Tau-related white-matter alterations along spatially selective pathways

BackgroundDiffusion tensor imaging (DTI) has been commonly used to characterize microstructural changes associated with aging and Alzheimer’s disease (AD) dementia. However, DTI ignores non‐Gaussian diffusion, which limits its ability to describe the more restricted diffusion present within cellular structures. Neurite Orientation Dispersion and Density Imaging (NODDI) and Mean Apparent Propagator (MAP) MRI are two alternative diffusion MRI (DWI) models which can be used to calculate parameters that better characterize alterations observed in tissue microstructure.To investigate the sensitivity of DTI, NODDI, and MAP to white matter changes associated with aging and dementia, we examined the relationship between 15 DWI parameters, age, and AD clinical status in 418 aging adults along the AD continuum.Methods418 participants (328 cognitively unimpaired [CU], 60 Mild Cognitive Impairment [MCI], 30 AD) from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center were imaged with multi‐shell DWI (Table 1). 15 DWI parameters were extracted from the cingulum, including 4 DTI (FA, MD, RD, AxD), 3 NODDI (ODI, NDI, ISO) and 8 MAP (RTOP, RTAP, RTPP, MSD, QIV, NG, NG||, NG⊥) metrics. All measures were fit to the following linear model:Diffusion Metric = β0+β1 *(age)+ β2 *(sex)+β3 *(status)+β4*(age*status)+ΕResultsDWI age trajectories and model parameters are provided in Figures 1‐2 and Tables 2‐4, respectively. 12 of 15 DWI metrics were significantly associated with age, including all 8 MAP measures. Additionally, 6 metrics were associated with AD and 8 were associated with MCI. The interaction between age and AD was significant for RTOP, NDI, FA, and AxD, while the interaction between age and MCI was significant for RTOP, RTPP, MSD, NG, NG⊥, ISO, MD, RD, and AxD (Pcorr = 0.05).ConclusionThe significant effects of clinical status and interactions between age and clinical status, may indicate that these metrics are sensitive to microstructural alterations specific to dementia. Furthermore, the meaningful interactions between age and MCI in 5 of 8 MAP parameters suggests that MAP MRI may be particularly useful for detecting early WM degeneration along the AD continuum. Future work will translate this analysis to larger cohorts and assess MAP parameters in gray matter.

NODDI in gray matter is a sensitive marker of aging and early AD changes.

Advanced diffusion MRI tract signatures in Alzheimer's disease, dementia with Lewy bodies, and the FTD/PPA spectrum.

Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer’s disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.

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.

BackgroundBrain amyloid (AB) and neurodegeneration (assessed using PET and MRI) are key AD pathology biomarkers, particularly during transitions from cognitively unimpaired (CU) to cognitively impaired (CI) states. Importantly, the role of brain white matter microstructural measures in these changes, such as those derived from neurite orientation dispersion and density imaging (NODDI), are poorly understood. We explored roles of cognitive status, brain amyloid pathology, and neurodegeneration on NODDI Free Water (FW), a potential neuroinflammation biomarker.MethodParticipants enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort (N=74, Table 1) underwent neuropsychological assessment, T1/NODDI MRI, and PiB PET. Participants were adjudicated as cognitively impaired (MCI or AD) or unimpaired using NIA‐AA criteria. T1 MRI were processed (FreeSurfer v5.3) to generate regions of interest (ROIs). An “AD‐signature” cortical thickness value was calculated, and thresholded to classify participants as neurodegeneration‐positive (N+) or ‐negative (N‐). A set of AD‐sensitive cortical ROIs was used to extract mean PET signal, which was thresholded to adjudicate AB positivity (AB‐/AB+). NODDI was processed to yield parameter maps using AMICO; mean isotropic volume fraction (FW) was calculated over a set of white matter tract ROIs. A two‐way ANOVA/ANCOVA (covariates: age, sex, education) assessed effects of neurodegeneration and AB on FW (N+/N‐ x AB+/AB‐), and a two‐way ANOVA/ANCOVA assessed effects of cognitive impairment and AB on FW (CU/CI x AB+/AB‐).ResultWe first examined effects of neurodegeneration and AB on NODDI FW. We found a significant main effect of neurodegeneration (p=.042), such that N+ had higher FW; this was marginal after adjustment (p=.07). We also observed a significant main effect of AB (p<.001; adjusted p=.012), with AB+ having higher FW. There was no significant interaction. We next explored how cognitive status and AB interacted on FW. We found a significant main effect of cognitive impairment, with CI having higher FW (p=.018; adjusted p=.013). We also found an interaction between diagnosis and AB (p=.037; adjusted p=.044) such that among AB+ individuals, presence of CI related to increased FW (Figure 1).ConclusionNODDI FW increases were significantly affected by the presence of neurodegeneration, AB positivity, and cognitive impairment.

