Brain mapping is dedicated to using brain imaging techniques such as MRI, CT, PET, EEG, and fNIRS to understand the brain anatomy, structure, and function, and how it contributes to cognition, behavior, and deficits of brain diseases. Recently, machine learning is in a stage of rapid development, and various new technologies are continuously introduced into the field, from traditional approaches (e.g., supervised/unsupervised learning and reinforcement learning) to deep learning-based approaches (e.g., Convolutional Neural Network, Recurrent Neural Network, Graph Neural Network, Transformer). With the help of these advanced machine learning approaches, the field of brain mapping has achieved great success in the following research directions over the past 20 years: 1) brain neuroanatomy; 2) brain connectivity, circuit, and connectome; 3) brain functional network and interaction; 4) brain registration, segmentation, and atlas reconstruction; 5) brain development and 6) computer-aided diagnosis of brain disorders and diseases.
This Research Topic calls for original and innovative contributions which address the key challenges and applications in brain mapping based on advanced machine learning approaches. Submissions should focus on the development of a novel methodologies/frameworks for brain mapping analysis, and all the developed methodologies/frameworks should be evaluated and validated on real brain imaging data.
Topics of interest include, but are not limited to:
? Advanced machine learning approaches for brain neuroanatomy analysis
? Advanced machine learning approaches for brain connectivity, circuit, and connectome analysis
? Advanced machine learning approaches for brain functional network/interaction analysis
? Advanced machine learning approaches for brain registration, segmentation, or atlas reconstruction
? Advanced machine learning approaches for multi-modal brain imaging data fusion.
? Advanced machine learning approaches for brain development analysis
? Advanced machine learning approaches for brain evolution analysis across species
? Advanced machine learning approaches for computer-aided diagnosis of brain disorders and diseases
Brain mapping is dedicated to using brain imaging techniques such as MRI, CT, PET, EEG, and fNIRS to understand the brain anatomy, structure, and function, and how it contributes to cognition, behavior, and deficits of brain diseases. Recently, machine learning is in a stage of rapid development, and various new technologies are continuously introduced into the field, from traditional approaches (e.g., supervised/unsupervised learning and reinforcement learning) to deep learning-based approaches (e.g., Convolutional Neural Network, Recurrent Neural Network, Graph Neural Network, Transformer). With the help of these advanced machine learning approaches, the field of brain mapping has achieved great success in the following research directions over the past 20 years: 1) brain neuroanatomy; 2) brain connectivity, circuit, and connectome; 3) brain functional network and interaction; 4) brain registration, segmentation, and atlas reconstruction; 5) brain development and 6) computer-aided diagnosis of brain disorders and diseases.
This Research Topic calls for original and innovative contributions which address the key challenges and applications in brain mapping based on advanced machine learning approaches. Submissions should focus on the development of a novel methodologies/frameworks for brain mapping analysis, and all the developed methodologies/frameworks should be evaluated and validated on real brain imaging data.
Topics of interest include, but are not limited to:
? Advanced machine learning approaches for brain neuroanatomy analysis
? Advanced machine learning approaches for brain connectivity, circuit, and connectome analysis
? Advanced machine learning approaches for brain functional network/interaction analysis
? Advanced machine learning approaches for brain registration, segmentation, or atlas reconstruction
? Advanced machine learning approaches for multi-modal brain imaging data fusion.
? Advanced machine learning approaches for brain development analysis
? Advanced machine learning approaches for brain evolution analysis across species
? Advanced machine learning approaches for computer-aided diagnosis of brain disorders and diseases
