Title |
Neurocognitive therapeutics: from concept to application in the treatment of negative attention bias
|
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Published in |
Biology of Mood & Anxiety Disorders, April 2015
|
DOI | 10.1186/s13587-015-0016-y |
Pubmed ID | |
Authors |
David M Schnyer, Christopher G Beevers, Megan T deBettencourt, Stephanie M Sherman, Jonathan D Cohen, Kenneth A Norman, Nicholas B Turk-Browne |
Abstract |
There is growing interest in the use of neuroimaging for the direct treatment of mental illness. Here, we present a new framework for such treatment, neurocognitive therapeutics. What distinguishes neurocognitive therapeutics from prior approaches is the use of precise brain-decoding techniques within a real-time feedback system, in order to adapt treatment online and tailor feedback to individuals' needs. We report an initial feasibility study that uses this framework to alter negative attention bias in a small number of patients experiencing significant mood symptoms. The results are consistent with the promise of neurocognitive therapeutics to improve mood symptoms and alter brain networks mediating attentional control. Future work should focus on optimizing the approach, validating its effectiveness, and expanding the scope of targeted disorders. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 40% |
Canada | 1 | 20% |
United Kingdom | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 40% |
Scientists | 2 | 40% |
Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
United States | 1 | <1% |
Brazil | 1 | <1% |
Unknown | 100 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 23 | 22% |
Student > Master | 17 | 17% |
Researcher | 14 | 14% |
Student > Bachelor | 10 | 10% |
Student > Doctoral Student | 9 | 9% |
Other | 22 | 21% |
Unknown | 8 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 50 | 49% |
Neuroscience | 17 | 17% |
Medicine and Dentistry | 6 | 6% |
Computer Science | 3 | 3% |
Agricultural and Biological Sciences | 2 | 2% |
Other | 10 | 10% |
Unknown | 15 | 15% |