Multi-echo Independent Component Analysis Research Articles

Improving the Sensitivity of Task-Based Multi-Echo Functional Magnetic Resonance Imaging via T2* Mapping Using Synthetic Data-Driven Deep Learning.

(1) Background: Functional magnetic resonance imaging (fMRI) utilizing multi-echo gradient echo-planar imaging (ME-GE-EPI) has demonstrated higher sensitivity and stability compared to utilizing single-echo gradient echo-planar imaging (SE-GE-EPI). The direct derivation of T2* maps from fitting multi-echo data enables accurate recording of dynamic functional changes in the brain, exhibiting higher sensitivity than echo combination maps. However, the widely employed voxel-wise log-linear fitting is susceptible to inevitable noise accumulation during image acquisition. (2) Methods: This work introduced a synthetic data-driven deep learning (SD-DL) method to obtain T2* maps for multi-echo (ME) fMRI analysis. (3) Results: The experimental results showed the efficient enhancement of the temporal signal-to-noise ratio (tSNR), improved task-based blood oxygen level-dependent (BOLD) percentage signal change, and enhanced performance in multi-echo independent component analysis (MEICA) using the proposed method. (4) Conclusion: T2* maps derived from ME-fMRI data using the proposed SD-DL method exhibit enhanced BOLD sensitivity in comparison to T2* maps derived from the LLF method.

Leveraging Multi-Echo EPI to Enhance BOLD Sensitivity in Task-based Olfactory fMRI.

Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast relies on gradient echo echo-planar imaging (GE-EPI) to quantify dynamic susceptibility changes associated with the hemodynamic response to neural activity. However, acquiring BOLD fMRI in human olfactory regions is particularly challenging due to their proximity to the sinuses where large susceptibility gradients induce magnetic field distortions. BOLD fMRI of the human olfactory system is further complicated by respiratory artifacts that are highly correlated with event onsets in olfactory tasks. Multi-echo EPI (ME-EPI) acquires gradient echo data at multiple echo times (TEs) during a single acquisition and can leverage signal evolution over the multiple echo times to enhance BOLD sensitivity and reduce artifactual signal contributions. In the current study, we developed a ME-EPI acquisition protocol for olfactory task-based fMRI and demonstrated significant improvement in BOLD signal sensitivity over conventional single-echo EPI (1E-EPI). The observed improvement arose from both an increase in BOLD signal changes through a T 2 * -weighted echo combination and a reduction in non-BOLD artifacts through the application of the Multi-Echo Independent Components Analysis (ME-ICA) denoising method. This study represents one of the first direct comparisons between 1E-EPI and ME-EPI in high-susceptibility regions and provides compelling evidence in favor of using ME-EPI for future task-based fMRI studies.

Simultaneous cortical, subcortical, and brainstem mapping of sensory activation.

Nonpainful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging studies have highlighted the value of whole-brain, systems-level investigation for examining sensory processing. However, whole-brain functional magnetic resonance imaging studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high-resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo functional magnetic resonance imaging acquisition at 3T with multi-echo independent component analysis denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to nonpainful brushing of the right hand, left hand, and right foot (n= 10 per location), and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we differentiated the adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Comparing the efficacy of data-driven denoising methods for a multi-echo fMRI acquisition at 7T

