Interpolation of diffusion weighted imaging datasets

White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis

BACKGROUND

Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD.

METHODS

Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments.

RESULTS

The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05).

CONCLUSION

Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI

Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high-resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high-resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep-learning-based super-resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR-CEST, to reduce the acquisition time for CEST MRI by reconstructing high-resolution images from fast low-resolution acquisitions. This is achieved by first pretraining the DLSR-CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine-tune the weights. Using the trained DLSR-CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal-to-noise ratio and structural similarity index measure metrics at all downsampling factors (2-8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR-CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z-spectrum information. Therefore, our DLSR-CEST demonstrated a robust reconstruction of high-resolution CEST source images from fast low-resolution acquisitions, thereby improving the spatial resolution and preserving most Z-spectrum information.

Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.

Pervasive alterations of intra-axonal volume and network organization in young children with a 16p11.2 deletion

Reciprocal Copy Number Variants (CNVs) at the 16p11.2 locus confer high risk for autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDDs). Morphometric MRI studies have revealed large and pervasive volumetric alterations in carriers of a 16p11.2 deletion. However, the specific neuroanatomical mechanisms underlying such alterations, as well as their developmental trajectory, are still poorly understood. Here we explored differences in microstructural brain connectivity between 24 children carrying a 16p11.2 deletion and 66 typically developing (TD) children between 2 and 8 years of age. We found a large pervasive increase of intra-axonal volume widespread over a high number of white matter tracts. Such microstructural alterations in 16p11.2 deletion children were already present at an early age, and led to significant changes in the global efficiency and integration of brain networks mainly associated to language, motricity and socio-emotional behavior, although the widespread pattern made it unlikely to represent direct functional correlates. Our results shed light on the neuroanatomical basis of the previously reported increase of white matter volume, and align well with analogous evidence of altered axonal diameter and synaptic function in 16p11.2 mice models. We provide evidence of a prevalent mechanistic deviation from typical maturation of brain structural connectivity associated with a specific biological risk to develop ASD. Future work is warranted to determine how this deviation contributes to the emergence of symptoms observed in young children diagnosed with ASD and other NDDs.

Dissection of the Temporofrontal Extreme Capsule Fasciculus Using Diffusion MRI Tractography and Association with Lexical Retrieval

The well-known arcuate fasciculus that connects the posterior superior temporal region with the language production region in the ventrolateral frontal cortex constitutes the classic peri-Sylvian dorsal stream of language. A second temporofrontal white matter tract connects ventrally the anterior to intermediate lateral temporal cortex with frontal areas via the extreme capsule. This temporofrontal extreme capsule fasciculus (TFexcF) constitutes the ventral stream of language processing. The precise origin, course, and termination of this pathway has been examined in invasive tract tracing studies in macaque monkeys, but there have been no standard protocols for its reconstruction in the human brain using diffusion imaging tractography. Here we provide a protocol for the dissection of the TFexcF in vivo in the human brain using diffusion magnetic resonance imaging (MRI) tractography which provides a solid basis for exploring its functional role. A key finding of the current dissection protocol is the demonstration that the TFexcF is left hemisphere lateralized. Furthermore, using the present dissection protocol, we demonstrate that the TFexcF is related to lexical retrieval scores measured with the category fluency test, in contrast to the classical arcuate fasciculus (the dorsal language pathway) that was also dissected and was related to sentence repetition.

versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database

The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.

Rapid microscopic 3D-diffusion tensor imaging fiber-tracking of mouse brain in vivo by super resolution reconstruction: validation on MAP6-KO mouse model

OBJECT

Exploring mouse brains by rapid 3D-Diffusion Tensor Imaging (3D-DTI) of high spatial resolution (HSR) is challenging in vivo. Here we use the super resolution reconstruction (SRR) postprocessing method to demonstrate its performance on Microtubule-Associated-Protein6 Knock-Out (MAP6-KO) mice.

MATERIALS AND METHODS

Two spin-echo DTI were acquired (9.4T, CryoProbe RF-coil): (i)-multislice 2D-DTI, (echo-planar integrating reversed-gradient) acquired in vivo in the three orthogonal orientations (360 μm slice-thickness, 120 × 120 μm in-plane resolution, 56 min scan duration); used in SRR software to reconstruct SRR 3D-DTI with HSR in slice-plane (120 × 120 × 120 µm) and (ii)-microscopic 3D-DTI (µ-3D-DTI), (100 × 100 × 100 µm; 8 h 6 min) on fixed-brains ex vivo, that were removed after paramagnetic contrast-agent injection to accelerate scan acquisition using short repetition-times without NMR-signal sensitivity loss.

RESULTS

White-matter defects, quantified from both 3D-DTI fiber-tracking were found very similar. Indeed, as expected the fornix and cerebral-peduncle volume losses were - 39% and - 35% in vivo (SRR 3D-DTI) versus - 34% and - 32% ex vivo (µ-3D-DTI), respectively (p<0.001). This finding is robust since the µ-3D-DTI feasibility on MAP6-KO ex vivo was already validated by fluorescent-microscopy of cleared brains.

DISCUSSION

First performance of the SRR to generate rapid HSR 3D-DTI of mouse brains in vivo is demonstrated. The method is suitable in neurosciences for longitudinal studies to identify molecular and genetic abnormalities in mouse models that are of growing developments.

