Keywords: Data Processing, fMRI

Motivation: Noise and artifacts significantly corrupt fMRI data.

Goal(s): It  has a significant potential in enhancing research outcomes for challenging populations like children and older subjects whose data prone to have noise, facilitating reliable fMRI studies.

Approach: We present a deep learning-based Automatic fMRI Scrubbing via Graph Attention (ASGA), to perform fMRI data “scrubbing” by automatically identifying and removing contaminated volumes. To achieve this, we firstly design an easy-to-implement carpet plot-based labeling tool for human labelling, which is fed to ASGA model training. By applying ASGA to two large-cohort studies (BCP and CBCP).

Results:   our method effectively removed noise-contaminated volumes without human interference.

Impact: Compared to other fMRI data censoring approaches, ASGA is automatic, targeting on general noise and artifacts, can better enhance fMRI analysis accuracy and research outcomes, especially useful for challenging populations such as children, older subjects, and patients. 

Keywords: Data Processing, Data Analysis, Geometric Distortion Correction, EPI, fMRI, Functional Mapping Accuracy, Submillimetre resolution, and Ultra-high field

Motivation: Distortion correction in EPI has been utilised in numerous fMRI studies. However, the impact of the distortion correction on submillimetre fMRI analysis remains largely unexplored. 

Goal(s): This work performs quantitative and qualitative evaluation of geometric distortion correction with various criteria in submillimetre fMRI at 7T. 

Approach: Submillimetre EPI (0.73 × 0.73 mm²) was employed for visual fMRI, and the distortion-corrected data were evaluated in terms of spatial resolution, functional mapping accuracy, and histogram distribution. 

Results: This work demonstrates the effectiveness of distortion correction in submillimetre fMRI, revealing substantially enhanced mapping accuracy without significant deterioration in spatial resolution or functional activation distribution.

Impact: The quantitative and qualitative evaluation of EPI distortion correction presented in our work demonstrates the effectiveness of distortion correction in submillimetre fMRI at 7T, revealing substantially enhanced mapping accuracy without a significant deterioration in spatial resolution or functional activation distribution.

Keywords: Data Processing, fMRI (task based)

Motivation: It is imperative to study individual brain functioning when performing tasks.

Goal(s): This study aims to develop a novel method to investigate the individuality of human brain functions.

Approach: The temporal correlation of a task-evoked activity with the time signal of every point in the brain quantifies the whole brain’s functional co-activity (FC). The spatial correlation of the FC maps of two task trials over the entire brain quantifies the degree of their co-activity, which measures the variation of brain activity when performing these two tasks.

Results: The measured trial-to-trial variation of the whole brain’s activity quantified individual brain functioning when performing tasks.

Impact: This study presents a novel method to investigate individual brain functioning when performing tasks. The quantified relationship of the whole brain activity with each performed task trial may characterizes the neural bases responsible for individual behavioral and clinical traits.

Keywords: Data Processing, Elastography

Motivation: The stiffness value in two-dimensional liver MR elastography (MRE) is measured manually to avoid local errors in the stiffness estimation (dark/hot spots). Observer variability with this manual measurement is one of the obstacles to the clinical usage of MRE.

Goal(s): Our goal was to automatically remove dark/hot spots from the measurement area.

Approach: We introduced a new automated technique (coherent-wave auto-selection: CHASE) for measuring the stiffness value and tested it for the liver of five healthy volunteers.

Results: CHASE automatically generated a measurement area scarcely including dark/hot spots, resulting in high uniformity within that area.

Impact: The combination of our new technique and confidence mapping (clinical method) can reduce the process of manual measurement of stiffness value in liver MR elastography, resulting in improved variability and diagnostic performance for fibrosis staging.

Keywords: Data Processing, DSC & DCE Perfusion, Blood-brain barrier; Biology, methods and models; Modelling; Permeability

Motivation: Different models of DCE-MRI tracer leakage may fit better in different brain regions in cerebrovascular disease and could provide additional insight into BBB function.

Goal(s): Demonstrate the utility of voxel-wise DCE-MRI model selection in stroke.

Approach: Fit the Extended Tofts, Patlak, and Intravascular models of DCE-MRI tracer leakage to data from controls, ischaemic stroke, and intracerebral haemorrhage patients on a voxel-wise basis, and select the best-fitting model for each voxel using the Akaike Information Criterion.

Results: Different models are preferred in different tissue types and disease groups. Model selection increases inter-patient variance of K^trans compared to Patlak alone.

