Document Type
Article
Publication Date
12-1-2021
Abstract
Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.
Recommended Citation
Wu, Chengyuan; Ferreira, Francisca; Fox, Michael; Harel, Noam; Hattangadi-Gluth, Jona; Horn, Andreas; Jbabdi, Saad; Kahan, Joshua; Oswal, Ashwini; Sheth, Sameer A; Tie, Yanmei; Vakharia, Vejay; Zrinzo, Ludvic; and Akram, Harith, "Clinical applications of magnetic resonance imaging based functional and structural connectivity" (2021). Department of Neurosurgery Faculty Papers. Paper 165.
https://jdc.jefferson.edu/neurosurgeryfp/165
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
PubMed ID
34648960
Language
English
Comments
This article is the author’s final published version in NeuroImage, Volume 244, December 2021, Article number 118649.
The published version is available at https://doi.org/10.1016/j.neuroimage.2021.118649. Copyright © Wu et al.