Revolutionizing stress-related disorder regulation through neuroinformatics and data analysis: An editorial

Arosh S. Perera Molligoda Arachchige; Yash Verma; Arosh S. Perera Molligoda Arachchige; Yash Verma

doi:10.3934/Neuroscience.2023019

AIMS Neuroscience

2023, Volume 10, Issue 3: 252-254. doi: 10.3934/Neuroscience.2023019

Previous Article Next Article

Editorial Special Issues

Revolutionizing stress-related disorder regulation through neuroinformatics and data analysis: An editorial

Department of Biomedical Sciences, Humanitas University, Milan, Italy

Received: 15 July 2023 Accepted: 30 August 2023 Published: 04 September 2023

Citation: Arosh S. Perera Molligoda Arachchige, Yash Verma. Revolutionizing stress-related disorder regulation through neuroinformatics and data analysis: An editorial[J]. AIMS Neuroscience, 2023, 10(3): 252-254. doi: 10.3934/Neuroscience.2023019

Related Papers:

Conflicts of interest

The author has no conflicts of interest to declare.

References

[1]	Neuroinformatics and data analysis in the regulation of stress-related disorders. Accessed on 14/07/2023 at https://www.aimspress.com/aimsn/article/6523/special-articles
[2]	Gianaros PJ, O'Connor M-F (2018) .Academic Press 543-563. Accessed on 14/07/2023 at http://bnl.pitt.edu/documents/Gianaros_O'COnnor_Contrada_14717_R2_CH39_07-13-10_543-564%20copy.pdf
[3]	Iniesta R, Stahl D, McGuffin P (2016) Machine learning, statistical learning and the future of biological research in psychiatry. Psychol Med 46: 2455-2465. https://doi.org/10.1017/S0033291716001367
[4]	Talaat FM, El-Balka RM (2023) Stress monitoring using wearable sensors: IoT techniques in medical field. Neural Comput Appl 35: 18571-18584. https://doi.org/10.1007/s00521-023-08681-z
[5]	Sheikh M, Qassem M, Kyriacou PA (2021) Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Front Digital health 3: 662811. https://doi.org/10.3389/fdgth.2021.662811
[6]	Dhama K, Latheef SK, Dadar M, et al. (2019) Biomarkers in Stress Related Diseases/Disorders: Diagnostic, Prognostic, and Therapeutic Values. Front Mol Biosci 6: 91. https://doi.org/10.3389/fmolb.2019.00091
[7]	Calhoun VD, Pearlson GD, Sui J (2021) Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples. Curr Opin Neurol 34: 469-479. https://doi.org/10.1097/WCO.0000000000000967
[8]	Schneiderman N, Ironson G, Siegel SD (2005) Stress and health: psychological, behavioral, and biological determinants. Annu Rev Clin Psycho 1: 607-628. https://doi.org/10.1146/annurev.clinpsy.1.102803.144141
[9]	Chekroud AM, Bondar J, Delgadillo J, et al. (2021) The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatr 20: 154-170. https://doi.org/10.1002/wps.20882
[10]	Jwa AS, Poldrack RA (2022) Addressing privacy risk in neuroscience data: from data protection to harm prevention. J Law Biosci 9: lsac025. https://doi.org/10.1093/jlb/lsac025
[11]	Baldwin JR, Pingault JB, Schoeler T, et al. (2022) Protecting against researcher bias in secondary data analysis: challenges and potential solutions. Eur J Epidemiol 37: 1-10. https://doi.org/10.1007/s10654-021-00839-0
[12]	Sathyanarayanan A, Mueller TT, Ali Moni M, et al. (2023) Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharm 69: 26-46. https://doi.org/10.1016/j.euroneuro.2023.01.001

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)