Brain atrophy emerges as a distinctive hallmark in various neurodegenerative diseases, demonstrating a progressive trajectory across diverse disease stages and concurrently manifesting in tandem with a discernible decline in cognitive abilities. Understanding the individualized patterns of brain atrophy is critical for precision medicine and the prognosis of neurodegenerative diseases. However, it is difficult to obtain longitudinal data to compare changes before and after the onset of diseases. In this study, we present a deep disentangled generative model (DDGM) for capturing individualized atrophy patterns via disentangling patient images into "realistic" healthy counterfactual images and abnormal residual maps. The proposed DDGM consists of four modules: normal MRI synthesis, residual map synthesis, input reconstruction module, and mutual information neural estimator (MINE). The MINE and adversarial learning strategy together ensure independence between disease-related features and features shared by both disease and healthy controls. In addition, we proposed a comprehensive evaluation of the effectiveness of synthetic pseudo-healthy images, focusing on both their healthiness and subject identity. The results indicated that the proposed DDGM effectively preserves these characteristics in the synthesized pseudo-healthy images, outperforming existing methods. The proposed method demonstrates robust generalization capabilities across two independent datasets from different races and sites. Analysis of the disease residual/saliency maps revealed specific atrophy patterns associated with Alzheimer's disease (AD), particularly in the hippocampus and amygdala regions. These accurate individualized atrophy patterns enhance the performance of AD classification tasks, resulting in an improvement in classification accuracy to 92.50 $\pm$ 2.70%.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.

Numerous studies have shown that accurate analysis of neurological disorders contributes to the early diagnosis of brain disorders and provides a window to diagnose psychiatric disorders due to brain atrophy. The emergence of geometric deep learning approaches provides a new way to characterize geometric variations on brain networks. However, brain network data suffer from high heterogeneity and noise. Consequently, geometric deep learning methods struggle to identify discriminative and clinically meaningful representations from complex brain networks, resulting in poor diagnostic accuracy. Hence, the primary challenge in the diagnosis of brain diseases is to enhance the identification of discriminative features. To this end, this paper presents a dual-attention deep manifold harmonic discrimination (DA-DMHD) method for early diagnosis of neurodegenerative diseases. Here, a low-dimensional manifold projection is first learned to comprehensively exploit the geometric features of the brain network. Further, attention blocks with discrimination are proposed to learn a representation, which facilitates learning of group-dependent discriminant matrices to guide downstream analysis of group-specific references. Our proposed DA-DMHD model is evaluated on two independent datasets, ADNI and ADHD-200. Experimental results demonstrate that the model can tackle the hard-to-capture challenge of heterogeneous brain network topological differences and obtain excellent classifying performance in both accuracy and robustness compared with several existing state-of-the-art methods.

BACKGROUND: The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity. RESULTS: In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = -0.38, P < 0.001; three classes classification: R = -0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls. CONCLUSIONS: The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD.

Intuitively, higher intelligence might be assumed to correspond to more efficient information transfer in the brain, but no direct evidence has been reported from the perspective of brain networks. In this study, we performed extensive analyses to test the hypothesis that individual differences in intelligence are associated with brain structural organization, and in particular that higher scores on intelligence tests are related to greater global efficiency of the brain anatomical network. We constructed binary and weighted brain anatomical networks in each of 79 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Based on their IQ test scores, all subjects were divided into general and high intelligence groups and significantly higher global efficiencies were found in the networks of the latter group. Moreover, we showed significant correlations between IQ scores and network properties across all subjects while controlling for age and gender. Specifically, higher intelligence scores corresponded to a shorter characteristic path length and a higher global efficiency of the networks, indicating a more efficient parallel information transfer in the brain. The results were consistently observed not only in the binary but also in the weighted networks, which together provide convergent evidence for our hypothesis. Our findings suggest that the efficiency of brain structural organization may be an important biological basis for intelligence.

The valley-city is the type of the city whose main part has been formed and developed in the valley. Valley-city is an area which is strongly influenced by the urbanization process. Its spatial limit and temporal quality tends to cause undesirable landscape patterns, and has been an axial point of the problems of urban ecological landscape. For example, Lanzhou is one of the cities heaviest polluted in China because of its industry system whose key industry is oil and chemistry industry and limited polluted degree that urban air and water system in valley-Basin can bear. The study of landscape structure and change within the valley-city with RS/GIS is useful in determining the spatial features of the city and the succession of landscape factors as indicators of the relationship between urbanization and ecological process. This paper is in the direction of the landscape ecology, Geography, Economics, and takes Lanzhou City as an example. Profited from RS and GIS technology, the paper has studied landscape ecology situation and LUCC, based on Landsat TM images of the recent 20 years from 1980 to 2000. The analysis indices of landscape pattern included Proportion of Area, Patch Density, Patch Size, Diversity Index, Evenness Index, Fragmentation Index, Fractal Dimension Index, and Medial Nearest Neighbor Index. In conclusion, the status and function of the valley city was in need of definition and description. The assessment of these features can establish a baseline for discussion of an equitable landscape pattern and open space that are necessary to develop a natural ecological system in a dense urban environment and to improve regional ecological function.