Brain Tissue Microstructure Research Articles

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

Maternal prenatal immune activation associated with brain tissue microstructure and metabolite concentrations in newborn infants

Few human studies have assessed the association of prenatal maternal immune activation (MIA) with measures of brain development and psychiatric risk in newborn offspring. Our goal was to identify the effects of MIA during the 2nd and 3rd trimesters of pregnancy on newborn measures of brain metabolite concentrations, tissue microstructure, and motor development. This was a prospective longitudinal cohort study conducted with nulliparous pregnant women who were aged 14 to 19 years and recruited in their 2nd trimester, as well as their children who were followed through 14 months of age. MIA was indexed by maternal interleukin-6 (IL-6) and C-reactive protein (CRP) in both trimesters of pregnancy.Primary outcomes included: (1) newborn brain metabolite concentrations as ratios to creatine (N-acetylaspartate (NAA)/creatine (Cr) and choline (Cho)/Cr) measured using Magnetic Resonance Spectroscopy; (2) newborn fractional anisotropy and mean diffusivity, measured using Diffusion Tensor Imaging; and (3) indices of motor development, assessed prenatally and postnatally at ages 4- and 14-months. Maternal IL-6 and CRP levels associated significantly with both metabolites in the putamen, thalamus, insula, and the internal capsule. Maternal IL-6 associated significantly with fractional anisotropy in the putamen, caudate, thalamus, insula, and precuneus, and with mean diffusivity in the inferior parietal and middle temporal gyrus. CRP associated significantly with fractional anisotropy in the thalamus, insula, and putamen. Significant associations were found in common regions across imaging modalities, though the direction of associations differed by immune marker. In addition, both maternal IL-6 and CRP (in both trimesters) prenatally associated significantly with offspring motor development at 4- and 14-months of age. The left thalamus mediated effects of IL-6 on postnatal motor development. These findings demonstrate that levels of MIA in mid- to late pregnancy in a generally healthy sample associate with tissue characteristics in newborn brain regions that primarily support motor integration and coordination, as well as behavioral regulation. Those brain effects may contribute to differences in motor development.

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

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

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22–37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92–0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

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

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

The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood.

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.

Likelihood-free posterior estimation and uncertainty quantification for diffusion MRI models

Abstract Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well as the connectivity of the white matter (known as tractography). Accurate estimation of the model parameters (by solving the inverse problem) is thus very important to infer the underlying biophysical tissue properties and fiber orientations. Although there has been extensive research on this topic with a myriad of dMRI models, most models use standard nonlinear optimization techniques and only provide an estimate of the model parameters without any information (quantification) about uncertainty in their estimation. Further, the effect of this uncertainty on the estimation of the derived dMRI microstructural measures downstream (e.g., fractional anisotropy) is often unknown and is rarely estimated. To address this issue, we first design a new deep-learning algorithm to identify the number of crossing fibers in each voxel. Then, at each voxel, we propose a robust likelihood-free deep learning method to estimate not only the mean estimate of the parameters of a multi-fiber dMRI model (e.g., the biexponential model), but also its full posterior distribution. The posterior distribution is then used to estimate the uncertainty in the model parameters as well as the derived measures. We perform several synthetic and in-vivo quantitative experiments to demonstrate the robustness of our approach for different noise levels and out-of-distribution test samples. Besides, our approach is computationally fast and requires an order of magnitude less time than standard nonlinear fitting techniques. The proposed method demonstrates much lower error (compared to existing methods) in estimating several metrics, including number of fibers in a voxel, fiber orientation, and tensor eigenvalues. The proposed methodology is quite general and can be used for the estimation of the parameters from any other dMRI model.

Magnetic resonance elastography captures a transient benefit of exercise intervention on forebrain stiffness in a rat model of fetal alcohol spectrum disorders.

