Hypercomplex Graph Neural Network: Towards Deep Intersection of Multi-modal Brain Networks.

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks.

Education, gender, and frequent pain among middle-aged and older adults in the United States, England, China, and India.

Using cross-sectional data from the United States, England, China, and India, we examined the relationship between education and frequent pain, alongside the modification role of gender in this relationship. We further examined patterns of 3 pain dimensions among participants who reported frequent pain, including pain severity, interference with daily activities, and medication use (these pain dimension questions were not administered in all countries). Our analytical sample included 92,204 participants aged 50 years and above.

Accelerating denoising diffusion probabilistic model via truncated inverse processes for medical image segmentation.

Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process.

Recent Progress in Brain Network Models for Medical Applications: A Review.

Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies.

MR-based spatiotemporal anisotropic atrophy evaluation of hippocampus in Alzheimer's disease progression by multiscale skeletal representation.

Increasing evidence has shown a higher sensitivity of Alzheimer's disease (AD) progression by local hippocampal atrophy rather than the whole volume. However, existing morphological methods based on subfield-volume or surface in imaging studies are not capable to describe the comprehensive process of hippocampal atrophy as sensitive as histological findings. To map histological distinctive measurements onto medical magnetic resonance (MR) images, we propose a multiscale skeletal representation (m-s-rep) to quantify focal hippocampal atrophy during AD progression in longitudinal cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

CReg-KD: Model refinement via confidence regularized knowledge distillation for brain imaging.

One of the core challenges of deep learning in medical image analysis is data insufficiency, especially for 3D brain imaging, which may lead to model over-fitting and poor generalization. Regularization strategies such as knowledge distillation are powerful tools to mitigate the issue by penalizing predictive distributions and introducing additional knowledge to reinforce the training process. In this paper, we revisit knowledge distillation as a regularization paradigm by penalizing attentive output distributions and intermediate representations.

Mapping Multi-Modal Brain Connectome for Brain Disorder Diagnosis via Cross-Modal Mutual Learning.

Recently, the study of multi-modal brain connectome has recorded a tremendous increase and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural networks, e.g.

Spatial-temporal patterns of brain disconnectome in Alzheimer's disease.

Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas-guided computational framework based on brain disconnectome to assess the spatial-temporal patterns of WMH-related structural disconnectivity within a longitudinal investigation.

A deep connectome learning network using graph convolution for connectome-disease association study.

Multivariate analysis approaches provide insights into the identification of phenotype associations in brain connectome data. In recent years, deep learning methods including convolutional neural network (CNN) and graph neural network (GNN), have shifted the development of connectome-wide association studies (CWAS) and made breakthroughs for connectome representation learning by leveraging deep embedded features. However, most existing studies remain limited by potentially ignoring the exploration of region-specific features, which play a key role in distinguishing brain disorders with high intra-class variations, such as autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD).

Ensemble learning supervision augmentation for white matter hyperintensity segmentation.

Since the ambiguous boundary of the lesion and inter-observer variability, white matter hyperintensity segmentation annotations are inherently noisy and uncertain. On the other hand, the high capacity of deep neural networks (DNN) enables them to overfit labels with noise and uncertainty, which may lead to biased models with weak generalization ability. This challenge has been addressed by leveraging multiple annotations per image.

MRF-Net: A multi-branch residual fusion network for fast and accurate whole-brain MRI segmentation.

Whole-brain segmentation from T1-weighted magnetic resonance imaging (MRI) is an essential prerequisite for brain structural analysis, e.g., locating morphometric changes for brain aging analysis.

Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity.

Neuroimaging-driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism.

Clinical and Preclinical Neuroimaging Changes in Spinocerebellar Ataxia Type 12: A Study of Three Chinese Pedigrees.

Background: Spinocerebellar ataxia type 12 (SCA12) is a rare SCA subtype with unclear clinical and imaging features. Also, the radiological changes in prodromal and early stages remain unknown.

Methods: Ten symptomatic and two pre-symptomatic cases from three Chinese pedigrees received clinical assessments and imaging studies including routine magnetic resonance imaging (MRI), diffusion kurtosis imaging (DKI), and positron emission tomography (PET) using 18F-flurodeoxyglucose (FDG) to investigate glucose metabolism in brain and 18F-vesicle monoamine transporter 2 (VMAT2) to inspect the integrity of the dopaminergic neuron.

A Multi-Atlas-Based [18F]9-Fluoropropyl-(+)-Dihydrotetrabenazine Positron Emission Tomography Image Segmentation Method for Parkinson's Disease Quantification.

Objectives: [18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson's disease (PD). In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for PD quantification assessment.

Methods: A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study, and both brain PET and magnetic resonance (MR) scans were conducted.

Three-Dimensional Digital Reconstruction of the Cerebellar Cortex: Lobule Thickness, Surface Area Measurements, and Layer Architecture.

The cerebellum is ontogenetically one of the first structures to develop in the central nervous system; nevertheless, it has been only recently reconsidered for its significant neurobiological, functional, and clinical relevance in humans. Thus, it has been a relatively under-studied compared to the cerebrum. Currently, non-invasive imaging modalities can barely reach the necessary resolution to unfold its entire, convoluted surface, while only histological analyses can reveal local information at the micrometer scale.

Coupling of brain activity and structural network in multiple sclerosis: A graph frequency analysis study.

The brain activities and the underlying wiring diagrams are vulnerable in multiple sclerosis (MS). Also, it remains unknown whether the complex coupling between these functional and structural brain properties would be affected. To address this issue, we adopted graph frequency analysis to quantify the high-order structural-functional interactions based on a combination of brain diffusion and functional MRI data.

An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.

Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments associated with Alzheimer's disease (AD). The knowledge of white matter anatomy facilitates lesion-symptom mapping associated with cognition, and provides important spatial information for lesion segmentation algorithms. However, deep learning-based methods in the white matter hyperintensity (WMH) segmentation realm do not take full advantage of anatomical knowledge in decision-making and lesion localization processes.

Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup.

Multi-institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels.