White matter microstructural abnormalities and default network degeneration are associated with early memory deficit in Alzheimer's disease continuum.
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ABSTRACT:
SUBMITTER: Ji F
PROVIDER: S-EPMC6426923 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionMicrostructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression.MethodsWe developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD.ResultsWe quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions.DiscussionIncorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD.
| S-EPMC7025138 | biostudies-literature
Project description:This study assessed white matter microstructural integrity and behavioral correlates for children with severe congenital hypothyroidism (CH) who were identified and treated early following newborn screening. Eighteen children with severe CH and 21 healthy controls underwent a battery of behavioral measures of hearing, language and communication, along with diffusion MR imaging. Tract-based spatial statistics were performed on standard diffusion parameters of fractional anisotropy and diffusivity metrics. Microscopic diffusion anisotropy mapping based on the Spherical Mean Technique was also used to evaluate biologically specific metrics. Compared with age-matched controls, children with severe CH had poorer hearing and communication skills, albeit generally within normal limits. Children with severe CH had fractional anisotropy that was significantly lower in the cerebellum, bilateral thalami and right temporal lobe, and radial diffusivity that was significantly higher in the cerebellum and bilateral thalami. Microscopic fractional anisotropy and intra-neurite volume fraction were also significantly decreased, and transverse microscopic diffusivity was significantly increased, in the CH group in areas including the cerebellum, thalamus, occipital lobe, and corpus callosum, and in the white matter adjacent to sensorimotor cortex, particularly in the left hemisphere. Significant and widespread correlations were observed between behavioral measures and measures of white matter microstructural integrity in children with CH. The results indicate that children with severe CH who are identified through newborn screening may have significant brain white matter microstructural abnormalities despite early treatment.
| S-EPMC6713841 | biostudies-other
Project description:To investigate the association of soccer heading with subclinical evidence of traumatic brain injury.With institutional review board approval and compliance with HIPAA guidelines, 37 amateur soccer players (mean age, 30.9 years; 78% [29] men, 22% [eight] women) gave written informed consent and completed a questionnaire to quantify heading in the prior 12 months and lifetime concussions. Diffusion-tensor magnetic resonance (MR) imaging at 3.0 T was performed (32 directions; b value, 800 sec/mm(2); 2 × 2 × 2-mm voxels). Cognitive function was measured by using a computerized battery of tests. Voxelwise linear regression (heading vs fractional anisotropy [FA]) was applied to identify significant regional associations. FA at each location and cognition were tested for a nonlinear relationship to heading by using an inverse logit model that incorporated demographic covariates and history of concussion.Participants had headed 32-5400 times (median, 432 times) over the previous year. Heading was associated with lower FA at three locations in temporo-occipital white matter with a threshold that varied according to location (885-1550 headings per year) (P < .00001). Lower levels of FA were also associated with poorer memory scores (P < .00001), with a threshold of 1800 headings per year. Lifetime concussion history and demographic features were not significantly associated with either FA or cognitive performance.Heading is associated with abnormal white matter microstructure and with poorer neurocognitive performance. This relationship is not explained by a history of concussion.
| S-EPMC3750422 | biostudies-other
Project description:Recent functional connectivity magnetic resonance imaging and diffusion tensor imaging (DTI) studies have suggested atypical functional connectivity and reduced integrity of long-distance white matter fibers in autism spectrum disorder (ASD). However, evidence for short-distance white matter fibers is still limited, despite some speculation of potential sparing of local connectivity in ASD. Short-distance U-fibers are an important component of neural networks and are thought to play a crucial role in cognitive function. In the present study, we applied tract-based spatial statistics to derive short- and long-distance white matter tracts in frontal, parietal, and temporal lobes in both hemispheres. DTI data were acquired from 26 children with ASD and 24 typically developing (TD) children. A mean fractional anisotropy (FA) image was created and thinned to represent centers of all common tracts. Evidence of compromised short-distance tracts for the ASD group was found in frontal lobe (reduced FA, increased mean diffusivity [MD] and radial diffusivity) as well as in temporal and parietal lobes (increased MD and radial diffusivity). Significant positive correlations between age and FA and negative correlations between age and MD and radial diffusivity were also found for short-distance tracts in each lobe in the TD, but not the ASD group. These results suggest white matter compromise in short-distance tracts in ASD. Absence of typical age-related correlations with DTI indices may reflect altered maturation of short-distance tracts in ASD. Our results are inconsistent with a notion of selective sparing of short-distance connectivity in ASD.
