Crossing Fiber Regions Research Articles

Plasma phospho‐tau predicts differences in white matter microstructural complexity and cognition in non‐demented older adults

AbstractBackgroundWhite matter (WM) measures from diffusion magnetic resonance imaging (dMRI) are sensitive to Alzheimer’s disease (AD) pathology and cognitive impairment. Whereas most extant dMRI studies focused on uniformly‐oriented WM fiber tracts, variations in crossing fiber regions also show sensitivity to mild cognitive impairment (MCI) and AD diagnoses. Such crossing fiber alterations may reflect differential disease effects on constituent fibers, suggesting that quantifying crossing fibers may provide unique markers of early WM changes and neurocognitive risk in AD and MCI. Although few methods exist for quantifying crossing fibers, recent findings show older age is associated with increased microstructural complexity (CX) a novel method to characterize in crossing fibers. The present study evaluated CX in relation to variation in plasma AD biomarkers and cognitive performance in older adults without dementia.MethodData from 48 participants included clinical evaluation, neuropsychological assessment, dMRI neuroimaging and venipuncture. SIMOA assays quantified the AD plasma biomarkers Aβ42, Aβ40, and tau phosphorylated at threonine181 (pTau‐181). Participants were clinically characterized as cognitively normal (n=19) or with MCI (amnestic: n=18; non‐amnestic: n=11). Diagnostic groups did not differ with respect to years of age and education, systolic and diastolic blood pressure, proportions of men and women, proportions of APOE ε4 allele carriers, or plasma biomarker levels. Processing of dMRI data followed the MRtrix fixel‐based analysis (FBA) framework to estimate voxelwise CX data for all participants.ResultVoxelwise regression of whole‐brain CX on pTau‐181 levels revealed a significant positive effect in the dorsal cingulum bundle and adjacent corpus callosum. Post hoc regression of mean CX sampled from significant voxels on pTau‐181 showed greater pTau‐181 level strongly predicts higher CX in this region (partial‐R=0.579, p<0.001), even while controlling for age, education, and Montreal Cognitive Assessment (MoCA) score. Path analysis linking pTau‐181 and cognition showed elevated circulating pTau‐181 levels strongly predict increased white matter complexity in mid‐cingulum bundle, which in turn predicts reduced cognitive performance on measures of working memory, list learning, and semantic fluency.ConclusionMicrostructural complexity in crossing fiber WM regions provides a sensitive marker of early WM alterations associated with elevated plasma phospho‐tau and cognitive decrements in non‐demented older adults.

Relationship of grey matter and white matter changes the visibility of perivascular space across normative lifespan

