相关论文: Predicting age of human subjects based on structur…

Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection…

Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which pathological changes begin many years before the onset of clinical symptoms, making early detection essential for timely intervention. T1-weighted (T1w) Magnetic…

Bone age assessment gives us evidence to analyze the children growth status and the rejuvenation involved chronological and biological ages. All the previous works consider left-hand X-ray image of a child in their works. In this paper, we…

Large-scale medical studies such as the UK Biobank examine thousands of volunteer participants with medical imaging techniques. Combined with the vast amount of collected metadata, anatomical information from these images has the potential…

Brain aging synthesis is a critical task with broad applications in clinical and computational neuroscience. The ability to predict the future structural evolution of a subject's brain from an earlier MRI scan provides valuable insights…

The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of brain white matter networks are often overlooked in convolutional network…

Brain network analysis is essential for diagnosing and intervention for Alzheimer's disease (AD). However, previous research relied primarily on specific time-consuming and subjective toolkits. Only few tools can obtain the structural brain…

Stroke is a common disabling neurological condition that affects about one-quarter of the adult population over age 25; more than half of patients still have poor outcomes, such as permanent functional dependence or even death, after the…

Human skill learning requires fine-scale coordination of distributed networks of brain regions that are directly linked to one another by white matter tracts to allow for effective information transmission. Yet how individual differences in…

Changes over time in brain anatomy can provide important insight for treatment design or scientific analyses. We present a method that predicts how a brain MRI for an individual will change over time. We model changes using a diffeomorphic…

Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity…

Myelination plays an important role in the neurological development of infant brain and MRI can visualize the myelination extension as T1 high and T2 low signal intensity at white matter. We tried to construct a convolutional neural network…

This study introduces a novel method for predicting cognitive age using psychophysiological tests. To determine cognitive age, subjects were asked to complete a series of psychological tests measuring various cognitive functions, including…

In this work, we study the extent to which structural connectomes and topological derivative measures are unique to individual changes within human brains. To do so, we classify structural connectome pairs from two large longitudinal…

Aging is associated with the accumulation of damage throughout a persons life. Individual health can be assessed by the Frailty Index (FI). The FI is calculated simply as the proportion $f$ of accumulated age related deficits relative to…

Alzheimer's disease (AD) is the most common age-related dementia. Mild cognitive impairment (MCI) is the early stage of cognitive decline before AD. It is crucial to predict the MCI-to-AD conversion for precise management, which remains…

Human braingraphs or connectomes are widely studied in the last decade to understand the structural and functional properties of our brain. In the last several years our research group has computed and deposited thousands of human…

A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the white matter microstructure. However, a head-to-head…

Diffusion MRI (dMRI) is an advanced imaging technique characterizing tissue microstructure and white matter structural connectivity of the human brain. The demand for high-quality dMRI data is growing, driven by the need for better…

Deep learning models have achieved state-of-the-art results in estimating brain age, which is an important brain health biomarker, from magnetic resonance (MR) images. However, most of these models only provide a global age prediction, and…