BackgroundObesity 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.

BackgroundIsolated REM sleep behavior disorder (iRBD) is a parasomnia that reflects an evolving α‐synucleinopathy disorder, providing an opportunity to study early pathological changes. While diffusion tensor imaging (DTI) studies have shown white matter changes in iRBD, Neurite Orientation Dispersion and Density Imaging (NODDI) may offer better biological specificity in characterizing microstructural alterations through measures of Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and Free Water Fraction (FWF).MethodWe included 77 participants with polysomnography‐confirmed iRBD from the North American Prodromal Synucleinopathy (NAPS) Consortium and 154 age‐ and sex‐matched cognitively unimpaired controls from the Mayo Clinic Study of Aging. White matter microstructure was evaluated using standardized multi‐shell diffusion on 3T MRI, quantifying DTI metrics (fractional anisotropy, FA; and mean diffusivity, MD) and NODDI parameters across bilateral white matter tracts defined by the JHU “Eve” WM atlas. Group differences were assessed using conditional logistic regression, with correlations to motor performance evaluated using the Purdue Pegboard and Alternating Finger Tapping tests.ResultCompared to controls, iRBD participants demonstrated widespread and bidirectional white matter changes across major white matter pathways (see Figure 1 for glass brain visualizations). While predominantly showing decreases, FA exhibited some increases, particularly in the corticospinal tract. MD showed a largely opposite pattern with predominantly increased values across tracts. NODDI metrics revealed complex bidirectional patterns: ODI was broadly increased across multiple tracts with focal decreases, while NDI showed a pattern of predominantly decreased values alongside localized increases. FWF demonstrated a mixed pattern with predominant decreases across most tracts. In addition, both DTI and NODDI metrics showed extensive, moderate correlations with the Purdue Pegboard Test and Alternating Finger Tapping performance (T = 2.0, p < 0.05, Figure 2).ConclusionOur study highlighted widespread and complex bidirectional white matter microstructural alterations in individuals with iRBD, demonstrating significant correlations with dexterity and motor performance even during the prodromal stage. These results suggest that extensive white matter abnormalities occur early in prodromal α‐synucleinopathies and highlight the value of advanced diffusion imaging techniques in characterizing iRBD and its potential prediction of phenoconversion to overt neurodegenerative disorders.

White matter tracts that overlap with the thalamus and the putamen are protected against multiple sclerosis pathology

BackgroundDespite the strengths conventional structural imaging measures to detect atrophy, cross‐sectional relationships between neurodegeneration and Alzheimer’s disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers.MethodOne‐hundred and sixty‐seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS‐T1 protocol to form anterior and posterior MTL ROIs. Multi‐shell DTI data were collected in 113 non‐collinear directions with 1.5mm isotropic voxels, were pre‐processed using a custom pipeline, and fit with voxel‐wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise‐regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume.ResultNODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p‐Tau‐181, inclusion of NODDI metrics subsumed this effect (F‐change&gt;7.54, p&lt;0.0067), and reduced ODI was the best predictor of plasma pTau‐181 within cognitively impaired individuals, while isoVf best predicted pTau‐181 across all individuals. Posterior MTL volume did not predict plasma GFAP, but inclusion of ODI and icVf both significantly predicted plasma GFAP in both CN and impaired individuals (F‐change&gt;12.68, p&lt;.001). Anterior MTL icVf predicted both pTau‐181 and GFAP.ConclusionMicrostructural alterations measured by NODDI appear more closely linked to AD‐related molecular measures and, thus, may provide a more sensitive cross‐sectional measure of neurodegeneration of the MTL compared to conventional macrostructural metrics.

