Recent advances and the future generation of neuroinformatics infrastructure

The huge volume of multi-modal neuroimaging data across different neuroscience communities has posed a daunting challenge to traditional methods of data sharing, data archiving, data processing and data analysis. Neuroinformatics plays a crucial role in creating advanced methodologies and tools for the handling of varied and heterogeneous datasets in order to better understand the structure and function of the brain. These tools and methodologies not only enhance data collection, analysis, integration, interpretation, modeling, and dissemination of data, but also promote data sharing and collaboration. This Neuroinformatics Research Topic aims to summarize the state-of-art of the current achievements and explores the directions for the future generation of neuroinformatics infrastructure. We welcome submissions from the following 4 topic areas: 1) data archiving, 2) data processing and workflow, 3) data mining, and 4) system integration methodologies. The data archiving section focus on novel methods for efficient collection, storage and query of huge volume neuroimaging data, genetics data, and all other related measures as well as meta-data and processing results. The data processing and workflow section emphasizes methods that facilitate large-scale parallel data processing tasks under the heterogeneous computational environment. The data mining section emphasizes novel data analysis methodologies to extract meaningful information from the data. Finally the system integration section focus on the best practices and novel approaches to integrate multiple systems (database, workflow, ware house, etc). To be more specific, we would like to encourage a spectrum of subtopics from the 4 areas, including but not limited to:
1. Data archiving
1.1. Novel data annotation and visualization
1.2. Archiving automation/simulation/de-identification/compression/quality control/ontology, etc.
1.3. Novel storage architecture (e.g., grid, web storage, XML, OO, ..., etc.) and search engine
1.4. Data warehousing
2. Data processing and workflow
2.1. Novel neuroimaging data processing algorithms, packages and libraries
2.2. Distributed/parallel algorithms design (cluster/GPU/FPGA/cloud/HPC, etc)
2.3. Neuroimaging workflow design
2.3.1. Visualization/annotation
2.3.2. Workflow for distributed/collaborative environment
2.3.3. Provenance, meta data management
3. Data mining
3.1. Novel multi-modal integration methods
3.2. Feature extraction and visualization methods
3.3. Novel machine learning methods
3.4. Novel applications (biomarker detection/clinical application/imaging-genomics/brain-computer-interface/publication mining, etc.)
4. System integration methodologies, frameworks, architectures and best practices
4.1. Integration of different software components, e.g., database/data warehouse/workflow system/data analysis package and libraries.
4.2. Cross-platform, cross-modality integration
4.3. Cross-domain integration
4.3.1. Integration of imaging database with other types of databases
4.3.2. Integration of imaging workflow with other types of workflow
4.4. Distributed system integration
4.4.1. Data synchronization/query/transaction control/access control/single sign on/processing