| S-EPMC3482113 | biostudies-literature
Project description:BackgroundThis study aimed to investigate feasible gray matter microstructural biomarkers with high sensitivity for early Alzheimer's disease (AD) detection. We propose a diffusion tensor imaging (DTI) measure, "radiality", as an early AD biomarker. It is the dot product of the normal vector of the cortical surface and primary diffusion direction, which reflects the fiber orientation within the cortical column.MethodsWe analyzed neuroimages from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including images from 78 cognitively normal (CN), 50 early mild cognitive impairment (EMCI), 34 late mild cognitive impairment (LMCI), and 39 AD patients. We then evaluated the cortical thickness (CTh), mean diffusivity (MD), which are conventional AD magnetic resonance imaging (MRI) biomarkers, and the amount of accumulated amyloid and tau using positron emission tomography (PET). Radiality was projected on the gray matter surface to compare and validate the changes with different stages alongside other neuroimage biomarkers.ResultsThe results revealed decreased radiality primarily in the entorhinal, insula, frontal, and temporal cortex with further progression of disease. In particular, radiality could delineate the difference between the CN and EMCI groups, while the other biomarkers could not. We examined the relationship between radiality and other biomarkers to validate its pathological evidence in AD. Overall, radiality showed a high association with conventional biomarkers. Additional ROI analysis revealed the dynamics of AD-related changes as stages onward.ConclusionRadiality in cortical gray matter showed AD-specific changes and relevance with other conventional AD biomarkers with high sensitivity. Moreover, radiality could identify the group differences seen in EMCI, representative of changes in early AD, which supports its superiority in early diagnosis compared to that possible with conventional biomarkers. We provide evidence of structural changes with cognitive impairment and suggest radiality as a sensitive biomarker for identifying early AD.
| S-EPMC7532608 | biostudies-literature
Project description:OBJECTIVE:Working memory impairments serve as prognostic factors for patients with schizophrenia. Working memory deficits are mainly associated with gray matter (GM) thickness and volume. We investigated the association between GM diffusivity and working memory in controls and individuals with schizophrenia. METHODS:T1 and diffusion tensor images of the brain, working memory task (letter number sequencing) scores, and the demographic data of 90 individuals with schizophrenia and 97 controls were collected from the SchizConnect database. T1 images were parcellated into the 68 GM Regions of Interest (ROI). Axial Diffusivity (AD), Fractional Anisotropy (FA), Radial Diffusivity (RD), and Trace (TR) were calculated for each of the ROIs. RESULTS:Compared to the controls, schizophrenia group showed significantly increased AD, RD, and TR in specific regions on the frontal, temporal, and anterior cingulate area. Moreover, working memory was negatively correlated with AD, RD, and TR in the lateral orbitofrontal, superior temporal, inferior temporal, and rostral anterior cingulate area on left hemisphere in the individuals with schizophrenia. CONCLUSION:These results demonstrated GM microstructural abnormalities in the frontal, temporal, and anterior cingulate regions of individuals with schizophrenia. Furthermore, these regional GM microstructural abnormalities suggest a neuropathological basis for the working memory deficits observed clinically in individuals with schizophrenia.
| S-EPMC6444097 | biostudies-literature
Project description:Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer's disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.
