Quantitative Evaluations of Geometrical Distortion Corrections in Cortical Surface-Based Analysis of High-Resolution Functional MRI Data at 7T

Premovement activity in the mesocortical system links peak force but not initiation of force generation under incentive motivation

Motivation facilitates motor performance; however, the neural substrates of the psychological effects on motor performance remain unclear. We conducted a functional magnetic resonance imaging experiment while human subjects performed a ready-set-go task with monetary incentives. Although subjects were only motivated to respond quickly, increasing the incentives improved not only reaction time but also peak grip force. However, the trial-by-trial correlation between reaction time and peak grip force was weak. Extensive areas in the mesocortical system, including the ventral midbrain (VM) and cortical motor-related areas, exhibited motivation-dependent activity in the premovement "Ready" period when the anticipated monetary reward was displayed. This premovement activity in the mesocortical system correlated only with subsequent peak grip force, whereas the activity in motor-related areas alone was associated with subsequent reaction time and peak grip force. These findings suggest that the mesocortical system linking the VM and motor-related regions plays a role in controlling the peak of force generation indirectly associated with incentives but not the initiation of force generation.

Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL)

The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.

Mitigating susceptibility-induced distortions in high-resolution 3DEPI fMRI at 7T

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.

Alterations in Resting-State Interhemispheric Coordination With Refractory Auditory Verbal Hallucinations in Schizophrenia

OBJECTIVE

The purpose of this study was to investigate resting-state interhemispheric functional connectivity in patients with schizophrenia and refractory auditory verbal hallucinations (RAVHs) by using voxel-mirrored homotopic connectivity (VMHC).

METHODS

Thirty-four patients with schizophrenia and RAVHs (RAVH group), 23 patients with schizophrenia but no auditory verbal hallucinations (non-AVH group), and 28 matched healthy volunteers (healthy control group) were recruited in China. VMHC analyses were used to identify brain areas with significant differences in functional connectivity among the three groups, and correlations between symptom scores and neurological measures were examined.

RESULTS

VMHC analyses showed aberrant bilateral connectivity between several homotopic brain regions: the RAVH and non-AVH groups showed differences in bilateral connectivity of the superior and middle temporal gyri, and the RAVH and healthy control groups showed differences in bilateral connectivity of the gyrus rectus, inferior frontal gyrus, and putamen. In addition, interhemispheric connectivity of the superior and middle temporal gyri correlated with patients' positive symptom scores.

CONCLUSIONS

These findings may help to elucidate the pathophysiological mechanisms underlying auditory verbal hallucinations. The results revealed interhemispheric functional dysconnectivity among patients with schizophrenia and suggest that the dysconnectivity of homotopic brain regions may play an important role in the development of auditory verbal hallucinations.

Minimizing magnetic resonance image geometric distortion at 7 Tesla for frameless presurgical planning using skin-adhered fiducials

BACKGROUND

7T MRI offers significant benefits to spatial and contrast resolution compared to lower field strengths. This superior image quality can help better delineate targets in stereotactic neurosurgical procedures; however, the potential for increased geometric distortions at 7T has impaired its widespread use for these applications. Image geometric distortions can be due to distortions of B₀ arising from tissue magnetic susceptibility effects or inherent field inhomogeneities, and nonlinearity of the magnetic field gradients.

PURPOSE

The purpose of this study was to investigate the use of 7T MRI for neurosurgical frameless stereotactic navigation procedures. Image geometric distortions at the skin surface in 7T images were minimized and compared to results from clinical 3T frameless imaging protocols.

METHODS

A 3D-printed grid phantom filled with oil was designed to perform a fine calibration of the 7T imaging gradients, and an oil-filled head phantom with internal targets was used to determine ground truth (from computed tomography [CT]) positioning errors. Three volunteers and the head phantom were imaged consecutively at 3T and 7T. Ten skin-adhesive fiducial markers were placed on each subject's exposed skin surface at standard clinical placement locations for frameless procedures. Imaging sequences included MPRAGE (three bandwidths at 7T: 400, 690, and 1020 Hz/pixel, and one at 3T: 400 Hz/pixel), T2 SPACE, and T2 SPACE FLAIR acquisitions. An additional GRE field map was acquired on both scanners using a multi-echo GRE sequence. Custom Matlab code was used to perform additional distortion correction of the images using the unwrapped field maps. Fiducial localization was performed with 3D Slicer, with absolute fiducial positioning errors determined in phantom experiments following rigid registration to the CT images. For human experiments, 3T and 7T images were registered and relative differences in fiducial locations were compared using two-tailed paired t-tests.

