The Analysis Core integrates Penn's world-class research and development programs in neuroimaging data analysis to provide consultation and customization for experimental and statistical design, image processing and analysis, and data visualization and interpretation. In addition to advanced techniques and tools, that cover the range of data analysis and processing tasks relevant to statistical infer13nce in structural and functional imaging studies of the brain, many of which originated at Penn, the Core collaborates with the Informatics Core to establish robust neuroimage processing pipelines with minimal failure rate that scale to the cluster and cloud computing needed for large studies, and with the Acquisition Core to match analysis strategies to acquisition methods. Supported effort, leveraging the expertise and experience of Core investigators, allows services that specialize these standard workflows to improve detection power in novel study designs. These resources together with other stand-alone capabilities can be accessed either through local user installations or using Informatics Core facilities. In addition to individual consultation and training, dissemination to the general NNC community occurs through tutorials, seminars by Core personnel, institutionally sponsored workshops, and other programmed outreach activities that promote open science at Penn and beyond. In the next project period, the Core will continue its successful track record of linking with the Acquisition and Informatics Cores to drive development of, and transfer to NNC users, the next generation of methodological innovations in neuroimaging analysis.
The Analysis Core integrates resources and expertise in neuroimaging analysis at the University of Pennsylvania to provide support for both preclinical and clinical neuroimaging research relevant to the NINDS mission to reduce the burden of neurological disease.
|Mattar, Marcelo G; Olkkonen, Maria; Epstein, Russell A et al. (2018) Adaptation decorrelates shape representations. Nat Commun 9:3812|
|Adler, Daniel H; Wisse, Laura E M; Ittyerah, Ranjit et al. (2018) Characterizing the human hippocampus in aging and Alzheimer's disease using a computational atlas derived from ex vivo MRI and histology. Proc Natl Acad Sci U S A 115:4252-4257|
|Dolui, Sudipto; Wang, Ze; Shinohara, Russell T et al. (2017) Structural Correlation-based Outlier Rejection (SCORE) algorithm for arterial spin labeling time series. J Magn Reson Imaging 45:1786-1797|
|Spitschan, Manuel; Bock, Andrew S; Ryan, Jack et al. (2017) The human visual cortex response to melanopsin-directed stimulation is accompanied by a distinct perceptual experience. Proc Natl Acad Sci U S A 114:12291-12296|
|Fang, Zhuo; Jung, Wi Hoon; Korczykowski, Marc et al. (2017) Post-conventional moral reasoning is associated with increased ventral striatal activity at rest and during task. Sci Rep 7:7105|
|Wisse, L E M; Adler, D H; Ittyerah, R et al. (2017) Comparison of In Vivo and Ex Vivo MRI of the Human Hippocampal Formation in the Same Subjects. Cereb Cortex 27:5185-5196|
|Aguirre, Geoffrey K; Butt, Omar H; Datta, Ritobrato et al. (2017) Postretinal Structure and Function in Severe Congenital Photoreceptor Blindness Caused by Mutations in the GUCY2D Gene. Invest Ophthalmol Vis Sci 58:959-973|
|Ming, Qingsen; Zhong, Xue; Zhang, Xiaocui et al. (2017) State-Independent and Dependent Neural Responses to Psychosocial Stress in Current and Remitted Depression. Am J Psychiatry 174:971-979|
|Jung, Wi Hoon; Prehn, Kristin; Fang, Zhuo et al. (2016) Moral competence and brain connectivity: A resting-state fMRI study. Neuroimage 141:408-415|
|Mattar, Marcelo G; Kahn, David A; Thompson-Schill, Sharon L et al. (2016) Varying Timescales of Stimulus Integration Unite Neural Adaptation and Prototype Formation. Curr Biol 26:1669-1676|
Showing the most recent 10 out of 161 publications