Neurite Orientation Dispersion And Density Imaging Model Research Articles

High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics.

To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.

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

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

Increased adiposity is related to reduced neurite complexity in the hippocampus, fornix, and uncinate fasciculus of cognitively unimpaired adults

AbstractBackgroundObesity is a risk factor for dementia. However, results from studies associating obesity to volume in the hippocampus and parahippocampal gyrus, regions underpinning memory, are inconsistent. Null findings could be due to the insensitivity of volumetric MRI to detect early neurodegenerative change. The neurite orientation dispersion and density imaging (NODDI) model provides unique information about neurite microstructure that may be relevant to the earliest stages of neurodegeneration. In cognitively healthy participants, we used NODDI to examine if body mass index (BMI) and waist circumference (WC) were related to the microstructure of the hippocampus and parahippocampal gyrus and white matter (WM) tracts linking these gray matter (GM) regions to the brain (i.e., hippocampal cingulum, uncinate fasciculus, and fornix).MethodParticipants were enrolled in the Wisconsin Alzheimer’s Disease Research Center (ADRC) Clinical Core, had T‐1 weighted and multi‐shell diffusion MRI, and lacked history of stroke and cognitive impairment (Table 1). Microstructure was assessed using neurite density (NDI) and orientation dispersion (ODI) indices obtained from the NODDI model. Higher NDI indicates higher neurite density; higher ODI reflects lower neurite coherence and greater neurite dispersion. N=383 had NODDI indices, height, weight and covariate data. Waist circumference (WC) (n=382) was dichotomized according to sex‐specific thresholds for cardiovascular/diabetes disease risk (females >88cm; males >102cm). Linear regression tested the relationship of BMI and WC to NDI and ODI in regions of interest controlling for age, sex, education, diabetes, hypertension, depression, and APOE4 carrier status.ResultIndividuals with higher BMI and in the higher‐risk WC category had significantly lower hippocampal ODI and fornix NDI when adjusting for covariates (Table 2, Figure 1). Higher BMI was also related to higher uncinate fasciculus ODI.ConclusionIncreased adiposity was related to lower neurite dispersion in the hippocampus and lower neurite density in the fornix. BMI alone was associated with lower neurite coherence in the uncinate fasciculus. These results suggest that increased adiposity may be related to less complex neurite microstructure in regions important for memory. Additional research is needed to determine if adiposity‐related microstructural differences in GM and WM are related to memory decline and presage future development of dementia.

Single-shell NODDI using dictionary-learner-estimated isotropic volume fraction.

Neurite orientation dispersion and density imaging (NODDI) enables the assessment of intracellular, extracellular, and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous studies caused by failure when fitting, especially for the neurite density index (NDI). Here, we investigated the possibility of creating robust NODDI parameter maps with single-shell data, using the isotropic volume fraction (fISO ) as a prior. Prior estimation was made independent of the NODDI model constraint using a dictionary learning approach. First, we used a stochastic sparse dictionary-based network (DictNet), which is trained with data obtained from in vivo and simulated diffusion MRI data, to predict fISO . In single-shell cases, the mean diffusivity and raw T2 signal with no diffusion weighting (S0 ) was incorporated in the dictionary for the fISO estimation. Then, the NODDI framework was used with the known fISO to estimate the NDI and orientation dispersion index (ODI). The fISO estimated using our model was compared with other fISO estimators in the simulation. Further, using both synthetic data simulation and human data collected on a 3 T scanner (both high-quality HCP and clinical dataset), we compared the performance of our dictionary-based learning prior NODDI (DLpN) with the original NODDI for both single-shell and multi-shell data. Our results suggest that DLpN-derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and the protocol with b= 2000 s/mm2 performs the best (error ~ 5% in white and gray matter). This may allow NODDI evaluation of studies on single-shell data by multi-shell scanning of two subjects for DictNet fISO training.

