Fast 3D fMRI acquisition with high spatial resolutions over a reduced FOV.

PURPOSE: To develop and demonstrate a fast 3D fMRI acquisition technique with high spatial resolution over a reduced FOV, named k-t 3D reduced FOV imaging (3D-rFOVI). METHODS: Based on 3D gradient-echo EPI, k-t 3D-rFOVI used a 2D RF pulse to reduce the FOV in the in-plane phase-encoding direction, boosting spatial resolution without increasing echo train length. For image acceleration, full sampling was applied in the central k-space region along the through-slab direction (kz) for all time frames, while randomized undersampling was used in outer kz regions at different time frames. Images were acquired at 3T and reconstructed using a method based on partial separability. fMRI detection sensitivity of k-t 3D-rFOVI was quantitively analyzed with simulation data. Human visual fMRI experiments were performed to evaluate k-t 3D-rFOVI and compare it with a commercial multiband EPI sequence. RESULTS: The simulation data showed that k-t 3D-rFOVI can detect 100% of fMRI activations with an acceleration factor (R) of 2 and ˜80% with R = 6. In the human fMRI data acquired with 1.5-mm spatial resolution and 800-ms volume TR (TRvol), k-t 3D-rFOVI with R = 4 detected 46% more activated voxels in the visual cortex than the multiband EPI. Additional fMRI experiments showed that k-t 3D-rFOVI can achieve TRvol of 480 ms with R = 6, while reliably detecting visual activation. CONCLUSIONS: k-t 3D-rFOVI can simultaneously achieve a high spatial resolution (1.5-mm isotropically) and short TRvol (480-ms) at 3T. It offers a robust acquisition technique for fast fMRI studies over a focused brain volume.

Three-dimensional echo-shifted EPI with simultaneous blip-up and blip-down acquisitions for correcting geometric distortion.

PURPOSE: EPI with blip-up/down acquisition (BUDA) can provide high-quality images with minimal distortions by using two readout trains with opposing phase-encoding gradients. Because of the need for two separate acquisitions, BUDA doubles the scan time and degrades the temporal resolution when compared to single-shot EPI, presenting a major challenge for many applications, particularly fMRI. This study aims at overcoming this challenge by developing an echo-shifted EPI BUDA (esEPI-BUDA) technique to acquire both blip-up and blip-down datasets in a single shot. METHODS: A 3D esEPI-BUDA pulse sequence was designed by using an echo-shifting strategy to produce two EPI readout trains. These readout trains produced a pair of k-space datasets whose k-space trajectories were interleaved with opposite phase-encoding gradient directions. The two k-space datasets were separately reconstructed using a 3D SENSE algorithm, from which time-resolved B0 -field maps were derived using TOPUP in FSL and then input into a forward model of joint parallel imaging reconstruction to correct for geometric distortion. In addition, Hankel structured low-rank constraint was incorporated into the reconstruction framework to improve image quality by mitigating the phase errors between the two interleaved k-space datasets. RESULTS: The 3D esEPI-BUDA technique was demonstrated in a phantom and an fMRI study on healthy human subjects. Geometric distortions were effectively corrected in both phantom and human brain images. In the fMRI study, the visual activation volumes and their BOLD responses were comparable to those from conventional 3D echo-planar images. CONCLUSION: The improved imaging efficiency and dynamic distortion correction capability afforded by 3D esEPI-BUDA are expected to benefit many EPI applications.

The Relationship Between Sleep and Brain Function in Older Adults With Chronic Kidney Disease and Self-Identified Cognitive Impairment.

