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Introduction and Aim: Mental stress may impact dramatically on dynamic autonomic control on heart. Many studies have demonstrated association of high body mass index (BMI) with greater risk for cardiovascular disease with disturbance in autonomic neuronal activity. Analysis of Heart rate variability (HRV)during acute mental stress assesses the autonomic status of the individual. Hence, we aimed to study the effect of acute mental stress on time domain measures in obese adults. Materials and Methods:Sixty male volunteers of 30 each in study group (obese individuals) and control group (non-obese individuals) were recruited for the study. A basal recording of ECG in lead II was done on all the individuals. Then they underwent mental arithmetic stress task for 5 minutes during which again ECG was recorded. The change in time domain measures of HRV during rest and stress task was analyzed and compared between both the groups. Results: Analysis of time domain measures of HRV revealed a statistically significant increase (p ? 0.001) in mean heart rate in both obese and non-obese individuals, while rMSSD(root mean square differences of successive RR interval) and SDNN (standard deviation of all NN intervals) showed a statistically significant (p? 0.001) decrease in obese individuals and non-obese individuals did not show any statistically significant change during the mental stress task. Conclusion: In response to acute mental stress there was increased heart rate in both the groups. But the autonomic neuronal activity differed by way of sympathetic dominance in non-obese individuals and parasympathetic withdrawal in obese individuals.

Dentatorubral-pallidoluysian atrophy (DRPLA) is an autosomal dominant neurodegenerative disorder characterized by slowly progressive cerebellar ataxia. Previously, autonomic symptoms or dysfunction have not been reported. To evaluate subclinical autonomic dysfunction regarding thermoregulatory function in SCA, we recorded sympathetic outflow to skin in a DRPLA patient confirmed by genetic analysis. We recorded skin sympathetic nerve activity (SSNA), which was elicited and recorded by using the microneurographical technique. In results, the resting frequency of SSNA bursts was very low (8.2 ± 0.4 bursts/min [institutional normal range: 20.8 ± 2.4 bursts/min]). However, acceleration of SSNA bursts induced by mental arithmetic stress was confirmed. The amplitude of reflex bursts induced by electrical stimuli was slightly low (9.6 ± 1.6 μV [institutional normal range: 10.9 ± 2.2 μV]), and the reflex latency was mildly prolonged (872 ± 23.7 msec [institutional normal range: 761.9 ± 51.7 msec]). These results suggest potentially central autonomic dysfunction in this patient with DRPLA. To our knowledge, this is the first report to record SSNA and confirm subclinical autonomic dysfunction in a case with DRPLA.

The extent to which processing of abstract numerical concepts depends on perceptual representations is still an open question. In four experiments, we examined the association between contrast polarity and mental arithmetic, as well as its possible source. Undergraduate psychology students verified the correctness of single-digit arithmetic problems such as 2 + 5 = 7 or 9 − 6 = 5. Problems appeared either in white or black on a grey background, thus creating positive or negative contrast polarity, respectively. When the correct response was Yes (No), participants were faster (slower) in verifying positive than negative addition problems and in verifying negative than positive subtraction problems. Experiment 2 confirmed that the same result also held for written word problems (e.g., SEVEN + SIX = THIRTEEN). However, Experiment 3 found that the effect of contrast polarity observed in Experiments 1 and 2 disappeared in a blocked design where arithmetic operation was a between-participant factor. In addition, Experiment 4 revealed that the effect of contrast polarity does not generalise to multiplication and division. Overall, available evidence suggests that participants spontaneously associate the abstract relation between addition and subtraction (more-less) with a similar relation between contrast polarities (bright-dark).

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.

[Purpose] The main objective of this study was to assess the reliability of the method for testing the attention distribution ability of the elderly using mental arithmetic response time. [Participants and Methods] The participants included in the study were 30 healthy elderly people (over 65 years old), 11 males and 19 females, eight of whom had experienced falls in the past year.In the quiet standing position and the free walking state, we recorded the mental calculation response time of the participants by calculating the two-digit plus or minus one-digit values within 100. The test of the two states were tested in 24 hour intervals. [Results] In the quiet standing and free walking state, the correlation coefficient of the mental arithmetic response time group of the elderly was excellent. The intra-group correlation coefficient of mental arithmetic response time of more than three tests under free walking was greater than 0.9. [Conclusion] We found that the mental arithmetic response time can be used to objectively evaluate the ability of attention distribution in the elderly.

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.

Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive imaging technology with widespread use in cognitive sciences and clinical studies. It indirectly measures neural activation by measuring alterations of oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) in tissues. This study used mental arithmetic task for analyzing the activation of the frontal cortex. Materials and methods: The fNIRS instrument was used for measuring the alterations of HbO2 and Hb in healthy subjects during the task. Then the recorded signals were filtered in the frequency range of 3 to 80 mHz. The Continuous Wavelet Transform (CWT) of each of the HbO2 and Hb signals in each channel was calculated in the intended frequency range, followed by the calculation of the energy of obtained coefficients. Finally, for the performed tasks, the average energy of each channel in each region was obtained. Then the energies of spatially symmetric channel pairs in the two hemispheres were compared using the t-test. Results: Results demonstrated that the average energy of HbO2 signal for corresponding channels in the temporal, Medial Prefrontal Cortex (MPFC), and Dorsolateral Prefrontal Cortex (DLPFC) regions had significant differences (P

Noninvasive assessment of autonomic nervous system (ANS) activity is of great importance, but the accuracy of the method used, which is primarily based on electrocardiogram-derived heart rate variability (HRV), has long been suspected. We investigated the feasibility of photoplethysmography (PPG) in ANS evaluation. Data of 32 healthy young men under four different ANS activation patterns were recorded: baseline, slow deep breathing (parasympathetic activation), cold pressor test (peripheral sympathetic activation), and mental arithmetic test (cardiac sympathetic activation). We extracted 110 PPG-based features to construct classification models for the four ANS activation patterns. Using interpretable models based on random forest, the main PPG features related to ANS activation were obtained. Results showed that pulse rate variability (PRV) exhibited similar changes to HRV across the different experiments. The four ANS patterns could be better classified using more PPG-based features compared with using HRV or PRV features, for which the classification accuracies were 0.80, 0.56, and 0.57, respectively. Sensitive features of parasympathetic activation included features of nonlinear (sample entropy), frequency, and time domains of PRV. Sensitive features of sympathetic activation were features of the amplitude and frequency domain of PRV of the PPG derivatives. Subsequently, these sensitive PPG-based features were used to fit the improved HRV parameters. The fitting results were acceptable (p < 0.01), which might provide a better method of evaluating ANS activity using PPG.