In functional magnetic resonance imaging (fMRI) of the brain the measured signal is corrupted by several (e.g. physiological, motion, and thermal) noise sources and depends on the image acquisition. Imaging at ultrahigh field strength is becoming increasingly popular as it offers increased spatial accuracy. The latter is of particular benefit in brainstem neuroimaging given the small cross-sectional area of most nuclei. However, physiological noise scales with field strength in fMRI acquisitions. Although this problem is in part solved by decreasing voxel size, it is clear that adequate physiological denoising is of utmost importance in brainstem-focused fMRI experiments. Multi-echo sequences have been reported to facilitate highly effective denoising through TE-dependence of Blood Oxygen Level Dependent (BOLD) signals, in a denoising method referred to as multi-echo independent component analysis (ME-ICA). It has not been explored previously how ME-ICA compares to other data-driven denoising approaches at ultrahigh field strength. In the current study, we compared the efficacy of several denoising methods, including anatomical component based correction (aCompCor), Automatic Removal of Motion Artifacts (ICA-AROMA) aggressive and non-aggressive options, ME-ICA, and a combination of ME-ICA and aCompCor. We assessed several data quality metrics, including temporal signal-to-noise ratio (tSNR), delta variation signal (DVARS), spectral density of the global signal, functional connectivity and Shannon spectral entropy. Moreover, we looked at the ability of each method to uncouple the global signal and respiration. In line with previous reports at lower field strengths, we demonstrate that after applying ME-ICA, the data is best post-processed in order to remove spatially diffuse noise with a method such as aCompCor. Our findings indicate that ME-ICA combined with aCompCor and the aggressive option of ICA-AROMA are highly effective denoising approaches for multi-echo data acquired at 7T. ME-ICA combined with aCompCor potentially preserves more signal-of-interest as compared to the aggressive option of ICA-AROMA.

A Comparison of Single- and Multi-Echo Processing of Functional MRI Data During Overt Autobiographical Recall.

Recent years have seen an increase in the use of multi-echo fMRI designs by cognitive neuroscientists. Acquiring multiple echoes allows one to increase contrast-to-noise; reduce signal dropout and thermal noise; and identify nuisance signal components in BOLD data. At the same time, multi-echo acquisitions increase data processing complexity and may incur a cost to the temporal and spatial resolution of the acquired data. Here, we re-examine a multi-echo dataset previously analyzed using multi-echo independent components analysis (ME-ICA) and focused on hippocampal activity during the overtly spoken recall of recent and remote autobiographical memories. The goal of the present series of analyses was to determine if ME-ICA’s theoretical denoising benefits might lead to a practical difference in the overall conclusions reached. Compared to single-echo (SE) data, ME-ICA led to qualitatively different findings regarding hippocampal contributions to autobiographical recall: whereas the SE analysis largely failed to reveal hippocampal activity relative to an active baseline, ME-ICA results supported predictions of the Standard Model of Consolidation and a time limited hippocampal involvement. These data provide a practical example of the benefits multi-echo denoising in a naturalistic memory paradigm and demonstrate how they can be used to address long-standing theoretical questions.

Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity

Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

Improving the Breath-Holding CVR Measurement Using the Multiband Multi-Echo EPI Sequence.

Blood oxygen level-dependent (BOLD) functional MRI (fMRI) is commonly used to measure cerebrovascular reactivity (CVR), which can convey insightful information about neurovascular health. Breath-holding (BH) has been shown to be a practical vasodilatory stimulus for measuring CVR in clinical settings. The conventional BOLD fMRI approach has some limitations, however, such as susceptibility-induced signal dropout at air tissue interfaces and low BOLD sensitivity especially in areas of low . These drawbacks can potentially be mitigated with multi-echo sequences, which acquire several images at different echo times in one shot. When combined with multiband techniques, high temporal resolution images can be acquired. This study compared an advanced multiband multi-echo (MBME) echo planar imaging (EPI) sequence with an existing multiband single-echo (MB) sequence to evaluate the repeatability and sensitivity of BH activation and CVR mapping. Images were acquired from 28 healthy volunteers, of which 18 returned for repeat imaging. Both MBME and MB data were pre-processed using both standard and advanced denoising techniques. The MBME data was further processed by combining echoes using a -weighted approach and denoising using multi-echo independent component analysis. BH activation was calculated using a general linear model and the respiration response function. CVR was computed as the percent change related to the activation. To account for differences in CVR related to TE, relative CVR (rCVR) was computed and normalized to the mean gray matter CVR. Test–retest metrics were assessed with the Dice coefficient, rCVR difference, within subject coefficient of variation, and the intraclass correlation coefficient. Our findings demonstrate that rCVR for MBME scans were significantly higher than for MB scans across most of the gray matter. In areas of high susceptibility-induced signal dropout, however, MBME rCVR was significantly less than MB rCVR due to artifactually high rCVR for MB scans in these regions. MBME rCVR showed improved test–retest metrics compared with MB. Overall, the MBME sequence displayed superior BOLD sensitivity, improved specificity in areas of signal dropout on MBME scans, enhanced reliability, and reduced variability across subjects compared with MB acquisitions. Our results suggest that the MBME EPI sequence is a promising tool for imaging CVR.