Background: Perivascular space (PVS), also known as Virchow-Robin space, has been recently recognized as one of the most important components in the glymphatic system of the brain,1 which is closely related to waste and toxins clearance in the brain. On T1- and T2-weighted images, PVS has similar signal intensities to cerebrospinal fluid (CSF), which are frequently observed in the centrum semiovale (CSO) and basal ganglia (BG) regions. A previous study2 has shown that PVS increases with age and enlarged PVS was deemed associated with neurodegenerative and vascular abnormalities in the elderly.3 Although the exact mechanism is still elusive, more evidence showed visible PVS in younger adults or even at the adolescent stage. In this study, we use the human connectome project (HCP) lifespan pilot cohort to characterize the relationship of neuronal tissue to PVS visibility across the normative lifespan.
 Methods: T1-weighted (0.8mm isotropic, TE/TR=2.12/2400 ms), T2-weighted (0.8mm isotropic, TE/TR=563/3200 ms), and diffusion magnetic resonance imaging (MRI) (1.5 mm isotropic, dir80) data were used from HCP lifespan pilot project (N=27, F/M: 12/15, age 8-75 years). PVS segmentation in white matter (WM) and BG was performed using the same method described in Sepehrband et al.4 (Figure 1). WM PVS volume fraction was calculated by dividing the total PVS volume by WM volume. Pearson correlation and one-way ANOVA were used for statistical analysis.
 Results: The grey matter (GM) volume fraction is negatively correlated with WM PVS volume (p<0.001, R2=0.374). In addition, we have observed an increase in PVS volume fraction in WM across the lifespan, but there is no significant difference in PVS volume fraction before the age of 55. From an age group range of 8-35 years old, the visible PVS is mostly located in CSO, less PVS is observed in the BG region. However, on the other hand, in the elderly group (age >65 years), high-visibility of PVS is observed both in WM and BG regions. Conclusions: Across the normative lifespan, PVS appearance is negatively correlated with the GM atrophy index, indicating the potential usage of PVS as a marker of neurodegeneration. In WM, we reported that increased PVS is associated with advancing age, which is consistent with previous reports.2 Interestingly, we found that in adolescent populations, the PVS occurs mostly in CSO but not in the BG region. In CSO, as shown from fiber orientation distribution analysis, the PVS is mainly located in crossing fiber regions with lower fiber density. However, In BG regions, the PVS occurs in deep GM nuclei (caudate, putamen) and major fiber tracts such as internal capsules or cortical spinal tracts, which are more densely packed compared to crossing-fibers in CSO. Previous studies have reported that the WM blood flow is inversely correlated with FA,5 the tightly structured fibers tend to show lower blood flow if compared to loosened ones. The high visibility of PVS in CSO may also be due to more loosened fiber structures, which lead to more axonal spaces for PVS fluid movement.

Biophysical compartment models for single-shell diffusion MRI in the human brain: a model fitting comparison

Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zero b-value (i.e. single-shell) and diffusion weighting of b = 1000 s mm−2. To produce microstructural parameter maps, the tensor model is usually used, despite known limitations. Although compartment models have demonstrated improved fits in multi-shell dMRI data, they are rarely used for single-shell parameter maps, where their effectiveness is unclear from the literature. Here, various compartment models combining isotropic balls and symmetric tensors were fitted to single-shell dMRI data to investigate model fitting optimization and extract the most information possible. Full testing was performed in 5 subjects, and 3 subjects with multi-shell data were included for comparison. The results were tested and confirmed in a further 50 subjects. The Markov chain Monte Carlo (MCMC) model fitting technique outperformed non-linear least squares. Using MCMC, the 2-fibre-orientation mono-exponential ball and stick model () provided artifact-free, stable results, in little processing time. The analogous ball and zeppelin model () also produced stable, low-noise parameter maps, though it required much greater computing resources (50 000 burn-in steps). In single-shell data, the gamma-distributed diffusivity ball and stick model () underperformed relative to other models, despite being an often-used software default. It produced artifacts in the diffusivity maps even with extremely long processing times. Neither increased diffusion weighting nor a greater number of gradient orientations improved fits. In white matter (WM), the tensor produced the best fit as measured by Bayesian information criterion. This result contrasts with studies using multi-shell data. However, in crossing fibre regions the tensor confounded geometric effects with fractional anisotropy (FA): the planar/linear WM FA ratio was 49%, while and retained 76% and 83% of restricted fraction, respectively. As a result, the and models are strong candidates to optimize information extraction from single-shell dMRI studies.

