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BACKGROUND: Being digitally literate allows health-based science students to access reliable, up-to-date information efficiently and expands the capacity for continuous learning. Digital literacy facilitates effective communication and collaboration among other healthcare providers. It helps to navigate the ethical implications of using digital technologies and aids the use of digital tools in managing healthcare processes. Our aim in this study is to determine the digital literacy level and awareness of our students receiving health-based education in our university and to pave the way for supporting the current curriculum with courses on digital literacy when necessary. METHOD: Students from Acibadem University who were registered undergraduate education for at least four years of health-based education, School of Medicine, Nutrition and Dietetics, Nursing, Physiotherapy and Rehabilitation, Psychology, Biomedical Engineering, Molecular Biology, and Genetics were included. The questionnaire consisted of 24 queries evaluating digital literacy in 7 fields: software and multimedia, hardware and technical problem solving, network and communication/collaboration, ethics, security, artificial intelligence (A.I.), and interest/knowledge. Two student groups representing all departments were invited for interviews according to the Delphi method. RESULTS: The survey was completed by 476 students. Female students had less computer knowledge and previous coding education. Spearman correlation test showed that there were weak positive correlations between the years and the "software and multimedia," "ethics," "interest and knowledge" domains, and the average score. The students from Nursing scored lowest in the query after those from the Nutrition and Dietetics department. The highest scores were obtained by Biomedical Engineering students, followed by the School of Medicine. Participants scored the highest in "network" and "A.I." and lowest in "interest-knowledge" domains. CONCLUSION: It is necessary to define the level of computer skills who start health-based education and shape the curriculum by determining which domains are weak. Creating an educational environment that fosters females' digital knowledge is recommended. Elective courses across faculties may be offered to enable students to progress and discuss various digital literacy topics. The extent to which students benefit from the digital literacy-supported curriculum may be evaluated. Thus, health-based university students are encouraged to acquire the computer skills required by today's clinical settings. REGISTRATION: This study was approved by Acibadem University and Acibadem Healthcare Institutions Medical Research Ethics Committee (ATADEK) (11 November 2022, ATADEK registration: 2022-17-138) All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from the participants.
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Inteligencia Artificial , Alfabetización , Femenino , Humanos , Educación en Salud , Estudiantes , CurriculumRESUMEN
Amyotrophic lateral sclerosis (ALS) is a lethal paralytic disease caused by the degeneration of motor neurons in the spinal cord, brain stem, and motor cortex. Mutations in the gene encoding copper/zinc superoxide dismutase (SOD1) are present in ~20% of familial ALS and ~2% of all ALS cases. The most common SOD1 gene mutation in North America is a missense mutation substituting valine for alanine (A4V). In this study, we analyze sodium channel currents in oocytes expressing either wild-type or mutant (A4V) SOD1 protein. We demonstrate that the A4V mutation confers a propensity to hyperexcitability on a voltage-dependent sodium channel (Nav1.3) mediated by heightened total Na(+) conductance and a hyperpolarizing shift in the voltage dependence of Nav1.3 activation. To estimate the impact of these channel effects on excitability in an intact neuron, we simulated these changes in the program NEURON; this shows that the changes induced by mutant SOD1 increase the spontaneous firing frequency of the simulated neuron. These findings are consistent with the view that excessive excitability of neurons is one component in the pathogenesis of this disease.
