Hemispheric lateralization of white matter microstructure in children and its potential role in sensory processing dysfunction

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.

Approximately 300-550 children per 100,000 sustain a mild traumatic brain injury (mTBI) each year, of whom ∼25-30% have long-term cognitive problems. Following mTBI, free water (FW) accumulation occurs in white matter (WM) tracts. Diffusion tensor imaging (DTI) can be used to investigate structural integrity following mTBI. Compared with conventional DTI, neurite orientation dispersion and density imaging (NODDI) orientation dispersion index (ODI) and fraction of isolated free water (FISO) metrics may allow a more advanced insight into microstructural damage following pediatric mTBI. In this longitudinal study, we used NODDI to explore whole-brain and tract-specific differences in ODI and FISO in children with persistent symptoms after mTBI (n = 80) and in children displaying clinical recovery (n = 32) at 1 and 2-3 months post-mTBI compared with healthy controls (HCs) (n = 21). Two-way repeated measures analysis of variance (ANOVA) and voxelwise two-sample t tests were conducted to compare whole-brain and tract-specific diffusion across groups. All results were corrected at positive false discovery rate (pFDR) <0.05. We also examined the association between NODDI metrics and clinical outcomes, using logistical regression to investigate the value of NODDI metrics in predicting future recovery from mTBI. Whole-brain ODI was significantly increased in symptomatic participants compared with HCs at both 1 and 2 months post-injury, where the uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF) were particularly implicated. Using region of interest (ROI) analysis in significant WM, bilateral IFOF and UF voxels, symptomatic participants had the highest ODI in all ROIs. ODI was lower in asymptomatic participants, and HCs had the lowest ODI in all ROIs. No changes in FISO were found across groups or over time. WM ODI was moderately correlated with a higher youth-reported post-concussion symptom inventory (PCSI) score. With 87% predictive power, ODI (1 month post-injury) and clinical predictors (age, sex, PCSI score, attention scores) were a more sensitive predictor of recovery at 2-3 months post-injury than fractional anisotropy (FA) and clinical predictors, or clinical predictors alone. FISO could not predict recovery at 2-3 months post-injury. Therefore, we found that ODI was significantly increased in symptomatic children following mTBI compared with HCs at 1 month post-injury, and progressively decreased over time alongside clinical recovery. We found no significant differences in FISO between groups or over time. WM ODI at 1 month was a more sensitive predictor of clinical recovery at 2-3 months post-injury than FA, FISO, or clinical measures alone. Our results show evidence of ongoing microstructural reorganization or neuroinflammation between 1 and 2-3 months post-injury, further supporting delayed return to play in children who remain symptomatic. We recommend future research examining the clinical utility of NODDI following mTBI to predict recovery or persistence of post-concussion symptoms and thereby inform management of mTBI.

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.

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.

Introduction: Chronic traumatic brain injury (cTBI) is associated with long-term cognitive, emotional, and functional impairments. It presents significant diagnostic and therapeutic challenges. Few studies have examined the effectiveness of artificial intelligence (AI) models and advanced diffusion imaging metrics to predict treatment outcomes in cTBI. This study investigated whether hybrid diffusion imaging (HYDI), a technique that employs multiple diffusion analyses, including diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), can identify imaging biomarkers predictive of treatment response. Methods: We prospectively enrolled 41 patients with cTBI who underwent HYDI scans at two timepoints. Neuropsychological outcomes were classified as favorable (clinical improvement) or unfavorable (no improvement or worsening). We assessed 6 diffusion metrics across 20 white matter regions using partial correlation analysis and 5 different AI algorithms. Outcome prediction models were trained and evaluated using k-fold cross-validation to ensure generalizability. Results: Results revealed significantly greater post-treatment changes in NODDI indices compared with DTI indices. In particular, regional changes in axonal density showed strong correlations with improvements in cognitive function. AI models incorporating both DTI and NODDI metrics showed promise for the prediction of the Mayo-Portland Adaptability Inventory, an assessment of postinjury adjustment in daily life and community reintegration. Overall, this study highlights the potential of HYDI-derived diffusion metrics, especially NODDI, as promising biomarkers for tracking microstructural changes and predicting therapeutic outcomes in cTBI. These findings support the use of advanced diffusion imaging and AI to inform treatment strategies and monitor recovery in patients with chronic brain injury.

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.

BackgroundWhite matter (WM) integrity declines with age and is affected by neurodegenerative diseases. Physical activity promotes neuronal health in animals, and is associated with WM microstructure metrics in humans. However, previous diffusion tensor imaging (DTI) studies were limited by small sample sizes and subjective questionnaire‐based physical activity assessments. Moreover, recently introduced neurite orientation dispersion and density imaging (NODDI) may offer complementary insights. Therefore, we examined the association between accelerometer‐based physical activity and DTI and NODDI measures of WM microstructure in a large population‐based cohort.MethodWe analyzed data of 2,455 participants (57.9% women, mean age: 54.7 years) with valid physical activity and MRI measurements from the Rhineland Study, a prospective cohort study in Bonn, Germany. We recorded physical activity using accelerometers and derived metabolic equivalent (MET)‐hours, step counts and time spent performing sedentary, light‐intensity and moderate‐to‐vigorous (MVPA) activities. DTI (fractional anisotropy (FA), mean diffusivity) and NODDI (orientation dispersion index, neurite density index) measures were obtained from 3T MR images using the microstructure diffusion toolbox. The association of physical activity with WM microstructure was assessed using multivariate polynomial regression, while adjusting for age, sex, education, body mass index and smoking status.ResultPhysical activity was associated with several global and tract‐specific DTI and NODDI measures. Effects were strongest at low physical activity quantities and weakened at higher levels. While higher MET‐hours, step counts and light and moderate‐to‐vigorous intensity activities were related to greater global FA, sedentary time was associated with reduced FA. We observed the strongest tract‐specific effects for moderate‐to‐vigorous intensity activities on the corpus callosum and corona radiata (standardized effects: MVPA on FA corpus callosum, ßlin = 0.0009 [95% CI: 0.0001; 0.0018] and ßqu = ‐0.0006 [95% CI: ‐0.0011; ‐0.0001]; MVPA on FA corona radiata, ßlin = 0.0011 [95% CI: 0.0001; 0.0021] and ßqu = ‐0.0009 [95% CI: ‐0.0014; ‐0.0003]). We obtained similar results for NODDI measures.ConclusionOur findings suggest that WM microstructure and particularly tracts connecting motor regions profit from physical activity. Sedentary individuals may benefit more from additional physical activity than already active individuals. NODDI metrics provide valuable complementary insights into the effects of physical activity on WM microstructure.