Objective To investigate sleepiness, fatigue, and performance following a 120‐minute nap during simulated 16‐hour night shifts based on subjective and objective assessments. Methods Fourteen females participated in this crossover comparative study. Three experimental nap conditions were used: naps from 22:00 to 00:00 (22‐NAP), 00:00 to 02:00 (00‐NAP), and 02:00 to 04:00 (02‐NAP), respectively. Measurement items were sleep parameters, sublingual temperature, a Visual Analog Scale for sleepiness and fatigue, a single‐digit mental arithmetic task (for 10 minutes), and heart rate variability. Participants wore an ActiGraph to estimate their sleep state. Results There was no difference in the sleep parameters at the time of naps among the three conditions. Immediately following a 120‐minute nap, sleepiness and fatigue increased, and the number of calculations performed in the single‐digit mental arithmetic task decreased in any of the conditions. In particular, immediately after the 02‐NAP, fatigue and high‐frequency power (HF) were higher than after the 22‐NAP. In the early morning (from 05:00 to 09:00), in the 22‐NAP, sleepiness and fatigue increased, and performance and sublingual temperature decreased more than in the 00‐NAP and 02‐NAP. Furthermore, the ratio of errors was significantly lower in the 00‐NAP than in the 22‐NAP in the early morning. Conclusions A 120‐minute nap taken from 22:00 to 02:00 may cause temporary sleepiness after waking, increase fatigue and reduce performance. Greater attention should be given to naps taken at a later time (ie, 02‐NAP). In addition, taking a nap starting at 00:00 might decrease the risks of errors in the morning.

Drug convicts are thought to have decreased performance of the brain to think, concentrate, and carry out logical functions. To monitor how the drug could affect the human brain, mental arithmetic is performed, and the EEG signals are recorded. In this study, addition task performance men show that some subjects have different counting accuracy. Mental arithmetic was monitored by International 10/20 system EEG with 19 scalp electrodes. The raw EEG data was processed and plotted to 2d mapping based on the power spectrum. The spectrum changes seen in Brain mapping, show how the mental arithmetic process takes place. The results of brain mapping can be a parameter that indicates the calculation process so that it can show the quality of the brain's work. Independent component label classifier performed, subject 7 obtain 79,8%, subject12 obtain 94,3%, subject15 obtain 92,3% accuracy as brain label.

Symbolic number ordering has been related to arithmetic fluency; however, the nature of this relation remains unclear. Here we investigate whether the implementation of strategies can explain the relation between number ordering and arithmetic fluency. In the first study, participants (N = 16) performed a symbolic number ordering task (i.e., "is a triplet of digits presented in order or not?") and verbally reported the strategy they used after each trial. The analysis of the verbal responses led to the identification of three main strategies: memory retrieval, triplet decomposition, and arithmetic operation. All the remaining strategies were grouped in the fourth category "other". In the second study, participants were presented with a description of the four strategies. Afterwards, they (N = 61) judged the order of triplets of digits as fast and as accurately as possible and, after each trial, they indicated the implemented strategy by selecting one of the four pre-determined strategies. Participants also completed a standardized test to assess their arithmetic fluency. Memory retrieval strategy was used more often for ordered trials than for non-ordered trials and more for consecutive than non-consecutive triplets. Reaction times on trials solved by memory retrieval were related to the participants' arithmetic fluency score. For the first time, we provide evidence that the relation between symbolic number ordering and arithmetic fluency is related to faster execution of memory retrieval strategies. ispartof: Journal of Cognition vol:4 issue:1 pages:1-13 ispartof: location:England status: published

The reliable and valid assessment of heart rate variability (HRV) is important to understanding autonomic functioning in youth. However, use of electrocardiogram (ECG) signals to derive estimates of parasympathetic-mediated HRV may present logistical barriers in applied settings. Thus, this study investigated the concordance between high-frequency HRV [HF-HRV] and root mean square of successive differences [RMSSD] derived from ECG and photoplethysmography (PPG) signals during a video baseline, resting baseline, and mental arithmetic among an ethnically diverse sample of ninety-six youths (Mage = 13.84; 51% male; 62.5% ethnic minority). Results showed moderate to almost perfect agreement between PPG- and ECG- derived HF-HRV and RMSSD for video and resting baselines (Lin's correlations ranged from 0.93 to 0.99) and limits of agreement (LoA) ranging from −0.48 to 0.58 (HF-HRV) and − 11.37 to 9.32 (RMSSD). Conversely, we found poor to moderate agreement for the mental arithmetic task (Lin's correlations ranged from 0.88 to 0.91) and LoA ranging from −0.68 + 0.94 (HF-HRV) and − 17.58 + 20.69 (RMSSD), though we did find ethnic minority youth had higher and moderate Lin's correlations (0.93 to 0.94). Overall, there was a bias towards higher HF-HRV and RMSSD values with PPG. Findings suggest that PPG-derived HF-HRV and RMSSD may be viable alternatives for ECG in baseline conditions, but tasks requiring movement or eliciting mild stress responses may result in less than perfect values and missing data patterns. It is imperative that future studies replicate these findings in other ethnically diverse youth samples and expand to younger children and applied settings.