White matter and neurochemical mechanisms underlying age-related differences in motor processing speed

Aging is associated with changes in the central nervous system and leads to reduced life quality. Here, we investigated the age-related differences in the CNS underlying motor performance deficits using magnetic resonance spectroscopy and diffusion MRI. MRS measured N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr) concentrations in the sensorimotor and occipital cortex, whereas dMRI quantified apparent fiber density (FD) in the same voxels to evaluate white matter microstructural organization. We found that aging was associated with increased reaction time and reduced FD and NAA concentration in the sensorimotor voxel. Both FD and NAA mediated the association between age and reaction time. The NAA concentration was found to mediate the association between age and FD in the sensorimotor voxel. We propose that the age-related decrease in NAA concentration may result in reduced axonal fiber density in the sensorimotor cortex which may ultimately account for the response slowness of older participants.

Increased meso-striatal connectivity mediates trait impulsivity in FTO variant carriers

Objective

While variations in the first intron of the fat mass and obesity-associated gene (FTO, rs9939609 T/A variant) have long been identified as a major contributor to polygenic obesity, the mechanisms underlying weight gain in risk allele carriers still remain elusive. On a behavioral level, FTO variants have been robustly linked to trait impulsivity. The regulation of dopaminergic signaling in the meso-striatal neurocircuitry by these FTO variants might represent one mechanism for this behavioral alteration. Notably, recent evidence indicates that variants of FTO also modulate several genes involved in cell proliferation and neuronal development. Hence, FTO polymorphisms might establish a predisposition to heightened trait impulsivity during neurodevelopment by altering structural meso-striatal connectivity. We here explored whether the greater impulsivity of FTO variant carriers was mediated by structural differences in the connectivity between the dopaminergic midbrain and the ventral striatum.

Methods

Eighty-seven healthy normal-weight volunteers participated in the study; 42 FTO risk allele carriers (rs9939609 T/A variant, FTO ⁺ group: AT, AA) and 39 non-carriers (FTO ^- group: TT) were matched for age, sex and body mass index (BMI). Trait impulsivity was assessed via the Barratt Impulsiveness Scale (BIS-11) and structural connectivity between the ventral tegmental area/substantia nigra (VTA/SN) and the nucleus accumbens (NAc) was measured via diffusion weighted MRI and probabilistic tractography.

Results

We found that FTO risk allele carriers compared to non-carriers, demonstrated greater motor impulsivity (p = 0.04) and increased structural connectivity between VTA/SN and the NAc (p< 0.05). Increased connectivity partially mediated the effect of FTO genetic status on motor impulsivity.

Conclusion

We report altered structural connectivity as one mechanism by which FTO variants contribute to increased impulsivity, indicating that FTO variants may exert their effect on obesity-promoting behavioral traits at least partially through neuroplastic alterations in humans.

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients

BACKGROUND AND PURPOSE

Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses.

MATERIALS AND METHODS

Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 - 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, M_age = 35.7y, age range 25 - 64y).

RESULTS

Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test-retest reliability of the fixel-wise metrics are warranted.

CONCLUSIONS

Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.

Column-based cortical depth analysis of the diffusion anisotropy and radiality in submillimeter whole-brain diffusion tensor imaging of the human cortical gray matter in vivo

High-resolution diffusion tensor imaging (DTI) can noninvasively probe the microstructure of cortical gray matter in vivo. In this study, 0.9-mm isotropic whole-brain DTI data were acquired in healthy subjects with an efficient multi-band multi-shot echo-planar imaging sequence. A column-based analysis that samples the fractional anisotropy (FA) and radiality index (RI) along radially oriented cortical columns was then performed to quantitatively analyze the FA and RI dependence on the cortical depth, cortical region, cortical curvature, and cortical thickness across the whole brain, which has not been simultaneously and systematically investigated in previous studies. The results showed characteristic FA and RI vs. cortical depth profiles, with an FA local maximum and minimum (or two inflection points) and a single RI maximum at intermediate cortical depths in most cortical regions, except for the postcentral gyrus where no FA peaks and a lower RI were observed. These results were consistent between repeated scans from the same subjects and across different subjects. They were also dependent on the cortical curvature and cortical thickness in that the characteristic FA and RI peaks were more pronounced i) at the banks than at the crown of gyri or at the fundus of sulci and ii) as the cortical thickness increases. This methodology can help characterize variations in microstructure along the cortical depth and across the whole brain in vivo, potentially providing quantitative biomarkers for neurological disorders.

Building a tissue-unbiased brain template of fiber orientation distribution and tractography with multimodal registration

PURPOSE

Brain templates provide an essential standard space for statistical analysis of brain structure and function. Despite recent advances, diffusion MRI still lacks a template of fiber orientation distribution (FOD) and tractography that is unbiased for both white and gray matter. Therefore, we aim to build up a set of such templates for better white-matter analysis and joint structural and functional analysis.

METHODS

We have developed a multimodal registration method to leverage the complementary information captured by T1 -weighted, T2 -weighted, and diffusion MRI, so that a coherent transformation is generated to register FODs into a common space and average them into a template. Consequently, the anatomically constrained fiber-tracking method was applied to the FOD template to generate a tractography template. Fiber-centered functional connectivity analysis was then performed as an example of the benefits of such an unbiased template.