Genetic and environmental influences of variation in diffusion MRI measures of white matter microstructure.

Quantitative neuroimaging studies in twin samples can investigate genetic contributions to brain structure and microstructure. Diffusion tensor imaging (DTI) studies with twin samples have shown moderate to high heritability in white matter microstructure. This study investigates the genetic and environmental contributions of another widely used diffusion MRI model not yet applied to twin studies, neurite orientation dispersion and density imaging (NODDI). The NODDI model is a multicompartment model of the diffusion-weighted MRI signal, providing estimates of neurite density (ND) and the orientation dispersion index (ODI). A cohort of monozygotic (MZ) and same-sex dizygotic (DZ) twins (N = 460 individuals) between 13 and 24years of age were scanned with a multi-shell diffusion weighted imaging protocol. Select white matter (WM) regions of interest (ROI) were extracted. Biometric structural equation modeling estimated the relative contributions from additive genetic (A) and common (C) and unique environmental (E) factors. Genetic factors for the NODDI measures accounted for 91% and 65% of the variation of global ND and ODI, respectively, compared with 83% for FA. We observed higher heritability for ND than both FA and ODI in 25 of 30 discrete white matter regions that we examined, suggesting ND may be more sensitive to underlying genetic sources of variation. This study demonstrated that genetic factors play a key role in the development of white matter microstructure using both DTI and NODDI.

Evaluating NODDI‐based biomarkers of Alzheimer’s disease

AbstractBackgroundMulti‐compartment (MC) diffusion MRI (dMRI) models are increasingly used to evaluate brain microstructure beyond the classic diffusion tensor model (DTI). The ability to separate the neural tissue into distinct compartments, i.e., intracellular volume fraction (ICVF), extracellular volume fraction (ECVF) and free‐water (FW), offers novel biomarkers for early detection of cognitive decline and Alzheimer’s disease. The most commonly used MC model used is Neurite Orientation Dispersion and Density Imaging (NODDI). Recent evidence shows that the voxel‐wise volume fractions compartment can be wrongly estimated if the same T2 relaxation time is assumed for the gray matter (GM), white matter (WM) and corticospinal fluid (CSF). To more accurately estimate the tissue compartments, here we use a novel method recently proposed by the DMIPY package (https://github.com/AthenaEPI/dmipy), that initially estimates a response function for each brain region (GM, WM and CSF) and uses these to better estimate the ICVF and FW compartments.MethodWe analyzed ADNI‐3 multi‐shell diffusion MRI (dMRI) data to estimate NODDI measures and to assess their ability to predict mild cognitive impairment (MCI). For each measure, we used regularized logistic regression to find cohesive clusters of brain tissue that contribute to correct classification. Data included 34 participants with MCI (mean age: 75.6±7.8yrs, 22M/9F) and 74 cognitively normal individuals (CN; mean age= 74.1±7.1yrs; 25M/39F). Siemens Prisma 3T multi‐shell dMRI data included 13 b0 images, 6 b=500, 48 b=1000, and 60 b=2000 s/mm2 diffusion weighted 32 images (voxel size=2mm, TE=71ms, TR=3300ms, δ=13.6ms, Δ=35ms). We fit three types of NODDI: the classic NODDI model, the NODDI model with a GM tissue response function, and with a WM response function.ResultThe NODDI model with the GM response function increased the accuracy of the logistic regression classification (see Table 1). The highest recall was achieved by the neurite dispersion index (ODI) in all three models.ConclusionAdding specific response functions for the GM and WM in NODDI improved classification accuracies and recall of MCI subjects. The distribution of the classifying regions was different across the three types of NODDI (Figures 1, 2 and 3).

White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the ‘FA fine’ metric of the RSI model and ‘orientation dispersion’ (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging.

