Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer's disease

OBJECTIVE

To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.

METHODS

A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.

RESULTS

Based on multivariate logistic regression, clinical dementia rating and Alzheimer's disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).

CONCLUSION

The present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.

Association of glycosylated hemoglobin concentrations with structural and functional brain changes in the normoglycemic population: A systematic review

Optimal glucose control is crucial for maintaining brain health and preventing metabolic and cognitive disorders in the general population. Glycosylated hemoglobin (HbA1c) serves as a key marker for assessing glucose intolerance and its impact on brain structure and function in healthy individuals. However, existing literature presents conflicting findings, necessitating a systematic review to consolidate current knowledge in this domain. This systematic review examines 26 English-language studies involving participants aged 15 years and above, investigating the relationship between HbA1c levels and brain health. Studies focusing on normal/general populations and utilizing magnetic resonance imaging (MRI) as the imaging modality were included. Exclusion criteria encompassed review articles, abstracts, letters, animal studies, and research involving neuropsychiatric or metabolic diseases. Data were gathered from PubMed, Scopus, and Web of Science databases up to November 2023. Analysis reveals significant associations between HbA1c levels and various brain metrics, including volume, cortical thickness, fractional anisotropy, mean diffusivity, activity, and connectivity. However, findings exhibit inconsistency, likely attributed to disparities in sample characteristics and study sizes. Notably, hippocampal volume, white matter hyperintensity, and ventral attention network connectivity emerge as frequently affected structures and functions, mirroring trends observed in diabetic populations. Despite inconclusive evidence, glucose intolerance appears to exert considerable influence on select brain structures and functions in individuals without diagnosed metabolic disorders. Understanding these associations is critical for mitigating the risk of cognitive decline and dementia in healthy populations. Future investigations should aim to elucidate the intricate relationship between HbA1c concentrations and brain health parameters in normoglycemic individuals.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer

The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.

Characterization of diffusion magnetic resonance imaging revealing relationships between white matter disconnection and behavioral disturbances in mild cognitive impairment: a systematic review

White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer's disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.

The Predictive Potential of Altered Voxel-Based Morphometry in Severely Obese Patients With Meibomian Gland Dysfunction

Objective

To investigate voxel-based morphometry (VBM) by using magnetic resonance imaging (MRI) in meibomian gland dysfunction patients with severe obesity (PATs) and to explore the application of VBM in the early diagnosis, prevention of cognitive impairment and targeted treatment of this disease.

Methods

Sixteen PATs and 12 healthy controls (HCs) were enrolled and underwent MRI. Whole-head images were analyzed using VBM and data were compared between groups using an independent samples t-test. Receiver operating characteristic (ROC) curves were utilized to assess the diagnostic value of this approach. Mini-mental state examination (MMSE) scores were used to assess cognitive impairment and were analyzed using an independent samples t-test.

Results

Compared with HCs, the VBM values in PATs were reduced in the left cerebellum and right thalamus but increased in the right brainstem, right precuneus and right paracentral lobule. The results of ROC curve analysis indicated that VBM may be useful in meibomian gland disease diagnosis. Comparison of MMSE scores between groups showed mild cognitive impairment in PATs.

Conclusion

PATs showed altered VBM values in some brain areas. These findings may provide information about the pathophysiology of meibomian gland dysfunction and may help to explain the underlying mechanisms of clinical manifestations in PATs, such as cognitive impairment. Abnormal VBM values in these brain areas may serve as predictive factors for development of meibomian gland disease in severely obese people and as indicators for individualized treatment.

Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network

Background

Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer’s disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance.

Methods

Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier.

Results

(1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI.

Conclusion

Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD.

Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

INTRODUCTION

Type 2 diabetes mellitus (T2DM) has been found to be associated with abnormalities of the central and peripheral vascular nervous system, which were considered to be involved in the development of cognitive impairments and erectile dysfunction (ED). In addition, altered brain function and structure were identified in patients with ED, especially psychological ED (pED). However, the similarities and the differences of the central neural mechanisms underlying pED and T2DM with ED (DM-ED) remained unclear.

METHODS

Diffusion tensor imaging data were acquired from 30 T2DM, 32 ED, and 31 DM-ED patients and 47 healthy controls (HCs). Then, whole-brain structural networks were constructed, which were mapped by connectivity matrices (90 × 90) representing the white matter between 90 brain regions parcellated by the anatomical automatic labeling template. Finally, the method of network-based statistic (NBS) was applied to assess the group differences of the structural connectivity.

RESULTS

Our NBS analysis demonstrated three subnetworks with reduced structural connectivity in DM, pED, and DM-ED patients when compared to HCs, which were predominantly located in the prefrontal and subcortical areas. Compared with DM patients, DM-ED patients had an impaired subnetwork with increased structural connectivity, which were primarily located in the parietal regions. Compared with pED patients, an altered subnetwork with increased structural connectivity was identified in DM-ED patients, which were mainly located in the prefrontal and cingulate areas.

CONCLUSION

These findings highlighted that the reduced structural connections in the prefrontal and subcortical areas were similar mechanisms to those associated with pED and DM-ED. However, different connectivity patterns were found between pED and DM-ED, and the increased connectivity in the frontal-parietal network might be due to the compensation mechanisms that were devoted to improving erectile function.