RESULTS

Our FOD template preserves fine structural details in white matter and also, importantly, clear folding patterns in the cortex and good contrast in the subcortex. Quantitatively, our templates show better individual-template agreement at the whole-brain scale and segmentation scale. The tractography template aligns well with the gray matter, which led to fiber-centered functional connectivity showing high cross-group consistency.

CONCLUSION

We have proposed a novel methodology for building a tissue-unbiased FOD and anatomically constrained tractography template based on multimodal registration. Our templates provide a standard space and statistical platform for not only white-matter analysis but also joint structural and functional analysis, therefore filling an important gap in multimodal neuroimage analysis.

Diffusion MRI approaches for investigating microstructural complexity in a rat model of traumatic brain injury

Our study explores the potential of conventional and advanced diffusion MRI techniques including diffusion tensor imaging (DTI), and single-shell 3-tissue constrained spherical deconvolution (SS3T-CSD) to investigate complex microstructural changes following severe traumatic brain injury in rats at a chronic phase. Rat brains after sham-operation or lateral fluid percussion (LFP) injury were scanned ex vivo in a 9.4 T scanner. Our region-of-interest-based approach of tensor-, and SS3T-CSD derived fixel-, 3-tissue signal fraction maps were sensitive to changes in both white matter (WM) and grey matter (GM) areas. Tensor-based measures, such as fractional anisotropy (FA) and radial diffusivity (RD), detected more changes in WM and GM areas as compared to fixel-based measures including apparent fiber density (AFD), peak FOD amplitude and primary fiber bundle density, while 3-tissue signal fraction maps revealed distinct changes in WM, GM, and phosphate-buffered saline (PBS) fractions highlighting the complex tissue microstructural alterations post-trauma. Track-weighted imaging demonstrated changes in track morphology including reduced curvature and average pathlength distal from the primary lesion in severe TBI rats. In histological analysis, changes in the diffusion MRI measures could be associated to decreased myelin density, loss of myelinated axons, and increased cellularity, revealing progressive microstructural alterations in these brain areas five months after injury. Overall, this study highlights the use of combined conventional and advanced diffusion MRI measures to obtain more precise insights into the complex tissue microstructural alterations in chronic phase of severe brain injury.

Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions

It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.

Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations

Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS.

Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI

BACKGROUND

Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM.

OBJECTIVE

In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls.

METHODS

By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons.

RESULTS

Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus.

CONCLUSION

This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.

TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography

TractoInferno is the world's largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.

Intersession Repeatability of Diffusion‐Tensor Imaging in the Supraspinatus and the Infraspinatus Muscles of Volunteers

BACKGROUND

Quantifying the rotator cuff (RC) muscles' viscoelasticity could provide outcome relevant information in patients with RC tears. MR-elastography requires robust diffusion-tensor imaging (DTI) to account for tissue anisotropy in muscles stiffness computation.

PURPOSE

To assess the repeatability of DTI parameters in the supraspinatus and infraspinatus muscles and to explore DTI tractography conformity with the muscles' anatomy.

STUDY TYPE

Prospective.

SUBJECTS

Six healthy volunteers underwent three consecutive shoulder MRI sessions about 10 minutes apart.

FIELD STRENGTH/SEQUENCE

3T/T1-vibe Dixon and Spin echo EPI DTI (12 gradient encoding directions, b-values 500 and 800 sec/mm² ).

ASSESSMENT

Supraspinatus and infraspinatus muscles were segmented on the T1-vibe Dixon sequence. DTI image quality was assessed using a quantitative threshold based on the signal-to-noise ratio (SNR). The eigenvalues ( λ 1 , λ 2 , λ 3 ), fractional anisotropy (FA) and mean diffusivity were calculated. DTI tractography was visually assessed.

STATISTICAL TESTS

DTI parameters within-subject intersession repeatability was assessed with Bland-Altman analysis and the coefficient of variation (CV). Repeatability was considered good for CV < 10%.

RESULTS

The SNR between diffusion-weighted and non-diffusion-weighted images was greater than 3, which aligns with standards for estimating DTI parameters. The FA showed the lowest mean bias (-0.007; 95% confidence interval [CI] -0.031 to 0.018) whereas the λ₁ had the highest mean bias (0.146 × 10^-3  mm² /sec; CI -0.034 to 0.326 × 10^-3  mm² /sec). CVs of the DTI parameters varied between 3.5% (FA) and 8.4% (λ₃ ) for the supraspinatus and between 3.2% (λ₁ ) and 6.8% (λ₃ ) for the infraspinatus. Tractography provided muscle fiber representations in three-dimensional space concordant with RC anatomy.

DATA CONCLUSION

DTI of the supraspinatus and infraspinatus muscles achieved an adequate SNR, allowing the measurement of the DTI metrics with good repeatability, and thus can be used for optimizing stiffness estimation in these anisotropic tissues.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Surface-based tracking for short association fibre tractography

It is estimated that in the human brain, short association fibres (SAF) represent more than 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 proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.

