Temporal dynamics of white and gray matter plasticity during motor skill acquisition: a comparative diffusion tensor imaging and multiparametric mapping analysis.

Learning new motor skills relies on neural plasticity within motor and limbic systems. This study uniquely combined diffusion tensor imaging and multiparametric mapping MRI to detail these neuroplasticity processes. We recruited 18 healthy male participants who underwent 960 min of training on a computer-based motion game, while 14 were scanned without training. Diffusion tensor imaging, which quantifies tissue microstructure by measuring the capacity for, and directionality of, water diffusion, revealed mostly linear changes in white matter across the corticospinal-cerebellar-thalamo-hippocampal circuit. These changes related to performance and reflected different responses to upper- and lower-limb training in brain areas with known somatotopic representations. Conversely, quantitative MRI metrics, sensitive to myelination and iron content, demonstrated mostly quadratic changes in gray matter related to performance and reflecting somatotopic representations within the same brain areas. Furthermore, while myelin and iron-sensitive multiparametric mapping MRI was able to describe time lags between different cortical brain systems, diffusion tensor imaging detected time lags within the white matter of the motor systems. These findings suggest that motor skill learning involves distinct phases of white and gray matter plasticity across the sensorimotor network, with the unique combination of diffusion tensor imaging and multiparametric mapping MRI providing complementary insights into the underlying neuroplastic responses.

Expectation Cues and False Percepts Generate Stimulus-Specific Activity in Distinct Layers of the Early Visual Cortex.

Perception has been proposed to result from the integration of feedforward sensory signals with internally generated feedback signals. Feedback signals are believed to play an important role in driving false percepts, that is, seeing things that are not actually there. Feedforward and feedback influences on perception can be studied using layer-specific fMRI, which we used here to interrogate neural activity underlying high-confidence false percepts while healthy human participants (N = 25, male and female) performed a perceptual orientation discrimination task. Auditory cues implicitly signaled the most likely upcoming orientation (referred to here as expectations). These expectations induced orientation-specific templates in the deep and superficial layers of V2, without affecting perception. In contrast, the orientation of falsely perceived stimuli with high confidence was reflected in the middle input layers of V2, suggesting a feedforward signal contributing to false percepts. The prevalence of high-confidence false percepts was related to everyday hallucination severity in a separate online sample (N = 100), suggesting a possible link with abnormal perceptual experiences. These results reveal a potential feedforward mechanism underlying false percepts, reflected by spontaneous stimulus-like activity in the input layers of the visual cortex, independent of top-down signals reflecting cued orientations.SIGNIFICANCE STATEMENT False percepts have been suggested to arise through excessive feedback signals. However, feedforward contributions to false percepts have remained largely understudied. Laminar fMRI has been shown to be useful in distinguishing feedforward from feedback activity as it allows the imaging of different cortical layers. In the present study we demonstrate that although cued orientations are encoded in the feedback layers of the visual cortex, the content of the false percepts are encoded in the feedforward layers and did not rely on these cued orientations. This shows that false percepts can in principle emerge from random feedforward signals in the visual cortex, with possible implications for disorders hallmarked by hallucinations like schizophrenia and Parkinson's disease.

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.

Coherent, time-shifted patterns of microstructural plasticity during motor-skill learning.

Motor skill learning relies on neural plasticity in the motor and limbic systems. However, the spatial and temporal characteristics of these changes-and their microstructural underpinnings-remain unclear. Eighteen healthy males received 1 h of training in a computer-based motion game, 4 times a week, for 4 consecutive weeks, while 14 untrained participants underwent scanning only. Performance improvements were observed in all trained participants. Serial myelin- and iron-sensitive multiparametric mapping at 3T during this period of intensive motor skill acquisition revealed temporally and spatially distributed, performance-related microstructural changes in the grey and white matter across a corticospinal-cerebellar-hippocampal circuit. Analysis of the trajectory of these transient changes suggested time-shifted cascades of plasticity from the dominant sensorimotor system to the contralateral hippocampus. In the cranial corticospinal tracts, changes in myelin-sensitive metrics during training in the posterior limb of the internal capsule were of greater magnitude in those who trained their upper limbs vs. lower limb trainees. Motor skill learning is associated with waves of grey and white matter plasticity, across a broad sensorimotor network.

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

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI.

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.

Prior expectations evoke stimulus-specific activity in the deep layers of the primary visual cortex.

