Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Mert R Sabuncu"'
Autor:
Batuhan K Karaman, Elizabeth C Mormino, Mert R Sabuncu, Alzheimer’s Disease Neuroimaging Initiative
Publikováno v:
PLoS ONE, Vol 17, Iss 11, p e0277322 (2022)
Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or preventio
Externí odkaz:
https://doaj.org/article/b1ba1ff8a336436b86c1ef5c1ea8cc41
Autor:
Mohammad Haft-Javaherian, Linjing Fang, Victorine Muse, Chris B Schaffer, Nozomi Nishimura, Mert R Sabuncu
Publikováno v:
PLoS ONE, Vol 14, Iss 3, p e0213539 (2019)
The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to ou
Externí odkaz:
https://doaj.org/article/cee1836a038747e5ba495e69ade572bc
Publikováno v:
PLoS Genetics, Vol 14, Iss 2, p e1007228 (2018)
[This corrects the article DOI: 10.1371/journal.pgen.1006711.].
Externí odkaz:
https://doaj.org/article/18598431600f45d4944754a86fb7a790
Publikováno v:
PLoS Genetics, Vol 13, Iss 4, p e1006711 (2017)
Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological a
Externí odkaz:
https://doaj.org/article/ae10b0ead08a41cf904780facae7a05e
Autor:
Rahul S Desikan, Mert R Sabuncu, Nicholas J Schmansky, Martin Reuter, Howard J Cabral, Christopher P Hess, Michael W Weiner, Alessandro Biffi, Christopher D Anderson, Jonathan Rosand, David H Salat, Thomas L Kemper, Anders M Dale, Reisa A Sperling, Bruce Fischl, Alzheimer's Disease Neuroimaging Initiative
Publikováno v:
PLoS ONE, Vol 5, Iss 9, p e12853 (2010)
Alzheimer's disease (AD) and its transitional state mild cognitive impairment (MCI) are characterized by amyloid plaque and tau neurofibrillary tangle (NFT) deposition within the cerebral neocortex and neuronal loss within the hippocampal formation.
Externí odkaz:
https://doaj.org/article/737b22cfbd7f4f55bed6f1b6ee122283
Autor:
Joshua Kahan, Hanley Ong, Hailan Elnaas, Judy Cha'ng, Santosh B Murthy, Alexander E Merkler, Mert R Sabuncu, Ajay Gupta, Hooman Kamel
Publikováno v:
Journal of Neurotrauma.
Autor:
Seyed Hani Hojjati, Gloria C. Chiang, Tracy A. Butler, Mony De Leon, Ajay Gupta, Yi Li, Mert R. Sabuncu, Farnia Feiz, Siddharth Nayak, Jacob Shteingart, Sindy Ozoria, Saman Gholipour Picha, Antonio Fernández, Yaakov Stern, José A. Luchsinger, Davangere P. Devanand, Qolamreza R. Razlighi
Studies on the histopathology of Alzheimer’s disease (AD) strongly suggest that extracellular β-amyloid (Aβ) plaques promote the spread of neurofibrillary tau tangles. Despite well-documented spatial discrepancies between these two proteinopathie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1336824410d9538750367797c9f593b6
https://doi.org/10.1101/2023.03.31.23288013
https://doi.org/10.1101/2023.03.31.23288013
Autor:
Benjamin Liechty, Zhuoran Xu, Zhilu Zhang, Cheyanne Slocum, Cagla D. Bahadir, Mert R. Sabuncu, David J. Pisapia
Publikováno v:
Scientific Reports. 12
While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing these models and contrasting them with human experts. We present a detailed empirical analysis comp
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Nature Communications
Nature Communications
White matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whol
Publikováno v:
Academic radiology.
Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and localization of prostate cancer (PCa). Thanks to the great success of deep learning models in computer vision, the potential application for early det