Volume 14 Supplement 1

Abstracts from the Twenty Second Annual Computational Neuroscience Meeting: CNS*2013

Open Access

Prediction was predictable from human brain activity in fronto-parietal cortex

BMC Neuroscience201314(Suppl 1):P181

https://doi.org/10.1186/1471-2202-14-S1-P181

Published: 8 July 2013

In partially observable environments, prediction of the future is a key to make appropriate decisions [1, 2]. Whether or not to open the door (decision making) would depend on whether one could expect someone is behind the door (prediction) even in a well-known environment such as an office. Such interpolation of pre-observed information was obtained by integrating the past observations and the environment model. Recent evidence suggests that such model-based decision-making activates fronto-parietal cortex [36]. Despite popularity of neural decoding, research on mapping from neural signals into prediction of pre-observed information has rarely been reported.

Here, we show that prediction decoding is possible from individuals' functional magnetic resonance imaging (fMRI) activity. We asked four healthy subjects to perform a scene prediction task, which required discriminating a true next scene in a three-dimensional navigation environment. We decoded the predicted scene consisting of three views; forward-center (fC), forward-left (fL) and forward-right (fR), information from fMRI activity in anatomically defined region of interests (ROIs): lateral prefrontal cortex (LPF), medial prefrontal cortex (MPF) and parietal cortex (PC). The decoding analysis was conducted individually for each subject and ROIs and the performance values across subjects were then averaged.

All subjects carried out the scene prediction task 92.6% ± 3.2% correct and 4.8% ± 3.5% incorrect. Using a leave-one-trial-out procedure, we decoded individuals predicted scene per view from the fMRI data of correct trials. When using PC activity, these decoders allowed us to read out predicted all views (fC: 70.94%; p < 0.01 × 10-1, fL: 60.43%; p < 0.01, fR: 65.18%; p < 0.05). In contrast, above-chance performance was obtained fC view only in LPF and MPF (LPF: 67.78%; p < 0.01 × 10-3, MPF: 62.97%; p < 0.05). Remarkably, PC bit decoders that trained by correct trials can also read out incorrect scene selected by the subject (fC: 66.77%; p < 0.01, fL: 58.19%; p = 0.43, fR: 75.43%; p < 0.05 × 10-1).

In this study, we demonstrated decoding of scene prediction from individuals fMRI activity. Because fC is the most important view for the subjects to determine the next motion, the fC decoders show the high decoding accuracy for all ROIs. These results suggest that prediction could be performed in the fronto-parietal network such to reflect the degree of contribution to the subsequent decision-making. Our findings have outlined the decision making system employed in complicated environments, and implied useful characters of decoders which can be used for brain machine interface of practical navigation systems.

Declarations

Acknowledgements

This research was supported by JSPS KAKENHI Grant Number 715121400004 and a contract with the Ministry of Internal Affairs and Communications entitled, 'Novel and innovative R&D making use of brain structures'.

Authors’ Affiliations

(1)
Graduate School of Informatics, Kyoto University
(2)
ATR Neural Information Analysis Laboratories

References

  1. Sutton RS, Barto AG: Reinforcement Learning: An Introduction. 1998, MIT PressGoogle Scholar
  2. Kaelbling LP, Littman ML, Cassandra AR: Planning and acting in partially observable stochastic domains. Artif Intell. 1998, 101 (1-2): 99-134. 10.1016/S0004-3702(98)00023-X.View ArticleGoogle Scholar
  3. Daw N, Gershman SJ, Seymour B, Dayan P, Dolan RJ: Model-based influences on humans' choices and striatal prediction errors. Neuron. 2011, 69 (6): ; 1204-1215.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Gläscher J, Daw N, Dayan P, O'Doherty JP: States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron. 2010, 66 (4): 585-595. 10.1016/j.neuron.2010.04.016.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Yoshida W, Ishii S: Resolution of uncertainty in prefrontal cortex. Neuron. 2006, 50 (5): 781-789. 10.1016/j.neuron.2006.05.006.View ArticlePubMedGoogle Scholar
  6. Bollinger J, Rubens MT, Zanto TP, Gazzaley A: Expectation-driven changes in cortical functional connectivity influence working memory and long-term memory performance. J Neurosci. 2010, 30 (43): 14399-14410. 10.1523/JNEUROSCI.1547-10.2010.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Shikauchi and Shin; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement