Functional magnetic resonance imaging.

By L. Shah, Jeffrey S. Anderson, James N. Lee, R. Wiggins. Seminars in roentgenology, 2010

1,964 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/826c681f75031db4973fcbdb85089f5dce725e18

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Stephen M. Smith
2. Russell A. Poldrack
3. Thomas E. Nichols
4. Xi-Nian Zuo
5. Chaogan Yan
6. Jeanette A. Mumford
7. James V. Haxby
8. Yufeng Zang

In Radiology, Nuclear Medicine and Imaging

1. Stephen M. Smith
2. Xi-Nian Zuo
3. Saâd Jbabdi
4. Jesper L. R. Andersson
5. Thomas E. Nichols
6. Russell A. Poldrack
7. Chaogan Yan
8. Mark Jenkinson

Citations in the corpus

• The Importance of Neurophysiological Constraints for Modelling the Emergence of Modularity (background): “12 discussed in some detail by Stevens (1989), Kaas (2000), Changizi (2001) and Karbowski (2003), with the general conclusion that the degree of connectedness should fall as the brain size increases.”
• Biometric Measures for Interactive Advertising Research (2011, background): “Keywords: consumer neuroscience, brain waves, eye movements, high-technology in advertising research, visual attention, emotions, unconsciousnes”
• Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data (2010, methodology): “…noise compromise the functional signal-to-noise ratio and the sensitivity and specificity of analytical results derived from brain blood-oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) data (Thomas et al., 2002; Huettel et al., 2004a; Raichle and Snyder, 2007).”
• Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy. (2010, background): “…measure the same haemodynamic response, generally fMRI techniques have an intrinsically limited acquisition rate usually at a minimum of one hertz (Huettel et al., 2003; but see Weishaupt et al., 2008), whereas fNIRS can acquire data rapidly, up to hundreds of hertz, thus providing a more…”
• The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data (2015, background): “Multiple classifiers have been compared across different data sets, with variable results (Cox and Savoy, 2003; Pereira et al., 2009; Misaki et al., 2010).”
• On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data (2013, background): “First of all, the noise in fMRI images does not correspond to the background noise of MRI images (see [6] for an excellent overview).”
• DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “By detecting local concentrations of deoxygenated and oxygenated hemoglobin, fMRI blood-oxygen level dependent (BOLD) signals allow inferring blood flow changes associated with differential neural activity during distinct tasks (Huettel et al., 2004).”
• Aesthetic package design: A behavioral, neural, and psychological investigation (2010, positive): “Here, research generally differentiates between anticipated reward (i.e., in the striatum) and reward outcome (i.e., in the ventromedial prefrontal cortex) (Knutson & Cooper, 2005).”
• Handbook of Functional MRI Data Analysis: Index (2011, positive): “One approach that has been suggested is the use of custom templates that are matched to the age of the group being studied (Buckner et al., 2004).”
• Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “2 Quality Control (QC) is a very important issue in brain imaging, as poorly executed application (or lack thereof) can compromise the trustworthiness of a study (Bennett and Miller, 2010). This topic has been explored in the literature, although very often research is mostly focused on Quality Assu”
• CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave (2016, methodology): “The use of multivariate pattern analysis has in the last decade become popular in functional magnetic resonance imaging (fMRI) research (Edelman et al., 1998; Haxby et al., 2001; Cox and Savoy, 2003; Mitchell et al., 2004; Haynes and Rees, 2006; Norman et al., 2006).”
• Functional magnetic resonance imaging in consumer research: A review and application (2011, background): “This section provides a brief methodological background on some of the basic concepts of fMRI (Buxton, 2002; Huettel, Song, & McCarthy, 2004; Reimann & Weber, 2010).”