In vivo probabilistic atlas of white matter tracts of the human subthalamic area combining track density imaging and optimized diffusion tractography

The human subthalamic area is a region of high anatomical complexity, tightly packed with tiny fiber bundles. Some of them, including the pallidothalamic, cerebello-thalamic, and mammillothalamic tracts, are relevant targets in functional neurosurgery for various brain diseases. Diffusion-weighted imaging-based tractography has been suggested as a useful tool to map white matter pathways in the human brain in vivo and non-invasively, though the reconstruction of these specific fiber bundles is challenging due to their small dimensions and complex anatomy. To the best of our knowledge, a population-based, in vivo probabilistic atlas of subthalamic white matter tracts is still missing. In the present work, we devised an optimized tractography protocol for reproducible reconstruction of the tracts of subthalamic area in a large data sample from the Human Connectome Project repository. First, we leveraged the super-resolution properties and high anatomical detail provided by short tracks track-density imaging (stTDI) to identify the white matter bundles of the subthalamic area on a group-level template. Tracts identification on the stTDI template was also aided by visualization of histological sections of human specimens. Then, we employed this anatomical information to drive tractography at the subject-level, optimizing tracking parameters to maximize between-subject and within-subject similarities as well as anatomical accuracy. Finally, we gathered subject level tracts reconstructed with optimized tractography into a large-scale, normative population atlas. We suggest that this atlas could be useful in both clinical anatomy and functional neurosurgery settings, to improve our understanding of the complex morphology of this important brain region.

Manifold-aware synthesis of high-resolution diffusion from structural imaging

The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to eight times larger than those of T1w images. The detailed information contained in accessible high-resolution T1w images could help in the synthesis of diffusion images with a greater level of detail. However, the non-Euclidean nature of diffusion imaging hinders current deep generative models from synthesizing physically plausible images. In this work, we propose the first Riemannian network architecture for the direct generation of diffusion tensors (DT) and diffusion orientation distribution functions (dODFs) from high-resolution T1w images. Our integration of the log-Euclidean Metric into a learning objective guarantees, unlike standard Euclidean networks, the mathematically-valid synthesis of diffusion. Furthermore, our approach improves the fractional anisotropy mean squared error (FA MSE) between the synthesized diffusion and the ground-truth by more than 23% and the cosine similarity between principal directions by almost 5% when compared to our baselines. We validate our generated diffusion by comparing the resulting tractograms to our expected real data. We observe similar fiber bundles with streamlines having <3% difference in length, <1% difference in volume, and a visually close shape. While our method is able to generate diffusion images from structural inputs in a high-resolution space within 15 s, we acknowledge and discuss the limits of diffusion inference solely relying on T1w images. Our results nonetheless suggest a relationship between the high-level geometry of the brain and its overall white matter architecture that remains to be explored.

Associations of body composition with regional brain volumes and white matter microstructure in very preterm infants

OBJECTIVE

To determine associations between body composition and concurrent measures of brain development including (1) Tissue-specific brain volumes and (2) White matter microstructure, among very preterm infants at term equivalent age.

DESIGN

Prospective observational study.

SETTING

Single-centre academic level III neonatal intensive care unit.

PATIENTS

We studied 85 infants born <33 weeks' gestation.

METHODS

At term equivalent age, infants underwent air displacement plethysmography to determine body composition, and brain MRI from which we quantified tissue-specific brain volumes and fractional anisotropy (FA) of white matter tracts. We estimated associations of fat and lean mass Z-scores with each brain outcome, using linear mixed models adjusted for intrafamilial correlation among twins and potential confounding variables.

RESULTS

Median gestational age was 29 weeks (range 23.4-32.9). One unit greater lean mass Z-score was associated with larger total brain volume (10.5 cc, 95% CI 3.8 to 17.2); larger volumes of the cerebellum (1.2 cc, 95% CI 0.5 to 1.9) and white matter (4.5 cc, 95% CI 0.7 to 8.3); and greater FA in the left cingulum (0.3%, 95% CI 0.1% to 0.6%), right uncinate fasciculus (0.2%, 95% CI 0.0% to 0.5%), and right posterior limb of the internal capsule (0.3%, 95% CI 0.03% to 0.6%). Fat Z-scores were not associated with any outcome.

CONCLUSIONS

Lean mass-but not fat-at term was associated with larger brain volume and white matter microstructure differences that suggest improved maturation. Lean mass accrual may index brain growth and development.

Bridging cognition and action: executive functioning mediates the relationship between white matter fiber density and complex motor abilities in older adults

Aging may be associated with motor decline that is attributed to deteriorating white matter microstructure of the corpus callosum (CC), among other brain-related factors. Similar to motor functioning, executive functioning (EF) typically declines during aging, with age-associated changes in EF likewise being linked to altered white matter connectivity in the CC. Given that both motor and executive functions rely on white matter connectivity via the CC, and that bimanual control is thought to rely on EF, the question arises whether EF can at least party account for the proposed link between CC-connectivity and motor control in older adults. To address this, diffusion magnetic resonance imaging data were obtained from 84 older adults. A fiber-specific approach was used to obtain fiber density (FD), fiber cross-section (FC), and a combination of both metrics in eight transcallosal white matter tracts. Motor control was assessed using a bimanual coordination task. EF was determined by a domain-general latent EF-factor extracted from multiple EF tasks, based on a comprehensive test battery. FD of transcallosal prefrontal fibers was associated with cognitive and motor performance. EF partly accounted for the relationship between FD of prefrontal transcallosal pathways and motor control. Our results underscore the multidimensional interrelations between callosal white matter connectivity (especially in prefrontal brain regions), EF across multiple domains, and motor control in the older population. They also highlight the importance of considering EF when investigating brain-motor behavior associations in older adults.

