Innovative Tools and Methodologies for Big Data Exploration, Visualization, and Analytics for Neuroscience
- Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), Doha, KuwaitInterests: signal processing; 4G/5G design; radio resource management; energy-efficiency improvement for next generation wireless networks; interference mitigation; short-range communications; WLAN; OFDM; SC-FDMA; HetNet; artificial intelligence; fuzzy logic and machine learning techniques with applications to algorithm and protocol design in wireless communications
Big data has brought revolutionary transformation in diverse fields associated with medical and allied health care sectors. Emerging advances in electronic health records (EHR) aids in the preservation of critical medical data, which would open unlimited opportunities for machine learning algorithms to work. Big data analytics in neuroscience opens an unequaled understanding of the function of neurons and gives potential cues in correlating behavior with human activity. Correlation of Brain imaging data representing their activity in response to various simulations with brain lobes through wiring diagrams revealed the cell clusters associated with specific emotions. Integration of artificial neural networks (ANN) in the visualization of magnetic resonance imaging provided a very detailed connectivity map of the human brain. The high resolution offered by ANN allows researchers to map to resolve of about one cubic millimeter, which is far better than before. Big data empowered Meta-analysis remarkably associate electroencephalography images with human cognitive abilities and forecast their behavior which is highly applicable in defense sectors. Advances in signal neuronal visualization techniques gave deep insights into brain synapses and revealed many more intricate functions of the brain ever than before. Machine learning algorithms capable of versatile fusion strategies precisely fuse brain activities map with the hormonal state of humans, which reveal diverse functionalities of the human mind and the impact of simulations on hormonal regulation of neuronal function.
Advanced analytics tools and big data facilitate exploring the brain's functionality, which aids in the diagnosis and treatment of many neuron-associated diseases. However, potential challenges in advanced methodologies for visualization and big data analytics in neuroscience must be evaluated before global adoption and recommendation. Primarily the integration of big data analytics in neuroscience is still in its infancy and needs to be improved. Machine learning algorithms to identify brain dynamics in decision-making are exceedingly complex and need improvements. The multimodal nature of medical data (associated with neuroscience) needs innovative fusion strategies to mine useful information. Modeling neurons' conscious and subconscious information processing is challenging due to the less accurate documentation technologies. Probing and identifying subtle neuronal changes with enormous consequences is challenging to perform, requiring more research. On substantial improvement, innovative methodologies and tools in big data analytics of neuroscience would be a revolutionary tool in future medical sectors. Additionally, they would serve as a driving force in identifying potential therapeutic strategies for many mental disorders.
Therefore this special issue aims to discuss and highlight various aspects of machine learning algorithms, neuronal systems, magnetic resonance brain imaging, brain-behavior, brain mapping, human connectome, hormonal regulation, and much more. We invite researchers from various fields to present their research articles, reviews, case studies, short communications, and perspectives in Innovative Tools and Methodologies for Big Data Exploration, Visualization, and Analytics for Neuroscience.
Topics of interest include [but are not limited to the following]:
1. Emerging research in the visualization of brain dynamics for assessment of structural and functional connectome
2. Research in big data-enabled neural foundations for intelligent behavior modeling
3. Advances in neural methodologies for the systemic design of learning mechanisms
4. Big enabled analytics for insight gaining in neuro-linguistics and neuro-education
5. Implications of brain imaging in the executive function of higher-order cognitive relationships
6. Innovations in the systematic design of multimodal brain data for human connectome map distribution
7. Brain big data analytics for neuron reconstruction and disease prognosis
8. Advances in brain data grids for visualization of disease-related markers in brain
9. Machine learning empowered fMRI and PET image analytics for multilevel brain networks and management
10. Research in the cloud and semantic brain data services for multi-threshold simulation of brain activity
11. Advanced statistical methodologies for the combinatorial fusion of brain imaging and optogenetics
12. Implications of social networks, social media, and e-learning for mental healthcare modulation and computational psychiatry
13. Role brain simulations in modeling and repair of damaged brain
Assis. Prof. Mohammad Shabaz, Assoc. Prof. Gaganpreet Kaur and Assoc. Prof. Abolfazl Mehbodniya
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