Impact: The Patlak model may not be the most appropriate model for DCE-MRI measurements of blood-brain barrier leakage in ischaemic stroke. Best-fitting model maps could help delineate the extent of “leaky” vs “non leaky” regions based on nested model assumptions.

Keywords: Data Processing, fMRI, brain stimulation

Motivation: In concurrent MRI-brain-stimulation studies various coil setups are used which show either limited access or limited spatial signal coverage.

Goal(s): Our goal is to come up with an optimized coil setup and data processing strategy that allow for open access to the head while maintaining whole brain imaging capabilities.

Approach: Three commonly used coil setups (head array, wrapped body array, TMS surface coil) where used and spatial signal homogenization as well as multi-echo fMRI postprocessing strategies were applied.

Results: By comparing various data processing strategies we were able to improve similarity of activation patterns across the different coil setups used.

Impact: Our results will help to improve researchers to chose optimized data acquistion and postprocessing strategies for whole brain and network based fMRI studies in concurrent brain-stimulation and MRI investigations.

Keywords: Data Processing, Arterial spin labelling

Motivation: ASL data has distinct feature in Fourier domain, potentially enabling development of Fourier-based ASL data analysis. 

Goal(s): To introduce a Fourier-based method to analyze ASL data including fMRI data and verify the robustness to abrupt and periodic artifacts. 

Approach: Contribution of the abrupt artifacts in the perfusion frequency component was estimated and eliminated to recover the perfusion signal. For the robustness to the periodic artifacts, weighted regression was applied for correlation calculation in the ASL fMRI analysis. 

Results: The Fourier-based ASL analysis yielded higher SNR in abrupt artifacts and more robust fMRI maps with periodic artifacts.

Impact: The proposed Fourier-based ASL analysis method is robust to various artifacts, yielding higher SNR and more robust fMRI maps. The study demonstrated for the first time that ASL perfusion fMRI data can be analyzed in Fourier domain, providing new perspectives.

Keywords: fMRI Analysis, fMRI, Denoising, DNN

Motivation: We expected that using a fMRI denoising neural network (DNN) that requires the denoised signal to correlate with a task design matrix in combination with a GLM analysis, can lead to biased results.

Goal(s): To find a DNN cost function that is independent of the task design.

Approach: We suggested a cost function that is based on the preserved frequency content and the correlation with motion regressors, with non-brain signals and with the non-denoised signal.

Results: We found that the DNN with the proposed cost function performed best in reducing the noise while preserving the BOLD signal.

Impact: Our intension with this abstract is to make researchers to think more carefully about the conditions included in their customized cost function in DNN-like denoising models and to check critically the denoised output.

Keywords: fMRI Analysis, fMRI, Physiological Denoising, Subject Identifiability

Motivation: Denoising systemic low-frequency oscillations (sLFOs) using global signal regression (GSR) can possibly impact the neural component of the BOLD fMRI signal.

Goal(s): Our goal was to use the spatial relationship between the sLFO component of the BOLD signal and brain vasculature to perform a less aggressive form of GSR.

Approach: We collected structural and functional images at 3T in ten subjects. We then used to temporal and spatial characteristics of the sLFOs to denoise the BOLD signal.

Results: The spatial correlation between the sLFOs and venograms confirmed their underlying vascular origin. The performance of our novel denoising technique still needs to be evaluated.

Impact: We propose a novel sLFO denoising method that uses the temporal and spatial patterns of physiological noise to preserve a larger fraction of the neural activity. 

Keywords: fMRI Analysis, Perfusion

Motivation: Test a denoising method, block matching with 4D filtering (BM4D), to improve the performance of a low signal-to-noise modality, functional imaging using arterial spin labeling (ASL)

Goal(s): Apply BM4D to improve the detection of brain activity

Approach: Use task-based fMRI in human data and in simulation to test how well the BM4D denoised data corresponds to ground truth activation compared to non-denoised data

Results: The BM4D denoised data performs better than non-denoised data when spatial Gaussian smoothing is not used prior to analysis but fails to outperform when adequate spatial Gaussian smoothing is used.

Impact: For functional arterial spin labeling (ASL), denoising algorithms may show an improvement in detecting brain activity in simulation, but not when comparisons are made after adequate spatial Gaussian smoothing.

Keywords: fMRI Analysis, fMRI

Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices.

Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform.  

Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals.

Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation.  

Impact: It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.

