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Converging evidence from studies of human and nonhuman animals suggests that the hippocampus contributes to sequence learning by using temporal context to bind sequentially occurring items. The fornix is a white matter pathway containing the major input and output pathways of the hippocampus, including projections from medial septum and to diencephalon, striatum, lateral septum and prefrontal cortex. If the fornix meaningfully contributes to hippocampal function, then individual differences in fornix microstructure might predict sequence memory. Here, we tested this prediction by performing tractography in 51 healthy adults who had undertaken a sequence memory task. Microstructure properties of the fornix were compared with those of tracts connecting medial temporal lobe regions but not predominantly the hippocampus: the Parahippocampal Cingulum bundle (PHC) (conveying retrosplenial projections to parahippocampal cortex) and the Inferior Longitudinal Fasciculus (ILF) (conveying occipital projections to perirhinal cortex). Using principal components analysis, we combined Free-Water Elimination Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging measures obtained from multi-shell diffusion MRI into two informative indices: the first (PC1) capturing axonal packing/myelin and the second (PC2) capturing microstructural complexity. We found a significant correlation between fornix PC2 and implicit reaction-time indices of sequence memory, indicating that greater fornix microstructural complexity is associated with better sequence memory. No such relationship was found with measures from the PHC and ILF. This study highlights the importance of the fornix in aiding memory for objects within a temporal context, potentially reflecting a role in mediating inter-regional communication within an extended hippocampal system.

Background: Structural network damage is a potentially important mechanism by which cerebral small vessel disease (SVD) can cause cognitive impairment. As a central hub of the structural network, the role of thalamus in SVD-related cognitive impairments remains unclear. We aimed to determine the associations between the structural alterations of thalamic subregions and cognitive impairments in SVD. Methods: In this cross-sectional study, 205 SVD participants without thalamic lacunes from the third follow-up (2020) of the prospective RUN DMC study (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort), which was initiated in 2006, Nijmegen, were included. Cognitive functions included processing speed, executive function, and memory. Probabilistic tractography was performed from thalamus to 6 cortical regions, followed by connectivity-based thalamic segmentation to assess each thalamic subregion volume and connectivity (measured by mean diffusivity [MD] of the connecting white matter tracts) with the cortex. Least absolute shrinkage and selection operator regression analysis was conducted to identify the volumes or connectivity of the total thalamus and 6 thalamic subregions that have the strongest association with cognitive performance. Linear regression and mediation analyses were performed to test the association of least absolute shrinkage and selection operator-selected thalamic subregion volume or MD with cognitive performance, while adjusting for age and education. Results: We found that higher MD of the thalamic-motor tract was associated with worse processing speed (β=−0.27; P P =0.001), and memory (β=−0.28; P Conclusions: Our results suggest that the structural alterations of thalamus are linked to cognitive impairment in SVD, largely depending on the damage pattern of the white matter tracts connecting specific thalamic subregions and cortical regions.