Early white matter connectivity and plasticity in post stroke aphasia recovery

A disruption of white matter connectivity is negatively associated with language (recovery) in patients with aphasia after stroke, and behavioral gains have been shown to coincide with white matter neuroplasticity. However, most brain-behavior studies have been carried out in the chronic phase after stroke, with limited generalizability to earlier phases. Furthermore, few studies have investigated neuroplasticity patterns during spontaneous recovery (i.e., not related to a specific treatment) in the first months after stroke, hindering the investigation of potential early compensatory mechanisms. Finally, the majority of previous research has focused on damaged left hemisphere pathways, while neglecting the potential protective value of their right hemisphere counterparts for language recovery. To address these outstanding issues, we present a longitudinal study of thirty-two patients with aphasia (21 males and 11 females, M = 69.47 years, SD = 10.60 years) who were followed up for a period of 1 year with test moments in the acute (1–2 weeks), subacute (3–6 months) and chronic phase (9–12 months) after stroke. Constrained Spherical Deconvolution-based tractography was performed in the acute and subacute phase to measure Fiber Bundle Capacity (FBC), a quantitative connectivity measure that is valid in crossing fiber regions, in the bilateral dorsal arcuate fasciculus (AF) and the bilateral ventral inferior fronto-occipital fasciculus (IFOF). First, concurrent analyses revealed positive associations between the left AF and phonology, and between the bilateral IFOF and semantics in the acute – but not subacute - phase, supporting the dual-stream language model. Second, neuroplasticity analyses revealed a decrease in connection density of the bilateral AF – but not the IFOF – from the acute to the subacute phase, possibly reflecting post stroke white matter degeneration in areas adjacent to the lesion. Third, predictive analyses revealed no contribution of acute FBC measures to the prediction of later language outcomes over and above the initial language scores, suggesting no added value ofthe diffusion measures for languageprediction. Our study provides new insights on (changes in) connectivity of damaged and undamaged language pathways in patients with aphasia in the first months after stroke, as well as if/how such measures are related to language outcomes at different stages of recovery. Individual results are discussed in the light of current frameworks of language processing and aphasia recovery.

Advanced diffusion‐MRI models reveals sex‐specific differences in white matter organization in patients with mild cognitive impairment

AbstractBackgroundRecent literature suggests that free‐water corrected (fiso) diffusion MRI (dMRI) measures provide higher sensitivity as compared to conventional single‐tensor (ST) dMRI measures to understand white‐matter (WM) changes due to neurodegeneration. However, fiso‐corrected dMRI measures are still biased at crossing‐fiber regions (CFR) of WM which are shown to be around 90% of the total WM voxels with conventional dMRI data acquisition. In this study, we systematically compared dMRI measures from ST‐dMRI, fiso‐corrected ST‐dMRI measures, and measures from advanced dMRI models such as Diffusion Kurtosis Imaging (DKI) and Neurite Orientation Distribution and Density Imaging (NODDI) which are sensitive to CFRs. We hypothesize that these advanced dMRI models will be sensitive in understanding sex‐specific WM organizational changes at regional level in patients with Alzheimer’s disease (AD)‐related mild cognitive impairment (MCI).Method40 healthy controls [HC; 21 Females (F), 19 Males (M)] and 38 MCI participants [13F, 25 M] were recruited at our center. MCI diagnoses were based on expert consensus between a neurologist (AR) and a neuropsychologist (JC) using results of a neuropsychological test battery and the Clinical Dementia Rating (CDR) interview. Isotropic 1.5mm3 multi‐shell dMRI (b‐values=500s/mm2, 1000s/mm2, and 2500s/mm2) each with identical 71 diffusion‐encoding directions was acquired on a 3T Siemens Skyra scanner for every participant using the CMRR sequence. 8 uniformly interspersed non‐diffusion‐weighted (b0) images were also acquired in the same run. ST, fiso‐corrected, DKI, and NODDI dMRI measures were estimated for every brain voxel in‐house using the standard analytic tools. WM was skeletonized using FSL tools and statistical comparisons were done non‐parametrically using PALM tools. GLM was utilized to test for sex‐specific group effect. Significance was established at family‐wise error correction (FWE) pcorr<0.05.ResultThalamocortical tracts of males in both HC and MCI showed a significantly higher axonal kurtosis (AK) and orientation density (ODI) derived from DKI and NODDI when compared to within‐group females. DKI also revealed presence of higher axonal water fraction (AWF) along the entire WM‐tracts between HC‐female and HC‐male, and lower mean kurtosis (MK) along the thalamic‐cortical tracts in HC‐females as compared to MCI‐females.ConclusionAdvanced dMRI‐models are sensitive to understand sex‐specific WM disorganization due to healthy ageing and MCI.

