Eran Dayan
Affiliations: | School of Medicine | University of North Carolina, Chapel Hill, Chapel Hill, NC |
Area:
brain connectivity, network neuroscience, motor systems, neuromodulationWebsite:
dayanlab.web.unc.eduGoogle:
"Eran Dayan"Mean distance: 14.01 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentor| Tamar Flash | grad student | 2010 | Weizmann Institute | |
| (Coupling between Visual Perception and Motor Production- Evidence from Neuroimaging and Psychophysics.) | ||||
| Rafael Malach | grad student | 2010 | Weizmann Institute | |
| (Coupling between Visual Perception and Motor Production- Evidence from Neuroimaging and Psychophysics.) | ||||
| Leonardo G. Cohen | post-doc | NINDS | ||
Children
Sign in to add trainee| Stephanie Langella | grad student | UNC Chapel Hill | |
| William Stanford | grad student | UNC Chapel Hill | |
| Kichang Kwak | post-doc | UNC Chapel Hill | |
| Belfin Robinson | post-doc | UNC Chapel Hill | |
| Muhammad Usman Sadiq | post-doc | UNC Chapel Hill | |
| Célia Rousseau | post-doc | 2017-2018 | UNC Chapel Hill |
| Chia-Hao Shih | post-doc | 2017-2019 | UNC Chapel Hill |
BETA: Related publications
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Publications
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| Robinson B, Bhamidi S, Dayan E. (2024) The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment. Neurobiology of Aging. 136: 70-77 |
| Robinson B, Bhamidi S, Dayan E. (2023) The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment. Medrxiv : the Preprint Server For Health Sciences |
| Stanford W, Mucha PJ, Dayan E. (2023) Age-related changes in network controllability are mitigated by redundancy in large-scale brain networks. Biorxiv : the Preprint Server For Biology |
| Kozak S, Dezachyo O, Stanford W, et al. (2022) Elevated integration within the reward network underlies vulnerability to distress. Cerebral Cortex (New York, N.Y. : 1991) |
| Stanford WC, Mucha PJ, Dayan E. (2022) A robust core architecture of functional brain networks supports topological resilience and cognitive performance in middle- and old-aged adults. Proceedings of the National Academy of Sciences of the United States of America. 119: e2203682119 |
| Kwak K, Stanford W, Dayan E. (2022) Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion. Human Brain Mapping |
| Nasr K, Haslacher D, Dayan E, et al. (2022) Breaking the boundaries of interacting with the human brain using adaptive closed-loop stimulation. Progress in Neurobiology. 216: 102311 |
| Sadiq MU, Kwak K, Dayan E, et al. (2022) Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies. Alzheimer's Research & Therapy. 14: 16 |
| Kwak K, Giovanello KS, Bozoki A, et al. (2021) Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns. Cell Reports. Medicine. 2: 100467 |
| Langella S, Mucha PJ, Giovanello KS, et al. (2021) The association between hippocampal volume and memory in pathological aging is mediated by functional redundancy. Neurobiology of Aging. 108: 179-188 |