Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates.

PURPOSE: The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation. METHODS: Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model. RESULTS: The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects. CONCLUSION: For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.

Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy.

Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction ( f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction ( f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation of f AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics of f A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmental T 2 and can substantially enhance the comparability between EM- and DWI-based metrics of f A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-based f A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.

Axisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study.

PURPOSE: To compare the estimation accuracy of axisymmetric diffusion kurtosis imaging (DKI) and standard DKI in combination with Rician bias correction (RBC). METHODS: Axisymmetric DKI is more robust against noise-induced variation in the measured signal than standard DKI because of its reduced parameter space. However, its susceptibility to Rician noise bias at low signal-to-noise ratios (SNR) is unknown. Here, we investigate two main questions: first, does RBC improve estimation accuracy of axisymmetric DKI?; second, is estimation accuracy of axisymmetric DKI increased compared to standard DKI? Estimation accuracy was investigated on the five axisymmetric DKI tensor metrics (AxTM): the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor, using a noise simulation study based on synthetic data of tissues with varying fiber alignment and in-vivo data focusing on white matter. RESULTS: RBC mainly increased accuracy for the parallel AxTM in tissues with highly to moderately aligned fibers. For the perpendicular AxTM, axisymmetric DKI without RBC performed slightly better than with RBC. However, the combination of axisymmetric DKI with RBC was the overall best performing algorithm across all five AxTM in white matter and axisymmetric DKI itself substantially improved accuracy in axisymmetric tissues with low fiber alignment. CONCLUSION: Combining axisymmetric DKI with RBC facilitates accurate DKI parameter estimation at unprecedented low SNRs ( ≈ 15 $$ \approx 15 $$ ) in white matter, possibly making it a valuable tool for neuroscience and clinical research studies where scan time is a limited resource. The tools used here are available in the open-source ACID toolbox for SPM.

Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling.

PURPOSE: The effective transverse relaxation rate ( R 2 * $$ {\mathrm{R}}_2^{\ast } $$ ) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fiber orientation with respect to the main field ( θ $$ \uptheta $$ ) complicate interpretation. The α- and θ $$ \uptheta $$ -dependence stem from the existence of multiple sub-voxel micro-environments (e.g., myelin and non-myelin water compartments). Ordinarily, it is challenging to quantify these sub-compartments; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a single R 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartment R 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. METHODS: We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartment R 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. Based on our numeric observations, we introduced a linear model that partitions R 2 * $$ {\mathrm{R}}_2^{\ast } $$ into α-dependent and α-independent components and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence, and reproducibility empirically. RESULTS: R 2 * $$ {\mathrm{R}}_2^{\ast } $$ increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although it suffered from marginally higher noise sensitivity. CONCLUSION: We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartment R 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

Finding the best clearing approach - Towards 3D wide-scale multimodal imaging of aged human brain tissue.

The accessibility of new wide-scale multimodal imaging techniques led to numerous clearing techniques emerging over the last decade. However, clearing mesoscopic-sized blocks of aged human brain tissue remains an extremely challenging task. Homogenizing refractive indices and reducing light absorption and scattering are the foundation of tissue clearing. Due to its dense and highly myelinated nature, especially in white matter, the human brain poses particular challenges to clearing techniques. Here, we present a comparative study of seven tissue clearing approaches and their impact on aged human brain tissue blocks (> 5 mm). The goal was to identify the most practical and efficient method in regards to macroscopic transparency, brief clearing time, compatibility with immunohistochemical processing and wide-scale multimodal microscopic imaging. We successfully cleared 26 × 26 × 5 mm3-sized human brain samples with two hydrophilic and two hydrophobic clearing techniques. Optical properties as well as light and antibody penetration depths highly vary between these methods. In addition to finding the best clearing approach, we compared three microscopic imaging setups (the Zeiss Laser Scanning Microscope (LSM) 880 , the Miltenyi Biotec Ultramicroscope ll (UM ll) and the 3i Marianas LightSheet microscope) regarding optimal imaging of large-scale tissue samples. We demonstrate that combining the CLARITY technique (Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging compatible Tissue hYdrogel) with the Zeiss LSM 880 and combining the iDISCO technique (immunolabeling-enabled three-dimensional imaging of solvent-cleared organs) with the Miltenyi Biotec UM ll are the most practical and efficient approaches to sufficiently clear aged human brain tissue and generate 3D microscopic images. Our results point out challenges that arise from seven clearing and three imaging techniques applied to non-standardized tissue samples such as aged human brain tissue.

Towards a representative reference for MRI-based human axon radius assessment using light microscopy.

