Cargando…
Connectomic disturbances underlying insomnia disorder and predictors of treatment response
OBJECTIVE: Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimula...
Autores principales: | Lu, Qian, Zhang, Wentong, Yan, Hailang, Mansouri, Negar, Tanglay, Onur, Osipowicz, Karol, Joyce, Angus W., Young, Isabella M., Zhang, Xia, Doyen, Stephane, Sughrue, Michael E., He, Chuan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399490/ https://www.ncbi.nlm.nih.gov/pubmed/36034119 http://dx.doi.org/10.3389/fnhum.2022.960350 |
Ejemplares similares
-
Connectomics underlying motor functional outcomes in the acute period following stroke
por: Bian, Rong, et al.
Publicado: (2023) -
Use of machine learning to identify functional connectivity changes in a clinical cohort of patients at risk for dementia
por: Shen, Ying, et al.
Publicado: (2022) -
Functional connectivity of the language area in migraine: a preliminary classification model
por: Gou, Chen, et al.
Publicado: (2023) -
An agile, data‐driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets
por: Young, Isabella M., et al.
Publicado: (2023) -
Using a ResNet-18 Network to Detect Features of Alzheimer’s Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on Odusami et al. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071
por: Nicholas, Peter J., et al.
Publicado: (2022)