Fixel-based evidence of microstructural damage in crossing pathways improves language mapping in Post-stroke aphasia

BackgroundThe complex crossing-fiber characteristics in the dual-stream system have been ignored by traditional diffusion tensor models regarding disconnections in post-stroke aphasia. It is valuable to identify microstructural damage of crossing-fiber pathways and reveal accurate fiber-specific language mapping in patients with aphasia. MethodsThis cross-sectional study collected magnetic resonance imaging data from 29 participants with post-stroke aphasia in the subacute stage and from 33 age- and sex-matched healthy controls. Fixel-based analysis was performed to examine microstructural fiber density (FD) and bundle cross-section alterations of specific fiber populations in crossing-fiber regions. Group comparisons were performed, and relationships with language scores were assessed. ResultsThe aphasic group exhibited significant fixel-wise FD reductions in the dual-stream tracts, including the left inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus, and superior longitudinal fasciculus (SLF) III (family-wise-error-corrected p < 0.05). Voxel- and fixel-wise comparisons revealed mismatched distributions in regions with crossing-fiber nexuses. Fixel-wise correlation analyses revealed significant associations between comprehension impairment and reduced FD in the temporal and frontal segments of the left IFOF, and also mapped naming ability to the IFOF. Average features along the whole course of dominant tracts assessed with tract-wise analyses attributed word-level comprehension to the IFOF (r = 0.723, p < 0.001) and revealed a trend-level correlation between sentence-level comprehension and FD of the SLF III (r = 0.451, p = 0.021). The mean FD of the uncinate fasciculus (UF) and IFOF correlated with total and picture naming scores, and the IFOF also correlated with responsive naming subdomains (Bonferroni corrected p < 0.05). ConclusionsFD reductions of dual streams suggest that intra-axonal volume reduction constitutes the microstructural damage of white matter integrity in post-stroke aphasia. Fixel-based analysis provides a complementary method of language mapping that identifies fiber-specific tracts in the left hemisphere language network with greater specificity than voxel-based analysis. It precisely locates the precise segments of the IFOF for comprehension, yields fiber-specific evidence for the debated UF-naming association, and reveals dissociative subdomain associations with distinct tracts.

Characterisation of white matter asymmetries in the healthy human brain using diffusion MRI fixel-based analysis

The diffusion tensor model for diffusion MRI has been used extensively to study asymmetry in the human brain white matter. However, given the limitations of the tensor model, the nature of any underlying asymmetries remains uncertain, particularly in crossing fibre regions. Here, we provide a more robust characterisation of human brain white matter asymmetries based on fibre-specific diffusion MRI metrics and a whole-brain data-driven approach. We used high-quality diffusion MRI data (n = 100) from the Human Connectome Project, the spherical deconvolution model for fibre orientation distribution estimation, and the Fixel-Based Analysis framework to utilise crossing fibre information in registration, data smoothing and statistical inference. We found many significant asymmetries, widespread throughout the brain white matter, with both left>right and right>left dominances observed in different pathways. No influences of sex, age, or handedness on asymmetry were found. We also report on the relative contributions of microstructural and morphological white matter properties toward the asymmetry findings. Our findings should provide important information to future studies focussing on how these asymmetries are affected by disease, development/ageing, or how they correlate to functional/cognitive measures.

Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration

Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter. Several multiple fiber models have been proposed that seem to overcome the limitations of the tensor, with few providing per-bundle dMRI-derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation. To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures. We correlated and compared histology to per-bundle descriptors derived from three methodologies for dMRI analysis (constrained spherical deconvolution and two multi-tensor representations). We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density and fractional anisotropy (derived from dMRI). The multi–fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions. Our proposed framework is useful to validate other current and future dMRI methods.

