Neuronal impairments or neurological disorders (NDs) continue to attract much research attention owing to both their unknown etiology and the interwoven complexities of their underlying molecular and signaling pathways that often make the identification of practical classifications and therapeutic targets difficult. However, with recent advancement in computational methods, investigators are able to employ several databases and tools to demystify these complex networks for the identification of better therapeutic or mechanistic targets [1, 2]. These resources are not only useful for the study of bio-molecular interaction networks, but also help in network analysis, visualization and the establishment of relationships at the level of their genes and protein products. Databases serve as repositories for the retrieval of gene or protein interaction data, while the computational tools help withn data visualization, the establishment of interaction networks and data analysis. The various forms of, edical interaction networks being analyzed include: gene interaction (GI), protein-protein interaction (PPI), protein-DNA interactions, and protein-RNA interactions [3, 4]. Computational analysis of these interactions has revolutionized the modern understanding of NDs and provides better prospects for drug discovery.

Use of computational a approach to the study of NDs constitutes the field of Neuroinformatics. Recently, research in Neuroinformatics has gained ground, as it appears to be more promising in providing understanding of neural networks and disease. Research in Neuroinformatics extends to theory and methodology, including analytical tools, database design, meta-analysis, data sharing and discussions on computational modeling. The pool of data is vast and heterogeneous in the field of experimental neuroscience and the integration and analysis of these increasingly large-volume, high-dimensional data sets require Neuroinformatics to further understanding of the nervous system [5, 6, 7, 8]. Neuroinformatics is at the crossroads of neuroscience and information science [9, 10, 11]. It involves the study of the nervous system, its structure, function and diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), Amyotrophic lateral sclerosis (ALS), Prion disease amongst others. Research in neuroscience contains many different sub-disciplines that analyze data from multiple levels of the brain and information science is concerned with the collection, classification, and dissemination of information [12, 13, 14]. To solve increasingly complex problems, clinicians and research scientists use computational tools, mathematical models and Neuroinformatic databases provided by neuroinformaticians to collaborate, share information and quantitatively support working theories [15].

Computational neuroscience employs theoretical analysis, mathematical models and abstractions of the brain to understand the principles that govern the development, structure, physiology, information processing and cognitive abilities of the nervous system. Computational neuroscientists employ mathematical and computational approaches to understand how the brain processes information and informatics helps accelerate this neuroscientific discovery. Neuroinformatics tools such as image analysis, computer simulation and database integration help facilitate neural modeling and collaboration in the field of computational neuroscience. The nature of this sub-discipline of neuroscience is primarily quantitative, so Neuroinformatics is beneficial in the organization and analysis of data derived from computational research. Computational neuroscience differs in more qualitative experiments such as finding the optimal design of nervous systems or exploring various neural connectivity schemes in model networks [16, 17].

Neuroinformatics is rapidly evolving due to manifold ways of applying information technology to demystify complex problems related to neuroscience. These ways include development of databases and tools necessary for the analysis, modeling, management, and sharing of neuroscience data (Fig. 1). Thus, Neuroinformatics is multidisciplinary, with each discipline having a reasonable contributory role. Hence, fields related to Neuroinformatics are summarized as; (a) Psychology: Cognitive development and information processing theory, (b) Computer Science: Natural and Bio-inspired computation, (c) Philosophy: Computational theory of mind, (d) Engineering: Brain-Computer Interface technology, (e) Medicine: Aging and mental diseases like depression and anxiety, (f) Physical Sciences: Physical processes within neural cells and neuronal networks, (g) Mathematics: Statistical algorithms and Quantifying neuronal differentiation, (h) Chemistry: Molecular structures and their interactions in nervous system, and (i) Biology: Chemical processes and molecular structure.

Fig. 1.

Schematic representation of various NDs-associated informations which can be extracted using biological database.

With an increasing number of large databases, data sharing in neuroscience may gradually become as common and useful as it is in genomics, where the existence of very large bodies of data is leading to increased knowledge as well as products and services linked to the improvement of human health. Numerous databases are being employed to study NDs; these databases can be open source (freely accessible) or proprietary (commercially available). Regardless of the source, databases for the study of NDs can be broadly divided into four as shown in Fig. 2.

