White matter characterization with diffusional kurtosis imaging

Diffusion tensor subspace imaging of double diffusion-encoded MRI delineates small fibers and gray-matter microstructure not visible with single encoding techniques

PURPOSE

Double diffusion encoding (DDE) acquisition strategies promise specificity for small-dimensional structures inaccessible to single diffusion encoding (SDE). For DDE-weighted MRI scans to become relevant for whole brain imaging, signal reconstruction frameworks must accurately report microstructural features of interest-especially microscale anisotropy in complex tissue environments. This study examined the recently developed diffusion tensor subspace imaging (DiTSI) framework and its radial and spherical anisotropy metrics (RA and SA, respectively) in postmortem human brain tissue specimens.

METHODS

MRI microscopy including multishell SDE-weighted and DDE-weighted imaging was performed for healthy brain stem and temporal lobe specimens and for specimens with Alzheimer's disease pathology and neurodegeneration. The DiTSI framework was compared with four other diffusion MRI frameworks, and angular and radial DDE sampling were evaluated.

RESULTS

DDE acquisition and the DiTSI metric maps of SA and RA in temporal lobe and brain-stem specimens were found to be distinct from fractional anisotropy and orientation dispersion index in providing complementary and selective contrast of microscale anisotropy at the gray-matter/white-matter interface in the cortex and in hippocampal layers. DiTSI maps also unmasked small fascicles in the brain stem that were not detectable by SDE techniques and provided selective contrast across the major fiber pathways. Results also revealed prominent reductions of SA and RA in tissue with Alzheimer's disease pathology that were not observed for any other framework.

CONCLUSIONS

New contrasts were evident for DiTSI framework metrics over a range of tissue environments with promise toward providing novel markers of pathology.

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3-Ex vivo imaging: Data processing, comparisons with microscopy, and tractography

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Multiparametric MRI Can Detect Enhanced Myelination in the Ex Vivo Gli1-/- Mouse Brain

This study investigated the potential of combining multiple MR parameters to enhance the characterization of myelin in the mouse brain. We collected ex vivo multiparametric MR data at 7 T from control and Gli1-/- mice; the latter exhibit enhanced myelination at Postnatal Day 10 (P10) in the corpus callosum and cortex. The MR data included relaxivity, magnetization transfer, and diffusion measurements, each targeting distinct myelin properties. This analysis was followed by and compared to myelin basic protein (MBP) staining of the same samples. Although a majority of the MR parameters included in this study showed significant differences in the corpus callosum between the control and Gli1-/- mice, only T2, T1/T2, and radial diffusivity (RD) demonstrated a significant correlation with MBP values. Based on data from the corpus callosum, partial least square regression suggested that combining T2, T1/T2, and inhomogeneous magnetization transfer ratio could explain approximately 80% of the variance in the MBP values. Myelin predictions based on these three parameters yielded stronger correlations with the MBP values in the P10 mouse brain corpus callosum than any single MR parameter. In the motor cortex, combining T2, T1/T2, and radial kurtosis could explain over 90% of the variance in the MBP values at P10. This study demonstrates the utility of multiparametric MRI in improving the detection of myelin changes in the mouse brain.

Sex-differences in brain multimodal estimates of white matter microstructure during early adolescence: Sex-specific associations with biological factors

Adolescence is marked by significant maturation of brain white matter microstructure, with evidence for sex-specific maturational trajectory. Most studies have examined conventional diffusion tensor imaging (DTI) metrics, which lack specificity to the underlying tissue modifications. In this study, we characterized sex-differences in white matter microstructure cross-sectionally using DTI, advanced diffusion spectrum imaging (DSI) and diffusion kurtosis imaging (DKI), as well as the white matter tract integrity-Watson (WMTI-W) biophysical model. We also aimed to explore the effect of age and biological systems undergoing sex-specific changes during adolescence, namely pubertal hormones, hypothalamic-pituitary-adrenal (HPA)-axis function, and glutathione-redox cycle homeostasis. The results indicate widespread sex-differences in all the white matter derived metrics, suggesting more advanced maturation in females compared to males as well as distinct tissue modifications underlying white matter maturation between males and females during this narrow developmental period. Additionally, the three biological factors explored appeared to be associated with indices of white matter maturation in females specifically, emphasizing this period as critical in female white matter development and sensitivity to environmental factors.

Aging of gray matter microstructure: A brain-wide characterization of age group differences using NODDI

This study aimed to provide a complete characterization of age group differences in cortical lobar, hippocampal, and subcortical gray matter microstructure using a multi-compartment diffusion-weighted MRI (DWI) approach with parameters optimized for gray matter (Neurite Orientation Dispersion and Density Imaging, NODDI). 76 younger (undergraduate students) and 64 older (surrounding communities) adults underwent diffusion-, T1-, and susceptibility-weighted MRI. Results revealed eight unique patterns across the 12 regions of interest in the relative direction and magnitude of age effects across NODDI metrics, which were grouped into three prominent patterns: cortical gray matter had predominantly higher free diffusion in older than younger adults, the hippocampus and amygdala had predominantly higher dispersion of diffusion and intracellular diffusion in older than younger adults, and the putamen and globus pallidus had lower dispersion of diffusion in older than younger adults. Results remained largely unchanged after controlling for normalized regional volume, suggesting that higher free diffusion in older than younger adults in cortical gray matter was not driven by macrostructural atrophy. Results also remained largely unchanged after controlling for iron content (QSM, R2*), even in iron-rich subcortical regions. Taken together, these patterns of age effects across NODDI metrics provide evidence of region-specific neurobiological substrates of aging of gray matter microstructure.

