BACKGROUND: Gait impairment is closely related to quality of life in patients with Parkinson's disease (PD). This study aimed to explore alterations in brain microstructure in PD patients and healthy controls (HCs) and to identify the correlation of gait impairment in the ON and OFF states of patients with PD, respectively. METHODS: We enrolled 24 PD patients and 29 HCs from the Movement Disorders Program at Beijing Friendship Hospital Capital Medical University between 2019 and 2020. We acquired magnetic resonance imaging (MRI) scans and processed the diffusion kurtosis imaging (DKI) images. Preprocessing of diffusion-weighted data was performed with Mrtrix3 software, using a directional distribution function to track participants' main white matter fiber bundles. Demographic and clinical characteristics were recorded. Quantitative gait and clinical scales were used to assess the status of medication ON and OFF in PD patients. RESULTS: The axial kurtosis (AK), mean kurtosis (MK), and radial kurtosis (RK) of five specific white matter fiber tracts, the bilateral corticospinal tract, left superior longitudinal fasciculus, left anterior thalamic radiation, forceps minor, and forceps major were significantly higher in PD patients compared to HCs. Additionally, the MK values were negatively correlated with Timed Up and Go Test (TUG) scores in both the ON and OFF in PD patients. Within the PD group, higher AK, MK, and RK values, whether the patients were ON or OFF, were associated with better gait performance (i.e., higher velocity and stride length). CONCLUSIONS PD exhibits characteristic regional patterns of white matter microstructural degradation. Correlations between objective gait parameters and DKI values suggest that dopamine-responsive gait function depends on preserved white matter microstructure. DKI-based Tract-Based Spatial Statistics (TBSS) analysis may serve as a tool for evaluating PD-related motor impairments (e.g., gait impairment) and could yield potential neuroimaging biomarkers.
Humans ; Parkinson Disease/diagnostic imaging* ; White Matter/physiopathology* ; Male ; Female ; Middle Aged ; Aged ; Gait/physiology* ; Diffusion Magnetic Resonance Imaging/methods* ; Diffusion Tensor Imaging/methods*

OBJECTIVES: To investigate the risk factors for white matter damage (WMD) in preterm infants with necrotizing enterocolitis (NEC). METHODS: A retrospective analysis was conducted on the clinical data of 249 preterm infants with NEC admitted to Children's Hospital of Fudan University between January 2021 and December 2023. Based on brain magnetic resonance imaging (MRI) white matter scores, the infants were categorized into a WMD group (≥7 points) and a non-injury group (<7 points). A multivariable logistic regression analysis was performed to identify risk factors for WMD. RESULTS: Compared with the non-injury group, the WMD group had significantly higher rates of Gram-negative bacterial infection (43.1% vs 28.2%), surgical treatment (47.2% vs 23.2%), and moderate-to-severe abnormalities on video electroencephalography (VEEG) (51.4% vs 11.9%) (all P<0.05). The multivariable logistic regression analysis showed that surgical treatment (OR=1.822, 95%CI: 1.199-2.777), longer hospital stay (OR=1.041, 95%CI: 1.004-1.080), and moderate-to-severe VEEG abnormalities (OR=7.045, 95%CI: 3.349-14.855) were independent risk factors for WMD (all P<0.05). CONCLUSIONS Surgical treatment, prolonged hospitalization, and moderate-to-severe VEEG abnormalities are independent risk factors for WMD in preterm infants with NEC, providing a basis for early clinical identification and intervention to improve neurological outcomes.
Humans ; Enterocolitis, Necrotizing/complications* ; Infant, Newborn ; Male ; Female ; Risk Factors ; Retrospective Studies ; Infant, Premature ; White Matter/diagnostic imaging* ; Logistic Models ; Magnetic Resonance Imaging