Improved resting state functional connectivity sensitivity and reproducibility using a multiband multi-echo acquisition

Recent advances in functional MRI techniques include multiband (MB) imaging and multi-echo (ME) imaging. In MB imaging multiple slices are acquired simultaneously leading to significant increases in temporal and spatial resolution. Multi-echo imaging enables multiple echoes to be acquired in one shot, where the ME images can be used to denoise the BOLD time series and increase BOLD sensitivity. In this study, resting state fMRI (rs-fMRI) data were collected using a combined MBME sequence and compared to an MB single echo sequence. In total, 29 subjects were imaged, and 18 of them returned within two weeks for repeat imaging. Participants underwent one MBME scan with three echoes and one MB scan with one echo. Both datasets were processed using standard denoising and advanced denoising. Advanced denoising included multi-echo independent component analysis (ME-ICA) for the MBME data and ICA-AROMA for the MB data. Resting state functional connectivity (RSFC) was evaluated using both selective seed-based and whole grey matter (GM) region-of-interest (ROI) based approaches. The reproducibility of connectivity metrics was also analyzed in the repeat subjects. In addition, functional connectivity density (FCD), a data-driven approach that counts the number of significant connections, both within a local cluster and globally, with each voxel was analyzed. Regardless of the standard or advanced denoising technique, all seed-based RSFC was significantly higher for MBME compared to MB. Much more GM ROI combinations showed significantly higher RSFC for MBME vs. MB. Reproducibility, evaluated using the dice coefficient was significantly higher for MBME relative to MB data. Finally, FCD was also higher for MBME vs. MB data. This study showed higher RSFC for MBME vs. MB data using selected seed-based, whole GM ROI-based, and data-driven approaches. Reproducibility found also higher for MBME data. Taken together, these results indicate that MBME is a promising technique for rs-fMRI.

Integrated multi-echo denoising strategy improves identification of inherent language laterality.

Although increasingly used in both neuroscience and clinical studies, a major challenge facing resting-state FMRI (rs-FMRI) still lies in isolating BOLD signal fluctuations resulting from neuronal activity from noise. In this study, we investigated the effect of a newly proposed denoising approach, integrated multi-echo rs-FMRI analysis, on language mapping. Multiband multi-echo rs-FMRI data were acquired, along with language task FMRI that identified language areas in the left hemisphere of 12 subjects. The language laterality and specificity of the language mapping given by seed-based correlation analysis were compared among the rs-FMRI data sets pre-processed using 3 different approaches: multi-echo data with integrated multi-echo independent component analysis, denoising that uses the TE-dependency of each signal component to judge its origin, and multi-echo and single-echo data with conventional denoising. The laterality index was automatically computed without setting any threshold to minimize the arbitrariness and to ensure the generality of the result. A repeated measures analysis of variance followed by post hoc tests showed that optimal combination of the 3-echo data succeeded in increasing the correlation within the targeted language system. With the physically principled multi-echo denoising approach, the integrated strategy further succeeded in revealing areas of synchronization more specific to the language system compared with conventional denoising approach, which eventually improved the identification of the laterality of the system. By successfully reducing non-specific correlations spreading over the brain, integrated multi-echo approach improved language mapping and identification of the laterality of the system using rs-FMRI.

Multiband multi-echo simultaneous ASL/BOLD for task-induced functional MRI.