Older adults with chronic kidney disease (CKD) are at risk for cognitive impairment and sleep disturbances. The purpose of the current study was to investigate the relationship between sleep and brain structure/function in older adults with CKD and self-identified cognitive impairment. The sample (N = 37) had a mean age of 68 years (SD = 4.9 years), estimated glomerular filtration rate of 43.7 mL/min/1.73m2 (SD = 10.98), median sleep time of 7.4 hours, and was 70% female. Sleeping <7.4 hours, compared to ≥7.4 hours, was associated with better attention/information processing (ß = 11.46, 95% confidence interval [CI] [3.85, 19.06]) and better learning/memory (ß = 2.06, 95% CI [0.37, 3.75]). Better sleep efficiency was associated with better global cerebral blood flow (ß = 3.30, 95% CI [0.65, 5.95]). Longer awake length after sleep onset was associated with worse fractional anisotropy of the cingulum (ß = -0.01, 95% CI [-0.02, -0.003]). Sleep duration and continuity may be related to brain function in older adults with CKD and self-identified cognitive impairment. [Journal of Gerontological Nursing, 49(7), 31-39.].

Time-dependent diffusion MRI using multiple stimulated echoes.

PURPOSE: To develop a time-efficient pulse sequence that acquires multiple diffusion-weighted images with distinct diffusion times in a single shot by using multiple stimulated echoes (mSTE) with variable flip angles (VFA). METHODS: The proposed diffusion-weighted mSTE with VFA (DW-mSTE-VFA) sequence begins with two 90° RF pulses that straddle a diffusion gradient lobe (GD ) to excite and restore one half of the magnetization into the longitudinal axis. The restored longitudinal magnetization was successively re-excited by a series of RF pulses with VFA, each followed by another GD , to generate a set of stimulated echoes. Each of the multiple stimulated echoes was acquired with an EPI echo train. As such, the train of multiple stimulated echoes produced a set of diffusion-weighted images with varying diffusion times in a single shot. This technique was experimentally demonstrated on a diffusion phantom, a fruit, and healthy human brain and prostate at 3 T. RESULTS: In the phantom experiment, the mean ADC measured at different diffusion times using DW-mSTE-VFA were highly consistent (r = 0.999) with those from a commercial spin-echo diffusion-weighted EPI sequence. In the fruit and brain experiments, DW-mSTE-VFA exhibited similar diffusion-time dependence to a standard diffusion-weighted stimulated echo sequence. The ADC showed significant time dependence in the human brain (p = 0.003 in both white matter and gray matter) and prostate tissues (p = 0.003 in both peripheral zone and central gland). CONCLUSION: DW-mSTE-VFA offers a time-efficient tool for investigating the diffusion-time dependency in diffusion MRI studies.

Simultaneous multi-segment (SMSeg) EPI over multiple focal regions.

Objective.This study aimed at developing a simultaneous multi-segment (SMSeg) imaging technique using a two-dimensional (2D) RF pulse in conjunction with echo planar imaging (EPI) to image multiple focal regions.Approach.The SMSeg technique leveraged periodic replicates of the excitation profile of a 2D RF pulse to simultaneously excite multiple focal regions at different locations. These locations were controlled by rotating and scaling transmit k-space trajectories. The resulting multiple isolated focal regions were projected into a composite 'slice' for display. GRAPPA-based parallel imaging was incorporated into SMSeg by taking advantage of coil sensitivity variations in both the phase-encoded and slice-selection directions. The SMSeg technique was implemented at 3 T in a single-shot gradient-echo EPI sequence and demonstrated in a phantom and human brains for both anatomic imaging and functional imaging.Main results.In both the phantom and the human brain, SMSeg images from three focal regions were simultaneously acquired. SMSeg imaging enabled up to a six-fold acceleration in parallel imaging without causing appreciable residual aliasing artifacts when compared with a conventional gradient-echo EPI sequence with the same acceleration factor. In the functional imaging experiment, BOLD activations associated with a visuomotor task were simultaneously detected in two non-coplanar segments (each with a size of 240 × 30 mm2), corresponding to visual and motor cortices, respectively.Significance.Our study has demonstrated that SMSeg imaging can be a viable method for studying multiple focal regions simultaneously.

Gradient-echo-train-based sub-millisecond periodic event encoded dynamic imaging with random (k, t)-space undersampling: k-t get-SPEEDI.