Sensory processing dysfunction (SPD) is linked to altered white matter (WM) microstructure in school-age children. Sensory over-responsivity (SOR), a form of SPD, affects at least 2.5% of all children and has substantial deleterious impact on learning and mental health. However, SOR has not been well studied using microstructural imaging such as diffusion MRI (dMRI). Since SOR involves hypersensitivity to external stimuli, we test the hypothesis that children with SOR require compensatory neuroplasticity in the form of superior WM microstructural integrity to protect against internalizing behavior, leaving those with impaired WM microstructure vulnerable to somatization and depression. Children ages 8-12years old with neurodevelopmental concerns were assessed for SOR using a comprehensive structured clinical evaluation, the Sensory Processing 3 Dimensions Assessment, and underwent 3 Tesla MRI with multishell multiband dMRI. Tract-based spatial statistics was used to measure diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics from global WM and nineteen selected WM tracts. Correlations of DTI and NODDI measures with measures of somatization and emotional disturbance from the Behavioral Assessment System for Children, 3rd edition (BASC-3), were computed in the SOR group and in matched children with neurodevelopmental concerns but not SOR. Global WM fractional anisotropy (FA) is negatively correlated with somatization and with emotional disturbance in the SOR group but not the non-SOR group. Also observed in children with SOR are positive correlations of radial diffusivity (RD) and free water fraction (FISO) with somatization and, in most cases, emotional disturbance. These effects are significant in boys with SOR, whereas the study is underpowered for girls. The most affected white matter are medial lemniscus and internal capsule sensory tracts, although effects of SOR are observed in many cerebral, cerebellar, and brainstem tracts. White matter microstructure is related to affective behavior in children with SOR.

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.

In Alzheimer's disease (AD), neurodegenerative processes are ongoing for years prior to the time that cortical atrophy can be reliably detected using conventional neuroimaging techniques. Recent advances in diffusion-weighted imaging have provided new techniques to study neural microstructure, which may provide additional information regarding neurodegeneration. In this study, we used neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion model, in order to investigate cortical microstructure along the clinical continuum of mild cognitive impairment (MCI) and AD dementia. Using gray matter-based spatial statistics (GBSS), we demonstrated that neurite density index (NDI) was significantly lower throughout temporal and parietal cortical regions in MCI, while both NDI and orientation dispersion index (ODI) were lower throughout parietal, temporal, and frontal regions in AD dementia. In follow-up ROI analyses comparing microstructure and cortical thickness (derived from T1-weighted MRI) within the same brain regions, differences in NODDI metrics remained, even after controlling for cortical thickness. Moreover, for participants with MCI, gray matter NDI-but not cortical thickness-was lower in temporal, parietal, and posterior cingulate regions. Taken together, our results highlight the utility of NODDI metrics in detecting cortical microstructural degeneration that occurs prior to measurable macrostructural changes and overt clinical dementia.

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

Motivation: Little is known about whether microstructural changes in the brainstem nuclei can differentiate three novel data-driven PD subtypes defined by a retrospective multidiscipline dataset. Goal(s): To compare various diffusion MRI metrics between patients with three novel data-driven PD subtypes. Approach: Neurite orientation dispersion and density Imaging (NODDI) and diffusion tensor imaging (DTI) metrics were reconstructed to compare three subtypes. Results: Only a NODDI index, free water fraction in the right substantia nigra can distinguish the symptomatically severest subtype from other two subtypes (AUC=0.798). Impact: NODDI assists in diagnosing the data-driven PD subtype with the most severe motor/non-motor comorbidities.

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.

This study aimed to investigate the regional changes of brain white matter (WM) in DE patients using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). A total of 25 dry eye patients (PAT) and 25 healthy controls (HC) were recruited. All subjects underwent DTI and NODDI, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), isotropic volume fraction (FISO), intra-cellular volume fraction (FICVF), and orientation dispersion index (ODI) were obtained respectively. Then complete Hospital Anxiety and Depression Scale (HADS), anxiety score (AS) or depression scores (DS) were obtained. Receiver operating characteristic (ROC) curve analysis was used to evaluate the reliability of DTI and NODDI in distinguishing the two groups. DTI revealed that PAT had lower FA in both the left superior longitudinal fasciculus (LSLF) and the corpus callosum (CC), and higher MD in the LSLF, the right posterior limb of the internal capsule and the right posterior thalamic radiation. PAT had significant AD changes in regions including the genu of the CC, the right posterior limb of internal capsule, and the right splenium of the CC. From NODDI, PAT showed increased ODI in the LSLF and increased FISO in the right splenium of the CC. FICVF showed a significant decrease in the LSLF while increased in the left anterior corona radiata and the CC. Furthermore, the average values of MD and FICVF were significantly correlated with DS and AS. Hence the results of this study suggest that there are regional changes in WM in DE patients which may contribute to further understanding of the pathological mechanism of DE.