The way we perceive the world is strongly influenced by our expectations. In line with this, much recent research has revealed that prior expectations strongly modulate sensory processing. However, the neural circuitry through which the brain integrates external sensory inputs with internal expectation signals remains unknown. In order to understand the computational architecture of the cortex, we need to investigate the way these signals flow through the cortical layers. This is crucial because the different cortical layers have distinct intra- and interregional connectivity patterns, and therefore determining which layers are involved in a cortical computation can inform us on the sources and targets of these signals. Here, we used ultra-high field (7T) functional magnetic resonance imaging (fMRI) to reveal that prior expectations evoke stimulus-specific activity selectively in the deep layers of the primary visual cortex (V1). These findings are in line with predictive processing theories proposing that neurons in the deep cortical layers represent perceptual hypotheses and thereby shed light on the computational architecture of cortex.

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.

Restoring statistical validity in group analyses of motion-corrupted MRI data.

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data-driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.

Correcting inter-scan motion artifacts in quantitative R1 mapping at 7T.

PURPOSE: Inter-scan motion is a substantial source of error in R1 estimation methods based on multiple volumes, for example, variable flip angle (VFA), and can be expected to increase at 7T where B1 fields are more inhomogeneous. The established correction scheme does not translate to 7T since it requires a body coil reference. Here we introduce two alternatives that outperform the established method. Since they compute relative sensitivities they do not require body coil images. THEORY: The proposed methods use coil-combined magnitude images to obtain the relative coil sensitivities. The first method efficiently computes the relative sensitivities via a simple ratio; the second by fitting a more sophisticated generative model. METHODS: R1 maps were computed using the VFA approach. Multiple datasets were acquired at 3T and 7T, with and without motion between the acquisition of the VFA volumes. R1 maps were constructed without correction, with the proposed corrections, and (at 3T) with the previously established correction scheme. The effect of the greater inhomogeneity in the transmit field at 7T was also explored by acquiring B1+ maps at each position. RESULTS: At 3T, the proposed methods outperform the baseline method. Inter-scan motion artifacts were also reduced at 7T. However, at 7T reproducibility only converged on that of the no motion condition if position-specific transmit field effects were also incorporated. CONCLUSION: The proposed methods simplify inter-scan motion correction of R1 maps and are applicable at both 3T and 7T, where a body coil is typically not available. The open-source code for all methods is made publicly available.

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.

Simultaneous voxel-wise analysis of brain and spinal cord morphometry and microstructure within the SPM framework.

To validate a simultaneous analysis tool for the brain and cervical cord embedded in the statistical parametric mapping (SPM) framework, we compared trauma-induced macro- and microstructural changes in spinal cord injury (SCI) patients to controls. The findings were compared with results obtained from existing processing tools that assess the brain and spinal cord separately. A probabilistic brain-spinal cord template (BSC) was generated using a generative semi-supervised modelling approach. The template was incorporated into the pre-processing pipeline of voxel-based morphometry and voxel-based quantification analyses in SPM. This approach was validated on T1-weighted scans and multiparameter maps, by assessing trauma-induced changes in SCI patients relative to controls and comparing the findings with the outcome from existing analytical tools. Consistency of the MRI measures was assessed using intraclass correlation coefficients (ICC). The SPM approach using the BSC template revealed trauma-induced changes across the sensorimotor system in the cord and brain in SCI patients. These changes were confirmed with established approaches covering brain or cord, separately. The ICC in the brain was high within regions of interest, such as the sensorimotor cortices, corticospinal tracts and thalamus. The simultaneous voxel-wise analysis of brain and cervical spinal cord was performed in a unique SPM-based framework incorporating pre-processing and statistical analysis in the same environment. Validation based on a SCI cohort demonstrated that the new processing approach based on the brain and cord is comparable to available processing tools, while offering the advantage of performing the analysis simultaneously across the neuraxis.

Imperfect spoiling in variable flip angle T1 mapping at 7T: Quantifying and minimizing impact.