We evaluate the test-retest repeatability and study the tissue properties of multicompartment relaxometry-based myelin water imaging (MCR-MWI) derived from different gradient echo (GRE) acquisition settings. Additionally, the variable flip angle acquisition scheme is optimised based on numerical simulations to reduce the acquisition time of MCR-MWI in a clinically practical range without using advanced image acquisition methods. For the test-retest analysis, in vivo imaging was performed to collect data from three healthy volunteers in two identical sessions. Three GRE sequence settings with different echo times and repetition times imitating various scanner setups were evaluated. The in vivo data was also used to validate the optimal variable flip angle combination derived from simulations. Bundle-specific profiles of MCR-MWI derived microstructural parameters were investigated, as well as the cross-correlations of those parameters. Good cross-session repeatability is observed for MCR-MWI. While good correlations can also be found in myelin water fraction (MWF) across protocols, systematic differences, particularly for protocols with different repetition times, are observed. Numerical simulations indicate that MCR-MWI can be performed with a minimum of three flip angles covering a wide range of T1 weighting without adding significant measurement bias and the result is supported by the in vivo experiment allowing whole brain 1.5mm isotropic MWF maps to be acquired in 9 minutes. Bundles-specific MWF analysis reveals that certain white matter bundles are similar in all three participants. We also found that microstructure relaxation parameters have low correlations with MWF. MCR-MWI is a reproducible measure of myelin. However, attention should be paid to considering the protocol related MWF differences for comparison studies, especially when different repetition times are used as this can introduce biases up to 0.5% of MWF in our tested protocols. The optimised flip angle acquisition scheme can reduce the total scan time to 40% of the original implementation without significant quality degradation.Highlights-Multi-compartment relaxometry based myelin water imaging (MCR-MWI) can be performed with data comprising as few as 3 flip angles without introducing substantial bias or instability in the fitting procedure;-MCR-MWI is a reproducible measure of myelin water fraction (MWF) and incorporating DWI can further improve the measurement reproducibility;-MCR-MWI allows the acquisition of whole brain 1.5mm isotropic MWF maps in 9 minutes, even without the use of advanced model-based reconstructions;-Small MWF bias can present in cross-protocol comparison if the MT effect is not constant across GRE protocols (e.g., different TRs or flip angle combinations);-Compartmental relaxation parameters derived from MCR-MWI possess complimentary information beyond myelin water concentration.

Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside. The authors propose Detect, a browser-based anomaly detection framework for diffusion magnetic resonance imaging tractometry data. The tool leverages normative microstructural brain features derived from healthy participants using deep autoencoders to detect anomalies at the individual level.

Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure. Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD. First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n=78) aged 8-18 (mean=12.4, SD=2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultra-strong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from 0 – 6000 s/mm2. Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing b-value (b=6000 s/mm2, median R2 = .34; b=4000 s/mm2, median R2 = .29; b=2400 s/mm2, median R2 = .21; b=1200 s/mm2, median R2 = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties.

White matter (WM) alterations have been observed in Huntington’s disease (HD) but their role in the disease-pathophysiology remains unknown. We assessed WM changes in premanifest HD by exploiting ultra-strong-gradient magnetic resonance imaging (MRI). This allowed to separately quantify magnetization transfer ratio (MTR) and hindered and restricted diffusion-weighted signal fractions, and assess how they drove WM microstructure differences between patients and controls. We used tractometry to investigate region-specific alterations across callosal segments with well-characterized early- and late-myelinating axon populations, while brain-wise differences were explored with tract-based cluster analysis (TBCA). Behavioural measures were included to explore disease-associated brain-function relationships. We detected lower MTR in patients’ callosal rostrum (tractometry: p = 0.03; TBCA: p = 0.03), but higher MTR in their splenium (tractometry: p = 0.02). Importantly, patients’ mutation-size and MTR were positively correlated (all p-values < 0.01), indicating that MTR alterations may directly result from the mutation. Further, MTR was higher in younger, but lower in older patients relative to controls (p = 0.003), suggesting that MTR increases are detrimental later in the disease. Finally, patients showed higher restricted diffusion signal fraction (FR) from the Composite Hindered and Restricted Model of Diffusion (CHARMED) in the cortico-spinal tract (p = 0.03), which correlated positively with MTR in the posterior callosum (p = 0.033), potentially reflecting compensatory mechanisms. In summary, this first comprehensive, ultra-strong gradient MRI study in HD provides novel evidence of mutation-driven MTR alterations at the premanifest disease stage which may reflect neurodevelopmental changes in iron, myelin or a combination of these.

