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- W3171803801 abstract "The rise of functional magnetic resonance imaging (fMRI) has led to a deeper understanding of cortical processing of pain. Central to these advances has been the identification and analysis of functional networks, often derived from groups of pre-selected pain regions. In this study our main objective was to identify functional brain networks related to pain perception by examining whole-brain activation, avoiding the need for a priori selection of regions. We applied a data-driven technique-Constrained Principal Component Analysis for fMRI (fMRI-CPCA)-that identifies networks without assuming their anatomical or temporal properties. Open-source fMRI data collected during a thermal pain task (33 healthy participants) were subjected to fMRI-CPCA for network extraction, and networks were associated with pain perception by modelling subjective pain ratings as a function of network activation intensities. Three functional networks emerged: a sensorimotor response network, a salience-mediated attention network, and the default-mode network. Together, these networks constituted a brain state that explained variability in pain perception, both within and between individuals, demonstrating the potential of data-driven, whole-brain functional network techniques for the analysis of pain imaging data." @default.
- W3171803801 created "2021-06-22" @default.
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- W3171803801 date "2021-06-08" @default.
- W3171803801 modified "2023-10-15" @default.
- W3171803801 title "Multiple Functional Brain Networks Related to Pain Perception Revealed by fMRI" @default.
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- W3171803801 doi "https://doi.org/10.1007/s12021-021-09527-6" @default.
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