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.

Conditional generative diffusion deep learning for accelerated diffusion tensor and kurtosis imaging

PURPOSE

The purpose of this study was to develop DiffDL, a generative diffusion probabilistic model designed to produce high-quality diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics from a reduced set of diffusion-weighted images (DWIs). This model addresses the challenge of prolonged data acquisition times in diffusion MRI while preserving metric accuracy.

METHODS

DiffDL was trained using data from the Human Connectome Project, including 300 training/validation subjects and 50 testing subjects. High-quality DTI and DKI metrics were generated using many DWIs and combined with subsets of DWIs to form training pairs. A UNet architecture was used for denoising, trained over 500 epochs with a linear noise schedule. Performance was evaluated against conventional DTI/DKI modeling and a reference UNet model using normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), and Pearson correlation coefficient (PCC).

RESULTS

DiffDL showed significant improvements in the quality and accuracy of fractional anisotropy (FA) and mean diffusivity (MD) maps compared to conventional methods and the baseline UNet model. For DKI metrics, DiffDL outperformed conventional DKI modeling and the UNet model across various acceleration scenarios. Quantitative analysis demonstrated superior NMAE, PSNR, and PCC values for DiffDL, capturing the full dynamic range of DTI and DKI metrics. The generative nature of DiffDL allowed for multiple predictions, enabling uncertainty quantification and enhancing performance.

CONCLUSION

The DiffDL framework demonstrated the potential to significantly reduce data acquisition times in diffusion MRI while maintaining high metric quality. Future research should focus on optimizing computational demands and validating the model with clinical cohorts and standard MRI scanners.

Diffusion MRI with double diffusion encoding and variable mixing times disentangles water exchange from transient kurtosis

Double diffusion encoding (DDE) makes diffusion MRI sensitive to a wide range of microstructural features, and the acquired data can be analysed using different approaches. Correlation tensor imaging (CTI) uses DDE to resolve three components of the diffusional kurtosis: isotropic, anisotropic, and microscopic kurtosis. The microscopic kurtosis is estimated from the contrast between single diffusion encoding (SDE) and parallel DDE signals at the same b-value. Another approach is multi-Gaussian exchange (MGE), which employs DDE to measure exchange. Sensitivity to exchange is obtained by contrasting SDE and DDE signals at the same b-value. CTI and MGE exploit the same signal contrast to quantify microscopic kurtosis and exchange, and this study investigates the interplay between these two quantities. We perform Monte Carlo simulations in different geometries with varying levels of exchange and study the behaviour of the parameters from CTI and MGE. We conclude that microscopic kurtosis from CTI is sensitive to the exchange rate and that intercompartmental exchange and the transient kurtosis of individual compartments are distinct sources of microscopic kurtosis. In an attempt to disentangle these two sources, we propose a heuristic signal representation referred to as tMGE (MGE incorporating transient kurtosis) that accounts for both effects by exploiting the distinct signatures of exchange and transient kurtosis with varying mixing time: exchange causes a slow dependence of the signal on mixing time while transient kurtosis arguably has a much faster dependence. We find that applying tMGE to data acquired with multiple mixing times for both parallel and orthogonal DDE may enable estimation of the exchange rate as well as isotropic, anisotropic, and transient kurtosis.

Associations between fronto-limbic white matter connections and internalizing symptoms in pediatric demyelinating disease

INTRODUCTION

Children with neuroinflammatory disorders, such as multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein-associated disorder (MOGAD), have high rates of anxiety and depression. These symptoms may be linked to disruptions in fronto-limbic white matter (WM) tracts, including the cingulum bundle (CB), inferior fronto-occipital fasciculus (IFOF), and uncinate fasciculus (UF), which support emotional regulation.

METHODS

We studied 33 children with neuroinflammatory disorders and 28 healthy controls. Diffusion tensor imaging and white matter tract integrity maps were generated, focusing on WM tracts of interest (CB, IFOF, UF) and a control tract (acoustic radiation). We examined differences in WM microstructure and internalizing symptoms between high and low symptom groups.

RESULTS

Participants with MS (40%), MOGAD (28%), and NMOSD (25%) reported high levels of internalizing symptoms. MOGAD participants showed lower axonal water fraction compared to MS and controls. Both MS and MOGAD groups exhibited reduced intra-axonal diffusivity and increased extra-axonal diffusivity, indicating demyelination and axonal changes. No significant differences were found between high and low internalizing groups, but higher relapse rates were linked to less WM disruption in those with high internalizing symptoms.

LIMITATIONS

The cross-sectional design limits causal interpretations, and medical covariates may affect WM structure.

CONCLUSION

Neuroinflammatory disorders are linked to fronto-limbic WM changes and high internalizing symptoms. Relapse may influence WM structure and psychological resilience in this population.

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.