Typical simultaneous blood oxygenation-level dependent (BOLD) and arterial spin labeling (ASL) sequences acquire two echoes, one perfusion-sensitive and one BOLD-sensitive. However, for ASL, spatial resolution and brain coverage are limited due to the T1 decay of the labeled blood. This study applies a sequence combining a multiband acquisition with four echoes for simultaneous BOLD and pseudo-continuous ASL (pCASL) echo planar imaging (MBME ASL/BOLD) for block-design task-fMRI. A multiband acceleration of four was employed to increase brain coverage and reduce slice-timing effects on the ASL signal. Multi-echo independent component analysis (MEICA) was implemented to automatically denoise the BOLD signal by regressing non-BOLD components. This technique led to increased temporal signal-to-noise ratio (tSNR) and BOLD sensitivity. The MEICA technique was also modified to denoise the ASL signal by regressing artifact and BOLD signals from the first echo time-series. The MBME ASL/BOLD sequence was applied to a finger-tapping task functional MRI (fMRI) experiment. Signal characteristics and activation were evaluated using single echo BOLD, combined ME BOLD, combined ME BOLD after MEICA denoising, perfusion-weighted (PW), and perfusion-weighted after MEICA denoising time-series. The PW data was extracted using both surround subtraction and high-pass filtering followed by demodulation. In addition, the CBF/BOLD response ratio and CBF/BOLD coupling were analyzed. Results showed that the MEICA denoising procedure significantly improved the BOLD signal, leading to increased BOLD sensitivity, tSNR, and activation statistics compared to conventional single echo BOLD data. At the same time, the denoised PW data showed increased tSNR and activation statistics compared to the non-denoised PW data. CBF/BOLD coupling was also increased using the denoised ASL and BOLD data. Our preliminary data suggest that the MBME ASL/BOLD sequence can be employed to collect whole-brain task-fMRI with improved data quality for both BOLD and PW time series, thus improving the results of block-design task fMRI.

Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity.

A novel sequence has been introduced that combines multiband imaging with a multi-echo acquisition for simultaneous high spatial resolution pseudo-continuous arterial spin labeling (ASL) and blood-oxygenation-level dependent (BOLD) echo-planar imaging (MBME ASL/BOLD). Resting-state connectivity in healthy adult subjects was assessed using this sequence. Four echoes were acquired with a multiband acceleration of four, in order to increase spatial resolution, shorten repetition time, and reduce slice-timing effects on the ASL signal. In addition, by acquiring four echoes, advanced multi-echo independent component analysis (ME-ICA) denoising could be employed to increase the signal-to-noise ratio (SNR) and BOLD sensitivity. Seed-based and dual-regression approaches were utilized to analyze functional connectivity. Cerebral blood flow (CBF) and BOLD coupling was also evaluated by correlating the perfusion-weighted timeseries with the BOLD timeseries. These metrics were compared between single echo (E2), multi-echo combined (MEC), multi-echo combined and denoised (MECDN), and perfusion-weighted (PW) timeseries. Temporal SNR increased for the MECDN data compared to the MEC and E2 data. Connectivity also increased, in terms of correlation strength and network size, for the MECDN compared to the MEC and E2 datasets. CBF and BOLD coupling was increased in major resting-state networks, and that correlation was strongest for the MECDN datasets. These results indicate our novel MBME ASL/BOLD sequence, which collects simultaneous high-resolution ASL/BOLD data, could be a powerful tool for detecting functional connectivity and dynamic neurovascular coupling during the resting state. The collection of more than two echoes facilitates the use of ME-ICA denoising to greatly improve the quality of resting state functional connectivity MRI.

Disrupted resting-state brain network properties in obesity: decreased global and putaminal cortico-striatal network efficiency.