RESULTS

Phantom measurements at 7T yielded gradient distance scaling errors of 1.1%, 2.2%, and 1.0% along the x-, y-, and z-axes, respectively. These system miscalibrations were traced back to phantom manufacturing deviations in the sphericity of the vendor's gradient calibration phantom. Correction factors along each gradient axis were applied, and afterward, geometric distortions of less than 1 mm were obtained in the 7T MR head phantom images for the 1020 Hz/pixel bandwidth MPRAGE sequence. For the human subjects, four fiducial locations were excluded from the analysis due to patient positioning differences. Differences between 3T and 7T MPRAGE with low/medium/high bandwidth were 2.2 /2.6/2.3 mm, respectively, before the correction, reducing to 1.6/1.3/1.0 mm after the correction (p < 0.001). T2 SPACE and T2 SPACE FLAIR yielded a similar pattern when the correction was applied, decreasing from 2.1 to 0.8 mm, and 2.6 to 1.0 mm, respectively.

CONCLUSIONS

7T MRI can be used to perform frameless presurgical planning with skin-adhesive fiducials. Geometric distortions can be reduced to a clinically relevant level (errors < ∼1 mm) with no significant susceptibility-related distortions, by using high receiver bandwidth, ensuring gradients are properly calibrated, and placing skin fiducials in areas where distortions from patient positioning are minimal.

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.

The dorsal premotor cortex encodes the step-by-step planning processes for goal-directed motor behavior in humans

The dorsal premotor cortex (PMd) plays an essential role in visually guided goal-directed motor behavior. Although there are several planning processes for achieving goal-directed behavior, the separate neural processes are largely unknown. Here, we created a new visuo-goal task to investigate the step-by-step planning processes for visuomotor and visuo-goal behavior in humans. Using functional magnetic resonance imaging, we found activation in different portions of the bilateral PMd during each processing step. In particular, the activated area for rule-based visuomotor and visuo-goal mapping was located at the ventrorostral portion of the bilateral PMd, that for action plan specification was at the dorsocaudal portion of the left PMd, that for transformation was at the rostral portion of the left PMd, and that for action preparation was at the caudal portion of the bilateral PMd. Thus, the left PMd was involved throughout all of the processes, but the right PMd was involved only in rule-based visuomotor and visuo-goal mapping and action preparation. The locations related to each process were generally spatially separated from each other, but they overlapped partially. These findings revealed that there are functional subregions in the bilateral PMd in humans and these subregions form a functional gradient to achieve goal-directed behavior.

Critical factors in achieving fine-scale functional MRI: Removing sources of inadvertent spatial smoothing

Ultra‐high Field (≥7T) functional magnetic resonance imaging (UHF‐fMRI) provides opportunities to resolve fine‐scale features of functional architecture such as cerebral cortical columns and layers, in vivo. While the nominal resolution of modern fMRI acquisitions may appear to be sufficient to resolve these features, several common data preprocessing steps can introduce unwanted spatial blurring, especially those that require interpolation of the data. These resolution losses can impede the detection of the fine‐scale features of interest. To examine quantitatively and systematically the sources of spatial resolution losses occurring during preprocessing, we used synthetic fMRI data and real fMRI data from the human visual cortex—the spatially interdigitated human V2 “thin” and “thick” stripes. The pattern of these cortical columns lies along the cortical surface and thus can be best appreciated using surface‐based fMRI analysis. We used this as a testbed for evaluating strategies that can reduce spatial blurring of fMRI data. Our results show that resolution losses can be mitigated at multiple points in preprocessing pathway. We show that unwanted blur is introduced at each step of volume transformation and surface projection, and can be ameliorated by replacing multi‐step transformations with equivalent single‐step transformations. Surprisingly, the simple approaches of volume upsampling and of cortical mesh refinement also helped to reduce resolution losses caused by interpolation. Volume upsampling also serves to improve motion estimation accuracy, which helps to reduce blur. Moreover, we demonstrate that the level of spatial blurring is nonuniform over the brain—knowledge which is critical for interpreting data in high‐resolution fMRI studies. Importantly, our study provides recommendations for reducing unwanted blurring during preprocessing as well as methods that enable quantitative comparisons between preprocessing strategies. These findings highlight several underappreciated sources of a spatial blur. Individually, the factors that contribute to spatial blur may appear to be minor, but in combination, the cumulative effects can hinder the interpretation of fine‐scale fMRI and the detectability of these fine‐scale features of functional architecture.