Retinal ganglion cell endowment is correlated with optic tract fiber cross section, not density

There is substantial variation between healthy individuals in the number of retinal ganglion cells (RGC) in the eye, with commensurate variation in the number of axons in the optic tracts. Fixel-based analysis of diffusion MR produces estimates of fiber density (FD) and cross section (FC). Using these fixel measurements along with retinal imaging, we asked if individual differences in RGC tissue volume are correlated with individual differences in FD and FC measurements obtained from the optic tracts, and subsequent structures along the cortical visual pathway. We find that RGC endowment is correlated with optic tract FC, but not with FD. RGC volume had a decreasing relationship with measurements from subsequent regions of the visual system (LGN volume, optic radiation FC/FD, and V1 surface area). However, we also found that the variations in each visual area were correlated with the variations in its immediately adjacent visual structure. We only observed these serial correlations when FC is used as the measure of interest for the optic tract and radiations, but no significant relationship was found when FD represented these white matter structures. From these results, we conclude that the variations in RGC endowment, LGN volume, and V1 surface area are better predicted by the overall cross section of the optic tract and optic radiations as compared to the intra-axonal restricted signal component of these white matter pathways. Additionally, the presence of significant correlations between adjacent, but not distant, anatomical structures suggests that there are multiple, local sources of anatomical variation along the visual pathway.

Reproducibility and repeatability of magnetic resonance imaging in dementia

PURPOSE

Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol.

METHODS

Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities.

RESULTS

Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better.

CONCLUSIONS

Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.

Quantitative comparison of corticospinal tracts arising from different cortical areas in humans

The corticospinal tract (CST), which plays a major role in the control of voluntary limb movements, arises from multiple motor- and somatosensory-related areas in monkeys. Although the cortical origin and quantitative differences in CSTs among the cortical areas are well-documented in monkeys, they are unclear in humans. We quantitatively investigated the CSTs from the cerebral cortex to the cervical cord in healthy volunteers using fiber tractography of diffusion-weighted magnetic resonance imaging. The corticospinal (CS) streamlines arose from nine cortical areas: primary motor area (mean ± SD = 49.71 ± 1.61%), dorsal (16.33 ± 1.37%) and ventral (11.02 ± 0.90%) premotor cortex, supplementary motor area (5.14 ± 0.36%), pre-supplementary motor area (2.46 ± 0.26%), primary somatosensory cortex (11.06 ± 0.91%), Brodmann area 5 (0.88 ± 0.09%), caudal cingulate zone (1.70 ± 0.30%), and posterior part of the rostral cingulate zone (1.70 ± 0.34%). In all cortical areas, the number of CS streamlines gradually decreased from the rostral to caudal spinal segments, but the proportion was maintained throughout the cervical cord. Over 75% of CS streamlines arose from the lateral surface of the frontal lobe, which may explain the voluntary control of dexterous and flexible limb movements in humans. (197/200 words).

Associations of Macronutrient Intake Determined by Point-of-Care Human Milk Analysis with Brain Development among very Preterm Infants

Point-of-care human milk analysis is now feasible in the neonatal intensive care unit (NICU) and allows accurate measurement of macronutrient delivery. Higher macronutrient intakes over this period may promote brain growth and development. In a prospective, observational study of 55 infants born at <32 weeks’ gestation, we used a mid-infrared spectroscopy-based human milk analyzer to measure the macronutrient content in repeated samples of human milk over the NICU hospitalization. We calculated daily nutrient intakes from unfortified milk and assigned infants to quintiles based on median intakes over the hospitalization. Infants underwent brain magnetic resonance imaging at term equivalent age to quantify total and regional brain volumes and fractional anisotropy of white matter tracts. Infants in the highest quintile of energy intake from milk, as compared with the lower four quintiles, had larger total brain volume (31 cc, 95% confidence interval [CI]: 5, 56), cortical gray matter (15 cc, 95%CI: 1, 30), and white matter volume (23 cc, 95%CI: 12, 33). Higher protein intake was associated with larger total brain (36 cc, 95%CI: 7, 65), cortical gray matter (22 cc, 95%CI: 6, 38) and deep gray matter (1 cc, 95%CI: 0.1, 3) volumes. These findings suggest innovative strategies to close nutrient delivery gaps in the NICU may promote brain growth for preterm infants.

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 ± 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts’ profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model’s performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

Network analysis shows decreased ipsilesional structural connectivity in glioma patients

Gliomas that infiltrate networks and systems, such as the motor system, often lead to substantial functional impairment in multiple systems. Network-based statistics (NBS) allow to assess local network differences and graph theoretical analyses enable investigation of global and local network properties. Here, we used network measures to characterize glioma-related decreases in structural connectivity by comparing the ipsi- with the contralesional hemispheres of patients and correlated findings with neurological assessment. We found that lesion location resulted in differential impairment of both short and long connectivity patterns. Network analysis showed reduced global and local efficiency in the ipsilesional hemisphere compared to the contralesional hemispheric networks, which reflect the impairment of information transfer across different regions of a network.

Tumors and their location distinctly alter both local and global brain connectivity within the ipsilesional hemisphere of glioma patients.

