About this Research Topic
Pediatric neurogenetic disorders are a broad spectrum of disorders caused by changes in genes or chromosomes, affecting the brain, spinal cord, nerves, and muscles. Symptoms can arise in the neonatal period or only become evident in later childhood. Neurogenetic disorders include intellectual disability, global developmental delay, autism, brain malformations, epilepsy, ataxia, abnormal muscle tone, movement disorders, and complex diseases that impact the brain. Children with neurogenetic disorders always go through the “diagnostic odysseys”. Therefore, the optimized diagnosis and management of these disorders required a multidisciplinary team. With the advancement in technology, multidimensional quantitative clinical phenotyping has emerged. From them, clinical imaging (MRI, MRS, fMRI, EEG, CT, PET) has played an important role in the diagnosis of neurogenetic disorders. Meanwhile, omics data, such as genomics, transcriptomics, proteomics, metabolomics, immunomics and microbiomics, can greatly assist the disease mechanism investigation and diagnostic decision-making. How to develop algorithms and pipelines to deal with multi-modality imaging and multi-omics dataset, and how to apply these multidimensional datasets in the mechanism investigation and the clinical diagnosis of pediatric neurogenetic disorders still remain a great challenge.
The brain is developing in childhood, especially in the neonatal period, where the brain structure and function are changing rapidly. Therefore, the construction of a baseline for the clinical imaging and omics dataset from an “apparently healthy” population is important, especially for the quantitative capture of abnormal signals in patients with genetic disorders. Currently, NIH has released the MRI study for brain and behavior development from birth to young adulthood, which has promoted the new algorithm design and data investigation. Thus, the construction of a cohort that integrates a multi-modality imaging or multi-omics dataset for brain development is necessary for the community. As for neurogenetic disorders, genomics information has achieved great success in disease diagnosis. The utility of omics data can elucidate more on the mechanism of disease, however, may be limited due to the accessibility of biological samples. The development of new algorithms or pipelines, for data manipulation or clinical application, can improve the interpretation of clinical datasets.
We’d like to call for papers in any type related to data analytics and clinical application in pediatrics.
Studies with multi-modality imaging or multi-omics are preferred but novel studies focusing on one modality or one omics are also welcomed. Topics include but are not limited to:
1. Creating a cohort or generating a public database with the integration of Multi-Modality Imaging and/or Multi-Omics dataset related to normal brain development or neurogenetic disorders
2. Developing a machine learning algorithm/pipeline to process imaging datasets for assisting the clinical diagnosis of pediatric neurogenetic disorders, especially for neonatal with rapid developmental processes
3. Developing a toolbox for Multi-Modality Imaging and/or Multi-Omics data processing related to pediatric neurogenetic disorders
4. Developing or performing analyses to assist disease mechanism investigation with the integration of Multi-Omics and/or Multi-Modality Imaging dataset from patients with pediatric neurogenetic disorders
5. Developing tools or performing analyses with the integration of Multi-Modality Imaging and/or Multi-Omics dataset to assist clinical diagnosis/prognosis of pediatric neurogenetic disorders.
The brain is developing in childhood, especially in the neonatal period, where the brain structure and function are changing rapidly. Therefore, the construction of a baseline for the clinical imaging and omics dataset from an “apparently healthy” population is important, especially for the quantitative capture of abnormal signals in patients with genetic disorders. Currently, NIH has released the MRI study for brain and behavior development from birth to young adulthood, which has promoted the new algorithm design and data investigation. Thus, the construction of a cohort that integrates a multi-modality imaging or multi-omics dataset for brain development is necessary for the community. As for neurogenetic disorders, genomics information has achieved great success in disease diagnosis. The utility of omics data can elucidate more on the mechanism of disease, however, may be limited due to the accessibility of biological samples. The development of new algorithms or pipelines, for data manipulation or clinical application, can improve the interpretation of clinical datasets.
We’d like to call for papers in any type related to data analytics and clinical application in pediatrics.
Studies with multi-modality imaging or multi-omics are preferred but novel studies focusing on one modality or one omics are also welcomed. Topics include but are not limited to:
1. Creating a cohort or generating a public database with the integration of Multi-Modality Imaging and/or Multi-Omics dataset related to normal brain development or neurogenetic disorders
2. Developing a machine learning algorithm/pipeline to process imaging datasets for assisting the clinical diagnosis of pediatric neurogenetic disorders, especially for neonatal with rapid developmental processes
3. Developing a toolbox for Multi-Modality Imaging and/or Multi-Omics data processing related to pediatric neurogenetic disorders
4. Developing or performing analyses to assist disease mechanism investigation with the integration of Multi-Omics and/or Multi-Modality Imaging dataset from patients with pediatric neurogenetic disorders
5. Developing tools or performing analyses with the integration of Multi-Modality Imaging and/or Multi-Omics dataset to assist clinical diagnosis/prognosis of pediatric neurogenetic disorders.
Keywords: pediatric neurogenetic disorders, multi-modality imaging, multi-omics technology, brain development, machine learning
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