Magnetic resonance‐based axonal microstructure measures are differentially modulated by interleukin‐6 in amyloid expressing TgCRND8 mice

Non-invasive assessments of microstructure changes linked to axonal pathology in Alzheimer's disease (AD) may be critical to early interventions to slow progression. Axons and synapses accumulate a variety of proteins, including amyloid precursor protein (APP) and β-amyloid (Aβ), which in turn alter functional activity in cognitive brain areas connected along major white matter pathways. In the extracellular space, the presence of ‘dystrophic neurites’ surrounding Aβ plaques and the presence of active neuroimmune cells can contribute to alterations in extracellular free water, tissue anisotropy and diffusion rates, as well as the density and geometric arrangement of axonal processes. In the present study, we used high field diffusion magnetic resonance imaging (dMRI) to determine the effects of increased levels Aβ and the inflammatory signaling cytokine interleukin-6 (IL6) on a range of tissue microstructure measures in mice. Subsets of transgenic (Tg)CRND8 mice with early onset expression of human APP (APPswe KM670671NL/APPInd V717F) and wildtype controls were induced to express brain IL6 using previously published adenoassociated viral vector (AAV) methods (n=48; 50% females). Mice were scanned at 11 Tesla using a 2-shell (b=600/2000) 54-gradient direction dMRI sequence. Images were post-processed using: (1) a bi-tensor model of diffusion that allows the partitioning of free water contributions to the diffusion tensor and (2) the neurite orientation dispersion and density imaging (NODDI) model. To avoid partial volume contamination of tissue microstructure measures by cerebroventricular water (CSF), major white matter (WM) tracts and hippocampus were manually segmented in ITKSNAP using fractional anisotropy (FA) maps and T2 anatomical scans. Sampled WM tracts included the splenium and genu of the corpus callosum, anterior commissure, cerebral peduncles, and fimbria. The resulting data using the free water correction method depicted a complex set of axonal and hippocampal microstructural changes that suggested restricted diffusion rates, reduced directionality of diffusion, and extracellular free water in Tg mice harboring human APP mutations. IL6 appeared to partially counteract this effect. In addition, results from the NODDI model suggested that the density of neurites and the geometric arrangement are increased by Aβ. IL6 effects on neurite density and orientation dispersion seemed modest but trended toward effects similar to Aβ. Our data support the development and use of microstructure imaging techniques to characterize early in vivo changes in neurodegenerative diseases and with aging. Support or Funding Information This work was supported by the National Institutes of Health (R01NS052318, R01NS075012, T32NS082168, P50NS091856 and P50AG047266). The authors acknowledge support from the Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) facility (National Science Foundation Cooperative Agreement No. DMR-1157490 and the State of Florida). Changes in white matter fractional anisotropy and free water in response to amyloidosis and interleukin-6 in mice scanned at 11 Tesla This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Application of neurite orientation dispersion and density imaging to characterize brain microstructural abnormalities in type-2 diabetics with mild cognitive impairment.

Diffusion-tensor-imaging (DTI) is sensitive in detecting white matter changes in type-2 diabetes mellitus (T2DM). However, DTI indices can be affected by either neurite density or spatial variation. A novel diffusion MRI technique, termed neurite orientation dispersion and density imaging (NODDI), can provide distinct indices of fiber density and dispersion. To characterize brain microstructural alterations in T2DM patients with mild cognitive impairment (MCI) using the NODDI model. Cross-sectional. Twenty T2DM patients with (DM-MCI group), 18 age- and gender-matched T2DM patients with normal cognition (DM-NC group), and 28 euglycemic healthy controls (HC). 3T/NODDI. Diffusion data were analyzed using tract-based-spatial-statistics (TBSS) analysis in white matter and voxel-based analysis in both white and gray matter. NODDI indices, including intracellular volume fraction (Vic) and orientation dispersion index (ODI), were estimated from multiple regions and compared among these groups. Differences between groups were compared by Student's t-test, Pearson chi-square test, or analysis of variance when appropriate. Correlation analyses were performed to investigate the relationship between NODDI variables and clinical measurements. Whole-brain TBSS revealed that 2.29% and 2.02% of the white matter regions exhibited decreased fractional anisotropy and Vic, respectively, between the DM-NC and HC, while considerably larger white matter areas showed decreased fractional anisotropy (38.38%) and Vic (34.64%) between the DM-MCI and HC (Student's t-test, P < 0.05). However, the angular variation of neurites, characterized by ODI, exhibited very little (0.1%, P < 0.05) or no difference (P > 0.05) between either the DM-MCI or DM-NC groups and HC. Decreased Vic values in the genu of the corpus callosum (R = 0.580, 0.551 and 0.586, P < 0.01) and thalamus (R = 0.570, 0.616, and 0.595, P < 0.05) correlated with glycosylated hemoglobin A1c level, disease duration, and neuropsychological scores, respectively. T2DM patients with cognitive decline had reduced Vic, which indicated decreased density of axons and dendrites. NODDI might be able to help probe microstructural changes in white and gray matter and provide information on diabetic encephalopathy, including those with cognitive impairment. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:889-898.

