Importance of serum albumin in machine learning-based prediction of cognitive function in the elderly using a basic blood test.

Introduction: In this study, we investigated the correlation between serum albumin levels and cognitive function, and examined the impact of including serum albumin values in the input layer on the prediction accuracy when forecasting cognitive function using deep learning and other machine learning models. Methods: We analyzed the electronic health record data from Osaka Medical and Pharmaceutical University Hospital between 2014 and 2021. The study included patients who underwent cognitive function tests during this period; however, patients from whom blood test data was not obtained up to 30 days before the cognitive function tests and those with values due to measurement error in blood test results were excluded. The Mini-Mental State Examination (MMSE) was used as the cognitive function test, and albumin levels were examined as the explanatory variable. Furthermore, we estimated MMSE scores from blood test data using deep learning models (DLM), linear regression models, support vector machines (SVM), decision trees, random forests, extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM). Results: Out of 5,017 patients who underwent cognitive function tests, 3,663 patients from whom blood test data had not been obtained recently and two patients with values due to measurement error were excluded. The final study population included 1,352 patients, with 114 patients (8.4%) aged below 65 and 1,238 patients (91.6%) aged 65 and above. In patients aged 65 and above, the age and male sex showed significant associations with MMSE scores of less than 24, while albumin and potassium levels showed negative associations with MMSE scores of less than 24. Comparing MMSE estimation performance, in those aged below 65, the mean squared error (MSE) of DLM was improved with the inclusion of albumin. Similarly, the MSE improved when using SVM, random forest and XGBoost. In those aged 65 and above, the MSE improved in all models. Discussion: Our study results indicated a positive correlation between serum albumin levels and cognitive function, suggesting a positive correlation between nutritional status and cognitive function in the elderly. Serum albumin levels were shown to be an important explanatory variable in the estimation of cognitive function for individuals aged 65 and above.

Enhancing dementia risk screening with GAN-synthesized periodontal examination and general blood test data.

Introduction: This study aimed to investigate the effectiveness of data augmentation to improve dementia risk prediction using machine learning models. Recent studies have shown that basic blood tests are cost-effective in predicting cognitive function. However, developing models that address various conditions poses challenges due to constraints associated with blood test results and cognitive assessments, including high costs, limited sample sizes, and missing data from tests not performed in certain facilities. Despite being often limited by small sample sizes, periodontal examination data have also emerged as a cost-effective screening tool. Methods: To address these challenges, this study explored the effectiveness of data augmentation using the Synthetic Minority Over-sampling Technique for Regression with Gaussian noise (SMOGN), a Generative Adversarial Network (GAN), and a Conditional Tabular GAN (CTGAN) on periodontal examination and blood test data. The datasets included parameters such as cognitive assessment results from the Mini-Mental State Examination (MMSE), demographic characteristics, periodontal examination data, and blood test results. Linear regression models, random forests, and deep neural networks were used to evaluate the effectiveness of the synthesized data. Results: This study used measured data from 108 participants and the synthesized data generated from the measured data. External validity was evaluated using a different dataset of 41 participants with missing items. The results suggested that normal GANs have the advantage of investigating models in data diversity, whereas CTGANs preserve the data structure and linear relationships in tabular data from the measured data, which drastically improves linear regression models. Discussion: Importantly, by interpolating sparse areas in the distribution, such as age, the synthesized models maintained prediction accuracy for test data with extreme inputs. These findings suggest that GAN-synthesized data can effectively address regression problems and improve dementia risk prediction.

Estimation of cognitive impairment in chronic pain patients and characteristics of estimated mild cognitive impairment.