| S-EPMC3669206 | biostudies-literature
Project description:Background and purposePrevious reports from the Framingham Heart Study have identified cross-sectional associations of arterial stiffness, as reflected by carotid-femoral pulse wave velocity (CFPWV) and systolic blood pressure with vascular brain injury. The purpose of this study is to examine free water (FW), fractional anisotropy (FA), and white matter hyperintensities (WMH) in relation to arterial stiffness among subjects of the Framingham Offspring and Third-Generation cohorts.MethodsIn 2422 participants aged 51.3±11.6 years, FA, FW, and WMH were related to CFPWV using voxel-based linear and generalized linear regressions, adjusting for relevant covariables. Mean FW, mean FA, and WMH burden (log transformed) were computed within white matter (WM) region and related to systolic blood pressure and CFPWV using multiple mediation analyses.ResultsCFPWV was found to be associated with higher FW, lower FA, and higher WMH incidence in WM areas covering, respectively, 356.1, 211.8, and 10.9 mL of the WM mask. Mediation analyses revealed that the effect of systolic blood pressure on FW was mediated by CFPWV (direct and indirect effects: a=0.040; P<0.001, and a'=0.020; P>0.05). Moreover, the effect of CFPWV on FA was mediated by FW (direct and indirect effects: b=-0.092; P<0.001, and b'=0.012; P>0.05), whose effect on WMH was, in turn, mediated by FA (direct and indirect effects: c=0.246; P<0.001, and c'=0.116; P>0.05).ConclusionsFrom these data, we propose a biomechanical hypothesis designed for future research experiments to explain how hemodynamic alteration may lead to WM injury by impacting cerebral water content and more subtly WM integrity, to finally lead to WMH development.
| S-EPMC5502744 | biostudies-literature
Project description:Neurite orientation dispersion and density imaging (NODDI) enables more specific characterization of tissue microstructure by estimating neurite density (NDI) and orientation dispersion (ODI), two key contributors to fractional anisotropy (FA). The present work compared NODDI- with diffusion tensor imaging (DTI)-derived indices for investigating white matter abnormalities in a clinical sample. We assessed the added value of NODDI parameters over FA, by contrasting group differences identified by both models. Diffusion-weighted images with multiple shells were acquired in a group of 8 healthy controls and 8 patients with an inherited metabolic disease. Both standard DTI and NODDI analyses were performed. Tract based spatial statistics (TBSS) was used for group inferences, after which overlap and unique contributions across different parameters were evaluated. Results showed that group differences in NDI and ODI were complementary, and together could explain much of the FA results. Further, compared to FA analysis, NDI and ODI gave a pattern of results that was more regionally specific and were able to capture additional discriminative voxels that FA failed to identify. Finally, ODI from single-shell NODDI analysis, but not NDI, was found to reproduce the group differences from the multi-shell analysis. To conclude, by using a clinically feasible acquisition and analysis protocol, we demonstrated that NODDI is of added value to standard DTI, by revealing specific microstructural substrates to white matter changes detected with FA. As the (simpler) DTI model was more sensitive in identifying group differences, NODDI is recommended to be used complementary to DTI, thereby adding greater specificity regarding microstructural underpinnings of the differences. The finding that ODI abnormalities can be identified reliably using single-shell data may allow the retrospective analysis of standard DTI with NODDI.
| S-EPMC5176300 | biostudies-literature
Project description:Background and purposeIncreasing DTI studies have demonstrated that white matter microstructural abnormalities play an important role in type 2 diabetes mellitus-related cognitive impairment. In this study, the diffusional kurtosis imaging method was used to investigate WM microstructural alterations in patients with type 2 diabetes mellitus and to detect associations between diffusional kurtosis imaging metrics and clinical/cognitive measurements.Materials and methodsDiffusional kurtosis imaging and cognitive assessments were performed on 58 patients with type 2 diabetes mellitus and 58 controls. Voxel-based intergroup comparisons of diffusional kurtosis imaging metrics were conducted, and ROI-based intergroup comparisons were further performed. Correlations between the diffusional kurtosis imaging metrics and cognitive/clinical measurements were assessed after controlling for age, sex, and education in both patients and controls.ResultsAltered diffusion metrics were observed in the corpus callosum, the bilateral frontal WM, the right superior temporal WM, the left external capsule, and the pons in patients with type 2 diabetes mellitus compared with controls. The splenium of the corpus callosum and the pons had abnormal kurtosis metrics in patients with type 2 diabetes mellitus. Additionally, altered diffusion metrics in the right prefrontal WM were significantly correlated with disease duration and attention task performance in patients with type 2 diabetes mellitus.ConclusionsWith both conventional diffusion and additional kurtosis metrics, diffusional kurtosis imaging can provide additional information on WM microstructural abnormalities in patients with type 2 diabetes mellitus. Our results indicate that WM microstructural abnormalities occur before cognitive decline and may be used as neuroimaging markers for predicting the early cognitive impairment in patients with type 2 diabetes mellitus.
| S-EPMC7959974 | biostudies-literature