OBJECTIVE:Parallel imaging is ubiquitous in MRI, enabling higher spatial and/or temporal resolution. However, successful unfolding is contingent on robust and accurate estimation of relative coil sensitivities, which often involves computation times that preclude online deployment. We present a computationally efficient method of robustly estimating coil sensitivities, and reconstructing under-sampled images using a data-driven regularised SENSE formalism that is commensurate with online deployment for Cartesian k-space acquisitions. MATERIALS AND METHODS:The proposed image reconstruction method via Multiple Orthogonal Reference Sensitivity Encoding (MORSE) estimates multiple sensitivities per voxel to address issues, such as rapidly varying sensitivities, chemical shift artefact, or insufficient fields of view. It simultaneously provides a data-driven regularisation term for noise control providing inherent adaptability to diverse imaging contexts. RESULTS:MORSE has been successfully deployed in multiple neuroimaging studies at both 3T and 7T, including functional studies of autobiographical memory processing, visual and auditory perception, and quantitative MRI studies of neurodegenerative diseases including Huntington's, Alzheimer's and Parkinson's. Exemplar image reconstructions are presented and compared with GRAPPA, ENLIVE, ESPIRiT and LORAKS. We also showcase application of MORSE outside of the brain via application in liver and knee imaging. MORSE consistently produced high-quality, artefact-free images with reconstruction times feasible for online deployment. DISCUSSION:The proposed method of sensitivity estimation and unfolding regularisation is flexible and robust. It is made available to the community in open-source as a library of functions within the vendor-agnostic Gadgetron image reconstruction framework.

Phase imaging applications such as QSM are highly sensitive to noise amplifications, phase singularities, and other artefacts, particularly in challenging scenarios such as ultra-high field (7T), under-sampled or single-echo acquisitions. We present a novel image reconstruction method, MORSE-PI, designed to produce high-SNR, artefact-free, and singularity-free phase images for both structural and functional phase-based brain imaging. MORSE-PI extends our previous approach, MORSE, by introducing a Virtual Reference Coil (VRC). The VRC is constructed as a linear combination of coil sensitivity maps, with correlations enhanced between coil elements using the noise covariance matrix. Such a VRC ensures robust signal support across the entire brain and is used to correct phase offsets in the MORSE-derived coil sensitivity estimates, resulting in artefact-free, high SNR phase. Compared to GRAPPA with ASPIRE phase correction, MORSE-PI demonstrates greater robustness to artefacts such as noise amplification and aliasing, and shows improved reproducibility in structural imaging at both 3T and 7T. Unlike ESPIRiT and GRAPPA combined with adaptive coil combination methods, MORSE-PI yields singularity-free phase maps. MORSE-PI enables high-SNR reconstructions even for the most challenging scenarios, such as single-echo EPI at 7T. Its efficient, containerised implementation using the Gadgetron framework supports deployment on the MRI scanner console during measurements. MORSE-PI offers a flexible and computationally efficient solution for generating high-SNR, artefact- and singularity-free phase images in both single- and multi-echo GRE and EPI acquisitions. This makes it particularly well-suited for structural and functional QSM, as well as other phase-based MRI applications. Its robustness and rapid computational time facilitate efficient deployment on scanners across field strengths.

Parallel imaging is ubiquitous in MRI, enabling diverse applications such as ultra-high-resolution functional and quantitative imaging with greater temporal resolution or reduced scan times respectively. Successful unfolding is contingent on robust and accurate estimation of the relative coil sensitivities, which often involves computation times that preclude real-time deployment. Here we present a computationally-efficient method of robustly estimating coil sensitivities, and reconstructing under-sampled images using a data-driven regularised SENSE formalism. Our MORSE scheme estimates multiple sensitivities per voxel to address issues such as rapidly varying sensitivities, chemical shift artefact, or insufficient fields of view and provides a data-driven regularisation term for noise control. Exemplar structural and functional image reconstructions at 3T and 7T are presented and compared with a vendor-provided GRAPPA reconstruction as well as state-of-the-art ESPIRiT and LORAKS algorithms. MORSE consistently produced high-quality, artefact-free images with reconstruction times feasible for real-time deployment. It is flexible and robust, and made available to the community in open-source as a library of functions within the vendor-agnostic Gadgetron image reconstruction framework.