Predicting Motor Outcomes in Stroke Patients Using Diffusion Spectrum MRI Microstructural Measures.

Improved understanding of neuroimaging signal changes and their relation to patient outcomes after ischemic stroke is needed to improve ability to predict motor improvement and make therapy recommendations. The posterior limb of the internal capsule (PLIC) is a hub of afferent and efferent motor signaling and this work proposes new, image-based methods for prognosis based on interhemispheric differences in the PLIC. In this work, nine acute supratentorial ischemic stroke patients with motor impairment received a baseline, 203-direction diffusion brain MRI and a clinical assessment 3–12 days post-stroke and were compared to nine age-matched healthy controls. Asymmetries based on the mean and Kullback-Leibler divergence in the ipsilesional and contralesional PLIC were calculated for diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) measures from the baseline MRI. Predictions of upper extremity Fugl-Meyer (FM) scores at 5-weeks follow-up from baseline measures of PLIC asymmetry in diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) models were evaluated. For the stroke participants, the baseline asymmetry measures in the PLIC for the orientation dispersion index of the neurite orientation dispersion and density imaging (NODDI) model were highly correlated with upper extremity FM outcomes (r2 = 0.83). Use of DSI and the NODDI orientation dispersion index parameter shows promise of being more predictive of stroke recovery and to help better understand white matter changes in stroke, beyond DTI measures. The new finding that baseline interhemispheric differences in the PLIC calculated from the orientation dispersion index of the NODDI model are highly correlated with upper extremity functional outcomes may lead to improved image-based motor-outcome prediction after middle cerebral artery ischemic stroke.

Empirical reproducibility, sensitivity, and optimization of acquisition protocol, for Neurite Orientation Dispersion and Density Imaging using AMICO

Neurite Orientation Dispersion and Density Imaging (NODDI) has been gaining prominence for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. To establish a pathway for adoption of NODDI in clinical studies, it is important to understand the sensitivity and reproducibility of NODDI metrics on empirical data in the context of acquisition protocol and brain anatomy. Previous studies addressed reproducibility across the 3 T scanners and within session and between subject reproducibility at 1.5 T and 3 T. However, empirical reproducibility on the performance of NODDI metrics based on b-value and diffusion-sensitized directions has not yet been addressed. In this study, we investigate a high angular resolution dataset with 11 repeats of a study with five b-values shells (1000, 1500, 2000, 2500 and 3000 s/mm2) and 96 directions per shell on a single subject. We validated the findings with a dataset from second subject with 10 repeats and 3 b-value shells (1000, 2000, 3000 s/mm2). The NODDI model was estimated using Accelerated Microstructure Imaging via Convex Optimization (AMICO) for different b-values and gradient directions on two-shell High Angular Resolution Density Imaging (HARDI) data fixing the lower shell at b = 1000 s/mm2. NODDI model applied to all acquired imaging data was used as a baseline gold standard for comparison. Additionally, we characterize orientation dispersion index (ODI) reproducibility using single-shell data. The experimental findings confirmed the sensitivity of intracellular volume fraction (Vic) with the choice of outer shell b-value more than with the choice of gradient directions. On the other hand, ODI is more sensitive to the number of gradient directions compared to b-value selection. Single-shell results for ODI are more comparable to 2-shell data at lower b-values than higher b-values. Recommended settings by region of interest and acquisition time are reported for the researchers considering using NODDI in human studies and/or comparing results across acquisition protocols.

Deciphering the microstructure of hippocampal subfields with in vivo DTI and NODDI: Applications to experimental multiple sclerosis

The hippocampus contains distinct populations of neurons organized into separate anatomical subfields and layers with differential vulnerability to pathological mechanisms. The ability of in vivo neuroimaging to pinpoint regional vulnerability is especially important for better understanding of hippocampal pathology at the early stage of neurodegenerative disorders and for monitoring future therapeutic strategies. This is the case for instance in multiple sclerosis whose neurodegenerative component can affect the hippocampus from the early stage. We challenged the capacity of two models, i.e. the classical diffusion tensor imaging (DTI) model and the neurite orientation dispersion and density imaging (NODDI) model, to compute quantitative diffusion MRI that could capture microstructural alterations in the individual hippocampal layers of experimental-autoimmune encephalomyelitis (EAE) mice, the animal model of multiple sclerosis. To achieve this, the hippocampal anatomy of a healthy mouse brain was first explored ex vivo with high resolution DTI and NODDI. Then, 18 EAE mice and 18 control mice were explored 20 days after immunization with in vivo diffusion MRI prior to sacrifice for the histological quantification of neurites and glial markers in each hippocampal layer. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) maps were computed from the DTI model while the orientation dispersion index (ODI), the neurite density index (NDI) and the volume fraction of isotropic diffusivity (isoVF) maps were computed from the NODDI model. We first showed in control mice that color-coded FA and ODI maps can delineate three main hippocampal layers. The quantification of FA, AD, RD, MD, ODI, NDI and isoVF presented differences within these 3 layers, especially within the molecular layer of the dentate gyrus which displayed a specific signature based on a combination of AD (or MD), ODI and NDI. Then, the comparison between EAE and control mice showed a decrease of AD (p = 0.036) and of MD (p = 0.033) selectively within the molecular layer of EAE mice while NODDI indices did not present any difference between EAE and control mice in any layer. Histological analyses confirmed the differential vulnerability of the molecular layer of EAE mice that exhibited decreased dendritic length and decreased dendritic complexity together with activated microglia. Dendritic length and intersections within the molecular layer were independent contributors to the observed decrease of AD (R2 = 0.37 and R2 = 0.40, p < 0.0001) and MD (R2 = 0.41 and R2 = 0.42, p < 0.0001). We therefore identified that NODDI maps can help to highlight the internal microanatomy of the hippocampus but NODDI still presents limitations in grey matter as it failed to capture selective dendritic alterations occurring at early stages of a neurodegenerative disease such as multiple sclerosis, whereas DTI maps were significantly altered.

Advanced diffusion imaging for assessing normal white matter development in neonates and characterizing aberrant development in congenital heart disease.

