Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Shella Keilholz"'
Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states
Autor:
Lisa Meyer-Baese, Nmachi Anumba, T. Bolt, L. Daley, T. J. LaGrow, Xiaodi Zhang, Nan Xu, Wen-Ju Pan, E. H. Schumacher, Shella Keilholz
Publikováno v:
Frontiers in Systems Neuroscience, Vol 18 (2024)
A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features suc
Externí odkaz:
https://doaj.org/article/f40653d6615144ddafa64dbfd9cc62b8
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
IntroductionBrain Network Models (BNMs) are mathematical models that simulate the activity of the entire brain. These models use neural mass models to represent local activity in different brain regions that interact with each other via a global stru
Externí odkaz:
https://doaj.org/article/cf836c23fbe943c4945209fe230ca448
Publikováno v:
NeuroImage, Vol 251, Iss , Pp 118987- (2022)
Externí odkaz:
https://doaj.org/article/a6f3c02cd0f1409eb52a48d62edf59bb
Publikováno v:
Frontiers in Neural Circuits, Vol 16 (2022)
Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase syn
Externí odkaz:
https://doaj.org/article/dec18a18da4344f188bf6f2d6d326316
Publikováno v:
Network Neuroscience, Vol 5, Iss 2, Pp 549-568 (2021)
AbstractWhile brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data’s apparent self-organization. To clarify how patt
Externí odkaz:
https://doaj.org/article/b1d7ce421e3542c4a33aa339408d4a01
Autor:
Nan Xu, Theodore J. LaGrow, Nmachi Anumba, Azalea Lee, Xiaodi Zhang, Behnaz Yousefi, Yasmine Bassil, Gloria P. Clavijo, Vahid Khalilzad Sharghi, Eric Maltbie, Lisa Meyer-Baese, Maysam Nezafati, Wen-Ju Pan, Shella Keilholz
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain’s physiological and pathologic
Externí odkaz:
https://doaj.org/article/e11f31905fc54a7a95a2b1657cc864c0
Autor:
Mani Salarian, Ravi Chakra Turaga, Shenghui Xue, Maysam Nezafati, Khan Hekmatyar, Jingjuan Qiao, Yinwei Zhang, Shanshan Tan, Oluwatosin Y. Ibhagui, Yan Hai, Jibiao Li, Rao Mukkavilli, Malvika Sharma, Pardeep Mittal, Xiaoyi Min, Shella Keilholz, Liqing Yu, Gengshen Qin, Alton Brad Farris, Zhi-Ren Liu, Jenny J. Yang
Publikováno v:
Nature Communications, Vol 10, Iss 1, Pp 1-14 (2019)
Non-invasive early diagnosis of liver fibrosis is important to prevent disease progression and direct treatment strategies. Here the authors developed a collagen-targeting contrast agent for the detection of early stage fibrosis and non-alcoholic ste
Externí odkaz:
https://doaj.org/article/7815a12c65474deb997bdf57a3b68b7b
Autor:
Patricia Pais-Roldán, Celine Mateo, Wen-Ju Pan, Ben Acland, David Kleinfeld, Lawrence H. Snyder, Xin Yu, Shella Keilholz
Publikováno v:
NeuroImage, Vol 245, Iss , Pp 118630- (2021)
Abstracts: Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state func
Externí odkaz:
https://doaj.org/article/a85de61839b14e028e943a09e2663c98
Autor:
Behnaz Yousefi, Shella Keilholz
Publikováno v:
NeuroImage, Vol 231, Iss , Pp 117827- (2021)
The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as functional networks and gradients. Dynamic analysis techniques have shown that function
Externí odkaz:
https://doaj.org/article/f440288c88c04ad09ad2d04159a0ed08
Autor:
Amrit Kashyap, Shella Keilholz
Publikováno v:
Network Neuroscience, Vol 3, Iss 2, Pp 405-426 (2019)
Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained
Externí odkaz:
https://doaj.org/article/3e7560527f2046c7988244e97647aeda