Sex based differences in functional connectivity during a working memory task: an fNIRS study

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.

Brain Activity Measurement during a Mental Arithmetic Task in fNIRS Signal Using Continuous Wavelet Transform

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<0.05). Also, a significant difference was observed in the temporal, medial prefrontal, and Ventrolateral Prefrontal Cortex (VLPFC) regions for Hb signal. Conclusion: The obtained results indicate activation in both HbO2 and Hb signals in the DLPFC, temporal, and MPFC regions, considering the performance of memory and the frontal cortex under mental arithmetic tasks. Therefore, it can be concluded that this technique is effective and appropriate for analyzing alterations of brain oxygen levels during cognitive activity.

Detection of Different Levels of Multiple Sclerosis by Assessing Nonlinear Characteristics of Posture

Background: Multiple sclerosis (MS) is a chronic disorder of the central nervous system that affects various parts of the brain and the spinal cord, leading to interruptions of the nervous, defense and movement systems, which usually affect balance and gait. Considering that the diagnosis of MS and its classification is a function of the expertise of the physician, the use of creative methods can help physicians to diagnose and classify different levels of the disease. Methods: The primary objective of the present study was to detect different levels of MS disease based on the nonlinear evaluation of body features. To do so, we studied eight MS patients and posture information of these patients such as the center of pressure (COP) were recorded at different levels with various degrees of Expanded Disability Status Scale (EDSS) by a motion analyzer device, while subjects were standing on the force plate in the eyes-opened and eyes-closed modes. After extracting and validating features that are used to assess posture disorders and explain the balancing behavior, the support vector machine (SVM) was employed to classify different levels of disease. Using the Spearman correlation test, each feature evaluated by the EDSS test. Results: The features obtained from Higuchi’s fractal dimensional algorithm in both anteriorposterior and mediolateral directions of the COP, which were significant (P<0.05) were selected and provided to SVM and neural network for classification of different levels. It found that SVM outperformed neural network and was able to carry out the classification with the accuracy of 90.7%. Conclusion: As an intelligent method, the non-linear evaluation of body features such as dimensional fractal analysis of the COP can help physicians diagnose different levels of MS with greater precision.

Modeling Multiple Sclerosis at Different Levels Using Reinforcement Learning

Background: Multiple sclerosis (MS) represents one of the most common disorders of the central nervous system, which leads to the dysfunction of different body systems and generates a myriad of problems for the affected individuals. Given the progressive nature of this disease, it can divide into several levels. The progression rate of the disease at each stage is essential for specialists, as it can help them to adopt appropriate therapeutic measures. Methods: One of the methods used in many MS neurological treatments is Expanded Disability Status Scale (EDSS), which allows physicians to give an estimate of the severity of the disease to patients, learn about the stage of the patient’s disease and prescribe appropriate medicines accordingly. Given the importance and impact of this disease on the quality of life of patients, researchers look for inexpensive and simple models with minimum side effects for examining different levels of MS and providing treatment solutions. Results: In this study, patients were asked to stand on a force plate. Then, the time series of the center of pressure and body oscillations of patients at various levels were recorded using a motion analyzer device, and a closed loop control system was proposed using the reverse pendulum (representing human body) and reinforcement learning. Conclusion: Based on the feedback received from the environment, the necessary rules for maintaining the balance of pendulum obtained, and, by observing the ankle torque at the output, a model presented that could examine different levels of MS.

STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY

Many investigations have been carried out on functional near infrared spectroscopy (fNIRS) applications in depression and schizophrenia patients that are most mysterious and costliest mental disorders in current society. fNIRS is a new optical method which assesses brain cortex hemodynamic and nervous activities non-invasively and it has been used in medicine as a study tool. Most of the researches of this approach have assessed the homodynamic response of frontal and temporal regions by means of various cognitive tasks. In this research, first, the cognitive task execution techniques have been explained concisely, and then some findings of fNIRS-based researches about depression and schizophrenia have been summarized and assessed. In fNIRS studies that have used various devices with different number of channels, the brain cortex functionality in schizophrenia and depressive patients has been investigated. The results demonstrate that a decrease in prefrontal regions activities can be observed in schizophrenia and depressive patients. Also more detailed studies illustrate ventrolateral, prefrontal and frontopolar region disorders. In severe depressive patients, a decrease in activities of prefrontal and temporal regions has been detected. Therefore, by paying attention to the deficiencies in these regions’ functions, it is possible to treat these diseases.

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

SEPARATION OF TWIN FETAL ECG FROM MATERNAL ECG USING EMPIRICAL MODE DECOMPOSITION TECHNIQUES

In this study, by using a combination of standard Empirical Mode Decomposition (EMD), Ensembling Empirical Mode Decomposition (EEMD), Completing Empirical Mode Decomposition (CEMD) and Principal Component Analysis (PCA), a new method was introduced to separate twin fetal heart rate (FHR) from maternal ECG. The data which were the results of modeling fetal and maternal ECG which be longed to 10 mothers with a sampling frequency of 250[Formula: see text]Hz. In this method, first R-wave of maternal ECG was determined, and then maternal QRS is removed. Further, to clarify these changes and increase resistance to environmental noises, PCA was used. In the next step, all FHRs related to twin fetuses were extracted from signals. Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was used for denoising. By using the proposed method for noise with an amplitude of over 10 dB, the FHR of the first and second (if any) fetuses were separated from maternal ECG with an accuracy of 93.3% and 91.1% respectively. The goal was to improve signal processing dimensions of fetal ECG and provides deeper insight about this issue using EEMD technique. It was tested on a twin fetus with the results suggesting its effectiveness even with increased number of fetuses with slight modifications.

Evaluating the features of the brain waves to quantify ADHD improvement by neurofeedback

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD), as one of the most common neurological disorders in children and adolescents, is characterized by decentralization, slow learning, distraction and hyperactivity. Studies have shown that in addition to medication, neurofeedback training can also be used to partially control the brain activity of these patients.

METHODS

In this study, using the brain signals processing before and after the treatment in 10 children treated by neurofeedback, the changes were evaluated by non-parametric statistical analysis and impact of neurofeedback on brain frequency bands was investigated. Finally, the results were compared with the protocols introduced in this paper and before researches.

RESULTS

The results of Kruskal-Wallis test showed an approximately significant increase in the relative power of gamma and an approximately significant reduction in the ratio of relative power of alpha/beta.

CONCLUSIONS

It represents the emotional response, elicited by the successful learning and diminished ratio of slow learning to active learning respectively.

Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS

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.