A patient-friendly 16-channel transmit/64-channel receive coil array for combined head-neck MRI at 7 Tesla.

PURPOSE: To extend the coverage of brain coil arrays to the neck and cervical-spine region to enable combined head and neck imaging at 7 Tesla (T) ultra-high field MRI. METHODS: The coil array structures of a 64-channel receive coil and a 16-channel transmit coil were merged into one anatomically shaped close-fitting housing. Transmit characteristics were evaluated in a B1+ -field mapping study and an electromagnetic model. Receive SNR and the encoding capability for accelerated imaging were evaluated and compared with a commercially available 7 T brain array coil. The performance of the head-neck array coil was demonstrated in human volunteers using high-resolution accelerated imaging. RESULTS: In the brain, the SNR matches the commercially available 32-channel brain array and showed improvements in accelerated imaging capabilities. More importantly, the constructed coil array improved the SNR in the face area, neck area, and cervical spine by a factor of 1.5, 3.4, and 5.2, respectively, in regions not covered by 32-channel brain arrays at 7 T. The interelement coupling of the 16-channel transmit coil ranged from -14 to -44 dB (mean = -19 dB, adjacent elements <-18 dB). The parallel 16-channel transmit coil greatly facilitates B1+ field shaping required for large FOV neuroimaging at 7 T. CONCLUSION: This new head-neck array coil is the first demonstration of a device of this nature used for combined full-brain, head-neck, and cervical-spine imaging at 7 T. The array coil is well suited to provide large FOV images, which potentially improves ultrahigh field neuroimaging applications for clinical settings.

Coil-to-coil physiological noise correlations and their impact on functional MRI time-series signal-to-noise ratio.

PURPOSE: Physiological nuisance fluctuations ("physiological noise") are a major contribution to the time-series signal-to-noise ratio (tSNR) of functional imaging. While thermal noise correlations between array coil elements have a well-characterized effect on the image Signal to Noise Ratio (SNR0 ), the element-to-element covariance matrix of the time-series fluctuations has not yet been analyzed. We examine this effect with a goal of ultimately improving the combination of multichannel array data. THEORY AND METHODS: We extend the theoretical relationship between tSNR and SNR0 to include a time-series noise covariance matrix Ψt , distinct from the thermal noise covariance matrix Ψ0 , and compare its structure to Ψ0 and the signal coupling matrix SSH formed from the signal intensity vectors S. RESULTS: Inclusion of the measured time-series noise covariance matrix into the model relating tSNR and SNR0 improves the fit of experimental multichannel data and is shown to be distinct from Ψ0 or SSH . CONCLUSION: Time-series noise covariances in array coils are found to differ from Ψ0 and more surprisingly, from the signal coupling matrix SSH . Correct characterization of the time-series noise has implications for the analysis of time-series data and for improving the coil element combination process. Magn Reson Med 76:1708-1719, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex.

Diffusion tensor MRI is sensitive to the coherent structure of brain tissue and is commonly used to study large-scale white matter structure. Diffusion in gray matter is more isotropic, however, several groups have observed coherent patterns of diffusion anisotropy within the cerebral cortical gray matter. We extend the study of cortical diffusion anisotropy by relating it to the local coordinate system of the folded cerebral cortex. We use 1mm and sub-millimeter isotropic resolution diffusion imaging to perform a laminar analysis of the principal diffusion orientation, fractional anisotropy, mean diffusivity and partial volume effects. Data from 6 in vivo human subjects, a fixed human brain specimen and an anesthetized macaque were examined. Large regions of cortex show a radial diffusion orientation. In vivo human and macaque data displayed a sharp transition from radial to tangential diffusion orientation at the border between primary motor and somatosensory cortex, and some evidence of tangential diffusion in secondary somatosensory cortex and primary auditory cortex. Ex vivo diffusion imaging in a human tissue sample showed some tangential diffusion orientation in S1 but mostly radial diffusion orientations in both M1 and S1.

Roles of default-mode network and supplementary motor area in human vigilance performance: evidence from real-time fMRI.

