About this Research Topic
Recent advances in artificial intelligence and scientific visualization, together with neuroimaging techniques, offer powerful models to decipher the secrets of our brain. These models are widely used to explain how the electrochemical signals generated by the human brain process sensory stimuli and produce information for a wide range of brain functions.
Machine Learning models are used to model data recorded in different modalities, such as fMRI, sMRI, EEG, MEG, fNIRS, and various forms of microscopy. The estimated models of the human brain can be visualized by three dimensional representation of various functionalities and states of the human brain, using the scientific visualization methods. These models enable neuroscientists to analyze and understand how the human brain works during the basic cognitive processes, such as attention, memory, sensation and perception; as well as high level cognitive processes, such as, language, problem solving, reasoning etc. These models are also used to develop brain-machine interfaces, driven by thoughts. Finally, resting state brain data can be modeled to analyze and diagnose various diseases
Although a wide range of machine learning methods are available in the literature, they are too short to analyze, interpret and predict brain imaging data. The models obtained at the output of a machine learning algorithm are rather poor approximations of a very restricted functionality of the human brain. Considering the fact that neurons are massively connected and all of the brain functions are interacting with each other, isolation of a single cognitive task or anatomic region represents a very narrow view of an extremely complex brain process. Additionally, most of the machine learning models are based on the findings of experimental neuroscience, which can be falsified by some other experiments. Finally, it is extremely difficult to measure the validity of the available models. Therefore, there is a long way of representing the structural or/and functional properties of the human brain by a rigorous machine learning model.
In this research topic, we welcome papers on theory and applications related to machine learning models for human brain to analyze, interpret and decode the brain imaging data in various modalities, such as, fMRI, sMRI, EEG, MEG, fNIRS, and microscopy. The research topic focuses on the following areas:
Recent advances in artificial intelligence and scientific visualization, together with neuroimaging techniques, offer powerful models to decipher the secrets of our brain. These models are widely used to explain how the electrochemical signals generated by the human brain process sensory stimuli and produce information for a wide range of brain functions.
Machine Learning models are used to model data recorded in different modalities, such as fMRI, sMRI, EEG, MEG, fNIRS, and various forms of microscopy. The estimated models of the human brain can be visualized by three dimensional representation of various functionalities and states of the human brain, using the scientific visualization methods. These models enable neuroscientists to analyze and understand how the human brain works during the basic cognitive processes, such as attention, memory, sensation and perception; as well as high level cognitive processes, such as, language, problem solving, reasoning etc. These models are also used to develop brain-machine interfaces, driven by thoughts. Finally, resting state brain data can be modeled to analyze and diagnose various diseases
Although a wide range of machine learning methods are available in the literature, they are too short to analyze, interpret and predict brain imaging data. The models obtained at the output of a machine learning algorithm are rather poor approximations of a very restricted functionality of the human brain. Considering the fact that neurons are massively connected and all of the brain functions are interacting with each other, isolation of a single cognitive task or anatomic region represents a very narrow view of an extremely complex brain process. Additionally, most of the machine learning models are based on the findings of experimental neuroscience, which can be falsified by some other experiments. Finally, it is extremely difficult to measure the validity of the available models. Therefore, there is a long way of representing the structural or/and functional properties of the human brain by a rigorous machine learning model.
In this research topic, we welcome papers on theory and applications related to machine learning models for human brain to analyze, interpret and decode the brain imaging data in various modalities, such as, fMRI, sMRI, EEG, MEG, fNIRS, and microscopy. The research topic focuses on the following areas:
• Artificial Neural Networks, specifically, Deep Neural Networks for Neuroimaging
* Disentanglement of neuroimaging data
* Brain data augmentation and transfer learning
* Brain encoding and decoding methods
• Learning and inference on neuroimaging data for
* cognitive state analysis and classification,
* modeling and analyzing the brain networks,
* multimodal data analysis
* multi-subject data analysis
* modeling and analyzing the neurological diseases.