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Fenómenos Electrofisiológicos/genética , Mutación , Canal de Sodio Activado por Voltaje NAV1.3/metabolismo , Superóxido Dismutasa-1/genética , Animales , Humanos , Neuronas/citología , Sodio/metabolismo , Xenopus laevisRESUMEN
Functional near infrared spectroscopy (fNIRS) is a promising method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of the task related neuronal activation. In this study, we use the anatomical resolution of functional magnetic resonance imaging (fMRI) to extract scalp and brain vascular signals separately and construct an optically weighted spatial average of the fMRI blood oxygen level-dependent (BOLD) signal for characterizing the scalp signal contribution to fNIRS measurements. We introduce an extended superficial signal regression (ESSR) method for canceling physiology-based systemic interference where the effects of cerebral and superficial systemic interference are treated separately. We apply and validate our method on the optically weighted BOLD signals, which are obtained by projecting the fMRI image onto optical measurement space by use of the optical forward problem. The performance of ESSR method in removing physiological artifacts is compared to i) a global signal regression (GSR) method and ii) a superficial signal regression (SSR) method. The retrieved signals from each method are compared with the neural signals that represent the 'ground truth' brain activation cleaned from cerebral systemic fluctuations. We report significant improvements in the recovery of task induced neural activation with the ESSR method when compared to the other two methods as reflected in the Pearson R(2) coefficient and mean square error (MSE) metrics (two tailed paired t-tests, p<0.05). The signal quality is enhanced most when ESSR method is applied with higher spatial localization, lower inter-trial variability, a clear canonical waveform and higher contrast-to-noise (CNR) improvement (60%). Our findings suggest that, during a cognitive task i) superficial scalp signal contribution to fNIRS signals varies significantly among different regions on the forehead and ii) using an average scalp measurement together with a local measure of superficial hemodynamics better accounts for the systemic interference inherent in the brain as well as superficial scalp tissue. We conclude that maximizing the overlap between the optical pathlength of superficial and deeper penetration measurements is of crucial importance for accurate recovery of the evoked hemodynamic response in fNIRS recordings.
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Mapeo Encefálico/métodos , Encéfalo/fisiología , Hemodinámica/fisiología , Procesamiento de Señales Asistido por Computador , Espectroscopía Infrarroja Corta/métodos , Humanos , Imagen por Resonancia MagnéticaRESUMEN
Differences in corticocerebral structure and function between males and females and their effects on behavior and the prevalence of various neuropsychiatric disorders have been considered as a fundamental topic in various fields of neuroscience. Recent studies on working memory (WM) reported the impact of sex on brain connectivity patterns, which reflect the important role of functional connectivity in the sex topic. Working memory, one of the most important cognitive tasks performed by regions of the PFC, can provide evidence regarding the presence of a difference between males and females. The present study aimed to assess sex differences in brain functional connectivity during working memory-related tasks by using functional near-infrared spectroscopy (fNIRS). In this regard, nine males and nine females completed a dual n-back working memory task with two target inputs of color and location stimuli in three difficulty levels (n = 0, 1, 2). Functional connectivity matrices were extracted for each subject for each memory load level. Females made less errors than males while spending more time performing the task for all workload levels except in 0-back related to the color stimulus, where the reaction time of females was shorter than males. The results of functional connectivity reveal the inverse behavior of two hemispheres at different memory workload levels between males and females. In the left hemisphere, males exhibited stronger connectivity compared to the females, while stronger connectivity was observed in the females' right hemisphere. Furthermore, an inverse trend was detected in the channel pairs with significant connectivity in the right hemisphere of males (falling) and females (rising) by enhancing working memory load level. Considering both behavioral and functional results for two sexes demonstrated a better performance in females due to the more effective use of the brain. The results indicate that sex affects functional connectivity between different areas in both hemispheres of the brain during cognitive tasks of varying difficulty levels although the general impression is that spatial capabilities are considered as a performance of the brain's right hemisphere. These results reinforce the presence of a sex effect in the functional imaging studies of hemodynamic function and emphasize the importance of evaluating brain network connectivity for achieving a better scientific understanding of sex differences.
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Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR ( N C R ¯ ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: N C R ¯ can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.
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Background.The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective.Objective.The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity.Approach. Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set.Main results.Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (92.2% and 90.16%, respectively,p= 0.005).Significance.Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.
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Conducta Impulsiva , Espectroscopía Infrarroja Corta , Adolescente , Algoritmos , Humanos , Redes Neurales de la Computación , Máquina de Vectores de SoporteRESUMEN
Objective: The aim of this study was to investigate the hemodynamic effects of occlusal splint therapy on masseter muscles of patients with myofascial pain accompanied by bruxism with near-infrared spectroscopy (NIRS). Methods: Twenty-four patients were randomly divided into two groups, where the study group (n = 12) received occlusal splint therapy and the control group (n = 12) underwent no therapy. Measurements were categorized into four subgroups: painful or painless control and painful or painless splint. Percent changes in deoxyhemoglobin (Hb), oxyhemoglobin (HbO2), and OXY (HbO2-Hb) values were calculated during a 1-month period. Results: Statistically significant inter-session differences between painful-splint and painful-control groups were detected for NIRS oxygenation parameters, whereas inter-session differences between painless groups were statistically insignificant. Conclusion: The results suggest that occlusal splint usage causes a decrease in hyperemic response, which is indicative of a decrease in masseter muscle contraction strength.
