2021 Fiscal Year Research-status Report
Hierarchical interactions of predictions and prediction errors in normal and schizophrenic brains
| Project/Area Number |
19K06906
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Chao Zenas 東京大学, ニューロインテリジェンス国際研究機構, 准教授 (30532113)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | Predictive coding / Prediction signal / Hierarchy / Theoretical model / Auditory sequence / Cortical oscillation / EEG |
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| Outline of Annual Research Achievements |
1. I have submitted a paper on this project titled "Frequency Ordering of Hierarchical Prediction in Human Brain", which is currently under review. Please see the abstract below.
2. I'm also preparing another related paper titled "Supramodal Hierarchical Prediction in Human Brain", which I aim to submit it in FY2022.
Abstract:
The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive. Here, we report the existence and function of hierarchical prediction signals in the human brain. We recorded electroencephalography during a hierarchical auditory prediction behavior and used a tensor decomposition analysis to model neural signal dependence and flow. Two prediction signals were identified in the period prior to auditory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions were found in the gamma band. Our findings reveal a “frequency ordering” of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Due to COVID-19 pandemic, we were not able to collect sufficient data from schizophrenic patients. Instead, we collected data from another 30 subjects on a novel task to investigate brain networks underlying cross-modal prediction, which could be a biomarker for prediction-related disorder, such as schizophrenia.
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| Strategy for Future Research Activity |
There are several ongoing projects based on the findings of the current project:
1. Temporal prediction. This project aims to extract prediction signals that encode complex timings within a temporal sequence and examine how they dynamically adjust during the sequence. This project will use human EEG, human ECoG, and marmoset ECoG.
2. Prediction signals in spontaneous activity. This project aims to extract prediction signals during spontaneous activity between epochs of sensory stimulation. This project will use human MEG.
3. Macrocircuit motifs for multimodal prediction. This project will identify macrocircuits in the human brain that learn and encode multilevel and multimodal predictions, and their operation in schizophrenia.
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| Causes of Carryover |
I have applied for extension of the grant to FY2022 to roll over the remaining \800,878 to FY2022. This is due to the delayed experiments due to COVID-19 situation.
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Research Products
(1 results)