Neural correlates with individual differences in temporal prediction during auditory-motor synchronization

Temporal prediction ability is vital for movement synchronization with external rhythmic stimuli (sensorimotor synchronization); however, little is known regarding individual variations in temporal prediction ability and its neural correlates. We determined the underlying neural correlates of temporal prediction and individual variations during auditory-motor synchronization. We hypothesized that the non-primary motor cortices, such as the premotor cortex and supplementary motor area, are the key brain regions that correlate individual variations in prediction ability. Functional magnetic resonance imaging (7T) was performed for 18 healthy volunteers who tapped to 3 types of auditory metronome beats: isochronous, tempo change, and random. The prediction ability was evaluated using prediction/tracking ratios that were computed based on cross-correlations between tap timing and pacing events. Participants with a higher prediction/tracking ratio (i.e. stronger predictive tendency) tapped to metronome beats more accurately and precisely. The prediction/tracking ratio was positively correlated with the activity in the bilateral dorsal premotor cortex (PMd), suggesting that the bilateral PMd explains the individual variation in prediction ability. These results indicate that the PMd is involved in generating a model for temporal prediction of auditory rhythm patterns and its activity would reflect model accuracy, which is critical for accurate and precise sensorimotor synchronization.

Cognitive control affects motor learning through local variations in GABA within the primary motor cortex

The primary motor cortex (M1) is crucial for motor learning; however, its interaction with other brain areas during motor learning remains unclear. We hypothesized that the fronto-parietal execution network (FPN) provides learning-related information critical for the flexible cognitive control that is required for practice. We assessed network-level changes during sequential finger tapping learning under speed pressure by combining magnetic resonance spectroscopy and task and resting-state functional magnetic resonance imaging. There was a motor learning-related increase in preparatory activity in the fronto-parietal regions, including the right M1, overlapping the FPN and sensorimotor network (SMN). Learning-related increases in M1-seeded functional connectivity with the FPN, but not the SMN, were associated with decreased GABA/glutamate ratio in the M1, which were more prominent in the parietal than the frontal region. A decrease in the GABA/glutamate ratio in the right M1 was positively correlated with improvements in task performance (p = 0.042). Our findings indicate that motor learning driven by cognitive control is associated with local variations in the GABA/glutamate ratio in the M1 that reflects remote connectivity with the FPN, representing network-level motor sequence learning formations.

The integrative role of the M1 in motor sequence learning

The primary motor cortex (M1) is crucial in motor learning. Whether the M1 encodes the motor engram for sequential finger tapping formed by an emphasis on speed is still inconclusive. The active states of engrams are hard to discriminate from the motor execution per se. As preparatory activity reflects the upcoming movement parameters, we hypothesized that the retrieval of motor engrams generated by different learning modes is reflected as a learning-related increase in the preparatory activity of the M1. To test this hypothesis, we evaluated the preparatory activity during the learning of sequential finger-tapping with the non-dominant left hand using a 7T functional MRI. Participants alternated between performing a tapping sequence as quickly as possible (maximum mode) or at a constant speed of 2 Hz paced by a sequence-specifying visual cue (constant mode). We found a training-related increase in preparatory activity in the network covering the bilateral anterior intraparietal sulcus and inferior parietal lobule extending to the right M1 during the maximum mode and the right M1 during the constant mode. These findings indicate that the M1, as the last effector of the motor output, integrates the motor engram distributed through the networks despite training mode differences.