Fetal alcohol spectrum disorders (FASD), a group of prevalent conditions resulting from prenatal alcohol exposure, affect the maturation of cerebral white matter as first identified with neuroimaging. However, traditional methods are unable to track subtle microstructural alterations to white matter. This preliminary study uses a highly sensitive and clinically translatable magnetic resonance elastography (MRE) protocol to assess brain tissue microstructure through its mechanical properties following an exercise intervention in a rat model of FASD. Female rat pups were either alcohol-exposed (AE) via intragastric intubation of alcohol in milk substitute (5.25 g/kg/day) or sham-intubated (SI) on postnatal days (PD) four through nine to model alcohol exposure during the brain growth spurt. On PD 30, half of AE and SI rats were randomly assigned to either a wheel-running or standard cage for 12 days. Magnetic resonance elastography was used to measure whole brain and callosal mechanical properties at the end of the intervention (around PD 42) and at 1 month post-intervention, and findings were validated with histological quantification of oligoglia. Alcohol exposure reduced forebrain stiffness (p = 0.02) in standard-housed rats. The adolescent exercise intervention mitigated this effect, confirming that increased aerobic activity supports proper neurodevelopmental trajectories. Forebrain damping ratio was lowest in standard-housed AE rats (p < 0.01), but this effect was not mitigated by intervention exposure. At 1 month post-intervention, all rats exhibited comparable forebrain stiffness and damping ratio (p > 0.05). Callosal stiffness and damping ratio increased with age. With cessation of exercise, there was a negative rebound effect on the quantity of callosal oligodendrocytes, irrespective of treatment group, which diverged fromour MRE results. This is the first application of MRE to measure the brain's mechanical properties in a rodent model of FASD. MRE successfully captured alcohol-related changes in forebrain stiffness and damping ratio. Additionally, MRE identified an exercise-related increase to forebrain stiffness in AE rats.

The impact of mechanical thrombectomy on the blood–brain barrier in patients with acute ischemic stroke: A non-contrast MR imaging study using DP-pCASL and NODDI

Background and purposeWhile mechanical thrombectomy (MT) achieves restoration of cerebral blood flow to the area at risk in patients with acute ischemic stroke (AIS), the influx of blood flow may exacerbate the blood–brain barrier (BBB) disruption and extravasation across the BBB, and it therefore remains unclear how reperfusion impacts the blood–brain barrier integrity. In this study, we use diffusion-prepared pseudocontinuous ASL (DP-pCASL) and Neurite Orientation Dispersion and Density Imaging (NODDI) sequence to measure the water exchange rate (kw) in patients who underwent either MT or medical management and determine its impact on the brain tissue microstructure in order to elucidate the impact of MT on BBB complex integrity. Materials and methodsWe prospectively enrolled 21 patients with AIS treated at our institution from 10/2021 to 6/2023 who underwent MR imaging at a 3.0-Tesla scanner. Patients underwent DP-pCASl and NODDI imaging in addition to the standard stroke protocol which generated cerebral blood flow (CBF), arterial transit time (ATT), water exchange rate (kw), orientation dispersion index (ODI), intracellular volume fraction (ICVF), and free water fraction (FWF) parametric maps. ResultsOf the 21 patients, 11 underwent MT and 10 were treated non-operatively. The average age and NIHSS for the MT cohort and non-MT cohorts were 69.3 ± 16.6 years old and 15.0 (12.0–20.0), and 70.2 ± 10.7 (p = 0.882) and 6.0 (3.8–9.0, p = 0.003) respectively. The average CBF, ATT, and kw in the infarcted territory of the MT cohort were 38.2 (18.4–59.6), 1347.6 (1182.5–1842.3), and 107.8 (79.2–140.1) respectively. The average CBF, ATT, and kw in the stroke ROI were 16.0 (8.8–36.6, p = 0.036), 1090.8 (937.1–1258.9, p = 0.013), 89.7 (68.0–122.7, p = 0.314) respectively. Linear regression analysis showed increasing CBF (p = 0.008) and undergoing mechanical thrombectomy (p = 0.048) were significant predictors of increased kw. ConclusionUsing our multimodal non-contrast MRI protocol, we demonstrate that increased CBF and mechanical thrombectomy increased kw, suggesting a better functioning BBB complex. Higher kw suggests less disruption of the BBB complex in the MT cohort.

