The brain is the control center of human behavior, consisting of billions of synapses, and sends signals in a wide range of patterns and sequences. These complex processes are responsible for the formation of every thought, emotion, function, or dysfunction we have. Radiomics was originally developed as a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Radiomics have been used to study a variety of diseases, such as brain tumors, highlighting the potential of radiomics to enhance clinical decision-making. The term "connectomics", like radiomics, derives from "genomics" because it takes a similar approach: big data analyzes of MRI massive data sets for building a digital atlas of human connectomes.
Although various studies from different fields in imaging have been published, for example, brain tumors, highlighting the potential of radiomics to enhance clinical decision-making. Connectomics also can be used to explore the pathological mechanisms of neurological diseases. However, the role of connectomics in the diagnosis and prognosis of neurological diseases needs to be further studied. In particular, methodologically, drawing on the analytical strategies of radiomics or the research framework of artificial intelligence may be able to improve this situation in connectomics. It is the emergence of big data, machine learning and artificial intelligence that can provide new opportunities for diagnosing neurological diseases based on brain network function and connectivity, transforming the future of diagnosis and treatment for every neurological disease in the world. The objective of this Research Topic is to collect the current progress of radiomics and/or connectomics, and related clinical applications in non-neoplastic neurological diseases, especially the new connectomic research protocols that draw on radiomics ideas and artificial intelligence for the diagnosis and clinical decision-making of neurological diseases. Such as Parkinson's disease, Alzheimer's disease, cerebral small vascular disease, epilepsy, and neuroimmune diseases.
We encourage contributions from neurology, neuroscience, neurocomputing, or neurotechnology perspectives, including interdisciplinary and multidisciplinary research related to the diagnosis, evaluation, and prognosis of neurological diseases related to radiomics and connectomics. In order to better understand its underlying mechanisms and develop new methods of diagnosis, treatment, and prognosis prediction.
Topics of interest include, but are not limited to:
- Diagnosis, quantitative evaluation, and prognosis related to radiomics or connectomics;
- AI application of MRI in neurological diseases;
- Overcoming technical challenges in the clinical practice of connectomics.
High-quality manuscripts such as original research, reviews, systematic reviews, brief research reports, methods, and mini-reviews are welcome.
The brain is the control center of human behavior, consisting of billions of synapses, and sends signals in a wide range of patterns and sequences. These complex processes are responsible for the formation of every thought, emotion, function, or dysfunction we have. Radiomics was originally developed as a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Radiomics have been used to study a variety of diseases, such as brain tumors, highlighting the potential of radiomics to enhance clinical decision-making. The term "connectomics", like radiomics, derives from "genomics" because it takes a similar approach: big data analyzes of MRI massive data sets for building a digital atlas of human connectomes.
Although various studies from different fields in imaging have been published, for example, brain tumors, highlighting the potential of radiomics to enhance clinical decision-making. Connectomics also can be used to explore the pathological mechanisms of neurological diseases. However, the role of connectomics in the diagnosis and prognosis of neurological diseases needs to be further studied. In particular, methodologically, drawing on the analytical strategies of radiomics or the research framework of artificial intelligence may be able to improve this situation in connectomics. It is the emergence of big data, machine learning and artificial intelligence that can provide new opportunities for diagnosing neurological diseases based on brain network function and connectivity, transforming the future of diagnosis and treatment for every neurological disease in the world. The objective of this Research Topic is to collect the current progress of radiomics and/or connectomics, and related clinical applications in non-neoplastic neurological diseases, especially the new connectomic research protocols that draw on radiomics ideas and artificial intelligence for the diagnosis and clinical decision-making of neurological diseases. Such as Parkinson's disease, Alzheimer's disease, cerebral small vascular disease, epilepsy, and neuroimmune diseases.
We encourage contributions from neurology, neuroscience, neurocomputing, or neurotechnology perspectives, including interdisciplinary and multidisciplinary research related to the diagnosis, evaluation, and prognosis of neurological diseases related to radiomics and connectomics. In order to better understand its underlying mechanisms and develop new methods of diagnosis, treatment, and prognosis prediction.
Topics of interest include, but are not limited to:
- Diagnosis, quantitative evaluation, and prognosis related to radiomics or connectomics;
- AI application of MRI in neurological diseases;
- Overcoming technical challenges in the clinical practice of connectomics.
High-quality manuscripts such as original research, reviews, systematic reviews, brief research reports, methods, and mini-reviews are welcome.