White matter (WM) alterations have been observed early in Huntington’s disease (HD) progression but their role in the disease-pathophysiology remains unknown. We exploited ultra-strong-gradient MRI to tease apart contributions of myelin (with the magnetization transfer ratio), and axon density (with the restricted volume fraction from the Composite Hindered and Restricted Model of Diffusion) to WM differences between premanifest HD patients and age- and sex-matched controls. Diffusion tensor MRI (DT-MRI) measures were also assessed. We used tractometry to investigate region-specific changes across callosal segments with well-characterized early- and late-myelinating axonal populations, while brain-wise alterations were explored with tract-based cluster analysis (TBCA). Behavioural measures were included to explore disease-associated brain-function relationships. We detected lower myelin in the rostrum of patients (tractometry: p = 0.0343; TBCA: p = 0.030), but higher myelin in their splenium (p = 0.016). Importantly, patients’ myelin and mutation size were positively associated (all p-values < 0.01), indicating that increased myelination might be a direct result of the mutation. Finally, myelin was higher than controls in younger patients but lower in older patients (p = 0.003), suggesting detrimental effects of increased myelination later in the course of the disease. Higher FR in patients’ left cortico-spinal tract (CST) (p = 0.03) was detected, and was found to be positively associated with MTR in the posterior callosum (p = 0.033), possibly suggesting compensation to myelin alterations. This comprehensive, ultra-strong gradient MRI investigation provides novel evidence of CAG-driven myelin alterations in premanifest HD which may reflect neurodevelopmental, rather than neurodegenerative disease-associated changes.

Background White matter (WM) impairments precede striatal atrophy and motor symptoms in Huntington’s disease (HD) but their aetiology remains unknown. Aims We exploited ultra-strong gradient MRI to disentangle the contribution of changes in axon microstructure versus changes in myelin to WM pathology in HD. Methods We assessed apparent myelin [with the magnetization transfer ratio (MTR)], and axon density [with the restricted volume fraction (FR) from the Composite Hindered and Restricted Model of Diffusion (CHARMED)] in premanifest HD patients and age- and sex-matched controls. Group differences in diffusion tensor MRI measures were also assessed. We investigated region-specific changes across the corpus callosum (CC) with tractometry and brain-wise WM microstructure abnormalities with tract-based cluster analysis (TBCA). Behavioural measures were included to explore disease-associated brain-function relationships. Results We detected lower apparent myelin in the posterior CC of patients (tractometry: p = 0.0343; TBCA: p = 0.030), and higher apparent myelin in the anterior CC (tractometry: p = 0.016). A positive association between apparent myelin and mutation size in patients (all p-values Conclusions We provide novel in vivo evidence for myelin-based WM alterations as an early feature of human HD. Critical pathogenic events were present in mutation carriers prior to clinical onset, emphasising the importance of understanding the mechanisms underlying early WM abnormalities for the discovery of new therapeutic approaches.

Aim A third of epilepsy patients suffer from medically refractory seizures. In patients eligible for surgical treatment, seizure freedom rates remain variable. Machine learning (ML) utilises large datasets to detect patterns to make predictions. We systematically review studies employing ML models for prediction of outcome following resective epilepsy surgery to evaluate their efficacy, applicability and value in determining surgical candidacy. Method MEDLINE, Cochrane and EMBASE databases were searched for literature published between 2010 – 2020 according to PRISMA guidance. Non-refractory epilepsy, non-clinical outcome prediction, or non-human studies were excluded. Clinical and demographic data, ML features, discrimination and prediction accuracy metrics were extracted. Results 15 studies were included. Median cohort size was 49 (range 16 – 4211). Heterogeneous input data sources were utilised: MRI (n = 10) , electrophysiology (n = 4), PET (n = 2), clinical data (n = 2), and neuropsychological testing (n = 1). The most common ML model used was support vector machines (n = 7). All studies had good discrimination (AUC > 0.70, range: 0.79 [95% CI NR] - 0.94 [95% CI 0.92 – 0.96]), and good prediction accuracy (> 0.70, range: 0.76 [95% CI NR] – 0.95 [95% CI NR]). Limitations included small sample sizes, limited external validation and lack of comparison with clinician-predicted outcomes. Conclusions Machine Learning for outcome prediction could enhance clinical decision-making for surgical candidacy in epilepsy, and lead to improved precision medicine delivery. Outcome reporting remains inconsistent, and further work is required to externally validate such models to implement these to large-scale clinical populations.

