Search results for: brain regions

Authors: Mouktafi Amine, Tahiri Asmaa

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A substantial number of traditional medical research has been put forward to managing and treating mental disorders. At the present time, to our best knowledge, it is believed that a fundamental understanding of the underlying causes of the majority of psychological disorders needs to be explored further to inform early diagnosis, managing symptoms and treatment. The emerging field of evolutionary psychology is a promising prospect to address the origin of mental disorders, potentially leading to more effective treatments. Schizophrenia as a topical mental disorder has been linked to the evolutionary adaptation of the human brain represented in the brain connectivity and asymmetry directly linked to humans' higher brain cognition in contrast to other primates being our direct living representation of the structure and connectivity of our earliest common African ancestors. As proposed in the evolutionary psychology scientific literature, the pathophysiology of schizophrenia is expressed and directly linked to altered connectivity between the Hippocampal Formation (HF) and Dorsolateral Prefrontal Cortex (DLPFC). This research paper presents the results of the use of tractography analysis using multiple open access Diffusion Weighted Imaging (DWI) datasets of healthy subjects, schizophrenia-affected subjects and primates to illustrate the relevance of the aforementioned brain regions' connectivity and the underlying evolutionary changes in the human brain. Deterministic fiber tracking and streamline analysis were used to generate connectivity matrices from the DWI datasets overlaid to compute distances and highlight disconnectivity patterns in conjunction with other fiber tracking metrics: Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD).

Keywords: tractography, diffusion weighted imaging, schizophrenia, evolutionary psychology

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Authors: Zelalem B. Gurmessa, Ignatius W. Ferreira, Henry F. Wissink

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The aim of this paper is to critically examine the factors contributing to academic brain drain in the Sub-Saharan Africa with specific reference to the higher education sector. Africa in general and Sub-Saharan African (SSA) countries, in particular, are experiencing an exodus of highly trained, qualified and competent human resources to other developing and developed countries thereby threatening the overall development of the relevant regions and impeding both public and private service delivery systems in the nation states. The region is currently in a dire situation in terms of health care services, education, science, and technology. The contribution of SSA countries to Science, Technology and Innovation is relatively minimal owing to the migration of skilled professionals due to both push and pull factors. The phenomenon calls for both international and trans-boundary, regional, national and institutional interventions to curb the exodus. Based on secondary data and the review of the literature, the article conceptualizes the antecedents and impacts of human capital flight or brain drain in the SSA countries from a higher education perspective. To this end, the article explores the magnitude, causes, and impacts of brain drain in the region. Despite the lack of consistent data on the magnitude of academic brain drain in the region, a critical analysis of the existing sources shows that pay disparity between developing and developed countries, the lack of enabling working conditions at source countries, fear of security due to political turmoil or unrest, the availability of green pastures and opportunity for development in the receiving countries were identified as major factors contributing to academic brain drain in the region. This hampers the socio-economic, technological and political development of the region. The paper also recommends that further research can be undertaken on the magnitude, causes, characteristics and impact of brain drain on the sustainability and competitiveness of SSA higher education institutions in the region.

Keywords: brain drain, higher education, sub-Saharan Africa, sustainable development

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Authors: Shin Ku Kim, Yun Ah Oh, Eun Hee Seo, Chang Min Dae, Yun Jung Bae