BackgroundElucidating developmental trajectories of white matter (WM) microstructure is critically important for understanding normal development and regional vulnerabilities in several brain disorders. Diffusion Weighted Imaging (DWI) is currently the method of choice for in-vivo white matter assessment. A majority of neonatal studies use the standard Diffusion Tensor Imaging (DTI) model although more advanced models such as the Neurite Orientation Dispersion and Density Imaging (NODDI) model and the Gaussian Mixture Model (GMM) have been used in adult population. In this study, we compare the ability of these three diffusion models to detect regional white matter maturation in typically developing control (TDC) neonates and regional abnormalities in neonates with congenital heart disease (CHD).MethodsMultiple b-value diffusion Magnetic Resonance Imaging (dMRI) data were acquired from TDC neonates (N = 16) at 38 to 47 gestational weeks (GW) and CHD neonates (N = 19) aged 37 weeks to 41 weeks. Measures calculated from the diffusion signal included not only Mean Diffusivity (MD) and Fractional Anisotropy (FA) derived from the standard DTI model, but also three advanced diffusion measures, namely, the fiber Orientation Dispersion Index (ODI), the isotropic volume fraction (Viso), and the intracellular volume fraction (Vic) derived from the NODDI model. Further, we used two novel measures from a non-parametric GMM, namely the Return-to-Origin Probability (RTOP) and Return-to-Axis Probability (RTAP), which are sensitive to axonal/cellular volume and density respectively. Using atlas-based registration, 22 white matter regions (6 projection, 4 association, and 1 callosal pathways bilaterally in each hemisphere) were selected and the mean value of all 7 measures were calculated in each region. These values were used as dependent variables, with GW as the independent variable in a linear regression model. Finally, we compared CHD and TDC groups on these measures in each ROI after removing age-related trends from both the groups.ResultsLinear analysis in the TDC population revealed significant correlations with GW (age) in 12 projection pathways for MD, Vic, RTAP, and 11 pathways for RTOP. Several association pathways were also significantly correlated with GW for MD, Vic, RTAP, and RTOP. The right callosal pathway was significantly correlated with GW for Vic. Consistent with the pathophysiology of altered development in CHD, diffusion measures demonstrated differences in the association pathways involved in language systems, namely the Uncinate Fasciculus (UF), the Inferior Fronto-occipital Fasciculus (IFOF), and the Superior Longitudinal Fasciculus (SLF). Overall, the group comparison between CHD and TDC revealed lower FA, Vic, RTAP, and RTOP for CHD bilaterally in the a) UF, b) Corpus Callosum (CC), and c) Superior Fronto-Occipital Fasciculus (SFOF). Moreover, FA was lower for CHD in the a) left SLF, b) bilateral Anterior Corona Radiata (ACR) and left Retrolenticular part of the Internal Capsule (RIC). Vic was also lower for CHD in the left Posterior Limb of the Internal Capsule (PLIC). ODI was higher for CHD in the left CC. RTAP was lower for CHD in the left IFOF, while RTOP was lower in CHD in the: a) left ACR, b) left IFOF and c) right Anterior Limb of the Internal Capsule (ALIC).ConclusionIn this study, all three methods revealed the expected changes in the WM regions during the early postnatal weeks; however, GMM outperformed DTI and NODDI as it showed significantly larger effect sizes while detecting differences between the TDC and CHD neonates. Future studies based on a larger sample are needed to confirm these results and to explore clinical correlates.

Analysis of the effects of noise, DWI sampling, and value of assumed parameters in diffusion MRI models.

PurposeThis study was a systematic evaluation across different and prominent diffusion MRI models to better understand the ways in which scalar metrics are influenced by experimental factors, including experimental design (diffusion‐weighted imaging [DWI] sampling) and noise.MethodsFour diffusion MRI models—diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator MRI (MAP‐MRI), and neurite orientation dispersion and density imaging (NODDI)—were evaluated by comparing maps and histogram values of the scalar metrics generated using DWI datasets obtained in fixed mouse brain with different noise levels and DWI sampling complexity. Additionally, models were fit with different input parameters or constraints to examine the consequences of model fitting procedures.ResultsExperimental factors affected all models and metrics to varying degrees. Model complexity influenced sensitivity to DWI sampling and noise, especially for metrics reporting non‐Gaussian information. DKI metrics were highly susceptible to noise and experimental design. The influence of fixed parameter selection for the NODDI model was found to be considerable, as was the impact of initial tensor fitting in the MAP‐MRI model.ConclusionAcross DTI, DKI, MAP‐MRI, and NODDI, a wide range of dependence on experimental factors was observed that elucidate principles and practical implications for advanced diffusion MRI. Magn Reson Med 78:1767–1780, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Neurite Orientation Dispersion and Density Imaging Color Maps to Characterize Brain Diffusion in Neurologic Disorders.