Geometric distortion is a major concern for cortical depth-dependent fMRI using EPI readouts that suffer from low bandwidth in the phase-encoded direction. Distortions are exacerbated at higher field strengths due to increased B0 field inhomogeneity. In this study, we quantitatively compared two distortion correction methods (B0 field-mapping and reversed-PE) applied to submillimeter (0.8mm isotropic) 3DEPI data at 7T. Cortical alignment was evaluated through comparison with an anatomically-faithful MP2RAGE reference by computing the dice coefficient and normalised mutual information. Both distortion correction methods usefully improve alignment. The reversed-PE approach performed better.

Introduction: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to modulate human brain and behavioural function in both research and clinical interventions. The combination of functional magnetic resonance imaging (fMRI) with tDCS enables researchers to directly test causal contributions of stimulated brain regions, answering questions about the physiology and neural mechanisms underlying behaviour. Despite the promise of the technique, advances have been hampered by technical challenges and methodological variability between studies, confounding comparability/replicability. Methods: Here tDCS-fMRI at 3T was developed for a series of experiments investigating language recovery after stroke. To validate the method, one healthy volunteer completed an fMRI paradigm with three conditions: No-tDCS, Sham-tDCS, Anodal-tDCS. MR data were analysed with region-of-interest (ROI) analyses of the electrodes and reference site. Results: Quality assessment indicated no visible signal dropouts or distortions in the brain introduced by the tDCS equipment. After modelling scanner drift, motion-related variance, and temporal autocorrelation, we found that functional MR sensitivity was not degraded or adversely affected by the tDCS set-up and stimulation protocol across conditions in grey matter and in the three ROIs. Discussion: Key safety factors and risk mitigation strategies that must be taken into consideration when integrating tDCS into an fMRI environment are outlined. To obtain reliable results, we provide practical solutions to technical challenges and complications of the method. It is hoped that sharing these data and Standard Operation Procedure (SOP) will promote methodological replication in future studies, enhancing the quality of tDCS-fMRI application, and improve the reliability of scientific results in this field. Conclusions: Our method and data provide a technically safe, reliable tDCS-fMRI procedure to obtain high quality MR data. The detailed framework of the SOP systematically reports the technical and procedural elements of our tDCS-fMRI approach, which can be adopted and prove useful in future studies.

Introduction: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to modulate human brain and behavioural function in both research and clinical interventions. The combination of functional magnetic resonance imaging (fMRI) with tDCS enables researchers to directly test causal contributions of stimulated brain regions, answering questions about the physiology and neural mechanisms underlying behaviour. Despite the promise of the technique, advances have been hampered by technical challenges and methodological variability between studies, confounding comparability/replicability. Methods: Here tDCS-fMRI at 3T was developed for a series of experiments investigating language recovery after stroke. To validate the method, one healthy volunteer completed an fMRI paradigm with three conditions: No-tDCS, Sham-tDCS, Anodal-tDCS. MR data were analysed with region-of-interest (ROI) analyses of the electrodes and reference site. Results: Quality assessment indicated no visible signal dropouts or distortions in the brain introduced by the tDCS equipment. After modelling scanner drift, motion-related variance, and temporal autocorrelation, we found that functional MR sensitivity was not degraded or adversely affected by the tDCS set-up and stimulation protocol across conditions in grey matter and in the three ROIs. Discussion: Key safety factors and risk mitigation strategies that must be taken into consideration when integrating tDCS into an fMRI environment are outlined. To obtain reliable results, we provide practical solutions to technical challenges and complications of the method. It is hoped that sharing these data and Standard Operation Procedure (SOP) will promote methodological replication in future studies, enhancing the quality of tDCS-fMRI application, and improve the reliability of scientific results in this field. Conclusions: Our method and data provide a technically safe, reliable tDCS-fMRI procedure to obtain high quality MR data. The detailed framework of the SOP systematically reports the technical and procedural elements of our tDCS-fMRI approach, which can be adopted and prove useful in future studies.

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI.

Non-invasive methods, such as neurofeedback training, could support cognitive symptom management in Huntington's disease by targeting brain regions whose function is impaired. The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of neurofeedback training in Huntington's disease by examining two different methods, activity and connectivity real-time functional MRI neurofeedback training. Thirty-two Huntington's disease gene-carriers completed 16 runs of neurofeedback training, using an optimized real-time functional MRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the supplementary motor area, and another receiving neurofeedback based on the correlation of supplementary motor area and left striatum activity (connectivity neurofeedback training), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during neurofeedback training sessions and after training at follow-up sessions. Transfer of training was measured by measuring the participants' ability to upregulate neurofeedback training target levels without feedback (near transfer), as well as by examining change in objective, a priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher neurofeedback training target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two neurofeedback training methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and learning success. We conclude that although there is evidence that neurofeedback training can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust.