Combined assessment of progressive apraxia of speech brain microstructure by diffusion tensor imaging tractography and multishell neurite orientation dispersion and density imaging.

Progressive apraxia of speech (PAOS) is characterized by difficulties with motor speech programming and planning. PAOS targets gray matter (GM) and white matter (WM) microstructure that can be assessed using diffusion tensor imaging (DTI) and multishell applications, such as neurite orientation dispersion and density imaging (NODDI). In this study, we aimed to apply DTI and NODDI to add further insight into PAOS tissue microstructure. Twenty-two PAOS patients and 26 age- and sex-matched controls, recruited by the Neurodegenerative Research Group (NRG) at Mayo Clinic, underwent diffusion MRI on 3T MRI. Brain maps of fractional anisotropy (FA) and mean diffusivity (MD) from DTI and intracellular volume fraction (ICVF) and isotropic volume fraction (IsoVF) from NODDI were generated. Global WM and GM, and specific WM tracts were identified using tractography and lobar GM regions. Global WM differences between PAOS and controls were greatest for ICVF, and global GM differences were greatest for MD and IsoVF. Abnormalities in key WM tracts involved in PAOS, including the body of the corpus callosum and frontal aslant tract, were identified with FA, MD, and ICVF, with excellent differentiation of PAOS from controls (area under the receiver operating characteristic curves >.90). MD and ICVF identified abnormalities in arcuate fasciculus, thalamic radiations, and corticostriatal tracts. Significant correlations were identified between an index of articulatory errors and DTI and NODDI metrics from the arcuate fasciculus, frontal aslant tract, and inferior longitudinal fasciculus. DTI and NODDI represent different aspects of brain tissue microstructure, increasing the number of potential biomarkers for PAOS.

Fast and robust quantification of uncertainty in non-linear diffusion MRI models

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.

Circadian sleep regulation and brain tissue microstructure in cognitively unimpaired older adults

AbstractBackgroundCircadian dysfunction increases with age, leading to changes in the circadian regulation of physiological and biological rhythms, including sleep. Actigraphy studies demonstrated that altered 24‐h rest‐activity patterns, an estimate of circadian sleep‐wake regulation, is related to cognitive decline and tightly linked to Alzheimer’s disease progression. Here, we assessed whether circadian sleep regulation, measured during a multiple naps protocol, is related to brain microstructural integrity in a group of cognitively unimpaired older adults.MethodEighty‐six retired older individuals (mean age [SD] = 69.9 ± 5.2 y.o., 32 females, Table 1) with no major sleep disorders or medical condition, underwent an in‐lab 40‐h multiple naps protocol and brain structural magnetic resonance imaging. As rapid eye movement (REM) sleep is under strong circadian control, circadian REM sleep amplitude was computed by fitting a Gaussian curve on REM sleep duration (% of total sleep time) measured for each nap opportunity. The multiparametric mapping protocol was used to derive indices of brain tissue microstructure using magnetization transfer saturation (MTsat), R1 and R2* maps (hMRI toolbox v0.2.2). Statistical models were adjusted for age, sex, education, and for additional covariates related to sleep architecture and its circadian modulation, including mean REM% and sleep efficiency (SE), the accumulation of REM sleep and sleep need (slope of a fitted first‐order polynomial curve), and circadian SE amplitude.ResultsWhole‐brain voxel‐based quantification analysis revealed that low circadian REM amplitude was related to low MTsat, R1 and R2* values in several white matter regions mostly located around the lateral ventricles (Figure 1A). In addition, a significant association was observed between low circadian REM amplitude and low R1 values in two grey matter clusters encompassing the hippocampus, entorhinal cortex, thalamus and hypothalamus (Figure 1B).ConclusionOur findings provide first evidence that changes in circadian sleep regulation is associated with microstructural changes in key brain regions sensitive to the aging process and involved in sleep‐wake regulation. These results further emphasize the relevance of monitoring circadian sleep regulation for the early detection of individuals at risk for neurodegeneration and cognitive decline.

