391 research outputs found
Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review
First published: 25 April 2020Neurofeedback training using real-time functional magnetic resonance imaging
(rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity.
It has sparked increased interest as a promising non-invasive treatment option in
neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance
are yet to be determined. In this work, we present the first extensive review
of acquisition, processing and quality control methods available to improve the quality
of the neurofeedback signal. Furthermore, we investigate the state of denoising
and quality control practices in 128 recently published rtfMRI-NF studies. We found:
(a) that less than a third of the studies reported implementing standard real-time
fMRI denoising steps, (b) significant room for improvement with regards to methods
reporting and (c) the need for methodological studies quantifying and comparing the
contribution of denoising steps to the neurofeedback signal quality. Advances in
rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic
effort is needed to build up evidence that disentangles the various mechanisms
influencing neurofeedback effects. To this end, we recommend that future
rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising
steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/),
(b) ensure the quality of the neurofeedback signal by calculating and reporting
community-informed quality metrics and applying offline control checks and (c) strive
to adopt transparent principles in the form of methods and data sharing and support
of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an
interactive environment to explore the study data, can be accessed at https://github.
com/jsheunis/quality-and-denoising-in-rtfmri-nf.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc
Feature analysis of functional MRI data for mapping epileptic networks
Issued as final reportUniversity of Pennsylvani
Improved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis
For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.Published versio
Single Shot Reversible GAN for BCG artifact removal in simultaneous EEG-fMRI
Simultaneous EEG-fMRI acquisition and analysis technology has been widely
used in various research fields of brain science. However, how to remove the
ballistocardiogram (BCG) artifacts in this scenario remains a huge challenge.
Because it is impossible to obtain clean and BCG-contaminated EEG signals at
the same time, BCG artifact removal is a typical unpaired signal-to-signal
problem. To solve this problem, this paper proposed a new GAN training model -
Single Shot Reversible GAN (SSRGAN). The model is allowing bidirectional input
to better combine the characteristics of the two types of signals, instead of
using two independent models for bidirectional conversion as in the past.
Furthermore, the model is decomposed into multiple independent convolutional
blocks with specific functions. Through additional training of the blocks, the
local representation ability of the model is improved, thereby improving the
overall model performance. Experimental results show that, compared with
existing methods, the method proposed in this paper can remove BCG artifacts
more effectively and retain the useful EEG information.Comment: 8 pages, 5 figures, 1 tabl
Advances in image acquisition and filtering for MRI neuroimaging at 7 tesla
Performing magnetic resonance imaging at high magnetic field strength promises many improvements over low fields that are of direct benefit in functional neuroimaging. This includes the possibility of improved signal-to-noise levels, and increased BOLD functional contrast and spatial specificity. However, human MRI at 7T and above suffers from unique engineering challenges that limit the achievable gains. In this thesis, three technological developments are introduced, all of which address separate issues associated with functional magnetic resonance neuroimaging at very high magnetic field strengths.
First, the image homogeneity problem is addressed by investigating methods of RF shimming — modifying the excitation portion of the MRI experiment for use with multi-channel RF coils. It is demonstrated that in 2D MRI experiments, shimming on a slice-by slice basis allows utilization of an extra degree of freedom available from the slice dimension, resulting in significant gains in image homogeneity and reduced RF power requirements.
After acceptable images are available, we move to address complications of high field imaging that manifest in the fMRI time series. In the second paper, the increased physiological noise present in BOLD time series at high field is addressed with a unique data-driven noise regressor scheme based upon information in the phase component of the MRI signal. It is demonstrated that this method identifies and removes a significant portion of physiological signals, and performs as good or better than other popular data driven methods that use only the magnitude signal information.
Lastly, the BOLD phase signal is again leveraged to address the confounding role of veins in resting state BOLD fMRI experiments. The phase regressor technique (previously developed by Dr. Menon) is modified and applied to resting state fMRI to remove macro vascular contributions in the datasets, leading to changes in spatial extent and connectivity of common resting state networks on single subjects and at the group level
Support vector machine classification of arterial volumeâ weighted arterial spin tagging images
IntroductionIn recent years, machineâ learning techniques have gained growing popularity in medical image analysis. Temporal brainâ state classification is one of the major applications of machineâ learning techniques in functional magnetic resonance imaging (fMRI) brain data. This article explores the use of support vector machine (SVM) classification technique with motorâ visual activation paradigm to perform brainâ state classification into activation and rest with an emphasis on different acquisition techniques.MethodsImages were acquired using a recently developed variant of traditional pseudocontinuous arterial spin labeling technique called arterial volumeâ weighted arterial spin tagging (AVAST). The classification scheme is also performed on images acquired using blood oxygenationâ level dependent (BOLD) and traditional perfusionâ weighted arterial spin labeling (ASL) techniques for comparison.ResultsThe AVAST technique outperforms traditional pseudocontinuous ASL, achieving classification accuracy comparable to that of BOLD contrast images.ConclusionThis study demonstrates that AVAST has superior signalâ toâ noise ratio and improved temporal resolution as compared with traditional perfusionâ weighted ASL and reduced sensitivity to scanner drift as compared with BOLD. Owing to these characteristics, AVAST lends itself as an ideal choice for dynamic fMRI and realâ time neurofeedback experiments with sustained activation periods.In this article, we test the performance of our recently introduced method for dynamic arterial blood volume imaging (AVAST) in the context of functional MRI data classification. AVAST is compared with blood oxygenationâ level dependent (BOLD) and arterial spin labeling (ASL) perfusion data collected during a simple motor task using a support vector machine algorithm to classify the brain state. Findings suggest that the AVAST technique has similar performance as BOLD imaging, while preserving the statistical benefits of ASL techniques.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135476/1/brb3549_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135476/2/brb3549.pd
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