Non-invasive methods, such as neurofeedback training (NFT), could support cognitive symptom management in Huntington’s disease (HD) by targeting brain regions whose function is impaired. The aim of our single-blind, sham-controlled study was to collect rigorous evidence regarding the feasibility of NFT in HD by examining two different methods, activity and connectivity real-time fMRI NFT. Thirty-two HD gene-carriers completed 16 runs of NFT training, using an optimized real-time fMRI protocol. Participants were randomized into four groups, two treatment groups, one receiving neurofeedback derived from the activity of the Supplementary Motor Area (SMA), and another receiving neurofeedback based on the correlation of SMA and left striatum activity (connectivity NFT), and two sham control groups, matched to each of the treatment groups. We examined differences between the groups during NFT training sessions and after training at follow-up sessions. Transfer of training was measured by measuring the participants’ ability to upregulate NFT target levels without feedback (near transfer), as well as by examining change in objective, a-priori defined, behavioural measures of cognitive and psychomotor function (far transfer) before and at 2 months after training. We found that the treatment group had significantly higher NFT target levels during the training sessions compared to the control group. However, we did not find robust evidence of better transfer in the treatment group compared to controls, or a difference between the two NFT methods. We also did not find evidence in support of a relationship between change in cognitive and psychomotor function and NFT learning success. We conclude that although there is evidence that NFT can be used to guide participants to regulate the activity and connectivity of specific regions in the brain, evidence regarding transfer of learning and clinical benefit was not robust. Although the intervention is non-invasive, given the costs and absence of reliable evidence of clinical benefit, we cannot recommend real-time fMRI NFT as a potential intervention in HD.

Gradient echo echo-planar imaging (GE EPI) is used for most fMRI studies but can suffer substantially from image distortions and BOLD sensitivity (BS) loss due to susceptibility-induced magnetic field inhomogeneities. While there are various post-processing methods for correcting image distortions, signal dropouts cannot be recovered and therefore need to be addressed at the data acquisition stage. Common approaches for reducing susceptibility-related BS loss in selected brain areas are: z-shimming, inverting the phase encoding (PE) gradient polarity, optimizing the slice tilt and increasing spatial resolution. The optimization of these parameters can be based on atlases derived from multiple echo-planar imaging (EPI) acquisitions. However, this requires resource and time, which imposes a practical limitation on the range over which parameters can be optimised meaning that the chosen settings may still be sub-optimal. To address this issue, we have developed an automated method that can be used to optimize across a large parameter space. It is based on numerical signal simulations of the BS loss predicted by physical models informed by a large database of magnetic field (B0) maps acquired on a broad cohort of participants. The advantage of our simulation-based approach compared to previous methods is that it saves time and expensive measurements and allows for optimizing EPI protocols by incorporating a broad range of factors, including different resolutions, echo times or slice orientations. To verify the numerical optimisation, results are compared to those from an earlier study and to experimental BS measurements carried out in six healthy volunteers.

Background and aims Methods that can normalize neuronal function, such as neurofeedback (NF) training, could support symptom management in HD as primary or adjunct treatments to other disease-modifying therapies. During NF training, participants receive feedback on the activity of a target brain region and learn by trial and error to regulate it. The aim of the present study was to test whether HD patients can learn to regulate their brain activity using NF training and to compare two different NF training protocols to establish which one is more effective. Methods Thirty-two HD patients completed a real-time fMRI NF training paradigm consisting of 4 training visits (4–14 days apart). Half of the participants received feedback derived from their own brain activity measured using BOLD contrast fMRI (treatment group), while the other half received sham feedback derived from unrelated participants in the treatment group (control group). Participants were further divided in two subgroups, one receiving neurofeedback derived from the Supplementary Motor Area (SMA), and another receiving neurofeedback based on the cross-correlation of SMA and left striatum activity (a measure of connectivity). Participants were blinded to group allocation. Results To evaluate successful learning, we tested for a linear increase in participants’ ability to enhance fMRI activity/connectivity voluntarily across visits. Participants in the SMA-activity NF group were the only group that showed a linear increase across visits. We also evaluated participants’ capacity to upregulate their brain activity after the end of training in the absence of explicit feedback (transfer effects). The SMA-activity NF group was again the only group that showed successful upregulation. Conclusions Our results suggest that SMA-activity NF training is a more promising approach than connectivity-based NF training. Future larger trials focusing only on activity-based NF training, are now needed to enable us to gather more evidence on the efficacy of the method in symptom-management in HD.