Assessing the Validity of Diffusion Weighted Imaging Models: A Study in Patients with Post-Surgical Lower-Grade Glioma

Background: Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they relate to each other is currently unknown. Therefore, this study assesses the validity and agreement of these models. Methods: Fourteen post-treatment LGG patients and six healthy controls (HC) underwent DWI MRI on a 3T MRI scanner. DWI processing included diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), white matter tract integrity (WMTI), neurite orientation dispersion and density imaging (NODDI), and fixel-based analysis (FBA). Validity was assessed by delineating surgical cavity, peri-surgical cavity, and normal-appearing white matter (NAWM) in LGG patients, and white matter (WM) in HC. Spearman correlation assessed the agreement between DWI parameters. Results: All obtained parameters differed significantly across tissue types. Remarkably, WMTI showed that intra-axonal diffusivity was high in the surgical cavity and low in NAWM and WM. Most DWI parameters correlated well with each other, except for WMTI-derived intra-axonal diffusivity. Conclusion: This study shows that all parameters relevant for tumour monitoring and DWI-derived parameters for axonal fibre-bundle integrity (except WMTI-IAS-Da) could be used interchangeably, enhancing inter-DWI model interpretability.

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.

Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer's disease, Parkinson's disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques-Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging-in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact.

Microstructurally informed subject-specific parcellation of the corpus callosum using axonal water fraction

The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as Hofer and Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas known to consist of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC known to consist of a greater number of large diameter fibers. Based on the degree of change in ƒ along the callosum, seven callosal subregions were consistently delineated for each individual. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.

Compensatory mechanisms amidst demyelinating disorders: insights into cognitive preservation

Demyelination disrupts the transmission of electrical signals in the brain and affects neurodevelopment in children with disorders such as multiple sclerosis and myelin oligodendrocyte glycoprotein-associated disorders. Although cognitive impairments are prevalent in these conditions, some children maintain cognitive function despite substantial structural injury. These findings raise an important question: in addition to the degenerative process, do compensatory neural mechanisms exist to mitigate the effects of myelin loss? We propose that a multi-dimensional approach integrating multiple neuroimaging modalities, including diffusion tensor imaging, magnetoencephalography and eye-tracking, is key to investigating this question. We examine the structural and functional connectivity of the default mode and executive control networks due to their significant roles in supporting higher-order cognitive processes. As cognitive proxies, we examine saccade reaction times and direction errors during an interleaved pro- (eye movement towards a target) and anti-saccade (eye movement away from a target) task. 28 typically developing children, 18 children with multiple sclerosis and 14 children with myelin oligodendrocyte glycoprotein-associated disorders between 5 and 18.9 years old were scanned at the Hospital for Sick Children. Tractography of diffusion MRI data examined structural connectivity. Intracellular and extracellular microstructural parameters were extracted using a white matter tract integrity model to provide specific inferences on myelin and axon structure. Magnetoencephalography scanning was conducted to examine functional connectivity. Within groups, participants had longer saccade reaction times and greater direction errors on the anti- versus pro-saccade task; there were no group differences on either task. Despite similar behavioural performance, children with demyelinating disorders had significant structural compromise and lower bilateral high gamma, higher left-hemisphere theta and higher right-hemisphere alpha synchrony relative to typically developing children. Children diagnosed with multiple sclerosis had greater structural compromise relative to children with myelin oligodendrocyte glycoprotein-associated disorders; there were no group differences in neural synchrony. For both patient groups, increased disease disability predicted greater structural compromise, which predicted longer saccade reaction times and greater direction errors on both tasks. Structural compromise also predicted increased functional connectivity, highlighting potential adaptive functional reorganisation in response to structural compromise. In turn, increased functional connectivity predicted faster saccade reaction times and fewer direction errors. These findings suggest that increased functional connectivity, indicated by increased alpha and theta synchrony, may be necessary to compensate for structural compromise and preserve cognitive abilities. Further understanding these compensatory neural mechanisms could pave the way for the development of targeted therapeutic interventions aimed at enhancing these mechanisms, ultimately improving cognitive outcomes for affected individuals.

Free water elimination tractometry for aging brains

Tractometry of diffusion-weighted magnetic resonance imaging (dMRI) non-invasively quantifies tissue properties of brain connections. It is widely used in aging studies but could be less reliable in aging brains due to increased white matter free water. We demonstrate that computational free water elimination (FWE) increases reliability and accuracy of tractometry in a large (n = 339) cohort of older adults (66 - 103 y.o.). We found substantial (up to ~37%) improvements in reliability in a split-half comparison at every stage of the pipeline: estimation of voxel-level fiber orientation distribution functions, delineation of major pathway trajectories, and assessment of tissue properties along the pathways. FWE also improves inferences from tractometry, producing more accurate cross-validated predictions of clinician Fazekas scores. By sub-sampling a multi-b-value dataset, we demonstrated that these findings generalize to both single-b-value data, which is important for many datasets where only one b-value may be available. Overall, the results highlight the importance of accounting for free water in tractometry studies, especially in aging brains. We provide open-source software for free-water elimination that can be applied to a wide range of clinical and research datasets (https://github.com/nrdg/fwe).