Neurite orientation dispersion and density imaging (NODDI) has recently been developed to overcome diffusion technique limitations in modeling biological systems. This manuscript reports a preliminary investigation into the use ofa singlecolor-coded map to represent NODDI-derived information. An optimized diffusion-weighted imaging protocol was acquired in several clinical neurological contexts including demyelinating disease, neoplastic process, stroke, and toxic/metabolic disease. The NODDI model was fitted to the diffusion datasets. NODDI is based on a three-compartment diffusion model and provides maps that quantify the contributions to the total diffusion signal in each voxel. The NODDI compartment maps were combined into a single 4-dimensional volume visualized as RGB image (red for anisotropic Gaussian diffusion, green for non-Gaussian anisotropic diffusion, and blue for isotropic Gaussian diffusion), in which the relative contributions of the different microstructural compartments can be easily appreciated. The NODDI color maps better describe the heterogeneity of neoplastic as well inflammatory lesions by identifying different tissue components within areas apparently homogeneous on conventional imaging. Moreover, NODDI color maps seem to be useful for identifying vasogenic edema differently from tumor-infiltrated edema. In multiple sclerosis, the NODDI color maps enable a visual assessment of the underlying microstructural changes, possibly highlighting an increased inflammatory component, within lesions and potentially in normal-appearing white matter. The NODDI color maps could make this technique valuable ina clinicalsetting, providingcomprehensiveand accessible information in normal and pathological brain tissues in different neurological pathologies.

Data of NODDI diffusion metrics in the brain and computer simulation of hybrid diffusion imaging (HYDI) acquisition scheme.

This article provides NODDI diffusion metrics in the brains of 52 healthy participants and computer simulation data to support compatibility of hybrid diffusion imaging (HYDI), “Hybrid diffusion imaging”[1] acquisition scheme in fitting neurite orientation dispersion and density imaging (NODDI) model, “NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain”[2]. HYDI is an extremely versatile diffusion magnetic resonance imaging (dMRI) technique that enables various analyzes methods using a single diffusion dataset. One of the diffusion data analysis methods is the NODDI computation, which models the brain tissue with three compartments: fast isotropic diffusion (e.g., cerebrospinal fluid), anisotropic hindered diffusion (e.g., extracellular space), and anisotropic restricted diffusion (e.g., intracellular space). The NODDI model produces microstructural metrics in the developing brain, aging brain or human brain with neurologic disorders. The first dataset provided here are the means and standard deviations of NODDI metrics in 48 white matter region-of-interest (ROI) averaging across 52 healthy participants. The second dataset provided here is the computer simulation with initial conditions guided by the first dataset as inputs and gold standard for model fitting. The computer simulation data provide a direct comparison of NODDI indices computed from the HYDI acquisition [1] to the NODDI indices computed from the originally proposed acquisition [2]. These data are related to the accompanying research article “Age Effects and Sex Differences in Human Brain White Matter of Young to Middle-Aged Adults: A DTI, NODDI, and q-Space Study” [3].