Accelerated data acquisition with simultaneous multi-slice (SMS) imaging for functional MRI studies leads to interacting and opposing effects that influence the sensitivity to blood oxygen level-dependent (BOLD) signal changes. Image signal to noise ratio (SNR) is decreased with higher SMS acceleration factors and shorter repetition times (TR) due to g-factor noise penalties and saturation of longitudinal magnetization. However, the lower image SNR is counteracted by greater statistical power from more samples per unit time and a higher temporal Nyquist frequency that allows for better removal of spurious non-BOLD high frequency signal content. This study investigated the dependence of the BOLD sensitivity on these main driving factors and their interaction, and provides a framework for evaluating optimal acceleration of SMS-EPI sequences. functional magnetic resonance imaging (fMRI) data from a scenes/objects visualization task was acquired in 10 healthy volunteers at a standard neuroscience resolution of 3 mm on a 3T MRI scanner. SMS factors 1, 2, 4, and 8 were used, spanning TRs of 2800 ms to 350 ms. Two data processing methods were used to equalize the number of samples over the SMS factors. BOLD sensitivity was assessed using g-factors maps, temporal SNR (tSNR), and t-score metrics. tSNR results show a dependence on SMS factor that is highly non-uniform over the brain, with outcomes driven by g-factor noise amplification and the presence of high frequency noise. The t-scoremetrics also show a high degree of spatial dependence: the lower g-factor noise area of V1 shows significant improvements at higher SMS factors; the moderate-level g-factor noise area of the parahippocampal place area shows only a trend of improvement; and the high g-factor noise area of the ventral-medial pre-frontal cortex shows a trend of declining t-scores at higher SMS factors. This spatial variability suggests that the optimal SMS factor for fMRI studies is region dependent. For task fMRI studies done with similar parameters as were used here (3T scanner, 32-channel RF head coil, whole brain coverage at 3 mm isotropic resolution), we recommend SMS accelerations of 4x (conservative) to 8x (aggressive) for most studies and a more conservative acceleration of 2x for studies interested in anterior midline regions.

Neuroimaging studies of speech perception have consistently indicated a left-hemisphere dominance in the temporal lobes' responses to intelligible auditory speech signals (McGettigan and Scott, 2012). However, there are important communicative cues that cannot be extracted from auditory signals alone, including the direction of the talker's gaze. Previous work has implicated the superior temporal cortices in processing gaze direction, with evidence for predominantly right-lateralized responses (Carlin & Calder, 2013). The aim of the current study was to investigate whether the lateralization of responses to talker gaze differs in an auditory communicative context. Participants in a functional MRI experiment watched and listened to videos of spoken sentences in which the auditory intelligibility and talker gaze direction were manipulated factorially. We observed a left-dominant temporal lobe sensitivity to the talker's gaze direction, in which the left anterior superior temporal sulcus/gyrus and temporal pole showed an enhanced response to direct gaze - further investigation revealed that this pattern of lateralization was modulated by auditory intelligibility. Our results suggest flexibility in the distribution of neural responses to social cues in the face within the context of a challenging speech perception task.

