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Real-Time Self-Regulation of Emotion Networks in Patients with Depression

Overview of attention for article published in PLOS ONE, June 2012
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Title
Real-Time Self-Regulation of Emotion Networks in Patients with Depression
Published in
PLOS ONE, June 2012
DOI 10.1371/journal.pone.0038115
Pubmed ID
Authors

David E. J. Linden, Isabelle Habes, Stephen J. Johnston, Stefanie Linden, Ranjit Tatineni, Leena Subramanian, Bettina Sorger, David Healy, Rainer Goebel

Abstract

Many patients show no or incomplete responses to current pharmacological or psychological therapies for depression. Here we explored the feasibility of a new brain self-regulation technique that integrates psychological and neurobiological approaches through neurofeedback with functional magnetic resonance imaging (fMRI). In a proof-of-concept study, eight patients with depression learned to upregulate brain areas involved in the generation of positive emotions (such as the ventrolateral prefrontal cortex (VLPFC) and insula) during four neurofeedback sessions. Their clinical symptoms, as assessed with the 17-item Hamilton Rating Scale for Depression (HDRS), improved significantly. A control group that underwent a training procedure with the same cognitive strategies but without neurofeedback did not improve clinically. Randomised blinded clinical trials are now needed to exclude possible placebo effects and to determine whether fMRI-based neurofeedback might become a useful adjunct to current therapies for depression.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 6 1%
Germany 5 <1%
Canada 5 <1%
United Kingdom 4 <1%
Netherlands 3 <1%
France 2 <1%
Portugal 2 <1%
China 2 <1%
Japan 2 <1%
Other 9 2%
Unknown 550 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 116 20%
Researcher 98 17%
Student > Master 80 14%
Student > Bachelor 71 12%
Student > Doctoral Student 35 6%
Other 103 17%
Unknown 87 15%
Readers by discipline Count As %
Psychology 189 32%
Neuroscience 91 15%
Medicine and Dentistry 68 12%
Agricultural and Biological Sciences 31 5%
Engineering 20 3%
Other 68 12%
Unknown 123 21%