Background: Patients with chronic pain suffer from psychological effects such as anxiety due to the pain itself. Pain can not only impair activities of daily living (ADL) and quality of life (QOL), but also impair cognitive function. Therefore, in this study, we aimed to estimate the cognitive function of chronic pain patients using a deep neural network (DNN) model that has already been implemented in society. We investigated the characteristics of patients presumed to have mild cognitive impairment (MCI) and, at the same time, verified the relationship with the questionnaire commonly used in chronic pain research, which is administered by 43 university affiliated hospitals and medical institutions participating in the chronic pain research group of the Ministry of Health, Labor and Welfare in Japan (assessment batteries). Method: The study included 114 outpatients from a multidisciplinary pain clinic, and we estimated their Mini-Mental State Examination (MMSE) scores based on age and basic blood test data (23 items). Furthermore, we classified the estimated MMSE scores of chronic pain patients into two groups based on a cutoff score of 27, which indicates MCI, and compared the blood data and assessment batteries. Additionally, we used a control group of 252 healthy adults aged 45 years or older who visited a dementia prevention outpatient clinic for comparison with the MMSE scores of chronic pain patients. Result: The MMSE scores in chronic pain patients were below the cutoff for MCI. When classified into two groups based on the estimated MMSE score of 27 points, WBC, RBC, Hb, Hct, PLT, UA, BUN, creatinine, Triglyceride, and γ-GT were significantly higher in the blood data. In the MCI group, PDAS values were significantly lower. Furthermore, only in the non-MCI group, a significant correlation was found between the estimated MMSE value and BPI, PDAS, and Locomo. The estimated MMSE scores were significantly lower in chronic pain patients than in healthy adults (p = 0.04). Conclusion: Patients with chronic pain may exhibit cognitive impairment due to systemic metabolic disturbances. This suggests that chronic pain affects activities of daily living, resulting in systemic metabolic disorders.

Bifidobacterium breve M-16V regulates the autonomic nervous system via the intestinal environment: A double-blind, placebo-controlled study.

We conducted a randomized controlled trial to investigate the potential of Bifidobacterium breve M-16 V to improve mood in humans. In this evaluation, we incorporated the use of near-infrared spectroscopy (NIRS), which has been used to evaluate mood states in studies with small sample sizes. Participants were given B. breve M-16 V (20 billion cells/day) for 6 weeks, and their mood state was assessed before and after ingestion. NIRS data were collected at rest and during a mental arithmetic task (under stress). Intake of B. breve M-16 V decreased the heart rate under stress and increased levels of the GABA-like substance pipecolic acid in stool samples. In addition, B. breve M-16 V improved mood and sleep scores in participants with high anxiety levels. These results suggest that B. breve M-16 V affects the metabolites of the gut microbiota and has the potential to modulate the autonomic nervous system and to improve mood and sleep.

Relationship Between Left-Right Dominancy of Prefrontal Cortex Activity and Heart Rate During Rest and Task Periods: An fNIRS Study.

BackgroundFunctional near-infrared spectroscopy (fNIRS) studies demonstrated that regulation of stress response of the autonomic nervous system is mediated by the left-right asymmetry of prefrontal cortex (PFC) activity. However, it is not yet clear whether PFC regulation of stress response is functioning only when the subject was under stress or even at rest without stress. In addition, the temporal responsivity of PFC regulation of stress response is not known.AimThis study aims to investigate the relationship between the left-right asymmetry of PFC activity and heart rate during both resting state and stressful state while performing a working memory task.ApproachTwenty-nine subjects were recruited to rest and conduct 2-back task, during which fNIRS and ECG were measured simultaneously.ResultsWe found weak correlation (r = 0.28, p = 0.137) between laterality index (LI) and heart rate in the task session, but no correlation in rest sessions at a group level. Moreover, weak but significant correlation was found only in the task session for all analysis intervals ranged from 2 s to 1 min.ConclusionIt is suggested that regulation of stress responses was mediated by the left-right asymmetry of PFC activity only when the subject was under stress stimuli and embody stress response did not affect PFC in reverse. This regulation can be observed at an analysis interval of no less than 2 s.

Relationship Between Cognitive Function, Oral Conditions and Systemic Metabolic Function in the Elderly.