BackgroundDespite the strengths conventional structural imaging measures to detect atrophy, cross‐sectional relationships between neurodegeneration and Alzheimer’s disease (AD) biomarkers in early stages is limited. Neurite orientation dispersion and density imaging (NODDI) measures microstructural alterations that are unique from conventional structural metrics. Alterations in microstructure have been detected in individuals with AD compared to cognitively normal individuals, but there is sparse data regarding the relationship between these metrics and AD biomarkers. Here we sought to characterize how NODDI metrics in the medial temporal lobe (MTL) are associated with plasma AD biomarkers.MethodOne‐hundred and sixty‐seven cognitively normal (CN) individuals, 47 individuals with mild cognitive impairment, and 16 individuals with AD underwent diffusion and T1w imaging. T1w images were segmented using the ASHS‐T1 protocol to form anterior and posterior MTL ROIs. Multi‐shell DTI data were collected in 113 non‐collinear directions with 1.5mm isotropic voxels, were pre‐processed using a custom pipeline, and fit with voxel‐wise NODDI models. ANTs was used to linearly register NODDI and T1w images to allow transformation of MTL ROIs to diffusion space. Stepwise‐regression models examined relationships between anterior and posterior MTL NODDI metrics with plasma biomarkers in CN and impaired (MCI/AD) groups after accounting for MTL volume.ResultNODDI metrics of intracellular volume fraction (icVf) and orientation dispersion index (ODI) were decreased, and isometric volume fraction (isoVf) was increased in cognitively impaired compared to CN. Amyloid positivity was associated with decreased ODI and increased isoVf in cognitively impaired individuals. While posterior MTL volume predicted plasma p‐Tau‐181, inclusion of NODDI metrics subsumed this effect (F‐change&gt;7.54, p&lt;0.0067), and reduced ODI was the best predictor of plasma pTau‐181 within cognitively impaired individuals, while isoVf best predicted pTau‐181 across all individuals. Posterior MTL volume did not predict plasma GFAP, but inclusion of ODI and icVf both significantly predicted plasma GFAP in both CN and impaired individuals (F‐change&gt;12.68, p&lt;.001). Anterior MTL icVf predicted both pTau‐181 and GFAP.ConclusionMicrostructural alterations measured by NODDI appear more closely linked to AD‐related molecular measures and, thus, may provide a more sensitive cross‐sectional measure of neurodegeneration of the MTL compared to conventional macrostructural metrics.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive neuroimaging technique that measures the displacement of water molecules in tissue over time. Due to its sensitivity to micron-scale water movement, which is influenced by cellular structures like membranes, axons, and myelin, dMRI is a unique method for probing tissue microstructure. Among dMRI analysis approaches, neurite orientation dispersion and density imaging (NODDI) is a biophysical modeling technique that enables the characterization of cytoarchitectural and myeloarchitectural features in the brain. The early postnatal period is characterized by rapid and dynamic biological processes such as axonal growth, dendritic arborization, and synaptogenesis—changes that alter the microstructural environment in ways that are detectable by NODDI. Thus, NODDI presents a promising approach for characterizing early brain development, offering biologically specific markers of tissue organization that are responsive to these maturational events. This review presents emerging literature on NODDI applications during early infancy, demonstrating its utility in mapping normative developmental trajectories, investigating alterations in preterm populations, and linking microstructural properties to environmental influences and emerging behavioral outcomes. While current literature offers initial insights into early microstructural development patterns, NODDI applications in infancy remain limited, and existing studies are constrained by small sample sizes, limited age coverage, and lack of longitudinal data. Nonetheless, initial evidence suggests that NODDI can complement conventional diffusion metrics and may provide novel insights into early neural maturation and plasticity. Continued application and methodological refinement of NODDI in infancy may help delineate sensitive periods of brain development and improve the interpretation of emerging neurobehavioral phenotypes.

