• Title/Summary/Keyword: Brain network

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Functional Neuroanatomy of Memory (기억의 기능적 신경 해부학)

  • Lee, Sung-Hoon
    • Sleep Medicine and Psychophysiology
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    • v.4 no.1
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    • pp.15-28
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    • 1997
  • Longterm memory is encoded in the neuronal connectivities of the brain. The most successful models of human memory in their operations are models of distributed and self-organized associative memory, which are founded in the principle of simulaneous convergence in network formation. Memory is not perceived as the qualities inherent in physical objects or events, but as a set of relations previously established in a neural net by simultaneousy occuring experiences. When it is easy to find correlations with existing neural networks through analysis of network structures, memory is automatically encoded in cerebral cortex. However, in the emergence of informations which are complicated to classify and correlated with existing networks, and conflictual with other networks, those informations are sent to the subcortex including hippocampus. Memory is stored in the form of templates distributed across several different cortical regions. The hippocampus provides detailed maps for the conjoint binding and calling up of widely distributed informations. Knowledge about the distribution of correlated networks can transform the existing networks into new one. Then, hippocampus consolidats new formed network. Amygdala may enable the emotions to influence the information processing and memory as well as providing the visceral informations to them. Cortico-striatal-pallido-thalamo-cortical loop also play an important role in memory function with analysis of language and concept. In case of difficulty in processing in spite of parallel process of informations, frontal lobe organizes theses complicated informations of network analysis through temporal processing. With understanding of brain mechanism of memory and information processing, the brain mechanism of mental phenomena including psychopathology can be better explained in terms of neurobiology and meuropsychology.

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Large-Scale Network Analysis using Effective Connectivity for Effective Brain Functional Imaging Analysis (효과적인 뇌기능 영상 분석을 위한 유효 연결성을 이용한 대규모 네트워크 분석)

  • Park, Ki-Hee;Lee, Seong-Whan
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06c
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    • pp.377-378
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    • 2011
  • 본 논문은 뇌기능 연구에 크게 기여하는 기능적 자기공명영상을 효과적으로 분석하기 위한 유효 연결성(Effective Connectivity, EC)을 이용한 대규모 네트워크(Large-Scale Network, LSN) 분석(LSN-EC)을 제안한다. 유효 연결성은 뇌영역간의 시공간적 인과관계를 표현한 연결성이며, 뇌의 기능적 연결성 및 구조탐색 사용된다. LSN-EC는 뇌영역간의 EC를 표현하고 그룹간의 차이분석을 통하여 뇌질환 분석 및 진단 연구로의 응용이 가능하다. 실험결과에서 알츠하이머병과 관련이 높다고 알려진 후대상피질(Posterior Cingulate Cortex)과 해마(Hippocampus)가 포함된 변연엽(Limbic Lobe), 기저핵 및 시상(Basal Ganglion and Thalamus) 주변 영역에서 감소된 EC를 확인하였다.

Contribution of ERP/EEG Measurements for Monitoring of Neurological Disorders

  • Lamia Bouafif;Cherif Adnen
    • International Journal of Computer Science & Network Security
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    • v.24 no.6
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    • pp.59-66
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    • 2024
  • Measurable electrophysiological changes in the scalp are frequently linked to brain activities. These progressions are called related evoked potentials (ERP), which are transient electrical responses recorded by electroencephalography (EEG) in light of tactile, mental, or motor enhancements. This painless strategy is gradually being used as a conclusion and clinical help. In this article, we will talk about the main ways to monitor brain activities in people with neurological diseases like Alzheimer's disease by analyzing EEG signals using ERP. We will also talk about how this method helps to detect the disease at an early stage.

VGG-based BAPL Score Classification of 18F-Florbetaben Amyloid Brain PET

  • Kang, Hyeon;Kim, Woong-Gon;Yang, Gyung-Seung;Kim, Hyun-Woo;Jeong, Ji-Eun;Yoon, Hyun-Jin;Cho, Kook;Jeong, Young-Jin;Kang, Do-Young
    • Biomedical Science Letters
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    • v.24 no.4
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    • pp.418-425
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    • 2018
  • Amyloid brain positron emission tomography (PET) images are visually and subjectively analyzed by the physician with a lot of time and effort to determine the ${\beta}$-Amyloid ($A{\beta}$) deposition. We designed a convolutional neural network (CNN) model that predicts the $A{\beta}$-positive and $A{\beta}$-negative status. We performed 18F-florbetaben (FBB) brain PET on controls and patients (n=176) with mild cognitive impairment and Alzheimer's Disease (AD). We classified brain PET images visually as per the on the brain amyloid plaque load score. We designed the visual geometry group (VGG16) model for the visual assessment of slice-based samples. To evaluate only the gray matter and not the white matter, gray matter masking (GMM) was applied to the slice-based standard samples. All the performance metrics were higher with GMM than without GMM (accuracy 92.39 vs. 89.60, sensitivity 87.93 vs. 85.76, and specificity 98.94 vs. 95.32). For the patient-based standard, all the performance metrics were almost the same (accuracy 89.78 vs. 89.21), lower (sensitivity 93.97 vs. 99.14), and higher (specificity 81.67 vs. 70.00). The area under curve with the VGG16 model that observed the gray matter region only was slightly higher than the model that observed the whole brain for both slice-based and patient-based decision processes. Amyloid brain PET images can be appropriately analyzed using the CNN model for predicting the $A{\beta}$-positive and $A{\beta}$-negative status.

Detecting Stress Based Social Network Interactions Using Machine Learning Techniques

  • S.Rajasekhar;K.Ishthaq Ahmed
    • International Journal of Computer Science & Network Security
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    • v.23 no.8
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    • pp.101-106
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    • 2023
  • In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

Snake Robot Motion Scheme Using Image and Voice (감각 정보를 이용한 뱀 로봇의 행동구현)

  • 강준영;김성주;조현찬;전홍태
    • Proceedings of the IEEK Conference
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    • 2002.06c
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    • pp.127-130
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    • 2002
  • Human's brain action can divide by recognition and intelligence. recognition is sensing voice, image and smell and Intelligence is logical judgment, inference, decision. To this concept, Define function of cerebral cortex, and apply the result. Current expert system is lack, that reasoning by cerebral cortex and thalamus, hoppocampal and so on. In this paper, With human's brain action, wish to embody human's action artificially Embody brain mechanism using Modular Neural Network, Applied this result to snake robot.

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Revolutionizing Brain Tumor Segmentation in MRI with Dynamic Fusion of Handcrafted Features and Global Pathway-based Deep Learning

  • Faizan Ullah;Muhammad Nadeem;Mohammad Abrar
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.1
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    • pp.105-125
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    • 2024
  • Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

Gut-Brain Connection: Microbiome, Gut Barrier, and Environmental Sensors

  • Min-Gyu Gwak;Sun-Young Chang
    • IMMUNE NETWORK
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    • v.21 no.3
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    • pp.20.1-20.18
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    • 2021
  • The gut is an important organ with digestive and immune regulatory function which consistently harbors microbiome ecosystem. The gut microbiome cooperates with the host to regulate the development and function of the immune, metabolic, and nervous systems. It can influence disease processes in the gut as well as extra-intestinal organs, including the brain. The gut closely connects with the central nervous system through dynamic bidirectional communication along the gut-brain axis. The connection between gut environment and brain may affect host mood and behaviors. Disruptions in microbial communities have been implicated in several neurological disorders. A link between the gut microbiota and the brain has long been described, but recent studies have started to reveal the underlying mechanism of the impact of the gut microbiota and gut barrier integrity on the brain and behavior. Here, we summarized the gut barrier environment and the 4 main gut-brain axis pathways. We focused on the important function of gut barrier on neurological diseases such as stress responses and ischemic stroke. Finally, we described the impact of representative environmental sensors generated by gut bacteria on acute neurological disease via the gut-brain axis.

Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

  • Lee, Ki-Baek;Kim, Ko Keun;Song, Jaeseung;Ryu, Jiwoo;Kim, Youngjoo;Park, Cheolsoo
    • Journal of Electrical Engineering and Technology
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    • v.11 no.6
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    • pp.1812-1824
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    • 2016
  • The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

A Study on Fog Forecasting Method through Data Mining Techniques in Jeju (데이터마이닝 기법들을 통한 제주 안개 예측 방안 연구)

  • Lee, Young-Mi;Bae, Joo-Hyun;Park, Da-Bin
    • Journal of Environmental Science International
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    • v.25 no.4
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    • pp.603-613
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    • 2016
  • Fog may have a significant impact on road conditions. In an attempt to improve fog predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, multinomial logistic regression, neural network and support vector machine. To validate machine learning models, the results from the simulation was compared with the fog data observed over Jeju(184 ASOS site) and Gosan(185 ASOS site). Predictive rates proposed by six data mining methods are all above 92% at two regions. Additionally, we validated the performance of machine learning models with WRF (weather research and forecasting) model meteorological outputs. We found that it is still not good enough for operational fog forecast. According to the model assesment by metrics from confusion matrix, it can be seen that the fog prediction using neural network is the most effective method.