Neurite Orientation Dispersion And Density Imaging Parameters Research Articles

Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies

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.

Assessing brain microstructural changes in chronic kidney disease: a diffusion imaging study using multiple models.

To explore the effectiveness of diffusion quantitative parameters derived from advanced diffusion models in detecting brain microstructural changes in patients with chronic kidney disease (CKD). The study comprised 44 CKD patients (eGFR<59 mL/min/1.73 m2) and 35 age-and sex-matched healthy controls. All patients underwent diffusion spectrum imaging (DSI) and conventional magnetic resonance imaging. Reconstructed to obtain diffusion MRI models, including diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI) and Mean Apparent Propagator (MAP)-MRI, were processed to obtain multi-parameter maps. The Tract-Based Spatial Statistics (TBSS) analysis was utilized for detecting microstructural differences and Pearson correlation analysis assessed the relationship between renal metabolism markers and diffusion parameters in the brain regions of CKD patients. Receiver operating characteristic (ROC) curve analysis assessed the diagnostic performance of diffusion models, with AUC comparisons made using DeLong's method. Significant differences were noted in DTI, NODDI, and MAP-MRI parameters between CKD patients and controls (p < 0.05). DTI indicated a decrease in Fractional Anisotropy(FA) and an increase in Mean and Radial Diffusivity (MD and RD) in CKD patients. NODDI indicated decreased Intracellular and increased Extracellular Volume Fractions (ICVF and ECVF). MAP-MRI identified extensive microstructural changes, with elevated Mean Squared Displacement (MSD) and Q-space Inverse Variance (QIV) values, and reduced Non-Gaussianity (NG), Axial Non-Gaussianity (NGAx), Radial Non-Gaussianity (NGRad), Return-to-Origin Probability (RTOP), Return-to-Axis Probability (RTAP), and Return-to-Plane Probability (RTPP). There was a moderate correlation between serum uric acid (SUA) and diffusion parameters in six brain regions (p < 0.05). ROC analysis showed the AUC values of DTI_FA ranged from 0.70 to 0.793. MAP_NGAx in the Retrolenticular part of the internal capsule R reported a high AUC value of 0.843 (p < 0.05), which was not significantly different from other diffusion parameters (p > 0.05). The advanced diffusion models (DTI, NODDI, and MAP-MRI) are promising for detecting brain microstructural changes in CKD patients, offering significant insights into CKD-affected brain areas.

Alzheimer’s disease biomarkers of amyloid and tau influence white matter neurite alterations in dementia with Lewy bodies: a NODDI study

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐ß plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition.MethodConsecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas.ResultCompared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings.ConclusionWidespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐ß deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum.

Alzheimer’s disease biomarkers of amyloid and tau influence white matter neurite alterations in dementia with Lewy bodies: a NODDI study

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐β plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition.MethodConsecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas.ResultCompared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings.ConclusionWidespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐β deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum.

The Effects of Hypertension on White Matter Microstructural and Vascular Imaging Metrics

AbstractBackgroundHypertension is one of the key vascular risk factors of AD and related dementia. Cerebral blood flow (CBF) and white matter (WM) integrity may play an essential role in understanding the vascular contributions to dementia (VCID). We investigated if hypertension is related to WM microstructural and vascular imaging metrics in WM.MethodFour‐hundred‐seven subjects (age: 69.7±8.1 years) enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core underwent MRI, including neurite orientation dispersion and density imaging (NODDI) and eight‐phase pseudo‐continuous ASL (PCASL). The subjects included 158 MCI, 111 APOE e4 allele carriers, 224 with hypertension, and 249 with impaired glycemic status which includes type 2 diabetes and prediabetics. Hypertension status was defined as SBP≥130mmHG and DBP≥80mmHG. The microstructural imaging metrics including intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI) were obtained from NODDI. The vascular imaging metric included CBF. CBF was obtained from PCASL sequence. Voxel‐wise linear regression analyses were performed to examine if CBF, ICVF, ISOVF, or ODI were related to hypertension adjusting for cognitive status, glycemic status, APOE e4 allele, age, sex, and years of education. All images were normalized into standard space. Tract‐based spatial statistics (TBSS) were used to normalize NODDI images. The false positive cluster thresholding was applied.ResultHypertensive subjects showed impairments in both microstructural and vascular imaging metrics. Based on Figures 1‐(A), 2‐(A) and 3‐(A), there are overlapping brain image voxels between the microstructural and vascular imaging metrics. Additionally, the hypertensive subjects showed significantly lower CBF, higher ISOVF, and higher ODI than normal subjects within the voxel clusters (Figures 1‐(B), 2‐(B), and 3‐(B)). Note that APOE e4 allele, glycemic status, and cognitive status also showed statistical significance in the NODDI parameters, but there was no association in CBF.ConclusionHypertension showed the potential association between the microstructural and vascular impairments in the WM. Each risk factor and cognitive status differently affected WM microstructural and vascular dynamic imaging parameters. These findings warrant further investigations of contributions of hypertension to the microstructural and vascular abnormalities in dementia.