Mechanical behavior and microstructure of porcine brain tissues under pulsed electric fields.

Pulsed electric fields are extensively utilized in clinical treatments, such as subthalamic deep brain stimulation, where electric field loading is in direct contact with brain tissue. However, the alterations in brain tissue's mechanical properties and microstructure due to changes in electric field parameters have not received adequate attention. In this study, the mechanical properties and microstructure of the brain tissue under pulsed electric fields were focused on. Herein, a custom indentation device was equipped with a module for electric field loading. Parameters such as pulse amplitude and frequency were adjusted. The results demonstrated that following an indentation process lasting 5s and reaching a depth of 1000 μm, and a relaxation process of 175s, the average shear modulus of brain tissue was reduced, and viscosity decreased. At the same amplitude, high-frequency pulsed electric fields had a smaller effect on brain tissue than low-frequency ones. Furthermore, pulsed electric fields induced cell polarization and reduced the proteoglycan concentration in brain tissue. As pulse frequency increased, cell polarization diminished, and proteoglycan concentration decreased significantly. High-frequency pulsed electric fields applied to brain tissue were found to reduce impedance fluctuation amplitude. This study revealed the effect of pulsed electric fields on the mechanical properties and microstructure of ex vivo brain tissue, providing essential information to promote the advancement of brain tissue electrotherapy in clinical settings.

Tract-wise microstructural analysis informs on current and future disability in early multiple sclerosis

ObjectivesMicrostructural characterization of patients with multiple sclerosis (MS) has been shown to correlate better with disability compared to conventional radiological biomarkers. Quantitative MRI provides effective means to characterize microstructural brain tissue changes both in lesions and normal-appearing brain tissue. However, the impact of the location of microstructural alterations in terms of neuronal pathways has not been thoroughly explored so far. Here, we study the extent and the location of tissue changes probed using quantitative MRI along white matter (WM) tracts extracted from a connectivity atlas.MethodsWe quantified voxel-wise T1 tissue alterations compared to normative values in a cohort of 99 MS patients. For each WM tract, we extracted metrics reflecting tissue alterations both in lesions and normal-appearing WM and correlated these with cross-sectional disability and disability evolution after 2 years.ResultsIn early MS patients, T1 alterations in normal-appearing WM correlated better with disability evolution compared to cross-sectional disability. Further, the presence of lesions in supratentorial tracts was more strongly associated with cross-sectional disability, while microstructural alterations in infratentorial pathways yielded higher correlations with disability evolution. In progressive patients, all major WM pathways contributed similarly to explaining disability, and correlations with disability evolution were generally poor.ConclusionsWe showed that microstructural changes evaluated in specific WM pathways contribute to explaining future disability in early MS, hence highlighting the potential of tract-wise analyses in monitoring disease progression. Further, the proposed technique allows to estimate WM tract-specific microstructural characteristics in clinically compatible acquisition times, without the need for advanced diffusion imaging.

Focal epilepsy without overt epileptogenic lesions: no evidence of microstructural brain tissue damage in multi-parametric quantitative MRI.

