Disentangling micro from mesostructure by diffusion MRI: A Bayesian approach

A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI

Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as variable dimensionalities (reflecting the unknown number of distinct white matter fiber populations in a voxel), low signal-to-noise ratios, and non-linear forward models. These challenges have led many existing methods to use biologically implausible simplified models to stabilize estimation, for instance, assuming shared microstructure across all fiber populations within a voxel. In this work, we introduce a novel sequential method for multi-fiber parameter inference that decomposes the task into a series of manageable subproblems. These subproblems are solved using deep neural networks tailored to problem-specific structure and symmetry, and trained via simulation. The resulting inference procedure is largely amortized, enabling scalable parameter estimation and uncertainty quantification across all model parameters. Simulation studies and real imaging data analysis using the Human Connectome Project (HCP) demonstrate the advantages of our method over standard alternatives. In the case of the standard model of diffusion, our results show that under HCP-like acquisition schemes, estimates for extra-cellular parallel diffusivity are highly uncertain, while those for the intra-cellular volume fraction can be estimated with relatively high precision.

White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life.

Cross-Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change

The concept of brain age (BA) describes an integrative imaging marker of brain health, often suggested to reflect aging processes. However, the degree to which cross-sectional MRI features, including BA, reflect past, ongoing, and future brain changes across different tissue types from macro- to microstructure remains controversial. Here, we use multimodal imaging data of 39,325 UK Biobank participants, aged 44-82 years at baseline and 2,520 follow-ups within 1.12-6.90 years to examine BA changes and their relationship to anatomical brain changes. We find insufficient evidence to conclude that BA reflects the rate of brain aging. However, modality-specific differences in brain ages reflect the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers.

Amygdala neurodegeneration differentiates brain-first and body-first Parkinson's disease: An MRI study

BACKGROUND

Two distinct patterns of alpha-synuclein spread in Parkinson's disease were proposed with a body-first and a brain-first subtype. The body-first subtype originates in the periphery, while the brain-first subtype initiates in the central nervous system, notably affecting the amygdala.

OBJECTIVES

This retrospective cross-sectional study compared the integrity of the substantia nigra and amygdala between body-first and brain-first Parkinson's disease subtypes.

METHODS

We analyzed data from 30 Parkinson's disease patients, classified into body-first (n = 21) and brain-first (n = 9) subtypes based on REM sleep behavior disorder history. Microstructural integrity was assessed using diffusion microstructure MRI.

RESULTS

No significant differences were found in the substantia nigra between subtypes. However, amygdala degeneration was significantly pronounced in the "brain-first" compared to the "body-first" group reflected by increased free interstitial fluid (p = 0.02, Cohen's d = -1.22).

CONCLUSIONS

The degeneration of amygdala is distinctively pronounced in "brain-first" Parkinson's disease, supporting differential disease progression patterns between subtypes.

A cross-sectional multicentre study of multishell diffusion MRI in multiple sclerosis

BACKGROUND AND OBJECTIVES

White matter (WM) microstructural properties from advanced multishell diffusion MRI (dMRI) have been linked to clinical disability in multiple sclerosis (MS). This multicentre study used multishell dMRI to compute WM metrics and test for differences between people with MS (pwMS) and healthy controls (HCs).

METHODS

We included multishell dMRI data from 251 pwMS or clinically isolated syndrome (CIS) (mean age 40.7 years, 72.4 % women, 88.8 % relapsing remitting MS) at six MAGNIMS centres and 543 HCs. Eleven scalar metric maps were estimated from multishell dMRI sequences, based on diffusion tensor imaging (DTI) and restriction spectrum imaging (RSI). The maps were analysed using tract-based spatial statistics (TBSS). The diffusion output was submitted to paired sampled t-tests to test for case-control differences and linear regression models to test for associations with Expanded Disability Status Scale (EDSS) scores, while accounting for confounders. In a sub-sample from Oslo, we tested for correlations between EDSS and dMRI metrics within WM lesions.

RESULTS

Significant group differences were found in nine out of eleven dMRI metrics. Linear regression models revealed significant correlations between EDSS and fractional anisotropy (FA) fast (β=-4.54, p = 0.01) and apparent diffusion coefficient (ADC) fast (β=10.92, p = 8.7 × 10-3).

CONCLUSIONS

Diffusion MRI based on clinically feasible multishell sequences uncovers WM group differences between pwMS and HCs, but only a selection of the advanced multishell parameters were sensitive to disability, and no statistically significant correlations with disability remained after Bonferroni correction.

Performance Comparison of Diffusion Kurtosis Imaging (DKI), Neurite Orientation Dispersion and Density Imaging (NODDI), and Diffusion Microstructure Imaging (DMI) in Predicting Adult-Type Glioma Subtype-A Pilot Study