The Effects of Hypertension on White Matter Microstructural and Vascular Imaging Metrics

AbstractBackgroundHypertension is one of the key vascular risk factors of AD and related dementia. Cerebral blood flow (CBF) and white matter (WM) integrity may play an essential role in understanding the vascular contributions to dementia (VCID). We investigated if hypertension is related to WM microstructural and vascular imaging metrics in WM.MethodFour‐hundred‐seven subjects (age: 69.7±8.1 years) enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core underwent MRI, including neurite orientation dispersion and density imaging (NODDI) and eight‐phase pseudo‐continuous ASL (PCASL). The subjects included 158 MCI, 111 APOE e4 allele carriers, 224 with hypertension, and 249 with impaired glycemic status which includes type 2 diabetes and prediabetics. Hypertension status was defined as SBP = 130mmHG and DBP = 80mmHG. The microstructural imaging metrics including intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index (ODI) were obtained from NODDI. The vascular imaging metric included CBF. CBF was obtained from PCASL sequence. Voxel‐wise linear regression analyses were performed to examine if CBF, ICVF, ISOVF, or ODI were related to hypertension adjusting for cognitive status, glycemic status, APOE e4 allele, age, sex, and years of education. All images were normalized into standard space. Tract‐based spatial statistics (TBSS) were used to normalize NODDI images. The false positive cluster thresholding was applied.ResultHypertensive subjects showed impairments in both microstructural and vascular imaging metrics. Based on Figures 1‐(A), 2‐(A) and 3‐(A), there are overlapping brain image voxels between the microstructural and vascular imaging metrics. Additionally, the hypertensive subjects showed significantly lower CBF, higher ISOVF, and higher ODI than normal subjects within the voxel clusters (Figures 1‐(B), 2‐(B), and 3‐(B)). Note that APOE e4 allele, glycemic status, and cognitive status also showed statistical significance in the NODDI parameters, but there was no association in CBF.ConclusionHypertension showed the potential association between the microstructural and vascular impairments in the WM. Each risk factor and cognitive status differently affected WM microstructural and vascular dynamic imaging parameters. These findings warrant further investigations of contributions of hypertension to the microstructural and vascular abnormalities in dementia.

Association of adverse childhood experiences and cortical neurite density alterations with posttraumatic stress disorder symptoms in autism spectrum disorder.

Posttraumatic stress disorder (PTSD) can be a source of significant social and daily distress in autism spectrum disorder (ASD). Compared to typically developed (TD) individuals, people with ASD are at an increased risk of adverse childhood experiences (ACEs), which can result in abnormal neuronal development. However, whether or how ACEs influence abnormal neural development and PTSD symptoms in ASD has not been fully elucidated. Thirty-nine TD individuals and 41 individuals with ASD underwent T1-weighted magnetic resonance imaging and neurite orientation dispersion and density imaging (NODDI), with axonal and dendritic densities assessed in terms of the orientation dispersion index and neurite density index (NDI), respectively. Voxel-based analyses were performed to explore the brain regions associated with PTSD symptoms, and the relationships between the severity of ACEs and PTSD symptoms and NODDI parameters in the extracted brain regions were examined. There was a significant positive association between PTSD symptom severity and NDI in the bilateral supplementary motor area; right superior frontal, left supramarginal, and right superior temporal gyrus; and right precuneus in the ASD group, but not in the TD group. ACE severity was significantly associated with NDI in the right superior frontal and left supramarginal gyrus and right precuneus in the ASD group. Moreover, NDI in the right precuneus mainly predicted the severity of PTSD symptoms in the ASD group, but not the TD group. These results suggest that ACE-associated higher neurite density is of clinical importance in the pathophysiology of PTSD symptoms in ASD.

White matter microstructural disruption in minimal hepatic encephalopathy: a neurite orientation dispersion and density imaging (NODDI) study.

