Fractionation of neural reward processing into independent components by novel decoding principle
Shitong Xiang,
Tianye Jia,
Chao Xie,
Zhichao Zhu,
Wei Cheng,
Gunter Schumann,
Trevor W. Robbins,
Jianfeng Feng
Affiliations
Shitong Xiang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
Tianye Jia
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, SE5 8AF, United Kingdom; Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
Chao Xie
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
Zhichao Zhu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
Wei Cheng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China
Gunter Schumann
Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Department of Psychiatry and Psychotherapy, Centre for Population Neuroscience and Precision Medicine (PONS), CCM, Charite Universitaetsmedizin, Berlin, Germany
Trevor W. Robbins
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Department of Psychology and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
Jianfeng Feng
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Ministry of Education, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; School of Mathematical Sciences and Centre for Computational Systems Biology, Fudan University, Shanghai, China; Corresponding authors at: Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China.
How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso‑limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.