The injurious effects of mental stress on the human body and mind are well known. Many researchers have focused on developing stress monitoring systems using physiological signals obtained from the body to alleviate stress. This study aims to provide a comparative analysis of four physiological signals – Electrodermal Activity (EDA), Heart Rate (HR), Skin Temperature (SKT), and Blood Volume Pulse (BVP), recorded using the Empatica E4 Wristband, in building stress classification models. We collect a dataset on 21 participants comprising their physiological signals while they perform a mental arithmetic task, which acts as a stress inducer. We compare the classification accuracy of machine learning classifiers trained on feature sets built using the four signals taken one at a time, two at a time, three at a time and all together. We achieve the highest accuracy of 99.92% using all the four signals. When we consider three signals at a time, EDA, HR, and BVP feature set achieves the best accuracy of 99.88%, and taking two signals at a time, EDA and HR feature set obtains the best accuracy of 99.09%. This paper can act as a guide for a manufacturer to select an optimal set of physiological signals for building efficient stress monitoring systems.

Theoretical models of adolescent depression postulate that one possible individual vulnerability factor for the development of depressive symptoms is autonomic dysregulation. However, there is limited and mixed support for these models among ethnically diverse and higher risk stress-exposed youth. Therefore, this study investigated the relations between both tonic and phasic indices of parasympathetic autonomic functioning (i.e., resting high-frequency heart rate variability [HF-HRV] and root mean square of successive differences [RMSSD]; HF-HRV and RMSSD reactivity to a mental arithmetic stressor) and depressive symptoms among 80 severely stress-exposed youth (51% female; 11-17 years of age) from diverse backgrounds (61.3% ethnic minority; caregiver-reported median family income = $20,000-$49,999 per year). Results demonstrated that lower resting HF-HRV and RMSSD, but not HF-HRV and RMSSD reactivity, was associated with greater youth depressive symptoms. Our findings suggest that lower resting parasympathetic autonomic functioning may be a potential vulnerability factor of depressive symptoms among stress-exposed youth, instead of specific emotional responses to stressors. These findings will need to be replicated in larger samples of stress-exposed youth and youth at higher risk for or exhibiting clinical levels of depressive symptoms to better elucidate relations with autonomic functioning and depressive symptoms among adolescents.

Background Postflight orthostatic challenge (OC), resulting from blood pooling in lower extremities, is a major health concern among astronauts that fly long-duration missions. Additionally, astronauts undergo physical demanding tasks resulting in acute fatigue, which can affect performance. However, the effects of concurrent OC and acute physical fatigue on performance have not been adequately investigated. Objective The purpose of this study was to determine the relationship between acute physical fatigue and cognitive function during OC. Methods Sixteen healthy participants performed the mental arithmetic task and psychomotor tracking tasks in the absence and presence of a prior 1-hour physically fatiguing exercise, on separate days under OC (induced via lower body negative pressure). We recorded task performances on the cognitive tests and prefrontal cortex oxygenation using functional near-infrared spectroscopy, along with physiological and subjective responses. Results The introduction of the cognitive tasks during OC increased cerebral oxygenation; however, oxygenation decreased significantly with the cognitive tasks under the acute fatigue conditions, particularly during the tracking task and in males. These differences were accompanied by comparable task performances. Discussion The findings suggest that mental arithmetic is a more effective countermeasure than psychomotor tracking under acute physical fatigue during OC. Whereas females did not show a significant difference in cerebral oxygenation due to task, males did, suggesting that it may be important to consider gender differences when developing countermeasures against OC.

Graph network analysis (GNA) showed a remarkable role for understanding brain functions, but its application is mainly nar-rowed to fMRI research. Connectivity analysis (CA) is introduced as a signal-to-graph mapping in a time-causality framework. In this paper, we investigate the application of GNA/CA in fNIRS. To solve the inher-ent challenges of using CA, we also propose a novel metric: a maximum cross-lag magnitude (MCLM) that eÿciently extracts major causality in-formation. We tested MCLM in four types of cognitive activities (mental arithmetic, motor imagery, word generation, and brain workload) from 55 participants. CA/MCLM showed a compelling modeling capacity and re-vealed unexpected cross-subject network patterns. We found that motion imagery and mental arithmetic share a background network structure, and that the right prefrontal cortex, in AFp8, is an invariable destina-tion for information flows in every stimuli and participant. Therefore, CA/MCLM-fNIRS showed potential for its use along with fNIRS in clin-ical studies. This work is financially supported by the Research Council of Norway to the Project No. 273599, "Patient-Centric Engineering in Rehabilitation (PACER)".

The incidence of stress-induced psychological and somatic diseases has been increasing rapidly, and it is important to clarify the neurophysiological mechanisms of stress response in order to establish effective stress management methods. We previously reported that the prefrontal cortex (PFC) plays an important role in stress response. In the present study, we employed near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to investigate the characteristics of PFC activity during mental arithmetic tasks. A two-channel NIRS device was used to measure hemoglobin (Hb) concentration changes in the bilateral PFC during a mental arithmetic task (2 min) in normal adults. Simultaneously, EEG was used to also measure bilateral PFC activity during the same task. We evaluated concentration changes of oxy-Hb induced by the task while analyzing α wave changes using power spectrum analysis. It was observed that oxy-Hb in the bilateral PFC increased significantly during the task (p < 0.05), while α wave power in the PFC decreased significantly (p < 0.01). The present results indicate that mental stress tasks caused the activation of the bilateral PFC. Simultaneous measurements of NIRS and EEG are useful for evaluating the neurophysiological mechanism of stress responses in the brain.