PURPOSE: The gradient-echo-train-based Sub-millisecond Periodic Event Encoded Dynamic Imaging (get-SPEEDI) technique provides ultrahigh temporal resolutions (∼0.6 ms) for detecting rapid physiological activities, but its practical adoption can be hampered by long scan times. This study aimed at developing a more efficient variant of get-SPEEDI for reducing the scan time without degrading temporal resolution or image quality. METHODS: The proposed pulse sequence, named k-t get-SPEEDI, accelerated get-SPEEDI acquisition by undersampling the k-space phase-encoding lines semi-randomly. At each time frame, k-space was fully sampled in the central region whereas randomly undersampled in the outer regions. A time-series of images was reconstructed using an algorithm based on the joint partial separability and sparsity constraints. To demonstrate the performance of k-t get-SPEEDI, images of human aortic valve opening and closing were acquired with 0.6-ms temporal resolution and compared with those from conventional get-SPEEDI. RESULTS: k-t get-SPEEDI achieved a 2-fold scan time reduction over the conventional get-SPEEDI (from ∼6 to ∼3 min), while achieving comparable SNRs and contrast-to-noise ratio (CNRs) for visualizing the dynamic process of aortic valve: SNR/CNR ≈$$ \approx $$ 70/38 vs. 73/39 in the k-t and conventional get-SPEEDI scans, respectively. The time courses of aortic valve area also matched well between these two sequences with a correlation coefficient of 0.86. CONCLUSIONS: The k-t get-SPEEDI pulse sequence was able to half the scan time without compromising the image quality and ultrahigh temporal resolution. Additional scan time reduction may also be possible, facilitating in vivo adoptions of SPEEDI techniques.

Three-dimensional reduced field-of-view imaging (3D-rFOVI).

PURPOSE: This study aimed at developing a 3D reduced field-of-view imaging (3D-rFOVI) technique using a 2D radiofrequency (RF) pulse, and demonstrating its ability to achieve isotropic high spatial resolution and reduced image distortion in echo planar imaging (EPI). METHODS: The proposed 3D-rFOVI technique takes advantage of a 2D RF pulse to excite a slab along the conventional slice-selection direction (i.e., z-direction) while limiting the spatial extent along the phase-encoded direction (i.e., y-direction) within the slab. The slab is phase-encoded in both through-slab and in-slab phase-encoded directions. The 3D-rFOVI technique was implemented at 3T in gradient-echo and spin-echo EPI pulse sequences for functional MRI (fMRI) and diffusion-weighted imaging (DWI), respectively. 3D-rFOVI experiments were performed on a phantom and human brain to illustrate image distortion reduction, as well as isotropic high spatial resolution, in comparison with 3D full-FOV imaging. RESULTS: In both the phantom and the human brain, image voxel dislocation was substantially reduced by 3D-rFOVI when compared with full-FOV imaging. In the fMRI experiment with visual stimulation, 3D isotropic spatial resolution of (2 × 2 × 2 mm3 ) was achieved with an adequate signal-to-noise ratio (81.5) and blood oxygen level-dependent (BOLD) contrast (2.5%). In the DWI experiment, diffusion-weighted brain images with an isotropic resolution of (1 × 1 × 1 mm3 ) was obtained without appreciable image distortion. CONCLUSION: This study indicates that 3D-rFOVI is a viable approach to 3D neuroimaging over a zoomed region.

Diffusion in Sephadex Gel Structures: Time Dependency Revealed by Multi-Sequence Acquisition over a Broad Diffusion Time Range.