In patients with epilepsies of structural origin, brain atrophy and pathological alterations of the tissue microstructure extending beyond the putative epileptogenic lesion have been reported. However, in patients without any evidence of epileptogenic lesions on diagnostic magnetic resonance imaging (MRI), impairment of the brain microstructure has been scarcely elucidated. Using multiparametric quantitative (q) magnetic resonance imaging MRI, we aimed to investigate diffuse impairment of the microstructural tissue integrity in MRI-negative focal epilepsy patients. 27 MRI-negative patients with focal epilepsy (mean age 33.1 ± 14.2 years) and 27 matched healthy control subjects underwent multiparametric qMRI including T1, T2, and PD mapping at 3 T. After tissue segmentation based on synthetic anatomies, mean qMRI parameter values were extracted from the cerebral cortex, the white matter (WM) and the deep gray matter (GM) and compared between patients and control subjects. Apart from calculating mean values for the qMRI parameters across the respective compartments, voxel-wise analyses were performed for each tissue class. There were no significant differences for mean values of quantitative T1, T2, and PD obtained from the cortex, the WM and the deep GM between the groups. Furthermore, the voxel-wise analyses did not reveal any clusters indicating significant differences between patients and control subjects for the qMRI parameters in the respective compartments. Based on the employed methodology, no indication for an impairment of the cerebral microstructural tissue integrity in MRI-negative patients with focal epilepsy was found in this study. Further research will be necessary to identify relevant factors and mechanisms contributing to microstructural brain tissue damage in various subgroups of patients with epilepsy.

Microstructural brain tissue changes contribute to cognitive and mood deficits in adults with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) patients show brain tissue changes in mood and cognitive regulatory sites, but the nature and extent of tissue injury and their associations with symptoms are unclear. Our aim was to examine brain tissue damage in T2DM over controls using mean diffusivity (MD) computed from diffusion tensor imaging (DTI), and assess correlations with mood and cognitive symptoms in T2DM. We collected DTI series (MRI), mood, and cognitive data, from 169 subjects (68 T2DM and 101 controls). Whole-brain MD-maps were calculated, normalized, smoothed, and compared between groups, as well as correlated with mood and cognition scores in T2DM subjects. Type 2 diabetes patients showed altered cognitive and mood functions over control subjects. Multiple brain sites in T2DM patients showed elevated MD values, indicating chronic tissue changes, including the cerebellum, insula, and frontal and prefrontal cortices, cingulate, and lingual gyrus. Associations between MD values and mood and cognition scores appeared in brain sites mediating these functions. Type 2 diabetes patients show predominantly chronic brain tissue changes in areas mediating mood and cognition functions, and tissue changes from those regions correlate with mood and cognitive symptoms suggesting that the microstructural brain changes may account for the observed functional deficits.

High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

Characterization of diffusion MRI using the mean apparent propagator model in hemodialysis patients: A pilot study

To better understand documented cognitive decline in hemodialysis (HD) patients, diffusion MRI (dMRI) has been used to characterize brain anatomical deficits relative to controls. Studies to this point have primarily used diffusion tensor imaging (DTI) to model the three-dimensional diffusion of water in HD patients, with DTI parameters reflecting underlying microstructural changes of brain tissue. Since DTI has some limitations in characterizing tissue microstructure, some of which may be complicated by HD, we explored the use of the mean apparent propagator (MAP) model to describe diffusion in HD patients. We collected anatomical T1 and T2 FLAIR MRIs as well as multi-shell dMRI in ten HD participants and ten age-matched controls. The T1 and T2 FLAIR MRIs were used for tissue segmentation and identification of white matter hyperintensity, respectively. Multi-shell dMRI data were used to estimate MAP and DTI diffusion models. Each model was then used to characterize the differences between the HD cohort and the age-matched controls in normal appearing white matter, subcortical gray matter, corpus callosum (CC) and bilateral radiata (Rad). As expected, parameters of both DTI and MAP models of dMRI were significantly different in HD participants compared to controls. However, some MAP parameters suggested additional tissue microstructural changes in HD participants, such as increased axonal diameter. Measurements of non-Gaussianity indicated that MAP provided better a diffusion estimate than DTI, and MAP appeared to provide a more accurate measure of anisotropy in Rad, based on measures of the Rad/CC ratio. In conclusion, parameters of the MAP and DTI models were both sensitive to changes in diffusivity in HD participants compared to controls; however, the MAP model appeared to provide additional detailed information about changes in brain tissue microstructure.

