Hierarchical interactions of predictions and prediction errors in normal and schizophrenic brains

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K06906
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 46010:Neuroscience-general-related
• Research Institution: The University of Tokyo
• 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)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: Predictive coding / Brain network / Hierarchy

Outline of Final Research Achievements

The human brain is proposed to harbor a hierarchical predictive coding neuronal network. In support of this theory, feedforward signals for prediction error have been reported, but feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography, and identify their neural signatures across two functional hierarchies. Two prediction signals are identified: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory. The above results are published: Chao Z. et al. (2022), Comms Biology, 5(1), 1076.

Free Research Field

Neuroscience

Academic Significance and Societal Importance of the Research Achievements

科学レベルでは、予測コーディングは、脳が利用できる圧倒的な量の感覚データを理解するための解決策になる可能性があり、その理解はニューロモルフィック エンジニアリングとニューロロボティクスのさらなる発展に役立つ可能性があります。 臨床レベルでは、個々の予測信号と予測誤差信号を識別し、健康な個人と精神病患者の両方でそれらの調整を監視することで、統合失調症や自閉症などの精神障害の予後および/または診断のための神経マーカーの開発に役立つ可能性があります。