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Bruxismo , Ferulas Oclusales , Manejo del Dolor , Bruxismo/complicaciones , Electromiografía , Hemodinámica , Humanos , Músculo Masetero , DolorRESUMEN
The aim of this study is to investigate relation between cardiopulmonary performance and muscular microcirculation in patients with fibromyalgia syndrome (FMS). Twenty-one female sedentary patients who were diagnosed as FMS, and 15 sedentary females were enrolled in to the study. All participants underwent a modified Bruce multistage maximal treadmill protocol with metabolic measurements and Near-Infrared Spectroscopy measurements. Exercise sessions were performed 3 times a week for 8 weeks. The results of the study suggest that cardiopulmonary system in charge of delivering oxygen to whole body and muscular microcirculation may have dysfunction in patients with FMS.
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Ejercicio Físico , Fibromialgia/fisiopatología , Consumo de Oxígeno/fisiología , Prueba de Esfuerzo , Femenino , Fibromialgia/rehabilitación , Humanos , Oxígeno/sangre , Oxígeno/metabolismo , Dimensión del DolorRESUMEN
BACKGROUND AND OBJECTIVE: Anthracyclines are well established and highly efficacious antineoplastic agents for various haematopoietic and solid tumours, such as breast cancer. The main adverse effect of anthracycline therapy is cardiotoxicity. The aim of this prospective study was to determine the role of plasma levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) in assessing left ventricular function in early breast cancer patients receiving adjuvant anthracycline treatment. METHODS: Thirty-three newly diagnosed breast cancer patients who received a total doxorubicin dosage of 240 mg/m2 over four treatment cycles as part of adjuvant chemotherapy after curative breast surgery were included in this study. Venous NT-proBNP levels were measured before and at the end of doxorubicin therapy. Left ventricular function was measured by echocardiography conducted 3 weeks after surgery and at the end of doxorubicin therapy. RESULTS: NT-proBNP levels were significantly higher in patients (n=10) with decreased left ventricular ejection fraction (LVEF) [p=0.02]. There was no difference in LVEF (p=0.164) or NT-proBNP levels (p=0.844) between the patients who had high NT-proBNP levels and those who had normal NT-proBNP levels before doxorubicin chemotherapy. None of the factors studied (breast cancer grade, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor 2 status, age) was found to be significantly related to NT-proBNP. CONCLUSION: The association between higher NT-proBNP levels and reduced LVEF in asymptomatic breast cancer patients after doxorubicin administration could be an early indication of subclinical acute anthracycline cardiotoxicity. Furthermore, breast cancer patients experiencing a progressive increase in NT-proBNP levels might be in a higher risk group for acute anthracycline cardiotoxicity.
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Antibióticos Antineoplásicos/efectos adversos , Neoplasias de la Mama/tratamiento farmacológico , Enfermedades Cardiovasculares/inducido químicamente , Doxorrubicina/efectos adversos , Péptido Natriurético Encefálico/sangre , Fragmentos de Péptidos/sangre , Función Ventricular Izquierda , Adulto , Antibióticos Antineoplásicos/administración & dosificación , Antibióticos Antineoplásicos/uso terapéutico , Biomarcadores/sangre , Neoplasias de la Mama/sangre , Neoplasias de la Mama/fisiopatología , Enfermedades Cardiovasculares/sangre , Quimioterapia Adyuvante , Doxorrubicina/administración & dosificación , Doxorrubicina/uso terapéutico , Femenino , Humanos , Estudios ProspectivosRESUMEN
OBJECTIVE: The aim of this study was to introduce a novel methodology for classification of brain hemodynamic responses collected via functional near infrared spectroscopy (fNIRS) during rest, motor imagery (MI) and motor execution (ME) tasks which involves generating population-level training sets. APPROACH: A 48-channel fNIRS system was utilized to obtain hemodynamic signals from the frontal (FC), primary motor (PMC) and somatosensory cortex (SMC) of ten subjects during an experimental paradigm consisting of MI and ME of various right hand movements. Classification accuracies of random forest (RF), support vector machines (SVM), and artificial neural networks (ANN) were computed at the single subject level by training each classifier with subject specific features, and at the group level by training with features from all subjects for ME versus Rest, MI versus Rest and MI versus ME conditions. The performances were also computed for channel data restricted to FC, PMC and SMC regions separately to determine optimal probe location. MAIN RESULTS: RF, SVM and ANN had comparably high classification accuracies for ME versus Rest (%94, %96 and %98 respectively) and for MI versus Rest (%95, %95 and %98 respectively) when fed with group level feature sets. The accuracy performance of each algorithm in localized brain regions were comparable (>%93) to the accuracy performance obtained with whole brain channels (>%94) for both ME versus Rest and MI versus Rest conditions. SIGNIFICANCE: By demonstrating the feasibility of generating a population level training set with a high classification performance for three different classification algorithms, the findings pave the path for removing the necessity to acquire subject specific training data and hold promise for a novel, real-time fNIRS based BCI system design which will be most effective for application to disease populations for whom obtaining data to train a classification algorithm is not possible.
