Pathophysiological changes in the spinal cord white and grey matter resulting from injury can be observed with MRI techniques. These techniques provide sensitive markers of macrostructural and ...microstructural tissue integrity, which correlate with histological findings. Spinal cord MRI findings in traumatic spinal cord injury (tSCI) and nontraumatic spinal cord injury - the most common form of which is degenerative cervical myelopathy (DCM) - have provided important insights into the pathophysiological processes taking place not just at the focal injury site but also rostral and caudal to the spinal injury. Although tSCI and DCM have different aetiologies, they show similar degrees of spinal cord pathology remote from the injury site, suggesting the involvement of similar secondary degenerative mechanisms. Advanced quantitative MRI protocols that are sensitive to spinal cord pathology have the potential to improve diagnosis and, more importantly, predict outcomes in patients with tSCI or nontraumatic spinal cord injury. This Review describes the insights into tSCI and DCM that have been revealed by neuroimaging and outlines current activities and future directions for the field.
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.
Display omitted
Conduction velocity is the speed at which electrical signals travel along axons and is a crucial determinant of neural communication. Inferences about conduction velocity can now be made in vivo in ...humans using a measure called the magnetic resonance (MR) g-ratio. This is the ratio of the inner axon diameter relative to that of the axon plus the myelin sheath that encases it. Here, in the first application to cognition, we found that variations in MR g-ratio, and by inference conduction velocity, of the parahippocampal cingulum bundle were associated with autobiographical memory recall ability in 217 healthy adults. This tract connects the hippocampus with a range of other brain areas. We further observed that the association seemed to be with inner axon diameter rather than myelin content. The extent to which neurites were coherently organised within the parahippocampal cingulum bundle was also linked with autobiographical memory recall ability. Moreover, these findings were specific to autobiographical memory recall and were not apparent for laboratory-based memory tests. Our results offer a new perspective on individual differences in autobiographical memory recall ability, highlighting the possible influence of specific white matter microstructure features on conduction velocity when recalling detailed memories of real-life past experiences.
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.
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
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.
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.
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.
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.
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.
The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction ...velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration.
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.
Display omitted
•A pipeline for automated estimation of ensemble mean axon radii at MRI voxel scale•Incorporation of 4 human white matter brain tissue samples for training and testing•Accurate mapping of the ...MRI-visible radius in the presence of staining heterogeneity•Includes 2 to 4 orders of magnitude more axons than the current gold standard (EM)•The pipeline enables validation of biophysical, MRI-based radius estimation models
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.
PDF