To evaluate the ability of neurite orientation dispersion and density imaging (NODDI) for detecting white matter (WM) microstructural abnormalities in minimal hepatic encephalopathy (MHE). Diffusion-weighted images, enabling the estimation of NODDI and diffusion tensor imaging (DTI) parameters, were acquired from 20 healthy controls (HC), 22 cirrhotic patients without MHE (NHE), and 15 cirrhotic patients with MHE. Tract-based spatial statistics were used to determine differences in DTI (including fractional anisotropy [FA] and mean/axial/radial diffusivity [MD/AD/RD]) and NODDI parameters (including neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISO]). Voxel-wise analyses of correlations between diffusion parameters and neurocognitive performance determined by Psychometric Hepatic Encephalopathy Score (PHES) were completed. MHE patients had extensive NDI reduction and rare ODI reduction, primarily involving the genu and body of corpus callosum and the bilateral frontal lobe, corona radiata, external capsule, anterior limb of internal capsule, temporal lobe, posterior thalamic radiation, and brainstem. The extent of NDI and ODI reduction expanded from NHE to MHE. In both MHE and NHE groups, the extent of NDI change was quite larger than that of FA change. No significant intergroup difference in ISO/MD/AD/RD was observed. Tissue specificity afforded by NODDI revealed the underpinning of FA reduction in MHE. The NDI in left frontal lobe was significantly correlated with PHES. MHE is characterized by diffuse WM microstructural impairment (especially neurite density reduction). NODDI can improve the detection of WM microstructural impairments in MHE and provides more precise information about MHE-related pathology than DTI.

Relationship Between Microstructural Alterations and Cognitive Decline After Whole-Brain Radiation Therapy for Brain Metastases: An Exploratory Whole-Brain MR Analysis Based on Neurite Orientation Dispersion and Density Imaging.

Cognitive impairment frequently occurs in patients with brain metastases (BM) after whole-brain radiotherapy (WBRT). It is crucial to explore the underlying mechanisms of cognitive impairment in BM patients receiving WBRT. To detect brain microstructural alterations in patients after WBRT by neurite orientation dispersion and density imaging (NODDI), and evaluate the performance of microstructural alterations in predicting cognitive impairment. Prospective. Twenty-six patients (seven female; mean age, 60.9 years). 3-T, multi-shell diffusion-weighted single-shot echo-planar sequence. Three-dimensional magnetization-prepared rapid acquisition with gradient echo sequence. Mini-mental state examination (MMSE) evaluations were conducted prior to, following, 1 and 3 months after WBRT. The diffusion data were collected twice, 1 week before and 1 week after WBRT. NODDI analysis was conducted to assess microstructural alterations in whole brain (orientation dispersion index, neurite density index, volume fraction of isotropic water molecules). Reliable change indices (RCI) of MMSE were used to measure cognitive decline. The performance of support vector machine models based on NODDI parameters and clinical features (prednisone usage, tumor volume, etc.) in predicting MMSE-RCI was evaluated. Paired t-test to assess alterations of NODDI measures and MMSE during follow-up. Statistical significance level of P-value <0.05. Significantly decreased MMSE score was found at 3 months after WBRT. After WBRT, corpus callosum, medial prefrontal cortex, limbic lobe, occipital lobe, parietal lobe, putamen, globus pallidus lentiform, and thalamus demonstrated damage in NODDI parameters. The predicted MMSE-RCI based on NODDI features was significantly associated with the measured MMSE-RCI at 1 month (R = 0.573; P = 0.003) and 3 months (R = 0.687; P < 0.0001) after WBRT. Microstructural alterations in several brain regions including the middle prefrontal and limbic cortexes were observed in patients with BM following WBRT, which may contribute to subsequent cognitive decline. 2 TECHNICAL EFFICACY: Stage 2.

The role of MR diffusion kurtosis and neurite orientation dispersion and density imaging in evaluating gliomas.