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Interfaces Cerebro-Computador , Imaginación/fisiología , Corteza Motora/fisiología , Movimiento/fisiología , Corteza Somatosensorial/fisiología , Espectroscopía Infrarroja Corta/métodos , Adolescente , Algoritmos , Femenino , Humanos , Masculino , Redes Neurales de la Computación , Estimulación Luminosa/métodos , Adulto JovenRESUMEN
Accurate estimation of concentration changes in muscles by continuous wave near-IR spectroscopy for muscle measurements suffers from underestimation and crosstalk problems due to the presence of superficial skin and fat layers. Underestimation error is basically caused by a homogeneous medium assumption in the calculations leading to the partial volume effect. The homogeneous medium assumption and wavelength dependence of mean partial path length in the muscle layer cause the crosstalk. We investigate underestimation errors and crosstalk by Monte Carlo simulations with a three layered (skin-fat-muscle) tissue model for a two-wavelength system where the choice of first wavelength is in the 675- to 775-nm range and the second wavelength is in the 825- to 900-nm range. Means of absolute underestimation errors and crosstalk over the considered wavelength pairs are found to be higher for greater fat thicknesses. Estimation errors of concentration changes for Hb and HbO(2) are calculated to be close for an ischemia type protocol where both Hb and HbO(2) are assumed to have equal magnitude but opposite concentration changes. The minimum estimation errors are found for the 700825- and 725825-nm pairs for this protocol.
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Tejido Adiposo/metabolismo , Algoritmos , Artefactos , Modelos Biológicos , Músculo Esquelético/metabolismo , Oxígeno/análisis , Espectroscopía Infrarroja Corta/métodos , Simulación por Computador , Modelos Estadísticos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
BACKGROUND: It is a daunting task to identify all the metabolic pathways of brain energy metabolism and develop a dynamic simulation environment that will cover a time scale ranging from seconds to hours. To simplify this task and make it more practicable, we undertook stoichiometric modeling of brain energy metabolism with the major aim of including the main interacting pathways in and between astrocytes and neurons. MODEL: The constructed model includes central metabolism (glycolysis, pentose phosphate pathway, TCA cycle), lipid metabolism, reactive oxygen species (ROS) detoxification, amino acid metabolism (synthesis and catabolism), the well-known glutamate-glutamine cycle, other coupling reactions between astrocytes and neurons, and neurotransmitter metabolism. This is, to our knowledge, the most comprehensive attempt at stoichiometric modeling of brain metabolism to date in terms of its coverage of a wide range of metabolic pathways. We then attempted to model the basal physiological behaviour and hypoxic behaviour of the brain cells where astrocytes and neurons are tightly coupled. RESULTS: The reconstructed stoichiometric reaction model included 217 reactions (184 internal, 33 exchange) and 216 metabolites (183 internal, 33 external) distributed in and between astrocytes and neurons. Flux balance analysis (FBA) techniques were applied to the reconstructed model to elucidate the underlying cellular principles of neuron-astrocyte coupling. Simulation of resting conditions under the constraints of maximization of glutamate/glutamine/GABA cycle fluxes between the two cell types with subsequent minimization of Euclidean norm of fluxes resulted in a flux distribution in accordance with literature-based findings. As a further validation of our model, the effect of oxygen deprivation (hypoxia) on fluxes was simulated using an FBA-derivative approach, known as minimization of metabolic adjustment (MOMA). The results show the power of the constructed model to simulate disease behaviour on the flux level, and its potential to analyze cellular metabolic behaviour in silico. CONCLUSION: The predictive power of the constructed model for the key flux distributions, especially central carbon metabolism and glutamate-glutamine cycle fluxes, and its application to hypoxia is promising. The resultant acceptable predictions strengthen the power of such stoichiometric models in the analysis of mammalian cell metabolism.
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Astrocitos/metabolismo , Hipoxia Encefálica/fisiopatología , Neuronas/metabolismo , Animales , Ácido Glutámico/metabolismo , Glutamina/metabolismo , Humanos , Hipoxia Encefálica/metabolismo , Cinética , Lípidos/fisiología , Espectroscopía de Resonancia Magnética , Modelos Neurológicos , Neurotransmisores/fisiología , Ácido gamma-Aminobutírico/metabolismoRESUMEN
A theoretical framework, a partial correlation-based functional connectivity (PC-FC) analysis to functional near-infrared spectroscopy (fNIRS) data, is proposed. This is based on generating a common background signal from a high passed version of fNIRS data averaged over all channels as the regressor in computing the PC between pairs of channels. This approach has been employed to real data collected during a Stroop task. The results show a strong significance in the global efficiency (GE) metric computed by the PC-FC analysis for neutral, congruent, and incongruent stimuli (NS, CS, IcS; GEN=0.10±0.009, GEC=0.11±0.01, GEIC=0.13±0.015, p=0.0073). A positive correlation (r=0.729 and p=0.0259) is observed between the interference of reaction times (incongruent-neutral) and interference of GE values (GEIC-GEN) computed from [HbO] signals.
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Procesamiento Automatizado de Datos/métodos , Espectroscopía Infrarroja Corta , Test de StroopRESUMEN
Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from [Formula: see text] data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in [Formula: see text] concentrations from 12 healthy volunteers. We have considered 10% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition ([Formula: see text]) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions [[Formula: see text], [Formula: see text]]. The findings bring us closer to delivering a biomarker derived from fNIRS data that can be reliably and easily adopted by the clinicians.
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Migraine pain is considered to manifest itself as a result of an impaired cerebrovascular reactivity. Hence, proper quantification and diagnosis of this problem without causing more disturbance has always been a challenge in investigating migraine pathogenesis. Functional near-infrared spectroscopy system (fNIRS) is being proposed as an inexpensive, rapid, safe and accurate technique to monitor cerebrovascular dynamics. We have developed NIROXCOPE 201, a novel multi source and detector device of fNIRS, and attempted to investigate the cerebrovascular reactivity of migraine patients to a breath hold task which produces a metabolic perturbation. Six normals and six migraine patients performed four consecutive breath holding task. A typical brain hemodynamic response (BHR) is observed both for controls and migraineurs with an initial phase, main response and a recovery phase. Hence, fitting to a sum of three sequentially arranged gaussian curves proved that amplitudes of [Hb] and [HbO2] signals acquired by fNIRS are approximately two to five times higher in controls than migraine patients (P<0.01) for all phases. Moreover, amplitude change between successive breath holds tends to converge to a steady value for controls whereas an uncontrolled percent change is observed for migraineurs. Our results confirm an impaired cerebrovascular reactivity in the frontal cortex of migraine patients interictally.
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Circulación Cerebrovascular/fisiología , Hipercapnia/diagnóstico , Migraña sin Aura/diagnóstico , Migraña sin Aura/fisiopatología , Espectroscopía Infrarroja Corta , Adulto , Femenino , Hemodinámica/fisiología , Hemoglobinas/metabolismo , Humanos , Hipercapnia/complicaciones , Masculino , Migraña sin Aura/etiología , Dinámicas no Lineales , Oxihemoglobinas/metabolismoRESUMEN
Migraine is hypothesized to be a neurovascular coupling disorder where the cerebral vascular reactivity is malfunctioning and measuring hemodynamic changes during migraine without causing more disturbance has always been a challenge. Functional near infrared spectroscopy system (fNIRS) is being proposed as an inexpensive, rapid, safe and accurate alternative to fMRI, transcranial doppler sonography (TCD). We have developed NIROXCOPE 201, a novel device for fNIRS which offers 16 source-detector pairs distributed on a probe that is placed on the forehead. Measuring hemodynamic changes during migraine without causing more disturbance has always been a challenge. Using NIROXCOPE 201, we have attempted to investigate the cerebrovascular reactivity of migraine patients to a breath hold task which produces a metabolic perturbation. Six normals and six migraine patients performed four consecutive breath holding task. We calculated the peak and latencies of the initial dip and recovery phases for [Hb], [HbO(2)], [tHb], and [OXY] signals. [Hb], [tHb], and [OXY] ID and R amplitudes of normals are approximately a magnitude higher than migraine patients (P<0.01), while latencies showed no significant differences. Data suggests an altered neurovascular coupling in frontal cortex of migraine patients interictally. The application of NIROXCOPE 201 to patients suffering from other primary headache disorders will reveal diagnostic as well as therapeutic implications of the presented study.