PurposeBrief bursts of RF noise during MR data acquisition (“k‐space spikes”) cause disruptive image artifacts, manifesting as stripes overlaid on the image. RF noise is often related to hardware problems, including vibrations during gradient‐heavy sequences, such as diffusion‐weighted imaging. In this study, we present an application of the Robust Principal Component Analysis (RPCA) algorithm to remove spike noise from k‐space. Methods: Corrupted k‐space matrices were decomposed into their low‐rank and sparse components using the RPCA algorithm, such that spikes were contained within the sparse component and artifact‐free k‐space data remained in the low‐rank component. Automated center refilling was applied to keep the peaked central cluster of k‐space from misclassification in the sparse component. Results: This algorithm was demonstrated to effectively remove k‐space spikes from four data types under conditions generating spikes: (i) mouse heart T1 mapping, (ii) mouse heart cine imaging, (iii) human kidney diffusion tensor imaging (DTI) data, and (iv) human brain DTI data. Myocardial T1 values changed by 86.1 ± 171 ms following despiking, and fractional anisotropy values were recovered following despiking of DTI data. Conclusion: The RPCA despiking algorithm will be a valuable postprocessing method for retrospectively removing stripe artifacts without affecting the underlying signal of interest. Magn Reson Med 75:2517–2525, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Synchronized behavior (chanting, singing, praying, dancing) is found in all human cultures and is central to religious, military, and political activities, which require people to act collaboratively and cohesively; however, we know little about the neural underpinnings of many kinds of synchronous behavior (e.g., vocal behavior) or its role in establishing and maintaining group cohesion. In the present study, we measured neural activity using fMRI while participants spoke simultaneously with another person. We manipulated whether the couple spoke the same sentence (allowing synchrony) or different sentences (preventing synchrony), and also whether the voice the participant heard was “live” (allowing rich reciprocal interaction) or prerecorded (with no such mutual influence). Synchronous speech was associated with increased activity in posterior and anterior auditory fields. When, and only when, participants spoke with a partner who was both synchronous and “live,” we observed a lack of the suppression of auditory cortex, which is commonly seen as a neural correlate of speech production. Instead, auditory cortex responded as though it were processing another talker's speech. Our results suggest that detecting synchrony leads to a change in the perceptual consequences of one's own actions: they are processed as though they were other-, rather than self-produced. This may contribute to our understanding of synchronized behavior as a group-bonding tool. SIGNIFICANCE STATEMENT Synchronized human behavior, such as chanting, dancing, and singing, are cultural universals with functional significance: these activities increase group cohesion and cause participants to like each other and behave more prosocially toward each other. Here we use fMRI brain imaging to investigate the neural basis of one common form of cohesive synchronized behavior: joint speaking (e.g., the synchronous speech seen in chants, prayers, pledges). Results showed that joint speech recruits additional right hemisphere regions outside the classic speech production network. Additionally, we found that a neural marker of self-produced speech, suppression of sensory cortices, did not occur during joint synchronized speech, suggesting that joint synchronized behavior may alter self-other distinctions in sensory processing.

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2 × 2 × 3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Quantitative imaging aims to provide in vivo neuroimaging biomarkers with high research and diagnostic value that are sensitive to underlying tissue microstructure. In order to use these data to examine intra-cortical differences or to define boundaries between different myelo-architectural areas, high resolution data are required. The quality of such measurements is degraded in the presence of motion hindering insight into brain microstructure. Correction schemes are therefore vital for high resolution, whole brain coverage approaches that have long acquisition times and greater sensitivity to motion. Here we evaluate the use of prospective motion correction (PMC) via an optical tracking system to counter intra-scan motion in a high resolution (800µm isotropic) multi-parameter mapping (MPM) protocol. Data were acquired on six volunteers using a 2x2 factorial design permuting the following conditions: PMC on/off and motion/no motion. In the presence of head motion, PMC-based motion correction considerably improved the quality of the maps as reflected by fewer visible artefacts and improved consistency. The precision of the maps, parameterised by the coefficient of variation in cortical sub-regions, showed improvements of 11-24% in the presence of deliberate head motion. Importantly, in the absence of motion the PMC system did not introduce extraneous artefacts into the quantitative maps. The PMC system based on optical tracking offers a robust approach to minimising motion artefacts in quantitative anatomical imaging without extending scan times. Such a robust motion correction scheme is crucial in order to achieve the ultra-high resolution required of quantitative imaging for cutting edge in vivo histology applications.

Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows training of voluntary control over regionally specific brain activity. However, the neural basis of successful neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with neurofeedback training of visual cortex activity. Using dynamic causal modeling (DCM), we found that training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.

Relaxation rates provide important information about tissue microstructure. Multi-parameter mapping (MPM) estimates multiple relaxation parameters from multi-echo FLASH acquisitions with different basic contrasts, i.e., proton density (PD), T1 or magnetization transfer (MT) weighting. Motion can particularly affect maps of the apparent transverse relaxation rate R2(*), which are derived from the signal of PD-weighted images acquired at different echo times. To address the motion artifacts, we introduce ESTATICS, which robustly estimates R2(*) from images even when acquired with different basic contrasts. ESTATICS extends the fitted signal model to account for inherent contrast differences in the PDw, T1w and MTw images. The fit was implemented as a conventional ordinary least squares optimization and as a robust fit with a small or large confidence interval. These three different implementations of ESTATICS were tested on data affected by severe motion artifacts and data with no prominent motion artifacts as determined by visual assessment or fast optical motion tracking. ESTATICS improved the quality of the R2(*) maps and reduced the coefficient of variation for both types of data-with average reductions of 30% when severe motion artifacts were present. ESTATICS can be applied to any protocol comprised of multiple 2D/3D multi-echo FLASH acquisitions as used in the general research and clinical setting.