A Collaborative Framework for Standardizing Physiological Data Quality Assessment in Neuroimaging
Poster No:
1825
Submission Type:
Abstract Submission
Authors:
Rithwik Guntaka1, Shansita Sharma1, Richard Song1, Shiyu Wang1, Kimberly Rogge-Obando1, Sarah Goodale2, Haatef Pourmotabbed1, Jeffrey Harding1, Alex Douma1, Roza Bayrak1, Catie Chang3
Institutions:
1Vanderbilt University, Nashville, TN, 2Vanderbilt University Medical Center, Nashville, TN, 3Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
First Author:
Co-Author(s):
Introduction:
Functional MRI measures blood oxygenation level dependent (BOLD) signal as a proxy for neural activity. However, the BOLD signal is sensitive to peripheral physiological factors such as breathing and heart rate. While traditionally regarded as noise, systemic physiological processes are frequently shown to be linked with cognition and may contribute valuable information to fMRI studies (Shokri-Kojori et al., 2018; Mather et al., 2018; Yuan et al., 2013; Shams et al., 2021). Recognizing this, neuroimaging research increasingly draws upon concurrent physiological recordings to enhance fMRI analysis. However, the usefulness of physiological data is contingent upon the quality of the recordings as well as expertise in data handling, which can vary significantly. A critical first step toward developing automated quality assessment pipelines is the systematic documentation and characterization of artifacts by domain experts across different datasets and acquisition protocols.
Methods:
To create a comprehensive reference guide of physiological data artifacts, we documented common artifacts in photoplethysmography (PPG, Figure 1) and respiratory belt data (Figure 2) collected during fMRI scanning across five datasets. These included three public datasets -- HCP Young Adult (Van Essen et al., 2013), HCP Aging (Bookheimer et al., 2019), NKI Rockland Sample (Tobe et al., 2022) -- as well as two in-house datasets. Over 5000 raw physiological recordings were assessed using an in-house MATLAB GUI developed for physiological data visualization and annotation. Five experts with extensive experience in physiological data processing and familiarity with specific datasets conducted the assessment. For each dataset, we identify and characterize recurrent artifacts, document their features, and provide annotated examples (link to repo will be released upon acceptance). To understand sources of variability, we document key acquisition parameters including the types of physiological data collected (respiratory, cardiac, eye tracking), recording devices and any known specifications, experimental tasks, scanner information (e.g., 3T Siemens Prisma), sequence details (e.g., single vs. multi-echo), and participant demographics (age range).
Results:
Our initial assessment of five distinct datasets revealed recurring signal artifacts, which are visualized in Figure 1 for cardiac (PPG) and Figure 2 for respiratory recordings.
Conclusions:
The documentation of artifacts across different physiological signal acquisition protocols serves as a foundation for standardizing quality assessment procedures and developing more reliable automated tools in the future. A systematic characterization will help identify sources of variability as we expand to include more datasets. Moving forward, we propose a crowdsourcing approach: expanding our documentation through community contributions of new datasets and expert annotations, while developing an open platform to make this knowledge base accessible to all researchers. To facilitate this, we will launch a data annotation challenge in 2025. Through these initiatives, we aim to make physiological data quality assessment accessible to the neuroimaging community. In subsequent phases of this project, we aim to implement and validate established artifact correction methodologies, such as for clipping (Van Gent et al., 2018) and transient noise (Xu et al., 2023), while developing novel solutions where current approaches fall short. Concurrent with these efforts, we are developing an automated pipeline for the systematic classification and correction of artifacts in physiological recordings. We believe this collaborative effort will help establish robust standards for assessing physiological data in neuroimaging research.
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other 2
Keywords:
Data analysis
Data Organization
Informatics
Open Data
1|2Indicates the priority used for review
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Provide references using APA citation style.
Mather et al. (2018). How heart rate variability affects emotion regulation brain networks. Current opinion in behavioral sciences, 19, 98–104. https://doi.org/10.1016/j.cobeha.2017.12.017
Shams et al. (2021). The neuronal associations of respiratory-volume variability in the resting state. NeuroImage, 230, 117783. https://doi.org/10.1016/j.neuroimage.2021.117783
Shokri-Kojori et al. (2018). An Autonomic Network: Synchrony Between Slow Rhythms of Pulse and Brain Resting State Is Associated with Personality and Emotions. Cerebral cortex (New York, N.Y. : 1991), 28(9), 3356–3371. https://doi.org/10.1093/cercor/bhy144
Tobe et al. (2022). A longitudinal resource for studying connectome development and its psychiatric associations during childhood. Scientific data, 9(1), 300. https://doi.org/10.1038/s41597-022-01329-y
Van Essen et al. (2013). The WU-Minn Human Connectome Project: an overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
Yuan et al. (2013). Correlated slow fluctuations in respiration, EEG, and BOLD fMRI. NeuroImage, 79, 81–93. https://doi.org/10.1016/j.neuroimage.2013.04.068