Fiber-orientation independent component of R2* obtained from single-orientation MRI measurements in simulations and a post-mortem human optic chiasm.

The effective transverse relaxation rate (R2*) is sensitive to the microstructure of the human brain like the g-ratio which characterises the relative myelination of axons. However, the fibre-orientation dependence of R2* degrades its reproducibility and any microstructural derivative measure. To estimate its orientation-independent part (R2,iso*) from single multi-echo gradient-recalled-echo (meGRE) measurements at arbitrary orientations, a second-order polynomial in time model (hereafter M2) can be used. Its linear time-dependent parameter, ß1, can be biophysically related to R2,iso* when neglecting the myelin water (MW) signal in the hollow cylinder fibre model (HCFM). Here, we examined the performance of M2 using experimental and simulated data with variable g-ratio and fibre dispersion. We found that the fitted ß1 can estimate R2,iso* using meGRE with long maximum-echo time (TEmax ≈ 54 ms), but not accurately captures its microscopic dependence on the g-ratio (error 84%). We proposed a new heuristic expression for ß1 that reduced the error to 12% for ex vivo compartmental R2 values. Using the new expression, we could estimate an MW fraction of 0.14 for fibres with negligible dispersion in a fixed human optic chiasm for the ex vivo compartmental R2 values but not for the in vivo values. M2 and the HCFM-based simulations failed to explain the measured R2*-orientation-dependence around the magic angle for a typical in vivo meGRE protocol (with TEmax ≈ 18 ms). In conclusion, further validation and the development of movement-robust in vivo meGRE protocols with TEmax ≈ 54 ms are required before M2 can be used to estimate R2,iso* in subjects.

High-resolution quantitative and functional MRI indicate lower myelination of thin and thick stripes in human secondary visual cortex.

The characterization of cortical myelination is essential for the study of structure-function relationships in the human brain. However, knowledge about cortical myelination is largely based on post-mortem histology, which generally renders direct comparison to function impossible. The repeating pattern of pale-thin-pale-thick stripes of cytochrome oxidase (CO) activity in the primate secondary visual cortex (V2) is a prominent columnar system, in which histology also indicates different myelination of thin/thick versus pale stripes. We used quantitative magnetic resonance imaging (qMRI) in conjunction with functional magnetic resonance imaging (fMRI) at ultra-high field strength (7 T) to localize and study myelination of stripes in four human participants at sub-millimeter resolution in vivo. Thin and thick stripes were functionally localized by exploiting their sensitivity to color and binocular disparity, respectively. Resulting functional activation maps showed robust stripe patterns in V2 which enabled further comparison of quantitative relaxation parameters between stripe types. Thereby, we found lower longitudinal relaxation rates (R1) of thin and thick stripes compared to surrounding gray matter in the order of 1-2%, indicating higher myelination of pale stripes. No consistent differences were found for effective transverse relaxation rates (R2*). The study demonstrates the feasibility to investigate structure-function relationships in living humans within one cortical area at the level of columnar systems using qMRI.

Quantitative MRI maps of human neocortex explored using cell type-specific gene expression analysis.

Quantitative magnetic resonance imaging (qMRI) allows extraction of reproducible and robust parameter maps. However, the connection to underlying biological substrates remains murky, especially in the complex, densely packed cortex. We investigated associations in human neocortex between qMRI parameters and neocortical cell types by comparing the spatial distribution of the qMRI parameters longitudinal relaxation rate (${R_{1}}$), effective transverse relaxation rate (${R_{2}}^{\ast }$), and magnetization transfer saturation (MTsat) to gene expression from the Allen Human Brain Atlas, then combining this with lists of genes enriched in specific cell types found in the human brain. As qMRI parameters are magnetic field strength-dependent, the analysis was performed on MRI data at 3T and 7T. All qMRI parameters significantly covaried with genes enriched in GABA- and glutamatergic neurons, i.e. they were associated with cytoarchitecture. The qMRI parameters also significantly covaried with the distribution of genes enriched in astrocytes (${R_{2}}^{\ast }$ at 3T, ${R_{1}}$ at 7T), endothelial cells (${R_{1}}$ and MTsat at 3T), microglia (${R_{1}}$ and MTsat at 3T, ${R_{1}}$ at 7T), and oligodendrocytes and oligodendrocyte precursor cells (${R_{1}}$ at 7T). These results advance the potential use of qMRI parameters as biomarkers for specific cell types.