Timing of selective basal ganglia white matter loss in premanifest Huntington's disease

OBJECTIVES

To investigate the timeframe prior to symptom onset when cortico-basal ganglia white matter (white matter) loss begins in premanifest Huntington's disease (preHD), and which striatal and thalamic sub-region white matter tracts are most vulnerable.

METHODS

We performed fixel-based analysis, which allows resolution of crossing white matter fibres at the voxel level, on diffusion tractography derived white matter tracts of striatal and thalamic sub-regions in two independent cohorts; TrackON-HD, which included 72 preHD (approx. 11 years before disease onset) and 85 controls imaged at three time points over two years; and the HD young adult study (HD-YAS), which included 54 preHD (approx. 25 years before disease onset) and 53 controls, imaged at one time point. Group differences in fibre density and cross section (FDC) were investigated.

RESULTS

We found no significant group differences in cortico-basal ganglia sub-region FDC in preHD gene carriers 25 years before onset. In gene carriers 11 years before onset, there were reductions in striatal (limbic and caudal motor) and thalamic (premotor, motor and sensory) FDC at baseline, with no significant change over 2 years. Caudal motor-striatal, pre-motor-thalamic, and primary motor-thalamic FDC at baseline, showed significant correlations with the Unified Huntington's disease rating scale (UHDRS) total motor score (TMS). Limbic cortico-striatal FDC and apathy were also significantly correlated.

CONCLUSIONS

Our findings suggest that limbic and motor white matter tracts to the striatum and thalamus are most susceptible to early degeneration in HD but that approximately 25 years from onset, these tracts appear preserved. These findings may have importance in determining the optimum time to initiate future disease modifying therapies in HD.

Tractography in Curvilinear Coordinates

Coordinate invariance of physical laws is central in physics, it grants us the freedom to express observations in coordinate systems that provide computational convenience. In the context of medical imaging there are numerous examples where departing from Cartesian to curvilinear coordinates leads to ease of visualization and simplicity, such as spherical coordinates in the brain's cortex, or universal ventricular coordinates in the heart. In this work we introduce tools that enhance the use of existing diffusion tractography approaches to utilize arbitrary coordinates. To test our method we perform simulations that gauge tractography performance by calculating the specificity and sensitivity of tracts generated from curvilinear coordinates in comparison with those generated from Cartesian coordinates, and we find that curvilinear coordinates generally show improved sensitivity and specificity compared to Cartesian. Also, as an application of our method, we show how harmonic coordinates can be used to enhance tractography for the hippocampus.

The relationship between white matter microstructure, cardiovascular fitness, gross motor skills, and neurocognitive functioning in children

Recent evidence indicates that both cardiovascular fitness and gross motor skill performance are related to enhanced neurocognitive functioning in children by influencing brain structure and functioning. This study investigates the role of white matter microstructure in the relationship of both cardiovascular fitness and gross motor skills with neurocognitive functioning in healthy children. In total 92 children (mean age 9.1 years, range 8.0-10.7) were included in this study. Cardiovascular fitness and gross motor skill performance were assessed using performance-based tests. Neurocognitive functioning was assessed using computerized tests (working memory, inhibition, interference control, information processing, and attention). Diffusion tensor imaging was used in combination with tract-based spatial statistics to assess white matter microstructure as defined by fractional anisotropy (FA), axial and radial diffusivity (AD, RD). The results revealed positive associations of both cardiovascular fitness and gross motor skills with neurocognitive functioning. Information processing and motor response inhibition were associated with FA in a cluster located in the corpus callosum. Within this cluster, higher cardiovascular fitness and better gross motor skills were both associated with greater FA, greater AD, and lower RD. No mediating role was found for FA in the relationship of both cardiovascular fitness and gross motor skills with neurocognitive functioning. The results indicate that cardiovascular fitness and gross motor skills are related to neurocognitive functioning as well as white matter microstructure in children. However, this study provides no evidence for a mediating role of white matter microstructure in these relationships.

Uncovering a Role for the Dorsal Hippocampal Commissure in Recognition Memory

The dorsal hippocampal commissure (DHC) is a white matter tract that provides interhemispheric connections between temporal lobe brain regions. Despite the importance of these regions for learning and memory, there is scant evidence of a role for the DHC in successful memory performance. We used diffusion-weighted magnetic resonance imaging (DW-MRI) and white matter tractography to reconstruct the DHC in both humans (in vivo) and nonhuman primates (ex vivo). Across species, our findings demonstrate a close consistency between the known anatomy and tract reconstructions of the DHC. Anterograde tract-tracer techniques also highlighted the parahippocampal origins of DHC fibers in nonhuman primates. Finally, we derived diffusion tensor MRI metrics from the DHC in a large sample of human subjects to investigate whether interindividual variation in DHC microstructure is predictive of memory performance. The mean diffusivity of the DHC correlated with performance in a standardized recognition memory task, an effect that was not reproduced in a comparison commissure tract—the anterior commissure. These findings highlight a potential role for the DHC in recognition memory, and our tract reconstruction approach has the potential to generate further novel insights into the role of this previously understudied white matter tract in both health and disease.

