Mini Mental State

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities.

Brain connectivity alterations associated with mental disorders have been widely reported in both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information from the vast amount of information afforded by brain networks remains a great challenge. Capturing network topology, graph convolutional networks (GCNs) have demonstrated to be superior in learning network representations tailored for identifying specific brain disorders. Existing graph construction techniques generally rely on a specific brain parcellation to define regions-of-interest (ROIs) to construct networks, often limiting the analysis into a single spatial scale. In addition, most methods focus on the pairwise relationships between the ROIs and ignore high-order associations between subjects. In this letter, we propose a mutual multi-scale triplet graph convolutional network (MMTGCN) to analyze functional and structural connectivity for brain disorder diagnosis. We first employ several templates with different scales of ROI parcellation to construct coarse-to-fine brain connectivity networks for each subject. Then, a triplet GCN (TGCN) module is developed to learn functional/structural representations of brain connectivity networks at each scale, with the triplet relationship among subjects explicitly incorporated into the learning process. Finally, we propose a template mutual learning strategy to train different scale TGCNs collaboratively for disease classification. Experimental results on 1,160 subjects from three datasets with fMRI or dMRI data demonstrate that our MMTGCN outperforms several state-of-the-art methods in identifying three types of brain disorders.

A Mutual Multi-Scale Triplet Graph Convolutional Network for Classification of Brain Disorders Using Functional or Structural Connectivity

Introduction Microstructural alterations as assessed by diffusion tensor imaging (DTI) are key findings in both Alzheimer's disease (AD) and small vessel disease (SVD). We determined the contribution of each of these conditions to diffusion alterations. Methods We studied six samples (N = 365 participants) covering the spectrum of AD and SVD, including genetically defined samples. We calculated diffusion measures from DTI and free water imaging. Simple linear, multivariable random forest, and voxel-based regressions were used to evaluate associations between AD biomarkers (amyloid beta, tau), SVD imaging markers, and diffusion measures. Results SVD markers were strongly associated with diffusion measures and showed a higher contribution than AD biomarkers in multivariable analysis across all memory clinic samples. Voxel-wise analyses between tau and diffusion measures were not significant. Discussion In memory clinic patients, the effect of SVD on diffusion alterations largely exceeds the effect of AD, supporting the value of diffusion measures as markers of SVD.

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Small vessel disease more than Alzheimer's disease determines diffusion MRI alterations in memory clinic patients

Background Alzheimer's disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart. Method We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM. Results All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks. Conclusion Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

/pdf/alzheimer-s-disease-projection-from-normal-to-mild-dementia-371pbvuvlt.pdf

Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Our purpose is to evaluate the microstructural and metabolism property in the white matter that later become white matter hyperintensity (WMH), and of WMH that later disappeared. Forty subjects with two-year follow-up were included. Each subject had 3DT1, T2FLAIR, DTI and FDG-PET scans. White matter was classified into: constant WMH, growing WMH, shrinking WMH and normal appearing white matter (NAWM). The average DTI (FA and MD) and FDG-PET (standardized FDG-PET rSUV) of each of the above-mentioned region were extracted and compared. At baseline, the growing WMH had lower FA and FDG-PET rSUV than NAWM, but had higher FA than the constant WMH. Longitudinally, in NAWM, there was a more rapid decline in metabolism compared to WMH areas, while in the growing WMH, a progression in diffusion was found. Finally, we discovered that the shrinking WMH had a similar microstructural and metabolism property and progression to the constant WMH. Our results suggest there are dynamic changes in microstructural and metabolism in WMH. The metabolic change was mainly found in NAWM, while the microstructural change was mainly found in WMH region. Besides, the reduced volume in WMH, to a larger extent, is irrelevant to the microstructural or metabolism recovery.

https://journals.sagepub.com/doi/pdf/10.1177/0271678X18761438

Microstructural and metabolic changes in the longitudinal progression of white matter hyperintensities.

Structural and metabolic connectivity are advanced features that facilitate the diagnosis of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Connectivity from a single imaging modality, however, did not show evident discriminative value in predicting MCI-to-AD conversion, possibly because the inter-modal information was not considered when quantifying the relationship between brain regions. Here we introduce a novel approach that extracts connectivity based on both structural and metabolic information to improve AD early diagnosis. Principal component analysis was performed on each imaging modality to extract the key discriminative patterns of each brain region in an independent auxiliary domain composed of AD and normal control (NC) subjects, which were then used to project the two subtypes of MCI to the low-dimensional space. The connectivity between each target brain region and all other regions was quantified via a multi-task regression model using the projected data. The prediction performance was evaluated in 75 stable MCI (sMCI) patients and 51 progressive MCI (pMCI) patients who converted to AD within 3 years. We achieved 79.37% accuracy, with 74.51% sensitivity and 82.67% specificity, in predicting MCI-to-AD progression, superior to other existing algorithms using either structural and metabolic connectivities alone or a combination thereof. Our results demonstrate the effectiveness of multi-modal connectivity, serving as robust biomarker for early AD diagnosis.

/pdf/brain-connectivity-based-prediction-of-alzheimer-s-disease-1sqflotji9.pdf

Brain Connectivity Based Prediction of Alzheimer’s Disease in Patients With Mild Cognitive Impairment Based on Multi-Modal Images

BACKGROUND The underlying white matter impairment in patients with early and late-onset Alzheimer's disease (EOAD and LOAD) is still unclear, and this might due to the complex AD pathology. METHODS We included 31 EOAD, 45 LOAD, and 64 younger, 46 elder controls in our study to undergo MRI examinations. Fiber density (FD) and fiber bundle cross-section (FC) were measured using fixel-based analysis based on diffusion weighted images. On whole brain and tract-based level, we compared these parameters among different groups (p<0.05, FWE corrected). Moreover, we verified our results in another independent dataset using the same analyses. RESULTS Compared to young healthy controls, EOAD had significantly lower FD in the splenium of corpus callosum, limbic tracts, cingulum bundles, and posterior thalamic radiation, and higher FC in the splenium of corpus callosum, dorsal cingulum and posterior thalamic radiation. On the other hand, LOAD had lower FD and FC as well. Importantly, a similar pattern was found in the independent validation dataset. Among all groups, both the FD and FC were associated with cognitive function. Furthermore, FD of fornix column and body, and FC of ventral cingulum were associated with composite amyloid and tau level (r=-0.34 and -0.53, p<0.001) respectively. CONCLUSIONS EOAD and LOAD were characterized by distinct white matter impairment patterns, which may be attributable to their different neuropathologies.

/pdf/distinct-fiber-specific-white-matter-reductions-pattern-in-2ayfn2g6ua.pdf

Alzheimer’s Disease Neuroimaging Initiative

Papers

Microstructural and metabolic changes in the longitudinal progression of white matter hyperintensities.

Brain Connectivity Based Prediction of Alzheimer’s Disease in Patients With Mild Cognitive Impairment Based on Multi-Modal Images

Distinct fiber-specific white matter reductions pattern in early- and late-onset Alzheimer's disease.