Journal Article Epublish
PubMed: 39463889 DOI: 10.1162/imag_a_00325
NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis improves brain activity detection across rodent and human functional MRI contexts.
Imaging Neurosci (Camb) 2:1-18
Authors: Russell W Chan, Giles Hamilton-Fletcher, Bradley J Edelman, Muneeb A Faiq, Thajunnisa A Sajitha, Steen Moeller, Kevin C Chan
Affiliations
- Department of Ophthalmology, New York University Grossman School of Medicine, New York, NY, United States.
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States.
- Tech4Health Institute, New York University Grossman School of Medicine, New York, NY, United States.
- E-SENSE Innovation & Technology, Hong Kong, China.
- Hong Kong Centre for Cerebro-cardiovascular Health Engineering (COCHE), Hong Kong, China.
- Brain-Wide Circuits for Behavior Research Group, Max Planck Institute of Biological Intelligence, Planegg, Germany.
- Emotion Research Department, Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Ophthalmology, New York University Grossman School of Medicine, New York, NY, United States. Tech4Health Institute, New York University Grossman School of Medicine, New York, NY, United States.
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, United States.
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States.
- Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY, United States.
- Department of Ophthalmology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Abstract
NOise Reduction with DIstribution Corrected (NORDIC) principal component analysis (PCA) has been shown to selectively suppress thermal noise and improve the temporal signal-to-noise ratio (tSNR) in human functional magnetic resonance imaging (fMRI). However, the feasibility to improve data quality for rodent fMRI using NORDIC PCA remains uncertain. NORDIC PCA may also be particularly beneficial for improving topological brain mapping, as conventional mapping requires precise spatiotemporal signals from large datasets (ideally ~1 hour acquisition) for individual representations. In this study, we evaluated the effects of NORDIC PCA compared with "Standard" processing in various rodent fMRI contexts that range from task-evoked optogenetic fMRI to resting-state fMRI. We also evaluated the effects of NORDIC PCA on human resting-state and retinotopic mapping fMRI via population receptive field (pRF) modeling. In rodent optogenetic fMRI, apart from doubling the tSNR, NORDIC PCA resulted in a larger number of activated voxels and a significant decrease in the variance of evoked brain responses without altering brain morphology. In rodent resting-state fMRI, we found that NORDIC PCA induced a nearly threefold increase in tSNR and preserved task-free relative cerebrovascular reactivity (rCVR) across cortical depth. NORDIC PCA further improved the detection of TGN020-induced aquaporin-4 inhibition on rCVR compared with Standard processing without NORDIC PCA. NORDIC PCA also increased the tSNR for both human resting-state and pRF fMRI, and for the latter also increased activation cluster sizes while retaining retinotopic organization. This suggests that NORDIC PCA preserves the spatiotemporal precision of fMRI signals needed for pRF analysis, and effectively captures small activity changes with high sensitivity. Taken together, these results broadly demonstrate the value of NORDIC PCA for the enhanced detection of neural dynamics across various rodent and human fMRI contexts. This can in turn play an important role in improving fMRI image quality and sensitivity for translational and preclinical neuroimaging research.
MeSH
Principal Component Analysis, Brain, Optogenetics