Age-related alterations in human cortical microstructure across the lifespan: Insights from high-gradient diffusion MRI

The human brain undergoes age-related microstructural alterations across the lifespan. Soma and Neurite Density Imaging (SANDI), a novel biophysical model of diffusion MRI, provides estimates of cell body (soma) radius and density, and neurite density in gray matter. The goal of this cross-sectional study was to assess the sensitivity of high-gradient diffusion MRI toward age-related alterations in cortical microstructure across the adult lifespan using SANDI. Seventy-two cognitively unimpaired healthy subjects (ages 19-85 years; 40 females) were scanned on the 3T Connectome MRI scanner with a maximum gradient strength of 300mT/m using a multi-shell diffusion MRI protocol incorporating 8 b-values and diffusion time of 19 ms. Intra-soma signal fraction obtained from SANDI model-fitting to the data was strongly correlated with age in all major cortical lobes (r = -0.69 to -0.60, FDR-p < 0.001). Intra-soma signal fraction (r = 0.48-0.63, FDR-p < 0.001) and soma radius (r = 0.28-0.40, FDR-p < 0.04) were significantly correlated with cortical volume in the prefrontal cortex, frontal, parietal, and temporal lobes. The strength of the relationship between SANDI metrics and age was greater than or comparable to the relationship between cortical volume and age across the cortical regions, particularly in the occipital lobe and anterior cingulate gyrus. In contrast to the SANDI metrics, all associations between diffusion tensor imaging (DTI) and diffusion kurtosis imaging metrics and age were low to moderate. These results suggest that high-gradient diffusion MRI may be more sensitive to underlying substrates of neurodegeneration in the aging brain than DTI and traditional macroscopic measures of neurodegeneration such as cortical volume and thickness.

Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort

In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.

Advanced Brain Imaging in Central Nervous System Demyelinating Diseases

In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.

A framework for optimizing the acquisition protocol multishell diffusion-weighted imaging for multimodel assessment

Complementary aspects of tissue microstructure can be studied with diffusion-weighted imaging (DWI). However, there is no consensus on how to design a diffusion acquisition protocol for multiple models within a clinically feasible time. The purpose of this study is to provide a flexible framework that is able to optimize the shell acquisition protocol given a set of DWI models. Eleven healthy subjects underwent an extensive DWI acquisition protocol, including 15 candidate shells, ranging from 10 to 3500 s/mm2. The proposed framework aims to determine the optimized acquisition scheme (OAS) with a data-driven procedure minimizing the squared error of model-estimated parameters. We tested the proposed method over five heterogeneous DWI models exploiting both low and high b-values (i.e., diffusion tensor imaging [DTI], free water, intra-voxel incoherent motion [IVIM], diffusion kurtosis imaging [DKI], and neurite orientation dispersion and density imaging [NODDI]). A voxel-level and region of interest (ROI)-level analysis was conducted over the white matter and in 48 fiber bundles, respectively. Results showed that acquiring data for the five abovementioned models via OAS requires 14 min, compared with 35 min for the joint recommended acquisition protocol. The parameters derived from the reference acquisition scheme and the OAS are comparable in terms of estimated values, noise, and tissue contrast. Furthermore, the power analysis showed that the OAS retains the potential sensitivity to group-level differences in the parameters of interest, with the exception of the free water model. Overall, there is a linear correspondence (R2 = 0.91) between OAS and reference-derived parameters. In conclusion, the proposed framework optimizes the shell acquisition scheme for a given set of DWI models (i.e., DTI, free water, IVIM, DKI, and NODDI), combining low and high b-values while saving acquisition time.

Compartmental anisotropy of filtered exchange imaging (FEXI) in human white matter: What is happening in FEXI?

PURPOSE

To investigate the effects of compartmental anisotropy on filtered exchange imaging (FEXI) in white matter (WM).

THEORY AND METHODS

FEXI signals were measured using multiple combinations of diffusion filter and detection directions in five healthy volunteers. Additional filters, including a trace-weighted diffusion filter with trapezoidal gradients, a spherical b-tensor encoded diffusion filter, and a T2 filter, were tested with trace-weighted diffusion detection.

RESULTS

A large range of apparent exchange rates (AXR) and both positive and negative filter efficiencies (σ) were found depending on the mutual orientation of the filter and detection gradients relative to WM fiber orientation. The data demonstrated that the fast-diffusion compartment suppressed by diffusional filtering is not exclusively extra-cellular, but also intra-cellular. While not comprehensive, a simple two-compartment diffusion tensor model with water exchange was able to account qualitatively for the trends in positive and negative filtering efficiencies, while standard model imaging (SMI) without exchange could not. This two-compartment diffusion tensor model also demonstrated smaller AXR variances across subjects. When employing trace-weighted diffusion detection, AXR values were on the order of the R1 (=1/T1) of water at 3T for crossing fibers, while being less than R1 for parallel fibers.

CONCLUSION

Orientation-dependent AXR and σ values were observed when using multi-orientation filter and detection gradients in FEXI, indicating that WM FEXI models need to account for compartmental anisotropy. When using trace-weighted detection, AXR values were on the order of or less than R1, complicating the interpretation of FEXI results in WM in terms of biological exchange properties. These findings may contribute toward better understanding of FEXI results in WM.

Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses

Purpose

To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures.

Methods

Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma.

Results

In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior-altitudinal area of the right eye and the inferior-altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma.

Conclusions

Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.

Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates

PURPOSE

The purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation.

METHODS

Noise-free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast "199" acquisition protocol. First the noise-free "baseline" difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal-to-noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI-Watson model.

RESULTS

The parallel and perpendicular kurtosis and all of the WMTI-Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast "199" protocol but adaptive denoising can help counteract these effects.

CONCLUSION

For the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the "199" protocol worsens precision in noisy data but adaptive denoising mitigates these effects.

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.

