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Endogenous controllability of closed-loop brain-machine interfaces for pain

View ORCID ProfileSuyi Zhang, View ORCID ProfileWako Yoshida, Hiroaki Mano, View ORCID ProfileTakufumi Yanagisawa, View ORCID ProfileKazuhisa Shibata, View ORCID ProfileMitsuo Kawato, View ORCID ProfileBen Seymour
doi: https://doi.org/10.1101/369736
Suyi Zhang
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
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Wako Yoshida
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
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Hiroaki Mano
3Center for Information and Neural Networks, National Institute for Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka, Japan, 565-0871
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Takufumi Yanagisawa
4Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Japan
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Kazuhisa Shibata
5Department of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University, Japan
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Mitsuo Kawato
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
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Ben Seymour
1Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
2Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
3Center for Information and Neural Networks, National Institute for Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka, Japan, 565-0871
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Abstract

The ultimate aim of closed-loop brain-machine systems for pain is to directly titrate the ongoing level of an intervention to pain-related neural activity. However pain is highly susceptible to endogenous modulation, raising the possibility that active or passive changes in neural activity provoked by the operation of the system could enhance or interfere with the signals upon which it is based. We studied healthy subjects receiving intermittent pain stimuli in a real-time fMRI-based closed-loop feedback-stimulation task. We showed that multi-voxel pattern decoding of pain intensity could be used to train a control algorithm to learn to deliver less painful stimuli (adaptive decoded neurofeedback). However, the system engaged two types of endogenous processes in the brain. First, despite the inherent incentive for subjects to enhance the neural decodability of pain, decodability was either reduced or unchanged in classic pain-processing regions, including insula, dorsolateral prefrontal, and somatosensory cortices. However, increased decodability was observed in a putative pain modulatory region - the pregenual anterior cingulate cortex (pgACC). Second, we found that pain perception itself was modulated by an endogenous computational uncertainty signal engaged as subjects learned the success rate of the system in reducing pain - an effect that also correlated with pgACC responses. The results illustrate how regionally and computationally specific co-adaptive brain-machine learning influences the efficacy of closed-loop systems for pain, and shows that pgACC acts as a key hub in the endogenous controllability of pain.

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Posted July 16, 2018.
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Endogenous controllability of closed-loop brain-machine interfaces for pain
Suyi Zhang, Wako Yoshida, Hiroaki Mano, Takufumi Yanagisawa, Kazuhisa Shibata, Mitsuo Kawato, Ben Seymour
bioRxiv 369736; doi: https://doi.org/10.1101/369736
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Endogenous controllability of closed-loop brain-machine interfaces for pain
Suyi Zhang, Wako Yoshida, Hiroaki Mano, Takufumi Yanagisawa, Kazuhisa Shibata, Mitsuo Kawato, Ben Seymour
bioRxiv 369736; doi: https://doi.org/10.1101/369736

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