Control over brain activation and pain learned by using real-time functional MRI.

By R. DeCharms, F. Maeda, Gary H. Glover, D. Ludlow, John M. Pauly, D. Soneji, John D. E. Gabrieli, S. Mackey. Proceedings of the National Academy of Sciences of the United States of America, 2005

871 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Pharmacology, Physiology

View Paper: https://www.semanticscholar.org/paper/737e81d72d89c1c0de7683e8e872309ecbf09e41

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

In Pharmacology

In Physiology

Citations in the corpus

Real-time fMRI and its application to neurofeedback (2012, methodology): “Most studies showed that training effects (i.e., signal changes across sessions) primarily occurred in the neurofeedback target region (e.g., deCharms et al., 2004, 2005; Lee et al., 2011; Weiskopf et al., 2003).”
Brain–computer interfaces: communication and restoration of movement in paralysis (2007, positive): “DeCharms et al. (2005) reported its effects on pain perception.”
The biopsychosocial approach to chronic pain: scientific advances and future directions. (2007, background): “For example, a recent study by deCharms et al. (2005) addressed the following question: If a person is able to learn to directly control activation of localized regions within the brain associated with pain (e.g., the rostral anterior cingulate cortex, which is a region involved in pain perception…”
Harnessing neuroplasticity for clinical applications (2011, positive): “In another study, both healthy subjects and patients with chronic pain were able to use real time functional MRI training to learn to control activation of brain regions involved in modulation of pain, which was associated with a concomitant decrease in pain perception (deCharms et al., 2005).”
Beyond mind-reading: multi-voxel pattern analysis of fMRI data. (2006, methodology): “If so, can we use these methods to provide ‘cognitive biofeedback’ to help subjects learn to control their thoughts [52,53] or external devices?”
Real-time fMRI neurofeedback: Progress and challenges (2013, methodology): “One of the landmark works in the field found self-regulation of rACC could reduce pain scores in chronic pain patients compared to controls in a single session (deCharms et al., 2005).”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Additionally, fMRI-based BMIs can be potentially used as clinical devices for treating neurological conditions, such as stroke (185, 877), chronic pain (140), emotional disorders (113), and psychiatric disorders (430).”
Stress-induced analgesia. (2009, background): “magnetic resonance imaging (fMRI) to train individuals to increase their pain threshold (deCharms et al., 2005).”
Regulation of anterior insular cortex activity using real-time fMRI (2007, methodology): “…were presented with continuously updated time-course plot of the target ROI and they were also provided with the time-course of a background ROI and the difference between the two (deCharms et al., 2004), on-line motion correction (Weiskopf et al., 2003), or video images (deCharms et al., 2005).”
The cerebral signature for pain perception and its modulation. (2007, background): “We know also from more recent data how a painful experience can occur without a primary nociceptive input (Derbyshire et al., 2004; Eisenberger et al., 2003; Raij et al., 2005; Singer et al., 2004), further complicating the story but perhaps providing an alternative explanation for how pain might…”
Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. (2006, background): “These authors and others (DeCharms et al., 2005) demonstrated substantial effects of”
Medical image processing on the GPU - Past, present and future (2013, background): “It is also possible for the subject to monitor his or her own brain activity, for example to learn how to suppress chronic pain (deCharms et al., 2005).”
Brain Computer Interfaces, a Review (2012, background): “Neurofeedback provided by a BCI system may improve cognitive performance [302,303], speech skills [304], affection [305], and pain management [306], and has been used in the treatment of mental disorders, such as epilepsy [307,308], attention deficit [309], schizophrenia [310], depression [311], alc”