Frontier Research Institute for Interdisciplinary Sciences
Tohoku University

Research Projects

Creative Interdisciplinary Collaboration Program 2024

Assist. Prof. Takuro Ishii

Title Rodent-based Approaches for Functional and Structural Recording of Physiological Signals.
Priod 2024-2025

In biological systems, the inhibition of communication between the brain and peripheral organs can trigger or exacerbate diseases. Since comprehensive recording of signal propagation between organs is technically challenging, previous studies have focused on pathological and physiological mechanisms specific to certain organs. This study aims to establish a system that simultaneously acquires structural and functional information of organs by combining functional ultrasound imaging with electrophysiological recordings.

This research specifically develops the following two technical foundations:

  1. Ultrasound Imaging of Organ Structure and Blood Flow
    We will develop an ultrasound modality that simultaneously measures the 3D structure and blood flow of the brain and peripheral organs in rodents. Ultrasound signals from three probes with different frequencies and shapes will be analyzed to visualize comprehensive blood flow dynamics of the brain and abdominal organs.
  2. Comprehensive Recording of Ultrasound Imaging and Electrophysiology
    We will design an optimal sensor arrangement for electrophysiological measurements tailored to the US imaging system and perform simultaneous recording of ultrasound imaging and electrophysiological data. Changes in blood flow and biological signals in organs over time in a rodent body will be utilized to construct a predictive model of inflammation levels and signal propagation.

This research will be conducted by an interdisciplinary research group of Dr. Ishii (FRIS, Biomedical Engineering), who works on ultrasound signal processing and system design, and Dr. Hiroyuki Igarashi (Grad. School of Pharmaceutical Sciences), who handles electrophysiological recordings and the construction of predictive models for inflammation levels.

 
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