constructing a non-contact heart rate acquisition system. The proposed system was evaluated by utilizing data obtained from 13 healthy subjects and 1 subject with abnormal ECG who were simultaneously measured with ECG. The output of the BCG system was confirmed to change with the same period as the ECG data obtained as the correct answer, and the synchronization of the R-peak positions was confirmed for all cases. As a result of comparing the heart rate intervals estimated from BCG and those obtained from ECG, it was confirmed that the same heart rate variability (HRV) features could be obtained even for abnormal ECG subject.

Frontotemporal EEG as potential biomarker for early MCI: a case-control study.

BACKGROUND: Previous studies using EEG (electroencephalography) as biomarker for dementia have attempted to research, but results have been inconsistent. Most of the studies have extremely small number of samples (average N = 15) and studies with large number of data do not have control group. We identified EEG features that may be biomarkers for dementia with 120 subjects (dementia 10, MCI 33, against control 77). METHODS: We recorded EEG from 120 patients with dementia as they stayed in relaxed state using a single-channel EEG device while conducting real-time noise reduction and compared them to healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' severity, were examined using the ratio of power spectrum at each frequency. RESULTS: In comparing healthy controls and dementia patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. CONCLUSIONS: A study with a larger sample size should be conducted to gauge reproducibility, but the results implied the effectiveness of EEG in clinical practice as a biomarker of MCI (mild cognitive impairment) and/or dementia.

A Study on Legume-Based Noodles as Staple Food for Office Workers.

This study aims to verify the effects of "legume-based noodles" as a staple food for lunch, specifically: blood glucose, cognitive function tests, Kansei value, work questionnaires, typing, and body weight. The experiment is divided into two groups: the intervention group (legumes-based noodle) and the control group (regular lunch). Both groups have similar menu except the staple food. The intervention group resulted in a statistically significant lower blood glucose area under the curve (AUC) and lower maximum blood glucose levels during the afternoon work hours on weekdays. In addition, the Kansei value "concentration" decreased at the end of the workday in the control group compared to before and after lunch but did not decrease in the intervention group. Furthermore, the number of typing accuracy was higher in the intervention group than in the control group, and the questionnaire responses for "work efficiency" and "motivation" were more positive. These results suggest that eating legume-based noodles may lead to improved performance of office workers.

Characteristics of single-channel electroencephalogram in depression during conversation with noise reduction technology.

BACKGROUND: Previous studies have attempted to characterize depression using electroencephalography (EEG), but results have been inconsistent. New noise reduction technology allows EEG acquisition during conversation. METHODS: We recorded EEG from 40 patients with depression as they engaged in conversation using a single-channel EEG device while conducting real-time noise reduction and compared them to those of 40 healthy subjects. Differences in EEG between patients and controls, as well as differences in patients' depression severity, were examined using the ratio of the power spectrum at each frequency. In addition, the effects of medications were examined in a similar way. RESULTS: In comparing healthy controls and depression patients, significant power spectrum differences were observed at 3 Hz, 4 Hz, and 10 Hz and higher frequencies. In the patient group, differences in the power spectrum were observed between asymptomatic patients and healthy individuals, and between patients of each respective severity level and healthy individuals. In addition, significant differences were observed at multiple frequencies when comparing patients who did and did not take antidepressants, antipsychotics, and/or benzodiazepines. However, the power spectra still remained significantly different between non-medicated patients and healthy individuals. LIMITATIONS: The small sample size may have caused Type II error. Patients' demographic characteristics varied. Moreover, most patients were taking various medications, and cannot be compared to the non-medicated control group. CONCLUSION: A study with a larger sample size should be conducted to gauge reproducibility, but the methods used in this study could be useful in clinical practice as a biomarker of depression.

Online improvisation training, hybrid improvisation training and on-site improvisation training; are they the same?

BACKGROUND: Since the outbreak of COVID-19 in Wuhan in December 2019, lifestyle has been changing to an online-based learning and working environment rather than on-site, and improvisation training is no exception. However, no research compares the efficacy of online versus on-site training. Although we believed that the most effective way to learn improvisation is an on-site format, it is important to explore how format differences can affect learners. METHOD: We offer three types of training such as on-site training (n = 6) (Consisting of 1 female with age ≥40 and <50, and 5 males with ages ≥20 and <50), hybrid training (Instructor participates from online and learners participate on-site) (n = 120) (Consisting of 55 female with age ≥15 and <20, and 65 males with ages ≥15 and <50), and online training (n = 20) (Consisting of 4 female with age ≥20 and <30, and 16 males with ages ≥20 and <50) We collected pretest, test, and posttest data by using the Kansei Analyzer, a simplified electroencephalograph (EEG) and Profile of Mood States (POMS) questionnaire. RESULTS: All formats of training displayed an increase in vigor and a decrease in depression, confusion, tension, anger, and fatigue. The online training displayed better results than the on-site training. Regardless of the format, all training displayed an increase in stress during the activities and a decrease in stress after the activity without changes in other indexes. Additionally, on-site training displayed an increase in sleepiness and stress during the activities. Some participants were tested twice but no significant differences were found between the initial results and the secondary results. CONCLUSION: In this study, we found evidence that online improvisation can lead to the prevention of depressive symptoms and can function as a method for the reduction of stress in conjunction with the increase of individual vigor. However, a future study is required due to the low number of participants and the absence of POMS data for the on-site training. Any future studies should account for these factors while examining other data such as blood pressure, blood sugar, and pulse.