Bundle-specific associations between white matter microstructure and Aβ and tau pathology in preclinical Alzheimer's disease

Beta-amyloid (Aβ) and tau proteins, the pathological hallmarks of Alzheimer's disease (AD), are believed to spread through connected regions of the brain. Combining diffusion imaging and positron emission tomography, we investigated associations between white matter microstructure specifically in bundles connecting regions where Aβ or tau accumulates and pathology. We focused on free-water-corrected diffusion measures in the anterior cingulum, posterior cingulum, and uncinate fasciculus in cognitively normal older adults at risk of sporadic AD and presymptomatic mutation carriers of autosomal dominant AD. In Aβ-positive or tau-positive groups, lower tissue fractional anisotropy and higher mean diffusivity related to greater Aβ and tau burden in both cohorts. Associations were found in the posterior cingulum and uncinate fasciculus in preclinical sporadic AD, and in the anterior and posterior cingulum in presymptomatic mutation carriers. These results suggest that microstructural alterations accompany pathological accumulation as early as the preclinical stage of both sporadic and autosomal dominant AD.

Anatomy of nerve fiber bundles at micrometer-resolution in the vervet monkey visual system

Although the primate visual system has been extensively studied, detailed spatial organization of white matter fiber tracts carrying visual information between areas has not been fully established. This is mainly due to the large gap between tracer studies and diffusion-weighted MRI studies, which focus on specific axonal connections and macroscale organization of fiber tracts, respectively. Here we used 3D polarization light imaging (3D-PLI), which enables direct visualization of fiber tracts at micrometer resolution, to identify and visualize fiber tracts of the visual system, such as stratum sagittale, inferior longitudinal fascicle, vertical occipital fascicle, tapetum and dorsal occipital bundle in vervet monkey brains. Moreover, 3D-PLI data provide detailed information on cortical projections of these tracts, distinction between neighboring tracts, and novel short-range pathways. This work provides essential information for interpretation of functional and diffusion-weighted MRI data, as well as revision of wiring diagrams based upon observations in the vervet visual system.

Fixel Based Analysis Reveals Atypical White Matter Micro- and Macrostructure in Adults With Autism Spectrum Disorder: An Investigation of the Role of Biological Sex

Atypical white matter (WM) microstructure is commonly implicated in the neuropathophysiology of autism spectrum disorder (ASD). Fixel based analysis (FBA), at the cutting-edge of diffusion-weighted imaging, can account for crossing WM fibers and can provide indices of both WM micro- and macrostructure. We applied FBA to investigate WM structure between 25 (12 males, 13 females) adults with ASD and 24 (12 males, 12 females) matched controls. As the role of biological sex on the neuropathophysiology of ASD is of increasing interest, this was also explored. There were no significant differences in WM micro- or macrostructure between adults with ASD and matched healthy controls. When data were stratified by sex, females with ASD had reduced fiber density and cross-section (FDC), a combined metric comprised of micro- and macrostructural measures, in the corpus callosum, a finding not detected between the male sub-groups. We conclude that micro- and macrostructural WM aberrations are present in ASD, and may be influenced by biological sex.

Evolving White Matter Injury following Pediatric Traumatic Brain Injury

This study is unique in that it examines the evolution of white matter injury very early and at 12 months post-injury in pediatric patients following traumatic brain injury (TBI). Diffusion tensor imaging (DTI) was acquired at two time-points: acutely at 6-17 days and 12 months following a complicated mild (cMild)/moderate (mod) or severe TBI. Regional measures of anisotropy and diffusivity were compared between TBI groups and against a group of age-matched healthy controls and used to predict performance on measures of attention, memory, and intellectual functioning at 12-months post-injury. Analysis of the acute DTI data using tract based spatial statistics revealed a small number of regional decreases in fractional anisotropy (FA) in both the cMild/mod and severe TBI groups compared with controls. These changes were observed in the occipital white matter, anterior limb of the internal capsule (ALIC)/basal ganglia, and corpus callosum. The severe TBI group showed regional differences in axial diffusivity (AD) in the brainstem and corpus callosum that were not seen in the cMild/mod TBI group. By 12-months, widespread decreases in FA and increases in apparent diffusion coefficient (ADC) and radial diffusivity (RD) were observed in both TBI groups compared with controls, with the overall number of regions with abnormal DTI metrics increasing over time. The early changes in regional DTI metrics were associated with 12-month performance IQ scores. These findings suggest that there may be regional differences in the brain's reparative processes or that mechanisms associated with the brain's plasticity to recover may also be region based.

Disentangling white-matter damage from physiological fibre orientation dispersion in multiple sclerosis