Conventional MRI sequences in neuro-oncology are insufficient for glioma grading. However, newly developed diffusion-weighted imaging techniques have been shown to have a great potential for glioma grading. This study examined the diagnostic performance of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and their combinations in glioma grading. Multishell diffusion tensor images were obtained with 3T MRI in 38 glioma patients (22 high-grade glioma [HGG], 16 low-grade glioma [LGG]). DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]), DKI (Axial kurtosis [AK], mean kurtosis [MK], radial kurtosis [RK]), and NODDI (intracellular volume fraction [ICVF], orientation distribution index, isotropic water fraction [ISO]) images were obtained after preprocessing. The average value of these parameters was calculated in the solid components of the tumors. The receiver operating characteristic curve analyses were performed to investigate the diagnostic performance and the curves were compared with the Delong test. FA shows an increase in HGG, while MD, RD, and AD exhibit a decrease. AK, MK, and RK were higher in HGG than LGG. ICVF increased in HGG, while ISO decreased. AK demonstrated the best diagnostic performance among all parameters, and kurtosis outperformed NODDI but not DTI. Combining these parameters did not yield a statistically significant improvement in diagnostic performance. DTI, DKI, and NODDI approaches can differentiate between HGG and LGG; however, kurtosis parameters perform better and adding NODDI parameters does not improve diagnostic performance. Using multishell b-value has not led to an increase in diagnostic performance.

Neurite orientation dispersion and density imaging and diffusion tensor imaging to facilitate distinction between infiltrating tumors and edemas in glioblastoma

BackgroundGlioblastomas are highly infiltrative tumors, and differentiating between non-enhancing tumors (NETs) and vasogenic edema (Edemas) occurring in the non-enhancing T2-weighted hyperintense area is challenging. Here, we differentiated between NETs and Edemas in glioblastomas using neurite orientation dispersion and density imaging (NODDI) and diffusion tensor imaging (DTI). Materials and methodsData were collected retrospectively from 21 patients with primary glioblastomas, three with metastasis, and two with meningioma as controls. MRI data included T2 weighted images and contrast enhanced T1 weighted images, NODDI, and DTI. Three neurosurgeons manually assigned volumes of interest (VOIs) to the NETs and Edemas. The DTI and NODDI-derived parameters calculated for each VOI were fractional anisotropy (FA), apparent diffusion coefficient (ADC), intracellular volume fraction (ICVF), isotropic volume fraction (ISOVF), and orientation dispersion index. ResultsSixteen and 14 VOIs were placed on NETs and Edemas, respectively. The ICVF, ISOVF, FA, and ADC values of NETs and Edemas differed significantly (p < 0.01). Receiver operating characteristic curve analysis revealed that using all parameters allowed for improved differentiation of NETs from Edemas (area under the curve = 0.918) from the use of NODDI parameters (0.910) or DTI parameters (0.899). Multiple logistic regression was performed with all parameters, and a predictive formula to differentiate between NETs and Edemas could be created and applied to the edematous regions of the negative control-group images; the tumor prediction degree was well below 0.5, confirming differentiation as edema. ConclusionsUsing NODDI and DTI may prove useful in differentiating NETs from Edemas in the non-contrast T2 hyperintensity region of glioblastomas.

Sleep-dependent structural neuroplasticity after a spatial navigation task: A diffusion imaging study.

Evidence for sleep-dependent changes in microstructural neuroplasticity remains scarce, despite the fact that it is a mandatory correlate of the reorganization of learning-related functional networks. We investigated the effects of post-training sleep on structural neuroplasticity markers measuring standard diffusion tensor imaging (DTI), mean diffusivity (MD), and the revised biophysical neurite orientation dispersion and density imaging (NODDI), free water fraction (FWF), and neurite density (NDI) parameters that enable disentangling whether MD changes result from modifications in neurites or in other cellular components (e.g., glial cells). Thirty-four healthy young adults were scanned using diffusion-weighted imaging (DWI) on Day1 before and after 40-min route learning (navigation) in a virtual environment, then were sleep deprived (SD) or slept normally (RS) for the night. After recovery sleep for 2 nights, they were scanned again (Day4) before and after 40-min route learning (navigation) in an extended environment. Sleep-related microstructural changes were computed on DTI (MD) and NODDI (NDI and FWF) parameters in the cortical ribbon and subcortical hippocampal and striatal regions of interest (ROIs). Results disclosed navigation learning-related decreased DWI parameters in the cortical ribbon (MD, FWF) and subcortical (MD, FWF, NDI) areas. Post-learning sleep-related changes were found at Day4 in the extended learning session (pre- to post-relearning percentage changes), suggesting a rapid sleep-related remodeling of neurites and glial cells subtending learning and memory processes in basal ganglia and hippocampal structures.

Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment : Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning.

This study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning algorithms. A total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K‑nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t‑test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI patients. The NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (pvalue range, < 0.001-0.042) and Viso values increased (pvalue range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores. NODDI combined with machine learning algorithms is apromising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

Microstructural Visual Pathway White Matter Alterations in Primary Open-Angle Glaucoma: A Neurite Orientation Dispersion and Density Imaging Study.