B 1 + $$ {B}_1

PURPOSE: Magnetization transfer saturation ( MTsat $$ \mathrm{MTsat} $$ ) is a useful marker to probe tissue macromolecular content and myelination in the brain. The increased B 1 + $$ {B}_1^{+} $$ -inhomogeneity at ≥ 7 $$ \ge 7 $$ T and significantly larger saturation pulse flip angles which are often used for postmortem studies exceed the limits where previous MTsat $$ \mathrm{MTsat} $$ B 1 + $$ {B}_1^{+} $$ correction methods are applicable. Here, we develop a calibration-based correction model and procedure, and validate and evaluate it in postmortem 7T data of whole chimpanzee brains. THEORY: The B 1 + $$ {B}_1^{+} $$ dependence of MTsat $$ \mathrm{MTsat} $$ was investigated by varying the off-resonance saturation pulse flip angle. For the range of saturation pulse flip angles applied in typical experiments on postmortem tissue, the dependence was close to linear. A linear model with a single calibration constant C $$ C $$ is proposed to correct bias in MTsat $$ \mathrm{MTsat} $$ by mapping it to the reference value of the saturation pulse flip angle. METHODS: C $$ C $$ was estimated voxel-wise in five postmortem chimpanzee brains. "Individual-based global parameters" were obtained by calculating the mean C $$ C $$ within individual specimen brains and "group-based global parameters" by calculating the means of the individual-based global parameters across the five brains. RESULTS: The linear calibration model described the data well, though C $$ C $$ was not entirely independent of the underlying tissue and B 1 + $$ {B}_1^{+} $$ . Individual-based correction parameters and a group-based global correction parameter ( C = 1 . 2 $$ C=1.2 $$ ) led to visible, quantifiable reductions of B 1 + $$ {B}_1^{+} $$ -biases in high-resolution MTsat $$ \mathrm{MTsat} $$ maps. CONCLUSION: The presented model and calibration approach effectively corrects for B 1 + $$ {B}_1^{+} $$ inhomogeneities in postmortem 7T data.

Brain structure and function: a multidisciplinary pipeline to study hominoid brain evolution.

To decipher the evolution of the hominoid brain and its functions, it is essential to conduct comparative studies in primates, including our closest living relatives. However, strong ethical concerns preclude in vivo neuroimaging of great apes. We propose a responsible and multidisciplinary alternative approach that links behavior to brain anatomy in non-human primates from diverse ecological backgrounds. The brains of primates observed in the wild or in captivity are extracted and fixed shortly after natural death, and then studied using advanced MRI neuroimaging and histology to reveal macro- and microstructures. By linking detailed neuroanatomy with observed behavior within and across primate species, our approach provides new perspectives on brain evolution. Combined with endocranial brain imprints extracted from computed tomographic scans of the skulls these data provide a framework for decoding evolutionary changes in hominin fossils. This approach is poised to become a key resource for investigating the evolution and functional differentiation of hominoid brains.

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

Combining navigator and optical prospective motion correction for high-quality 500 µm resolution quantitative multi-parameter mapping at 7T.

PURPOSE: High-resolution quantitative multi-parameter mapping shows promise for non-invasively characterizing human brain microstructure but is limited by physiological artifacts. We implemented corrections for rigid head movement and respiration-related B0-fluctuations and evaluated them in healthy volunteers and dementia patients. METHODS: Camera-based optical prospective motion correction (PMC) and FID navigator correction were implemented in a gradient and RF-spoiled multi-echo 3D gradient echo sequence for mapping proton density (PD), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*). We studied their effectiveness separately and in concert in young volunteers and then evaluated the navigator correction (NAVcor) with PMC in a group of elderly volunteers and dementia patients. We used spatial homogeneity within white matter (WM) and gray matter (GM) and scan-rescan measures as quality metrics. RESULTS: NAVcor and PMC reduced artifacts and improved the homogeneity and reproducibility of parameter maps. In elderly participants, NAVcor improved scan-rescan reproducibility of parameter maps (coefficient of variation decreased by 14.7% and 11.9% within WM and GM respectively). Spurious inhomogeneities within WM were reduced more in the elderly than in the young cohort (by 9% vs. 2%). PMC increased regional GM/WM contrast and was especially important in the elderly cohort, which moved twice as much as the young cohort. We did not find a significant interaction between the two corrections. CONCLUSION: Navigator correction and PMC significantly improved the quality of PD, R1, and R2* maps, particularly in less compliant elderly volunteers and dementia patients.

The traveling heads 2.0: Multicenter reproducibility of quantitative imaging methods at 7 Tesla.

OBJECT: This study evaluates inter-site and intra-site reproducibility at ten different 7 T sites for quantitative brain imaging. MATERIAL AND METHODS: Two subjects - termed the "traveling heads" - were imaged at ten different 7 T sites with a harmonized quantitative brain MR imaging protocol. In conjunction with the system calibration, MP2RAGE, QSM, CEST and multi-parametric mapping/relaxometry were examined. RESULTS: Quantitative measurements with MP2RAGE showed very high reproducibility across sites and subjects, and errors were in concordance with previous results and other field strengths. QSM had high inter-site reproducibility for relevant subcortical volumes. CEST imaging revealed systematic differences between the sites, but reproducibility was comparable to results in the literature. Relaxometry had also very high agreement between sites, but due to the high sensitivity, differences caused by different applications of the B1 calibration of the two RF coil types used were observed. CONCLUSION: Our results show that quantitative brain imaging can be performed with high reproducibility at 7 T and with similar reliability as found at 3 T for multicenter studies of the supratentorial brain.

Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography.

Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI-facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude-was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1-V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.

Biophysically motivated efficient estimation of the spatially isotropic R2* component from a single gradient-recalled echo measurement.

PURPOSE: To propose and validate an efficient method, based on a biophysically motivated signal model, for removing the orientation-dependent part of R2* using a single gradient-recalled echo (GRE) measurement. METHODS: The proposed method utilized a temporal second-order approximation of the hollow-cylinder-fiber model, in which the parameter describing the linear signal decay corresponded to the orientation-independent part of R2* . The estimated parameters were compared to the classical, mono-exponential decay model for R2* in a sample of an ex vivo human optic chiasm (OC). The OC was measured at 16 distinct orientations relative to the external magnetic field using GRE at 7T. To show that the proposed signal model can remove the orientation dependence of R2* , it was compared to the established phenomenological method for separating R2* into orientation-dependent and -independent parts. RESULTS: Using the phenomenological method on the classical signal model, the well-known separation of R2* into orientation-dependent and -independent parts was verified. For the proposed model, no significant orientation dependence in the linear signal decay parameter was observed. CONCLUSIONS: Since the proposed second-order model features orientation-dependent and -independent components at distinct temporal orders, it can be used to remove the orientation dependence of R2* using only a single GRE measurement.

NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter.

The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.

In vivo field-cycling relaxometry using an insert coil for magnetic field offset.

PURPOSE: The T(1) of tissue has a strong dependence on the measurement magnetic field strength. T(1) -dispersion could be a useful contrast parameter, but is unavailable to clinical MR systems which operate at fixed magnetic field strength. The purpose of this work was to implement a removable insert magnet coil for field-cycling T(1) -dispersion measurements on a vertical-field MRI scanner, by offsetting the static field over a volume of interest. METHODS: An insert magnet coil was constructed for use with a whole-body sized 59 milli-Tesla (mT) vertical-field, permanent-magnet based imager. The coil has diameter 38 cm and thickness 6.1 cm and a homogeneous region (± 5%) of 5 cm DSV, offset by 5 cm from the coil surface. Surface radiofrequency (RF) coils were also constructed. RESULTS: The insert coil was used in conjunction with a surface RF coil and a volume-localized inversion-recovery pulse sequence to plot T(1) -dispersion in a human volunteer's forearm over a range of field strengths from 1 mT to 70 mT. CONCLUSION: T(1) -dispersion measurements were demonstrated on a fixed-field MRI scanner, using an insert coil. This demonstrates the feasibility of relaxation dispersion measurements on an otherwise conventional MR imager, facilitating the exploitation of T(1) -dispersion contrast for enhanced diagnosis.

Rapid multi-field T(1) estimation algorithm for Fast Field-Cycling MRI.

Fast Field-Cycling MRI (FFC-MRI) is an emerging MRI technique that allows the main magnetic field to vary, allowing probing T1 at various magnetic field strengths. This technique offers promising possibilities but requires long scan times to improve the signal-to-noise ratio. This paper presents an algorithm derived from the two-point method proposed by Edelstein that can estimate T1 using only one image per field, thereby shortening the scan time by a factor of nearly two, taking advantage of the fact that the equilibrium magnetisation is proportional to the magnetic field strength. Therefore the equilibrium magnetisation only needs measuring once, then T1 can be found from inversion recovery experiments using the Bloch equations. The precision and accuracy of the algorithm are estimated using both simulated and experimental data, by Monte-Carlo simulations and by comparison with standard techniques on a phantom. The results are acceptable but usage is limited to the case where variations of the main magnetic field are fast compared with T1 and where the dispersion curve is relatively linear. The speed-up of T1-dispersion measurements resulting from the new method is likely to make FFC-MRI more acceptable when it is applied in the clinic.

Field-cycling NMR relaxometry with spatial selection.

Fast field-cycling MRI offers access to sources of endogenous information not available from conventional fixed-field imagers. One example is the T(1) dispersion curve: a plot of T(1) versus field strength. We present a pulse sequence that combines saturation-recovery/inversion-recovery T(1) determination with field cycling and point-resolved spectroscopy localization, enabling the measurement of dispersion curves from volumes selected from a pilot image. Compared with a nonselective sequence, our method of volume selection does not influence measurement accuracy, even for relatively long echo times and in the presence of radiofrequency field nonuniformity. The measured voxel profile, while not ideal, corresponds with that expected from the image slice profile. On a whole-body fast field-cycling scanner with 59-mT detection, the sensitivity of the experiment is sufficient to reveal distinctive "quadrupole dips" in dispersion curves of protein-rich human tissue in vivo.