Bingham–NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI

This paper presents Bingham–NODDI, a clinically-feasible technique for estimating the anisotropic orientation dispersion of neurites. Direct quantification of neurite morphology on clinical scanners was recently realised by a diffusion MRI technique known as neurite orientation dispersion and density imaging (NODDI). However in its current form NODDI cannot estimate anisotropic orientation dispersion, which is widespread in the brain due to common fanning and bending of neurites. This work proposes Bingham–NODDI that extends the NODDI formalism to address this limitation. Bingham–NODDI characterises anisotropic orientation dispersion by utilising the Bingham distribution to model neurite orientation distribution. The new model estimates the extent of dispersion about the dominant orientation, separately along the primary and secondary dispersion orientations. These estimates are subsequently used to estimate the overall dispersion about the dominant orientation and the dispersion anisotropy. We systematically evaluate the ability of the new model to recover these key parameters of anisotropic orientation dispersion with standard NODDI protocol, both in silico and in vivo. The results demonstrate that the parameters of the proposed model can be estimated without additional acquisition requirements over the standard NODDI protocol. Thus anisotropic dispersion can be determined and has the potential to be used as a marker for normal brain development and ageing or in pathology. We additionally find that the original NODDI model is robust to the effects of anisotropic orientation dispersion, when the quantification of anisotropic dispersion is not of interest.

Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study

Microstructural changes in human brain white matter of young to middle-aged adults were studied using advanced diffusion Magnetic Resonance Imaging (dMRI). Multiple shell diffusion-weighted data were acquired using the Hybrid Diffusion Imaging (HYDI). The HYDI method is extremely versatile and data were analyzed using Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI), and q-space imaging approaches. Twenty-four females and 23 males between 18 and 55years of age were included in this study. The impact of age and sex on diffusion metrics were tested using least squares linear regressions in 48 white matter regions of interest (ROIs) across the whole brain and adjusted for multiple comparisons across ROIs. In this study, white matter projections to either the hippocampus or the cerebral cortices were the brain regions most sensitive to aging. Specifically, in this young to middle-aged cohort, aging effects were associated with more dispersion of white matter fibers while the tissue restriction and intra-axonal volume fraction remained relatively stable. The fiber dispersion index of NODDI exhibited the most pronounced sensitivity to aging. In addition, changes of the DTI indices in this aging cohort were correlated mostly with the fiber dispersion index rather than the intracellular volume fraction of NODDI or the q-space measurements. While men and women did not differ in the aging rate, men tend to have higher intra-axonal volume fraction than women. This study demonstrates that advanced dMRI using a HYDI acquisition and compartmental modeling of NODDI can elucidate microstructural alterations that are sensitive to age and sex. Finally, this study provides insight into the relationships between DTI diffusion metrics and advanced diffusion metrics of NODDI model and q-space imaging.

Heritability of complex white matter diffusion traits assessed in a population isolate.

Diffusion weighted imaging (DWI) methods can noninvasively ascertain cerebral microstructure by examining pattern and directions of water diffusion in the brain. We calculated heritability for DWI parameters in cerebral white (WM) and gray matter (GM) to study the genetic contribution to the diffusion signals across tissue boundaries. Using Old Order Amish (OOA) population isolate with large family pedigrees and high environmental homogeneity, we compared the heritability of measures derived from three representative DWI methods targeting the corpus callosum WM and cingulate gyrus GM: diffusion tensor imaging (DTI), the permeability-diffusivity (PD) model, and the neurite orientation dispersion and density imaging (NODDI) model. These successively more complex models represent the diffusion signal modeling using one, two, and three diffusion compartments, respectively. We replicated the high heritability of the DTI-based fractional anisotropy (h(2) = 0.67) and radial diffusivity (h(2) = 0.72) in WM. High heritability in both WM and GM tissues were observed for the permeability-diffusivity index from the PD model (h(2) = 0.64 and 0.84), and the neurite density from the NODDI model (h(2) = 0.70 and 0.55). The orientation dispersion index from the NODDI model was only significantly heritable in GM (h(2) = 0.68). DWI measures from multicompartmental models were significantly heritable in WM and GM. DWI can offer valuable phenotypes for genetic research; and genes thus identified may reveal mechanisms contributing to mental and neurological disorders in which diffusion imaging anomalies are consistently found. Hum Brain Mapp 37:525-535, 2016. © 2015 Wiley Periodicals, Inc.