Multi-compartment microscopic diffusion imaging

Advanced Quantitative MRI Unveils Microstructural Thalamic Changes Reflecting Disease Progression in Multiple Sclerosis

BACKGROUND AND OBJECTIVES

In patients with multiple sclerosis (PwMS), thalamic atrophy occurs during the disease course. However, there is little understanding of the mechanisms leading to volume loss and of the relationship between microstructural thalamic pathology and disease progression. This cross-sectional and longitudinal study aimed to comprehensively characterize in vivo pathologic changes within thalamic microstructure in PwMS using advanced multiparametric quantitative MRI (qMRI).

METHODS

Thalamic microstructural integrity was evaluated using quantitative T1, magnetization transfer saturation, multishell diffusion, and quantitative susceptibility mapping (QSM) in 183 PwMS and 105 healthy controls (HCs). The same qMRI protocol was available for 127 PwMS and 73 HCs after a 2-year follow-up period. Inclusion criteria for PwMS encompassed either an active relapsing-remitting MS (RRMS) or inactive progressive MS (PMS) disease course. Thalamic alterations were compared between PwMS and HCs and among disease phenotypes. In addition, the study investigated the relationship between thalamic damage and clinical and conventional MRI measures of disease severity.

RESULTS

Compared with HCs, PwMS exhibited substantial thalamic alterations, indicative of microstructural and macrostructural damage, demyelination, and disruption in iron homeostasis. These alterations extended beyond focal thalamic lesions, affecting normal-appearing thalamic tissue diffusely. Over the follow-up period, PwMS displayed an accelerated decrease in myelin volume fraction [mean difference in annualized percentage change (MD-ApC) = -1.50; p = 0.041] and increase in quantitative T1 (MD-ApC = 0.92; p < 0.0001) values, indicating heightened demyelinating and neurodegenerative processes. The observed differences between PwMS and HCs were substantially driven by the subgroup with PMS, wherein thalamic degeneration was significantly accelerated, even in comparison with patients with RRMS. Thalamic qMRI alterations showed extensive correlations with conventional MRI, clinical, and cognitive disease burden measures. Disability progression over follow-up was associated with accelerated thalamic degeneration, as reflected by enhanced diffusion (β = -0.067; p = 0.039) and QSM (β = -0.077; p = 0.027) changes. Thalamic qMRI metrics emerged as significant predictors of neurologic and cognitive disability even when accounting for other established markers including white matter lesion load and brain and thalamic atrophy.

DISCUSSION

These findings offer deeper insights into thalamic pathology in PwMS, emphasizing the clinical relevance of thalamic damage and its link to disease progression. Advanced qMRI biomarkers show promising potential in guiding interventions aimed at mitigating thalamic neurodegenerative processes.

Restriction-induced time-dependent transcytolemmal water exchange: Revisiting the Kӓrger exchange model

The Kӓrger model and its derivatives have been widely used to incorporate transcytolemmal water exchange rate, an essential characteristic of living cells, into analyses of diffusion MRI (dMRI) signals from tissues. The Kӓrger model consists of two homogeneous exchanging components coupled by an exchange rate constant and assumes measurements are made with sufficiently long diffusion time and slow water exchange. Despite successful applications, it remains unclear whether these assumptions are generally valid for practical dMRI sequences and biological tissues. In particular, barrier-induced restrictions to diffusion produce inhomogeneous magnetization distributions in relatively large-sized compartments such as cancer cells, violating the above assumptions. The effects of this inhomogeneity are usually overlooked. We performed computer simulations to quantify how restriction effects, which in images produce edge enhancements at compartment boundaries, influence different variants of the Kӓrger-model. The results show that the edge enhancement effect will produce larger, time-dependent estimates of exchange rates in e.g., tumors with relatively large cell sizes (>10 μm), resulting in overestimations of water exchange as previously reported. Moreover, stronger diffusion gradients, longer diffusion gradient durations, and larger cell sizes, all cause more pronounced edge enhancement effects. This helps us to better understand the feasibility of the Kärger model in estimating water exchange in different tissue types and provides useful guidance on signal acquisition methods that may mitigate the edge enhancement effect. This work also indicates the need to correct the overestimated transcytolemmal water exchange rates obtained assuming the Kärger-model.

Improving the assessment of axonal injury in early multiple sclerosis

RATIONALE AND OBJECTIVES

Several quantitative magnetic resonance imaging (MRI) methods are available to measure tissue injury in multiple sclerosis (MS), but their pathological specificity remains limited. The multi-compartment diffusion imaging using the spherical mean technique (SMT) overcomes several technical limitations of the diffusion-weighted image signal, thus delivering metrics with increased pathological specificity. Given these premises, here we assess whether the SMT-derived apparent axonal volume (Vax) provides a better tissue classifier than the diffusion tensor imaging (DTI)-derived axial diffusivity (AD) in the white matter (WM) of MS brains.

METHODS

Forty-three treatment-naïve people with newly diagnosed MS, clinically isolated syndrome, or radiologically isolated syndrome and 18 healthy controls (HCs) underwent a 3.0 Tesla MRI inclusive of T1-weighted (T1-w) and T2-w fluid-attenuated inversion recovery (FLAIR) sequences, and multi-b shell diffusion-weighted imaging. In patients only, pre- and post-gadolinium diethylenetriamine penta-acetic acid T1-w sequences were obtained for the evaluation of contrast-active lesions (CELs). Vax and AD were calculated in T2-lesions, chronic black holes (cBHs), and normal appearing (NAWM) in patients and normal WM (NWM) in HCs. Vax and AD values were compared across all the possible combinations of these regions. CELs were excluded from the analyses.

RESULTS

Vax differed in all comparisons (p ≤ 0.047 by paired t-test); AD differed in most comparisons (p < 0.001) except between NAWM and NWM, and between cBHs and T2-lesions. Vax had higher accuracy (p ≤ 0.029 by DeLong test) and larger effect size (p ≤ 0.038 by paired t-test) than AD in differentiating areas with even minimal tissue injury.

CONCLUSIONS

Vax provides a better radiological quantitative discriminator of different degrees of axonal-mediated tissue injury even between areas with expected minimal pathology. Our data support further studies to assess the readiness of Vax as a measure of outcome for clinical trials on neuroprotection in MS.

Tutorial: a guide to diffusion MRI and structural connectomics

Diffusion magnetic resonance imaging (dMRI) is a versatile imaging technique that has gained popularity thanks to its sensitive ability to measure displacement of water molecules within a living tissue on a micrometer scale. Although dMRI has been around since the early 1990s, its applications are constantly evolving, primarily regarding the inference of structural connectomics from nerve fiber trajectories. However, these applications require expertise in image processing and statistics, and it can be difficult for a newcomer to choose an appropriate pipeline to fit their research needs, not least because dMRI is such a flexible methodology that dozens of acquisition and analysis pipelines have been developed over the years. This introductory guide is designed for graduate students and researchers in the neuroscience community who are interested in integrating this new methodology regardless of their background in neuroimaging and computational tools. The guide provides a brief overview of the basic dMRI methodologies but focuses on its applications in neuroplasticity and connectomics. The guide starts with dMRI experimental designs and a complete step-by-step pipeline for structural connectomics. The following section covers the basics of dMRI, including parameters and clinical applications (apparent diffusion coefficient, mean diffusivity, fractional anisotropy and microscopic fractional anisotropy), as well as different approaches and models. The final section focuses on structural connectomics, covering subjects from fiber tracking (techniques, evaluation and limitations) to structural networks (constructing, analyzing and visualizing a network).

Cognitive Impairment in Cerebral Small Vessel Disease Is Associated with Corpus Callosum Microstructure Changes Based on Diffusion MRI

The cerebral small vessel disease (cSVD) is one of the main causes of vascular and mixed cognitive impairment (CI), and it is associated, in particular, with brain ageing. An understanding of structural tissue changes in an intact cerebral white matter in cSVD might allow one to develop the sensitive biomarkers for early diagnosis and monitoring of disease progression.

PURPOSE OF THE STUDY

to evaluate microstructural changes in the corpus callosum (CC) using diffusion MRI (D-MRI) approaches in cSVD patients with different severity of CI and reveal the most sensitive correlations of diffusion metrics with CI.

METHODS

the study included 166 cSVD patients (51.8% women; 60.4 ± 7.6 years) and 44 healthy volunteers (65.9% women; 59.6 ± 6.8 years). All subjects underwent D-MRI (3T) with signal (diffusion tensor and kurtosis) and biophysical (neurite orientation dispersion and density imaging, NODDI, white matter tract integrity, WMTI, multicompartment spherical mean technique, MC-SMT) modeling in three CC segments as well as a neuropsychological assessment.

RESULTS

in cSVD patients, microstructural changes were found in all CC segments already at the subjective CI stage, which was found to worsen into mild CI and dementia. More pronounced changes were observed in the forceps minor. Among the signal models FA, MD, MK, RD, and RK, as well as among the biophysical models, MC-SMT (EMD, ETR) and WMTI (AWF) metrics exhibited the largest area under the curve (>0.85), characterizing the loss of microstructural integrity, the severity of potential demyelination, and the proportion of intra-axonal water, respectively. Conclusion: the study reveals the relevance of advanced D-MRI approaches for the assessment of brain tissue changes in cSVD. The identified diffusion biomarkers could be used for the clarification and observation of CI progression.

Using diffusion MRI to understand white matter damage and the link between brain microstructure and cognitive deficits in paediatric medulloblastoma patients

PURPOSE

Survivors of medulloblastoma face a range of challenges after treatment, involving behavioural, cognitive, language and motor skills. Post-treatment outcomes are associated with structural changes within the brain resulting from both the tumour and the treatment. Diffusion magnetic resonance imaging (MRI) has been used to investigate the microstructure of the brain. In this review, we aim to summarise the literature on diffusion MRI in patients treated for medulloblastoma and discuss future directions on how diffusion imaging can be used to improve patient quality.

METHOD

This review summarises the current literature on medulloblastoma in children, focusing on the impact of both the tumour and its treatment on brain microstructure. We review studies where diffusion MRI has been correlated with either treatment characteristics or cognitive outcomes. We discuss the role diffusion MRI has taken in understanding the relationship between microstructural damage and cognitive and behavioural deficits.

RESULTS

We identified 35 studies that analysed diffusion MRI changes in patients treated for medulloblastoma. The majority of these studies found significant group differences in measures of brain microstructure between patients and controls, and some of these studies showed associations between microstructure and neurocognitive outcomes, which could be influenced by patient characteristics (e.g. age), treatment, radiation dose and treatment type.

CONCLUSIONS

In future, studies would benefit from being able to separate microstructural white matter damage caused by the tumour, tumour-related complications and treatment. Additionally, advanced diffusion modelling methods can be explored to understand and describe microstructural changes to white matter.

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.

Processing speed mediates the relationship between DDR1 and psychosocial functioning in euthymic patients with bipolar disorder presenting psychotic symptoms

The DDR1 locus is associated with the diagnosis of schizophrenia and with processing speed in patients with schizophrenia and first-episode psychosis. Here, we investigated whether DDR1 variants are associated with bipolar disorder (BD) features. First, we performed a case‒control association study comparing DDR1 variants between patients with BD and healthy controls. Second, we performed linear regression analyses to assess the associations of DDR1 variants with neurocognitive domains and psychosocial functioning. Third, we conducted a mediation analysis to explore whether neurocognitive impairment mediated the association between DDR1 variants and psychosocial functioning in patients with BD. Finally, we studied the association between DDR1 variants and white matter microstructure. We did not find any statistically significant associations in the case‒control association study; however, we found that the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse neurocognitive performance in patients with BD with psychotic symptoms. In addition, the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse psychosocial functioning through processing speed. We did not find correlations between white matter microstructure abnormalities and the neurocognitive domains associated with the combined genotypes rs1264323AA-rs2267641AC/CC. Overall, the results suggest that DDR1 may be a marker of worse neurocognitive performance and psychosocial functioning in patients with BD, specifically those with psychotic symptoms.

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.

Tractometry of Human Visual White Matter Pathways in Health and Disease

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor

We aimed to compare the ability of diffusion tensor imaging and multi-compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy-six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid-attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate-severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R-squared value of .57, significantly outperforming the R-squared value of .24 for FA (p-value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS-related impairments.

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

PURPOSE

This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approach and a T2-based pore size estimation technique.

THEORY AND METHODS

A general diffusion-relaxation theoretical model for the spherical mean signal from water molecules within a distribution of cylinders with varying radii was introduced, encompassing the evaluated models as particular cases. Additionally, a new numerical approach was presented for estimating effective radii (i.e., MRI-visible mean radii) from the ground truth radii distributions, not reliant on previous theoretical approximations and adaptable to various acquisition sequences. The ground truth radii were obtained from scanning electron microscope images.

RESULTS

Both methods show a linear relationship between effective radii estimated from MRI data and ground-truth radii distributions, although some discrepancies were observed. The spherical mean power-law method overestimated fiber radii. Conversely, the T2-based method exhibited higher sensitivity to smaller fiber radii, but faced limitations in accurately estimating the radius in one particular phantom, possibly because of material-specific relaxation changes.

CONCLUSION

The study demonstrates the feasibility of both techniques to predict pore sizes of hollow microfibers. The T2-based technique, unlike the spherical mean power-law method, does not demand ultra-high diffusion gradients, but requires calibration with known radius distributions. This research contributes to the ongoing development and evaluation of neuroimaging techniques for fiber radius estimation, highlights the advantages and limitations of both methods, and provides datasets for reproducible research.

Increased extra-neurite conductivity of brain in patients with Alzheimer's disease: A pilot study

The objectives of this study were to investigate how the extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer's disease (AD) patients compared with old cognitively normal (CN) people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between those conductivity values and cognitive decline. To do this, high-frequency conductivity (HFC) at the Larmor frequency was obtained using MRI-based electrical property tomography (MREPT) and was decomposed into EC and IC using information of multi-shell multi-gradient direction diffusion tensor images. This prospective single-center study included 20 patients with mild or moderate AD, 25 patients with amnestic MCI, and 21 old CN participants. After decomposing EC and IC from HFC for all participants, we performed voxel-based and regions-of-interest analyses to compare conductivity between the three participant groups and to evaluate the association with either age or the Mini-Mental State Examination (MMSE) scores. We found increased EC in AD compared to CN and MCI. EC was significantly negatively associated with MMSE scores in the insula, and middle temporal gyrus. EC might be used as an imaging biomarker for helping to monitor cognitive function.

Sparse Blind Spherical Deconvolution of diffusion weighted MRI

Diffusion-weighted magnetic resonance imaging provides invaluable insights into in-vivo neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.e., the signal arising from individual fascicles within a voxel. In this paper, an algorithm of blind spherical deconvolution is proposed, which only assumes the axial symmetry of the response function instead of its exact knowledge. This algorithm provides a method for estimating the peaks of the ODF in a voxel without any explicit response function, as well as a method for estimating signals associated with the peaks of the ODF, regardless of how those peaks were obtained. The two stages of the algorithm are tested on Monte Carlo simulations, as well as compared to state-of-the-art methods on real in-vivo data for the orientation retrieval task. Although the proposed algorithm was shown to attain lower angular errors than the state-of-the-art constrained spherical deconvolution algorithm on synthetic data, it was outperformed by state-of-the-art spherical deconvolution algorithms on in-vivo data. In conjunction with state-of-the art methods for axon bundles direction estimation, the proposed method showed its potential for the derivation of per-voxel per-direction metrics on synthetic as well as in-vivo data.

Perivascular space diffusivity and brain microstructural measures are associated with circadian time and sleep quality

The glymphatic system is centred around brain cerebrospinal fluid flow and is enhanced during sleep, and the synaptic homeostasis hypothesis proposes that sleep acts on brain microstructure by selective synaptic downscaling. While so far primarily studied in animals, we here examine in humans if brain diffusivity and microstructure is related to time of day, sleep quality and cognitive performance. We use diffusion weighted images from 916 young healthy individuals, aged between 22 and 37 years, collected as part of the Human Connectome Project to assess diffusion tensor image analysis along the perivascular space index, white matter fractional anisotropy, intra-neurite volume fraction and extra-neurite mean diffusivity. Next, we examine if these measures are associated with circadian time of acquisition, the Pittsburgh Sleep Quality Index (high scores correspond to low sleep quality) and age-adjusted cognitive function total composite score. Consistent with expectations, we find that diffusion tensor image analysis along the perivascular space index and orbitofrontal grey matter extra-neurite mean diffusivity are negatively and white matter fractional anisotropy positively correlated with circadian time. Further, we find that grey matter intra-neurite volume fraction correlates positively with Pittsburgh Sleep Quality Index, and that this correlation is driven by sleep duration. Finally, we find positive correlations between grey matter intra-neurite volume fraction and cognitive function total composite score, as well as negative interaction effects between cognitive function total composite score and Pittsburgh Sleep Quality Index on grey matter intra-neurite volume fraction. Our findings propose that perivascular flow is under circadian control and that sleep downregulates the intra-neurite volume in healthy adults with positive impact on cognitive function.

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.

The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy

The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.

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.

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%.

Clinical and radiological correlates of apathy in multiple sclerosis

BACKGROUND

Although apathy has been associated with fronto-striatal dysfunction in several neurological disorders, its clinical and magnetic resonance imaging (MRI) correlates have been poorly investigated in people with multiple sclerosis (PwMS).

OBJECTIVES

To evaluate clinical variables and investigate microstructural integrity of fronto-striatal grey matter (GM) and white matter (WM) structures using diffusion tensor imaging (DTI).

METHODS

A total of 123 PwMS (age: 40.25 ± 11.5; female: 60.9%; relapsing-remitting multiple sclerosis: 75.6%) were prospectively enrolled and underwent neurological and neuropsychological evaluation, including Expanded Disability Status Scale (EDSS), Apathy Evaluation Scale (AES-S), Hospital Anxiety and Depression Scale (HADS), Modified Fatigue Impact Scale (MFIS) and brain 3T-MRI volumes of whole brain, frontal/prefrontal cortex (PFC) and subcortical regions were calculated. DTI-derived metrics were evaluated in the same GM regions and in connecting WM tracts.

RESULTS

Apathetic PwMS (32.5%) showed lower education levels, higher HADS, MFIS scores and WM lesions volume than nonapathetic PwMS. Significant differences in DTI metrics were found in middle frontal, anterior cingulate and superior frontal PFC subregions and in caudate nuclei. Significant alterations were found in the right cingulum and left striatal-frontorbital tracts.

CONCLUSIONS

Apathy in PwMS is associated with higher levels of physical disability, depression, anxiety and fatigue together with lower educational backgrounds. Microstructural damage within frontal cortex, caudate and fronto-striatal WM bundles is a significant pathological substrate of apathy in multiple sclerosis (MS).

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.

Study protocol - elucidating the neural correlates of functional remediation for older adults with bipolar disorder

Introduction

Beyond mood abnormalities, bipolar disorder (BD) includes cognitive impairments that worsen psychosocial functioning and quality of life. These deficits are especially severe in older adults with BD (OABD), a condition expected to represent most individuals with BD in the upcoming years. Restoring the psychosocial functioning of this population will thus soon represent a public health priority. To help tackle the problem, the Bipolar and Depressive Disorders Unit at the Hospital Clínic of Barcelona has recently adapted its Functional Remediation (FR) program to that population, calling it FROA-BD. However, while scarce previous studies localize the neural mechanisms of cognitive remediation interventions in the dorsal prefrontal cortex, the specific mechanisms are seldom unknown. In the present project, we will investigate the neural correlates of FR-OABD to understand its mechanisms better and inform for potential optimization. The aim is to investigate the brain features and changes associated with FROA-BD efficacy.

Methods

Thirty-two individuals with OABD in full or partial remission will undergo a magnetic resonance imaging (MRI) session before receiving FR-OABD. After completing the FR-OABD intervention, they will undergo another MRI session. The MRI sessions will include structural, diffusion-weighted imaging (DWI), functional MRI (fMRI) with working memory (n-back) and verbal learning tasks, and frontal spectroscopy. We will correlate the pre-post change in dorsolateral and dorsomedial prefrontal cortices activation during the n-back task with the change in psychosocial functioning [measured with the Functioning Assessment Short Test (FAST)]. We will also conduct exploratory whole-brain correlation analyses between baseline or pre-post changes in MRI data and other clinical and cognitive outcomes to provide more insights into the mechanisms and explore potential brain markers that may predict a better treatment response. We will also conduct separate analyses by sex.

Discussion

The results of this study may provide insights into how FROA-BD and other cognitive remediations modulate brain function and thus could optimize these interventions.

Increased white matter fibre dispersion and lower IQ scores in adults born preterm

Preterm birth has been associated with altered microstructural properties of the white matter and lower cognitive ability in childhood and adulthood. Due to methodological limitations of the diffusion tensor model, it is not clear whether alterations in myelination or variation in fibre orientation are driving these differences. Novel models applied to multi-shell diffusion imaging have been used to disentangle these effects, but to date this has not been used to study the preterm brain in adulthood. This study investigated whether novel advanced diffusion MRI metrics such as microscopic anisotropy and orientation dispersion are altered in adults born preterm, and whether this was associated with cognitive performance. Seventy-two preterm born participants (<37 weeks gestational age) were recruited from a 1982-1984 cohort (33 males, mean age 33.5 ± 1.0 years). Seventy-two term born (>37 weeks gestational age) controls (34 males, mean age 30.9 ± 4.0 years) were recruited from the general population. Tensor FA was calculated with FSL, while microscopic FA and orientation dispersion entropy (ODE) were estimated using the Spherical Mean Technique (SMT). Estimated Full Scale IQ (FSIQ), Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI) were obtained from the WASI-II (abbreviated) IQ test. Voxel-wise comparisons using FSL's tract-based spatial statistics were performed to test between-group differences in diffusion MRI metrics as well as within-group associations of diffusion MRI metrics and IQ outcomes. The preterm group had significantly lower FSIQ, VCI and PRI scores. Preterm subjects demonstrated widespread decreases in ODE reflecting increased fibre dispersion, but no differences in microscopic FA. Tensor FA was increased in a small area in the anterior corona radiata. Lower FA values in the preterm population were associated with lower FSIQ and PRI scores. An increase in fibre dispersion in white matter and lower IQ scores after preterm birth exist in adulthood. Advanced diffusion MRI metrics such as the orientation dispersion entropy can be used to monitor white matter alterations across the lifespan in preterm born individuals. Although not significantly different between preterm and term groups, tensor FA values in the preterm group were associated with cognitive outcome.