Systemic metabolic disorders, including lifestyle-related diseases, are known risk factors for dementia. Furthermore, oral diseases such as periodontal disease and tooth decay are also associated with systemic metabolic disorders such as lifestyle-related diseases, and have also been reported to be indicators of risk factors for developing dementia. In this study, we investigated the relationship between cognitive function, oral conditions and systemic metabolic function in the elderly. We investigated the number of healthy teeth, the number of prosthetic teeth fitted, the number of missing prosthetic teeth, etc., in 41 elderly patients (69.7 ± 5.6 years old). Cognitive function was evaluated by the Mini Mental State Examination (MMSE). We also estimated MMSE scores for each subject using deep learning-based assessment of MMSE scores. This deep learning method enables the estimation of the MMSE score based on basic blood test data from medical examinations and reflects the systemic metabolic state including lifestyle-related diseases. The estimated MMSE score correlated negatively with age (r = -0.381), correlated positively with the number of healthy teeth (r = 0.37), and correlated negatively with the number of missing prosthetic teeth (r = -0.39). This relationship was not found in the measured MMSE scores. A negative correlation (r = -0.36) was found between age and the current number of teeth and a positive correlation (r = 0.37) was found between age and the number of missing prosthetic teeth. A positive correlation was found between the number of teeth requiring prosthesis and lifestyle-related diseases. The deep learning-based estimation method of cognitive function clearly demonstrated the close relationship between oral health condition, systemic metabolic function and the risk of cognitive impairment. It was determined that the smaller the number of existing teeth and the larger the number of missing prosthetic teeth, the higher is the risk of cognitive impairment. Systemic metabolic function is presumed to affect oral health and cognitive function. Interestingly, no such relationship was found in the measured MMSE scores. There are two possible reasons for this. The first is that MMSE is a subjective test and is less accurate in assessing cognitive function. The second is that because the MMSE estimated based on blood data using deep learning is calculated based on the metabolic function, it has a stronger correlation with the oral health condition affected by the metabolic function. In conclusion, oral health condition may predict cognitive impairment in the elderly.

Effect of Exercise Therapy on Stress Response Evaluated by IoMT Monitoring System.

The Internet of Medical Things (IoMT) system plays a role in various areas of social activity, including healthcare. Telemetry of cardiovascular function, such as blood pressure and pulse, in daily life is useful in the treatment of cardiovascular disease and stress management. However, until now, brain function monitoring technology has not been installed in the IoMT system.In this study, we used near-infrared spectroscopy (NIRS) installed in the IoMT system to evaluate whether consumers who are not medical experts can measure their own brain function correctly. In addition, the IoMT system was used to assess the long-term effects of physical exercise on physical and mental health.We studied a total of 119 healthy adults recruited from a fitness gym in Koriyama, Japan. After receiving instruction in the usage of the IoMT monitoring system including NIRS, the subjects monitored their physical and mental conditions by themselves when they visited the gym. We evaluated the relations between blood pressure (BP), pulse rate (PR), body weight (BW) and age. In addition, we evaluated the left/right asymmetry of the prefrontal cortex (PFC) at rest and BP. We calculated the laterality index at rest (LIR) for assessment of left/right asymmetry of PFC activity; a positive LIR (>0) indicates right-dominant PFC activity associated with higher stress responses, while a negative LIR (<0) indicates left-dominant PFC activity associated with lower stress responses. We studied 47 out of 119 cases who monitored their physiological conditions before and after physical exercise for 6 months for this study.The results showed that the systolic blood pressure and mean blood pressure (p < 0.05) were significantly reduced after the physical exercise for 6 months; body weight did not change significantly (p > 0.05). In addition, NIRS demonstrated that LIR changed to plus values from minus values after exercise (p < 0.01).These results show that (1) consumers who are not-medical experts can measure their own brain function correctly using NIRS; (2) after long-term physical exercise, systemic blood pressure decreased, associated with modulation of PFC activity (i.e., from right-dominant PFC activity to left-dominant activity), indicating that long-term physical exercises caused relaxation in the brain and the autonomic nervous system.

Effect of Atorvastatin on Microcirculation Evaluated by Vascular Occlusion Test with Peripheral Near-Infrared Spectroscopy.