Background/Objectives: The purpose of this study was to evaluate the performance of diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI) in differentiating molecular subtypes of adult-type gliomas. Methods: Standardized MRI was performed and evaluated in 59 patients with adult-type glioma. DKI, NODDI, and DMI parameter values were quantitatively evaluated in ROIs in contrast-enhancing/solid tumor tissue and five concentric shells with peritumoral tissue. DKI, NODDI, and DMI parameters of (i) glioblastomas, Isocitrate dehydrogenase (IDH) wildtype; (ii) astrocytomas, IDH mutant; and (iii) oligodendrogliomas, IDH mutant were compared with analysis of variance (ANOVA). Receiver operating characteristic curve (ROC) curve analysis was conducted to discriminate firstly between IDH mutant and IDH wildtype gliomas and then between IDH mutant astrocytomas and oligodendrogliomas. Results: Significant differences between the three aforementioned subtypes were found for the apparent diffusion coefficient (ADC) and mean kurtosis (MK) and again for the orientation dispersion index (ODI) and intra-axonal volume fraction (v-intra). The diagnostic accuracy depended on the distance to the contrast-enhancing/solid tumor tissue. Some NODDI and DMI parameters significantly predicted the IDH status and significantly discriminated between astrocytomas and oligodendrogliomas; however, ADC and MK showed the best prediction in both ROC analyses (maximum AUC 0.910 (CI 0.824-0.995)). Conclusions: The evaluation of peritumoral tissue can be a valuable procedure, while NODDI and DMI appear to be promising but are currently inferior to DKI in predicting glioma subtypes categorized according to the WHO 2021 classification.

Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI

The central nervous system (CNS), comprised of both the brain and spinal cord, and is a complex network of white and gray matter responsible for sensory, motor, and cognitive functions. Advanced diffusion MRI (dMRI) techniques offer a promising mechanism to non-invasively characterize CNS architecture, however, most studies focus on the brain or spinal cord in isolation. Here, we implemented a clinically feasible dMRI protocol on a 3T scanner to simultaneously characterize neurite and soma microstructure of both the brain and spinal cord. The protocol enabled the use of Diffusion Tensor Imaging (DTI), Standard Model Imaging (SMI), and Soma and Neurite Density Imaging (SANDI), representing the first time SMI and SANDI have been evaluated in the cord, and in the cord and brain simultaneously. Our results demonstrate high image quality even at high diffusion weightings, reproducibility of SMI and SANDI derived metrics similar to those of DTI with few exceptions, and biologically feasible contrasts between and within white and gray matter regions. Reproducibility and contrasts were decreased in the cord compared to that of the brain, revealing challenges due to partial volume effects and image preprocessing. This study establishes a harmonized approach for brain and cord microstructural imaging, and the opportunity to study CNS pathologies and biomarkers of structural integrity across the neuroaxis.

In vivo human neurite exchange imaging (NEXI) at 500 mT/m diffusion gradients

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b -values up to 17.5 ms/μm2. The anisotropic Kärger model was applied to estimate the exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median±interquartile range) 13±8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the NEXI exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated NEXI exchange time and require extra attention when comparing NEXI results between various hardware setups.

Diffusion MRI with Machine Learning

Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

Inflammation-Triggered Enlargement of Choroid Plexus in Subacute COVID-19 Patients with Neurological Symptoms

OBJECTIVE

To investigate whether choroid plexus volumes in subacute coronavirus disease 2019 (COVID-19) patients with neurological symptoms could indicate inflammatory activation or barrier dysfunction and assess their association with clinical data.

METHODS

Choroid plexus volumes were measured in 28 subacute COVID-19 patients via cerebral magnetic resonance imaging (MRI), compared with those in infection-triggered non-COVID-19 encephalopathy patients (n = 25), asymptomatic individuals after COVID-19 (n = 21), and healthy controls (n = 21). Associations with inflammatory serum markers (peak counts of leukocytes, C-reactive protein [CRP], interleukin 6), an MRI-based marker of barrier dysfunction (CSF volume fraction [V-CSF]), and clinical parameters like olfactory performance and cognitive scores (Montreal Cognitive Assessment) were investigated.

RESULTS

COVID-19 patients showed significantly larger choroid plexus volumes than control groups (p < 0.001, η2 = 0.172). These volumes correlated significantly with peak leukocyte levels (p = 0.001, Pearson's r = 0.621) and V-CSF (p = 0.009, Spearman's rho = 0.534), but neither with CRP nor interleukin 6. No significant correlations were found with clinical parameters.

INTERPRETATION

In patients with subacute COVID-19, choroid plexus volume is a marker of central nervous system inflammation and barrier dysfunction in the presence of neurologic symptoms. The absence of plexus enlargement in infection-triggered non-COVID-19 encephalopathy suggests a specific severe acute respiratory syndrome coronavirus 2 effect. This study also documents an increase in choroid plexus volume for the first time as a parainfectious event. ANN NEUROL 2024;96:715-725.

Advanced diffusion MRI provides evidence for altered axonal microstructure and gradual peritumoral infiltration in GBM in comparison to brain metastases

PURPOSE

In contrast to peritumoral edema in metastases, GBM is histopathologically characterized by infiltrating tumor cells within the T2 signal alterations. We hypothesized that depending on the distance from the outline of the contrast-enhancing tumor we might reveal imaging evidence of gradual peritumoral infiltration in GBM and predominantly vasogenic edema around metastases. We thus investigated the gradual change of advanced diffusion metrics with the peritumoral zone in metastases and GBM.

METHODS

In 30 patients with GBM and 28 with brain metastases, peritumoral T2 hyperintensity was segmented in 33% partitions based on the total volume beginning at the enhancing tumor margin and divided into inner, middle and outer zones. Diffusion Tensor Imaging (DTI)-derived fractional anisotropy and mean diffusivity as well as Diffusion Microstructure Imaging (DMI)-based parameters Dax-intra, Dax-extra, V‑CSF and V-intra were employed to assess group-wise differences between inner and outer zones as well as within-group gradients between the inner and outer zones.