Background: Mathematics form is universally understood in modern society. It is natural to ask whether there is neurophysiological evidence for putative cognitive components of the task and the practice in mental arithmetic will lead to a detectable transition in primary locus of brain activity. Aims and Objectives: The purpose of this study was to determine the effect of real-time arithmetic recognition task for cognitive performance and electroencephalographic activities. Materials and Methods: While practicing the real-time arithmetic recognition task named SpeedMath developed by NeuroSky, Inc., electroencephalographic activities were also recorded by using the commercial lightweight electroencephalographic device, Mindwave Mobile, NeuroSky, Inc. Eight participants included learning disabilities children participating in this study. All participants were instructed and trained to practice the simple the arithmetic recognition task which included arithmetic and mathematic skills. Results: The results showed that both alpha and beta frequency bands were increased with statistically significant at the 0.05 level. Conclusions: The real-time arithmetic recognition task might improve the real-time arithmetic recognition task and performance. Asian Journal of Medical Sciences Vol.10(1) 2019 43-46

The autonomic and hormonal reactivity in the conditions of mental stress was studied in 248 children and adolescents aged 9–10 to 14–16 years. Changes in functional load level were evaluated based on the dynamics of heart rate variability (HRV) and the salivary cortisol level. A centile table was composed using the changes in these parameters during mental arithmetic tasks. Based on the table, six types of autonomic and hypothalamic-pituitary-adrenal reactivity were determined and gradations of deviations in various parts of the autonomic nervous system (ANS) refined. The reactivity of the ANS and hypothalamic-pituitary-adrenal axis (HPA-axis) was found to depend on the initial functional state and the age of subjects. In early adolescence, an increased level of psychoemotional stress associated with increased activity of the HPA-axis leads to a decrease in the reaction to mental stress. By an age of 14–16 years, the initial functional state of the ANS and HPA-axis tends to improve and, consequently, the reaction to mental stress is normalized.

Background: Mental stress is known as one of the main influential factors in development of different diseases including heart attack and stroke. Thus, quantification of stress level can be very important in preventing many diseases and in human health. Methods: The prefrontal cortex is involved in body regulation in response to stress. In this research, functional near infrared spectroscopy (fNIRS) signals were recorded from FP2 position in the international electroencephalographic 10–20 system during a stressful mental arithmetic task to be calculated within a limited period of time. After extracting the brain’s hemodynamic response from fNIRS signal, different linear and nonlinear features were extracted from the signal which are then used for stress levels classification both individually and in combination. Results: In this study, the maximum accuracy of 88.72% was achieved in classification between high and low stress levels, and 96.92% was obtained for the stress and rest states. Conclusion: Our results showed that using the proposed linear and nonlinear features it is possible to effectively classify stress levels from fNIRS signals recorded from only one site in the prefrontal cortex. Comparing to other methods, it is shown that the proposed algorithm outperforms other previously reported methods using the nonlinear features extracted from the fNIRS signal. These results clearly show the potential of fNIRS signal as a useful tool for early diagnosis and quantify stress.

Microneurographic recordings of human muscle sympathetic nerve activity responses to sudden sensory stimuli (ie, arousal) have revealed 2 intraindividually reproducible response profiles in healthy young males that predict different neural and blood pressure responses to more sustained stress. Approximately 50% of subjects inhibit muscle sympathetic nerve activity during arousal, whereas the remaining 50% do not, and the latter group displays a markedly greater blood pressure increase in response to arousal, as well as during and after 3 minutes of mental arithmetic. Studying a group of monozygotic twins (10 pairs, 2 excluded from analysis), the aim of the present study was to evaluate the degree of genetic determination of these sympathetic response profiles. Muscle sympathetic burst incidence at rest was similar in twins, with a within-pair burst incidence ratio of 0.87±0.02 (SEM) compared with 0.73±0.07 found in unrelated pairs ( P =0.002), confirming a previous study from our laboratory. In contrast, the sympathetic responses to arousal showed large twin within-pair variance (arousal inhibition ratio 0.56±0.11), which did not significantly differ ( P =0.939) from the variance in pairs of unrelated subjects (0.46±0.11). The finding that human muscle sympathetic nerve responses to arousal are less determined by genotype than the resting level of corresponding sympathetic nerve activity suggests that the arousal response pattern is more prone to be altered by environmental factors. This raises the possibility that these intraindividually reproducible sympathetic neural response profiles can be modified in a positive direction from a cardiovascular risk perspective.

Cuffless blood pressure (BP) measurement is an all-inclusive term for a method that aims to measure BP without using a cuff. In our previous article, we reported the development of a simple cuffless technique based on the haemodynamic expression, using only pulse rate (PR) and modified normalized pulse volume (mNPV) that can be measured using only a smartphone. The stressor used in our previous article was the cardiac-dominant pattern (mental arithmetic); however, here we have experimented with another stressor, namely, a vascular-dominant pattern in a cold pressor test, to verify this theory in 7 healthy young males and 6 healthy young females (N = 22). As a result, the mean arterial pressure accuracy was improved (r = 0.727) compared with that achieved in the previous study.

In recent years, research on stress has been actively conducted because of an increase in demand for mental health care. In this paper, simultaneous measurements of biosignals by electrocardiogram (ECG), near-infrared spectroscopy (NIRS) and nasal skin temperature (NST) were performed at stress loading due to mental arithmetic task. Feature extraction and selection of the biosignals were performed by random forest and stepwise method, and learned by SVM to construct a detection model of stress state. Effective features were investigated from the model. As a result of learning, the F1 score of the model by stepwise method was 0.765, which is the highest result compared with the model using all the features and the model by random forest. The results suggested that the slope of NST is most useful as a feature to detect stress.