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Objectives: The commonly used PRESS technique in magnetic resonance spectroscopy (MRS) has a limitation of incomplete water suppression. The recently developed sLASER technique is known for its improved effectiveness in suppressing water signal. However, no prior study has compared both sequences in a normal human brain. In this study, we firstly aimed to compare the performances of both techniques in brain MRS. Materials and methods: From January 2023 to July 2023, thirty healthy participants (mean age 38 years, 17 male, 13 female) without underlying neurological diseases were enrolled in this study. All participants underwent single-voxel MRS using both PRESS and sLASER techniques on 3T MRI. Two regions-of-interest were allocated in the left medial thalamus and left parietal white matter (WM) by a single reader. The SpectroView Analysis (SW5, Philips, Netherlands) provided automatic measurements, including signal-to-noise ratio (SNR) and peak_height of water, N-acetylaspartate (NAA)-water/Choline (Cho)-water/Creatine (Cr)-water ratios, and NAA-Cr/Cho-Cr ratios. The measurements from PRESS and sLASER techniques were compared using paired T-tests and Bland-Altman methods, and the variability was assessed using coefficients of variation (CV). Results: SNR and peak_heights of the water were significantly lower with sLASER compared to PRESS (left medial thalamus, sLASER SNR/peak_height 2092±475/328±85 vs. PRESS 2811±549/440±105); left parietal WM, 5422±1016/872±196 vs. 7152±1305/1150±278; all, P<0.001, respectively). Accordingly, NAA-water/Cho-water/Cr-water ratios and NAA-Cr/Cho-Cr ratios were significantly higher with sLASER than with PRESS (all, P< 0.001, respectively). The variabilities of NAA-water/Cho-water/Cr-water ratios and Cho-Cr ratio in the left medial thalamus were lower with sLASER than with PRESS (CV, sLASER vs. PRESS, 19.9 vs. 58.1/19.8 vs. 54.7/20.5 vs. 43.9 and 11.5 vs. 16.2) Conclusion: The sLASER technique demonstrated enhanced background water suppression, resulting in increased signals and reduced variability in brain metabolite measurements of MRS. Therefore, sLASER could offer a more precise and stable method for identifying brain metabolites.

Keywords: Magnetic resonance spectroscopy, Brain, sLASER, PRESS

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Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

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In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

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Authors: Alan C. Lin, Tran Anh Son

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This study proposes an approach to determine the undercut regions and their releasing directions for slider design of complex parts represented by the file format of STL (STereoLithography). In order to delineate the border of undercut regions, orthogonal cutting planes are firstly employed to automatically find the inner loops of a part model. To discover the facets belonging to undercut regions, attributes are then assigned to the facets of the part model based on the topological relationship of adjacent facets of each inner loop. After that, the undercut regions are separated from other facets in the model. Through the recognized facets of the undercut regions, the concept of 'visibility map (V-map)' is further applied to determine feasible releasing directions for each of the undercut regions. The undercut regions having the same releasing direction are finally grouped to form a slider in the injection mold.

Keywords: solid model, STL data, injection mold design, visibility map

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Authors: Joseph Wolff, Jeffrey Eilbott

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Autism Spectrum Disorders (ASD) are a spectrum of social disorders characterized by deficits in social communication, verbal ability, and interaction that can vary in severity. In recent years, researchers have used magnetic resonance imaging (MRI) to help detect how neural patterns in individuals with ASD differ from those of neurotypical (NT) controls for classification purposes. This study analyzed the classification of ASD within males and females using functional MRI data. Functional connectivity (FC) correlations among brain regions were used as feature inputs for machine learning algorithms. Analysis was performed on 558 cases from the Autism Brain Imaging Data Exchange (ABIDE) I dataset. When trained specifically on females, the algorithm underperformed in classifying the ASD subset of our testing population. Although the subject size was relatively smaller in the female group, the manual matching of both male and female training groups helps explain the algorithm’s bias, indicating the altered sex abnormalities in functional brain networks compared to typically developing peers. These results highlight the importance of taking sex into account when considering how generalizations of findings on males with ASD apply to females.

Keywords: autism spectrum disorder, machine learning, neuroimaging, sex differences

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Authors: Katalin Bándy

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The European emigration of highly educated immigrants draws attention to the problem of brain drain. Due to the Eastern European countries joining the EU and the opening of the Western European labour market the west-wards migration brisked up. By now another problem has been intensified correlated to migration: the migration of highly skilled workers related to brain waste tendencies. With some exceptions, educated immigrants from Eastern European countries are more likely to end up in unskilled jobs than residents. This paper is about to reveal the above-mentioned problems and this study is supported by the results of secondary pieces of research and the own survey made in the EU-15 among the Hungarian highly skilled (especially economics graduated) migrants, and it also examines the causes and in the focus there are the migrant motivations of the high-skilled young generation after the crisis.