Non-invasive assessment of axon radii via MRI bears great potential for clinical and neuroscience research as it is a main determinant of the neuronal conduction velocity. However, there is a lack of representative histological reference data at the scale of the cross-section of MRI voxels for validating the MRI-visible, effective radius (reff). Because the current gold standard stems from neuroanatomical studies designed to estimate the bulk-determined arithmetic mean radius (rarith) on small ensembles of axons, it is unsuited to estimate the tail-weighted reff. We propose CNN-based segmentation on high-resolution, large-scale light microscopy (lsLM) data to generate a representative reference for reff. In a human corpus callosum, we assessed estimation accuracy and bias of rarith and reff. Furthermore, we investigated whether mapping anatomy-related variation of rarith and reff is confounded by low-frequency variation of the image intensity, e.g., due to staining heterogeneity. Finally, we analyzed the error due to outstandingly large axons in reff. Compared to rarith, reff was estimated with higher accuracy (maximum normalized-root-mean-square-error of reff: 8.5 %; rarith: 19.5 %) and lower bias (maximum absolute normalized-mean-bias-error of reff: 4.8 %; rarith: 13.4 %). While rarith was confounded by variation of the image intensity, variation of reff seemed anatomy-related. The largest axons contributed between 0.8 % and 2.9 % to reff. In conclusion, the proposed method is a step towards representatively estimating reff at MRI voxel resolution. Further investigations are required to assess generalization to other brains and brain areas with different axon radii distributions.

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.

Longitudinal changes of spinal cord grey and white matter following spinal cord injury.

OBJECTIVES: Traumatic and non-traumatic spinal cord injury produce neurodegeneration across the entire neuraxis. However, the spatiotemporal dynamics of spinal cord grey and white matter neurodegeneration above and below the injury is understudied. METHODS: We acquired longitudinal data from 13 traumatic and 3 non-traumatic spinal cord injury patients (8-8 cervical and thoracic cord injuries) within 1.5 years after injury and 10 healthy controls over the same period. The protocol encompassed structural and diffusion-weighted MRI rostral (C2/C3) and caudal (lumbar enlargement) to the injury level to track tissue-specific neurodegeneration. Regression models assessed group differences in the temporal evolution of tissue-specific changes and associations with clinical outcomes. RESULTS: At 2 months post-injury, white matter area was decreased by 8.5% and grey matter by 15.9% in the lumbar enlargement, while at C2/C3 only white matter was decreased (-9.7%). Patients had decreased cervical fractional anisotropy (FA: -11.3%) and increased radial diffusivity (+20.5%) in the dorsal column, while FA was lower in the lateral (-10.3%) and ventral columns (-9.7%) of the lumbar enlargement. White matter decreased by 0.34% and 0.35% per month at C2/C3 and lumbar enlargement, respectively, and grey matter decreased at C2/C3 by 0.70% per month. CONCLUSIONS: This study describes the spatiotemporal dynamics of tissue-specific spinal cord neurodegeneration above and below a spinal cord injury. While above the injury, grey matter atrophy lagged initially behind white matter neurodegeneration, in the lumbar enlargement these processes progressed in parallel. Tracking trajectories of tissue-specific neurodegeneration provides valuable assessment tools for monitoring recovery and treatment effects.

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research.

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

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.

Dynamic changes in white matter microstructure in anorexia nervosa: findings from a longitudinal study.

BACKGROUND: Gray matter (GM) 'pseudoatrophy' is well-documented in patients with anorexia nervosa (AN), but changes in white matter (WM) are less well understood. Here we investigated the dynamics of microstructural WM brain changes in AN patients during short-term weight restoration in a combined longitudinal and cross-sectional study design. METHODS: Diffusion-weighted images were acquired in young AN patients before (acAN-Tp1, n = 56) and after (acAN-Tp2, n = 44) short-term weight restoration as well as in age-matched healthy controls (HC, n = 60). Images were processed using Tract-Based-Spatial-Statistics to compare fractional anisotropy (FA) across groups and timepoints. RESULTS: In the cross-sectional comparison, FA was significantly reduced in the callosal body in acAN-Tp1 compared with HC, while no differences were found between acAN-Tp2 and HC. In the longitudinal arm, FA increased with weight gain in acAN-Tp2 relative to acAN-Tp1 in large parts of the callosal body and the fornix, while it decreased in the right corticospinal tract. CONCLUSIONS: Our findings reveal that dynamic, bidirectional changes in WM microstructure in young underweight patients with AN can be reversed with brief weight restoration therapy. These results parallel those previously observed in GM and suggest that alterations in WM in non-chronic AN are also state-dependent and rapidly reversible with successful intervention.

Microstructural imaging of human neocortex in vivo.

