Neuroinformatics
Overview of Neuroinformatics
In recent two decades, with the development of neuroscience, a new method is needed to deal with the blooming research data. Researchers began to combine the ‘traditional’ bioinformatics with neuroscience and build databases to create the neuroinformatic in the early 1990s. After that, researchers continued to develop databases and deal with the complex types of neuroscience data. In 2004, a web portal “Society for Neuroscience” (SfN) was developed to catalog the databases and tools of neuroscience. With the achievements of SfN, Neuroscience Database gateway (NDG) organizes 178 databases into five main categories, and 15 classes.
The neuroinformatic has its goal to help understand the structure, function, and development of the health and disease nervous system. It requires the integration of different and complex data which is collected at multiple levels of investigation. The data includes gene and protein sequences in the nervous system; imaging of nerve cells; anatomic atlases of the brain; brain imaging by positron emission tomography (PET), electroencephalography (EEG), magnetic resonance imaging (MRI), and other methods; many electrophysiological recording methods; and clinical neurological data. To achieve this goal, researchers need to focus on three fields. First of all, large databases, where we can share various neuroscience data, need creating. Secondly, neuroscience data-analyzing tools need developing. Thirdly, computational models of neuroscience need developing. With these three problems solved, all data, needed to understand the nervous system, will ultimately be integrated. And this will help researchers to compare nervous systems from the level of molecules to behavior, in healthy, diseased, or injured human beings.
Examples of Neuroinformatic Database
- National center for biotechnology information (NCBI)
An “All database” search engine at NCBI can help researchers to concurrently search through 38 databases which include papers on Journals, literature, books, sequence databases, metadata, software, and other resources. That is useful to concurrently search the information of gene, protein, disease, and literature related to them in neuroscience.
Fig.1 All database search in NCBI.
- German Neuroinformatics Node (G-Node)
As a part of the International Neuroinformatics Coordinating Facility (INCF) and the German Bernstein Network for Computational Neuroscience (NNCN), G-Node provides the development and free use of tools for analyzing neurophysiological data. Besides, the tools developed in the G-Node are open source and freely available to researchers.
Neuroinformatics in Diseases
- Neuroinformatics in neurodegenerative diseases.
- Neuroinformatics in traumatic brain injury (TBI).
Neurodegenerative diseases are disorders with the impairment of motor and cognitive function. Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS) are the most common diseases of neurodegenerative diseases, which are often associated with psychiatric disorders. So far, it is still not curable.
With the increasing quantity of patients, neuroimaging data of neurodegenerative diseases has increased rapidly in the last few years. A free access policy to the databases of different organizations is very important to analyze those data. Just like the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, is the first database with worldwide open access, including blood and CSF test results and MRI/PET imaging data. That makes great development to strategy for treatments of neurodegenerative diseases.
Every year in the United States, there are more than 50,000 cases of mortality and 100,000 cases of serious neurological disability due to TBI. In the last few decades, the use of neuroimaging is an important way to get information in the treatment of TBI patients. And the massive neuroimaging data need neuroinformatic methods to analyzing, managing, and integrating with gene and protein sequences in the nervous system, nerve cells imaging, anatomic atlases of the brain, and other kinds of data.
Recently, researchers have made a multimodal integration of structural magnetic resonance imaging (sMRI) / diffusion-weighted imaging (DWI) / diffusion tensor imaging (DTI) to quantify white matter (WM) neural network abnormality associated with TBI-related cerebral microbleeds (CMBs). This study suggested that compared with healthy control simples, neuroinformatic methods can speed up the identification of patient-specific CMB-related connectomic changes.
Fig.2 Flowchart of neuroinformatic analysis for SWI/DWI volumes. (Maher, 2018)
Creative Biolabs & Neuroinformatics
Creative Biolabs is a professional provider for neuroscience research, and we can provide a wide range of products and custom services in this field. With our integrated global platform, we have great potential to accelerate your neuroscience research.
For more detailed information, please feel free to contact us for detailed information.
Reference
- Maher, Alexander S.; et al. Neuroinformatics and analysis of connectomic alterations due to cerebral microhemorrhages in geriatric mild neurotrauma: microhemorrhages in geriatric neurotrauma.Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2018: 165-171.