It has been increasingly reported that in biological tissues diffusion-weighted MRI signal attenuation deviates from mono-exponential decay, especially at high b-values. A number of diffusion models have been proposed to characterize this non-Gaussian diffusion behavior. One of these models is the continuous-time random-walk (CTRW) model, which introduces two new parameters: a fractional order time derivative α and a fractional order spatial derivative ß. These new parameters have been linked to intravoxel diffusion heterogeneities in time and space, respectively, and are believed to depend on diffusion times. Studies on this time dependency are limited, largely because the diffusion time cannot vary over a board range in a conventional spin-echo echo-planar imaging sequence due to the accompanying T2 decays. In this study, we investigated the time-dependency of the CTRW model in Sephadex gel phantoms across a broad diffusion time range by employing oscillating-gradient spin-echo, pulsed-gradient spin-echo, and pulsed-gradient stimulated echo sequences. We also performed Monte Carlo simulations to help understand our experimental results. It was observed that the diffusion process fell into the Gaussian regime at extremely short diffusion times whereas it exhibited a strong time dependency in the CTRW parameters at longer diffusion times.

MRI with sub-millisecond temporal resolution over a reduced field of view.

PURPOSE: To demonstrate an MRI pulse sequence-Sub-millisecond Periodic Event Encoded Dynamic Imaging with a reduced field of view (or rFOV-SPEEDI)-for decreasing the scan times while achieving sub-millisecond temporal resolution. METHODS: rFOV-SPEEDI was based on a variation of SPEEDI, known as get-SPEEDI, which used each echo in an echo-train to sample a distinct k-space raster by synchronizing with a cyclic event. This can produce a set of time-resolved images of the cyclic event with a temporal resolution determined by the echo spacing (typically < 1 ms). rFOV-SPEEDI incorporated a 2D radiofrequency (RF) pulse into get-SPEEDI to limit the field of view (FOV), leading to reduction in phase-encoding steps and subsequently decreased scan times without compromising the spatial resolution. Two experiments were performed at 3T to illustrate rFOV-SPEEDI's capability of capturing fast-changing electric currents in a phantom and the rapid opening and closing of aortic valve in human subjects over reduced FOVs. The results were compared with those from full FOV get-SPEEDI. RESULTS: In the first experiment, the rapidly varying currents (50-200 Hz) were successfully captured with a temporal resolution of 0.8 ms, and agreed well with the applied currents. In the second experiment, the rapid opening and closing processes of aortic valve were clearly visualized with a temporal resolution of 0.6 ms over a reduced FOV (12 × 12 cm2 ). In both experiments, the acquisition times of rFOV-SPEEDI were decreased by 33%-50% relative to full FOV get-SPEEDI acquisitions and the spatial resolution was maintained. CONCLUSION: Reducing the FOV is a viable approach to shortening the scan times in SPEEDI, which is expected to help stimulate SPEEDI applications for studying ultrafast, cyclic physiological and biophysical processes over a focal region.

Automated pipeline for EEG artifact reduction (APPEAR) recorded during fMRI.

Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.

Visualization of Human Aortic Valve Dynamics Using Magnetic Resonance Imaging with Sub-Millisecond Temporal Resolution.