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.

Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture

Brain tissue microarchitecture is characterized by heterogeneous degrees of diffusivity and rates of transverse relaxation. Unlike standard diffusion MRI with a single echo time (TE), which provides information primarily on diffusivity, relaxation-diffusion MRI involves multiple TEs and multiple diffusion-weighting strengths for probing tissue-specific coupling between relaxation and diffusivity. Here, we introduce a relaxation-diffusion model that characterizes tissue apparent relaxation coefficients for a spectrum of diffusion length scales and at the same time factors out the effects of intra-voxel orientation heterogeneity. We examined the model with an in vivo dataset, acquired using a clinical scanner, involving different health conditions. Experimental results indicate that our model caters to heterogeneous tissue microstructure and can distinguish fiber bundles with similar diffusivities but different relaxation rates. Code with sample data is available at https://github.com/dryewu/RDSI.

Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments

Most diffusion biophysical models capture basic properties of tissue microstructure, such as diffusivity and anisotropy. More realistic models that relate the diffusion-weighted signal to cell size and membrane permeability often require simplifying assumptions such as short gradient pulse and Gaussian phase distribution, leading to tissue features that are not necessarily quantitative. Here, we propose a method to quantify tissue microstructure without jeopardizing accuracy owing to unrealistic assumptions. Our method utilizes realistic signals simulated from the geometries of cellular microenvironments as fingerprints, which are then employed in a spherical mean estimation framework to disentangle the effects of orientation dispersion from microscopic tissue properties. We demonstrate the efficacy of microstructure fingerprinting in estimating intra-cellular, extra-cellular, and intra-soma volume fractions as well as axon radius, soma radius, and membrane permeability.

Neurite Orientation Dispersion and Density Imaging in Multiple Sclerosis: A Systematic Review

Diffusion-weighted imaging has been applied to investigate alterations in multiple sclerosis (MS). In the last years, advanced diffusion models were used to identify subtle changes and early lesions in MS. Among these models, neurite orientation dispersion and density imaging (NODDI) is an emerging approach, quantifying specific neurite morphology in both grey (GM) and white matter (WM) tissue and increasing the specificity of diffusion imaging. In this systematic review, we summarized the NODDI findings in MS. A search was conducted on PubMed, Scopus, and Embase, which yielded a total number of 24 eligible studies. Compared to healthy tissue, these studies identified consistent alterations in NODDI metrics involving WM (neurite density index), and GM lesions (neurite density index), or normal-appearing WM tissue (isotropic volume fraction and neurite density index). Despite some limitations, we pointed out the potential of NODDI in MS to unravel microstructural alterations. These results might pave the way to a deeper understanding of the pathophysiological mechanism of MS. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain

The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.

Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics

Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.

Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology

Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.

Examining post-concussion white matter change in a pediatric sample

Diffusion-Weight Imaging (DWI) is increasingly used to explore a range of outcomes in pediatric concussion, particularly the neurobiological underpinnings of symptom recovery. However, the DWI findings within the broader pediatric concussion literature are mixed, which can largely be explained by methodological heterogeneity. To address some of these limitations, the aim of the present study was to utilize internationally- recognized criteria for concussion and a consistent imaging timepoint to conduct a comprehensive, multi-parametric survey of white matter microstructure after concussion. Forty-three children presenting with concussion to the emergency department of a tertiary level pediatric hospital underwent neuroimaging and were classified as either normally recovering (n = 27), or delayed recovering (n = 14) based on their post-concussion symptoms at 2 weeks post-injury.We combined multiple DWI metrics across four modeling approaches using Linked Independent Component Analysis (LICA) to extract several independent patterns of covariation in tissue microstructure present in the study cohort. Our analysis did not identify significant differences between the symptomatic and asymptomatic groups and no component significantly predicted delayed recovery. If white matter microstructure changes are implicated in delayed recovery from concussion, these findings, alongside previous work, suggest that current diffusion techniques are insufficient to detect those changes at this time.

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter

Purpose

Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM.

Methods

Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI.

Results

ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM.

Conclusion

C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.

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.

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.

Estimation of brain tissue response by electrical stimulation in a subject-specific model implemented by conductivity tensor imaging

Electrical stimulation such as transcranial direct current stimulation (tDCS) is widely used to treat neuropsychiatric diseases and neurological disorders. Computational modeling is an important approach to understand the mechanisms underlying tDCS and optimize treatment planning. When applying computational modeling to treatment planning, uncertainties exist due to insufficient conductivity information inside the brain. In this feasibility study, we performed in vivo MR-based conductivity tensor imaging (CTI) experiments on the entire brain to precisely estimate the tissue response to the electrical stimulation. A recent CTI method was applied to obtain low-frequency conductivity tensor images. Subject-specific three-dimensional finite element models (FEMs) of the head were implemented by segmenting anatomical MR images and integrating a conductivity tensor distribution. The electric field and current density of brain tissues following electrical stimulation were calculated using a conductivity tensor-based model and compared to results using an isotropic conductivity model from literature values. The current density by the conductivity tensor was different from the isotropic conductivity model, with an average relative difference |rD| of 52 to 73%, respectively, across two normal volunteers. When applied to two tDCS electrode montages of C3-FP2 and F4-F3, the current density showed a focused distribution with high signal intensity which is consistent with the current flowing from the anode to the cathode electrodes through the white matter. The gray matter tended to carry larger amounts of current densities regardless of directional information. We suggest this CTI-based subject-specific model can provide detailed information on tissue responses for personalized tDCS treatment planning.