RESULTS

In metastases, fractional anisotropy and Dax-extra were significantly reduced in the inner zone compared to the outer zone (FA p = 0.01; Dax-extra p = 0.03). In GBM, we noted a reduced Dax-extra and significantly lower intraaxonal volume fraction (Dax-extra p = 0.008, V‑intra p = 0.006) accompanied by elevated axial intraaxonal diffusivity in the inner zone (p = 0.035). Between-group comparison of the outer to the inner zones revealed significantly higher gradients in metastases over GBM for FA (p = 0.04) as well as the axial diffusivity in the intra- (p = 0.02) and extraaxonal compartment (p < 0.001).

CONCLUSION

Our findings provide evidence of gradual alterations within the peritumoral zone of brain tumors. These are compatible with predominant (vasogenic) edema formation in metastases, whereas our findings in GBM are in line with an axonal destructive component in the immediate peritumoral area and evidence of tumor cell infiltration with accentuation in the tumor's vicinity.

The Larmor frequency shift of a white matter magnetic microstructure model with multiple sources

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to the macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense medium of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here, we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron, and so forth. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI measurements of an ex vivo mouse brain at ultra-high field.

In Search for a Pathogenesis of Major Depression and Suicide-A Joint Investigation of Dopamine and Fiber Tract Anatomy Focusing on the Human Ventral Mesencephalic Tegmentum: Description of a Workflow

Major depressive disorder (MDD) is prevalent with a high subjective and socio-economic burden. Despite the effectiveness of classical treatment methods, 20-30% of patients stay treatment-resistant. Deep Brain Stimulation of the superolateral branch of the medial forebrain bundle is emerging as a clinical treatment. The stimulation region (ventral tegmental area, VTA), supported by experimental data, points to the role of dopaminergic (DA) transmission in disease pathology. This work sets out to develop a workflow that will allow the performance of analyses on midbrain DA-ergic neurons and projections in subjects who have committed suicide. Human midbrains were retrieved during autopsy, formalin-fixed, and scanned in a Bruker MRI scanner (7T). Sections were sliced, stained for tyrosine hydroxylase (TH), digitized, and integrated into the Montreal Neurological Institute (MNI) brain space together with a high-resolution fiber tract atlas. Subnuclei of the VTA region were identified. TH-positive neurons and fibers were semi-quantitatively evaluated. The study established a rigorous protocol allowing for parallel histological assessments and fiber tractographic analysis in a common space. Semi-quantitative readings are feasible and allow the detection of cell loss in VTA subnuclei. This work describes the intricate workflow and first results of an investigation of DA anatomy in VTA subnuclei in a growing naturalistic database.

A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.

Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank

Background

During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging.

Methods

We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data.

Results

Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages.

Conclusions

We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.

Complemental Value of Microstructural and Macrostructural MRI in the Discrimination of Neurodegenerative Parkinson Syndromes

PURPOSE

Various MRI-based techniques were tested for the differentiation of neurodegenerative Parkinson syndromes (NPS); the value of these techniques in direct comparison and combination is uncertain. We thus compared the diagnostic performance of macrostructural, single compartmental, and multicompartmental MRI in the differentiation of NPS.

METHODS

We retrospectively included patients with NPS, including 136 Parkinson's disease (PD), 41 multiple system atrophy (MSA) and 32 progressive supranuclear palsy (PSP) and 27 healthy controls (HC). Macrostructural tissue probability values (TPV) were obtained by CAT12. The microstructure was assessed using a mesoscopic approach by diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and diffusion microstructure imaging (DMI). After an atlas-based read-out, a linear support vector machine (SVM) was trained on a training set (n = 196) and validated in an independent test cohort (n = 40). The diagnostic performance of the SVM was compared for different inputs individually and in combination.

RESULTS

Regarding the inputs separately, we observed the best diagnostic performance for DMI. Overall, the combination of DMI and TPV performed best and correctly classified 88% of the patients. The corresponding area under the receiver operating characteristic curve was 0.87 for HC, 0.97 for PD, 1.0 for MSA, and 0.99 for PSP.

CONCLUSION

We were able to demonstrate that (1) MRI parameters that approximate the microstructure provided substantial added value over conventional macrostructural imaging, (2) multicompartmental biophysically motivated models performed better than the single compartmental DTI and (3) combining macrostructural and microstructural information classified NPS and HC with satisfactory performance, thus suggesting a complementary value of both approaches.

Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction and fatigue

After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition, marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with Post-COVID-Condition, healthy controls, and individuals that contracted COVID-19 without long-term symptoms. We reveal widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in close anatomical-functional relationship with the respective symptoms.

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.

The connection of motor improvement after deep brain stimulation in Parkinson's disease and microstructural integrity of the substantia nigra and subthalamic nucleus

BACKGROUND

Nigrostriatal microstructural integrity has been suggested as a biomarker for levodopa response in Parkinson's disease (PD), which is a strong predictor for motor response to deep brain stimulation (DBS) of the subthalamic nucleus (STN). This study aimed to explore the impact of microstructural integrity of the substantia nigra (SN), STN, and putamen on motor response to STN-DBS using diffusion microstructure imaging.

METHODS

Data was collected from 23 PD patients (mean age 63 ± 7, 6 females) who underwent STN-DBS, had preoperative 3 T diffusion magnetic resonance imaging including multishell diffusion-weighted MRI with b-values of 1000 and 2000 s/mm2 and records of motor improvement available.