Orientation Prior and Consistent Model Selection Increase Sensitivity of Tract-Based Spatial Statistics in Crossing-Fiber Regions

The goal of this paper is to increase the statistical power of crossing-fiber statistics in voxelwise analyses of diffusion-weighted magnetic resonance imaging (DW-MRI) data. In the proposed framework, a fiber orientation atlas and a model complexity atlas were used to fit the ball-and-sticks model to diffusion-weighted images of subjects in a prospective population-based cohort study. Reproducibility and sensitivity of the partial volume fractions in the ball-and-sticks model were analyzed using TBSS (tract-based spatial statistics) and compared to a reference framework. The reproducibility was investigated on two scans of 30 subjects acquired with an interval of approximately three weeks by studying the intraclass correlation coefficient (ICC). The sensitivity to true biological effects was evaluated by studying the regression with age on 500 subjects from 65 to 90 years old. Compared to the reference framework, the ICC improved significantly when using the proposed framework. Higher t-statistics indicated that regression coefficients with age could be determined more precisely with the proposed framework and more voxels correlated significantly with age. The application of a fiber orientation atlas and a model complexity atlas can significantly improve the reproducibility and sensitivity of crossing-fiber statistics in TBSS.

Motor Abilities in Adolescents Born Preterm Are Associated With Microstructure of the Corpus Callosum.

Background: Preterm birth is associated with increased risk of neuromotor impairment. Rates of major neuromotor impairment (cerebral palsy) have decreased; however, in a large proportion of those who do not develop cerebral palsy impaired neuromotor function is observed and this often has implications for everyday life. The aim of this study was to investigate motor performance in preterm born adolescents without cerebral palsy, and to examine associations with alterations of motor system pathway structure.Design/Methods: Thirty-two adolescents (12 males) without cerebral palsy, born before 33 weeks of gestation (mean 27.4 weeks, SD 2.4; birth weight mean 1,084.5 g; SD 387.2), treated at a single tertiary unit, were assessed (median age 16 years; min 14, max 18). Timed performance and quality of movements were assessed with the Zürich Neuromotor Assessment. Neuroimaging included Diffusion Magnetic Resonance Imaging for tractography of the major motor tracts and measurement of fractional anisotropy as a measure of microstructure of the tracts along the major motor pathways. Separate analyses were conducted for areas with predominantly single and predominantly crossing fiber regions.Results: Motor performance in both tasks assessing timed performance and quality of movements, was poorer than expected in the preterm group in relation to norm population. The strongest significant correlations were seen between performance in tasks assessing movement quality and fractional anisotropy in corpus callosum fibers connecting primary motor, primary somatosensory and premotor areas. In addition, timed motor performance was significantly related to fractional anisotropy in the cortico-spinal and thalamo-cortical to premotor area fibers, and the corpus callosum.Conclusions: Impairments in motor abilities are present in preterm born adolescents without major neuromotor impairment and in the absence of focal brain injury. Altered microstructure of the corpus callosum microstructure appears a crucial factor, in particular for movement quality.

Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition.

To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI. Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion. The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

White matter alterations and their associations with motor function in young adults born preterm with very low birth weight.