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Hemodinámica/fisiología , Trastornos Migrañosos/fisiopatología , Espectroscopía Infrarroja Corta , Adulto , Femenino , Hemoglobinas/análisis , Humanos , Masculino , Oxihemoglobinas/análisisRESUMEN
BACKGROUND: Although the underlying mechanisms responsible for cardiac dysfunction after prolonged exercise remains to be elucidated, it has reported cardiac deterioration following exhaustive exercise in the absence of underlying cardiovascular diseases, which has been attributed to cardiac fatigue. The study was designed to investigate the effects of after fatiguing exercise on oxygen kinetics. METHODS: Six athletes have taken examination, firstly by echocardiography, secondly by cardiopulmonary exercise testing and then by near-infrared spectroscopy (NIRS), before 2 days (pre-race) and after 1 day (post-race) marathon competition. RESULTS: We found decrease in left ventricular systolic and diastolic functions, and peak oxygen consumption while increasing half time of muscular oxygen delivery after race period. CONCLUSION: Cardiopulmonary exercise testing in conjunction with oxygen kinetics of skeletal muscle measured by NIRS may be a tool for detecting cardiac fatigue.
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Ejercicio Físico/fisiología , Músculo Esquelético/fisiología , Oxígeno/metabolismo , Disfunción Ventricular Izquierda/fisiopatología , Adulto , Humanos , Masculino , Consumo de Oxígeno/fisiología , Espectroscopía Infrarroja Corta , Función Ventricular Izquierda/fisiologíaRESUMEN
We address the problem of prototypical waveform extraction in cognitive experiments using functional near-infrared spectroscopy (fNIRS) signals. These waveform responses are evoked with visual stimuli provided in an oddball type experimental protocol. As the statistical signal-processing tool, we consider the linear signal space representation paradigm and use independent component analysis (ICA). The assumptions underlying ICA is discussed in the light of the signal measurement and generation mechanisms in the brain. The ICA-based waveform extraction is validated based both on its conformance to the parametric brain hemodynamic response (BHR) model and to the coherent averaging technique. We assess the intra-subject and inter-subject waveform and parameter variability.
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Encéfalo/fisiología , Cognición/fisiología , Simulación por Computador , Procesamiento de Imagen Asistido por Computador , Espectroscopía Infrarroja Corta/métodos , Adulto , Algoritmos , Humanos , Masculino , Modelos Biológicos , Espectroscopía Infrarroja Corta/instrumentaciónRESUMEN
OBJECTIVE: In this paper, we aimed for the robust estimation of the parameters and states of the hemodynamic model by using blood oxygen level dependent signal. APPROACH: In the fMRI literature, there are only a few successful methods that are able to make a joint estimation of the states and parameters of the hemodynamic model. In this paper, we implemented a maximum likelihood based method called the particle smoother expectation maximization (PSEM) algorithm for the joint state and parameter estimation. MAIN RESULTS: Former sequential Monte Carlo methods were only reliable in the hemodynamic state estimates. They were claimed to outperform the local linearization (LL) filter and the extended Kalman filter (EKF). The PSEM algorithm is compared with the most successful method called square-root cubature Kalman smoother (SCKS) for both state and parameter estimation. SCKS was found to be better than the dynamic expectation maximization (DEM) algorithm, which was shown to be a better estimator than EKF, LL and particle filters. SIGNIFICANCE: PSEM was more accurate than SCKS for both the state and the parameter estimation. Hence, PSEM seems to be the most accurate method for the system identification and state estimation for the hemodynamic model inversion literature. This paper do not compare its results with Tikhonov-regularized Newton-CKF (TNF-CKF), a recent robust method which works in filtering sense.