RESULTS

The association between a poorer DBS-response and increased free interstitial fluid showed notable effect sizes (rho > |0.4|) in SN and STN, but not in putamen. However, this did not reach significance after Bonferroni correction and controlling for sex and age.

CONCLUSION

Microstructural integrity of SN and STN are potential biomarkers for the prediction of therapy efficacy following STN-DBS, but further studies are required to confirm these associations.

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

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

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

Brain asymmetries from mid- to late life and hemispheric brain age

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

Advanced diffusion imaging reveals microstructural characteristics of primary CNS lymphoma, allowing differentiation from glioblastoma

Background

Primary CNS lymphoma (PCNSL) and glioblastoma (GBM) both represent frequent intracranial malignancies with differing clinical management. However, distinguishing PCNSL from GBM with conventional MRI can be challenging when atypical imaging features are present. We employed advanced dMRI for noninvasive characterization of the microstructure of PCNSL and differentiation from GBM as the most frequent primary brain malignancy.

Methods

Multiple dMRI metrics including Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging, and Diffusion Microstructure Imaging were extracted from the contrast-enhancing tumor component in 10 PCNSL and 10 age-matched GBM on 3T MRI. Imaging findings were correlated with cell density and axonal markers obtained from histopathology.

Results

We found significantly increased intra-axonal volume fractions (V-intra and intracellular volume fraction) and microFA in PCNSL compared to GBM (all P < .001). In contrast, mean diffusivity (MD), axial diffusivity (aD), and microADC (all P < .001), and also free water fractions (V-CSF and V-ISO) were significantly lower in PCNSL (all P < .01). Receiver-operating characteristic analysis revealed high predictive values regarding the presence of a PCNSL for MD, aD, microADC, V-intra, ICVF, microFA, V-CSF, and V-ISO (area under the curve [AUC] in all >0.840, highest for MD and ICVF with an AUC of 0.960). Comparative histopathology between PCNSL and GBM revealed a significantly increased cell density in PCNSL and the presence of axonal remnants in a higher proportion of samples.

Conclusions

Advanced diffusion imaging enables the characterization of the microstructure of PCNSL and reliably distinguishes PCNSL from GBM. Both imaging and histopathology revealed a relatively increased cell density and a preserved axonal microstructure in PCNSL.

Cerebellar, Not Nigrostriatal Degeneration Impairs Dexterity in Multiple System Atrophy

BACKGROUND

Multiple system atrophy (MSA) clinically manifests with either predominant nigrostriatal or cerebellopontine degeneration. This corresponds to two different phenotypes, one with predominant Parkinson's symptoms (MSA-P [multiple system atrophy-parkinsonian subtype]) and one with predominant cerebellar deficits (MSA-C [multiple system atrophy-cerebellar subtype]). Both nigrostriatal and cerebellar degeneration can lead to impaired dexterity, which is a frequent cause of disability in MSA.

OBJECTIVE

The aim was to disentangle the contribution of nigrostriatal and cerebellar degeneration to impaired dexterity in both subtypes of MSA.

METHODS

We thus investigated nigrostriatal and cerebellopontine integrity using diffusion microstructure imaging in 47 patients with MSA-P and 17 patients with MSA-C compared to 31 healthy controls (HC). Dexterity was assessed using the 9-Hole Peg Board (9HPB) performance.

RESULTS

Nigrostriatal degeneration, represented by the loss of cells and neurites, leading to a larger free-fluid compartment, was present in MSA-P and MSA-C when compared to HCs. Whereas no intergroup differences were observed between the MSAs in the substantia nigra, MSA-P showed more pronounced putaminal degeneration than MSA-C. In contrast, a cerebellopontine axonal degeneration was observed in MSA-P and MSA-C, with stronger effects in MSA-C. Interestingly, the degeneration of cerebellopontine fibers is associated with impaired dexterity in both subtypes, whereas no association was observed with nigrostriatal degeneration.

CONCLUSION

Cerebellar dysfunction contributes to impaired dexterity not only in MSA-C but also in MSA-P and may be a promising biomarker for disease staging. In contrast, no significant association was observed with nigrostriatal dysfunction. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding

Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.

Mesoscopic Assessment of Microstructure in Glioblastomas and Metastases by Merging Advanced Diffusion Imaging with Immunohistopathology

BACKGROUND AND PURPOSE

Glioblastomas and metastases are the most common malignant intra-axial brain tumors in adults and can be difficult to distinguish on conventional MR imaging due to similar imaging features. We used advanced diffusion techniques and structural histopathology to distinguish these tumor entities on the basis of microstructural axonal and fibrillar signatures in the contrast-enhancing tumor component.

MATERIALS AND METHODS

Contrast-enhancing tumor components were analyzed in 22 glioblastomas and 21 brain metastases on 3T MR imaging using DTI-fractional anisotropy, neurite orientation dispersion and density imaging-orientation dispersion, and diffusion microstructural imaging-micro-fractional anisotropy. Available histopathologic specimens (10 glioblastomas and 9 metastases) were assessed for the presence of axonal structures and scored using 4-level scales for Bielschowsky staining (0: no axonal structures, 1: minimal axonal fragments preserved, 2: decreased axonal density, 3: no axonal loss) and glial fibrillary acid protein expression (0: no glial fibrillary acid protein positivity, 1: limited expression, 2: equivalent to surrounding parenchyma, 3: increased expression).

