Functional neurological disorder (FND) affect the function of the body rather than being caused by the physical neurological disease or disorder. FND can encompass a diverse range of neurological symptoms that are genuine, but not due to a neurological condition. FND study involves the integration of many disciplines, especially genomics, imaging, brain network, behavioral studies, and artificial intelligence. Advances in sequencing technology help us understand genetic alterations accurately, quickly and cheaply, and gain a more fundamental understanding of FND mechanisms. Imaging technology and artificial intelligence allow us to noninvasively visualize the brain structure, understand the brain network and strengthen the cognition of the brain. Therefore, FND study aims to explore potential mechanisms and biomarkers for diagnosis genuinely. It should be carried out from microscale to macroscale, from structure to function, from node to brain network.
At present, diagnosis and treatment approaches for FND were gained from several well-conducted randomized controlled trials and behavioral studies. Still, there is no systemic and comprehensive research on its underlying mechanism. With the development of machine learning, functional magnetic resonance imaging, big data analysis, and neuromodulation, remarkable achievements have been made in many research fields. In recent years, there is a lack of a significant breakthrough in FND studies, so the underlying mechanism of FND needs to be further explored through combining these methods. Based on different perspectives and multidisciplinary crossover, we may have a new understanding of the FND.
This Research Topic is focused on the frontiers in the accurate diagnosis and treatment, and study the underlying mechanism of FND through neurogenomics, signaling pathways, molecular mechanism, neuromodulation, brain network, behavioral study and artificial intelligence. Functional neurological disorders are related to how the brain functions, rather than impairment to the brain's structure (such as from a stroke, infection or injury). Largely understudied until recently, the framing of FND as a “software” problem is well-received. This Topic also covers studies addressing other typical “software” problems like parkinsonism, dementia, epilepsy and abnormal psychology.
We welcome contributions advance in understanding functional neurological disorders and related conditions from neurogenomics, cellular level, signaling pathways, molecular mechanism and neuromodulation. Studies can be focused on human, animal models and in vitro approaches. Bioinformatics and statistical methods, including machine learning and other algorithms based on omics data, are particularly of interest. We welcome Original Research, Reviews and Mini-Reviews of both clinical and preclinical studies falling under the the following themes:
• Alteration of FNDs and related conditions in molecular and cellular level.
• Integrative genomics studies identify new genes associated with FNDs and related conditions.
• Multi-omics studies identify FNDs and related conditions pathogenesis mechanisms and potential therapeutic targets.
• Offering new hypotheses or insights into disease etiology or pathogenesis based on machine learning and other statistical approaches.
• Identification of novel signaling pathways and molecular mechanisms.
• Elucidation of the pathophysiological mechanisms of neurodegeneration in FNDs and related conditions.
• Imaging assessments of structural and functional alterations in the brain, to improve understanding of pathophysiology, prognosis and underlying clinical subtypes of FNDs and related conditions.
Functional neurological disorder (FND) affect the function of the body rather than being caused by the physical neurological disease or disorder. FND can encompass a diverse range of neurological symptoms that are genuine, but not due to a neurological condition. FND study involves the integration of many disciplines, especially genomics, imaging, brain network, behavioral studies, and artificial intelligence. Advances in sequencing technology help us understand genetic alterations accurately, quickly and cheaply, and gain a more fundamental understanding of FND mechanisms. Imaging technology and artificial intelligence allow us to noninvasively visualize the brain structure, understand the brain network and strengthen the cognition of the brain. Therefore, FND study aims to explore potential mechanisms and biomarkers for diagnosis genuinely. It should be carried out from microscale to macroscale, from structure to function, from node to brain network.
At present, diagnosis and treatment approaches for FND were gained from several well-conducted randomized controlled trials and behavioral studies. Still, there is no systemic and comprehensive research on its underlying mechanism. With the development of machine learning, functional magnetic resonance imaging, big data analysis, and neuromodulation, remarkable achievements have been made in many research fields. In recent years, there is a lack of a significant breakthrough in FND studies, so the underlying mechanism of FND needs to be further explored through combining these methods. Based on different perspectives and multidisciplinary crossover, we may have a new understanding of the FND.
This Research Topic is focused on the frontiers in the accurate diagnosis and treatment, and study the underlying mechanism of FND through neurogenomics, signaling pathways, molecular mechanism, neuromodulation, brain network, behavioral study and artificial intelligence. Functional neurological disorders are related to how the brain functions, rather than impairment to the brain's structure (such as from a stroke, infection or injury). Largely understudied until recently, the framing of FND as a “software” problem is well-received. This Topic also covers studies addressing other typical “software” problems like parkinsonism, dementia, epilepsy and abnormal psychology.
We welcome contributions advance in understanding functional neurological disorders and related conditions from neurogenomics, cellular level, signaling pathways, molecular mechanism and neuromodulation. Studies can be focused on human, animal models and in vitro approaches. Bioinformatics and statistical methods, including machine learning and other algorithms based on omics data, are particularly of interest. We welcome Original Research, Reviews and Mini-Reviews of both clinical and preclinical studies falling under the the following themes:
• Alteration of FNDs and related conditions in molecular and cellular level.
• Integrative genomics studies identify new genes associated with FNDs and related conditions.
• Multi-omics studies identify FNDs and related conditions pathogenesis mechanisms and potential therapeutic targets.
• Offering new hypotheses or insights into disease etiology or pathogenesis based on machine learning and other statistical approaches.
• Identification of novel signaling pathways and molecular mechanisms.
• Elucidation of the pathophysiological mechanisms of neurodegeneration in FNDs and related conditions.
• Imaging assessments of structural and functional alterations in the brain, to improve understanding of pathophysiology, prognosis and underlying clinical subtypes of FNDs and related conditions.
