Neuroinformatics Applications of Data Science and Artificial Intelligence.


Journal

Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069

Informations de publication

Date de publication:
24 Sep 2024
Historique:
medline: 24 9 2024
pubmed: 24 9 2024
entrez: 24 9 2024
Statut: aheadofprint

Résumé

Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.

Identifiants

pubmed: 39316274
doi: 10.1007/s12021-024-09692-4
pii: 10.1007/s12021-024-09692-4
doi:

Types de publication

Editorial

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Références

Arbib, M. A., Bonaiuto, J. J., Bornkessel-Schlesewsky, I., Kemmerer, D., MacWhinney, B., Nielsen, F. Å., & Oztop, E. (2014). Action and language mechanisms in the brain: Data, models and neuroinformatics. Neuroinformatics, 12, 209–225.
doi: 10.1007/s12021-013-9210-5 pubmed: 24234916 pmcid: 4101894
Baloh, R. W. (2024). Brain Electricity Book SubtitleThe Interwoven History of Electricity and Neuroscience. UK: Springer.
doi: 10.1007/978-3-031-62994-5
Barabási, D. L., Bianconi, G., Bullmore, E., Burgess, M., Chung, S., Eliassi-Rad, T., George, D., Kovács, I. A., Makse, H., & Nichols, T. E. (2023). Neuroscience needs network science. Journal of Neuroscience., 43(34), 5989–5995.
doi: 10.1523/JNEUROSCI.1014-23.2023 pubmed: 37612141
Bisiani, J., Anugu, A., & Pentyala, S. (2023). It’s Time to Go Quantum in Medicine. Journal of Clinical Medicine, 12(13), 4506. https://doi.org/10.3390/jcm12134506
doi: 10.3390/jcm12134506 pubmed: 37445540 pmcid: 10342414
Dinov, I., & Velev, M. (2021). Data science: time complexity, inferential uncertainty, and spacekime analytics (1st ed., p. 450). Berlin/Boston: De Gruyter.
doi: 10.1515/9783110697827
Dinov, I. D. (2023). Data Science and Predictive Analytics: Biomedical and Health Applications using R. Springer.
doi: 10.1007/978-3-031-17483-4
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T. S., & Li, Q. (2024). A survey on rag meeting llms: Towards retrieval-augmented large language models. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6491–6501).
Gao, X., Wang, Y., Chen, X., & Gao, S. (2021). Interface, interaction, and intelligence in generalized brain–computer interfaces. Trends in Cognitive Sciences., 25(8), 671–684.
doi: 10.1016/j.tics.2021.04.003 pubmed: 34116918
Górriz, J. M., Ramírez, J., Ortiz, A., Martinez-Murcia, F. J., Segovia, F., Suckling, J., Leming, M., Zhang, Y.-D., Álvarez-Sánchez, J. R., & Bologna, G. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237–270.
doi: 10.1016/j.neucom.2020.05.078
Kasabov, N. (2013). Springer Handbook of Bio-neuro-informatics. UK: Springer Science & Business Media.
Rao, R. P. (2023). Brain co-processors: Using AI to restore and augment brain function (pp. 1225–1260). Springer.
Voigtlaender, S., Pawelczyk, J., Geiger, M., Vaios, E. J., Karschnia, P., Cudkowicz, M., Dietrich, J., Haraldsen, I. R. H., Feigin, V., & Owolabi, M. (2024). Artificial intelligence in neurology: Opportunities, challenges, and policy implications. Journal of Neurology., 271(5), 2258–2273.
doi: 10.1007/s00415-024-12220-8 pubmed: 38367046
Zhang, R., Zhang, Y., Liu, Y., Guo, Y., Shen, Y., Deng, D., Qiu, Y. J., & Dinov, I. D. (2022). Kimesurface representation and tensor linear modeling of longitudinal data. Neural Computing and Applications., 34, 6377–6396. https://doi.org/10.1007/s00521-021-06789-8
doi: 10.1007/s00521-021-06789-8 pubmed: 35936508
Zhao, L., Zhang, L., Wu, Z., Chen, Y., Dai, H., Yu, X., Liu, Z., Zhang, T., Hu, X., & Jiang, X. (2023). When brain-inspired ai meets agi. Meta-Radiology, 1, 100005.
doi: 10.1016/j.metrad.2023.100005

Auteurs

Ivo D Dinov (ID)

University of Michigan, Ann Arbor, MI, USA. statistics@umich.edu.

Classifications MeSH