Very low birth weight (VLBW: ≤ 1500 g) individuals have an increased risk of white matter alterations and neurodevelopmental problems, including fine and gross motor problems. In this hospital-based follow-up study, the main aim was to examine white matter microstructure and its relationship to fine and gross motor function in 31 VLBW young adults without cerebral palsy compared with 31 term-born controls, at mean age 22.6 ± 0.7 years. The participants were examined with tests of fine and gross motor function (Trail Making Test-5: TMT-5, Grooved Pegboard, Triangle from Movement Assessment Battery for Children-2: MABC-2 and High-level Mobility Assessment Tool: HiMAT) and diffusion tensor imaging (DTI). Probabilistic tractography of motor pathways of the corticospinal tract (CST) and corpus callosum (CC) was performed. Fractional anisotropy (FA) was calculated in non-crossing (capsula interna in CST, body of CC) and crossing (centrum semiovale) fibre regions along the tracts and examined for group differences. Associations between motor test scores and FA in the CST and CC were investigated with linear regression. Tract-based spatial statistics (TBSS) was used to examine group differences in DTI metrics in all major white matter tracts. The VLBW group had lower scores on all motor tests compared with controls, however, only statistically significant for TMT-5. Based on tractography, FA in the VLBW group was lower in non-crossing fibre regions and higher in crossing fibre regions of the CST compared with controls. Within the VLBW group, poorer fine motor function was associated with higher FA in crossing fibre regions of the CST, and poorer bimanual coordination was additionally associated with lower FA in crossing fibre regions of the CC. Poorer gross motor function was associated with lower FA in crossing fibre regions of the CST and CC. There were no associations between motor function and FA in non-crossing fibre regions of the CST and CC within the VLBW group. In the TBSS analysis, the VLBW group had lower FA and higher mean diffusivity compared with controls in all major white matter tracts. The findings in this study may indicate that the associations between motor function and FA are caused by other tracts crossing the CST and CC, and/or by alterations in the periventricular white matter in the centrum semiovale. Some of the associations were in the opposite direction than hypothesized, thus higher FA does not always indicate better function. Furthermore, widespread white matter alterations in VLBW individuals persist into young adulthood.

Assessing White Matter Microstructure in Brain Regions with Different Myelin Architecture Using MRI.

ObjectiveWe investigate how known differences in myelin architecture between regions along the cortico-spinal tract and frontal white matter (WM) in 19 healthy adolescents are reflected in several quantitative MRI parameters that have been proposed to non-invasively probe WM microstructure. In a clinically feasible scan time, both conventional imaging sequences as well as microstructural MRI parameters were assessed in order to quantitatively characterise WM regions that are known to differ in the thickness of their myelin sheaths, and in the presence of crossing or parallel fibre organisation.ResultsWe found that diffusion imaging, MR spectroscopy (MRS), myelin water fraction (MWF), Magnetization Transfer Imaging, and Quantitative Susceptibility Mapping were myelin-sensitive in different ways, giving complementary information for characterising WM microstructure with different underlying fibre architecture. From the diffusion parameters, neurite density (NODDI) was found to be more sensitive than fractional anisotropy (FA), underlining the limitation of FA in WM crossing fibre regions. In terms of sensitivity to different myelin content, we found that MWF, the mean diffusivity and chemical-shift imaging based MRS yielded the best discrimination between areas.ConclusionMultimodal assessment of WM microstructure was possible within clinically feasible scan times using a broad combination of quantitative microstructural MRI sequences. By assessing new microstructural WM parameters we were able to provide normative data and discuss their interpretation in regions with different myelin architecture, as well as their possible application as biomarker for WM disorders.

Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter.

Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

Comparison of 3D orientation distribution functions measured with confocal microscopy and diffusion MRI

The ability of diffusion MRI (dMRI) fiber tractography to non-invasively map three-dimensional (3D) anatomical networks in the human brain has made it a valuable tool in both clinical and research settings. However, there are many assumptions inherent to any tractography algorithm that can limit the accuracy of the reconstructed fiber tracts. Among them is the assumption that the diffusion-weighted images accurately reflect the underlying fiber orientation distribution (FOD) in the MRI voxel. Consequently, validating dMRI's ability to assess the underlying fiber orientation in each voxel is critical for its use as a biomedical tool. Here, using post-mortem histology and confocal microscopy, we present a method to perform histological validation of orientation functions in 3D, which has previously been limited to two-dimensional analysis of tissue sections. We demonstrate the ability to extract the 3D FOD from confocal z-stacks, and quantify the agreement between the MRI estimates of orientation information obtained using constrained spherical deconvolution (CSD) and the true geometry of the fibers. We find an orientation error of approximately 6° in voxels containing nearly parallel fibers, and 10–11° in crossing fiber regions, and note that CSD was unable to resolve fibers crossing at angles below 60° in our dataset. This is the first time that the 3D white matter orientation distribution is calculated from histology and compared to dMRI. Thus, this technique serves as a gold standard for dMRI validation studies — providing the ability to determine the extent to which the dMRI signal is consistent with the histological FOD, and to establish how well different dMRI models can predict the ground truth FOD.