Short association fibres (SAF) of the human brain are estimated to represent over a half of the total white matter volume, and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and close proximity to the cortical mantle, leading to partial volume effects and exacerbating the influence of the gyral bias. This work considers the choice of scanner, acquisition, voxel size, seeding strategy and filtering techniques to propose a whole-brain, surface-based tractography approach with the aim of providing a method for investigating SAF ≤30-40 mm. The framework is designed to: (1) ensure a greater cortical surface coverage through spreading streamline seeds more uniformly; (2) introduce precise filtering mechanics which are particularly important when dealing with small, morphologically diverse structures; and (3) allow the use of surface-based registration for dataset comparisons which can be superior to volume-based registration in the cortical vicinity. The indexation of surface vertices at each streamline end enables direct interfacing between streamlines and the cortical surface without dependence on the voxel grid. SAF tractograms generated using recent test- retest data from our institution are carefully characterised and measures of consistency using streamline-, voxel- and surface-wise comparisons calculated to inform researchers and serve as a benchmark for future methodological developments.

Introduction Pre-operative white matter tract reconstruction of the Meyer’s loop (ML) of the optic radiation using diffusion MRI (tractography) can be used to prevent post-operative visual-field deficit. Due to its complex anatomy, precise reconstruction of the ML is challenging and often underestimated. Previous work has suggested that an innovative tractography technique using oriented priors called MAGNET better approximates reconstruction to reported histological prosections. This proof-of-context study validates the MAGNET methodology in predicting visual-field deficits in patients undergoing TLE surgery. Method Diffusion MRI datasets were used to reconstruct pre-operative ML using MAGNET in five patients. These were overlaid on post-operative T2-MRI series demonstrating the surgically resected area to measure overlap between resection and reconstructed ML. A correlation with post-operative visual-field defects was established. Results There was no evidence of visual field deficit in the cases where there was no overlap between the reconstructed ML and the resected region. In the cases with overlap with reconstructed ML and resection, there was visual deficit found. There was no correlation between proportion of resected ML and visual deficit. Conclusions This pilot demonstrates that MAGNET accurately reconstructs ML in pre-surgical TLE cases compared to standard tractography techniques and can be used to augment neurosurgical planning and resection.

Cognitive difficulties are common and a key concern for people with multiple sclerosis. Advancing knowledge of the role of white matter pathology in multiple sclerosis-related cognitive impairment is essential as both occur early in the disease with implications for early intervention. Consequently, this cross-sectional study asked whether quantifying the relationships between lesions and specific white matter structures could better explain co-existing cognitive differences than whole brain imaging measures. Forty participants with relapse-onset multiple sclerosis underwent cognitive testing and MRI at 3 Tesla. They were classified as cognitively impaired (n = 24) or unimpaired (n = 16) and differed across verbal fluency, learning and recall tasks corrected for intelligence and education (corrected P-values = 0.007–0.04). The relationships between lesions and white matter were characterized across six measures: conventional voxel-based T2 lesion load, whole brain tractogram load (lesioned volume/whole tractogram volume), whole bundle volume, bundle load (lesioned volume/whole bundle volume), Tractometry (diffusion-tensor and high angular resolution diffusion measures sampled from all bundle streamlines) and lesionometry (diffusion measures sampled from streamlines traversing lesions only). The tract-specific measures were extracted from corpus callosum segments (genu and isthmus), striato-prefrontal and -parietal pathways, and the superior longitudinal fasciculi (sections I, II and III). White matter measure-task associations demonstrating at least moderate evidence against the null hypothesis (Bayes Factor threshold < 0.2) were examined using independent t-tests and covariate analyses (significance level P, Graphical Abstract Graphical Abstract, A key priority in multiple sclerosis is to uncover the neural mechanisms associated with cognitive change. Winter et al. demonstrate that the relationships between white matter pathology and the structure of specific tracts explain differences in learning and recall tasks in long-standing relapse-onset multiple sclerosis.

