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| 001 | 904428 | ||
| 005 | 20220131120426.0 | ||
| 024 | 7 | _ | |a 10.1016/j.neuroimage.2021.118207 |2 doi |
| 024 | 7 | _ | |a 1053-8119 |2 ISSN |
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| 100 | 1 | _ | |a Haugg, Amelie |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis |
| 260 | _ | _ | |a Orlando, Fla. |c 2021 |b Academic Press |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. |
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| 700 | 1 | _ | |a Götzendorfer, Sebastian J. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Sladky, Ronald |0 P:(DE-HGF)0 |b 5 |
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| 700 | 1 | _ | |a Cohen Kadosh, Kathrin |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Zich, Catharina |0 P:(DE-HGF)0 |b 17 |
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| 773 | _ | _ | |a 10.1016/j.neuroimage.2021.118207 |g Vol. 237, p. 118207 - |0 PERI:(DE-600)1471418-8 |p 118207 - |t NeuroImage |v 237 |y 2021 |x 1053-8119 |
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