RESULTS

When we compared glioblastomas and metastases, fractional anisotropy was significantly increased and orientation dispersion was decreased in glioblastomas (each P < .001), with a significant shift toward increased glial fibrillary acid protein and Bielschowsky scores. Positive associations of fractional anisotropy and negative associations of orientation dispersion with glial fibrillary acid protein and Bielschowsky scores were revealed, whereas no association between micro-fractional anisotropy with glial fibrillary acid protein and Bielschowsky scores was detected. Receiver operating characteristic curves revealed high predictive values of both fractional anisotropy (area under the curve = 0.8463) and orientation dispersion (area under the curve = 0.8398) regarding the presence of a glioblastoma.

CONCLUSIONS

Diffusion imaging fractional anisotropy and orientation dispersion metrics correlated with histopathologic markers of directionality and may serve as imaging biomarkers in contrast-enhancing tumor components.

Automated signal intensity analysis of the spinal cord for detection of degenerative cervical myelopathy - a matched-pair MRI study

PURPOSE

Detection of T2 hyperintensities in suspected degenerative cervical myelopathy (DCM) is done subjectively in clinical practice. To gain objective quantification for dedicated treatment, signal intensity analysis of the spinal cord is purposeful. We investigated fully automated quantification of the T2 signal intensity (T2-SI) of the spinal cord using a high-resolution MRI segmentation.

METHODS

Matched-pair analysis of prospective acquired cervical 3D T2-weighted sequences of 114 symptomatic patients and 88 healthy volunteers. Cervical spinal cord was segmented automatically through a trained convolutional neuronal network with subsequent T2-SI registration slice-by-slice. Received T2-SI curves were subdivided for each cervical level from C2 to C7. Additionally, all levels were subjectively classified concerning a present T2 hyperintensity. For T2-positive levels, corresponding T2-SI curves were compared to curves of age-matched volunteers at the identical level.

RESULTS

Forty-nine patients showed subjective T2 hyperintensities at any level. The corresponding T2-SI curves showed higher signal variabilities reflected by standard deviation (18.51 vs. 7.47 a.u.; p < 0.001) and range (56.09 vs. 24.34 a.u.; p < 0.001) compared to matched controls. Percentage of the range from the mean absolute T2-SI per cervical level, introduced as "T2 myelopathy index" (T2-MI), was correspondingly significantly higher in T2-positive segments (23.99% vs. 10.85%; p < 0.001). ROC analysis indicated excellent differentiation for all three parameters (AUC 0.865-0.920).

CONCLUSION

This fully automated T2-SI quantification of the spinal cord revealed significantly increased signal variability for DCM patients compared to healthy volunteers. This innovative procedure and the applied parameters showed sufficient diagnostic accuracy, potentially diagnosing radiological DCM more objective to optimize treatment recommendation.

TRIAL REGISTRATION

DRKS00012962 (17.01.2018) and DRKS00017351 (28.05.2019).

The impact of head orientation with respect to B0 on diffusion tensor MRI measures

Diffusion tensor MRI (DT-MRI) remains the most commonly used approach to characterise white matter (WM) anisotropy. However, DT estimates may be affected by tissue orientation w.r.t. B → 0 due to local gradients and intrinsic T 2 orientation dependence induced by the microstructure. This work aimed to investigate whether and how diffusion tensor MRI-derived measures depend on the orientation of the head with respect to the static magnetic field, B → 0 . By simulating WM as two compartments, we demonstrated that compartmental T 2 anisotropy can induce the dependence of diffusion tensor measures on the angle between WM fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and increased axial diffusivity were observed in white matter fibres perpendicular to B → 0 compared to those parallel to B → 0 . Fractional anisotropy varied by up to 20 % as a function of the angle between WM fibres and the orientation of the main magnetic field. To conclude, fibre orientation w.r.t. B → 0 is responsible for up to 7 % variance in diffusion tensor measures across the whole brain white matter from all subjects and head orientations. Fibre orientation w.r.t. B → 0 may introduce additional variance in clinical research studies using diffusion tensor imaging, particularly when it is difficult to control for (e.g., fetal or neonatal imaging, or when the trajectories of fibres change due to, e.g., space occupying lesions).

Brain-wide associations between white matter and age highlight the role of fornix microstructure in brain ageing

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different levels of white matter damage in rodent optic nerves. 2) We obtained ex vivo DW-MRI data with b-tensor encoding schemes and calculated quantitative metrics using Q-space trajectory imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI data analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.

Bio-psycho-social factors' associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking ~50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

Parameter estimation for WMTI-Watson model of white matter using encoder-decoder recurrent neural network

PURPOSE

Biophysical modeling of the diffusion MRI (dMRI) signal provides estimates of specific microstructural tissue properties. Although non-linear least squares (NLLS) is the most widespread fitting method, it suffers from local minima and high computational cost. Deep learning approaches are steadily replacing NLLS, but come with the limitation that the model needs to be retrained for each acquisition protocol and noise level. In this study, a novel fitting approach was proposed based on the encoder-decoder recurrent neural network (RNN) to accelerate model estimation with good generalization to various datasets.

METHODS

The white matter tract integrity (WMTI)-Watson model as an implementation of the Standard Model of diffusion in white matter derives its parameters indirectly from the diffusion and kurtosis tensors (DKI). The RNN-based solver, which estimates the WMTI-Watson model from DKI, is therefore more readily translatable to various data, irrespective of acquisition protocols as long as the DKI was pre-computed from the signal. An embedding approach was also used to render the model insensitive to potential differences in distributions between training data and experimental data. The analytical solution, NLLS, RNN-, and a multilayer perceptron (MLP)-based methods were evaluated on synthetic and in vivo datasets of rat and human brain.