We used real-time functional magnetic resonance imaging (fMRI) to determine which regions of the human brain have a role in vigilance as measured by reaction time (RT) to variably timed stimuli. We first identified brain regions where activation before stimulus presentation predicted RT. Slower RT was preceded by greater activation in the default-mode network, including lateral parietal, precuneus, and medial prefrontal cortices; faster RT was preceded by greater activation in the supplementary motor area (SMA). We examined the roles of these brain regions in vigilance by triggering trials based on brain states defined by blood oxygenation level-dependent activation measured using real-time fMRI. When activation of relevant neural systems indicated either a good brain state (increased activation of SMA) or a bad brain state (increased activation of lateral parietal cortex and precuneus) for performance, a target was presented and RT was measured. RTs on trials triggered by a good brain state were significantly faster than RTs on trials triggered by a bad brain state. Thus human performance was controlled by monitoring brain states that indicated high or low vigilance. These findings identify neural systems that have a role in vigilance and provide direct evidence that the default-mode network has a role in human performance. The ability to control and enhance human behavior based on brain state may have broad implications.

Atypical brain activation patterns during a face-to-face joint attention game in adults with autism spectrum disorder.

Joint attention behaviors include initiating one's own and responding to another's bid for joint attention to an object, person, or topic. Joint attention abilities in autism are pervasively atypical, correlate with development of language and social abilities, and discriminate children with autism from other developmental disorders. Despite the importance of these behaviors, the neural correlates of joint attention in individuals with autism remain unclear. This paucity of data is likely due to the inherent challenge of acquiring data during a real-time social interaction. We used a novel experimental set-up in which participants engaged with an experimenter in an interactive face-to-face joint attention game during fMRI data acquisition. Both initiating and responding to joint attention behaviors were examined as well as a solo attention (SA) control condition. Participants included adults with autism spectrum disorder (ASD) (n = 13), a mean age- and sex-matched neurotypical group (n = 14), and a separate group of neurotypical adults (n = 22). Significant differences were found between groups within social-cognitive brain regions, including dorsal medial prefrontal cortex (dMPFC) and right posterior superior temporal sulcus (pSTS), during the RJA as compared to SA conditions. Region-of-interest analyses revealed a lack of signal differentiation between joint attention and control conditions within left pSTS and dMPFC in individuals with ASD. Within the pSTS, this lack of differentiation was characterized by reduced activation during joint attention and relative hyper-activation during SA. These findings suggest a possible failure of developmental neural specialization within the STS and dMPFC to joint attention in ASD.

A 64-channel 3T array coil for accelerated brain MRI.

A 64-channel brain array coil was developed and compared to a 32-channel array constructed with the same coil former geometry to precisely isolate the benefit of the 2-fold increase in array coil elements. The constructed coils were developed for a standard clinical 3T MRI scanner and used a contoured head-shaped curved former around the occipital pole and tapered in at the neck to both improve sensitivity and patient comfort. Additionally, the design is a compact, split-former design intended for robust daily use. Signal-to-noise ratio and noise amplification (G-factor) for parallel imaging were quantitatively evaluated in human imaging and compared to a size and shape-matched 32-channel array coil. For unaccelerated imaging, the 64-channel array provided similar signal-to-noise ratio in the brain center to the 32-channel array and 1.3-fold more signal-to-noise ratio in the brain cortex. Reduced noise amplification during highly parallel imaging of the 64-channel array provided the ability to accelerate at approximately one unit higher at a given noise amplification compared to the sized-matched 32-channel array. For example, with a 4-fold acceleration rate, the central brain and cortical signal-to-noise ratio of the 64-channel array was 1.2- and 1.4-fold higher, respectively, compared to the 32-channel array. The characteristics of the coil are demonstrated in accelerated brain imaging.

7-T MRI of the spinal cord can detect lateral corticospinal tract abnormality in amyotrophic lateral sclerosis.

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting lower and upper motor neurons. Degeneration of the lateral corticospinal tract (CST) is a key finding in ALS cervical spinal cord autopsies. We hypothesized that in vivo ultra-high-field MRI of the cervical spinal cord can detect abnormality in the CST. METHODS: A patient with ALS (disease duration 23 months) and a healthy control were scanned at 7-T MRI using a 19-channel coil. Multi-echo T2*-weighted imaging was performed in the spinal cord, covering C2-C6. Cross-sectional resolution was 0.37 × 0.37 mm(2). RESULTS: We detected clear signal hyperintensity in both segments of the lateral CST in the ALS patient, which was significant when compared with the normal control subject (P < 10(-7)). CONCLUSION: We believe there are potential benefits of 7-T MRI for increased sensitivity and spatial accuracy in characterizing pathology in the spinal cord.

Brain basis of phonological awareness for spoken language in children and its disruption in dyslexia.