In this experiment, psychogenic (mental arithmetic), thermogenic (mean body temperature elevation of 0.6 °C) and combined thermo-psychogenic treatments were used to explore eccrine sweat-gland recruitment from glabrous (volar hand and forehead) and non-glabrous skin surfaces (chest). It was hypothesised that each treatment would activate the same glands, and that glandular activity would be intermittent. Nine individuals participated in a single trial with normothermic and mildly hyperthermic phases. When normothermic, a 10-min arithmetical challenge was administered, during which sudomotor activity was recorded. Following passive heating and thermal clamping, sweating responses were again evaluated (10 min). A second arithmetical challenge (10 min) was administered during clamped hyperthermia, with its sudorific impact recorded. The activity of individual sweat glands was recorded at 60-s intervals, using precisely positioned, and uniformly applied, starch-iodide papers. Those imprints were digitised and analysed. Peak activity typically occurred during the thermo-psychogenic treatment, revealing physiologically active densities of 128 (volar hand), 165 (forehead) and 77 glands.cm−2 (chest). Except for the hand (46%), glands uniquely activated by one treatment were consistently

The assessment of changes in the autonomous nervous system (ANS), have important prognostic and diagnostic value, and can be used to assess stress levels. There are many approaches to directly measure the sympathetic and parasympathetic nervous system, although, most of them are invasive and unable to provide continuous monitoring. Heart rate variability (HRV) and Electrodermal activity (EDA) are noninvasive methods to assess the autonomous nervous system, by computing the spectral analysis of both HRV and EDA biosignals. In order to provide continuous monitoring, a wearable device is used, obtaining HRV features with photoplethysmography signals from the wrist and EDA from the fingers. The extraction of the HRV and EDA features, were obtained by submitting the subjects to a mental arithmetic stress test. The distinct response to stress was then classified using machine-learning techniques. The constructed models have the ability to predict how the subjects will respond, with an accuracy of approximately 80% in terms of HRV features in baseline and an accuracy of approximately 77% in terms of HRV and EDA simultaneous baseline features, when submitted to a situation of stress.

The current study evaluated the efficacy of self-chosen music listening for the function of affect regulation comparing effects in younger and older adults. Forty younger (18–30 years, M = 19.75, SD = 2.57, 14 males) and forty older (60–81 years, M = 68.48, SD = 6.07, 21 males) adults visited the laboratory and were randomised to either the intervention (10 minutes of listening to self-chosen music) or the active control condition (10 minutes of listening to an experimenter-chosen radio documentary). Negative affect (NA) was induced in all participants using a speech preparation and mental arithmetic task, followed by the intervention/control condition. Measures of self-reported affect were taken at baseline, post-induction and post-intervention. Controlling for baseline affect and reactivity to the NA induction, in comparison with the active control group the music listening group demonstrated greater reduction in NA. Supporting developmental theories of positive ageing, analyses also found significant main effects for age, with older adults experiencing greater reduction of NA than younger adults, regardless of condition. Results of the current study provide preliminary insights into the effects of self-chosen music on induced NA, however, additional experimental control conditions comparing self-chosen and experimenter-chosen music with self-chosen and experimenter-chosen active controls are needed to fully understand music listening effects for affect regulation.

A large number of studies on altered functional brain network tend to focus only on the alternation in topological metric of functional connectivity, rather than on the details of graph adjustment that cause topological metric changes, such as significant adjusted route and the nodes on it. In this paper, we first used the brain atlas of Dosenbach to generate the functional brain networks of the 21 participants recruited in the mental arithmetic experiment. Then, the nodal efficiency of each brain region in the network were calculated and statistically compared between mental arithmetic cognitive states. The brain regions with significant alternation in nodal efficiency were taken as seeds for searching adjusted routes. The brain regions that have significant changes in network efficiency with the seed nodes were considered as destined nodes of the relative seed nodes. Finally, the details of two adopted indicators on altered functional brain network by comparing the adjusted route between the two endpoints of the adjusted route were given and used as clues for the better understanding of the cognitive pattern of mental arithmetic. In this paper, the average number of adjusted routes contributed by brain region is used to indicate the degree of contribution of the brain region to the route adjustment, and the interaction degree within specific network is indicated by the density of adjusted routes. The results show that both indicators of fronto-parietal network is significantly higher than that of other networks, which indicates the brain regions and the routes within fronto-parietal network are the most active. In summary, the method proposed in this paper provides a new perspective to study the causes of functional brain network alternation in mental arithmetic. However, due to the participants’ variation of adjusted routes and the nodes on it, a better understanding of these functional brain network alternation for individual participant with the proposed method needs more in-depth research.

The human stress level is rather intangible, and therefore cannot be directly measured. However, this stress can be correlated to external and/or internal factors, which are rather tangible things, such as heart rate, Systolic-Diastolic Blood Pressure, salivary α -amylase, and skin temperature. These factors measurements are provided in wide ranges and vary individually lends itself naturally to fuzzy sets and fuzzy logic. This research is aimed at assessing respondents stress level through the changes of physiological factors above over time. The subjects of this study are 10 healthy adults, aged between 23-35 years old. The measurement time divided into four periods, namely pre-stressor, stressor-I, stressor-II and post stressor period. Pauli-test and mental arithmetic test was used as stressors. Obtained data were analyzed using Fuzzy Inference System (FIS) toolbox in MATLAB. By using fuzzy inference system model, the system succeeds to predict subject stress value/index based on subject physiological value. The result showed that the changes in human factors affects human stress value/index, although the subjects stated that their stress level on the same level their physiological factors were changed during measurement, and the models showed the changes of subject stress value during different period of measurement time. Keywords

