About this Research Topic
Age-related neurodegenerative diseases, particularly Alzheimer’s disease and Parkinson’s disease are a global challenge affecting more than 40 million people worldwide. Due to the complex nature of the diseases, the interplays of genomic, environmental and lifestyle factors throughout life, as well as the coexistence of multiple pathologies in the elderly patients, the clinical manifestations are often different in different groups of patients, which brings a great challenge to the accurate diagnosis of these diseases.
Imaging biomarkers in vivo have become essential in the clinical practice for patients with neurodegenerative diseases in the past few decades. In particular, positron emission tomography (PET) imaging can quantify molecular targets in vivo with nanomolar sensitivity with the development of new ligands. Magnetic resonance imaging (MRI), benefited from its high spatial resolution can explore microstructure, iron deposition, neuromelanin level and functional connectivity with advanced acquisition technology and analysis methods. These tools can provide objective indicators to show the presence and progression of neurodegenerative diseases, which not only greatly promote the diagnosis for patients, but also can be used for targeted recruitment of clinical trials as well as treatment efficacy evaluation. Identifying clinical subtypes based on the characteristics of biomarkers in vivo can improve the grouping of patients into clinical trials, and ultimately promote the development of precision medicine strategies.
The aim of this Research Topic therefore is to introduce the state-of-the-art multi-tracer and multi-modal neuroimaging approaches for age-related neurodegenerative diseases, combining new deep learning technologies in the analysis of neuroimaging data to develop effective image-based biomarkers.
We welcome studies within the above mentioned advanced neuroimaging approaches to address one of the following sub-areas:
1. Early diagnosis and differential diagnosis of AD and PD
2. Prediction of clinical outcome from the preclinical stage
3. Treatment efficacy evaluation
4. Development of new PET tracers and clinical transformation
5. Association or causality between different models
6. Parcellation and atlas-free segmentation of multimodal imaging
7. Rapid or low-dose multi-tracer and multi-modal imaging
8. Deep learning for spatial normalization of neuroimaging
Imaging biomarkers in vivo have become essential in the clinical practice for patients with neurodegenerative diseases in the past few decades. In particular, positron emission tomography (PET) imaging can quantify molecular targets in vivo with nanomolar sensitivity with the development of new ligands. Magnetic resonance imaging (MRI), benefited from its high spatial resolution can explore microstructure, iron deposition, neuromelanin level and functional connectivity with advanced acquisition technology and analysis methods. These tools can provide objective indicators to show the presence and progression of neurodegenerative diseases, which not only greatly promote the diagnosis for patients, but also can be used for targeted recruitment of clinical trials as well as treatment efficacy evaluation. Identifying clinical subtypes based on the characteristics of biomarkers in vivo can improve the grouping of patients into clinical trials, and ultimately promote the development of precision medicine strategies.
The aim of this Research Topic therefore is to introduce the state-of-the-art multi-tracer and multi-modal neuroimaging approaches for age-related neurodegenerative diseases, combining new deep learning technologies in the analysis of neuroimaging data to develop effective image-based biomarkers.
We welcome studies within the above mentioned advanced neuroimaging approaches to address one of the following sub-areas:
1. Early diagnosis and differential diagnosis of AD and PD
2. Prediction of clinical outcome from the preclinical stage
3. Treatment efficacy evaluation
4. Development of new PET tracers and clinical transformation
5. Association or causality between different models
6. Parcellation and atlas-free segmentation of multimodal imaging
7. Rapid or low-dose multi-tracer and multi-modal imaging
8. Deep learning for spatial normalization of neuroimaging
Keywords: Multi-tracer, PET, Neuroimaging, Alzheimer’s disease, Parkinson’s disease, MRI, Deep learning, Multi-modal
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