RESULTS

The proposed RNN solver showed highly reduced computation time over the analytical solution and NLLS, with similar accuracy but improved robustness, and superior generalizability over MLP.

CONCLUSION

The RNN estimator can be easily applied to various datasets without retraining, which shows great potential for a widespread use.

Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from only axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown distribution of axonal orientations. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated although needed for modeling axons - especially in the context of multi-compartmental modeling. We introduce a new general method for the estimation of both the axial and radial axonal diffusivities at strong diffusion weightings based on kernel zonal modeling. The method could lead to estimates that are free from partial volume bias with gray matter or other isotropic compartments. The method is tested on publicly available data from the MGH Adult Diffusion Human Connectome project. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii from only two shells. The estimation problem is also addressed from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.

Application of diffusion microstructure imaging in musculoskeletal radiology - translation from head to shoulders

OBJECTIVES

Quantitative MRI techniques, such as diffusion microstructure imaging (DMI), are increasingly applied for advanced tissue characterization. We determined its value in rotator cuff (RC) muscle imaging by studying the association of DMI parameters to isometric strength and fat fraction (FF).

METHODS

Healthy individuals prospectively underwent 3T-MRI of the shoulder using DMI and chemical shift encoding-based water-fat imaging. RC muscles were segmented and quantitative MRI metrics (V-ISO, free fluid; V-intra, compartment inside of muscle fibers; V-extra, compartment outside of muscle fibers, and FF) were extracted. Isometric shoulder strength was quantified using specific clinical tests. Sex-related differences were assessed with Student's t. Association of DMI-metrics, FF, and strength was tested. A factorial two-way ANOVA was performed to compare the main effects of sex and external/internal strength-ratio and their interaction effects on quantitative imaging parameters ratios of infraspinatus/subscapularis.

RESULTS

Among 22 participants (mean age: 26.7 ± 3.1 years, 50% female, mean BMI: 22.6 ± 1.9 kg/m²), FF of the individual RC muscles did not correlate with strength or DMI parameters (all p > 0.05). Subjects with higher V-intra (r = 0.57 to 0.87, p < 0.01) and lower V-ISO (r = -0.6 to -0.88, p < 0.01) had higher internal and external rotation strength. Moreover, V-intra was higher and V-ISO was lower in all RC muscles in males compared to female subjects (all p < 0.01). There was a sex-independent association of external/internal strength-ratio with the ratio of V-extra of infraspinatus/subscapularis (p = 0.02).

CONCLUSIONS

Quantitative DMI parameters may provide incremental information about muscular function and microstructure in young athletes and may serve as a potential biomarker.

KEY POINTS

• Diffusion microstructure imaging was successfully applied to non-invasively assess the microstructure of rotator cuff muscles in healthy volunteers. • Sex-related differences in the microstructural composition of the rotator cuff were observed. • Muscular microstructural metrics correlated with rotator cuff strength and may serve as an imaging biomarker of muscular integrity and function.

Differentiation of Perilesional Edema in Glioblastomas and Brain Metastases: Comparison of Diffusion Tensor Imaging, Neurite Orientation Dispersion and Density Imaging and Diffusion Microstructure Imaging

Although the free water content within the perilesional T2 hyperintense region should differ between glioblastomas (GBM) and brain metastases based on histological differences, the application of classical MR diffusion models has led to inconsistent results regarding the differentiation between these two entities. Whereas diffusion tensor imaging (DTI) considers the voxel as a single compartment, multicompartment approaches such as neurite orientation dispersion and density imaging (NODDI) or the recently introduced diffusion microstructure imaging (DMI) allow for the calculation of the relative proportions of intra- and extra-axonal and also free water compartments in brain tissue. We investigate the potential of water-sensitive DTI, NODDI and DMI metrics to detect differences in free water content of the perilesional T2 hyperintense area between histopathologically confirmed GBM and brain metastases. Respective diffusion metrics most susceptible to alterations in the free water content (MD, V-ISO, V-CSF) were extracted from T2 hyperintense perilesional areas, normalized and compared in 24 patients with GBM and 25 with brain metastases. DTI MD was significantly increased in metastases (p = 0.006) compared to GBM, which was corroborated by an increased DMI V-CSF (p = 0.001), while the NODDI-derived ISO-VF showed only trend level increase in metastases not reaching significance (p = 0.060). In conclusion, diffusion MRI metrics are able to detect subtle differences in the free water content of perilesional T2 hyperintense areas in GBM and metastases, whereas DMI seems to be superior to DTI and NODDI.

Diffusion microstructure imaging in progressive supranuclear palsy: reduced axonal volumes in the superior cerebellar peduncles, dentato-rubro-thalamic tracts, ventromedial thalami, and frontomesial white matter

Differentiating between Parkinson's disease (PD) and atypical Parkinson syndromes such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and corticobasal degeneration is challenging. Diffusion microstructure imaging (DMI) was analyzed in patients with clinically suspected atypical Parkinson syndromes and healthy controls. In an exploration cohort, the spatial distribution of PSP-related changes of DMI parameters were evaluated in a voxel-wise analysis and a region-of-interest (ROI)-based approach was established. The diagnostic performance was subsequently tested in an independent validation cohort. In the exploration cohort, 53 PSP patients were compared to a pooled comparison group of 19 patients with PD, 26 patients with MSA, 7 patients with corticobasal syndrome, and 25 healthy controls. PSP patients showed widespread axonal loss in the superior cerebellar peduncles, the dentato-rubro-thalamic tracts, the thalami and the frontal white matter (each P < 0.001). In the validation cohort consisting of 12 patients with PSP vs. 13 patients with other movement disorders, the accuracy of this ROI-based approach for identifying the PSP was highest in the thalamus and the frontal white matter (accuracy 0.96 each). This DMI approach can identify PSP patients on an individual level in a collective with suspected atypical Parkinson syndromes and allows further insight on microstructural alterations in vivo.