Currently, mental stress is a common social problem affecting people. Stress reduces human functionality during routine work and may lead to severe health defects. Detecting stress is important in education and industry to determine the efficiency of teaching, to improve education, and to reduce risks from human errors that might occur due to workers&rsquo, stressful situations. Therefore, the early detection of mental stress using machine learning (ML) techniques is essential to prevent illness and health problems, improve quality of education, and improve industrial safety. The human brain is the main target of mental stress. For this reason, an ML system is proposed which investigates electroencephalogram (EEG) signal for thirty-six participants. Extracting useful features is essential for an efficient mental stress detection (MSD) system. Thus, this framework introduces a hybrid feature-set that feeds five ML classifiers to detect stress and non-stress states, and classify stress levels. To produce a reliable, practical, and efficient MSD system with a reduced number of electrodes, the proposed MSD scheme investigates the electrodes placements on different sites on the scalp and selects that site which has the higher impact on the accuracy of the system. Principal Component analysis is employed also, to reduce the features extracted from such electrodes to lower model complexity, where the optimal number of principal components is examined using sequential forward procedure. Furthermore, it examines the minimum number of electrodes placed on the site which has greater impact on stress detection and evaluation. To test the effectiveness of the proposed system, the results are compared with other feature extraction methods shown in literature. They are also compared with state-of-the-art techniques recorded for stress detection. The highest accuracies achieved in this study are 99.9%(sd = 0.015) and 99.26% (sd = 0.08) for identifying stress and non-stress states, and distinguishing between stress levels, respectively, using only two frontal brain electrodes for detecting stress and non-stress, and three frontal electrodes for evaluating stress levels respectively. The results show that the proposed system is reliable as the sensitivity is 99.9(0.064), 98.35(0.27), specificity is 99.94(0.02), 99.6(0.05), precision is 99.94(0.06), 98.9(0.23), and the diagnostics odd ratio (DOR) is &ge, 100 for detecting stress and non-stress, and evaluating stress levels respectively. This shows that the proposed framework has compelling performance and can be employed for stress detection and evaluation in medical, educational and industrial fields. Finally, the results verified the efficiency and reliability of the proposed system in predicting stress and non-stress on new patients, as the accuracy achieved 98.48% (sd = 1.12), sensitivity = 97.78% (sd = 1.84), specificity = 97.75% (sd = 2.05), precision = 99.26% (sd = 0.67), and DOR &ge, 100 using only two frontal electrodes.

American Indian adults are at an increased risk for cardiovascular disease compared with non-Hispanic white adults. Scant research exists examining the underlying physiological and psychological mechanisms associated with these risks. This study aimed to examine possible psychological and physiological stress-related mechanisms related to cardiovascular disease risk in healthy American Indian and non-Hispanic white adults. Forty American Indian (60% female, Mean age = 19.93, SD = 2.08 years) and 45 non-Hispanic white (70% female, Mean age = 20.18, SD = 2.22 years) participants attended an in-person laboratory session. Salivary cortisol and cardiovascular activity were measured before (baseline), during, and after exposure to a 10-minute mental arithmetic task. Compared to non-Hispanic white participants, American Indian had diminished salivary cortisol (p < .001), blood pressure (p’s < .001), and heart rate (p = .041) responses to acute psychological stress. These effects could not be accounted for by differences in task performance or self-reported engagement. Previous research has shown that exaggerated responses to stress are associated with increased risk of cardiovascular disease. However, diminished responses to stress are associated with early childhood stress and future adverse behaviors (e.g., addiction, obesity). Diminished reactivity may influence behaviors that can impact future development of cardiovascular disease in American Indian populations.

Background: Cognitive dysfunction in Type 2 diabetes mellitus (DM) can be unveiled by stress test even before it develops at rest. Mental stress testing is easier to administer and can be precisely regulated by the investigator. Mental arithmetic using serial subtraction is the most widely used method to administer stress. Reaction time (RT) widely used to assess cognitive domains such as attention, execution, and psychomotor speed. Investigators have shown that choice RTs are delayed in Type 2 DM. Aim and Objectives: The objective of the present study was to assess cognitive dysfunction in Type 2 DM during acute mental stress without overt cerebrovascular disease or other vascular risk factors. Materials and Methods: After getting medical ethical clearance of our institution, 60 subjects between the given age group (35–55 years) were enlisted in the study. They were categorized into two groups. Group 1 had 30 diagnosed cases of Type 2 DM at least 2 years of duration. Group 2 had 30 gender and age-matched controls. Mini-mental status examination was performed to assess global cognitive function in these groups. Simple and choice auditory and visual RTs (VRTs) were measured during rest and during acute mental stress in these groups to assess cognitive function. Results: There was a significant difference in auditory RT and VRT, both simple and choice in Type 2 DM and controls and these RTs further increased during mental stress (P < 0.001). Conclusion: Cognitive function in Type 2 DM deteriorates with acute mental stress.

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.

There has been an increase interest for functional near-infrared spectroscopy (NIRS) in recent years since it is a non-invasive technique as well as few restrictions to the subjects and not affected by electrical noise. In this study, we analyzed mental arithmetic based NIRS signals that it can be helpful for patients like dyscalculia where difficulty learning or lack of attention problem exists. So, it is important that the mental arithmetic is effectively separated from NIRS signal. For this purpose, first, we determined change in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations by applying the modified Beer-Lambert law to NIRS data set. After Hilbert transform (HT)+ sum derivative (SD) based features were extracted from pre-processed HbR and HbO, these features were classified by k-nearest neighbors. The average classification accuracy (CA) rates of 82.87% and 84.94% were calculated from the HT+SD based features that best determine the mental arithmetic of the HbR and HbO signals, respectively. It can be said that the proposed method is effective for this dataset, in view of the fact that these values are 2.17% and 1.34% higher than CAs calculated in the literature for HbR and HbO, respectively.

We investigated the effect of age on heart rate (HR) responses and subjective mental workload during mental tasks. In this study, 55 male participants performed a mental arithmetic (MA) task and a mirror tracing (MT) task for 5 min each. Electrocardiogram (ECG) was recorded during these mental tasks and resting periods. Low frequency component (LF), high frequency component (HF), and LF/HF ratio of heart rate variability (HRV) were derived from ECG signals. NASA Task Load Index (NASA-TLX) was used to evaluate the subjective mental workload during each task. In the results, HR was significantly larger in the MA task than the resting period. LF was significantly smaller in the MA than the resting period before the task. On the other hand, no HR changes from the baseline in the MT task were found, suggesting that Pattern 2 response might appear. In the results of HRV indices, decrease in LF suggests the inhibition of the parasympathetic nervous system activity by the task. These results are consistent with those of our previous studies. No significant main interaction with age and blocks (MA, MT and resting periods) were found in physiological responses except for LF/HF and subjective mental workload score. Therefore, the physiological responses induced by mental tasks might be more susceptible to task characteristic than to aging. This study partly supported the previous study.

