Zobrazeno 1 - 10
of 947
pro vyhledávání: '"Mostofsky, Stewart"'
Autor:
Henry, Teague R., Duffy, Kelly A., Rudolph, Marc D., Nebel, Mary Beth, Mostofsky, Stewart H., Cohen, Jessica R.
Publikováno v:
Network Neuroscience, Vol 4, Iss 1, Pp 70-88 (2020)
Whole-brain network analysis is commonly used to investigate the topology of the brain using a variety of neuroimaging modalities. This approach is notable for its applicability to a large number of domains, such as understanding how brain network or
Externí odkaz:
https://doaj.org/article/768249c889ce46d08203e18c25876c34
Publikováno v:
In Journal of Psychiatric Research December 2024 180:103-112
Autor:
D'Souza, Niharika Shimona, Nebel, Mary Beth, Crocetti, Deana, Wymbs, Nicholas, Robinson, Joshua, Mostofsky, Stewart, Venkataraman, Archana
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized autoencoder infers a
Externí odkaz:
http://arxiv.org/abs/2105.14409
Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder and dementi
Externí odkaz:
http://arxiv.org/abs/2101.04809
Autor:
D'Souza, Niharika Shimona, Nebel, Mary Beth, Crocetti, Deana, Wymbs, Nicholas, Robinson, Joshua, Mostofsky, Stewart H., Venkataraman, Archana
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of b
Externí odkaz:
http://arxiv.org/abs/2008.12410
Autor:
D'Souza, Niharika Shimona, Nebel, Mary Beth, Wymbs, Nicholas, Mostofsky, Stewart H., Venkataraman, Archana
We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks
Externí odkaz:
http://arxiv.org/abs/2009.03238
Autor:
D'Souza, Niharika Shimona, Nebel, Mary Beth, Crocetti, Deana, Wymbs, Nicholas, Robinson, Joshua, Mostofsky, Stewart, Venkataraman, Archana
We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract predictive biomarkers of a disease.
Externí odkaz:
http://arxiv.org/abs/2007.01931
Autor:
D'Souza, Niharika Shimona, Nebel, Mary Beth, Wymbs, Nicholas, Mostofsky, Stewart, Venkataraman, Archana
We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral data. The dictionary learning objective decomposes patient correlatio
Externí odkaz:
http://arxiv.org/abs/2007.01930
Autor:
D'Souza, Niharika Shimona, Nebel, Mary Beth, Wymbs, Nicholas, Mostofsky, Stewart, Venkataraman, Archana
The problem of linking functional connectomics to behavior is extremely challenging due to the complex interactions between the two distinct, but related, data domains. We propose a coupled manifold optimization framework which projects fMRI data ont
Externí odkaz:
http://arxiv.org/abs/2007.01929
Autor:
Schirmer, Markus D., Venkataraman, Archana, Rekik, Islem, Kim, Minjeong, Mostofsky, Stewart H., Nebel, Mary Beth, Rosch, Keri, Seymour, Karen, Crocetti, Deana, Irzan, Hassna, Hütel, Michael, Ourselin, Sebastien, Marlow, Neil, Melbourne, Andrew, Levchenko, Egor, Zhou, Shuo, Kunda, Mwiza, Lu, Haiping, Dvornek, Nicha C., Zhuang, Juntang, Pinto, Gideon, Samal, Sandip, Zhang, Jennings, Bernal-Rusiel, Jorge L., Pienaar, Rudolph, Chung, Ai Wern
Large, open-source consortium datasets have spurred the development of new and increasingly powerful machine learning approaches in brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable inform
Externí odkaz:
http://arxiv.org/abs/2006.03611