Anesthesia-related brain microstructure modulations detected by diffusion magnetic resonance imaging

Recent studies have shown significant changes to brain microstructure during sleep and anesthesia. In vivo optical microscopy and magnetic resonance imaging (MRI) studies have attributed these changes to anesthesia and sleep-related modulation of the brain's extracellular space (ECS). Isoflurane anesthesia is widely used in preclinical diffusion MRI (dMRI) and it is therefore important to investigate if the brain's microstructure is affected by anesthesia to an extent detectable with dMRI. Here, we employ diffusion kurtosis imaging (DKI) to assess brain microstructure in the awake and anesthetized mouse brain (n = 22). We find both mean diffusivity (MD) and mean kurtosis (MK) to be significantly decreased in the anesthetized mouse brain compared with the awake state (p < 0.001 for both). This effect is observed in both gray matter and white matter. To further investigate the time course of these changes we introduce a method for time-resolved fast DKI. With this, we show the time course of the microstructural alterations in mice (n = 5) as they transition between states in an awake-anesthesia-awake paradigm. We find that the decrease in MD and MK occurs rapidly after delivery of gas isoflurane anesthesia and that values normalize only slowly when the animals return to the awake state. Finally, time-resolved fast DKI is employed in an experimental mouse model of brain edema (n = 4), where cell swelling causes the ECS volume to decrease. Our results show that isoflurane affects DKI parameters and metrics of brain microstructure and point to isoflurane causing a reduction in the ECS volume. The demonstrated DKI methods are suitable for in-bore perturbation studies, for example, for investigating microstructural modulations related to sleep/wake-dependent functions of the glymphatic system. Importantly, our study shows an effect of isoflurane anesthesia on rodent brain microstructure that has broad relevance to preclinical dMRI.

Tractometry of the Human Connectome Project: resources and insights

Introduction

The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.

Methods

We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.

Results

We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

Biophysical modeling and diffusion kurtosis imaging reveal microstructural alterations in normal-appearing white-matter regions of the brain in obstructive sleep apnea

Study Objectives

Studies have indicated that sleep abnormalities are a strong risk factor for developing cognitive impairment, cardiomyopathies, and neurodegenerative disorders. However, neuroimaging modalities are unable to show any consistent markers in obstructive sleep apnea (OSA) patients. We hypothesized that, compared with those of the control cohort, advanced diffusion MRI metrics could show subtle microstructural alterations in the brains of patients with OSA.

Methods

Sixteen newly diagnosed patients with moderate to severe OSA and 15 healthy volunteers of the same age and sex were considered healthy controls. Multishell diffusion MRI data of the brain, along with anatomical data (T1 and T2 images), were obtained on a 3T MRI system (Siemens, Germany) after a polysomnography (PSG) test for sleep abnormalities and a behavioral test battery to evaluate cognitive and executive brain functions. Diffusion MRI data were used to compute diffusion tensor imaging and diffusion kurtosis imaging (DKI) parameters along with white-matter tract integrity (WMTI) metrics for only parallel white-matter fibers.

Results

OSA was diagnosed when the patient's apnea-hypopnea index was ≥ 15. No significant changes in cognitive or executive functions were observed in the OSA cohort. DKI parameters can show significant microstructural alterations in the white-matter region, while the WMTI metric, the axonal-water-fraction (fp), reveals a significant decrease in OSA patients concerning the control cohort.

Conclusions

Advanced diffusion MRI-based microstructural alterations in the white-matter region of the brain suggest that white-matter tracts are more sensitive to OSA-induced intermittent hypoxia.

PyDesigner v1.0: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Magnetic Resonance Imaging

PyDesigner is a Python-based software package based on the original Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline (Dv1) for dMRI preprocessing and tensor estimation. This software is openly provided for non-commercial research and may not be used for clinical care. PyDesigner combines tools from FSL and MRtrix3 to perform denoising, Gibbs ringing correction, eddy current motion correction, brain masking, image smoothing, and Rician bias correction to optimize the estimation of multiple diffusion measures. It can be used across platforms on Windows, Mac, and Linux to accurately derive commonly used metrics from DKI, DTI, WMTI, FBI, and FBWM datasets as well as tractography ODFs and .fib files. It is also file-format agnostic, accepting inputs in the form of .nii, .nii.gz, .mif, and dicom format. User-friendly and easy to install, this software also outputs quality control metrics illustrating signal-to-noise ratio graphs, outlier voxels, and head motion to evaluate data integrity. Additionally, this dMRI processing pipeline supports multiple echo-time dataset processing and features pipeline customization, allowing the user to specify which processes are employed and which outputs are produced to meet a variety of user needs.

Multi-parametric MRI can detect enhanced myelination in the Gli1 -/- mouse brain

This study investigated the potential of combining multiple MR parameters to enhance the characterization of myelin in the mouse brain. We collected ex vivo multi-parametric MR data at 7 Tesla from control and Gli1 -/- mice; the latter exhibit enhanced myelination at postnatal day 10 (P10) in the corpus callosum and cortex. The MR data included relaxivity, magnetization transfer, and diffusion measurements, each targeting distinct myelin properties. This analysis was followed by and compared to myelin basic protein (MBP) staining of the same samples. Although a majority of the MR parameters included in this study showed significant differences in the corpus callosum between the control and Gli1 -/- mice, only T 2 , T 1 /T 2, and radial diffusivity (RD) demonstrated a significant correlation with MBP values. Based on data from the corpus callosum, partial least square regression suggested that combining T 2 , T 1 /T 2 , and inhomogeneous magnetization transfer ratio could explain approximately 80% of the variance in the MBP values. Myelin predictions based on these three parameters yielded stronger correlations with the MBP values in the P10 mouse brain corpus callosum than any single MR parameter. In the motor cortex, combining T 2 , T 1 /T 2, and radial kurtosis could explain over 90% of the variance in the MBP values at P10. This study demonstrates the utility of multi-parametric MRI in improving the detection of myelin changes in the mouse brain.

Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study

Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.

Functional connectivity in autism spectrum disorder evaluated using rs-fMRI and DKI

We evaluated functional connectivity (FC) in patients with adult autism spectrum disorder (ASD) using resting-state functional MRI (rs-fMRI) and diffusion kurtosis imaging (DKI). We acquired rs-fMRI data from 33 individuals with ASD and 33 healthy controls (HC) and DKI data from 18 individuals with ASD and 17 HC. ASD showed attenuated FC between the right frontal pole (FP) and the bilateral temporal fusiform cortex (TFusC) and enhanced FC between the right thalamus and the bilateral inferior division of lateral occipital cortex, and between the cerebellar vermis and the right occipital fusiform gyrus (OFusG) and the right lingual gyrus, compared with HC. ASD demonstrated increased axial kurtosis (AK) and mean kurtosis (MK) in white matter (WM) tracts, including the right anterior corona radiata (ACR), forceps minor (FM), and right superior longitudinal fasciculus (SLF). In ASD, there was also a significant negative correlation between MK and FC between the cerebellar vermis and the right OFusG in the corpus callosum, FM, right SLF and right ACR. Increased DKI metrics might represent neuroinflammation, increased complexity, or disrupted WM tissue integrity that alters long-distance connectivity. Nonetheless, protective or compensating adaptations of inflammation might lead to more abundant glial cells and cytokine activation effectively alleviating the degeneration of neurons, resulting in increased complexity. FC abnormality in ASD observed in rs-fMRI may be attributed to microstructural alterations of the commissural and long-range association tracts in WM as indicated by DKI.

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis

RATIONALE AND OBJECTIVES

This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoost) model was developed to effectively classify individuals with ASD and typical developing children (TDC).

METHODS AND MATERIALS

Multi-shell diffusion weighted images were acquired from 62 children with ASD and 44 TDC. Using the Pydesigner procedure, diffusion tensor (DT), diffusion kurtosis (DK), and white matter tract integrity (WMTI) metrics were computed. Subsequently, TBSS analysis was applied to discern differences in these diffusion parameters between ASD and TDC groups. The XGBoost model was then trained using metrics showing significant differences, and Shapley Additive explanations (SHAP) values were computed to assess the feature importance in the model's predictions.

RESULTS

TBSS analysis revealed a significant reduction in axonal diffusivity (AD) in the left posterior corona radiata and the right superior corona radiata. Among the DK indicators, mean kurtosis, axial kurtosis, and kurtosis fractional anisotropy were notably increased in children with ASD, with no significant difference in radial kurtosis. WMTI metrics such as axonal water fraction, axonal diffusivity of the extra-axonal space (EAS_AD), tortuosity of the extra-axonal space (EAS_TORT), and diffusivity of intra-axonal space (IAS_Da) were significantly increased, primarily in the corpus callosum and fornix. Notably, there was no significant difference in radial diffusivity of the extra-axial space (EAS_RD). The XGBoost model demonstrated excellent classification ability, and the SHAP analysis identified EAS_TORT as the feature with the highest importance in the model's predictions.

CONCLUSION

This study utilized TBSS analyses with multi-shell diffusion data to examine white matter abnormalities in pediatric autism. Additionally, the developed XGBoost model showed outstanding performance in classifying ASD and TDC. The ranking of SHAP values based on the XGBoost model underscored the significance of features in influencing model predictions.

Dissecting unique and common variance across body and brain health indicators using age prediction

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

A multimodal submillimeter MRI atlas of the human cerebellum

The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties. Here, we introduce a multifaceted cerebellar atlas based on high-resolution multimodal MRI, facilitating the understanding of the neurodevelopment and neurodegeneration of the cerebellum based on cortical morphology, tissue microstructure, and intra-cerebellar and cerebello-cerebral connectivity.

Purpose in life as a resilience factor for brain health: diffusion MRI findings from the Midlife in the U.S. study

Introduction

A greater sense of purpose in life is associated with several health benefits relevant for active aging, but the mechanisms remain unclear. We evaluated if purpose in life was associated with indices of brain health.

Methods

We examined data from the Midlife in the United States (MIDUS) Neuroscience Project. Diffusion weighted magnetic resonance imaging data (n=138; mean age 65.2 years, age range 48-95; 80 females; 37 black, indigenous, and people of color) were used to estimate microstructural indices of brain health such as axonal density, and axonal orientation. The seven-item purpose in life scale was used. Permutation analysis of linear models was used to examine associations between purpose in life scores and the diffusion metrics in white matter and in the bilateral hippocampus, adjusting for age, sex, education, and race.

Results and discussion

Greater sense of purpose in life was associated with brain microstructural features consistent with better brain health. Positive associations were found in both white matter and the right hippocampus, where multiple convergent associations were detected. The hippocampus is a brain structure involved in learning and memory that is vulnerable to stress but retains the capacity to grow and adapt through old age. Our findings suggest pathways through which an enhanced sense of purpose in life may contribute to better brain health and promote healthy aging. Since purpose in life is known to decline with age, interventions and policy changes that facilitate a greater sense of purpose may extend and improve the brain health of individuals and thus improve public health.

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

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.