Cognitive workload, which is the level of mental effort required for a cognitive task, can be assessed by monitoring the changes in neurophysiological measures such as electroencephalogram (EEG). This study investigates the performance of an EEG-based Brain-Computer Interface (BCI) to discriminate different difficulty levels in performing a mental arithmetic task. EEG data from 10 subjects were collected while performing mental addition with 3 difficulty levels (easy, medium and hard). EEG features were then extracted using band power and Common Spatial Pattern features and subsequently features were selected using Fisher Ratio to train a Linear Discriminant Classifier. The results from 10-fold cross-validation yielded averaged accuracy of 90% for 2 classes (easy versus hard tasks) and 66% for 3 classes (easy versus medium versus hard tasks). Hence the results showed the feasibility of using EEG-based BCI to measure cognitive workload in performing mental arithmetic.

Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (&ge, 10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR, bits/min). To alleviate the ITR degradation, we propose a more practical hBCI operated by intuitive mental tasks, such as mental arithmetic (MA) and word chain (WC) tasks, performed within a short trial length (5 s). In addition, the suitability of the WC as a BCI task was assessed, which has so far rarely been used in the BCI field. In this experiment, EEG and NIRS data were simultaneously recorded while participants performed MA and WC tasks without preliminary training and remained relaxed (baseline, BL). Each task was performed for 5 s, which was a shorter time than previous hBCI studies. Subsequently, a classification was performed to discriminate MA-related or WC-related brain activations from BL-related activations. By using hBCI in the offline/pseudo-online analyses, average classification accuracies of 90.0 &plusmn, 7.1/85.5 &plusmn, 8.1% and 85.8 &plusmn, 8.6/79.5 &plusmn, 13.4% for MA vs. BL and WC vs. BL, respectively, were achieved. These were significantly higher than those of the unimodal EEG- or NIRS-BCI in most cases. Given the short trial length and improved classification accuracy, the average ITRs were improved by more than 96.6% for MA vs. BL and 87.1% for WC vs. BL, respectively, compared to those reported in previous studies. The suitability of implementing a more practical hBCI based on intuitive mental tasks without preliminary training and with a shorter trial length was validated when compared to previous studies.

Acute stress responses are known to include increases in heart rate and blood pressure, as well as increases in the number of circulating immune cells, all of which are governed by the autonomic nervous system. This laboratory practical measures cardiovascular and circulating immune cell responses to a passive (cold pressor) and active (mental arithmetic) acute stress task in student participants. The results allow them to examine the different patterns of autonomic response they elicit (approximated by heart rate and blood pressure responses), and knowledge of these responses can then be used to infer the governing autonomic aspect of the increases in circulating immune cells from the results. This activity can be either adapted from teacher-led methods to inquiry, asking students to design the details of the acute stress tasks, or developed by asking students to design a follow-up experiment that could be used to provide direct evidence for their conclusions. Data collected provide a platform for teaching data analysis and interpretation, as well as critical thinking.

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the “one-versus-one” (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

Spatial-numerical associations (small numbers-left/lower space and large numbers-right/upper space) are regularly found in simple number categorization tasks. These associations were taken as evidence for a spatially oriented mental number line. However, the role of spatial-numerical associations during more complex number processing, such as counting or mental arithmetic is less clear. Here, we investigated whether counting is associated with a movement along the mental number line. Participants counted aloud upward or downward in steps of 3 for 45 s while looking at a blank screen. Gaze position during upward counting shifted rightward and upward, while the pattern for downward counting was less clear. Our results, therefore, confirm the hypothesis of a movement along the mental number line for addition. We conclude that space is not only used to represent number magnitudes but also to actively operate on numbers in more complex tasks such as counting, and that the eyes reflect this spatial mental operation.

The eyes, often called the window to our minds, reveal the focus of spatial attention and are therefore a powerful research tool for the study of spatial processing and spatially related higher cognitive functions. The aim of this paper is to highlight the potential of eye movement analysis in the domain of numerical cognition, to review several relevant findings, and to provide an outlook for future research.

Objective We examined associations of different adiposity measures with cortisol responses during the Trier Social Stress Test for children (TSST-C). Design Descriptive study. Setting Holdsworth Memorial Hospital, Mysore, India. Participants Adolescents aged 13.5y from a birth cohort were recruited (N=269, 133 boys). Methods The stressor (TSST-C) was 5-minutes each of public speaking and mental arithmetic tasks in front of two unfamiliar ‘judges’. Salivary cortisol concentrations were measured at baseline and at regular intervals after TSST-C. Weight, height, sub scapular and triceps skinfold thickness, and waist and hip circumference were measured, and percentage body fat was estimated (fat%; bioimpedance). Body mass index (BMI) and Waist-to-hip ratio (WHR) were calculated. All variables were converted into within-cohort SD scores before analysis. Stressinduced change in cortisol concentrations from baseline (cortisol response) was examined in relation to adiposity. Results Stress increased cortisol concentrations significantly from baseline (mean (SD): 5.5 (6.4) ng/mL; PPP=0.004). Sum of skinfold thickness and BMI were not associated with cortisol responses. Conclusions Abdominal adiposity is associated with reduced hypothalamic-pituitary-adrenal axis reactivity to stress in this adolescent population.