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

Objective To investigate the changes in plasma amyloid-β (Aβ) level and their relationship with white matter microstructure in the patients with amnesic mild cognitive impairment(aMCI) and vascular mild cognitive impairment (vMCI).Methods A total of 36 aMCI patients,20 vMCI patients,and 34 sex and age matched healthy controls (HC) in the outpatient and inpatient departments of the First Affiliated Hospital of Anhui Medical University were enrolled in this study.Neuropsychological scales,including the Mini-Mental State Examination,the Montreal Cognitive Assessment,and the Activity of Daily Living Scale,were employed to assess the participants.Plasma samples of all the participants were collected for the measurement of Aβ42 and Aβ40 levels.All the participants underwent magnetic resonance scanning to obtain diffusion tensor imaging (DTI) data.The DTI indexes of 48 white matter regions of each individual were measured (based on the ICBM-DTI-81 white-matter labels atlas developed by Johns Hopkins University),including fractional anisotropy (FA) and mean diffusivity (MD).The cognitive function,plasma Aβ42,Aβ40,and Aβ42/40 levels,and DTI index were compared among the three groups.The correlations between the plasma Aβ42/40 levels and DTI index of aMCI and vMCI patients were analyzed.Results The Mini-Mental State Examination and the Montreal Cognitive Assessment scores of aMCI and vMCI groups were lower than those of the HC group (all P<0.001).There was no significant difference in the Activity of Daily Living Scale score among the three groups (P=0.654).The plasma Aβ42 level showed no significant difference among the three groups (P=0.227).The plasma Aβ40 level in the vMCI group was higher than that in the HC group (P=0.014),while it showed no significant difference between aMCI and HC groups (P=1.000).The plasma Aβ42/40 levels in aMCI and vMCI groups showed no significant differences from that in the HC group (P=1.000,P=0.105),while the plasma Aβ42/40 level was lower in the vMCI group than in the aMCI group (P=0.016).The FA value of the left anterior limb of internal capsule in the vMCI group was lower than those in HC and aMCI groups (all P=0.001).The MD values of the left superior corona radiata,left external capsule,left cingulum (cingulate gyrus),and left superior fronto-occipital fasciculus in the vMCI group were higher than those in HC (P=0.024,P=0.001,P=0.003,P<0.001) and aMCI (P=0.015,P=0.004,P=0.019,P=0.001) groups,while the MD values of the right posterior limb of internal capsule (P=0.005,P=0.001) and left cingulum (hippocampus) (P=0.017,P=0.031) in the aMCI and vMCI groups were higher than those in the HC group.In the aMCI group,plasma Aβ42/40 level was positively correlated with FA of left posterior limb of internal capsule (r=0.403,P=0.015) and negatively correlated with MD of the right fonix (r=-0.395,P=0.017).In the vMCI group,plasma Aβ42/40 level was positively correlated with FA of the right superior cerebellar peduncle and the right anterior limb of internal capsule (r=0.575,P=0.008;r=0.639,P=0.002),while it was negatively correlated with MD of the right superior cerebellar peduncle and the right anterior limb of internal capsule (r=-0.558,P=0.011;r=-0.626,P=0.003).Conclusions Plasma Aβ levels vary differently in the patients with aMCI and vMCI.The white matter regions of impaired microstructural integrity differ in the patients with different dementia types in the early stage.The plasma Aβ levels in the patients with aMCI and vMCI are associated with the structural integrity of white matter,and there is regional specificity between them.
Humans ; Diffusion Tensor Imaging ; White Matter/diagnostic imaging* ; Cognitive Dysfunction ; Outpatients ; Cognition ; Amyloid beta-Peptides

Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
Humans ; White Matter/diagnostic imaging* ; Diffusion Tensor Imaging/methods* ; Alzheimer Disease/complications* ; Reproducibility of Results ; Cognition ; Cognitive Dysfunction/complications* ; Brain/diagnostic imaging*

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.
Brain ; White Matter/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging/methods* ; Algorithms ; Databases, Factual ; Image Processing, Computer-Assisted/methods*

Alzheimer Disease ; Brain ; Humans ; White Matter/diagnostic imaging*

Brain/diagnostic imaging* ; Humans ; Magnetic Resonance Imaging ; Neuroimaging ; White Matter/diagnostic imaging*

Diffusion tensor imaging technology can provide information on the white matter of the brain, which can be used to explore changes in brain tissue structure, but it lacks the specific description of the microstructure information of brain tissue. The neurite orientation dispersion and density imaging make up for its shortcomings. But in order to accurately estimate the brain microstructure, a large number of diffusion gradients are needed, and the calculation is complex and time-consuming through maximum likelihood fitting. Therefore, this paper proposes a kind of microstructure parameters estimation method based on the proximal gradient network, which further avoids the classic fitting paradigm. The method can accurately estimate the parameters while reducing the number of diffusion gradients, and achieve the purpose of imaging quality better than the neurite orientation dispersion and density imaging model and accelerated microstructure imaging via convex optimization model.
Brain/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Neurites ; White Matter

As the two essential components, the white matter and gray matter compose the central nervous system of the brain. Widely known that axons of neurons mainly form the white matter, and these formed nerve fibers are responsible for transmitting information among various brain regions to achieve the coordinated operation of the entire brain. Early research on the white matter could only be done by dissecting living animals or human cadavers, until Basser et al. proposed diffusion tensor imaging (DTI) technology in 1994, which could detect the diffusion characteristics of water in the brain in vivo noninvasively. Accordingly, this technology could be applied to investigate the diffusion movement of water in white matter to obtain the information of direction and micro-anatomy of white matter fiber bundles. With the advancement on the display and analysis of the anatomical structure of white matter fiber bundles, the exploration of microscopic pathological changes, and the assistance of clinical diagnosis and neurophysiological research, DTI technology has become one of the most popular topics in brain science research. Chronic pain refers to pain lasting more than three months, which not only seriously affects the patient's physical and social functions, but also dramatically reduces the quality of life. It was reported that long-term pain stimulation might cause pathological remodeling of the central nervous system, and abnormalities in white matter were found in imaging examinations of patients with chronic pain. This review introduces the quantitative analysis methods of white matter fiber bundle microstructure based on DTI and its application in chronic pain, and further discusses the application value of DTI technology on clinical research of chronic pain.
Animals ; Brain ; Chronic Pain/diagnostic imaging* ; Diffusion Tensor Imaging ; Humans ; Quality of Life ; White Matter/diagnostic imaging*