The vascular occlusion test (VOT) with peripheral near-infrared spectroscopy (NIRS) is a non-invasive method to evaluate peripheral microcirculation. Statin therapy is widely used for patients with dyslipidaemia and contributes to reducing low-density lipoprotein cholesterol (LDL-C) levels and adverse cardiovascular events. However, it is not yet clear whether statin treatment improves peripheral microcirculation assessed by VOT with NIRS. In the present study, using VOT with NIRS, we evaluated the effect of statin therapy on peripheral microcirculation in patients with dyslipidaemia before and after statin therapy. METHODS: A total of six consecutive patients with dyslipidaemia who had not received statin therapy (6 males, mean age 71.8 ± 7.4 years) were enrolled. All patients were administered atorvastatin and their peripheral microcirculation assessed using VOT with NIRS (NIRO-200NX, Hamamatsu Photonics K.K., Japan) before and after statin therapy. The NIRS probe was attached to the right thenar eminence and brachial artery blood flow was blocked for 3 min at 50 mmHg above the resting systolic blood pressure. Maximum and minimum values of NIRS parameters after the VOT were used to determine concentration changes for total haemoglobin (ΔcHb), oxyhaemoglobin (ΔO2Hb), deoxyhaemoglobin (ΔHHb), and tissue oxygenation index (ΔTOI). RESULTS: During the follow-up period (mean 30.3 ± 6.5 days), LDL-C level decreased from 129.7 ± 26.3 to 67.5 ± 20.2 mg/dL (p-value = 0.031), ΔTOI increased from 24.0 ± 5.3 to 33.7 ± 6.3% (p-value = 0.023), and ΔO2Hb increased from 16.4 ± 5.3 to 20.0 ± 6.6 µmol/L (p-value = 0.007). ΔcHb and ΔHHb did not change significantly. CONCLUSION: ΔO2Hb and ΔTOI were significantly increased during the follow-up period. These findings suggest that ΔO2Hb and ΔTOI could assess the improvement of peripheral microcirculation by statin therapy. Compared to ΔTOI, ΔO2Hb seems to be a more useful parameter to evaluate peripheral microcirculation.

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning.

Background: Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy. Methods: We used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items. Results: There was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = -0.29) and platelet counts (PLT) (r = -0.26). GM-BHQ correlated with BUN (r = -0.30), GLU (r = -0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ. Discussion: Aging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject-this method may thus contribute to personalized care.

Event-Related NIRS and EEG Analysis for Mental Stress Monitoring.

Mental disorders caused by chronic stress are difficult to identify, and colleagues in the work environment may suddenly report symptoms. Social barriers exist including the financial cost of medical services and the lack of a perceived need for treatment even if potential patients have a desire to receive mental healthcare. Self-report inventories such as the Beck Depression Inventory (BDI-II) and State-Trait Anxiety Inventory (STAI) can assess the emotional valence for mental health assessment, but medical expertise may be required for interpretation of the results. Contingency plans for clinical supervision and referral sources are necessary for sufficient mental healthcare using self-report inventories. On the other hand, the laterality index at rest (LIR) has been proposed for evaluation of the mental stress level from near-infrared spectroscopy (NIRS) data in the prefrontal cortex in the resting state. However, the potential for long-term monitoring has not been investigated with sufficient evaluation results. In this study, feature values were extracted from both NIRS and EEG signals each week for 10 weeks in four young participants with an average BDI-II score of 17.7, i.e., indicative of mild depression. Temporal changes in LIR and heart rate (HR) were compared with STAI-Y1 and BDI-II scores. We found cross-correlations between the time series of LIR and STAI-Y1 within one-week delay. In addition, the time series of LIR was also correlated with BDI-II with one-week delay. Importantly, by annotating the larger changes in LIR and HR on daily life events, the changes in LIR and HR were different depending on the type of life event that affected these moods.

Changes in Prefrontal Cortex Asymmetry Due to Standing Load in Stroke Patients Measured by NIRS.