Phonological awareness, knowledge that speech is composed of syllables and phonemes, is critical for learning to read. Phonological awareness precedes and predicts successful transition from language to literacy, and weakness in phonological awareness is a leading cause of dyslexia, but the brain basis of phonological awareness for spoken language in children is unknown. We used functional magnetic resonance imaging to identify the neural correlates of phonological awareness using an auditory word-rhyming task in children who were typical readers or who had dyslexia (ages 7-13) and a younger group of kindergarteners (ages 5-6). Typically developing children, but not children with dyslexia, recruited left dorsolateral prefrontal cortex (DLPFC) when making explicit phonological judgments. Kindergarteners, who were matched to the older children with dyslexia on standardized tests of phonological awareness, also recruited left DLPFC. Left DLPFC may play a critical role in the development of phonological awareness for spoken language critical for reading and in the etiology of dyslexia.

When the brain is prepared to learn: enhancing human learning using real-time fMRI.

The rate of learning or memory formation varies over time for any individual, partly due to moment-to-moment fluctuation of brain state. Functional neuroimaging has revealed the neural correlates of learning and memory, but here we asked if neuroimaging can causally enhance human learning by detection of brain states that reveal when a person is prepared or not prepared to learn. The parahippocampal cortex (PHC) is essential for memory formation for scenes. Here, activation in PHC was monitored in real-time, and scene presentations were triggered when participants entered "good" or "bad" brain states for learning of novel scenes. Subsequent recognition memory was more accurate for scenes presented in "good" than "bad" brain states. These findings show that neuroimaging can identify in real-time brain states that enhance or depress learning and memory formation, and knowledge about such brain states may be useful for accelerating education and training. Further, the use of functional neuroimaging as a causal, rather than correlative, tool to study the human brain may open new insights into the neural basis of human cognition.

Physiological noise and signal-to-noise ratio in fMRI with multi-channel array coils.

Sensitivity in BOLD fMRI is characterized by the signal to noise ratio (SNR) of the time-series (tSNR), which contains fluctuations from thermal and physiological noise sources. Alteration of an acquisition parameter can affect the tSNR differently depending on the relative magnitude of the physiological and thermal noise, therefore knowledge of this ratio is essential for optimizing fMRI acquisitions. In this study, we compare image and time-series SNR from array coils at 3T with and without parallel imaging (GRAPPA) as a function of image resolution and acceleration. We use the "absolute unit" SNR method of Kellman and McVeigh to calculate the image SNR (SNR(0)) in a way that renders it comparable to tSNR, allowing determination of the thermal to physiological noise ratio, and the pseudo-multiple replica method to quantify the image noise alterations due to the GRAPPA reconstruction. The Kruger and Glover noise model, in which the physiological noise standard deviation is proportional to signal strength, was found to hold for the accelerated and non-accelerated array coil data. Thermal noise dominated the EPI time-series for medium to large voxel sizes for single-channel and 12-channel head coil configurations, but physiological noise dominated the 32-channel array acquisition even at 1 mm × 1mm × 3 mm resolution. At higher acceleration factors, image SNR is reduced and the time-series becomes increasingly thermal noise dominant. However, the tSNR reduction is smaller than the reduction in image SNR due to the presence of physiological noise.

Differential selectivity for dynamic versus static information in face-selective cortical regions.

Neuroimaging studies have identified multiple face-selective regions in human cortex but the functional division of labor between these regions is not yet clear. A central hypothesis, with some empirical support, is that face-selective regions in the superior temporal sulcus (STS) are particularly responsive to dynamic information in faces, whereas the fusiform face area (FFA) computes the static or invariant properties of faces. Here we directly tested this hypothesis by measuring the magnitude of response in each region to both dynamic and static stimuli. Consistent with the hypothesis, we found that the response to movies of faces was not significantly different from the response to static images of faces from these same movies in the right FFA and right occipital face area (OFA). By contrast the face-selective region in the right posterior STS (pSTS) responded nearly three times as strongly to dynamic faces as to static faces, and a face-selective region in the right anterior STS (aSTS) responded to dynamic faces only. Both of these regions also responded more strongly to moving faces than to moving bodies, indicating that they are preferentially engaged in processing dynamic information from faces, not in more general processing of any dynamic social stimuli. The response to dynamic and static faces was not significantly different in a third face-selective region in the posterior continuation of the STS (pcSTS). The strong selectivity of face-selective regions in the pSTS and aSTS, but not the FFA, OFA or pcSTS, for dynamic face information demonstrates a clear functional dissociation between different face-selective regions, and provides further clues into their function.