The efficient organization and communication of brain networks underlie cognitive processing and their disruption can lead to pathological behaviours. Few studies have focused on whole-brain networks in obesity and binge eating disorder (BED). Here we used multi-echo resting-state functional magnetic resonance imaging (rsfMRI) along with a data-driven graph theory approach to assess brain network characteristics in obesity and BED. Multi-echo rsfMRI scans were collected from 40 obese subjects (including 20 BED patients) and 40 healthy controls and denoised using multi-echo independent component analysis (ME-ICA). We constructed a whole-brain functional connectivity matrix with normalized correlation coefficients between regional mean blood oxygenation level-dependent (BOLD) signals from 90 brain regions in the Automated Anatomical Labeling atlas. We computed global and regional network properties in the binarized connectivity matrices with an edge density of 5%-25%. We also verified our findings using a separate parcellation, the Harvard-Oxford atlas parcellated into 470 regions. Obese subjects exhibited significantly reduced global and local network efficiency as well as decreased modularity compared with healthy controls, showing disruption in small-world and modular network structures. In regional metrics, the putamen, pallidum and thalamus exhibited significantly decreased nodal degree and efficiency in obese subjects. Obese subjects also showed decreased connectivity of cortico-striatal/cortico-thalamic networks associated with putaminal and cortical motor regions. These findings were significant with ME-ICA with limited group differences observed with conventional denoising or single-echo analysis. Using this data-driven analysis of multi-echo rsfMRI data, we found disruption in global network properties and motor cortico-striatal networks in obesity consistent with habit formation theories. Our findings highlight the role of network properties in pathological food misuse as possible biomarkers and therapeutic targets.

Evaluation of multi-echo ICA denoising for task based fMRI studies: Block designs, rapid event-related designs, and cardiac-gated fMRI

Multi-echo fMRI, particularly the multi-echo independent component analysis (ME-ICA) algorithm, has previously proven useful for increasing the sensitivity and reducing false positives for functional MRI (fMRI) based resting state connectivity studies. Less is known about its efficacy for task-based fMRI, especially at the single subject level. This work, which focuses exclusively on individual subject results, compares ME-ICA to single-echo fMRI and a voxel-wise T2⁎ weighted combination of multi-echo data for task-based fMRI under the following scenarios: cardiac-gated block designs, constant repetition time (TR) block designs, and constant TR rapid event-related designs. Performance is evaluated primarily in terms of sensitivity (i.e., activation extent, activation magnitude, percent detected trials and effect size estimates) using five different tasks expected to evoke neuronal activity in a distributed set of regions. The ME-ICA algorithm significantly outperformed all other evaluated processing alternatives in all scenarios. Largest improvements were observed for the cardiac-gated dataset, where ME-ICA was able to reliably detect and remove non-neural T1 signal fluctuations caused by non-constant repetition times. Although ME-ICA also outperformed the other options in terms of percent detection of individual trials for rapid event-related experiments, only 46% of all events were detected after ME-ICA; suggesting additional improvements in sensitivity are required to reliably detect individual short event occurrences. We conclude the manuscript with a detailed evaluation of ME-ICA outcomes and a discussion of how the ME-ICA algorithm could be further improved. Overall, our results suggest that ME-ICA constitutes a versatile, powerful approach for advanced denoising of task-based fMRI, not just resting-state data.

DISRUPTED COMPLEX BRAIN NETWORK PROPERTIES IN OBESE SUBJECTS

ObjectiveObesity and binge eating disorder have been suggested to be associated with impulsivity and disruption on the cortico-striatal brain network. Here we aimed to examine global network structure in resting...

Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA

The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts.

Separating slow BOLD from non-BOLD baseline drifts using multi-echo fMRI

The functional magnetic resonance (fMRI) baseline is known to drift over the course of an experiment and is often attributed to hardware instability. These ultraslow fMRI fluctuations are inseparable from blood oxygenation level dependent (BOLD) changes in standard single echo fMRI and they are therefore typically removed before further analysis in both resting-state and task paradigms. However, some part of these fluctuations may be of neuronal origin, as neural activity can indeed fluctuate at the scale of several minutes or even longer, such as after the administration of drugs or during the ultradian rhythms. Here, we show that it is possible to separate the slow BOLD and non-BOLD drifts automatically using multi-echo fMRI and multi-echo independent components analysis (ME-ICA) denoising by demonstrating the detection of a visual signal evoked from a flickering checkerboard with slowly changing contrast.