Daniel Handwerker

National Institute of Mental Health, Bethesda, MD, United States 
Hemodynamic signals, aging
"Daniel Handwerker"
Mean distance: 13.35 (cluster 23)


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Steven Yantis research assistant Johns Hopkins
Mark D'Esposito grad student 2000-2005 UC Berkeley
 (Assessing variability of the fMRI BOLD response to neural activity.)
Peter A. Bandettini post-doc NIMH
Roland Henry post-doc UCSF
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Gonzalez-Castillo J, Caballero-Gaudes C, Topolski N, et al. (2019) Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest. Neuroimage. 116129
Yu Y, Huber L, Yang J, et al. (2019) Layer-specific activation of sensory input and predictive feedback in the human primary somatosensory cortex. Science Advances. 5: eaav9053
Chai Y, Handwerker DA, Marrett S, et al. (2019) Visual temporal frequency preference shows a distinct cortical architecture using fMRI. Neuroimage
Xie H, Gonzalez-Castillo J, Handwerker DA, et al. (2019) Time-varying whole-brain functional network connectivity coupled to task engagement. Network Neuroscience (Cambridge, Mass.). 3: 49-66
Xie H, Zheng CY, Handwerker DA, et al. (2018) Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information. Neuroimage
Huber L, Handwerker DA, Jangraw DC, et al. (2017) High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron
Jangraw DC, Gonzalez-Castillo J, Handwerker DA, et al. (2017) A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task. Neuroimage
Huber L, Ivanov D, Handwerker DA, et al. (2016) Techniques for blood volume fMRI with VASO: From low-resolution mapping towards sub-millimeter layer-dependent applications. Neuroimage
Gonzalez-Castillo J, Panwar P, Buchanan LC, et al. (2016) Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI. Neuroimage
Gorgolewski KJ, Auer T, Calhoun VD, et al. (2016) The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data. 3: 160044
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