In comparison to other natural systems, the temporal dynamics of the human brain's growth, structure, and function are notably intricate. The human brain is comprised of an estimated 86.1 to 8.0 billion neurons and a comparable non-neural glial cells number. Additionally, the brain contains neuronal systems with over 100 trillion connections. The modeling, analysis, and comprehension of these complex structures require the use of code and automation. Neuroinformatics methodologies are employed to manage, retrieve, and integrate the copious quantities of data produced through clinical documentation, scientific literature, and specialized databases. Conversely, computational neuroscience, which draws heavily upon the fields of biology, physics, mathematics, and computation, tackles these issues. Neuroinformatics is the interdisciplinary field that integrates computational neuroscience and neuroscientific experimentation. This paper functions as an introductory guide for individuals who lack familiarity with the domains of neuroinformatics and computational neuroscience, along with their consistentsophisticated software, resources, and tools.
R. K. Manthari, P. Koyya, and S. L. Pandrangi, “Brain-derived neurotrophic factor - the protective agent against neurological disorders,” CNS Neurol. Disord. Drug Targets, 2023.
K. Misaki et al., “Pos0942 the significance of pre-screening of subclinical malignancies before the treatment of biologic agents or Jak inhibitors to connective tissue disease patients,” Ann. Rheum. Dis., vol. 82, no. Suppl 1, p. 783.1-783, 2023.
M. Fahle, “3. How can language cope with color? Functional aspects of the nervous system,” in Speaking of Colors and Odors, Amsterdam: John Benjamins Publishing Company, 2007, pp. 35–60.
S. M. BagheriMofidi, M. Pouladian, S. B. Jameie, and A. Abbaspour Tehrani-Fard, “Computational modeling of neuronal current MRI signals with rat somatosensory cortical neurons,” Interdiscip. Sci., vol. 8, no. 3, pp. 253–262, 2016.
J. Oesterle, N. Krämer, P. Hennig, and P. Berens, “Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models,” J. Comput. Neurosci., 2023.
P. K. Mandal, A. Banerjee, M. Tripathi, and A. Sharma, “A comprehensive review of magnetoencephalography (MEG) studies for brain functionality in healthy aging and Alzheimer’s disease (AD),” Front. Comput. Neurosci., vol. 12, p. 60, 2018.
F. W.K.Lor and R. H.Y.So, “Computational ergonomics‚Äîa possible extension of computational neuroscience? Definitions, potential benefits, and a case study on cybersickness,” in Contemporary Ergonomics, CRC Press, 2004.
M. Marino et al., “Neuronal dynamics enable the functional differentiation of resting state networks in the human brain,” Hum. Brain Mapp., vol. 40, no. 5, pp. 1445–1457, 2019.
R. Huang, B. Zheng, and X. Song, “Rank decomposition and symmetric rank decomposition over arbitrary fields,” Linear Multilinear Algebra, vol. 70, no. 20, pp. 5888–5901, 2022.
M. A. Al-Shabi, “Design of a network intrusion detection system using complex deep neuronal networks,” Nternational J. Commun. Netw. Inf. Secur., vol. 13, no. 3, 2022.
F. Bezanilla, “Ion channels: from conductance to structure,” Neuron, vol. 60, no. 3, pp. 456–468, 2008.
E. Cortés, J. S. Pak, and E. Özkan, “Structure and evolution of neuronal wiring receptors and ligands,” Dev. Dyn., vol. 252, no. 1, pp. 27–60, 2023.
&na;, “Ionic basis for intrinsic 40Hz neuronal oscillations,” Neuroreport, vol. 5, no. 4, p. 531, 1994.
Z. Zhao, H. Ji, C. Zhang, J. Pei, X. Zhang, and Y. Yuan, “Modulation effects of low-intensity transcranial ultrasound stimulation on the neuronal firing activity and synaptic plasticity of mice,” Neuroimage, vol. 270, no. 119952, p. 119952, 2023.
B. Liss and J. Roeper, “Individual dopamine midbrain neurons: functional diversity and flexibility in health and disease,” Brain Res. Rev., vol. 58, no. 2, pp. 314–321, 2008.
J. Ma et al., “Pre-stimulus gamma power in human posteromedial cortex shows supra-modal mechanisms in predicting the amplitude and latency of task-induced suppression,” Cereb. Cortex, vol. 33, no. 12, pp. 7843–7856, 2023.
M. Carrero-Pazos, B. Vilas-Estévez, and A. Vázquez-Martínez, “Digital imaging techniques for recording and analysing prehistoric rock art panels in Galicia (NW Iberia),” Digit. Appl. Archaeol. Cult. Herit., vol. 8, pp. 35–45, 2018.
A. Dragomir and A. Omurtag, “Brain’s networks and their functional significance in cognition,” in Handbook of Neuroengineering, Singapore: Springer Singapore, 2021, pp. 1–30.