Keywords: fMRI Analysis, fMRI (task based)

Motivation: Selecting the right Gaussian kernel size for fMRI lacks standardization. For the detection of subtle brain activations, smaller kernels are needed. However, their reliability in ensuring accurate and trustworthy results remains uncertain.

Goal(s): Evaluate the effectiveness of small Gaussian filter kernel on fMRI data.

Approach: Assessment of BOLD signal integrity, accuracy, and data normality- employing 7T fMRI simulated time series and resting-state data.

Results: The study underscored the efficiency of smaller kernels in minimizing noise and upholding accurate signal detection. Residuals largely followed a Gaussian distribution.

Impact: Our study provides factual support for using small Gaussian kernel sizes in 7T fMRI data for their reliability in both functionality and compliance with RFT requirements.

Keywords: fMRI Analysis, fMRI

Motivation: Alternative distortion-reducing methods are needed since NUFFT reconstruction of modestly undersampled radial k-space creates streaking artifacts that influence the entire image and fMRI study's ROI.

Goal(s): Our objective is to demonstrate that the ROI in our fMRI study is less exposed to aliasing artifacts when PFT reconstruction is used as opposed to NUFFT reconstruction.

Approach: Radial-based fMRI images with 2x undersampling were reconstructed using PFT. The neural activity map was then compared to NUFFT-based reconstruction.

Results: Compared to the NUFFT, our proposed approach achieved a qualitatively and quantitatively improved activation map thanks to the distinct artifact characteristic of the PFT. 

Impact: Global streaking artifacts in reconstructed images from undersampled radial k-space may seriously affect fMRI study ROI and lead to incorrect brain activity maps. This work used the PFT approach to reduce ROI aliasing artifact in fMRI studies.

Keywords: fMRI Analysis, Machine Learning/Artificial Intelligence, HiHi fMRI, Data Analysis

Motivation: Because there is significant variability in the hemodynamic response of individuals, fitting a simple function may lead to false negatives (i.e., a bad fit leads to larger residuals than the noise level would dictate).

Goal(s): Develop a robust data-driven based approach for detecting BOLD.

Approach: A semi-supervised automatic detection (SAD) method based on a bidirectional long/short-term memory neural network to find BOLD responses in the entire brain and assess classification performance on simulated fMRI data. 

Results: The proposed detection method exhibits robustness across various HRF shapes at realistic contrast-to-noise ratios.

Impact: We proposed a method for detecting BOLD responses in high temporal resolution fMRI data that is based on a Bidirectional long/short-term memory neural network. Classification performance was excellent as tested using simulated data with different HRFs and contrast-to-noise ratios. 

Keywords: fMRI Analysis, fMRI (resting state), Denoising, thermal noise, data preprocessing

Motivation: Noise sources, including thermal noise, affect signal-to-noise ratio (SNR) in resting-state fMRI, limiting utility and impact of this type of data.

Goal(s): This study aims at enhancing data quality by integrating Component Analysis based on Standard-deviation Attenuation (CASA) technique with standard denoising methods.

Approach: Employing rs-fMRI data from 19 controls, we compared the regression of motion, white matter and CSF signals (MWC approach) and the integrated CASA denoising + MWC approach, based on tSNR and RSN comparison.

Results: The study showed significant enhancement in tSNR employing CASA + MWC approach and led to RSNs free from artifact-related patterns seen with the MWC method.

Impact: Resting-state fMRI data with our Component Analysis based on Standard-deviation Attenuation (CASA) denoising have greater signal quality and reduced contribution of unstructured thermal noise, which is greatly beneficial for reliably evaluating functional connectivity in resting state fMRI studies. 

Keywords: fMRI Analysis, Data Analysis, fMRI (resting state), dynamic connectivity, microstate

Motivation: Implement a well-established method (microstate analysis) to characterize state and state-transitions from EEG to fMRI. Such an approach will provide information on dynamic network changes and could provide a novel way to assess brain function in health and disease.

Goal(s): To provide a proof-of-concept that EEG-microstate analysis can be adapted to fMRI.  

Approach: Adapt EEG-microstate analysis to fMRI data and characterize dynamic brain network changes.  

Results: We demonstrate that this novel approach can identify distinct network states, their average duration, frequency of occurrence, total coverage of entire scan and transition probabilities between states.

Impact: We provide proof-of-concept that commonly used EEG-microstate analysis can be adapted to characterize dynamic changes in brain network states in fMRI data. This novel analysis could provide novel insights in alterations of brain functionality in various disorders.