Whole-brain diffusion tensor imaging predicts 6-month functional outcome in acute intracerebral haemorrhage

IntroductionSmall vessel disease (SVD) causes most spontaneous intracerebral haemorrhage (ICH) and is associated with widespread microstructural brain tissue disruption, which can be quantified via diffusion tensor imaging (DTI) metrics: mean diffusivity (MD) and fractional anisotropy (FA). Little is known about the impact of whole-brain microstructural alterations after SVD-related ICH. We aimed to investigate: (1) association between whole-brain DTI metrics and functional outcome after ICH; and (2) predictive ability of these metrics compared to the pre-existing ICH score.MethodsSixty-eight patients (38.2% lobar) were retrospectively included. We assessed whole-brain DTI metrics (obtained within 5 days after ICH) in cortical and deep grey matter and white matter. We used univariable logistic regression to assess the associations between DTI and clinical-radiological variables and poor outcome (modified Rankin Scale > 2). We determined the optimal predictive variables (via LASSO estimation) in: model 1 (DTI variables only), model 2 (DTI plus non-DTI variables), model 3 (DTI plus ICH score). Optimism-adjusted C-statistics were calculated for each model and compared (likelihood ratio test) against the ICH score.ResultsDeep grey matter MD (OR 1.04 [95% CI 1.01–1.07], p = 0.010) and white matter MD (OR 1.11 [95% CI 1.01–1.23], p = 0.044) were associated (univariate analysis) with poor outcome. Discrimination values for model 1 (0.67 [95% CI 0.52–0.83]), model 2 (0.71 [95% CI 0.57–0.85) and model 3 (0.66 [95% CI 0.52–0.82]) were all significantly higher than the ICH score (0.62 [95% CI 0.49–0.75]).ConclusionOur exploratory study suggests that whole-brain microstructural disruption measured by DTI is associated with poor 6-month functional outcome after SVD-related ICH. Whole-brain DTI metrics performed better at predicting recovery than the existing ICH score.

Parkinson’s disease may disrupt overlapping subthalamic nucleus and pallidal motor networks

There is an ongoing debate about differential clinical outcome and associated adverse effects of deep brain stimulation (DBS) in Parkinson’s disease (PD) targeting the subthalamic nucleus (STN) or the globus pallidus pars interna (GPi). Given that functional connectivity profiles suggest beneficial DBS effects within a common network, the empirical evidence about the underlying anatomical circuitry is still scarce. Therefore, we investigate the STN and GPi-associated structural covariance brain patterns in PD patients and healthy controls.We estimate GPi’s and STN’s whole-brain structural covariance from magnetic resonance imaging (MRI) in a normative mid- to old-age community-dwelling cohort (n = 1184) across maps of grey matter volume, magnetization transfer (MT) saturation, longitudinal relaxation rate (R1), effective transversal relaxation rate (R2*) and effective proton density (PD*). We compare these with the structural covariance estimates in patients with idiopathic PD (n = 32) followed by validation using a reduced size controls’ cohort (n = 32).In the normative data set, we observed overlapping spatially distributed cortical and subcortical covariance patterns across maps confined to basal ganglia, thalamus, motor, and premotor cortical areas. Only the subcortical and midline motor cortical areas were confirmed in the reduced size cohort. These findings contrasted with the absence of structural covariance with cortical areas in the PD cohort.We interpret with caution the differential covariance maps of overlapping STN and GPi networks in patients with PD and healthy controls as correlates of motor network disruption. Our study provides face validity to the proposed extension of the currently existing structural covariance methods based on morphometry features to multiparameter MRI sensitive to brain tissue microstructure.