Most diffusion MRI (dMRI) studies of disease rely on statistical comparisons between large groups of patients and healthy controls to infer altered tissue state. Such studies often require data from a significant number of patients before robust inferences can be made, and clinical heterogeneity can greatly challenge their discriminative power. Moreover, for clinicians and researchers studying small datasets, rare cases, or individual patients, this approach is clearly inappropriate. There is a clear and unmet need to shift away from the current standard approach of group-wise comparisons to methods with the sensitivity for detection of altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalised-medicine in translational imaging. To this end, Detect was developed to advance dMRI-based Tractometry towards single-subject analysis. By: 1) operating on the manifold of white matter pathways; and 2) learning normative microstructural features to better discriminate patients from controls, our framework captures idiosyncrasies in patterns along brain white matter pathways in the individual. This novel approach paves the way from traditional group-based comparisons to true personalised radiology, taking microstructural imaging from the bench to the bedside.

Diffusion MRI techniques are used widely to study the characteristics of the human brain connectome in vivo. However, to resolve and characterise white matter (WM) fibres in heterogeneous MRI voxels remains a challenging problem typically approached with signal models that rely on prior information and constraints. We have recently introduced a 5D relaxation–diffusion correlation framework wherein multidimensional diffusion encoding strategies are used to acquire data at multiple echo‐times to increase the amount of information encoded into the signal and ease the constraints needed for signal inversion. Nonparametric Monte Carlo inversion of the resulting datasets yields 5D relaxation–diffusion distributions where contributions from different sub‐voxel tissue environments are separated with minimal assumptions on their microscopic properties. Here, we build on the 5D correlation approach to derive fibre‐specific metrics that can be mapped throughout the imaged brain volume. Distribution components ascribed to fibrous tissues are resolved, and subsequently mapped to a dense mesh of overlapping orientation bins to define a smooth orientation distribution function (ODF). Moreover, relaxation and diffusion measures are correlated to each independent ODF coordinate, thereby allowing the estimation of orientation‐specific relaxation rates and diffusivities. The proposed method is tested on a healthy volunteer, where the estimated ODFs were observed to capture major WM tracts, resolve fibre crossings, and, more importantly, inform on the relaxation and diffusion features along with distinct fibre bundles. If combined with fibre‐tracking algorithms, the methodology presented in this work has potential for increasing the depth of characterisation of microstructural properties along individual WM pathways., Diffusion MRI techniques designed to resolve fibre crossings within a given white matter (WM) voxel typically assume that the voxel‐level microstructural features can be represented by a single signal response function; this precludes the investigation of microscopic differences between the sub‐voxel fibre populations. In this work, we build on a recently introduced 5D relaxation–diffusion correlation MRI framework and present an analysis protocol for deriving and visualising metrics informing on the relaxation rates and diffusivities of distinct fibres. Experiments on a healthy volunteer demonstrate that the presented approach can capture crossings between distinct WM tracts of the human brain and inform on their individual relaxation–diffusion properties.

We provide a rich multi-contrast microstructural MRI dataset acquired on an ultra-strong gradient 3T Connectom MRI scanner comprising 5 repeated sets of MRI microstructural contrasts in 6 healthy human participants. The availability of data sets that support comprehensive simultaneous assessment of test-retest reliability of multiple microstructural contrasts (i.e., those derived from advanced diffusion, multi-component relaxometry and quantitative magnetisation transfer MRI) in the same population is extremely limited. This unique dataset is offered to the imaging community as a test-bed resource for conducting specialised analyses that may assist and inform their current and future research. The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and computed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols. Our data demonstrate high reproducibility of several microstructural MRI measures across scan sessions as shown by intra-class correlation coefficients and coefficients of variation. To illustrate a potential use of the MICRA dataset, we computed sample sizes required to provide sufficient statistical power a priori across different white matter pathways and microstructure measures for different statistical comparisons. We also demonstrate whole brain white matter voxel-wise repeatability in several microstructural maps. The MICRA dataset will be of benefit to researchers wishing to conduct similar reliability tests, power estimations or to evaluate the robustness of their own analysis pipelines.

