Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising

•We propose a unified deep attentive CNN framework for automatic rs-fMRI denoising.•It simultaneously learns spatio-temporal features of noise in a data-driven manner.•We provide visual explanations to depict how the CNNs work for noise detection.•It achieves high performance on various datasets inc...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Fla.), 2022-07, Vol.254, p.119127-119127, Article 119127
Hauptverfasser:	Heo, Keun-Soo, Shin, Dong-Hee, Hung, Sheng-Che, Lin, Weili, Zhang, Han, Shen, Dinggang, Kam, Tae-Eui
Format:	Artikel
Sprache:	eng
Schlagworte:	Blood pressure Brain mapping Convolutional neural network Deep learning Denoising Functional magnetic resonance imaging Independent component analysis Infants Machine learning Neuroimaging Noise Physiology Resting-state fMRI Temporal variations Time series
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Beschreibung
Zusammenfassung:	•We propose a unified deep attentive CNN framework for automatic rs-fMRI denoising.•It simultaneously learns spatio-temporal features of noise in a data-driven manner.•We provide visual explanations to depict how the CNNs work for noise detection.•It achieves high performance on various datasets including infant cohorts.•It can be integrated into any pipelines by accelerating speed (
ISSN:	1053-8119 1095-9572
DOI:	10.1016/j.neuroimage.2022.119127