Axon fiber orientation as the source of T1 relaxation anisotropy in white matter: A study on corpus callosum in vivo and ex vivo

PURPOSE

Recent studies indicate that T₁ in white matter (WM) is influenced by fiber orientation in B₀ . The purpose of the study was to investigate the interrelationships between axon fiber orientation in corpus callosum (CC) and T₁ relaxation time in humans in vivo as well as in rat brain ex vivo.

METHODS

Volunteers were scanned for relaxometric and diffusion MRI at 3 T and 7 T. Angular T₁ plots from WM were computed using fractional anisotropy and fiber-to-field-angle maps. T₁ and fiber-to-field angle were measured in five sections of CC to estimate the effects of inherently varying fiber orientations on T₁ within the same tracts in vivo. Ex vivo rat-brain preparation encompassing posterior CC was rotated in B₀ and T₁ , and diffusion MRI images acquired at 9.4 T. T₁ angular plots were determined at several rotation angles in B₀ .

RESULTS

Angular T₁ plots from global WM provided reference for estimated fiber orientation-linked T₁ changes within CC. In anterior midbody of CC in vivo, where small axons are dominantly present, a shift in axon orientation is accompanied by a change in T₁ , matching that estimated from WM T₁ data. In CC, where large and giant axons are numerous, the measured T₁ change is about 2-fold greater than the estimated one. Ex vivo rotation of the same midsagittal CC region of interest produced angular T₁ plots at 9.4 T, matching those observed at 7 T in vivo.

CONCLUSION

These data causally link axon fiber orientation in B₀ to the T₁ relaxation anisotropy in WM.

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.

The influence of axonal beading and undulation on axonal diameter mapping

We consider the effect of non-cylindrical axonal shape on axonal diameter mapping with diffusion MRI. Practical sensitivity to axon diameter is attained at strong diffusion weightings b , where the deviation from the 1 / b scaling yields the finite transverse diffusivity, which is then translated into axon diameter. While axons are usually modeled as perfectly straight, impermeable cylinders, the local variations in diameter (caliber variation or beading) and direction (undulation) have been observed in microscopy data of human axons. Here we quantify the influence of cellular-level features such as caliber variation and undulation on axon diameter estimation. For that, we simulate the diffusion MRI signal in realistic axons segmented from 3-dimensional electron microscopy of a human brain sample. We then create artificial fibers with the same features and tune the amplitude of their caliber variations and undulations. Numerical simulations of diffusion in fibers with such tunable features show that caliber variations and undulations result in under- and over-estimation of axon diameters, correspondingly; this bias can be as large as 100%. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.

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

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

Role of cortical excitatory/inhibitory imbalance in autism spectrum disorders from a symptom severity trajectories framework: a study protocol

BACKGROUND

There is considerable evidence reporting an excitatory/inhibitory (E/I) cortical imbalance in autism spectrum disorders (ASD). However, previous findings on the direction of this imbalance and its relationship to ASD symptomatology are heterogeneous. Some factors contributing to these mixed results might be the methodological differences between studies assessing the E/I ratio and the intrinsic variability within the autistic spectrum. Studying the evolution of ASD symptoms and the factors that modulate it might help to explain and reduce this variability. Here we present a study protocol to explore the longitudinal role of E/I imbalance in ASD symptoms, combining different approaches to measure the E/I ratio and using the trajectories of symptom severity as a framework.

METHODS

This observational two time-point prospective study assesses the E/I ratio and the evolution of the behavioural symptoms in a sample of at least 98 participants with ASD. Participants are enrolled at 12 to 72 months of age and followed from 18 to 48 months after. A comprehensive battery of tests is applied to evaluate ASD clinical symptoms. The E/I ratio is approached from electrophysiology, magnetic resonance, and genetics. We will calculate the individual change for the main ASD symptoms and, based on that, we will define the trajectories of symptom severity. Then, we will investigate the correlation between measures of excitation/inhibition balance and autistic symptomatology cross-sectionally, as well as the ability of these measurements to predict changes in symptoms over time.

DISCUSSION

This study presents a robust multisystemic approach to the E/I imbalance theory in autism and its relation to divergent symptom trajectories. That setting will allow us to relate and compare the neurobiological information coming from different sources and its impact on behavioural symptoms while accounting for the high variability in ASD. The findings derived from this study could contribute to the ASD biomarkers research and might provide valuable evidence for the development of more personalized treatments in ASD.

Patterns of inflammation, microstructural alterations, and sodium accumulation define multiple sclerosis subtypes after 15 years from onset

Introduction

Conventional MRI is routinely used for the characterization of pathological changes in multiple sclerosis (MS), but due to its lack of specificity is unable to provide accurate prognoses, explain disease heterogeneity and reconcile the gap between observed clinical symptoms and radiological evidence. Quantitative MRI provides measures of physiological abnormalities, otherwise invisible to conventional MRI, that correlate with MS severity. Analyzing quantitative MRI measures through machine learning techniques has been shown to improve the understanding of the underlying disease by better delineating its alteration patterns.

Methods

In this retrospective study, a cohort of healthy controls (HC) and MS patients with different subtypes, followed up 15 years from clinically isolated syndrome (CIS), was analyzed to produce a multi-modal set of quantitative MRI features encompassing relaxometry, microstructure, sodium ion concentration, and tissue volumetry. Random forest classifiers were used to train a model able to discriminate between HC, CIS, relapsing remitting (RR) and secondary progressive (SP) MS patients based on these features and, for each classification task, to identify the relative contribution of each MRI-derived tissue property to the classification task itself.