PURPOSE: The variable flip angle (VFA) approach to T1 mapping assumes perfectly spoiled transverse magnetisation at the end of each repetition time (TR). Despite radiofrequency (RF) and gradient spoiling, this condition is rarely met, leading to erroneous T1 estimates ( T1app ). Theoretical corrections can be applied but make assumptions about tissue properties, for example, a global T2 time. Here, we investigate the effect of imperfect spoiling at 7T and the interaction between the RF and gradient spoiling conditions, additionally accounting for diffusion. We provide guidance on the optimal approach to maximise the accuracy of the T1 estimate in the context of 3D multi-echo acquisitions. METHODS: The impact of the spoiling regime was investigated through numerical simulations, phantom and invivo experiments. RESULTS: The predicted dependence of T1app on tissue properties, system settings, and spoiling conditions was observed in both phantom and in vivo experiments. Diffusion effects modulated the dependence of T1app on both B1+ efficiency and T2 times. CONCLUSION: Error in T1app can be minimized by using an RF spoiling increment and gradient spoiler moment combination that minimizes T2 -dependence and safeguards image quality. Although the diffusion effect was comparatively small at 7T, correction factors accounting for this effect are recommended.

Locus coeruleus integrity in old age is selectively related to memories linked with salient negative events.

The locus coeruleus (LC) is the principal origin of noradrenaline in the brain. LC integrity varies considerably across healthy older individuals, and is suggested to contribute to altered cognitive functions in aging. Here we test this hypothesis using an incidental memory task that is known to be susceptible to noradrenergic modulation. We used MRI neuromelanin (NM) imaging to assess LC structural integrity and pupillometry as a putative index of LC activation in both younger and older adults. We show that older adults with reduced structural LC integrity show poorer subsequent memory. This effect is more pronounced for emotionally negative events, in accord with a greater role for noradrenergic modulation in encoding salient or aversive events. In addition, we found that salient stimuli led to greater pupil diameters, consistent with increased LC activation during the encoding of such events. Our study presents novel evidence that a decrement in noradrenergic modulation impacts on specific components of cognition in healthy older adults. The findings provide a strong motivation for further investigation of the effects of altered LC integrity in pathological aging.

Interruptions of the FXN GAA Repeat Tract Delay the Age at Onset of Friedreich's Ataxia in a Location Dependent Manner.

Friedreich's ataxia (FRDA) is a comparatively rare autosomal recessive neurological disorder primarily caused by the homozygous expansion of a GAA trinucleotide repeat in intron 1 of the FXN gene. The repeat expansion causes gene silencing that results in deficiency of the frataxin protein leading to mitochondrial dysfunction, oxidative stress and cell death. The GAA repeat tract in some cases may be impure with sequence variations called interruptions. It has previously been observed that large interruptions of the GAA repeat tract, determined by abnormal MboII digestion, are very rare. Here we have used triplet repeat primed PCR (TP PCR) assays to identify small interruptions at the 5' and 3' ends of the GAA repeat tract through alterations in the electropherogram trace signal. We found that contrary to large interruptions, small interruptions are more common, with 3' interruptions being most frequent. Based on detection of interruptions by TP PCR assay, the patient cohort (n = 101) was stratified into four groups: 5' interruption, 3' interruption, both 5' and 3' interruptions or lacking interruption. Those patients with 3' interruptions were associated with shorter GAA1 repeat tracts and later ages at disease onset. The age at disease onset was modelled by a group-specific exponential decay model. Based on this modelling, a 3' interruption is predicted to delay disease onset by approximately 9 years relative to those lacking 5' and 3' interruptions. This highlights the key role of interruptions at the 3' end of the GAA repeat tract in modulating the disease phenotype and its impact on prognosis for the patient.

Does hippocampal volume explain performance differences on hippocampal-dependant tasks?

Marked disparities exist across healthy individuals in their ability to imagine scenes, recall autobiographical memories, think about the future and navigate in the world. The importance of the hippocampus in supporting these critical cognitive functions has prompted the question of whether differences in hippocampal grey matter volume could be one source of performance variability. Evidence to date has been somewhat mixed. In this study we sought to mitigate issues that commonly affect these types of studies. Data were collected from a large sample of 217 young, healthy adult participants, including whole brain structural MRI data (0.8 mm isotropic voxels) and widely-varying performance on scene imagination, autobiographical memory, future thinking and navigation tasks. We found little evidence that hippocampal grey matter volume was related to task performance in this healthy sample. This was the case using different analysis methods (voxel-based morphometry, partial correlations), when whole brain or hippocampal regions of interest were examined, when comparing different sub-groups (divided by gender, task performance, self-reported ability), and when using latent variables derived from across the cognitive tasks. Hippocampal grey matter volume may not, therefore, significantly influence performance on tasks known to require the hippocampus in healthy people. Perhaps only in extreme situations, as in the case of licensed London taxi drivers, are measurable ability-related hippocampus volume changes consistently exhibited.