Identifying microstructural changes in diffusion MRI; How to circumvent parameter degeneracy

Biophysical models that attempt to infer real-world quantities from data usually have many free parameters. This over-parameterisation can result in degeneracies in model inversion and render parameter estimation ill-posed. However, in many applications, we are not interested in quantifying the parameters per se, but rather in identifying changes in parameters between experimental conditions (e.g. patients vs controls). Here we present a Bayesian framework to make inference on changes in the parameters of biophysical models even when model inversion is degenerate, which we refer to as Bayesian EstimatioN of CHange (BENCH). We infer the parameter changes in two steps; First, we train models that can estimate the pattern of change in the measurements given any hypothetical direction of change in the parameters using simulations. Next, for any pair of real data sets, we use these pre-trained models to estimate the probability that an observed difference in the data can be explained by each model of change. BENCH is applicable to any type of data and models and particularly useful for biophysical models with parameter degeneracies, where we can assume the change is sparse. In this paper, we apply the approach in the context of microstructural modelling of diffusion MRI data, where the models are usually over-parameterised and not invertible without injecting strong assumptions. Using simulations, we show that in the context of the standard model of white matter our approach is able to identify changes in microstructural parameters from conventional multi-shell diffusion MRI data. We also apply our approach to a subset of subjects from the UK-Biobank Imaging to identify the dominant standard model parameter change in areas of white matter hyperintensities under the assumption that the standard model holds in white matter hyperintensities.

Disentangling nigral and putaminal contribution to motor impairment and levodopa response in Parkinson's disease

The extent to which the degeneration of the substantia nigra (SN) and putamen each contribute to motor impairment in Parkinson's disease (PD) is unclear, as they are usually investigated using different imaging modalities. To examine the pathophysiological significance of the SN and putamen in both motor impairment and the levodopa response in PD using diffusion microstructure imaging (DMI). In this monocentric retrospective cross-sectional study, DMI parameters from 108 patients with PD and 35 healthy controls (HC) were analyzed using a voxel- and region-based approach. Linear models were applied to investigate the association between individual DMI parameters and Movement Disorder Society Unified Parkinson's Disease Rating Scale-Part 3 performance in ON- and OFF-states, as well as the levodopa response, controlling for age and sex. Voxel- and region-based group comparisons of DMI parameters between PD and HC revealed significant differences in the SN and putamen. In PD, a poorer MDS-UPDRS-III performance in the ON-state was associated with increased free fluid in the SN (b-weight = 65.79, p = 0.004) and putamen (b-weight = 86.00, p = 0.006), and contrariwise with the demise of cells in both structures. The levodopa response was inversely associated with free fluid both in the SN (b-weight = -83.61, p = 0.009) and putamen (b-weight = -176.56, p < 0.001). Interestingly, when the two structures were assessed together, the integrity of the putamen, but not the SN, served as a predictor for the levodopa response (b-weight = -158.03, p < 0.001). Structural alterations in the SN and putamen can be measured by diffusion microstructure imaging in PD. They are associated with poorer motor performance in the ON-state, as well as a reduced response to levodopa. While both nigral and putaminal integrity are required for good performance in the ON-state, it is putaminal integrity alone that determines the levodopa response. Therefore, the structural integrity of the putamen is crucial for the improvement of motor symptoms to dopaminergic medication, and might therefore serve as a promising biomarker for motor staging.

Widespread white matter oedema in subacute COVID-19 patients with neurological symptoms

While neuropathological examinations in patients who died from COVID-19 revealed inflammatory changes in cerebral white matter, cerebral MRI frequently fails to detect abnormalities even in the presence of neurological symptoms. Application of multi-compartment diffusion microstructure imaging (DMI), that detects even small volume shifts between the compartments (intra-axonal, extra-axonal and free water/CSF) of a white matter model, is a promising approach to overcome this discrepancy. In this monocentric prospective study, a cohort of 20 COVID-19 inpatients (57.3 ± 17.1 years) with neurological symptoms (e.g. delirium, cranial nerve palsies) and cognitive impairments measured by the Montreal Cognitive Assessment (MoCA test; 22.4 ± 4.9; 70% below the cut-off value <26/30 points) underwent DMI in the subacute stage of the disease (29.3 ± 14.8 days after positive PCR). A comparison of whole-brain white matter DMI parameters with a matched healthy control group (n = 35) revealed a volume shift from the intra- and extra-axonal space into the free water fraction (V-CSF). This widespread COVID-related V-CSF increase affected the entire supratentorial white matter with maxima in frontal and parietal regions. Streamline-wise comparisons between COVID-19 patients and controls further revealed a network of most affected white matter fibres connecting widespread cortical regions in all cerebral lobes. The magnitude of these white matter changes (V-CSF) was associated with cognitive impairment measured by the MoCA test (r = -0.64, P = 0.006) but not with olfactory performance (r = 0.29, P = 0.12). Furthermore, a non-significant trend for an association between V-CSF and interleukin-6 emerged (r = 0.48, P = 0.068), a prominent marker of the COVID-19 related inflammatory response. In 14/20 patients who also received cerebral 18F-FDG PET, V-CSF increase was associated with the expression of the previously defined COVID-19-related metabolic spatial covariance pattern (r = 0.57; P = 0.039). In addition, the frontoparietal-dominant pattern of neocortical glucose hypometabolism matched well to the frontal and parietal focus of V-CSF increase. In summary, DMI in subacute COVID-19 patients revealed widespread volume shifts compatible with vasogenic oedema, affecting various supratentorial white matter tracts. These changes were associated with cognitive impairment and COVID-19 related changes in 18F-FDG PET imaging.

