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

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task

Introduction

Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features.

Methods

For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups.

Results

The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%.

Discussion

The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

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 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.

Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T

Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric "T2* contrast efficiency" for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.

Transcranial focused ultrasound stimulation of cortical and thalamic somatosensory areas in human

The effects of transcranial focused ultrasound (FUS) stimulation of the primary somatosensory cortex and its thalamic projection (i.e., ventral posterolateral nucleus) on the generation of electroencephalographic (EEG) responses were evaluated in healthy human volunteers. Stimulation of the unilateral somatosensory circuits corresponding to the non-dominant hand generated EEG evoked potentials across all participants; however, not all perceived stimulation-mediated tactile sensations of the hand. These FUS-evoked EEG potentials (FEP) were observed from both brain hemispheres and shared similarities with somatosensory evoked potentials (SSEP) from median nerve stimulation. Use of a 0.5 ms pulse duration (PD) sonication given at 70% duty cycle, compared to the use of 1 and 2 ms PD, elicited more distinctive FEP peak features from the hemisphere ipsilateral to sonication. Although several participants reported hearing tones associated with FUS stimulation, the observed FEP were not likely to be confounded by the auditory sensation based on a separate measurement of auditory evoked potentials (AEP) to tonal stimulation (mimicking the same repetition frequency as the FUS stimulation). Off-line changes in resting-state functional connectivity (FC) associated with thalamic stimulation revealed that the FUS stimulation enhanced connectivity in a network of sensorimotor and sensory integration areas, which lasted for at least more than an hour. Clinical neurological evaluations, EEG, and neuroanatomical MRI did not reveal any adverse or unintended effects of sonication, attesting its safety. These results suggest that FUS stimulation may induce long-term neuroplasticity in humans, indicating its neurotherapeutic potential for various neurological and neuropsychiatric conditions.

Improved Resting-State Functional MRI Using Multi-Echo Echo-Planar Imaging on a Compact 3T MRI Scanner with High-Performance Gradients

In blood-oxygen-level-dependent (BOLD)-based resting-state functional (RS-fMRI) studies, usage of multi-echo echo-planar-imaging (ME-EPI) is limited due to unacceptable late echo times when high spatial resolution is used. Equipped with high-performance gradients, the compact 3T MRI system (C3T) enables a three-echo whole-brain ME-EPI protocol with smaller than 2.5 mm isotropic voxel and shorter than 1 s repetition time, as required in landmark fMRI studies. The performance of the ME-EPI was comprehensively evaluated with signal variance reduction and region-of-interest-, seed- and independent-component-analysis-based functional connectivity analyses and compared with a counterpart of single-echo EPI with the shortest TR possible. Through the multi-echo combination, the thermal noise level is reduced. Functional connectivity, as well as signal intensity, are recovered in the medial orbital sulcus and anterior transverse collateral sulcus in ME-EPI. It is demonstrated that ME-EPI provides superior sensitivity and accuracy for detecting functional connectivity and/or brain networks in comparison with single-echo EPI. In conclusion, the high-performance gradient enabled high-spatial-temporal resolution ME-EPI would be the method of choice for RS-fMRI study on the C3T.

Neuroplasticity Following Stroke from a Functional Laterality Perspective: A fNIRS Study

To explore alterations of resting-state functional connectivity (rsFC) in sensorimotor cortex following strokes with left or right hemiplegia considering the lateralization and neuroplasticity. Seventy-three resting-state functional near-infrared spectroscopy (fNIRS) files were selected, including 26 from left hemiplegia (LH), 21 from right hemiplegia (RH) and 26 from normal controls (NC) group. Whole-brain analyses matching the Pearson correlation were used for rsFC calculations. For right-handed normal controls, rsFC of motor components (M1 and M2) in the left hemisphere displayed a prominent intensity in comparison with the right hemisphere (p < 0.05), while for stroke groups, this asymmetry has disappeared. Additionally, RH rather than LH showed stronger rsFC between left S1 and left M1 in contrast to normal controls (p < 0.05), which correlated inversely with motor function (r = - 0.53, p < 0.05). Regarding M1, rsFC within ipsi-lesioned M1 has a negative correlation with motor function of the affected limb (r = - 0.60 for the RH group and - 0.43 for the LH group, p < 0.05). The rsFC within contra-lesioned M1 that innervates the normal side was weakened compared with that of normal controls (p < 0.05). Stronger rsFC of motor components in left hemisphere was confirmed by rs-fNIRS as the "secret of dominance" for the first time, while post-stroke hemiplegia broke this cortical asymmetry. Meanwhile, a statistically strengthened rsFC between left S1 and M1 only in right-hemiplegia group may act as a compensation for the impairment of the dominant side. This research has implications for brain-computer interfaces synchronizing sensory feedback with motor performance and transcranial magnetic regulation for cortical excitability to induce cortical plasticity.

Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction

PURPOSE

To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping.

METHODS

In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B₀ information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments.

RESULTS

The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI.

CONCLUSION

GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

Cognitive decline is associated with frequency-specific resting state functional changes in normal aging

Resting state low-frequency brain activity may aid in our understanding of the mechanisms of aging-related cognitive decline. Our purpose was to explore the characteristics of the amplitude of low-frequency fluctuations (ALFF) in different frequency bands of fMRI to better understand cognitive aging. Thirty-seven cognitively normal older individuals underwent a battery of neuropsychological tests and MRI scans at baseline and four years later. ALFF from five different frequency bands (typical band, slow-5, slow-4, slow-3, and slow-2) were calculated and analyzed. A two-way ANOVA was used to explore the interaction effects in voxel-wise whole brain ALFF of the time and frequency bands. Paired-sample t-test was used to explore within-group changes over four years. Partial correlation analysis was performed to assess associations between the altered ALFF and cognitive function. Significant interaction effects of time × frequency were distributed over inferior frontal gyrus, superior frontal gyrus, right rolandic operculum, left thalamus, and right putamen. Significant ALFF reductions in all five frequency bands were mainly found in the right hemisphere and the posterior cerebellum; whereas localization of the significantly increased ALFF were mainly found in the cerebellum at typical band, slow-5 and slow-4 bands, and left hemisphere and the cerebellum at slow-3, slow-2 bands. In addition, ALFF changes showed frequency-specific correlations with changes in cognition. These results suggest that changes of local brain activity in cognitively normal aging should be investigated in multiple frequency bands. The association between ALFF changes and cognitive function can potentially aid better understanding of the mechanisms underlying normal cognitive aging.

A Geometric View of Signal Sensitivity Metrics in multi-echo fMRI

In multi-echo fMRI (ME-fMRI), two metrics have been widely used to measure the performance of various acquisition and analysis approaches. These are temporal SNR (tSNR) and differential contrast-to-noise ratio (dCNR). A key step in ME-fMRI is the weighted combination of the data from multiple echoes, and prior work has examined the dependence of tSNR and dCNR on the choice of weights. However, most studies have focused on only one of these two metrics, and the relationship between the two metrics has not been examined. In this work, we present a geometric view that offers greater insight into the relation between the two metrics and their weight dependence. We identify three major regimes: (1) a tSNR robust regime in which tSNR is robust to the weight selection with most weight variants achieving close to optimal performance, whereas dCNR shows a pronounced dependence on the weights with most variants achieving suboptimal performance; (2) a dCNR robust regime in which dCNR is robust to the weight selection with most weight variants achieving close to optimal performance, while tSNR exhibits a strong dependence on the weights with most variants achieving significantly lower than optimal performance; and (3) a within-type robust regime in which both tSNR and dCNR achieve nearly optimal performance when the form of the weights are variants of their respective optimal weights and exhibit a moderate decrease in performance for other weight variants. Insight into the behavior observed in the different regimes is gained by considering spherical representations of the weight dependence of the components used to form each metric. For multi-echo acquisitions, dCNR is shown to be more directly related than tSNR to measures of CNR and signal-to-noise ratio (SNR) for both task-based and resting-state fMRI scans, respectively.

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.

Contribution of the multi-echo approach in accelerated functional magnetic resonance imaging multiband acquisition

We wanted to verify the effect of combining multi‐echo (ME) functional magnetic resonance imaging (fMRI) with slice acceleration in simultaneous multi‐slice acquisition. The aim was to shed light on the benefits of multiple echoes for various acquisition settings, especially for levels of slice acceleration and flip angle. Whole‐brain ME fMRI data were obtained from 26 healthy volunteers (using three echoes; seven runs with slice acceleration 1, 4, 6, and 8; and two different flip angles for each of the first three acceleration factors) and processed as single‐echo (SE) data and ME data based on optimal combinations weighted by the contrast‐to‐noise ratio. Global metrics (temporal signal‐to‐noise ratio, signal‐to‐noise separation, number of active voxels, etc.) and local characteristics in regions of interest were used to evaluate SE and ME data. ME results outperformed SE results in all runs; the differences became more apparent for higher acceleration, where a significant decrease in data quality is observed. ME fMRI can improve the observed data quality metrics over SE fMRI for a wide range of accelerated fMRI acquisitions.

A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis

Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.