DTI studies of patients with primary open-angle glaucoma have demonstrated that glaucomatous degeneration is not confined to the retina but involves the entire visual pathway. Due to the lack of direct biologic interpretation of DTI parameters, the structural nature of this degeneration is still poorly understood. We used neurite orientation dispersion and density imaging (NODDI) to characterize the microstructural changes in the pregeniculate optic tracts and the postgeniculate optic radiations of patients with primary open-angle glaucoma, to better understand the mechanisms underlying these changes. T1- and multishell diffusion-weighted scans were obtained from 23 patients with primary open-angle glaucoma and 29 controls. NODDI parametric maps were produced from the diffusion-weighted scans, and probabilistic tractography was used to track the optic tracts and optic radiations. NODDI parameters were computed for the tracked pathways, and the measures were compared between both groups. The retinal nerve fiber layer thickness and visual field loss were assessed for the patients with glaucoma. The optic tracts of the patients with glaucoma showed a higher orientation dispersion index and a lower neurite density index compared with the controls (P < .001 and P = .001, respectively), while their optic radiations showed a higher orientation dispersion index only (P = .003). The pregeniculate visual pathways of the patients with primary open-angle glaucoma exhibited a loss of both axonal coherence and density, while the postgeniculate pathways exhibited a loss of axonal coherence only. Further longitudinal studies are needed to assess the progression of NODDI alterations in the visual pathways of patients with primary open-angle glaucoma across time.

Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer's disease.

Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric-fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO-is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer's disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.

Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio

IntroductionQuantitative microstructural MRI, such as myelin-sensitive magnetisation transfer ratio (MTR) or saturation (MTsat), axon-sensitive water diffusion Neurite Orientation Dispersion and Density Imaging (NODDI), and the aggregate g-ratio, may provide more specific markers of white matter integrity than conventional MRI for early patient stratification in relapsing-remitting multiple sclerosis (RRMS). The aim of this study was to determine the sensitivity of such markers to longitudinal pathological change within cerebral white matter lesions (WML) and normal-appearing white matter (NAWM) in recently diagnosed RRMS. MethodsSeventy-nine people with recently diagnosed RRMS, from the FutureMS longitudinal cohort, were recruited to an extended MRI protocol at baseline and one year later. Twelve healthy volunteers received the same MRI protocol, repeated within two weeks. Ethics approval and written informed consent were obtained.3T MRI included magnetisation transfer, and multi-shell diffusion-weighted imaging. NAWM and whole brain were segmented from 3D T1-weighted MPRAGE, and WML from T2-weighted FLAIR. MTR, MTsat, NODDI isotropic (ISOVF) and intracellular (ICVF) volume fractions, and g-ratio (calculated from MTsat and NODDI data) were measured within WML and NAWM. Brain parenchymal fraction (BPF) was also calculated.Longitudinal change in BPF and microstructural metrics was assessed with paired t-tests (α = 0.05) and linear mixed models, adjusted for confounding factors with False Discovery Rate (FDR) correction for multiple comparisons. Longitudinal changes were compared with test-retest Bland-Altman limits of agreement from healthy control white matter. The influence of longitudinal change on g-ratio was explored through post-hoc analysis in silico by computing g-ratio with realistic simulated MTsat and NODDI values. ResultsIn NAWM, g-ratio and ICVF increased, and MTsat decreased over one year (adjusted mean difference = 0.007, 0.005, and −0.057 respectively, all FDR-corrected p < 0.05). There was no significant change in MTR, ISOVF, or BPF. In WML, MTsat, NODDI ICVF and ISOVF increased over time (adjusted mean difference = 0.083, 0.024 and 0.016, respectively, all FDR-corrected p < 0.05). Group-level longitudinal changes exceeded test-retest limits of agreement for NODDI ISOVF and ICVF in WML only. In silico analysis showed g-ratio may increase due to a decrease in MTsat or ISOVF, or an increase in ICVF. DiscussionG-ratio and MTsat changes in NAWM over one year may indicate subtle myelin loss in early RRMS, which were not apparent with BPF or NAWM MTR. Increases in NAWM and WML NODDI ICVF were not anticipated, and raise the possibility of axonal swelling or morphological change. Increases in WML MTsat may reflect myelin repair. Changes in NODDI ISOVF are more likely to reflect alterations in water content. Competing MTsat and ICVF changes may account for the absence of g-ratio change in WML. Longitudinal changes in microstructural measures are significant at a group level, however detection in individual patients in early RRMS is limited by technique reproducibility. ConclusionMTsat and g-ratio are more sensitive than MTR to early pathological changes in RRMS, but complex dependence of g-ratio on NODDI parameters limit the interpretation of aggregate measures in isolation. Improvements in technique reproducibility and validation of MRI biophysical models across a range of pathological tissue states are needed.