Multiple sclerosis leads to diffuse damage of the central nervous system, affecting also the normal-appearing white matter. Demyelination and axonal degeneration reduce regional fractional anisotropy in normal-appearing white matter, which can be routinely mapped with diffusion tensor imaging. However, the standard fractional anisotropy metric is also sensitive to physiological variations in orientation dispersion of white matter fibres. This complicates the detection of disease-related damage in large parts of cerebral white matter where microstructure physiologically displays a high degree of fibre dispersion. To resolve this ambiguity, we employed a novel tensor-valued encoding method for diffusion MRI, which yields a microscopic fractional anisotropy metric that is unaffected by regional variations in orientation dispersion. In 26 patients with relapsing-remitting multiple sclerosis, 14 patients with primary-progressive multiple sclerosis and 27 age-matched healthy controls, we compared standard fractional anisotropy mapping with the novel microscopic fractional anisotropy mapping method, focusing on normal-appearing white matter. Mean microscopic fractional anisotropy and standard fractional anisotropy of normal-appearing white matter were significantly reduced in both patient groups relative to healthy controls, but microscopic fractional anisotropy yielded a better reflection of disease-related white-matter alterations. The reduction in mean microscopic fractional anisotropy showed a significant positive linear relationship with physical disability, as reflected by the expanded disability status scale. Mean reduction of microscopic fractional anisotropy in normal-appearing white matter also scaled positively with individual cognitive dysfunction, as measured with the symbol digit modality test. Mean microscopic fractional anisotropy reduction in normal-appearing white matter also showed a positive relationship with total white-matter lesion load as well as lesion load in specific tract systems. None of these relationships between normal-appearing white-matter microstructure and clinical, cognitive or structural measures emerged when using mean fractional anisotropy. Together, the results provide converging evidence that microscopic fractional anisotropy mapping substantially advances the assessment of cerebral white matter in multiple sclerosis by disentangling microstructure damage from variations in physiological fibre orientation dispersion at the stage of data acquisition. Since tensor-valued encoding can be implemented in routine diffusion MRI, microscopic fractional anisotropy mapping bears considerable potential for the future assessment of disease progression in normal-appearing white matter in both relapsing-remitting and progressive forms of multiple sclerosis as well as other white-matter-related brain diseases.

Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model

BACKGROUND AND AIMS

Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model.

METHODS

Seventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b ​= ​2000 ​s/mm²). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n ​= ​48) and abnormal scores (functional score: 1-5, mild-profound; n ​= ​29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 ​mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome.

RESULTS

For the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome.

DISCUSSION/CONCLUSION

This study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models.

Impact of b-value on estimates of apparent fibre density

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 b = 0 to 6,000 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 = 6,000 s/mm², median R² = .34; b = 4,000 s/mm², median R² = .29; b = 2,400 s/mm², median R² = .21; b = 1,200 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.

Fiber-specific variations in anterior transcallosal white matter structure contribute to age-related differences in motor performance

Age-related differences in bimanual motor performance have been extensively documented, but their underlying neural mechanisms remain less clear. Studies applying diffusion MRI in the aging population have revealed evidence for age-related white matter variations in the corpus callosum (CC) which are related to bimanual motor performance. However, the diffusion tensor model used in those studies is confounded by partial volume effects in voxels with complex fiber geometries which are present in up to 90% of white matter voxels, including the bilateral projections of the CC. A recently developed whole-brain analysis framework, known as fixel-based analysis (FBA), enables comprehensive statistical analyses of white matter quantitative measures in the presence of such complex fiber geometries. To investigate the contribution of age-related fiber-specific white matter variations to age-related differences in bimanual performance, a cross-sectional lifespan sample of healthy human adults (N ​= ​95; 20-75 years of age) performed a bimanual tracking task. Furthermore, diffusion MRI data were acquired and the FBA metrics associated with fiber density, cross-section, and combined fiber density and cross-section were estimated. Whole-brain FBA revealed significant negative associations between age and fiber density, cross-section, and combined metrics of multiple white matter tracts, including the bilateral projections of the CC, indicative of white matter micro- and macrostructural degradation with age. More importantly, mediation analyses demonstrated that age-related variations in the combined (fiber density and cross-section) metric of the genu, but not splenium, of the CC contributed to the observed age-related differences in bimanual coordination performance. These findings highlight the contribution of variations in interhemispheric communication between prefrontal (non-motor) cortices to age-related differences in motor performance.

Structural abnormalities in thalamo-prefrontal tracks revealed by high angular resolution diffusion imaging predict working memory scores in concussed children

Because of their high prevalence, heterogeneous clinical presentation, and wide-ranging sequelae, concussions are a challenging neurological condition, especially in children. Shearing forces transmitted across the brain during concussions often result in white matter damage. The neuropathological impact of concussions has been discerned from animal studies and includes inflammation, demyelination, and axonal loss. These pathologies can overlap during the sub-acute stage of recovery. However, due to the challenges of accurately modeling complex white matter structure, these neuropathologies have not yet been differentiated in children in vivo. In the present study, we leveraged recent advances in diffusion imaging modeling, tractography, and tractometry to better understand the neuropathology underlying working memory problems in concussion. Studying a sample of 16 concussed and 46 healthy youths, we used novel tractography methods to isolate 11 working memory tracks. Along these tracks, we measured fractional anisotropy, diffusivities, track volume, apparent fiber density, and free water fraction. In three tracks connecting the right thalamus to the right dorsolateral prefrontal cortex (DLPFC), we found microstructural differences suggestive of myelin alterations. In another track connecting the left anterior-cingulate cortex with the left DLPFC, we found microstructural changes suggestive of axonal loss. Structural differences and tractography reconstructions were reproduced using test-retest analyses. White matter structure in the three thalamo-prefrontal tracks, but not the cingulo-prefrontal track, appeared to play a key role in working memory function. The present results improve understanding of working memory neuropathology in concussions, which constitutes an important step toward developing neuropathologically informed biomarkers of concussion in children.

Early white matter development is abnormal in tuberous sclerosis complex patients who develop autism spectrum disorder

Background

Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants.

Methods

TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD.

Results

Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups.

Conclusions

Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.

Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer's disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus.

Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases

Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.