Autonomic disorders such as orthostatic hypotension often become a problem during the early mobilization of poststroke patients. We reported that the prefrontal cortex (PFC) oxyhemoglobin changes at rest are often on the right, and a positive correlation was observed between the left and right activity balance and the change in oxy-Hb. In this study, we focused on the asymmetrical changes associated with the standing load from rest. We assessed the left-right asymmetry of the PFC oxyhemoglobin changes at rest and standing load by calculating the Laterality Index at Rest (LIR) and laterality index during activity (LIA); positive values indicate the right-dominant activity, while negative values indicate left-dominant activity. As for left-right asymmetry LIA, the active dominant PFC was reversed in five patients. It should be noted that in almost all of the 13 cases, the active PFC and the lesion side matched. The detailed mechanism of overactivity up to the prefrontal cortex on the lesion side is unknown, but it may be a recovery mechanism that elicits plasticity in the brain network.

Machine Learning-Based Assessment of Cognitive Impairment Using Time-Resolved Near-Infrared Spectroscopy and Basic Blood Test.

We have demonstrated that machine learning allows us to predict cognitive function in aged people using near-infrared spectroscopy (NIRS) data or basic blood test data. However, the following points are not yet clear: first, whether there are differences in prediction accuracy between NIRS and blood test data; second, whether there are differences in prediction accuracy for cognitive function in linear models and non-linear models; and third, whether there are changes in prediction accuracy when both NIRS and blood test data are added to the input layer. We used a linear regression model (LR) for the linear model and random forest (RF) and deep neural network (DNN) for the non-linear model. We studied 250 participants (mean age = 73.3 ± 12.6 years) and assessed cognitive function using the Mini Mental State Examination (MMSE) (mean MMSE scores = 22.9 ± 6.1). We used time-resolved NIRS (TNIRS) to measure absolute concentrations of hemoglobin and optical pathlength at rest in the bilateral prefrontal cortices. A basic blood test was performed on the same day. We compared predicted MMSE scores and grand truth MMSE scores; prediction accuracies were evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE). We found that (1) the DNN-based prediction using TNIRS data exhibited lower MAE and MAPE compared with those using blood test data, (2) the difference in MAPE between TNIRS and blood test data was only 0.3%, (3) adding TNIRS data to the blood test data of the input layer only improved MAPE by 1.0% compared to the use of blood test data alone, whereas the use of the blood test data alone exhibited the prediction accuracy with 81.8% sensitivity and 91.3% specificity (N = 202, repeated five-fold cross validation). Given these findings and the benefits of using blood test data (low cost and large-scale screening possible), we concluded that the DNN model using blood test data is still the most suitable for mass screening.

Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination.

Background: In order to develop a new screening test of cognitive impairment, we studied whether cognitive function can be estimated from basic blood test data by applying deep learning models. This model was constructed based on the effects of systemic metabolic disorders on cognitive function. Methods: We employed a deep neural network (DNN) to predict cognitive function based on subject's age and blood test items (23 items). We included 202 patients (73.48 ± 13.1 years) with various systemic metabolic disorders for training of the DNN model, and the following groups for validation of the model: (1) Patient group, 65 patients (73.6 ± 11.0 years) who were hospitalized for rehabilitation after stroke; (2) Healthy group, 37 subjects (62.0 ± 8.6 years); (3) Health examination group, 165 subjects (54.0 ± 8.6 years) admitted for a health examination. The subjects underwent the Mini-Mental State Examination (MMSE). Results: There were significant positive correlations between the predicted MMSE scores and ground truth scores in the Patient and Healthy groups (r = 0.66, p < 0.001). There were no significant differences between the predicted MMSE scores and ground truth scores in the Patient group (p > 0.05); however, in the Healthy group, the predicted MMSE scores were slightly, but significantly, lower than the ground truth scores (p < 0.05). In the Health examination group, the DNN model classified 94 subjects as normal (MMSE = 27-30), 67 subjects as having mild cognitive impairment (24-26), and four subjects as having dementia (≤ 23). In 37 subjects in the Health examination group, the predicted MMSE scores were slightly lower than the ground truth MMSE (p < 0.05). In contrast, in the subjects with neurological disorders, such as subarachnoid hemorrhage, the ground truth MMSE scores were lower than the predicted scores. Conclusions: The DNN model could predict cognitive function accurately. The predicted MMSE scores were significantly lower than the ground truth scores in the Healthy and Health examination groups, while there was no significant difference in the Patient group. We suggest that the difference between the predicted and ground truth MMSE scores was caused by changes in atherosclerosis with aging, and that applying the DNN model to younger subjects may predict future cognitive impairment after the onset of atherosclerosis.