The neocortex of the human brain is the seat of higher brain function. Modern imaging techniques, chief among them magnetic resonance imaging (MRI), allow non-invasive imaging of this important structure. Knowledge of the microstructure of the neocortex has classically come from post-mortem histological studies of human tissue, and extrapolations from invasive animal studies. From these studies, we know that the scale of important neocortical structure spans six orders of magnitude, ranging from the size of axonal diameters (microns), to the size of cortical areas responsible for integrating sensory information (centimetres). MRI presents an opportunity to move beyond classical methods, because MRI is non-invasive and MRI contrast is sensitive to neocortical microstructure over all these length scales. MRI thus allows inferences to be made about neocortical microstructure in vivo, i.e. MRI-based in vivo histology. We review recent literature that has applied and developed MRI-based in vivo histology to probe the microstructure of the human neocortex, focusing specifically on myelin, iron, and neuronal fibre mapping. We find that applications such as cortical parcellation (using [Formula: see text] maps as proxies for myelin content) and investigation of cortical iron deposition with age (using [Formula: see text] maps) are already contributing to the frontiers of knowledge in neuroscience. Neuronal fibre mapping in the cortex remains challenging in vivo, but recent improvements in diffusion MRI hold promise for exciting applications in the near future. The literature also suggests that utilising multiple complementary quantitative MRI maps could increase the specificity of inferences about neocortical microstructure relative to contemporary techniques, but that further investment in modelling is required to appropriately combine the maps. In vivo histology of human neocortical microstructure is undergoing rapid development. Future developments will improve its specificity, sensitivity, and clinical applicability, granting an ever greater ability to investigate neuroscientific and clinical questions about the human neocortex.

Four in vivo g-ratio-weighted imaging methods: Comparability and repeatability at the group level.

A recent method, denoted in vivo g-ratio-weighted imaging, has related the microscopic g-ratio, only accessible by ex vivo histology, to noninvasive MRI markers for the fiber volume fraction (FVF) and myelin volume fraction (MVF). Different MRI markers have been proposed for g-ratio weighted imaging, leaving open the question which combination of imaging markers is optimal. To address this question, the repeatability and comparability of four g-ratio methods based on different combinations of, respectively, two imaging markers for FVF (tract-fiber density, TFD, and neurite orientation dispersion and density imaging, NODDI) and two imaging markers for MVF (magnetization transfer saturation rate, MT, and, from proton density maps, macromolecular tissue volume, MTV) were tested in a scan-rescan experiment in two groups. Moreover, it was tested how the repeatability and comparability were affected by two key processing steps, namely the masking of unreliable voxels (e.g., due to partial volume effects) at the group level and the calibration value used to link MRI markers to MVF (and FVF). Our data showed that repeatability and comparability depend largely on the marker for the FVF (NODDI outperformed TFD), and that they were improved by masking. Overall, the g-ratio method based on NODDI and MT showed the highest repeatability (90%) and lowest variability between groups (3.5%). Finally, our results indicate that the calibration procedure is crucial, for example, calibration to a lower g-ratio value (g = 0.6) than the commonly used one (g = 0.7) can change not only repeatability and comparability but also the reported dependency on the FVF imaging marker. Hum Brain Mapp 39:24-41, 2018. © 2017 Wiley Periodicals, Inc.

The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI.

Diffusion tensor imaging (DTI) is a promising approach for investigating the white matter microstructure of the spinal cord. However, it suffers from severe susceptibility, physiological, and instrumental artifacts present in the cord. Retrospective correction techniques are popular approaches to reduce these artifacts, because they are widely applicable and do not increase scan time. In this paper, we present a novel outlier rejection approach (reliability masking) which is designed to supplement existing correction approaches by excluding irreversibly corrupted and thus unreliable data points from the DTI index maps. Then, we investigate how chains of retrospective correction techniques including (i) registration, (ii) registration and robust fitting, and (iii) registration, robust fitting, and reliability masking affect the statistical power of a previously reported finding of lower fractional anisotropy values in the posterior column and lateral corticospinal tracts in cervical spondylotic myelopathy (CSM) patients. While established post-processing steps had small effect on the statistical power of the clinical finding (slice-wise registration: -0.5%, robust fitting: +0.6%), adding reliability masking to the post-processing chain increased it by 4.7%. Interestingly, reliability masking and registration affected the t-score metric differently: while the gain in statistical power due to reliability masking was mainly driven by decreased variability in both groups, registration slightly increased variability. In conclusion, reliability masking is particularly attractive for neuroscience and clinical research studies, as it increases statistical power by reducing group variability and thus provides a cost-efficient alternative to increasing the group size.

Local striatal reward signals can be predicted from corticostriatal connectivity.