Locus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases.

Pathological alterations to the locus coeruleus, the major source of noradrenaline in the brain, are histologically evident in early stages of neurodegenerative diseases. Novel MRI approaches now provide an opportunity to quantify structural features of the locus coeruleus in vivo during disease progression. In combination with neuropathological biomarkers, in vivo locus coeruleus imaging could help to understand the contribution of locus coeruleus neurodegeneration to clinical and pathological manifestations in Alzheimer's disease, atypical neurodegenerative dementias and Parkinson's disease. Moreover, as the functional sensitivity of the noradrenergic system is likely to change with disease progression, in vivo measures of locus coeruleus integrity could provide new pathophysiological insights into cognitive and behavioural symptoms. Locus coeruleus imaging also holds the promise to stratify patients into clinical trials according to noradrenergic dysfunction. In this article, we present a consensus on how non-invasive in vivo assessment of locus coeruleus integrity can be used for clinical research in neurodegenerative diseases. We outline the next steps for in vivo, post-mortem and clinical studies that can lay the groundwork to evaluate the potential of locus coeruleus imaging as a biomarker for neurodegenerative diseases.

Maximising BOLD sensitivity through automated EPI protocol optimisation.

Gradient echo echo-planar imaging (GE EPI) is used for most fMRI studies but can suffer substantially from image distortions and BOLD sensitivity (BS) loss due to susceptibility-induced magnetic field inhomogeneities. While there are various post-processing methods for correcting image distortions, signal dropouts cannot be recovered and therefore need to be addressed at the data acquisition stage. Common approaches for reducing susceptibility-related BS loss in selected brain areas are: z-shimming, inverting the phase encoding (PE) gradient polarity, optimizing the slice tilt and increasing spatial resolution. The optimization of these parameters can be based on atlases derived from multiple echo-planar imaging (EPI) acquisitions. However, this requires resource and time, which imposes a practical limitation on the range over which parameters can be optimised meaning that the chosen settings may still be sub-optimal. To address this issue, we have developed an automated method that can be used to optimize across a large parameter space. It is based on numerical signal simulations of the BS loss predicted by physical models informed by a large database of magnetic field (B0) maps acquired on a broad cohort of participants. The advantage of our simulation-based approach compared to previous methods is that it saves time and expensive measurements and allows for optimizing EPI protocols by incorporating a broad range of factors, including different resolutions, echo times or slice orientations. To verify the numerical optimisation, results are compared to those from an earlier study and to experimental BS measurements carried out in six healthy volunteers.

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.

Establishing intra- and inter-vendor reproducibility of T1 relaxation time measurements with 3T MRI.

PURPOSE: Parametric imaging methods (e.g., T1 relaxation time mapping) have been shown to be more reproducible across time and vendors than weighted (e.g., T1 -weighted) images. The purpose of this work was to more extensively evaluate the validity of this assertion. METHODS: Seven volunteers underwent twice-repeated acquisitions of variable flip-angle T1 mapping, including B1+ calibration, on a 3T Philips Achieva and 3T Siemens Trio scanner. Intra-scanner and inter-vendor T1 variability were calculated. To determine T1 reproducibility levels in longitudinal settings, or after changing hardware or software, four additional data sets were acquired from two of the participants; one participant was scanned on a different 3T Siemens Trio scanner and another on the same 3T Philips Achieva scanner but after a software upgrade. RESULTS: Intra-scanner variability of voxel-wise T1 values was consistent between the two vendors, averaging 0.7/0.7/1.3/1.4% in white matter/cortical gray matter/subcortical gray matter/cerebellum, respectively. We observed, however, a systematic bias between the two vendors of https://doi.org/10.0/7.8/8.6/10.0%, respectively. The T1 bias across two scanners of the same model was greater than intra-scanner variability, although still only at 1.4/1.0/1.9/2.3%, respectively. A greater bias was identified for data sets acquired before/after software upgrade in white matter/cortical gray matter (3.6/2.7%) whereas variability in subcortical gray matter/cerebellum was comparable (1.7/1.9%). CONCLUSION: We established intra- and inter-vendor reproducibility levels for a widely used T1 mapping protocol. We anticipate that these results will guide the design of multi-center studies, particularly those encompassing multiple vendors. Furthermore, this baseline level of reproducibility should be established or surpassed during the piloting phase of such studies.