Neuroplasticity refers to changes in functions or structures that occur in the central nervous system (particularly the brain) to adapt to external and/or internal factors. Impaired neuroplasticity in affected brain structures and networks is thought to represent a critical pathological mechanism underlying multiple chronic diseases such as gastrointestinal diseases and neuropsychiatric disorders. In recent years, accumulating evidence suggests that complementary and alternative medicine (CAM) therapies hold great promise in treating diseases by altering neuroplasticity change. Understanding how neural changes underlie CAM treatments, and how to induce optimal neural changes are critical for developing effective new treatments, improving existing ones, and predicting treatment response.
CAM therapies refer to a diverse range of healing techniques that are not considered established or standard practices in western medicine. Traditional and novel CAM modalities include but are not limited to acupuncture, Mind-body interventions such as Tai Chi, aerobic exercise, music therapy, neuromodulation such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), herbal medicines, supplements, and so on.
Neuroplasticity can occur at micro or macro levels of the central nerve system, from cells to synapses, from myelin to axons, from individual regions to large-scale brain networks. As a consequence, CAM may treat diseases via facilitating neural plasticity from micro or macro pathways. Over several decades, the development of neurobiological technology and electrophysiology have shed light on neural mechanisms of CAM in animal models. Also, advanced neuroimaging technologies provide important information about how brain systems change as a result of CAM therapies in humans.
Recently, innovative research strategies such as machine learning (ML) have also demonstrated their great potential in brain plasticity. Those ML models can detect subtle and complex variations in brain plasticity, combined ML methods and brain-based biomarkers can not only assist in diagnosing diseases but also predict therapeutic outcomes and offer personalized tailoring of interventions.
This Research Topic aims to provide a state-of-the-art insight on the relationship between CAM and neuroplasticity, derived from the studies performed using different research tools, including neurophysiological and neuroimaging techniques which may address neuroplasticity at multiple system levels. We are particularly interested in articles combining innovative strategies such as machine learning to evaluate the molecular and cellular neuroplasticity mechanisms of CAM, in order to facilitate translational research from neural mechanisms to clinical applications.
We welcome articles covering, but not limited to, the themes below:
? Neuroplasticity-related mechanism of CAM via neurobiological or neuroimaging technologies.
? Reviews summarizing the neuroplasticity-related progress in the field of CAM.
? Innovative neuroplasticity-related techniques, such as ML, combing neuroimaging and genetics/biochemical indicators, or clinical features. Especially, translational studies for extracting neurobiological/genetic features of diseases through ML, extending to clinical decision making (including diagnosis, treatment of diseases, etc.).
Neuroplasticity refers to changes in functions or structures that occur in the central nervous system (particularly the brain) to adapt to external and/or internal factors. Impaired neuroplasticity in affected brain structures and networks is thought to represent a critical pathological mechanism underlying multiple chronic diseases such as gastrointestinal diseases and neuropsychiatric disorders. In recent years, accumulating evidence suggests that complementary and alternative medicine (CAM) therapies hold great promise in treating diseases by altering neuroplasticity change. Understanding how neural changes underlie CAM treatments, and how to induce optimal neural changes are critical for developing effective new treatments, improving existing ones, and predicting treatment response.
CAM therapies refer to a diverse range of healing techniques that are not considered established or standard practices in western medicine. Traditional and novel CAM modalities include but are not limited to acupuncture, Mind-body interventions such as Tai Chi, aerobic exercise, music therapy, neuromodulation such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), herbal medicines, supplements, and so on.
Neuroplasticity can occur at micro or macro levels of the central nerve system, from cells to synapses, from myelin to axons, from individual regions to large-scale brain networks. As a consequence, CAM may treat diseases via facilitating neural plasticity from micro or macro pathways. Over several decades, the development of neurobiological technology and electrophysiology have shed light on neural mechanisms of CAM in animal models. Also, advanced neuroimaging technologies provide important information about how brain systems change as a result of CAM therapies in humans.
Recently, innovative research strategies such as machine learning (ML) have also demonstrated their great potential in brain plasticity. Those ML models can detect subtle and complex variations in brain plasticity, combined ML methods and brain-based biomarkers can not only assist in diagnosing diseases but also predict therapeutic outcomes and offer personalized tailoring of interventions.
This Research Topic aims to provide a state-of-the-art insight on the relationship between CAM and neuroplasticity, derived from the studies performed using different research tools, including neurophysiological and neuroimaging techniques which may address neuroplasticity at multiple system levels. We are particularly interested in articles combining innovative strategies such as machine learning to evaluate the molecular and cellular neuroplasticity mechanisms of CAM, in order to facilitate translational research from neural mechanisms to clinical applications.
We welcome articles covering, but not limited to, the themes below:
? Neuroplasticity-related mechanism of CAM via neurobiological or neuroimaging technologies.
? Reviews summarizing the neuroplasticity-related progress in the field of CAM.
? Innovative neuroplasticity-related techniques, such as ML, combing neuroimaging and genetics/biochemical indicators, or clinical features. Especially, translational studies for extracting neurobiological/genetic features of diseases through ML, extending to clinical decision making (including diagnosis, treatment of diseases, etc.).