BACKGROUND: Visualization of aortic valve dynamics is important in diagnosing valvular diseases but is challenging to perform with magnetic resonance imaging (MRI) due to the limited temporal resolution. PURPOSE: To develop an MRI technique with sub-millisecond temporal resolution and demonstrate its application in visualizing rapid aortic valve opening and closing in human subjects in comparison with echocardiography and conventional MRI techniques. STUDY TYPE: Prospective. POPULATION: Twelve healthy subjects. FIELD STRENGTH/SEQUENCE: 3 T; gradient-echo-train-based sub-millisecond periodic event encoded imaging (get-SPEEDI) and balanced steady-state free precession (bSSFP). ASSESSMENT: Images were acquired using get-SPEEDI with a temporal resolution of 0.6 msec. get-SPEEDI was triggered by an electrocardiogram so that each echo in the gradient echo train corresponded to an image at a specific time point, providing a time-resolved characterization of aortic valve dynamics. For comparison, bSSFP was also employed with 12 msec and 24 msec temporal resolutions, respectively. The durations of the aortic valve rapid opening (Tro ), rapid closing (Trc ), and the maximal aortic valve area (AVA) normalized to height were measured with all three temporal resolutions. M-mode echocardiograms with a temporal resolution of 0.8 msec were obtained for further comparison. STATISTICAL TEST: Parameters were compared between the three sequences, together with the echocardiography results, with a Mann-Whitney U test. RESULTS: Significantly shorter Tro (mean ± SD: 27.5 ± 6.7 msec) and Trc (43.8 ± 11.6 msec) and larger maximal AVA/height (2.01 ± 0.29 cm2 /m) were measured with get-SPEEDI compared to either bSSFP sequence (Tro of 56.3 ± 18.8 and 63.8 ± 20.2 msec; Trc of 68.2 ± 16.6 and 72.8 ± 18.2 msec; maximal AVA/height of 1.63 ± 0.28 and 1.65 ± 0.32 cm2 /m for 12 msec and 24 msec temporal resolutions, respectively, P < 0.05). In addition, the get-SPEEDI results were more consistent with those measured using echocardiography, especially for Tro (29.0 ± 4.1 msec, P = 0.79) and Trc (41.6 ± 4.3 msec, P = 0.16). DATA CONCLUSION: get-SPEEDI allows for visualization of human aortic valve dynamics and provided values closer to those measured using echocardiography than the bSSFP sequences. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

Integration of Simultaneous Resting-State Electroencephalography, Functional Magnetic Resonance Imaging, and Eye-Tracker Methods to Determine and Verify Electroencephalography Vigilance Measure.

Background/Introduction: Concurrent electroencephalography and resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain with high temporal/spatial resolution. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixated cross, objective and verified experimental measures to quantify degree of vigilance are not readily available. Electroencephalography (EEG) is the modality extensively used for estimating vigilance, especially during eyes-closed resting state. However, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. Methods: Three 12-min resting scans (eyes open, fixating on the cross) were collected from 10 healthy control participants. We simultaneously collected EEG, fMRI, physiological, and eye-tracker data and investigated the correlation between EEG features, pupil size, and heart rate. Furthermore, we used pupil size and EEG features as regressors to find their correlations with blood-oxygen-level-dependent fMRI measures. Results: EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r = 0.306, p < 0.001). Moreover, FOBP also correlated with heart rate (r = 0.255, p < 0.001), as well as several brain regions in the anticorrelated network, including the bilateral insula and inferior parietal lobule. Discussion: In this study, we investigated whether simultaneous EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with vigilance measures during eyes-open rsfMRI. Our results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects. Impact statement We revealed an association between electroencephalography frontal and occipital beta power (FOBP) and pupil size changes during an eyes-open resting state, which supports the conclusion that FOBP could serve as an objective measure of vigilance in healthy human subjects. The results were validated by using simultaneously recorded heart rate and functional magnetic resonance imaging (fMRI). Interestingly, independently verified heart rate changes can also provide an easy-to-determine measure of vigilance during resting-state fMRI. These findings have important implications for an analysis and interpretation of dynamic resting-state fMRI connectivity studies in health and disease.

Improved autoregressive model for correction of noise serial correlation in fast fMRI.

PURPOSE: In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. METHODS: Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (ARAICc ) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). RESULTS: The measured false positive characteristics agreed well with the theoretical curve when using the ARAICc , and the activation maps in the smoothed data matched the ground truth. The ARAICc showed improved performance than the fixed AR(p) method. CONCLUSION: The ARAICc can effectively remove noise SC, and accurate statistical analysis results can be obtained with the ARAICc correction in fast fMRI.

Automatic cardiac cycle determination directly from EEG-fMRI data by multi-scale peak detection method.

