AIMC Topic: Neuroimaging
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Radiomics in glioma: emerging trends and challenges.
Annals of clinical and translational neurology
Feb 3, 2025
Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning alg...
Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.
NeuroImage
Feb 1, 2025
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...
AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.
Artificial intelligence in medicine
Jan 31, 2025
Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among infants and children. This trend emphasizes the critical importance of early detection, as timely di...
UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases.
NeuroImage
Jan 30, 2025
In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established met...
An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 27, 2025
INTRODUCTION: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.
Contrastive learning in brain imaging.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 26, 2025
Contrastive learning is a type of deep learning technique trying to classify data or examples without requiring data labeling. Instead, it learns about the most representative features that contrast positive and negative pairs of examples. In literat...
Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.
Biomedical physics & engineering express
Jan 22, 2025
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...
[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Jan 21, 2025
Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain...
Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
Methods (San Diego, Calif.)
Jan 19, 2025
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...
Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.
NeuroImage
Jan 16, 2025
Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data...
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