Effect of Mastication Muscle Activity on Prefrontal Cortex NIRS Measurement: A Pilot Study.

Changes in NIRS signals are related to changes in local cerebral blood flow or oxy-Hb concentration. On the other hand, recent studies have revealed the effect of chewing gum on cognitive performance, stress control etc. which accompanied brain activity in the prefrontal cortex (PFC). However, these relationships are still controversial. To evaluate the chewing effect on PFC, NIRS seems to be a suitable method of imaging such results. When measuring NIRS on PFC, blood volume in superficial tissues (scalp, skin, muscle) might have some affect. The aim of the present study was to clarify the effect of the anterior temporal muscle on NIRS signals during gum chewing. Eight healthy volunteers participated. Two-channel NIRS (HOT-1000, NeU, Japan), which can distinguish total-Hb concentrations in deep tissue and superficial tissue layers, was used. In addition to a conventional optode separation distance of 3.0 cm, Hot 1000 has a short distance of 1.0 cm (NEAR channel) to measure NIRS signals that originate exclusively from surface tissues. NIRS probes were placed at Fp1 and Fp2 in the normal probe setting. The headset was displaced to the left in order to allow the left probe to be placed over the left anterior temporal muscle. In the normal setting, the superficial signal curve shows no notable change; however, the neural (calculated and defined in HOT-1000) and deep curves show an increase during the gum chewing task. At the deviated setting, all three signals show marked changes during the task. Total-Hb concentration in the deviated probe setting is significantly large (p < 0.05) than that of in the normal probe setting. When using gum chewing as a task, it would be better to consider a probe position carefully so that the influence of muscle activity on NIRS signal can be distinguished.

Usefulness of a New Device to Monitor Cerebral Blood Oxygenation Using NIRS During Cardiopulmonary Resuscitation in Patients with Cardiac Arrest: A Pilot Study.

Recent guidelines on cardiopulmonary resuscitation (CPR) have stressed the necessity to improve the quality of CPR. Our previous studies demonstrated the usefulness of monitoring cerebral blood oxygenation (CBO) during CPR by near-infrared spectroscopy (NIRS). The present study evaluates whether the NIRO-CCR1, a new NIRS device, is as useful in the clinical setting as the NIRO-200NX. We monitored CBO in 20 patients with cardiac arrest by NIRS. On the arrival of patients at the emergency department, the attending physician immediately assessed whether the patient was eligible for this study after conventional advanced life support and, if eligible, measured CBO in the frontal lobe by NIRS. We found that in all patients, the cerebral blood flow waveform was in synchrony with the chest compressions. Moreover, the tissue oxygenation index increased following cardiopulmonary bypass (CPB) in patients undergoing CPB, including one patient in whom CBO was monitored using the NIRO-CCR1. In addition, although the NIRO-CCR1 could display the pulse rate (Tempo) in real time, Tempo was not always detected, despite detection of the cerebral blood flow waveform. This suggested that chest compressions may not have been effective, indicating that the NIRO-CCR1 also seems useful to assess the quality of CPR. This study suggests that the NIRO-CCR1 can measure CBO during CPR in patients with cardiac arrest as effectively as the NIRO-200NX; in addition, the new NIRO-CCR1 may be even more useful, especially in prehospital fields (e.g. in an ambulance), since it is easy to carry.

Application of Peripheral Near Infrared Spectroscopy to Assess Risk Factors in Patient with Coronary Artery Disease: Part 1.

Obesity, a risk factor of coronary artery disease, is known to cause peripheral microcirculatory disturbances. This study evaluated the relationship between the degree of obesity and peripheral microcirculatory disturbances, using peripheral near infrared spectroscopy (NIRS) with a vascular occlusion test (VOT). We compared correlations between the NIRS parameter changes induced by VOT and body mass index (BMI) in patients with and without statin therapy. A NIRS probe was set on the right thenar eminence, brachial artery blood flow was blocked for 3 min, and then released. Although total hemoglobin (ΔcHb), deoxyhemoglobin (ΔHHb) and tissue oxygenation index (ΔTOI) were not correlated with BMI, a significant negative correlation was found between oxyhemoglobin (ΔO2Hb) and BMI in the overall study population (r = -0.255, p-value 0.02). In addition, a significant negative correlation was found between ΔO2Hb and BMI in patients without statin therapy (r = -0.353, p-value 0.02) but not in patients with statin therapy (r = -0.181, p-value 0.27). These findings suggest that ΔO2Hb may be a useful indicator to assess peripheral microcirculation.