BACKGROUND: In simultaneous EEG-fMRI, identification of the period of cardioballistic artifact (BCG) in EEG is required for the artifact removal. Recording the electrocardiogram (ECG) waveform during fMRI is difficult, often causing inaccurate period detection. NEW METHOD: Since the waveform of the BCG extracted by independent component analysis (ICA) is relatively invariable compared to the ECG waveform, we propose a multiple-scale peak-detection algorithm to determine the BCG cycle directly from the EEG data. The algorithm first extracts the high contrast BCG component from the EEG data by ICA. The BCG cycle is then estimated by band-pass filtering the component around the fundamental frequency identified from its energy spectral density, and the peak of BCG artifact occurrence is selected from each of the estimated cycle. RESULTS: The algorithm is shown to achieve a high accuracy on a large EEG-fMRI dataset. It is also adaptive to various heart rates without the needs of adjusting the threshold parameters. The cycle detection remains accurate with the scan duration reduced to half a minute. Additionally, the algorithm gives a figure of merit to evaluate the reliability of the detection accuracy. COMPARISON WITH EXISTING METHOD: The algorithm is shown to give a higher detection accuracy than the commonly used cycle detection algorithm fmrib_qrsdetect implemented in EEGLAB. CONCLUSIONS: The achieved high cycle detection accuracy of our algorithm without using the ECG waveforms makes possible to create and automate pipelines for processing large EEG-fMRI datasets, and virtually eliminates the need for ECG recordings for BCG artifact removal.

Cardiorespiratory noise correction improves the ASL signal.

Cardiorespiratory fluctuations such as changes in heart rate or respiration volume influence the temporal dynamics of cerebral blood flow (CBF) measurements during arterial spin labeling (ASL) fMRI. This "physiological noise" can confound estimates of resting state network activity, and it may lower the signal-to-noise ratio of ASL during task-related experiments. In this study we examined several methods for minimizing the contributions of both synchronized and non-synchronized physiological noise in ASL measures of CBF, by combining the RETROICOR approach with different linear deconvolution models. We evaluated the amount of variance in CBF that could be explained by each method during physiological rest, in both resting state and task performance conditions. To further demonstrate the feasibility of this approach, we induced low-frequency cardiorespiratory deviations via peripheral adrenergic stimulation with isoproterenol, and determined how these fluctuations influenced CBF, before and after applying noise correction. By suppressing physiological noise, we observed substantial improvements in the signal-to-noise ratio at the individual and group activation levels. Our results suggest that variations in cardiac and respiratory parameters can account for a large proportion of the variance in resting and task-based CBF, and indicate that regressing out these non-neuronal signal variations improves the intrinsically low signal-to-noise ratio of ASL. This approach may help to better identify and control physiologically driven activations in ASL resting state and task-based analyses.

The Insular Cortex Dynamically Maps Changes in Cardiorespiratory Interoception.

Palpitations and dyspnea are fundamental to the human experience of panic anxiety, but it remains unclear how the brain dynamically represents changes in these interoceptive sensations. We used isoproterenol, a rapidly acting peripheral beta-adrenergic agonist similar to adrenaline, to induce sensations of palpitation and dyspnea in healthy individuals (n=23) during arterial spin labeling functional magnetic resonance imaging (fMRI). We hypothesized that the right mid-insular cortex, a central recipient of viscerosensory input, would preferentially respond during the peak period of cardiorespiratory stimulation. Bolus infusions of saline and isoproterenol (1 or 2 µg) were administered in a blinded manner while participants continuously rated the intensity of their cardiorespiratory sensation using a dial. Isoproterenol elicited dose-dependent increases in cardiorespiratory sensation, with all participants reporting palpitations and dyspnea at the 2 µg dose. Consistent with our hypothesis, the right mid-insula was maximally responsive during the peak period of sympathetic arousal, heart rate increase, and cardiorespiratory sensation. Furthermore, a shift in insula activity occurred during the recovery period, after the heart rate had largely returned to baseline levels, with an expansion of activation into anterior and posterior sectors of the right insula, as well as bilateral regions of the mid-insula. These results confirm the right mid-insula is a key node in the interoceptive network, and inform computational models proposing specific processing roles for insula subregions during homeostatic inference. The combination of isoproterenol and fMRI offers a powerful approach for evaluating insula function, and could be a useful probe for examining interoceptive dysfunction in psychiatric disorders.