Finding of the factors affecting the severity of COVID-19 based on mathematical models.

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.

Deep neural network-based classification of cardiotocograms outperformed conventional algorithms.

Cardiotocography records fetal heart rates and their temporal relationship to uterine contractions. To identify high risk fetuses, obstetricians inspect cardiotocograms (CTGs) by eye. Therefore, CTG traces are often interpreted differently among obstetricians, resulting in inappropriate interventions. However, few studies have focused on quantitative and nonbiased algorithms for CTG evaluation. In this study, we propose a newly constructed deep neural network model (CTG-net) to detect compromised fetal status. CTG-net consists of three convolutional layers that extract temporal patterns and interrelationships between fetal heart rate and uterine contraction signals. We aimed to classify the abnormal group (umbilical artery pH < 7.20 or Apgar score at 1 min < 7) and the normal group from CTG data. We evaluated the performance of the CTG-net with the F1 score and compared it with conventional algorithms, namely, support vector machine and k-means clustering, and another deep neural network model, long short-term memory. CTG-net showed the area under the receiver operating characteristic curve of 0.73 ± 0.04, which was significantly higher than that of long short-term memory. CTG-net, a quantitative and automated diagnostic aid system, enables early intervention for putatively abnormal fetuses, resulting in a reduction in the number of cases of hypoxic injury.

Evaluation of olive oil effects on human stress response by measuring cerebral blood flow.

In this paper, we evaluated the effects of olive oil on human's stress level. In recent years, mental stress from harsh working environment have been causing serious problems to human health, both mentally and physically. Symptoms of stress may include feelings of worthlessness, agitation, anxiety, lethargy, insomnia, and behavioral changes. Additionally, the harsh working environments may cause the workers to adopt unhealthy dietary habits, contributing to the health issue. On the other hand, olive oil has been known to provide stress-relieving effects both by ingestion and by inhaling the scent. Here, we examined the effects of extravirgin olive oil ingestion for mitigating stress from deskwork. Three best-selling extravirgin olive oil in Japan were tested, and typing task was selected to emulate deskwork situation. Near-infrared spectroscopy (NIRS) is utilized in this study to visualize the response in brain via cerebral blood flow analysis and to measure participants' stress level. Statistical analysis showed that the stress levels were lower during the olive oil ingestion experiment compared to no-oil experiment, even when measured one hour after the ingestion.

Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning.

Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particularly during activity. Here, we developed a system to objectively judge pain levels in walking subjects by recording their quantitative electroencephalography (qEEG) and analyzing data by machine learning. To do so, we enrolled 23 patients who had undergone total hip replacement for pain, and recorded their qEEG during a five-minute walk via a wearable device with a single electrode placed over the Fp1 region, based on the 10-20 Electrode Placement System, before and three months after surgery. We also assessed subject hip pain using a numerical rating scale. Brain wave amplitude differed significantly among subjects with different levels of hip pain at frequencies ranging from 1 to 35 Hz. qEEG data were also analyzed by a support vector machine using the Radial Basis Functional Kernel, a function used in machine learning. That approach showed that an individual's hip pain during walking can be recognized and subdivided into pain quartiles with 79.6% recognition Accuracy. Overall, we have devised an objective and non-invasive tool to monitor an individual's pain during walking.

Sleep Stage Estimation from Bed Leg Ballistocardiogram Sensors.

Ballistocardiogram (BCG) is a graphical representation of the subtle oscillations in body movements caused by cardiovascular activity. Although BCGs cause less burden to the user, electrocardiograms (ECGs) are still commonly used in the clinical scene due to BCG sensors' noise sensitivity. In this paper, a robust method for sleep time BCG measurement and a mathematical model for predicting sleep stages using BCG are described. The novel BCG measurement algorithm can be described in three steps: preprocessing, creation of heartbeat signal template, and template matching for heart rate variability detection. The effectiveness of this algorithm was validated with 99 datasets from 36 subjects, with photoplethysmography (PPG) to compute ground truth heart rate variability (HRV). On average, 86.9% of the inter-beat intervals were detected and the mean error was 8.5ms. This shows that our method successfully extracted beat-to-beat intervals from BCG during sleep, making its usability comparable to those of clinical ECGs. Consequently, compared to other conventional BCG systems, even more accurate sleep heart rate monitoring with a smaller burden to the patient is available. Moreover, the accuracy of the sleep stages mathematical model, validated with 100 datasets from 25 subjects, is 80%, which is higher than conventional five-stage sleep classification algorithms (max: 69%). Although, in this paper, we applied the mathematical model to heart rate interval features from BCG, theoretically, this sleep stage prediction algorithm can also be applied to ECG-extracted heart rate intervals.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.

INTRODUCTION: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep. METHODS: Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity. TRIAL REGISTRATION: UMIN000021396, University Hospital Medical Information Network (UMIN).

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one's cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.