Clinical feasibility of diffusion microstructure imaging (DMI) in acute ischemic stroke

BACKGROUND

Diffusion microstructure imaging (DMI) is a fast approach to higher-order diffusion-weighted magnetic resonance imaging that allows robust decomposition and characterization of diffusion properties of brain tissue into intra-axonal, extra-axonal, and a free water-compartment. We now report the application of this technique to acute ischemic stroke and demonstrate its potential applicability to the daily clinical routine.

METHODS

Thirty-eight patients diagnosed with acute ischemic stroke were scanned using an accelerated multi-shell diffusion-weighted imaging protocol (median delay between onset and MRI scan of 113 min). DMI metrics were calculated and the apparent diffusion coefficient (ADC) derived from conventional diffusion-weighted imaging was used for comparison. The resulting DMI parameter maps were analysed for their potential to improve infarct core delineation, and a receiver-operating characteristic (ROC) analysis was subsequently performed for automated infarct segmentation.

RESULTS

Robust parameter maps for diffusion microstructure properties were obtained in all cases. Within the ischemic tissue, an increase in the volume fraction of the intra-axonal compartment was accompanied by a volume fraction reduction in the other two compartments. Moreover, diffusivity was reduced in all three compartments, with intra-axonal diffusivity showing the highest degree of contrast. The intra-axonal diffusion coefficient maps were subsequently found to perform better than single-shell ADC-derived segmentation in terms of automatic segmentation of the infarct core (area under the curve = 0.98 vs 0.92).

CONCLUSIONS

The alterations to the ischemic core detected by DMI are in line with the "beading-model" of non-uniform neurite swelling under ischemic conditions. When compared to conventional single-shell diffusion-weighted imaging, DMI metrics are associated with improved discriminative power for delineating and characterizing ischemic changes. This might allow a more detailed assessment of infarct age, severity of damage, the degree of reversibility, and outcome.

Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems

Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40and80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.

Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.

Diffusion tensor estimation with transformer neural networks

Diffusion tensor imaging (DTI) is a widely used method for studying brain white matter development and degeneration. However, standard DTI estimation methods depend on a large number of high-quality measurements. This would require long scan times and can be particularly difficult to achieve with certain patient populations such as neonates. Here, we propose a method that can accurately estimate the diffusion tensor from only six diffusion-weighted measurements. Our method achieves this by learning to exploit the relationships between the diffusion signals and tensors in neighboring voxels. Our model is based on transformer networks, which represent the state of the art in modeling the relationship between signals in a sequence. In particular, our model consists of two such networks. The first network estimates the diffusion tensor based on the diffusion signals in a neighborhood of voxels. The second network provides more accurate tensor estimations by learning the relationships between the diffusion signals as well as the tensors estimated by the first network in neighboring voxels. Our experiments with three datasets show that our proposed method achieves highly accurate estimations of the diffusion tensor and is significantly superior to three competing methods. Estimations produced by our method with six diffusion-weighted measurements are comparable with those of standard estimation methods with 30-88 diffusion-weighted measurements. Hence, our method promises shorter scan times and more reliable assessment of brain white matter, particularly in non-cooperative patients such as neonates and infants.

Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.

Diffusion Microstructure Imaging to Analyze Perilesional T2 Signal Changes in Brain Metastases and Glioblastomas

Purpose: Glioblastomas (GBM) and brain metastases are often difficult to differentiate in conventional MRI. Diffusion microstructure imaging (DMI) is a novel MR technique that allows the approximation of the distribution of the intra-axonal compartment, the extra-axonal cellular, and the compartment of interstitial/free water within the white matter. We hypothesize that alterations in the T2 hyperintense areas surrounding contrast-enhancing tumor components may be used to differentiate GBM from metastases. Methods: DMI was performed in 19 patients with glioblastomas and 17 with metastatic lesions. DMI metrics were obtained from the T2 hyperintense areas surrounding contrast-enhancing tumor components. Resected brain tissue was assessed in six patients in each group for features of an edema pattern and tumor infiltration in the perilesional interstitium. Results: Within the perimetastatic T2 hyperintensities, we observed a significant increase in free water (p < 0.001) and a decrease in both the intra-axonal (p = 0.006) and extra-axonal compartments (p = 0.024) compared to GBM. Perilesional free water fraction was discriminative regarding the presence of GBM vs. metastasis with a ROC AUC of 0.824. Histologically, features of perilesional edema were present in all assessed metastases and absent or marginal in GBM. Conclusion: Perilesional T2 hyperintensities in brain metastases and GBM differ significantly in DMI-values. The increased free water fraction in brain metastases suits the histopathologically based hypothesis of perimetastatic vasogenic edema, whereas in glioblastomas there is additional tumor infiltration.

A critical review of connectome validation studies

Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.