Detection of step displacements in fMRI head motion data based on machine learning

Accurate artifacts detection in functional magnetic resonance imaging (fMRI) data is important in clinical applications and research. Subjects head motion remains the major source of fMRI artifacts, and retained head motion in the pre-processed fMRI data could also predict anthropomorphic, behavioral and clinical factors. However, an accurate characterization of subject head motion artifacts is lacking. We searched for step displacements in fMRI head motion data using machine learning approaches. Head motion data were defined using conventional six motion parameters as produced by fMRI realignment procedure. We created the semi-automatic markup tool to prepare head motion data for classification. This preparation was done using the sliding-window statistical anomaly detection and manual refinement of characteristic step artifacts. Marked up training dataset was used to train various classifiers to classify step-like head displacements. The best accuracy was achieved using neural and k-nearest neighborhood classifiers. Proposed approach could be used for an accurate detection of specific fMRI artifacts associated with head motions.

[1]  Christopher Rorden,et al.  Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank , 2017 .

[2]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[3]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[4]  Dimitri Van De Ville,et al.  OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis , 2017, NeuroImage.

[5]  Abraham Z. Snyder,et al.  Real-time motion analytics during brain MRI improve data quality and reduce costs , 2017, NeuroImage.

[6]  N. Vayatis,et al.  Selective review of offline change point detection methods , 2019 .

[7]  Svitlana Zinger,et al.  Quality and denoising in real‐time functional magnetic resonance imaging neurofeedback: A methods review , 2020, Human brain mapping.

[8]  A. V. Gaidel,et al.  Efficiency of machine learning algorithms and convolutional neural network for detection of pathological changes in MR images of the brain , 2020 .

[9]  Zhiliang Liu,et al.  Kernel Parameter Selection for Support Vector Machine Classification , 2014 .

[10]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[11]  Казанский Николай Львович,et al.  Реконструкция анатомических структур на основе статистической модели формы , 2017 .

[12]  Klaus Mathiak,et al.  Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI , 2012, NeuroImage.

[13]  Raghavendra Chalapathy University of Sydney,et al.  Deep Learning for Anomaly Detection: A Survey , 2019, ArXiv.

[14]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[15]  S. A. Bibikov,et al.  Correction of distortions in color images based on parametric identification , 2011, Pattern Recognition and Image Analysis.

[16]  Kenneth Sundaraj,et al.  A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals , 2014, BMC Bioinformatics.

[17]  Dimitri Van De Ville,et al.  No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI , 2019, NeuroImage.

[18]  Dimitri Van De Ville,et al.  Real-time fMRI data for testing OpenNFT functionality , 2017, Data in brief.