Toward tract-specific fractional anisotropy (TSFA) at crossing-fiber regions with clinical diffusion MRI.

White matter fractional anisotropy (FA), a measure suggesting microstructure, is significantly underestimated with single diffusion tensor model at crossing-fiber regions (CFR). We propose a tract-specific FA (TSFA), corrected for the effects of crossing-fiber geometry and free water at CFR, and adapted for tract analysis with diffusion MRI (dMRI) in clinical research. At CFR voxels, the proposed technique estimates free water fraction (fiso ) as a linear function of mean apparent diffusion coefficient (mADC), fits the dual tensors and estimates TSFA. Digital phantoms were designed for testing the accuracy of fiso and fitted dual-anisotropies at CFR. The technique was applied to clinical dMRI of normal subjects and hereditary spastic paraplegia (HSP) patients to test the effectiveness of TSFA. Phantom simulation showed unbiased estimates of dual-tensor anisotropies at CFR and high accuracy of fiso as a linear function of mADC. TSFA at CFR was highly consistent to the single tensor FA at non-CFR within the same tract with normal human dMRI. Additional HSP imaging biomarkers with significant correlation to clinical motor function scores could be identified with TSFA. Results suggest the potential of the proposed technique in estimating unbiased TSFA at CFR and conducting tract analysis in clinical research.

HOMOR: Higher Order Model Outlier Rejection for high b-value MR diffusion data

Diffusion MR images are prone to artefacts caused by head movement and cardiac pulsation. Previous techniques for the automated voxel-wise detection of signal intensity outliers have relied on the fit of the diffusion tensor to the data (RESTORE). However, the diffusion tensor cannot appropriately model more than a single fibre population, which may lead to inaccuracies when identifying outlier voxels in crossing fibre regions, particularly when high b-values are used to obtain increased angular contrast. HOMOR (higher order model outlier rejection) was developed to overcome this limitation and is introduced in this study. HOMOR is closely related to RESTORE, but employs a higher order model capable of resolving multiple fibre populations within a voxel. Using high b-value (b=3000 s/mm2) diffusion data from a population of 90 healthy participants, as well as simulations, HOMOR was found to identify a decreased number of outlier voxels compared to RESTORE primarily within areas of crossing, bending and fanning fibres. At lower b-values, however, RESTORE and HOMOR give similar results, which is demonstrated using diffusion data acquired at b=1000 s/mm2 in a mixed cohort. This study demonstrates that, although RESTORE is suitable for low b-value data, HOMOR is better suited for high b-value data.

Modeling the interactions of Alzheimer-related genes from the whole brain microarray data and diffusion tensor images of human brain