• Big Data Analytics
* Efficient analysis tools for large-scale brain data
* Summarization and information extraction from brain data
• Visualization and Scientific Computing
* High-Dimensional neuroimaging data visualization
* Brain network visualization and summarization
• Applications
* Resting state brain data analysis
* Cognitive states brain data analysis
* Diagnosis and analysis of neurological diseases
* Brain-computer interface
Machine Learning models are used to model data recorded in different modalities, such as fMRI, sMRI, EEG, MEG, fNIRS, and various forms of microscopy. The estimated models of the human brain can be visualized by three dimensional representation of various functionalities and states of the human brain, using the scientific visualization methods. These models enable neuroscientists to analyze and understand how the human brain works during the basic cognitive processes, such as attention, memory, sensation and perception; as well as high level cognitive processes, such as, language, problem solving, reasoning etc. These models are also used to develop brain-machine interfaces, driven by thoughts. Finally, resting state brain data can be modeled to analyze and diagnose various diseases
Although a wide range of machine learning methods are available in the literature, they are too short to analyze, interpret and predict brain imaging data. The models obtained at the output of a machine learning algorithm are rather poor approximations of a very restricted functionality of the human brain. Considering the fact that neurons are massively connected and all of the brain functions are interacting with each other, isolation of a single cognitive task or anatomic region represents a very narrow view of an extremely complex brain process. Additionally, most of the machine learning models are based on the findings of experimental neuroscience, which can be falsified by some other experiments. Finally, it is extremely difficult to measure the validity of the available models. Therefore, there is a long way of representing the structural or/and functional properties of the human brain by a rigorous machine learning model.
In this research topic, we welcome papers on theory and applications related to machine learning models for human brain to analyze, interpret and decode the brain imaging data in various modalities, such as, fMRI, sMRI, EEG, MEG, fNIRS, and microscopy. The research topic focuses on the following areas:
Recent advances in artificial intelligence and scientific visualization, together with neuroimaging techniques, offer powerful models to decipher the secrets of our brain. These models are widely used to explain how the electrochemical signals generated by the human brain process sensory stimuli and produce information for a wide range of brain functions.
Machine Learning models are used to model data recorded in different modalities, such as fMRI, sMRI, EEG, MEG, fNIRS, and various forms of microscopy. The estimated models of the human brain can be visualized by three dimensional representation of various functionalities and states of the human brain, using the scientific visualization methods. These models enable neuroscientists to analyze and understand how the human brain works during the basic cognitive processes, such as attention, memory, sensation and perception; as well as high level cognitive processes, such as, language, problem solving, reasoning etc. These models are also used to develop brain-machine interfaces, driven by thoughts. Finally, resting state brain data can be modeled to analyze and diagnose various diseases
Although a wide range of machine learning methods are available in the literature, they are too short to analyze, interpret and predict brain imaging data. The models obtained at the output of a machine learning algorithm are rather poor approximations of a very restricted functionality of the human brain. Considering the fact that neurons are massively connected and all of the brain functions are interacting with each other, isolation of a single cognitive task or anatomic region represents a very narrow view of an extremely complex brain process. Additionally, most of the machine learning models are based on the findings of experimental neuroscience, which can be falsified by some other experiments. Finally, it is extremely difficult to measure the validity of the available models. Therefore, there is a long way of representing the structural or/and functional properties of the human brain by a rigorous machine learning model.
In this research topic, we welcome papers on theory and applications related to machine learning models for human brain to analyze, interpret and decode the brain imaging data in various modalities, such as, fMRI, sMRI, EEG, MEG, fNIRS, and microscopy. The research topic focuses on the following areas:
• Artificial Neural Networks, specifically, Deep Neural Networks for Neuroimaging
* Disentanglement of neuroimaging data
* Brain data augmentation and transfer learning
* Brain encoding and decoding methods
• Learning and inference on neuroimaging data for
* cognitive state analysis and classification,
* modeling and analyzing the brain networks,
* multimodal data analysis
* multi-subject data analysis
* modeling and analyzing the neurological diseases.
• Big Data Analytics
* Efficient analysis tools for large-scale brain data
* Summarization and information extraction from brain data
• Visualization and Scientific Computing
* High-Dimensional neuroimaging data visualization
* Brain network visualization and summarization
• Applications
* Resting state brain data analysis
* Cognitive states brain data analysis
* Diagnosis and analysis of neurological diseases
* Brain-computer interface
Keywords: Computational neuroscience, Machine Learning, Scientific Visualization, Big Data Analytics, Neuroimaging
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