A. Parent, D. Paré, Y. Smith, and M. Steriade, “Basal forebrain cholinergic and noncholinergic projections to the thalamus and brainstem in cats and monkeys: BASAL FOREBRAIN PROJECTIONS,” J. Comp. Neurol., vol. 277, no. 2, pp. 281–301, 1988.
Z. Lu and X. Bai, “The processing differences between Chinese proper nouns and common nouns in the left and right hemispheres of the brain,” Brain Sci., vol. 13, no. 3, 2023.
P. Wang, H. Sang, and D. Ma, “Dissipativity for discrete-time switched positive delay systems: A dwell-time-dependent linear copositive storage functional method,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 7, pp. 4028–4038, 2023.
California Medi-Cal Type 2 Diabetes Study Group, “Closing the gap: effect of diabetes case management on glycemic control among low-income ethnic minority populations: the California Medi-Cal type 2 diabetes study,” Diabetes Care, vol. 27, no. 1, pp. 95–103, 2004.
Y. Liao et al., “The alternation of gray matter morphological topology in drug-naïve tourette’s syndrome in children,” Front. Aging Neurosci., vol. 14, p. 873148, 2022.
G. Videtta, L. Squarcina, M. G. Rossetti, P. Brambilla, G. Delvecchio, and M. Bellani, “White matter modifications of corpus callosum in bipolar disorder: A DTI tractography review,” J. Affect. Disord., vol. 338, pp. 220–227, 2023.
E. M. Tansey, “The synapse: people, words and connections,” Neuronal Signal., vol. 6, no. 2, p. NS20220017, 2022.
J. D. Medaglia, “Networks of cognitive processes: Functional and anatomical correlates of cognition, emotions, and social cognition,” in Cognitive Dimensions of Major Depressive Disorder, Oxford University Press, 2019, pp. 157–170.
J. G. Bjaalie, S. Grillner, and S. Usui, “Neuroinformatics: databases, tools, and computational modeling for studying the nervous system,” Neural Netw., vol. 21, no. 8, pp. 1045–1046, 2008.
S.-E. Park, Y.-J. Jeon, and H.-M. Baek, “Functional and structural brain abnormalities and clinical characteristics of male patients with alcohol dependence,” Brain Sci., vol. 13, no. 6, 2023.
L. H. Scholtens, R. Pijnenburg, S. C. de Lange, I. Huitinga, M. P. van den Heuvel, and Netherlands Brain Bank, “Common microscale and macroscale principles of connectivity in the human brain,” J. Neurosci., vol. 42, no. 20, pp. 4147–4163, 2022.
M. B. Abrams et al., “Correction to: A standards organization for open and FAIR neuroscience: The international neuroinformatics coordinating facility,” Neuroinformatics, vol. 20, no. 1, pp. 37–38, 2022.
F. Barré-Sinoussi and X. Montagutelli, “Animal models are essential to biological research: issues and perspectives,” Future Sci. OA, vol. 1, no. 4, p. FSO63, 2015.
S. Zhang, Q. Zhai, and Y. Li, “Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts,” Reliab. Eng. Syst. Saf., vol. 231, no. 109021, p. 109021, 2023.
F. Å. Nielsen, “The Brede database: a small database for functional neuroimaging,” Dtu.dk. [Online]. Available: http://www2.imm.dtu.dk/pubdb/edoc/imm2879.pdf. [Accessed: 01-Jul-2023].
J. Gao, J. Liu, Y. Xu, D. Peng, and Z. Wang, “Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer’s disease,” Front. Neurosci., vol. 17, 2023.
T. Jiang, “Brainnetome: a new -ome to understand the brain and its disorders,” Neuroimage, vol. 80, pp. 263–272, 2013.
H. Lee, J. Chen, and U. Hasson, “A functional neuroimaging dataset acquired during naturalistic movie watching and narrated recall of a series of short cinematic films,” Data Brief, vol. 46, no. 108788, p. 108788, 2023.
Z. Zhao, C. Chen, B. Mani Adhikari, L. E. Hong, P. Kochunov, and S. Chen, “Mediation analysis for high-dimensional mediators and outcomes with an application to multimodal imaging data,” Comput. Stat. Data Anal., vol. 185, 2023.
C. Gómez-Tapia, B. Bozic, and L. Longo, “On the minimal amount of EEG data required for learning distinctive human features for task-dependent biometric applications,” Front. Neuroinform., vol. 16, p. 844667, 2022.
P. Gleeson et al., “NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail,” PLoS Comput. Biol., vol. 6, no. 6, p. e1000815, 2010.
J. L. Lancaster, D. Eberly, A. Alyassin, J. H. Downs 3rd, and P. T. Fox, “A geometric model for measurement of surface distance, surface area, and volume from tomographic images,” Med. Phys., vol. 19, no. 2, pp. 419–431, 1992.
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Polly Jones
Polly Jones
Ural Federal University, Sverdlovsk Oblast, Russia, 620075.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Polly Jones, “Recent Developments in Neuroinformatics and Computational Neuroscience”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.2, pp. 118-128, July 2023. doi: 10.53759/0088/JBSHA202303012.