A defining feature of the basal ganglia is their anatomical organization into multiple cortico-striatal loops. A central tenet of this architecture is the idea that local striatal function is determined by its precise connectivity with cortex, creating a functional topography that is mirrored within cortex and striatum. Here we formally test this idea using both human anatomical and functional imaging, specifically asking whether within striatal subregions one can predict between-voxel differences in functional signals based on between-voxel differences in corticostriatal connectivity. We show that corticostriatal connectivity profiles predict local variation in reward signals in bilateral caudate nucleus and putamen, expected value signals in bilateral caudate nucleus, and response effector activity in bilateral putamen. These data reveal that, even within individual striatal regions, local variability in corticostriatal anatomical connectivity predicts functional differentiation.

Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study.

The blood oxygenation level-dependent (BOLD) signal is widely used for functional magnetic resonance imaging (fMRI) of brain function in health and disease. The statistical power of fMRI group studies is significantly hampered by high inter-subject variance due to differences in baseline vascular physiology. Several methods have been proposed to account for physiological vascularization differences between subjects and hence improve the sensitivity in group studies. However, these methods require the acquisition of additional reference scans (such as a full resting-state fMRI session or ASL-based calibrated BOLD). We present a vascular autorescaling (VasA) method, which does not require any additional reference scans. VasA is based on the observation that slow oscillations (<0.1Hz) in arterial blood CO2 levels occur naturally due to changes in respiration patterns. These oscillations yield fMRI signal changes whose amplitudes reflect the blood oxygenation levels and underlying local vascularization and vascular responsivity. VasA estimates proxies of the amplitude of these CO2-driven oscillations directly from the residuals of task-related fMRI data without the need for reference scans. The estimates are used to scale the amplitude of task-related fMRI responses, to account for vascular differences. The VasA maps compared well to cerebrovascular reactivity (CVR) maps and cerebral blood volume maps based on vascular space occupancy (VASO) measurements in four volunteers, speaking to the physiological vascular basis of VasA. VasA was validated in a wide variety of tasks in 138 volunteers. VasA increased t-scores by up to 30% in specific brain areas such as the visual cortex. The number of activated voxels was increased by up to 200% in brain areas such as the orbital frontal cortex while still controlling the nominal false-positive rate. VasA fMRI outperformed previously proposed rescaling approaches based on resting-state fMRI data and can be readily applied to any task-related fMRI data set, even retrospectively.

Synthetic quantitative MRI through relaxometry modelling.

Quantitative MRI (qMRI) provides standardized measures of specific physical parameters that are sensitive to the underlying tissue microstructure and are a first step towards achieving maps of biologically relevant metrics through in vivo histology using MRI. Recently proposed models have described the interdependence of qMRI parameters. Combining such models with the concept of image synthesis points towards a novel approach to synthetic qMRI, in which maps of fundamentally different physical properties are constructed through the use of biophysical models. In this study, the utility of synthetic qMRI is investigated within the context of a recently proposed linear relaxometry model. Two neuroimaging applications are considered. In the first, artefact-free quantitative maps are synthesized from motion-corrupted data by exploiting the over-determined nature of the relaxometry model and the fact that the artefact is inconsistent across the data. In the second application, a map of magnetization transfer (MT) saturation is synthesized without the need to acquire an MT-weighted volume, which directly leads to a reduction in the specific absorption rate of the acquisition. This feature would be particularly important for ultra-high field applications. The synthetic MT map is shown to provide improved segmentation of deep grey matter structures, relative to segmentation using T1 -weighted images or R1 maps. The proposed approach of synthetic qMRI shows promise for maximizing the extraction of high quality information related to tissue microstructure from qMRI protocols and furthering our understanding of the interrelation of these qMRI parameters.

Structure predicts function: combining non-invasive electrophysiology with in-vivo histology.

We present an approach for combining high resolution MRI-based myelin mapping with functional information from electroencephalography (EEG) or magnetoencephalography (MEG). The main contribution to the primary currents detectable with EEG and MEG comes from ionic currents in the apical dendrites of cortical pyramidal cells, aligned perpendicularly to the local cortical surface. We provide evidence from an in-vivo experiment that the variation in MRI-based myeloarchitecture measures across the cortex predicts the variation of the current density over individuals and thus is of functional relevance. Equivalent current dipole locations and moments due to pitch onset evoked response fields (ERFs) were estimated by means of a variational Bayesian algorithm. The myeloarchitecture was estimated indirectly from individual high resolution quantitative multi-parameter maps (MPMs) acquired at 800µm isotropic resolution. Myelin estimates across cortical areas correlated positively with dipole magnitude. This correlation was spatially specific: regions of interest in the auditory cortex provided significantly better models than those covering whole hemispheres. Based on the MPM data we identified the auditory cortical area TE1.2 as the most likely origin of the pitch ERFs measured by MEG. We can now proceed to exploit the higher spatial resolution of quantitative MPMs to identify the cortical origin of M/EEG signals, inform M/EEG source reconstruction and explore structure-function relationships at a fine structural level in the living human brain.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

A general linear relaxometry model of R1 using imaging data.

PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model.