Does the brain behave like a (complex) network? I. Dynamics DOI
David Papo, Javier M. Buldú

Physics of Life Reviews, Год журнала: 2023, Номер 48, С. 47 - 98

Опубликована: Дек. 12, 2023

Язык: Английский

Amyloid pathology related to aberrant structure-function coupling of brain networks in Alzheimer’s disease: insights from [18F]-florbetapir PET imaging DOI
Haojie Chen, Mingkai Zhang,

Min Wei

и другие.

European Journal of Nuclear Medicine and Molecular Imaging, Год журнала: 2025, Номер unknown

Опубликована: Март 7, 2025

Язык: Английский

Процитировано

1

Arsenic exposure activates microglia, inducing neuroinflammation and promoting the occurrence and development of Alzheimer's disease-like neurodegeneration in mice DOI Creative Commons

Bo Zhang,

Jiaojiao Wang, Junhong Chen

и другие.

Ecotoxicology and Environmental Safety, Год журнала: 2025, Номер 297, С. 118251 - 118251

Опубликована: Апрель 27, 2025

Язык: Английский

Процитировано

1

Alzheimer’s Disease Prediction via Brain Structural-Functional Deep Fusing Network DOI Creative Commons
Qiankun Zuo, Yanyan Shen, Na Zhong

и другие.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2023, Номер 31, С. 4601 - 4612

Опубликована: Янв. 1, 2023

Fusing structural-functional images of the brain has shown great potential to analyze deterioration Alzheimer's disease (AD). However, it is a big challenge effectively fuse correlated and complementary information from multimodal neuroimages. In this work, novel model termed cross-modal transformer generative adversarial network (CT-GAN) proposed functional structural contained in magnetic resonance imaging (fMRI) diffusion tensor (DTI). The CT-GAN can learn topological features generate connectivity data an efficient end-to-end manner. Moreover, swapping bi-attention mechanism designed gradually align common enhance between modalities. By analyzing generated features, identify AD-related connections. Evaluations on public ADNI dataset show that dramatically improve prediction performance detect regions effectively. also provides new insights into detecting abnormal neural circuits.

Язык: Английский

Процитировано

23

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers DOI Creative Commons
Chun Dang, Yanchao Wang, Qian Li

и другие.

Deleted Journal, Год журнала: 2023, Номер 3

Опубликована: Янв. 1, 2023

Abstract Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10–20 years before emergence clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during phase mild cognitive impairment (MCI) AD. The detection biomarkers has emerged as a promising tool for tracking efficacy potential therapies, making an early diagnosis, prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, single photon emission-computed have provided few application. MRI modalities described this review include structural MRI, functional diffusion tensor spectroscopy, arterial spin labelling. These techniques allow presymptomatic diagnostic brains cognitively normal elderly people might also be used monitor progression after onset This highlights biomarkers, merits, demerits different their value MCI patients. Further studies necessary explore more overcome limitations inclusion criteria

Язык: Английский

Процитировано

22

Does the brain behave like a (complex) network? I. Dynamics DOI
David Papo, Javier M. Buldú

Physics of Life Reviews, Год журнала: 2023, Номер 48, С. 47 - 98

Опубликована: Дек. 12, 2023

Язык: Английский

Процитировано

18