BackgroundIn recent years the genome-wide microarray-based gene expression profiles and diffusion tensor images (DTI) in human brain have been made available with accompanying anatomic and histology data. The challenge is to integrate various types of data to investigate the interactions of genes that are associated with specific neurological disorder.ResultsIn this study, we analyzed the whole brain microarray data and the physical connectivity of the hippocampus with other brain regions to identify the genes related to Alzheimer's disease and their interactions with proteins. We generated a physical connectivity map of the left and right hippocampuses with 12 other brain regions and identified 33 Alzheimer-related genes that interact with many proteins. These genes are highly linked to the development of Alzheimer's disease.ConclusionsIn Alzheimer's brain both brain regions and inter-regional communications through the white matter are often hampered. So far the connectivity of regions in Alzheimer's brain has been studied mostly at the functional level using functional MRI (fMRI). Analyzing the inter-regional fiber connectivity without tracking crossing-fiber regions often provides coarse and inaccurate results. A few deep brain fibers were analyzed but the inter-regional fiber connectivity was not analyzed in their studies. The inter-regional fiber connectivity analysis can provide comprehensive and measurable degradation of fiber tracts in AD patients' brains, but is not easy to perform. We tracked crossing-fiber regions and identified genes with high expression levels in the fiber pathways of the hippocampus. The interactions of the genes with other proteins can provide comprehensive and measurable degradation of fiber tracts in Alzheimer brains. To the best of our knowledge, this is the first attempt to integrate the whole brain microarray data with DTI data to identify specific genes and their interactions.

Corticospinal tractography with morphological, functional and diffusion tensor MRI: a comparative study of four deterministic algorithms used in clinical routine

Diffusion tensor imaging permits study of white matter fibre bundles; however, its main limitation is lack of validation on anatomical data, especially in crossing fibre regions. Our study aimed to compare four deterministic tractography algorithms used in clinical routine. We studied the corticospinal tract, the bundle mediating voluntary movement. Our study seeks to evaluate tractography provided by algorithms through comparative analysis by expert neuroradiologists. MRI data from 15 right-handed volunteers (30.8 years) were studied. Regions of interest (ROIs) were segmented on morphological and functional MRI. Diffusion weighted images (15 directions) were performed, then for each voxel the tensor was estimated. Tractography of the corticospinal tract was performed using four fibre-tracking algorithms. Three numerical integration methods Euler, Runge-Kutta second (RK2) and fourth order (RK4), and a tensor deflection method (TEND). Quantitative measurement was performed. Qualitative evaluation was carried out by two expert neuroradiologists using Kappa test concordance. For the quantitative aspect, only RK2 and TEND presented no significant difference concerning the number of fibres (p = 0.58). There was no difference between right and left side for each algorithm. Regarding the qualitative aspects, there was a lack of fibres from the ventrolateral part of the functional ROIs. Comparison by expert neuroradiologists revealed low rather than high concordance. The algorithm ranked first was RK2 according to expert preferences. Different algorithms used in clinical routine failed to show realistic anatomical bundles. The most mathematically robust algorithm was not selected, nor was the algorithm defining more fibres. Validation of anatomical data provided by tractography remains a challenge.

Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data

Diffusion-weighted imaging can potentially be used to infer the connectivity of the human brain in vivo using fibre-tracking techniques, and is therefore of great interest to neuroscientists and clinicians. A key requirement for fibre tracking is the accurate estimation of white matter fibre orientations within each imaging voxel. The diffusion tensor model, which is widely used for this purpose, has been shown to be inadequate in crossing fibre regions. A number of approaches have recently been proposed to address this issue, based on high angular resolution diffusion-weighted imaging (HARDI) data. In this study, an experimental model of crossing fibres, consisting of water-filled plastic capillaries, is used to thoroughly assess three such techniques: constrained spherical deconvolution (CSD), super-resolved CSD (super-CSD) and Q-ball imaging (QBI). HARDI data were acquired over a range of crossing angles and b-values, from which fibre orientations were computed using each technique. All techniques were capable of resolving the two fibre populations down to a crossing angle of 45°, and down to 30° for super-CSD. A bias was observed in the fibre orientations estimated by QBI for crossing angles other than 90°, consistent with previous simulation results. Finally, for a 45° crossing, the minimum b-value required to resolve the fibre orientations was 4000s/mm2 for QBI, 2000s/mm2 for CSD, and 1000s/mm2 for super-CSD. The quality of estimation of fibre orientations may profoundly affect fibre tracking attempts, and the results presented provide important additional information regarding performance characteristics of well-known methods.