Automatic EEG-assisted retrospective motion correction for fMRI (aE-REMCOR).

Head motions during functional magnetic resonance imaging (fMRI) impair fMRI data quality and introduce systematic artifacts that can affect interpretation of fMRI results. Electroencephalography (EEG) recordings performed simultaneously with fMRI provide high-temporal-resolution information about ongoing brain activity as well as head movements. Recently, an EEG-assisted retrospective motion correction (E-REMCOR) method was introduced. E-REMCOR utilizes EEG motion artifacts to correct the effects of head movements in simultaneously acquired fMRI data on a slice-by-slice basis. While E-REMCOR is an efficient motion correction approach, it involves an independent component analysis (ICA) of the EEG data and identification of motion-related ICs. Here we report an automated implementation of E-REMCOR, referred to as aE-REMCOR, which we developed to facilitate the application of E-REMCOR in large-scale EEG-fMRI studies. The aE-REMCOR algorithm, implemented in MATLAB, enables an automated preprocessing of the EEG data, an ICA decomposition, and, importantly, an automatic identification of motion-related ICs. aE-REMCOR has been used to perform retrospective motion correction for 305 fMRI datasets from 16 subjects, who participated in EEG-fMRI experiments conducted on a 3T MRI scanner. Performance of aE-REMCOR has been evaluated based on improvement in temporal signal-to-noise ratio (TSNR) of the fMRI data, as well as correction efficiency defined in terms of spike reduction in fMRI motion parameters. The results show that aE-REMCOR is capable of substantially reducing head motion artifacts in fMRI data. In particular, when there are significant rapid head movements during the scan, a large TSNR improvement and high correction efficiency can be achieved. Depending on a subject's motion, an average TSNR improvement over the brain upon the application of aE-REMCOR can be as high as 27%, with top ten percent of the TSNR improvement values exceeding 55%. The average correction efficiency over the 305 fMRI scans is 18% and the largest achieved efficiency is 71%. The utility of aE-REMCOR on the resting state fMRI connectivity of the default mode network is also examined. The motion-induced position-dependent error in the DMN connectivity analysis is shown to be reduced when aE-REMCOR is utilized. These results demonstrate that aE-REMCOR can be conveniently and efficiently used to improve fMRI motion correction in large clinical EEG-fMRI studies.

Ballistocardiogram artifact removal with a reference layer and standard EEG cap.

BACKGROUND: In simultaneous EEG-fMRI, the EEG recordings are severely contaminated by ballistocardiogram (BCG) artifacts, which are caused by cardiac pulsations. To reconstruct and remove the BCG artifacts, one promising method is to measure the artifacts in the absence of EEG signal by placing a group of electrodes (BCG electrodes) on a conductive layer (reference layer) insulated from the scalp. However, current BCG reference layer (BRL) methods either use a customized EEG cap composed of electrode pairs, or need to construct the custom reference layer through additional model-building experiments for each EEG-fMRI experiment. These requirements have limited the versatility and efficiency of BRL. The aim of this study is to propose a more practical and efficient BRL method and compare its performance with the most popular BCG removal method, the optimal basis sets (OBS) algorithm. NEW METHOD: By designing the reference layer as a permanent and reusable cap, the new BRL method is able to be used with a standard EEG cap, and no extra experiments and preparations are needed to use the BRL in an EEG-fMRI experiment. RESULTS: The BRL method effectively removed the BCG artifacts from both oscillatory and evoked potential scalp recordings and recovered the EEG signal. COMPARISON WITH EXISTING METHOD: Compared to the OBS, this new BRL method improved the contrast-to-noise ratios of the alpha-wave, visual, and auditory evoked potential signals by 101%, 76%, and 75%, respectively, employing 160 BCG electrodes. Using only 20 BCG electrodes, the BRL improved the EEG signal by 74%/26%/41%, respectively. CONCLUSION: The proposed method can substantially improve the EEG signal quality compared with traditional methods.