Associations and dissociations between default and self-reference networks in the human brain.

Neuroimaging has revealed consistent activations in medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) extending to precuneus both during explicit self-reference tasks and during rest, a period during which some form of self-reference is assumed to occur in the default mode of brain function. The similarity between these two patterns of midline cortical activation may reflect a common neural system for explicit and default-mode self-reference, but there is little direct evidence about the similarities and differences between the neural systems that mediate explicit self-reference versus default-mode self-reference during rest. In two experiments, we compared directly the brain regions activated by explicit self-reference during judgments about trait adjectives and by rest conditions relative to a semantic task without self-reference. Explicit self-reference preferentially engaged dorsal MPFC, rest preferentially engaged precuneus, and both self-reference and rest commonly engaged ventral MPFC and PCC. These findings indicate that there are both associations (shared components) and dissociations between the neural systems underlying explicit self-reference and the default mode of brain function.

Computing moment-to-moment BOLD activation for real-time neurofeedback.

Estimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method.

A 20-channel receive-only mouse array coil for a 3 T clinical MRI system.

A 20-channel phased-array coil for MRI of mice has been designed, constructed, and validated with bench measurements and high-resolution accelerated imaging. The technical challenges of designing a small, high density array have been overcome using individual small-diameter coil elements arranged on a cylinder in a hexagonal overlapping design with adjacent low impedance preamplifiers to further decouple the array elements. Signal-to-noise ratio (SNR) and noise amplification in accelerated imaging were simulated and quantitatively evaluated in phantoms and in vivo mouse images. Comparison between the 20-channel mouse array and a length-matched quadrature driven small animal birdcage coil showed an SNR increase at the periphery and in the center of the phantom of 3- and 1.3-fold, respectively. Comparison with a shorter but SNR-optimized birdcage coil (aspect ratio 1:1 and only half mouse coverage) showed an SNR gain of twofold at the edge of the phantom and similar SNR in the center. G-factor measurements indicate that the coil is well suited to acquire highly accelerated images.

Size-optimized 32-channel brain arrays for 3 T pediatric imaging.

Size-optimized 32-channel receive array coils were developed for five age groups, neonates, 6 months old, 1 year old, 4 years old, and 7 years old, and evaluated for pediatric brain imaging. The array consisted of overlapping circular surface coils laid out on a close-fitting coil-former. The two-section coil former design was obtained from surface contours of aligned three-dimensional MRI scans of each age group. Signal-to-noise ratio and noise amplification for parallel imaging were evaluated and compared to two coils routinely used for pediatric brain imaging; a commercially available 32-channel adult head coil and a pediatric-sized birdcage coil. Phantom measurements using the neonate, 6-month-old, 1-year-old, 4-year-old, and 7-year-old coils showed signal-to-noise ratio increases at all locations within the brain over the comparison coils. Within the brain cortex the five dedicated pediatric arrays increased signal-to-noise ratio by up to 3.6-, 3.0-, 2.6-, 2.3-, and 1.7-fold, respectively, compared to the 32-channel adult coil, as well as improved G-factor maps for accelerated imaging. This study suggests that a size-tailored approach can provide significant sensitivity gains for accelerated and unaccelerated pediatric brain imaging.

Ultra-high field (7T) functional magnetic resonance imaging in amyotrophic lateral sclerosis: a pilot study.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease of the central nervous system that results in a progressive loss of motor function and ultimately death. It is critical, yet also challenging, to develop non-invasive biomarkers to identify, localize, measure and/or track biological mechanisms implicated in ALS. Such biomarkers may also provide clues to identify potential molecular targets for future therapeutic trials. Herein we report on a pilot study involving twelve participants with ALS and nine age-matched healthy controls who underwent high-resolution resting state functional magnetic resonance imaging at an ultra-high field of 7 Tesla. A group-level whole-brain analysis revealed a disruption in long-range functional connectivity between the superior sensorimotor cortex (in the precentral gyrus) and bilateral cerebellar lobule VI. Post hoc analyses using atlas-derived left and right cerebellar lobule VI revealed decreased functional connectivity in ALS participants that predominantly mapped to bilateral postcentral and precentral gyri. Cerebellar lobule VI is a transition zone between anterior motor networks and posterior non-motor networks in the cerebellum, and is associated with a wide range of key functions including complex motor and cognitive processing tasks. Our observation of the involvement of cerebellar lobule VI adds to the growing number of studies implicating the cerebellum in ALS. Future avenues of scientific investigation should consider how high-resolution imaging at 7T may be leveraged to visualize differences in functional connectivity disturbances in various genotypes and phenotypes of ALS along the ALS-frontotemporal dementia spectrum.

Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age.

Understanding the neurophysiology of human cognitive development relies on methods that enable accurate comparison of structural and functional neuroimaging data across brains from people of different ages. A fundamental question is whether the substantial brain growth and related changes in brain morphology that occur in early childhood permit valid comparisons of brain structure and function across ages. Here we investigated whether valid comparisons can be made in children from ages 4 to 11, and whether there are differences in the use of volume-based versus surface-based registration approaches for aligning structural landmarks across these ages. Regions corresponding to the calcarine sulcus, central sulcus, and Sylvian fissure in both the hemispheres were manually labeled on T1-weighted structural magnetic resonance images from 31 children ranging in age from 4.2 to 11.2years old. Quantitative measures of shape similarity and volumetric-overlap of these manually labeled regions were calculated when brains were aligned using a 12-parameter affine transform, SPM's nonlinear normalization, a diffeomorphic registration (ANTS), and FreeSurfer's surface-based registration. Registration error for normalization into a common reference framework across participants in this age range was lower than commonly used functional imaging resolutions. Surface-based registration provided significantly better alignment of cortical landmarks than volume-based registration. In addition, registering children's brains to a common space does not result in an age-associated bias between older and younger children, making it feasible to accurately compare structural properties and patterns of brain activation in children from ages 4 to 11.

Locating the functional and anatomical boundaries of human primary visual cortex.

The primary visual cortex (V1) can be delineated both functionally by its topographic map of the visual field and anatomically by its distinct pattern of laminar myelination. Although it is commonly assumed that the specialized anatomy V1 exhibits corresponds in location with functionally defined V1, demonstrating this in human has not been possible thus far due to the difficulty of determining the location of V1 both functionally and anatomically in the same individual. In this study we use MRI to measure the anatomical and functional V1 boundaries in the same individual and demonstrate close agreement between them. Functional V1 location was measured by parcellating occipital cortex of 10 living humans into visual cortical areas based on the topographic map of the visual field measured using functional MRI. Anatomical V1 location was estimated for these same subjects using a surface-based probabilistic atlas derived from high-resolution structural MRI of the stria of Gennari in 10 intact ex vivo human hemispheres. To ensure that the atlas prediction was correct, it was validated against V1 location measured using an observer-independent cortical parcellation based on the laminar pattern of cell density in serial brain sections from 10 separate individuals. The close agreement between the independent anatomically and functionally derived V1 boundaries indicates that the whole extent of V1 can be accurately predicted based on cortical surface reconstructions computed from structural MRI scans, eliminating the need for functional localizers of V1. In addition, that the primary cortical folds predict the location of functional V1 suggests that the mechanism giving rise to V1 location is tied to the development of the cortical folds.

Resting-State Functional Connectivity of the Subthalamic Nucleus to Limbic, Associative, and Motor Networks.

The subthalamic nucleus (STN) is a small structure situated deep in the midbrain that exhibits wide-ranging functionality. In addition to its role in motor control, the STN is considered a hub for synchronizing aspects of emotion and cognition including attention, inhibitory control, motivation, and working memory. Evidence from neuroanatomical tracer studies suggests that the medial, ventromedial, and dorsolateral parts of the STN correspond to limbic, associative, and motor subdivisions, respectively. Although the extent of STN functional anatomical overlap remains unclear, blood oxygenation level dependent imaging of the STN may provide complementary information about the diverse functions of this structure. Methodological limitations in spatial and temporal resolutions, however, have prevented a comprehensive exploration of temporal correlations from the STN to the whole brain. In this study, we optimize spatial (2 mm isotropic) and temporal (TR = 1 s) resolutions to take full advantage of the time series signal-to-noise ratio capabilities of multichannel array coils and simultaneous multislice imaging. We interrogated STN seed-to-voxel resting-state functional MRI connectivity in a group of 30 healthy participants that included the whole brain at high-temporal and spatial resolutions. This analysis revealed STN functional connectivity to limbic, associative, and motor networks. Our findings contribute to the understanding of STN functional neuroanatomy in humans and are clinically relevant for ongoing research in deep brain stimulation.