Analysis of white matter tract integrity using diffusion kurtosis imaging reveals the correlation of white matter microstructural abnormalities with cognitive impairment in type 2 diabetes mellitus

Background

This study aimed to identify disruptions in white matter integrity in type 2 diabetes mellitus (T2DM) patients by utilizing the white matter tract integrity (WMTI) model, which describes compartment-specific diffusivities in the intra- and extra-axonal spaces, and to investigate the relationship between WMTI metrics and clinical and cognitive measurements.

Methods

A total of 73 patients with T2DM and 57 healthy controls (HCs) matched for age, sex, and education level were enrolled and underwent diffusional kurtosis imaging and cognitive assessments. Tract-based spatial statistics (TBSS) and atlas-based region of interest (ROI) analysis were performed to compare group differences in diffusional metrics, including fractional anisotropy (FA), mean diffusivity (MD), axonal water fraction (AWF), intra-axonal diffusivity (Daxon), axial extra-axonal space diffusivity (De,//), and radial extra-axonal space diffusivity (De,⊥) in multiple white matter (WM) regions. Relationships between diffusional metrics and clinical and cognitive functions were characterized.

Results

In the TBSS analysis, the T2DM group exhibited decreased FA and AWF and increased MD, De,∥, and De,⊥ in widespread WM regions in comparison with the HC group, which involved 56.28%, 32.07%, 73.77%, 50.47%, and 75.96% of the mean WM skeleton, respectively (P < 0.05, TFCE-corrected). De,⊥ detected most of the WM changes, which were mainly located in the corpus callosum, internal capsule, external capsule, corona radiata, posterior thalamic radiations, sagittal stratum, cingulum (cingulate gyrus), fornix (stria terminalis), superior longitudinal fasciculus, and uniform fasciculus. Additionally, De,⊥ in the genu of the corpus callosum was significantly correlated with worse performance in TMT-A (β = 0.433, P < 0.001) and a longer disease duration (β = 0.438, P < 0.001).

Conclusions

WMTI is more sensitive than diffusion tensor imaging in detecting T2DM-related WM microstructure abnormalities and can provide novel insights into the possible pathological changes underlying WM degeneration in T2DM. De,⊥ could be a potential imaging marker in monitoring disease progression in the brain and early intervention treatment for the cognitive impairment in T2DM.

Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry

Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T 2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.

Comparative study of stretched-exponential and kurtosis models of diffusion-weighted imaging in renal assessment to distinguish patients with primary aldosteronism from healthy controls

PURPOSE

To compare the ability of diffusion parameters obtained by stretched-exponential and kurtosis models of diffusion-weighted imaging (DWI) to distinguish between patients with primary aldosteronism (PA) and healthy controls (HCs) in renal assessment.

MATERIALS AND METHODS

A total of 44 participants (22 patients and 22 HCs) underwent renal MRI with an 11 b-value DWI sequence and a 3 b-value diffusion kurtosis imaging (DKI) sequence from June 2021 to April 2022. Binary logistic regression was used to construct regression models combining different diffusion parameters. Receiver-operating characteristic (ROC) curve analysis and comparisons were used to evaluate the ability of single diffusion parameters and combined diffusion models to distinguish between the two groups.

RESULTS

A total of six diffusion parameters (including the cortical anomalous exponent term [α_Cortex], medullary fractional anisotropy [FA_Medulla], cortical FA [FA_Cortex], cortical axial diffusivity [Da_Cortex], medullary mean diffusivity [MD_Medulla] and medullary radial diffusivity [Dr_Medulla]) were included, and 10 regression models were studied. The area under the curve (AUC) of Dr_Medulla was 0.855, comparable to that of FA_Cortex and FA_Medulla and significantly higher than that of α_Cortex, Da_Cortex and MD_Medulla. The AUC of the Model_all parameters was 0.967, comparable to that of Model_FA (0.946) and Model_DKI (0.966) and significantly higher than that of the other models. The sensitivity and specificity of Model_all parameters were 87.2% and 95%, respectively.

CONCLUSION

The Model_all parameters, Model_FA and Model_DKI were valid for differentiating between PA patients and HCs with similar differentiation efficacy and were superior to single diffusion parameters and other models.

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Microstructural changes of peripheral nerves in early multiple sclerosis: A prospective magnetic resonance neurography study

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a demyelinating disorder of the central nervous system (CNS). However, there is increasing evidence of peripheral nerve involvement. This study aims to characterize the pattern of peripheral nerve changes in patients with newly diagnosed MS using quantitative magnetic resonance (MR) neurography.

METHODS

In this prospective study, 25 patients first diagnosed with MS according to the revised McDonald criteria (16 female, mean age = 32.8 ± 10.6 years) and 14 healthy controls were examined with high-resolution 3-T MR neurography of the sciatic nerve using diffusion kurtosis imaging (DKI; 20 diffusional directions, b = 0, 700, 1200 s/mm2 ) and magnetization transfer imaging (MTI). In total, 15 quantitative MR biomarkers were analyzed and correlated with clinical symptoms, intrathecal immunoglobulin synthesis, electrophysiology, and lesion load on brain and spine MR imaging.

RESULTS

Patients showed decreased fractional anisotropy (mean = 0.51 ± 0.04 vs. 0.56 ± 0.03, p < 0.001), extra-axonal tortuosity (mean = 2.32 ± 0.17 vs. 2.49 ± 0.17, p = 0.008), and radial kurtosis (mean = 1.40 ± 0.23 vs. 1.62 ± 0.23, p = 0.014) and higher radial diffusivity (mean = 1.09 ∙ 10-3  mm2 /s ± 0.16 vs. 0.98 ± 0.11 ∙ 10-3  mm2 /s, p = 0.036) than controls. Groups did not differ in MTI. No significant association was found between MR neurography markers and clinical/laboratory parameters or CNS lesion load.

