Cargando…
Classification of multiple sclerosis women with voiding dysfunction using machine learning: Is functional connectivity or structural connectivity a better predictor?
INTRODUCTION: Machine learning (ML) is an established technique that uses sets of training data to develop algorithms and perform data classification without using human intervention/supervision. This study aims to determine how functional and anatomical brain connectivity (FC and SC) data can be us...
Autores principales: | Tran, Khue, Salazar, Betsy H., Boone, Timothy B., Khavari, Rose, Karmonik, Christof |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10071087/ https://www.ncbi.nlm.nih.gov/pubmed/37025479 http://dx.doi.org/10.1002/bco2.217 |
Ejemplares similares
-
High spatial correlation in brain connectivity between micturition and resting states within bladder-related networks using 7 T MRI in multiple sclerosis women with voiding dysfunction
por: Shi, Zhaoyue, et al.
Publicado: (2021) -
Data-Driven Machine-Learning Quantifies Differences in the Voiding Initiation Network in Neurogenic Voiding Dysfunction in Women With Multiple Sclerosis
por: Karmonik, Christof, et al.
Publicado: (2019) -
Predictors for outcomes of noninvasive, individualized transcranial magnetic neuromodulation in multiple sclerosis women with neurogenic voiding dysfunction
por: Jang, Yongchang, et al.
Publicado: (2022) -
Is the Brainstem Activation Different Between Healthy Young Male and Female Volunteers at Initiation of Voiding? A High Definition 7-Tesla Magnetic Resonance Imaging Study
por: Schott, Bradley, et al.
Publicado: (2023) -
Therapeutic effects of non-invasive, individualized, transcranial neuromodulation treatment for voiding dysfunction in multiple sclerosis patients: study protocol for a pilot clinical trial
por: Tran, Khue, et al.
Publicado: (2021)