The anisotropic microstructure of white matter is reflected in various MRI contrasts. Transverse relaxation rates can be probed as a function of fibre-orientation with respect to the main magnetic field, while diffusion properties are probed as a function of fibre-orientation with respect to an encoding gradient. While the latter is easy to obtain by varying the orientation of the gradient, as the magnetic field is fixed, obtaining the former requires re-orienting the head. In this work we deployed a tiltable RF-coil to study$$T_2$$T2- and diffusional anisotropy of the brain white matter simultaneously in diffusion-$$T_2$$T2correlation experiments.

Diffusion magnetic resonance imaging is a technique which has long been used to study white matter microstructure in vivo. Recent advancements in hardware and modelling techniques have opened up interest in disentangling tissue compartments in the grey matter. In this study, we evaluate the repeatability of soma and neurite density imaging in a sample of six healthy adults scanned five times on an ultra-strong gradient magnetic resonance scanner (300 mT/m). Repeatability was expressed as an intraclass correlation coefficient (ICC). Our findings reveal that measures of soma density (mean ICC \(=\) 0.976), neurite density (mean ICC \(=\) 0.959) and apparent soma size (mean ICC \(=\) 0.923) are highly reliable across multiple cortical and subcortical networks. Overall, we demonstrate the promise of moving advanced grey matter microstructural imaging towards applications of development, ageing, and disease.

The anisotropy of brain white matter microstructure manifests itself in orientational-dependence of various MRI contrasts, and can result in significant quantification biases if ignored. Understanding the origins of this orientation-dependence could enhance the interpretation of MRI signal changes in development, ageing and disease and ultimately improve clinical diagnosis. Using a novel experimental setup, this work studies the contributions of the intra- and extra-axonal water to the orientation-dependence of one of the most clinically-studied parameters, apparent transverse relaxation T2. Specifically, a tiltable receive coil is interfaced with an ultra-strong gradient MRI scanner to acquire multidimensional MRI data with an unprecedented range of acquisition parameters. Using this setup, compartmental T2 can be disentangled based on differences in diffusional-anisotropy, and its orientation-dependence further elucidated by re-orienting the head with respect to the main magnetic field B→0. A dependence of (compartmental) T2 on the fibre orientation w.r.t. B→0 was observed, and further quantified using characteristic representations for susceptibility- and magic angle effects. Across white matter, anisotropy effects were dominated by the extra-axonal water signal, while the intra-axonal water signal decay varied less with fibre-orientation. Moreover, the results suggest that the stronger extra-axonal T2 orientation-dependence is dominated by magnetic susceptibility effects (presumably from the myelin sheath) while the weaker intra-axonal T2 orientation-dependence may be driven by a combination of microstructural effects. Even though the current design of the tiltable coil only offers a modest range of angles, the results demonstrate an overall effect of tilt and serve as a proof-of-concept motivating further hardware development to facilitate experiments that explore orientational anisotropy. These observations have the potential to lead to white matter microstructural models with increased compartmental sensitivity to disease, and can have direct consequences for longitudinal and group-wise T2- and diffusion-MRI data analysis, where the effect of head-orientation in the scanner is commonly ignored.

In multiple sclerosis studies, lesion volume (or lesion load) derived from conventional T2 imaging correlates modestly with clinical assessment. Determining which specific white matter pathways are impacted by lesions may provide additional insights regarding task-specific clinical impairment. Using diffusion MRI, we introduce a set of tract-based metrics that go beyond traditional lesion load approaches and show how they relate to task performance (i.e., working memory, information processing and verbal fluency) in a cohort of 40 patients with multiple sclerosis.

At the typical spatial resolution of MRI in the human brain, approximately 60-90% of voxels contain multiple fiber populations. Quantifying microstructural properties of distinct fiber populations within a voxel is therefore challenging but necessary. While progress has been made for diffusion and T