Analysis of mental arithmetic based electroencephalography (EEG) signal can be helpful for patients who have difficulty learning or understanding arithmetic or have autism spectrum disorders. It is difficult to separate mental arithmetic from EEG signals since these signals are nonstatic and nonlinear. In this study, we extracted features based entropy, skewness and entropy + skewness from the EEG signal. Then, extracted features were classified by support vector machines. The average 85.69% classification accuracy (CA) was calculated from the entropy based features that best determine the mental arithmetic of the EEG signals. This value is 9.79% higher than the average 75.90% CA calculated in the literature. This result indicates that proposed method is effective for this data set.

Orthostatic challenge produced by upright posture may lead to syncope if the cardiovascular system is unable to maintain adequate brain perfusion. This review outlines orthostatic intolerance related to the aging process, long-term bedrest immobilization, drugs, and disease. Aging-associated illness or injury due to falls often leads to hospitalization. Given that older patients spend up to 83% of hospital admission lying in bed, immobilization - and its consequences such as physiological deconditioning, functional decline, and orthostatic intolerance - represents a central challenge in the care of the vulnerable older population. This review examines current scientific knowledge regarding orthostatic intolerance and how it comes about, thereby contributing to understanding of (patho-) physiological concepts of cardiovascular (in-) stability in ambulatory and bedrest confined senior citizens as well as in individuals with disease conditions (e.g. orthostatic intolerance in patients with diabetes mellitus, multiple sclerosis, Parkinson’s, spinal cord injury, SCI) or those on multiple medications (polypharmacy). Understanding these aspects, along with cardio- postural interactions, is particularly important as blood pressure destabilization leading to orthostatic intolerance affects 3-4% of the general population, and in 4 out of 10 cases the exact cause remains elusive. Reviewed also are countermeasures to orthostatic intolerance such as exercise, water drinking, mental arithmetic, cognitive training and respiration training in SCI patients. In addition, timing of countermeasure application is also discussed. We speculate that optimally applied countermeasures such as mental challenge maintain sympathetic activity, and improves venous return, stroke volume, and consequently, blood pressure during upright standing. Finally, this paper emphasizes the importance of an active life style in old age and why early re- mobilization following immobilization or bedrest is crucial in fall prevention in older persons.

We provide an open access dataset for hybrid brain–computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left versus right hand motor imagery; mental arithmetic versus resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be enhanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.

In this paper, the use of initial dips using functional near-infrared spectroscopy (fNIRS) for brain-computer interface (BCI) is investigated. Features and window sizes for detecting initial dips are also discussed. Three mental tasks including mental arithmetic, mental counting, and puzzle solving are performed in obtaining fNIRS signals from the prefrontal cortex. Vector-based phase analysis method combined with a threshold circle, as a decision criterion, are used to detect the initial dips. Eight healthy subjects participate in experiment. Linear discriminant analysis is used as a classifier. To classify initial dips, five features (signal mean, peak value, signal slope, skewness, and kurtosis) of oxy-hemoglobin (HbO) and four different window sizes (0 similar to 1, 0 similar to 1.5, 0 similar to 2, and 0 similar to 2.5 sec) are examined. It is shown that a combination of signal mean and peak value and a time period of 0 similar to 2.5 sec provide the best average classification accuracy of 57.5% for three classes. To further validate the result, three-class classification using the conventional hemodynamic response (HR) is also performed, in which two features (signal mean and signal slope) and 2 similar to 7 sec window size have yielded the average classification accuracy of 65.9%. This reveals that fNIRS-based BCI using initial dip detection can reduce the command generation time from 7 sec to 2.5 sec while the classification accuracy is a bit sacrificed from 65.9% to 57.5% for three mental tasks. Further improvement can be made by using deoxy hemoglobin signals in coping with the slow HR problem. (C) 2016 Optical Society of America

We investigated whether change of emotion related to sourness during chewing causes higher brain functions by using mental arithmetic test. Subjects comprised 120 healthy individuals. The mental arithmetic test was performed under four conditions: chewing lemon-flavored gum (Standard gum, as a delicious gum); gum with 6 times the citric acid component of the Standard gum (Strong-sour gum, as an unpleasant gum); the Standard gum with citric acid component removed (Without-sour gum); and no chewing (Control). After chewing gum, subjects completed a subjective sensory evaluation about taste and deliciousness. The number of answers was lowest while chewing Without-sour gum compared to chewing the other gums or control. No significant correlation was found between performances of the mental arithmetic test and subjective sensory evaluation, except for a weak negative correlation with taste. These results suggest that the sourness might have influence on higher brain functions.

OBJECTIVE: Autonomic dysfunction is widely recognized in both partial and generalized epilepsies. The aim of this study was to evaluate the vasomotor response in patients with generalized idiopathic epilepsy by the clinical autonomic function tests. METHODS: 124 consecutive subjects including 62 idiopathic generalized epileptic patients diagnosed for more than 3 months receiving monotherapy and 62 matched for sex and age healthy controls were assessed in this case-control study. The evaluation of the vasomotor system was made using a questionnaire and autonomic function tests including Cold pressor, Valsalva maneuver, mental arithmetic and hand-grip tests. RESULTS: Abnormal score of the Cold pressor test was seen in 59.7%, Valsalva maneuver in 64.5% and 33.9% in mental arithmetic test of epileptic patients. These results were different significantly in comparison to control group. If abnormal hand grip test was defined as an increase less than 11 mmHg in diastolic pressure, there was found no significant difference between two groups of case and control, also a few people in control group was reported normal. While defining abnormal test was interpreted by increase in only one parameter or none of systolic blood pressure, diastolic blood pressure or heart rate, 43.5% in case group versus 14.5% in controls had abnormal results with significantly difference. CONCLUSION: Vasomotor dysfunction evaluated by cardiovascular tests was present commonly in the patients with generalized epilepsy. Further confirmation requires detailed reviewing of central and peripheral limbs to the breakdown of the system to be felt.