Fig. 2.

Schematic classification of in-silico databases for NDs.

Further, some of these databases along with their roles in the study of neuronal impairment are enumerated in Table 1 (Ref. [3, 4, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]), while the commonly used ones are discussed below.

Many tools have been developed to study NDs; these can be freely accessible or commercially available.

Some Neuroinformatics tools are meant for analysis, modelling, and visualization of neural systems such as Cytoscape, Gephi and EEGNET. Others are simply plug-ins that provide interface for various analysis such as ClusterONE and CytoHubba, while others were developed for structuring data to create consistent terminology and formats for the sharing of data such as BIRN (Biomedical Informatics $|$Research Network). In silico tools for the study of NDs can be divided into four broad categories as shown in Fig. 3, while Table 2 (Ref. [3, 23, 24, 26, 34, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54]) summarizes some of them. Further, the commonly used tools are discussed below.

Fig. 3.

Schematic classification of in-silico tools for NDs.

Advancements and successes made in the field of Neuroinformatics unveil more challenging perspectives. This challenge combines two tasks; development of new tools for understanding of neural networks and diseases on one side, and effective use of those computational tools and approaches on the other. Firstly, brain consists of large number of neurons with high complexity of structural connectivity that changes with experience. In the recent years, there is rapid increase in an overwhelming amount of new data from both theory and computational modeling to further understand the complex dynamic system. The new accruing data cover the area of brain connectomics, transcriptomics and neurophysiology, effective data analysis and interpretation are key to understanding of brain diseases. This challenge does not only necessitate collaborative efforts across the related fields, but also increase the demand for development of novel analysis tools [57].

Secondly, experiments and intuition alone could not explain the complexity of brain system, consequently, modern computational neuroscience is expanding synergistically with experimental research. One of the major benefits of this synergy is the increased use of artificial intelligence (AI) and machine learning (ML) for computational modeling and data analysis in neuroscience [58]. However, the current limitation of ML in recognizing higher cognitive functions and complex network visualization will continue to put pressure on Neuroinfromatics to develop more dynamic tools and methodologies. With the continues influx of an overwhelming amount of computational databases and tools, development of infrastructure, methodologies, and trainings for effective handling of the heterogeneous datasets will constitute the future challenge in Neuroinformatics and neuroscience [2].

Considering the successes recorded in Neuroinformatics, more attention needs to be given to infrastructural development, funding, and training to ensure the sustainability of brain research. Training opportunities would attract young talents that will drive the future development of computational algorithms and analytical tools. It’s through regular training that professionals and young talents will be kept abreast of recent development. Therefore, having articles and chapters – like the present one - that summarize recent advances in analytical tools and databases would be a “training recipe”.

Computational approach to study of NDs is rapidly evolving. The increase in synergistic relationship between computational and experimental neuroscience leads to the emergence of more analytical tools and databases. For sustainability of research in neuroscience, development of infrastructure, methodologies, and trainings are necessary. Frequent update and review of the emerging databases and tools along with their functionalities would be a training recipe for young professionals and guide for researchers. This is in a bid to further understand the fundamentals of NDs, their unknown etiology, interwoven complex molecular and signaling pathways. Additionally, it will also help in the development of the drug and further improves the neurotherapeutics. Further study is recommended to explore more of such databases and tools for their pertinent role in Neurological studies.

NKJ, MBU and AA conceptualized, wrote, and edited the manuscript. SKJ, DKC, GG, SKS, SR, NK, FAK, HD and VU performed the literature survey, drafted and edited the manuscript. NKJ ideated the scheme, performed artwork. PP, KKK, GMA, SO, KD, FZ helped in revision and edited the manuscript. All authors contributed to the article and approved the submitted version.

Not applicable.

We would like to thanks the senior management of Sharda University for their constant support and encouragement.

This research received no external funding.

The authors declare no conflict of interest.