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called “virtual dissection.” Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. It is our opinion that if the field of dMRI tractography wants to be taken seriously as a widespread clinical tool, it is imperative to harmonize WM bundle segmentations and develop protocols aimed to be used in clinical settings. The EADC‐ADNI Harmonized Hippocampal Protocol achieved such standardization through a series of steps that must be reproduced for every WM bundle. This article is an observation of the problematic. A specific bundle segmentation protocol was used in order to provide a real‐life example, but the contribution of this article is to discuss the need for reproducibility and standardized protocol, as for any measurement tool. This study required the participation of 11 experts and 13 nonexperts in neuroanatomy and “virtual dissection” across various laboratories and hospitals. Intra‐rater agreement (Dice score) was approximately 0.77, while inter‐rater was approximately 0.65. The protocol provided to participants was not necessarily optimal, but its design mimics, in essence, what will be required in future protocols. Reporting tractometry results such as average fractional anisotropy, volume or streamline count of a particular bundle without a sufficient reproducibility score could make the analysis and interpretations more difficult. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction protocols in this era of open and collaborative science.

In magnetic resonance imaging (MRI), the image contrast is the result of the subtle interaction between the physicochemical properties of the imaged living tissue and the parameters used for image acquisition. By varying parameters such as the echo time (TE) and the inversion time (TI), it is possible to collect images that capture different expressions of this sophisticated interaction. Sensitization to diffusion summarized by the b-value - constitutes yet another explorable “dimension” to modify the image contrast which reflects the degree of dispersion of water in various directions within the tissue microstructure. The full exploration of this multidimensional acquisition parameter space offers the promise of a more comprehensive description of the living tissue but at the expense of lengthy MRI acquisitions, often unfeasible in clinical practice. The harnessing of multidimensional information passes through the use of intelligent sampling strategies for reducing the amount of images to acquire, and the design of methods for exploiting the redundancy in such information. This chapter reports the results of the MUDI challenge, comparing different strategies for predicting the acquired densely sampled multidimensional data from sub-sampled versions of it.

Investigative studies of white matter (WM) brain structures using diffusion MRI (dMRI) tractography frequently require manual WM bundle segmentation, often called "virtual dissection". Human errors and personal decisions make these manual segmentations hard to reproduce, which have not yet been quantified by the dMRI community. The contribution of this study is to provide the first large-scale, international, multi-center variability assessment of the "virtual dissection" of the pyramidal tract (PyT). Eleven (11) experts and thirteen (13) non-experts in neuroanatomy and "virtual dissection" were asked to perform 30 PyT segmentation and their results were compared using various voxel-wise and streamline-wise measures. Overall the voxel representation is always more reproducible 1. CC-BY-NC-ND 4.0 International license available under a not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which was this version posted April 30, 2019. ; https://doi.org/10.1101/623892 doi: bioRxiv preprint than streamlines (≈70% and ≈35% overlap respectively) and distances between segmen-tations are also lower for voxel-wise than streamline-wise measures (≈3mm and ≈6mm respectively). This needs to be seriously considered before using tract-based measures (e.g. bundle volume versus streamline count) for an analysis. We show and argue that future bundle segmentation protocols need to be designed to be more robust to human subjectivity. Coordinated efforts by the diffusion MRI tractography community are needed to quantify and account for reproducibility of WM bundle extraction techniques in this era of open and collaborative science.

We present a case of preserved corticospinal connectivity in a cortical tuber, in a 10 year-old boy with intractable epilepsy and tuberous sclerosis complex (TSC). The patient had multiple subcortical tubers, one of which was located in the right central sulcus. In preparation for epilepsy surgery, motor mapping, by neuronavigated transcranial magnetic stimulation (nTMS) coupled with surface electromyography (EMG) was performed to locate the primary motor cortical areas. The resulting functional motor map revealed expected corticospinal connectivity in the left precentral gyrus. Surprisingly, robust contralateral deltoid and tibialis anterior motor evoked potentials (MEPs) were also elicited with direct stimulation of the cortical tuber in the right central sulcus. MRI with diffusion tensor imaging (DTI) tractography confirmed corticospinal fibers originating in the tuber. As there are no current reports of preserved connectivity between a cortical tuber and the corticospinal tract, this case serves to highlight the functional interdigitation of tuber and eloquent cortex. Our case also illustrates the widening spectrum of neuropathological abnormality in TSC that is becoming apparent with modern MRI methodology. Finally, our finding underscores the need for further study of preserved function in tuber tissue during presurgical workup in patients with TSC.