Keywords: brain drain, brain waste, migration of highly-skilled, underemployment

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Authors: Meshandren Naidoo, Amy Gooden

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Implantable brain-computer interface (BCI) technologies have developed to the point where brain-computer communication is possible. This has great potential in the medical field, as it allows persons who have lost capacities. However, ethicists and regulators call for a strict approach to these technologies due to the impact on personhood. This research demonstrates that the personhood debate is more nuanced and that where an African approach to personhood is used, it may produce results more favorable to the development and use of this technology.

Keywords: artificial intelligence, law, neuroscience, ethics

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Authors: Abbas Shafiee, Mohammad Taghi Ahmadian, Maryam Hoviattalab

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The biomechanical behavior of brain tissue is needed for predicting the traumatic brain injury (TBI). Each year over 1.5 million people sustain a TBI in the USA. The appropriate coefficients for injury prediction can be evaluated using experimental data. In this study, an experimental setup on brain soft tissue was developed to perform unconfined compression tests at quasistatic strain rates ∈0.0004 s-1 and 0.008 s-1 and 0.4 stress relaxation test under unconfined uniaxial compression with ∈ 0.67 s-1 ramp rate. The fitted visco-hyperelastic parameters were utilized by using obtained stress-strain curves. The experimental data was validated using finite element analysis (FEA) and previous findings. Also, influence of friction coefficient on unconfined compression and relaxation test and effect of ramp rate in relaxation test is investigated. Results of the findings are implemented on the analysis of a human brain under high acceleration due to impact.

Keywords: brain soft tissue, visco-hyperelastic, finite element analysis (FEA), friction, quasistatic strain rate

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: S. Oudjemia, F. Alim, S. Seddiki

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This paper shows the application of multifractal analysis for additional help in cancer diagnosis. The medical image processing is a very important discipline in which many existing methods are in search of solutions to real problems of medicine. In this work, we present results of multifractal analysis of brain MRI images. The purpose of this analysis was to separate between healthy and cancerous tissue of the brain. A nonlinear method based on multifractal detrending moving average (MFDMA) which is a generalization of the detrending fluctuations analysis (DFA) is used for the detection of abnormalities in these images. The proposed method could make separation of the two types of brain tissue with success. It is very important to note that the choice of this non-linear method is due to the complexity and irregularity of tumor tissue that linear and classical nonlinear methods seem difficult to characterize completely. In order to show the performance of this method, we compared its results with those of the conventional method box-counting.

Keywords: irregularity, nonlinearity, MRI brain images, multifractal analysis, brain tumor

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Authors: H. Namazi, H. T. N. Kuan

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Brain activity can be measured by acquiring and analyzing EEG signals from an individual. In fact, the human brain response to external and internal stimuli is mapped in his EEG signals. During years some methods such as Fourier transform, wavelet transform, empirical mode decomposition, etc. have been used to analyze the EEG signals in order to find the effect of stimuli, especially external stimuli. But each of these methods has some weak points in analysis of EEG signals. For instance, Fourier transform and wavelet transform methods are linear signal analysis methods which are not good to be used for analysis of EEG signals as nonlinear signals. In this research we analyze the brain response to visual stimuli by extracting information in the form of various measures from EEG signals using a software developed by our research group. The used measures are Jeffrey’s measure, Fractal dimension and Hurst exponent. The results of these analyses are useful not only for fundamental understanding of brain response to visual stimuli but provide us with very good recommendations for clinical purposes.