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.

Linking Microstructural Integrity and Motor Cortex Excitability in Multiple Sclerosis.

Motor skills are frequently impaired in multiple sclerosis (MS) patients following grey and white matter damage with cortical excitability abnormalities. We applied advanced diffusion imaging with 3T magnetic resonance tomography for neurite orientation dispersion and density imaging (NODDI), as well as diffusion tensor imaging (DTI) in 50 MS patients and 49 age-matched healthy controls to quantify microstructural integrity of the motor system. To assess excitability, we determined resting motor thresholds using non-invasive transcranial magnetic stimulation. As measures of cognitive-motor performance, we conducted neuropsychological assessments including the Nine-Hole Peg Test, Trail Making Test part A and B (TMT-A and TMT-B) and the Symbol Digit Modalities Test (SDMT). Patients were evaluated clinically including assessments with the Expanded Disability Status Scale. A hierarchical regression model revealed that lower neurite density index (NDI) in primary motor cortex, suggestive for axonal loss in the grey matter, predicted higher motor thresholds, i.e. reduced excitability in MS patients (p = .009, adjusted r² = 0.117). Furthermore, lower NDI was indicative of decreased cognitive-motor performance (p = .007, adjusted r² = .142 for TMT-A; p = .009, adjusted r² = .129 for TMT-B; p = .006, adjusted r² = .142 for SDMT). Motor WM tracts of patients were characterized by overlapping clusters of lowered NDI (p <.05, Cohen’s d = 0.367) and DTI-based fractional anisotropy (FA) (p <.05, Cohen’s d = 0.300), with NDI exclusively detecting a higher amount of abnormally appearing voxels. Further, orientation dispersion index of motor tracts was increased in patients compared to controls, suggesting a decreased fiber coherence (p <.05, Cohen’s d = 0.232). This study establishes a link between microstructural characteristics and excitability of neural tissue, as well as cognitive-motor performance in multiple sclerosis. We further demonstrate that the NODDI parameters neurite density index and orientation dispersion index detect a larger amount of abnormally appearing voxels in patients compared to healthy controls, as opposed to the classical DTI parameter FA. Our work outlines the potential for microstructure imaging using advanced biophysical models to forecast excitability alterations in neuroinflammation.

Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging Assessment of Optic Pathway Function in Patients With Anterior Visual Pathway Compression.

In patients with sellar or parasellar tumors, it is crucial to evaluate visual field impairment in the preoperative stage and to predict visual field improvement after the surgery. The purpose of this study was to investigate the associations of diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) parameters in the optic radiations with preoperative and postoperative visual field impairment. This prospective study included 81 participants with sellar or parasellar tumors. Multishell diffusion imaging and a visual field impairment score (VFIS) were acquired before and after the surgery. The multishell diffusion-weighted imaging was acquired to measure the neurite density and neurite orientation dispersion, as well as the diffusivity. DTI parameters were fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, and radial diffusivity (RD). NODDI provided intracellular volume fraction (Vic), the orientation dispersion index, and isotropic volume fraction (Viso). The associations of DTI and NODDI parameters in the optic radiations with VFIS were investigated, adjusting for age, tumor height, and symptom duration. Among 162 optic radiations, 117 were functionally impaired in the preoperative stage. FA and Vic had significant negative correlations, whereas MD and RD had significant positive correlations with the VFIS (all P < 0.001). In the preoperative stage, lower FA (P = 0.001; odds ratio = 0.750) and Vic (P = 0.003; OR = 0.827) and higher MD (P = 0.007; OR = 1.244) and RD (P < 0.001; OR = 1.361) were significantly associated with the presence of visual field impairment. For the degree of postoperative improvement, preoperative lower Vic (P = 0.034; OR = 0.910) and higher MD (P = 0.037; OR = 1.103) and RD (P = 0.047; OR = 1.090) were significantly associated with more postoperative improvement. DTI and NODDI parameters in the optic radiations were correlated with VFIS and associated with preoperative visual field impairments and postoperative improvement. It may help in predicting visual field improvement after the surgery in patients with sellar or parasellar tumors.

Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis.

Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings.Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics.Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients.Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42).Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.