Assessing the impact of denoising on local correlation analysis with registration induced artefacts
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1591
Submission Type:
Abstract Submission
Authors:
Doneka Lonaiz Aranguren1,2, Aitor Alberdi Escudero2, Kristian Galea3, Nina Attard Montalto2, Christine Farrugia4, Paola Galdi5, Robert Smith6,7, Kenneth Scerri8, Liam Butler2, Claude Bajada2
Institutions:
1Mondragon University, Mondragon, Spain, 2Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Msida, Malta, 3University of Malta Magnetic Resonance Imaging Platform, Msida, Malta, 4Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom, 5School of Informatics, University of Edinburgh, Edinburgh, United Kingdom, 6The Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, 7Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia, 8Department of Systems & Control Engineering, Faculty of Engineering, University of Malta, Msida, Malta
First Author:
Doneka Lonaiz Aranguren
Mondragon University|Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Mondragon, Spain|Msida, Malta
Mondragon University|Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Mondragon, Spain|Msida, Malta
Co-Author(s):
Aitor Alberdi Escudero
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Nina Attard Montalto
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Christine Farrugia
Centre for Clinical Brain Sciences, University of Edinburgh
Edinburgh, United Kingdom
Centre for Clinical Brain Sciences, University of Edinburgh
Edinburgh, United Kingdom
Robert Smith
The Florey Institute of Neuroscience and Mental Health|Florey Department of Neuroscience and Mental Health, The University of Melbourne
Melbourne, Victoria|Parkville, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health|Florey Department of Neuroscience and Mental Health, The University of Melbourne
Melbourne, Victoria|Parkville, Victoria, Australia
Kenneth Scerri
Department of Systems & Control Engineering, Faculty of Engineering, University of Malta
Msida, Malta
Department of Systems & Control Engineering, Faculty of Engineering, University of Malta
Msida, Malta
Liam Butler
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Claude Bajada
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta
Msida, Malta
Introduction:
Local correlation analysis techniques, which assess the degree of correlation in time series within local voxel neighbourhoods, are gaining interest in the field of functional magnetic resonance imaging (fMRI) research. One such technique is the Vogt-Bailey index (VB index) (1,2), a local correlation approach based on spectral graph theory. It has previously been shown that confounds relating to data pre-processing can create artificial local correlations, confounding the interpretation of such parametric maps (3). Unlike the General Linear Model (GLM), such local connectivity metrics do not have the capability to regress confounding signals during the model fit. We hypothesized that instead directly regressing nuisance factors estimated during pre-processing from the fMRI time series may mitigate such corruption. This study aimed to investigate the manifestation of artifacts in local connectivity via the VB index, when applied to Rician noise (where no local correlations should appear).
Methods:
A research volunteer was recruited and scanned using a 3T Siemens Magnetom Vida MRI scanner with a gradient-echo echo planar imaging (EPI) pulse sequence. During the scanning, the volunteer was subjected to a simple block design motor task. fMRIPrep version 24.1.0 was used as a preprocessing pipeline using the "resampling" flag to extract the derivatives from the subject data. A Rician distribution simulating MRI scanner noise was parameterised based on the background fMRI signal intensities. Using this distribution, a synthetic version of the empirical fMRI voxel data consisting entirely of independent noise was generated. The set of pre-processing corrections initially estimated from the empirical data by fMRIPrep were applied to the synthetic data. The data was then provided to RsDenoise (4-6), which includes additional steps such as time series normalization and detrending, motion and tissue regression, and temporal filtering. The VB toolbox was finally applied to the preprocessed fMRI time series both with and without the use of RsDenoise.
Results:
The VB index computed from the pre-processed synthetic noise data without first applying rsDenoise manifested in an artifactual striping pattern (Fig 1a), as reported previously (3). This pattern does not appear when rsDenoise was first applied to the pre-processed synthetic noise data, followed by computation of the VB index (Fig 1b).
Conclusions:
The application of a denoising algorithm reduces the undesired artifacts in the VB index. Future work could focus on comparing the performance of alternate denoising algorithms and quantifying their effect in data analysis. Nevertheless, these preliminary results suggest that explicit regression of nuisance factors prior to local correlation analyses can help in suppressing confounding artifacts.
Modeling and Analysis Methods:
Motion Correction and Preprocessing 1
Other Methods 2
Keywords:
Data analysis
FUNCTIONAL MRI
Workflows
Other - VB Index
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
2. Bajada CJ et al. A tutorial and tool for exploring feature similarity gradients with MRI data. NeuroImage. 2020 Nov 1;221:117140.
3. Farrugia C et al. Effects of preprocessing on local homogeneity of fMRI data. 2023 Jul, OHBM Abstract.
4. Dubois J et al. Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. Personal Neurosci. 2018;1:e6.
5. Galdi P. et al. rsDenoise [Internet]. Available from: https://github.com/adolphslab/rsDenoise
6. Satterthwaite TD et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage. 2013 Jan 1;64:240–56.
Funding Sources
The study is financed by Xjenza Malta, for and on behalf of the Foundation for Science and Technology, through FUSION: Space Upstream Programme (Project: Operation TOM, Grant ID: SUP-2023-01). RS is supported by fellowship funding from the National Imaging Facility (NIF), an Australian Government National Collaborative Research Infrastructure Strategy (NCRIS) capability.
Acknowledgements
The authors gratefully acknowledge the provision of scanning services by the University of Malta’s MRI Platform (UMRI).