Keywords: visual stimuli, brain response, EEG signal, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: Y. J. Zhou, G. Lu

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In this paper, an analytical study is made for the dynamic behavior of human brain tissue under transient loading. In this analytical model the Mooney-Rivlin constitutive law is coupled with visco-elastic constitutive equations to take into account both the nonlinear and time-dependent mechanical behavior of brain tissue. Five ordinary differential equations representing the relationships of five main parameters (radial stress, circumferential stress, radial strain, circumferential strain, and particle velocity) are obtained by using the characteristic method to transform five partial differential equations (two continuity equations, one motion equation, and two constitutive equations). Analytical expressions of the attenuation properties for spherical wave in brain tissue are analytically derived. Numerical results are obtained based on the five ordinary differential equations. The mechanical responses (particle velocity and stress) of brain are compared at different radii including 5, 6, 10, 15 and 25 mm under four different input conditions. The results illustrate that loading curves types of the particle velocity significantly influences the stress in brain tissue. The understanding of the influence by the input loading cures can be used to reduce the potentially injury to brain under head impact by designing protective structures to control the loading curves types.

Keywords: analytical method, mechanical responses, spherical wave propagation, traumatic brain injury

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Authors: Dalia O. Saleha, Gehad A. Abdel Jaleela, Sally W. Al-Awdana

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Diabetes mellitus (DM) is known to exacerbate cerebral ischemic injury. The present study aimed to investigate the anti-oxidant and anti-inflammatory effects of oral supplementation of melatonin (MLN) on cerebral injury caused by middle cerebral artery occlusion and reperfusion (MCAO/Re) in streptozotocin (STZ)-induced hyperglycemic rats. Hyperglycemia was induced by a single injection of STZ (55mg/kg; i.p.), six weeks later the cerebral injury was induced by MCAO/Re. Twenty-four hours after the MCAO/Re the MLN (10 mg/kg) was injected for 14 consecutive days. Results of the present study revealed that MCAO/Re in STZ-induced hyperglycemia in rats causes an increase in the oxidative stress biomarkers; it increased brain lipid peroxidation (measured as malondialdehyde; MDA) and brain level of nitric oxide (NO). Moreover, MCAO/Reproduces a prominent increase in the brain inflammatory markers viz. interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis nuclear factor-alpha (TNF-α). Oral treatment of MCAO/Re in STZ-induced hyperglycemic rats with MLN (10 mg/kg) for two weeks restored the brain levels of MDA, GSH, NO, IL-6, IL-1β and the TNF-α. MLN succeeded to suppress the exacerbation of damage in the brain of hyperglycemic rats. These results suggest that daily intake of MLN attenuates the exacerbation of cerebral ischemic injury in a diabetic state, which may be attributed to anti-oxidant and anti-inflammatory effects in the brain.

Keywords: melatonin, brain injury, cerebral ischemia/reperfusion, hyperglycemia, rats

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Authors: N. Fuad, M. N. Taib, R. Jailani, M. E. Marwan

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This paper discusses on power spectral density (PSD) characteristics which are extracted from three-dimensional (3D) electroencephalogram (EEG) models. The EEG signal recording was conducted on 150 healthy subjects. Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram images. Then, the values of maximum PSD were extracted as features from the model. These features are analysed using mean relative power (MRP) and different mean relative power (DMRP) technique to observe the pattern among different brain balancing indexes. The results showed that by implementing these techniques, the pattern of brain balancing indexes can be clearly observed. Some patterns are indicates between index 1 to index 5 for left frontal (LF) and right frontal (RF).

Keywords: power spectral density, 3D EEG model, brain balancing, mean relative power, different mean relative power

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Authors: H. Namazi, H. T. N. Kuan

Abstract:

Brain activity can be measured by acquiring and analyzing EEG signals from an individual. In fact, the human brain response to external and internal stimuli is mapped in his EEG signals. During years some methods such as Fourier transform, wavelet transform, empirical mode decomposition, etc. have been used to analyze the EEG signals in order to find the effect of stimuli, especially external stimuli. But each of these methods has some weak points in analysis of EEG signals. For instance, Fourier transform and wavelet transform methods are linear signal analysis methods which are not good to be used for analysis of EEG signals as nonlinear signals. In this research we analyze the brain response to auditory stimuli by extracting information in the form of various measures from EEG signals using a software developed by our research group. The used measures are Jeffrey’s measure, Fractal dimension and Hurst exponent. The results of these analyses are useful not only for fundamental understanding of brain response to auditory stimuli but provide us with very good recommendations for clinical purposes.