CONCLUSIONS

This study provides further evidence of peripheral nerve involvement in MS already at initial diagnosis. The characteristic pattern of DKI parameters indicates predominant demyelination and suggests a primary coaffection of the peripheral nervous system in MS. This first human study using DKI for peripheral nerves shows its potential and clinical feasibility in providing novel biomarkers.

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.

White matter and literacy: A dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

White Matter Microstructure Analysis in Subjective Memory Complaints and Cognitive Impairment: Insights from Diffusion Kurtosis Imaging and Free-Water DTI

Background

Dementia is characterized by a cognitive decline in memory and other domains that lead to functional impairments. As people age, subjective memory complaints (SMC) become common, where individuals perceive cognitive decline without objective deficits on assessments. SMC can be an early sign and may precede amnestic mild cognitive impairment (MCI), which frequently advances to Alzheimer's disease (AD).

Objective

This study aims to investigate white matter microstructure in individuals with SMC, in cognitively impaired (CI) cohorts, and in cognitively normal individuals using diffusion kurtosis imaging (DKI) and free water imaging (FWI). The study also explores voxel-based correlations between DKI/FWI metrics and cognitive scores to understand the relationship between brain microstructure and cognitive function.

Methods

Twelve healthy controls (HCs), ten individuals with SMC, and eleven CI individuals (MCI or AD) were enrolled in this study. All participants underwent MRI 3T scan and the BNI Screen (BNIS) for Higher Cerebral Functions.

Results

The mean kurtosis tensor and anisotropy of the kurtosis tensor showed significant differences across the three groups, indicating altered white matter microstructure in CI and SMC individuals. The free water volume fraction (f) also revealed group differences, suggesting changes in extracellular water content. Notably, these metrics effectively discriminated between the CI and HC/SMC groups. Additionally, correlations between imaging metrics and BNIS scores were found for CI and SMC groups.

Conclusions

These imaging metrics hold promise in discriminating between individuals with CI and SMC. The observed differences indicate their potential as sensitive and specific biomarkers for early detection and differentiation of cognitive decline.

Identifying relevant diffusion MRI microstructure biomarkers relating to exposure to repeated head impacts in contact sport athletes

PURPOSE

Repeated head impacts (RHI) without concussion may cause long-term sequelae. A growing array of diffusion MRI metrics exist, both empiric and modeled and it is hard to know which are potentially important biomarkers. Common conventional statistical methods fail to consider interactions between metrics and rely on group-level comparisons. This study uses a classification pipeline as a means towards identifying important diffusion metrics associated with subconcussive RHI.

METHODS

36 collegiate contact sport athletes and 45 non-contact sport controls from FITBIR CARE were included. Regional/whole brain WM statistics were computed from 7 diffusion metrics. Wrapper-based feature selection was applied to 5 classifiers representing a range of learning capacities. Best 2 classifiers were interpreted to identify the most RHI-related diffusion metrics.

RESULTS

Mean diffusivity (MD) and mean kurtosis (MK) are found to be the most important metrics for discriminating between athletes with and without RHI exposure history. Regional features outperformed global statistics. Linear approaches outperformed non-linear approaches with good generalizability (test AUC 0.80-0.81).

CONCLUSION

Feature selection and classification identifies diffusion metrics that characterize subconcussive RHI. Linear classifiers yield the best performance and mean diffusion, tissue microstructure complexity, and radial extra-axonal compartment diffusion (MD, MK, De,⊥) are found to be the most influential metrics. This work provides proof of concept that applying such approach to small, multidimensional dataset can be successful given attention to optimizing learning capacity without overfitting and serves an example of methods that lead to better understanding of the myriad of diffusion metrics as they relate to injury and disease.

Microstructurally Informed Subject-Specific Parcellation of the Corpus Callosum using Axonal Water Fraction

The corpus callosum (CC) is the most important interhemispheric white matter (WM) structure composed of several anatomically and functionally distinct WM tracts. Resolving these tracts is a challenge since the callosum appears relatively homogenous in conventional structural imaging. Commonly used callosal parcellation methods such as the Hofer/Frahm scheme rely on rigid geometric guidelines to separate the substructures that are limited to consider individual variation. Here we present a novel subject-specific and microstructurally-informed method for callosal parcellation based on axonal water fraction (ƒ) known as a diffusion metric reflective of axon caliber and density. We studied 30 healthy subjects from the Human Connectome Project (HCP) dataset with multi-shell diffusion MRI. The biophysical parameter ƒ was derived from compartment-specific WM modeling. Inflection points were identified where there were concavity changes in ƒ across the CC to delineate callosal subregions. We observed relatively higher ƒ in anterior and posterior areas consisting of a greater number of small diameter fibers and lower ƒ in posterior body areas of the CC consisting of a greater number of large diameter fibers. Based on degree of change in ƒ along the callosum, seven callosal subregions can be consistently delineated for each individual. We observe that ƒ can capture differences in underlying tissue microstructures and seven subregions can be identified across CC. Therefore, this method provides microstructurally informed callosal parcellation in a subject-specific way, allowing for more accurate analysis in the corpus callosum.

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.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning

BACKGROUND

The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.

METHODS

The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.

RESULTS

Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.

CONCLUSIONS

Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.