Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Robyn L, Miller"'
Publikováno v:
Frontiers in Psychiatry, Vol 15 (2024)
IntroductionDynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clus
Externí odkaz:
https://doaj.org/article/e831d6e9f1cc4f39babf3167a70b7158
Publikováno v:
Entropy, Vol 26, Iss 7, p 545 (2024)
Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated mea
Externí odkaz:
https://doaj.org/article/8dcc4a52cc824936a30e4dba59401c7a
Publikováno v:
Neuroimage: Reports, Vol 3, Iss 4, Pp 100186- (2023)
Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain region
Externí odkaz:
https://doaj.org/article/b0d0c4c478754133835a49b5b5c0af78
Autor:
Charles A. Ellis, Mohammad S. E. Sendi, Rongen Zhang, Darwin A. Carbajal, May D. Wang, Robyn L. Miller, Vince D. Calhoun
Publikováno v:
Frontiers in Neuroinformatics, Vol 17 (2023)
IntroductionMultimodal classification is increasingly common in electrophysiology studies. Many studies use deep learning classifiers with raw time-series data, which makes explainability difficult, and has resulted in relatively few studies applying
Externí odkaz:
https://doaj.org/article/bb154bf172e24d809c656847873c5452
Autor:
Mohammad S.E. Sendi, Elaheh Zendehrouh, Charles A. Ellis, Zening Fu, Jiayu Chen, Robyn L. Miller, Elizabeth C. Mormino, David H. Salat, Vince D. Calhoun
Publikováno v:
NeuroImage: Clinical, Vol 37, Iss , Pp 103363- (2023)
Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer’s disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best o
Externí odkaz:
https://doaj.org/article/d307e7de9d3745868bc82c3b7b18c2c2
Publikováno v:
Informatics in Medicine Unlocked, Vol 37, Iss , Pp 101176- (2023)
The field of neuroimaging has increasingly sought to develop artificial intelligence-based models for neurological and neuropsychiatric disorder automated diagnosis and clinical decision support. However, if these models are to be implemented in a cl
Externí odkaz:
https://doaj.org/article/d8a641cb9d8a4d978b720677c13599c8
Autor:
Sofía Puvogel, Kris Blanchard, Bárbara S. Casas, Robyn L. Miller, Delia Garrido-Jara, Sebastián Arizabalos, Stevens K. Rehen, Magdalena Sanhueza, Verónica Palma
Publikováno v:
Frontiers in Cell and Developmental Biology, Vol 10 (2022)
Schizophrenia (SZ) is a severe mental disorder that arises from abnormal neurodevelopment, caused by genetic and environmental factors. SZ often involves distortions in reality perception and it is widely associated with alterations in brain connecti
Externí odkaz:
https://doaj.org/article/303e2ade3fe840b6b2a7e4033bdd13ed
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
BackgroundDynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a conventional dFNC pipeline, a clustering st
Externí odkaz:
https://doaj.org/article/d58b7018a93e490b9b5f04b2c2173a14
Publikováno v:
Frontiers in Neuroinformatics, Vol 16 (2022)
In recent years, the use of convolutional neural networks (CNNs) for raw resting-state electroencephalography (EEG) analysis has grown increasingly common. However, relative to earlier machine learning and deep learning methods with manually extracte
Externí odkaz:
https://doaj.org/article/bbfa376b7a6748c49f24a1ab0a5724b2
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
The study of brain network connectivity as a time-varying property began relatively recently and, to date, has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale s
Externí odkaz:
https://doaj.org/article/6990938f3b024dbfb9a525fa6db9caaf