Results and discussion

Average classification accuracy scores of 99 and 95% were obtained when discriminating HC and CIS vs. SP, respectively; 82 and 83% for HC and CIS vs. RR; 76% for RR vs. SP, and 79% for HC vs. CIS. Different patterns of alterations were observed for each classification task, offering key insights in the understanding of MS phenotypes pathophysiology: atrophy and relaxometry emerged particularly in the classification of HC and CIS vs. MS, relaxometry within lesions in RR vs. SP, sodium ion concentration in HC vs. CIS, and microstructural alterations were involved across all tasks.

Central vein sign and diffusion MRI differentiate microstructural features within white matter lesions of multiple sclerosis patients with comorbidities

Introduction

The Central Vein Sign (CVS) has been suggested as a potential biomarker to improve diagnostic specificity in multiple sclerosis (MS). Nevertheless, the impact of comorbidities on CVS performance has been poorly investigated so far. Despite the similar features shared by MS, migraine and Small Vessel Disease (SVD) at T2-weighted conventional MRI sequences, ex-vivo studies demonstrated their heterogeneous histopathological substrates. If in MS, inflammation, primitive demyelination and axonal loss coexist, in SVD demyelination is secondary to ischemic microangiopathy, while the contemporary presence of inflammatory and ischemic processes has been suggested in migraine. The aims of this study were to investigate the impact of comorbidities (risk factors for SVD and migraine) on the global and subregional assessment of the CVS in a large cohort of MS patients and to apply the Spherical Mean Technique (SMT) diffusion model to evaluate whether perivenular and non-perivenular lesions show distinctive microstructural features.

Methods

120 MS patients stratified into 4 Age Groups performed 3T brain MRI. WM lesions were classified in "perivenular" and "non-perivenular" by visual inspection of FLAIR* images; mean values of SMT metrics, indirect estimators of inflammation, demyelination and fiber disruption (EXTRAMD: extraneurite mean diffusivity, EXTRATRANS: extraneurite transverse diffusivity and INTRA: intraneurite signal fraction, respectively) were extracted.

Results

Of the 5303 lesions selected for the CVS assessment, 68.7% were perivenular. Significant differences were found between perivenular and non-perivenular lesion volume in the whole brain (p < 0.001) and between perivenular and non-perivenular lesion volume and number in all the four subregions (p < 0.001 for all). The percentage of perivenular lesions decreased from youngest to oldest patients (79.7%-57.7%), with the deep/subcortical WM of oldest patients as the only subregion where the number of non-perivenular was higher than the number of perivenular lesions. Older age and migraine were independent predictors of a higher percentage of non-perivenular lesions (p < 0.001 and p = 0.013 respectively). Whole brain perivenular lesions showed higher inflammation, demyelination and fiber disruption than non perivenular lesions (p = 0.001, p = 0.001 and p = 0.02 for EXTRAMD, EXTRATRANS and INTRA respectively). Similar findings were found in the deep/subcortical WM (p = 0.001 for all). Compared to non-perivenular lesions, (i) perivenular lesions located in periventricular areas showed a more severe fiber disruption (p = 0.001), (ii) perivenular lesions located in juxtacortical and infratentorial regions exhibited a higher degree of inflammation (p = 0.01 and p = 0.05 respectively) and (iii) perivenular lesions located in infratentorial areas showed a higher degree of demyelination (p = 0.04).

Discussion

Age and migraine have a relevant impact in reducing the percentage of perivenular lesions, particularly in the deep/subcortical WM. SMT may differentiate perivenular lesions, characterized by higher inflammation, demyelination and fiber disruption, from non perivenular lesions, where these pathological processes seemed to be less pronounced. The development of new non-perivenular lesions, especially in the deep/subcortical WM of older patients, should be considered a "red flag" for a different -other than MS- pathophysiology.

Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales

Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

High frequency conductivity decomposition by solving physically constraint underdetermined inverse problem in human brain

The developed magnetic resonance electrical properties tomography (MREPT) can visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data from magnetic resonance imaging (MRI). The recovered high-frequency conductivity (HFC) value is highly complex and heterogeneous in a macroscopic imaging voxel. Using high and low b-value diffusion weighted imaging (DWI) data, the multi-compartment spherical mean technique (MC-SMT) characterizes the water molecule movement within and between intra- and extra-neurite compartments by analyzing the microstructures and underlying architectural organization of brain tissues. The proposed method decomposes the recovered HFC into the conductivity values in the intra- and extra-neurite compartments via the recovered intra-neurite volume fraction (IVF) and the diffusion patterns using DWI data. As a form of decomposition of intra- and extra-neurite compartments, the problem to determine the intra- and extra-neurite conductivity values from the HFC is still an underdetermined inverse problem. To solve the underdetermined problem, we use the compartmentalized IVF as a criterion to decompose the electrical properties because the ion-concentration and mobility have different characteristics in the intra- and extra-neurite compartments. The proposed method determines a representative apparent intra- and extra-neurite conductivity values by changing the underdetermined equation for a voxel into an over-determined minimization problem over a local window consisting of surrounding voxels. To suppress the noise amplification and estimate a feasible conductivity, we define a diffusion pattern distance to weight the over-determined system in the local window. To quantify the proposed method, we conducted a simulation experiment. The simulation experiments show the relationships between the noise reduction and the spatial resolution depending on the designed local window sizes and diffusion pattern distance. Human brain experiments (five young healthy volunteers and a patient with brain tumor) were conducted to evaluate and validate the reliability of the proposed method. To quantitatively compare the results with previously developed methods, we analyzed the errors for reconstructed extra-neurite conductivity using existing methods and indirectly verified the feasibility of the proposed method.