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.

Mild traumatic brain injury (mTBI) is the signature injury experienced by military service members and is associated with poor neuropsychiatric outcomes. Yet, there is a lack of reliable clinical tools for mTBI diagnosis and prognosis. To examine the white matter microstructure and neuropsychiatric outcomes of service members with a remote history of mTBI (ie, mTBI that occurred over 2 years ago) using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). This case-control study examined 98 male service members enrolled in a study at the National Intrepid Center of Excellence. Eligible participants were active duty status or able to enroll in the Defense Enrollment Eligibility Reporting system, ages 18 to 60 years, and had a remote history of mTBI; controls were matched by age. Remote history of mTBI. White matter microstructure was assessed using a region-of-interest approach of skeletonized diffusion images, including DTI (fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity) and NODDI (orientation dispersion index [ODI], isotropic volume fraction, intra-cellular volume fraction). Neuropsychiatric outcomes associated with posttraumatic stress disorder (PTSD) and postconcussion syndrome were assessed. A total of 65 male patients with a remote history of mTBI (mean [SD] age, 40.5 [5.0] years) and 33 age-matched male controls (mean [SD] age, 38.9 [5.6] years) were included in analysis. Compared with the control cohort, the 65 service members with mTBI presented with significantly more severe PTSD-like symptoms (mean [SD] PTSD CheckList-Civilian [PCL-C] version scores: control, 19.0 [3.8] vs mTBI, 41.2 [11.6]; P < .001). DTI and NODDI metrics were altered in the mTBI group compared with the control, including intra-cellular volume fraction of the right cortico-spinal tract (β = -0.029, Cohen d = 0.66; P < .001), ODI of the left posterior thalamic radiation (β = -0.006, Cohen d = 0.55; P < .001), and ODI of the left uncinate fasciculus (β = 0.013, Cohen d = 0.61; P < .001). In service members with mTBI, fractional anisotropy of the left uncinate fasciculus was associated with postconcussion syndrome (β = 5.4 × 10-3; P = .003), isotropic volume fraction of the genu of the corpus callosum with PCL-C (β = 4.3 × 10-4; P = .01), and ODI of the left fornix and stria terminalis with PCL-C avoidance scores (β = 1.2 × 10-3; P = .02). In this case-control study of military-related mTBI, the results suggest that advanced magnetic resonance imaging techniques using NODDI can reveal white matter microstructural alterations associated with neuropsychiatric symptoms in the chronic phase of mTBI. Diffusion trends observed throughout widespread white matter regions-of-interest may reflect mechanisms of neurodegeneration as well as postinjury tissue scarring and reorganization.

Epilepsy is one of the largest neurological diseases in the world, and juvenile myoclonic epilepsy (JME) usually occurs in adolescents, giving patients tremendous burdens during growth, which really needs the early diagnosis. Advanced diffusion magnetic resonance imaging (MRI) could detect the subtle changes of the white matter, which could be a non-invasive early diagnosis biomarker for JME. Transfer learning can solve the problem of insufficient clinical samples, which could avoid overfitting and achieve a better detection effect. However, there is almost no research to detect JME combined with diffusion MRI and transfer learning. In this study, two advanced diffusion MRI methods, high angle resolved diffusion imaging (HARDI) and neurite orientation dispersion and density imaging (NODDI), were used to generate the connectivity matrix which can describe tiny changes in white matter. And three advanced convolutional neural networks (CNN) based transfer learning were applied to detect JME. A total of 30 participants (15 JME patients and 15 normal controls) were analyzed. Among the three CNN models, Inception_resnet_v2 based transfer learning is better at detecting JME than Inception_v3 and Inception_v4, indicating that the "short cut" connection can improve the ability to detect JME. Inception_resnet_v2 achieved to detect JME with the accuracy of 75.2% and the AUC of 0.839. The results support that diffusion MRI and CNN based transfer learning have the potential to improve the automated detection of JME.