Keywords: auditory stimuli, brain response, EEG signal, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: Farhad Forouharmajd, Hossein Ebrahimi, Siamak Pourabdian

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Introduction: Prevalent use of cell phones (mobile phones) has led to increasing worries about the effect of radiofrequency waves on the physiology of human body. This study was done to determine different reactions of the temperatures in different depths of brain tissue in confronting with radiofrequency waves of cell phones. Methodology: This study was an empirical research. A cow's brain tissue was placed in a compartment and the effects of radiofrequency waves of the cell phone was analyzed during confrontation and after confrontation, in three different depths of 2, 12, and 22 mm of the tissue, in 4 mm and 4 cm distances of the tissue to a cell phone, for 15 min. Lutron thermometer was used to measure the tissue temperatures. Data analysis was done by Lutron software. Findings: The rate of increasing the temperature at the depth of 22 mm was higher than 2 mm and 12mm depths, during confrontation of the brain tissue at the distance of 4 mm with the cell phone, such that the tissue temperatures at 2, 12, and 22 mm depths increased by 0.29 ˚C, 0.31 ˚C, and 0.37 ˚C, respectively, relative to the base temperature (tissue temperature before confrontation). Moreover, the temperature of brain tissue at the distance of 4 cm by increasing the tissue depth was more than other depths. Increasing the tissue temperature also existed by increasing the brain tissue depth after the confrontation with the cell phone. The temperature of the 22 mm depth increased with higher speed at the time confrontation. Conclusion: Not only radiofrequency waves of cell phones increased the tissue temperature in all the depths of the brain tissue, but also the temperature due to radiofrequency waves of the cell phone was more at the depths higher than 22 mm of the tissue. In fact, the thermal effect of radiofrequency waves was higher in higher depths.

Keywords: mobile phone, radio frequency waves, brain tissue, temperature

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Authors: K. M. Damodara Gowda

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Background: Maternal health and nutrition are considered as the predominant factors influencing brain functional development. If the mother is free of illness and genetic defects, maternal nutrition would be one of the most critical factors affecting the brain development. Calorie restrictions cause significant impairment in spatial learning ability and the levels of Brain Derived Neurotrophic Factor (BDNF) in rats. But, the mechanism by which the prenatal under-nutrition leads to impairment in brain learning and memory function is still unclear. In the present study, prenatal Astaxanthin supplementation on BDNF level, spatial learning and memory performance in the offspring’s of normal, calorie restricted and Astaxanthin supplemented rats was investigated. Methodology: The rats were administered with 6mg and 12 mg of astaxanthin /kg bw for 21 days following which acquisition and retention of spatial memory was tested in a partially-baited eight arm radial maze. The BDNF level in different regions of the brain (cerebral cortex, hippocampus and cerebellum) was estimated by ELISA method. Results: Calorie restricted animals treated with astaxanthin made significantly more correct choices (P < 0.05), and fewer reference memory errors (P < 0.05) on the tenth day of training compared to offsprings of calorie restricted animals. Calorie restricted animals treated with astaxanthin also made significantly higher correct choices (P < 0.001) than untreated calorie restricted animals in a retention test 10 days after the training period. The mean BDNF level in cerebral cortex, Hippocampus and cerebellum in Calorie restricted animals treated with astaxanthin didnot show significant variation from that of control animals. Conclusion: Findings of the study indicated that memory and learning was impaired in the offspring’s of calorie restricted rats which was effectively modulated by astaxanthin at the dosage of 12 mg/kg body weight. In the same way the BDNF level at cerebral cortex, Hippocampus and Cerebellum was also declined in the offspring’s of calorie restricted animals, which was also found to be effectively normalized by astaxanthin.