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.

Loss of aquaporin-4 results in glymphatic system dysfunction via brain-wide interstitial fluid stagnation

The glymphatic system is a fluid transport network of cerebrospinal fluid (CSF) entering the brain along arterial perivascular spaces, exchanging with interstitial fluid (ISF), ultimately establishing directional clearance of interstitial solutes. CSF transport is facilitated by the expression of aquaporin-4 (AQP4) water channels on the perivascular endfeet of astrocytes. Mice with genetic deletion of AQP4 (AQP4 KO) exhibit abnormalities in the brain structure and molecular water transport. Yet, no studies have systematically examined how these abnormalities in structure and water transport correlate with glymphatic function. Here, we used high-resolution 3D magnetic resonance (MR) non-contrast cisternography, diffusion-weighted MR imaging (MR-DWI) along with intravoxel-incoherent motion (IVIM) DWI, while evaluating glymphatic function using a standard dynamic contrast-enhanced MR imaging to better understand how water transport and glymphatic function is disrupted after genetic deletion of AQP4. AQP4 KO mice had larger interstitial spaces and total brain volumes resulting in higher water content and reduced CSF space volumes, despite similar CSF production rates and vascular density compared to wildtype mice. The larger interstitial fluid volume likely resulted in increased slow but not fast MR diffusion measures and coincided with reduced glymphatic influx. This markedly altered brain fluid transport in AQP4 KO mice may result from a reduction in glymphatic clearance, leading to enlargement and stagnation of fluid in the interstitial space. Overall, diffusion MR is a useful tool to evaluate glymphatic function and may serve as valuable translational biomarker to study glymphatics in human disease.

An investigation of the association between focal damage and global network properties in cognitively impaired and cognitively preserved patients with multiple sclerosis

Introduction

The presence of focal cortical and white matter damage in patients with multiple sclerosis (pwMS) might lead to specific alterations in brain networks that are associated with cognitive impairment. We applied microstructure-weighted connectomes to investigate (i) the relationship between global network metrics and information processing speed in pwMS, and (ii) whether the disruption provoked by focal lesions on global network metrics is associated to patients' information processing speed.

Materials and methods

Sixty-eight pwMS and 92 healthy controls (HC) underwent neuropsychological examination and 3T brain MRI including multishell diffusion (dMRI), 3D FLAIR, and MP2RAGE. Whole-brain deterministic tractography and connectometry were performed on dMRI. Connectomes were obtained using the Spherical Mean Technique and were weighted for the intracellular fraction. We identified white matter lesions and cortical lesions on 3D FLAIR and MP2RAGE images, respectively. PwMS were subdivided into cognitively preserved (CPMS) and cognitively impaired (CIMS) using the Symbol Digit Modalities Test (SDMT) z-score at cut-off value of -1.5 standard deviations. Statistical analyses were performed using robust linear models with age, gender, and years of education as covariates, followed by correction for multiple testing.

Results

Out of 68 pwMS, 18 were CIMS and 50 were CPMS. We found significant changes in all global network metrics in pwMS vs HC (p < 0.05), except for modularity. All global network metrics were positively correlated with SDMT, except for modularity which showed an inverse correlation. Cortical, leukocortical, and periventricular lesion volumes significantly influenced the relationship between (i) network density and information processing speed and (ii) modularity and information processing speed in pwMS. Interestingly, this was not the case, when an exploratory analysis was performed in the subgroup of CIMS patients.

Discussion

Our study showed that cortical (especially leukocortical) and periventricular lesions affect the relationship between global network metrics and information processing speed in pwMS. Our data also suggest that in CIMS patients increased focal cortical and periventricular damage does not linearly affect the relationship between network properties and SDMT, suggesting that other mechanisms (e.g. disruption of local networks, loss of compensatory processes) might be responsible for the development of processing speed deficits.

On the performance of multi-compartment relaxometry for myelin water imaging (MCR-MWI) - test-retest repeatability and inter-protocol reproducibility

In this study, we optimized the variable flip angle (VFA) acquisition scheme using numerical simulations to shorten the acquisition time of multicompartment relaxometry for myelin water imaging (MCR-MWI) to a clinically practical range in the absence of advanced image reconstruction methods. As the primary objective of this study, the test-retest repeatability of myelin water fraction (MWF) measurements of MCR-MWI is evaluated on three gradient echo (GRE) sequence settings using the optimized VFA schemes with different echo times and repetition times, emulating various scanner setups. The cross-protocol reproducibility of MCR-MWI and MCR with diffusion-informed myelin water imaging (MCR-DIMWI) is also examined. As a secondary objective, we explore the bundle-specific profiles of various microstructural parameters from MCR-(DI)MWI and their cross-correlations to determine if these parameters possess supplementary microstructure information beyond myelin concentration. Numerical simulations indicate that MCR-MWI can be performed with a minimum of three flip angles covering a wide range of T₁ weightings without adding significant bias. This is supported by the results of an in vivo experiment, allowing whole-brain 1.5 mm isotropic MWF maps to be acquired in 9 min, reducing the total scan time to 40% of the original implementation without significant quality degradation. Good test-retest repeatability is observed for MCR-MWI for all three GRE protocols. While good correlations can also be found in MWF across protocols, systematic differences are observed. Bundle-specific MWF analysis reveals that certain white matter bundles are similar in all participants. We also found that microstructure relaxation parameters have low linear correlations with MWF. MCR-MWI is a reproducible measure of myelin. However, attention should be paid to the protocol related MWF differences when comparing different studies, as the MWF bias up to 0.5% can be observed across the protocols examined in this work.