A neural network approach to fMRI binocular visual rivalry task analysis.
Nicola Bertolino, Stefania Ferraro, Anna Nigri, Maria Grazia Bruzzone, Francesco Ghielmetti
PLoS One 2014 DOI: 10.1371/journal.pone.0105206 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational fMRI study using multivariate pattern analysis and artificial neural networks Peer reviewed |
|---|---|
| Sample size | 12 |
| Population | Healthy participants |
| Key findings | An artificial neural network trained on fMRI data from a binocular non-rivalry task generalized to a binocular rivalry task and identified with high accuracy which stimulus participants consciously perceived. Network outputs corresponded significantly (p<0.05) with behavioral reports of the conscious percept in all 12 subjects, suggesting the approach can decode visual consciousness without relying on behavioral responses. |
Abstract
The purpose of this study was to investigate whether artificial neural networks (ANN) are able to decode participants' conscious experience perception from brain activity alone, using complex and ecological stimuli. To reach the aim we conducted pattern recognition data analysis on fMRI data acquired during the execution of a binocular visual rivalry paradigm (BR). Twelve healthy participants were submitted to fMRI during the execution of a binocular non-rivalry (BNR) and a BR paradigm in which two classes of stimuli (faces and houses) were presented. During the binocular rivalry paradigm, behavioral responses related to the switching between consciously perceived stimuli were also collected. First, we used the BNR paradigm as a functional localizer to identify the brain areas involved the processing of the stimuli. Second, we trained the ANN on the BNR fMRI data restricted to these regions of interest. Third, we applied the trained ANN to the BR data as a 'brain reading' tool to discriminate the pattern of neural activity between the two stimuli. Fourth, we verified the consistency of the ANN outputs with the collected behavioral indicators of which stimulus was consciously perceived by the participants. Our main results showed that the trained ANN was able to generalize across the two different tasks (i.e. BNR and BR) and to identify with high accuracy the cognitive state of the participants (i.e. which stimulus was consciously perceived) during the BR condition. The behavioral response, employed as control parameter, was compared with the network output and a statistically significant percentage of correspondences (p-value <0.05) were obtained for all subjects. In conclusion the present study provides a method based on multivariate pattern analysis to investigate the neural basis of visual consciousness during the BR phenomenon when behavioral indicators lack or are inconsistent, like in disorders of consciousness or sedated patients.
Citations in the library
Cites 3
- On the Role of Attention in Binocular Rivalry: Electrophysiological Evidence PLoS One July 22, 2011
- Decoding the neural correlates of consciousness. Current Opinion in Neurology December 1, 2010
- Predicting the stream of consciousness from activity in human visual cortex. Current Biology July 26, 2005