Keywords: calorie restiction, learning, Memory, Cerebral cortex, Hippocampus, Cerebellum, BDNF, Astaxanthin

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Authors: U.B Mahadevswamy UBM, Siraj Ahmed Siraj

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As the title of the paper indicates about brainwaves and its uses for various applications based on their frequencies and different parameters which can be implemented as real time application with the title a smart brain wave sensor system for paralyzed patients. Brain wave sensing is to detect a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brainwaves sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment. Keywords:-Brainwave sensor, BMI, Brain scan, EEG, MCH.

Keywords: Keywords:-Brainwave sensor , BMI, Brain scan, EEG, MCH

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Authors: Ramón Aparicio-García, Gustavo Juárez Gracia, Jesús Álvarez Cedillo

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A research line of the computer science that involve the study of the Human-Computer Interaction (HCI), which search to recognize and interpret the user intent by the storage and the subsequent analysis of the electrical signals of the brain, for using them in the control of electronic devices. On the other hand, the affective computing research applies the human emotions in the HCI process helping to reduce the user frustration. This paper shows the results obtained during the hardware and software development of a Brain Computer Interface (BCI) capable of recognizing the human emotions through the association of the brain electrical activity patterns. The hardware involves the sensing stage and analogical-digital conversion. The interface software involves algorithms for pre-processing of the signal in time and frequency analysis and the classification of patterns associated with the electrical brain activity. The methods used for the analysis and classification of the signal have been tested separately, by using a database that is accessible to the public, besides to a comparison among classifiers in order to know the best performing.

Keywords: affective computing, interface, brain, intelligent interaction

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Authors: Arezou Hajhashemi, Karim Asgari, Masoud Etemadifar, Maryam Keyvani, Ali Hekmatnia

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Multiple Sclerosis (MS) is one of the most prevalent demyelinating diseases in CNS. As in other chronic cerebral diseases, impairment in cognitive functioning and in memory is popular. Because of the inflammatory and demyelinating nature of the disease, the localization of plaques in different parts of the Prefrontal and Limbic System, may lead to memorial symptoms. This investigation was intended to study relationship between frequency of plaques and memorial symptoms arising from dysfunction limbic system and prefrontal of patients with MS. The sample was selected randomly from patients with MS with memory problem, who have been referred to Isfahan Multiple Sclerosis Society. Brain System Test and Memory Test was administered to the sample, and their MRI's were analyzed by specialist in order to indentify two different parts of plaques. The data was analyzed by SPSS. The results showed that there were significant relationship between MS plaques and prefrontal's dysfunction and memorial symptom related to prefrontal area; however, there were no significant relationship between MS plaques and limbic system's dysfunction and memorial symptoms related to limbic system area. The results of this study suggest that memorial symptoms due to injury regions of the brain have the most significant relationship to prefrontal. Better judgment about these results needs more studies in future.

Keywords: multiple sclerosis, magnetic image, brain injury, behavior disorder

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Authors: Hasan Vural

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Turkey is a favorable country to produce a great variety of agricultural products because of her different geographic and climatic conditions which have been used to divide the country into four main and seven sub regions. This classification into seven regions traditionally has been used in order to data collection and publication especially related with agricultural production. Afterwards, nine agricultural regions were considered. Recently, the governmental body which is responsible of data collection and dissemination (Turkish Institute of Statistics-TIS) has used 12 classes which include 11 sub regions and Istanbul province. This study aims to evaluate these classification efforts based on the acreage of ten main crops in a ten years time period (1996-2005). The panel data grouped in 11 subregions has been evaluated by cluster and multivariate statistical methods. It was concluded that from the agricultural production point of view, it will be rather meaningful to consider three main and eight sub-agricultural regions throughout the country.

Keywords: agricultural region, factorial analysis, cluster analysis,

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

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Authors: Bilal Ahmad

Abstract:

Alzheimer’s disease (AD) is one of the most common neurodegenerative illnesses. However, how Gut-microbiota plays a role in the pathogenesis of AD is not well elucidated. The purpose of this literature review is to summarize and understand the current findings that may elucidate the gut microbiota's role in the development of AD. Methods: A literature review of all the relevant papers known to the author was conducted. Relevant articles, abstracts and research papers were collected from well-accepted web sources like PubMed, PMC, and Google Scholar. Results: Recent studies have shown that Gut-microbiota has an important role in the progression of AD via Gut-Microbiota-Brain Axis. The onset of AD supports the ‘Hygiene Hypothesis’, which shows that AD might begin in the Gut, causing dysbiosis, which interferes with the intestinal barrier by releasing pro-inflammatory cytokines and making its way up to the brain via the blood-brain barrier (BBB). Molecular mechanisms lipopolysaccharides and serotonin kynurenine (tryptophan) pathways have a direct association with inflammation, the immune system, neurodegeneration, and AD. Conclusion: The studies helped to analyze the molecular basis of AD, other neurological conditions like depression, autism, and Parkinson's disease and how they are linked to Gut-microbiota. Further, studies to explore the therapeutic effects of probiotics in AD and cognitive enhancement should be warranted to provide significant clinical and practical value.

Keywords: gut-microbiota, Alzheimer’s disease, second brain aging, lipopolysaccharides, short-chain fatty acids

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Authors: Seung Ju Yeon, Seul Ki Min, Jun Sang Park, Yeo Seon Kwon, Hoo Cheol Lee, Hyun Jung Shim, Il-Doo Kim, Ja Kyeong Lee, Hwa Sung Shin

Abstract:

In brain injury, depleting oxidative stress is the most effective way to reduce the brain infarct size. C-phycocyanin (C-Pc) is a well-known antioxidant protein that has neuroprotective effects obtained from green microalgae. Astrocyte is glial cell that supports the nerve cell such as neuron, which account for a large portion of the brain. In brain injury, such as ischemia and reperfusion, astrocyte has an important rule that overcomes the oxidative stress and protect from brain reactive oxygen species (ROS) injury. However little is known about how C-Pc regulates the anti-oxidants effects of astrocyte. In this study, when the C-Pc was treated in oxidized astrocyte, we confirmed that inflammatory factors Interleukin-6 and Interleukin-3 were increased and antioxidants enzyme, Superoxide dismutase (SOD) and catalase was upregulated, and neurotrophic factors, brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) was alleviated. Also, it was confirmed to reduce infarct size of the brain in ischemia and reperfusion because C-Pc has anti-oxidant effects in middle cerebral artery occlusion (MCAO) animal model. These results show that C-Pc can help astrocytes lead neuroprotective activities in the oxidative stressed environment of the brain. In summary, the C-PC protects astrocytes from oxidative stress and has anti-oxidative, anti-inflammatory, neurotrophic effects under ischemic situations.

Keywords: c-phycocyanin, astrocyte, reactive oxygen species, ischemia and reperfusion, neuroprotective effect

Procedia PDF Downloads 294

Authors: Beata Spilakova, Daniel J. Shaw, Radek Marecek, Milan Brazdil

Abstract:

Hyperscanning affords a unique insight into the brain dynamics underlying human interaction by simultaneously scanning two or more individuals’ brain responses while they engage in dyadic exchange. This provides an opportunity to observe dynamic brain activations in all individuals participating in interaction, and possible interbrain effects among them. The present research aims to provide an experimental paradigm for hyperscanning research capable of delineating among different forms of interaction. Specifically, the goal was to distinguish between two dimensions: (1) interaction structure (concurrent vs. turn-based) and (2) goal structure (competition vs cooperation). Dual-fMRI was used to scan 22 pairs of participants - each pair matched on gender, age, education and handedness - as they played the Pattern Game. In this simple interactive task, one player attempts to recreate a pattern of tokens while the second player must either help (cooperation) or prevent the first achieving the pattern (competition). Each pair played the game iteratively, alternating their roles every round. The game was played in two consecutive sessions: first the players took sequential turns (turn-based), but in the second session they placed their tokens concurrently (concurrent). Conventional general linear model (GLM) analyses revealed activations throughout a diffuse collection of brain regions: The cooperative condition engaged medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC); in the competitive condition, significant activations were observed in frontal and prefrontal areas, insula cortices and the thalamus. Comparisons between the turn-based and concurrent conditions revealed greater precuneus engagement in the former. Interestingly, mPFC, PCC and insulae are linked repeatedly to social cognitive processes. Similarly, the thalamus is often associated with a cognitive empathy, thus its activation may reflect the need to predict the opponent’s upcoming moves. Frontal and prefrontal activation most likely represent the higher attentional and executive demands of the concurrent condition, whereby subjects must simultaneously observe their co-player and place his own tokens accordingly. The activation of precuneus in the turn-based condition may be linked to self-other distinction processes. Finally, by performing intra-pair correlations of brain responses we demonstrate condition-specific patterns of brain-to-brain coupling in mPFC and PCC. Moreover, the degree of synchronicity in these neural signals related to performance on the game. The present results, then, show that different types of interaction recruit different brain systems implicated in social cognition, and the degree of inter-player synchrony within these brain systems is related to nature of the social interaction.

Keywords: brain-to-brain coupling, hyperscanning, pattern game, social interaction

Procedia PDF Downloads 306

Authors: Alexandre H. Okano, Pedro M. D. Agrícola, Daniel G. Da S. Machado, Luiz I. Do N. Neto, Luiz F. Farias Junior, Paulo H. D. Nascimento, Rickson C. Mesquita, John F. Araujo, Eduardo B. Fontes, Hassan M. Elsangedy, Shinsuke Shimojo, Li M. Li

Abstract:

The prefrontal cortex is involved in the reward system and the insular cortex integrates the afferent inputs arriving from the body’ systems and turns into feelings. Therefore, modulating neuronal activity in these regions may change individuals’ perception in a given situation such as exercise. We tested whether transcranial direct current stimulation (tDCS) change cerebral oxygenation and pleasure during exercise. Fourteen volunteer healthy adult men were assessed into five different sessions. First, subjects underwent to a maximum incremental test on a cycle ergometer. Then, subjects were randomly assigned to a transcranial direct current stimulation (2mA for 15 min) intervention in a cross over design in four different conditions: anode and cathode electrodes on T3 and Fp2 targeting the insular cortex, and Fpz and F4 targeting prefrontal cortex, respectively; and their respective sham. These sessions were followed by 30 min of moderate intensity exercise. Brain oxygenation was measured in prefrontal cortex with a near infrared spectroscopy. Perceived exertion and pleasure were also measured during exercise. The asymmetry in prefrontal cortex oxygenation before the stimulation decreased only when it was applied over this region which did not occur after insular cortex or sham stimulation. Furthermore, pleasure was maintained during exercise only after prefrontal cortex stimulation (P > 0.7), while there was a decrease throughout exercise (P < 0.03) during the other conditions. We conclude that tDCS over the prefrontal cortex changes brain oxygenation in ventromedial prefrontal cortex and maintains perceived pleasure during exercise. Therefore, this technique might be used to enhance effective responses related to exercise.

Keywords: affect, brain stimulation, dopamine neuromodulation, pleasure, reward, transcranial direct current stimulation

Procedia PDF Downloads 294

Authors: Semih Sözer

Abstract:

One of the most common issues in economic systems is understanding characteristics of economic activities in cities and regions. Although there are critics to economic base models in conceptual and empirical aspects, these models are useful tools to examining the economic structure of a nation, regions or cities. This paper uses one of the methodologies of economic base models namely the location quotients model. Data for this model includes employment numbers of provinces and NUTS II regions in Turkey. Time series of data covers the years of 1990, 2000, 2003, and 2009. Aim of this study is finding which sectors are export-base and which sectors are import-base in provinces and regions. Model results show that big provinces or powerful regions (population, size etc.) mostly have basic sectors in their economic system. However, interesting facts came from different sectors in different provinces and regions in the model results.

Keywords: economic base, location quotients model, regional economics, regional development

Procedia PDF Downloads 409