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Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation
N.S. Davydov 1,2, V.V. Evdokimova 1,2, P.G. Serafimovich 1,2, V.I. Protsenko 1,2, A.G. Khramov 2, A.V. Nikonorov 1,2

1 IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS,
443001, Samara, Russia, Molodogvardeyskaya 151;
2 Samara National Research University,
443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 2788 kB

DOI: 10.18287/2412-6179-CO-1337

Pages: 991-1001.

Full text of article: Russian language.

Abstract:
Quality assessment and artifact detection in functional magnetic resonance imaging (fMRI) data is essential for clinical applications and brain research. Subject head motion remains the main source of artifacts - even the tiniest head movement can perturb the structural and functional data derived from the fMRI. In this paper, we propose an end-to-end neural network technology for detecting step anomalies with training on partially synthetic data with adaptation to a specific small set of real data. A procedure for generating a synthetic dataset for training and a module for automated labeling of real data is developed. A recurrent neural network model for detecting step anomalies is proposed. A method for the model adaptation to a small set of real data based on one-step meta-learning is developed. An experimental verification of the accuracy is carried out in the problem of detecting step anomalies using a sliding window of 10, 15, and 24 pixels. The experiments have shown the proposed technology to provide the detection of stepwise anomalies with an accuracy of 0.9546.

Keywords:
recurrent neural networks, anomaly detection, signal analysis, functional magnetic resonance imaging, meta-learning.

Citation:
Davydov NS, Evdokimova VV, Serafimovich PG, Protsenko VI, Khramov AG, Nikonorov AV. Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation. Computer Optics 2023; 47(6): 991-1001. DOI: 10.18287/2412-6179-CO-1337.

Acknowledgements:
The theoretical part and one-step meta-learning method were developed with the support from the Russian Science Foundation under RSF grant 22-19-00364.

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