Application of Peripheral Near Infrared Spectroscopy to Assess Risk Factors in Patient with Coronary Artery Disease: Part 2.

Epicardial adipose tissue (EAT) is associated with visceral fat and various cardiac disorders, such as atrial fibrillation and adverse cardiovascular events. Therefore, it is important to develop a simple and non-invasive inspection method to assess EAT, to prevent unfavorable cardiac events. This study assessed correlations between near-infrared spectroscopy (NIRS) changes induced by a vascular occlusion test (VOT) and EAT volume measured by cardiac computed tomography (CCT) in patients with suspected coronary artery disease. We also assessed correlations between body mass index (BMI) and EAT volume in the same population. In addition, these correlations were compared in patients treated with statin therapy and in those without statin therapy. A NIRS probe was set on the right thenar eminence, and brachial artery blood flow was blocked for 3 min before being released. A negative correlation was found between oxyhemoglobin (ΔO2Hb) and EAT volume in the overall study population (r = -0.236, p = 0.03). Interestingly, although a strong correlation was observed in patients without statin therapy (r = -0.488, p < 0.001), this correlation was not observed in patients with statin therapy (r = 0.157, p = 0.34). These findings suggest that NIRS measurements with VOT may be a useful method to identify patients with high EAT volume and high cardiovascular risks.

Prediction of MMSE Score Using Time-Resolved Near-Infrared Spectroscopy.

Time-resolved near-infrared spectroscopy (TRS) enables assessment of baseline concentrations of hemoglobin (Hb) in the prefrontal cortex, which reflects regional cerebral blood flow and neuronal activity at rest. In a previous study, we demonstrated that baseline concentrations of oxy-Hb, deoxy-Hb, total-Hb, and oxygen saturation (SO2) measured by TRS were correlated with mini mental state examination (MMSE) scores. In the present study, we investigated whether Hb concentrations measured with TRS at rest can predict MMSE scores in aged people with various cognitive functions. A total of 202 subjects (87 males, 115 females, age 73.4 ± 13 years) participated. First, MMSE was conducted to assess cognitive function, and then baseline concentrations of oxy-Hb, deoxy-Hb, total-Hb, and SO2 in the bilateral prefrontal cortex were measured by TRS. Then, we employed the deep neural network (DNN) to predict the MMSE score. From the comparison results, the DNN showed 91.5% accuracy by leave-one-out cross validation. We found that not only the baseline concentration of SO2 but also optical path lengths contributed to prediction of the MMSE score. These results suggest that TRS with the DNN is useful as a screening test for cognitive impairment.

PFC Blood Oxygenation Changes in Four Different Cognitive Tasks.

Aging often results in a decline in cognitive function, related to alterations in the prefrontal cortex (PFC) activation. Maintenance of this function in an aging society is an important issue. Some practices/drills, moderate exercise, mastication, and a cognitive task itself could enhance cognitive function. In this validation study, before evaluating the effects of some drills on the elderly, we examined the neural substrate of blood oxygenation changes by the use of four cognitive tasks and fNIRS. Seven healthy volunteers (mean age 25.3 years) participated in this study. Each task session was designed in a block manner; 4 periods of rests (30 s) and 3 blocks of four tasks (30 s). The tasks used were: a computerized Stroop test, a Wisconsin Card Sorting Test, a Sternberg working memory paradigm, and a semantic verbal fluency task. The findings of the study are that all four tasks activated PFC to some extent, without laterality except for the verbal fluency task. The results confirm that NIRS is suitable for measurement of blood oxygenation changes in frontal brain areas that are associated with all four cognitive tasks.