A Library for fMRI Real-Time Processing Systems in Python (RTPSpy) With Comprehensive Online Noise Reduction, Fast and Accurate Anatomical Image Processing, and Online Processing Simulation.

Masaya Misaki,Martin P Paulus,Jerzy Bodurka

doi:10.3389/fnins.2022.834827

Masaya Misaki, Martin P Paulus + Show 1 more

Open Access

https://doi.org/10.3389/fnins.2022.834827

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Abstract

Real-time fMRI (rtfMRI) has enormous potential for both mechanistic brain imaging studies or treatment-oriented neuromodulation. However, the adaption of rtfMRI has been limited due to technical difficulties in implementing an efficient computational framework. Here, we introduce a python library for real-time fMRI (rtfMRI) data processing systems, Real-Time Processing System in python (RTPSpy), to provide building blocks for a custom rtfMRI application with extensive and advanced functionalities. RTPSpy is a library package including (1) a fast, comprehensive, and flexible online fMRI image processing modules comparable to offline denoising, (2) utilities for fast and accurate anatomical image processing to define an anatomical target region, (3) a simulation system of online fMRI processing to optimize a pipeline and target signal calculation, (4) simple interface to an external application for feedback presentation, and (5) a boilerplate graphical user interface (GUI) integrating operations with RTPSpy library. The fast and accurate anatomical image processing utility wraps external tools, including FastSurfer, ANTs, and AFNI, to make tissue segmentation and region of interest masks. We confirmed that the quality of the output masks was comparable with FreeSurfer, and the anatomical image processing could complete in a few minutes. The modular nature of RTPSpy provides the ability to use it for a simulation analysis to optimize a processing pipeline and target signal calculation. We present a sample script for building a real-time processing pipeline and running a simulation using RTPSpy. The library also offers a simple signal exchange mechanism with an external application using a TCP/IP socket. While the main components of the RTPSpy are the library modules, we also provide a GUI class for easy access to the RTPSpy functions. The boilerplate GUI application provided with the package allows users to develop a customized rtfMRI application with minimum scripting labor. The limitations of the package as it relates to environment-specific implementations are discussed. These library components can be customized and can be used in parts. Taken together, RTPSpy is an efficient and adaptable option for developing rtfMRI applications.Code available at: https://github.com/mamisaki/RTPSpy

Highlights

Online evaluation of human brain activity with real-time functional magnetic resonance imaging expands the possibility of neuroimaging
We provide a boilerplate graphical user interface (GUI) application integrating operations with Real-Time Processing System in python (RTPSpy), and a sample application of neurofeedback presentation using PsychoPy (Peirce, 2008) to demonstrate how the RTPSpy is implemented in an application and to interface to another external application
The Windows showed relatively longer processing times regardless of the specification, which might be due to the overhead of the Windows subsystem for Linux. These results indicate that the PC requirement for RTPSpy is not high, at least for an ordinary real-time fMRI scan with a few seconds TR and less than a few hundred volumes

Summary

INTRODUCTION

Online evaluation of human brain activity with real-time functional magnetic resonance imaging (rtfMRI) expands the possibility of neuroimaging. This is an independent PsychoPy18 application from RTPspy but uses the RTP_SERVE module to communicate with an RTPSpy application. While this example script just displays the latest received value on the screen with text (Figure 11C), users can modify this part to make a decent feedback presentation In this example application, the GUI operation can be done in parallel to the online image processing as the watchdog in the RTP_WATCH module runs in a separate thread, on which the processing runs. We provide a full-fledged application of the left-amygdala neurofeedback session (Zotev et al, 2011; Young et al, 2017) in the “example/LA-NF” directory, which is explained in the Supplementary Material, “LA-NF application” and in GitHub (see text footnote 1)

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