Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

Hypoxia and its effect on the cellular system

Eukaryotic cells utilize oxygen for different functions of cell organelles owing to cellular survival. A balanced oxygen homeostasis is an essential requirement to maintain the regulation of normal cellular systems. Any changes in the oxygen level are stressful and can alter the expression of different homeostasis regulatory genes and proteins. Lack of oxygen or hypoxia results in oxidative stress and formation of hypoxia inducible factors (HIF) and reactive oxygen species (ROS). Substantial cellular damages due to hypoxia have been reported to play a major role in various pathological conditions. There are different studies which demonstrated that the functions of cellular system are disrupted by hypoxia. Currently, study on cellular effects following hypoxia is an important field of research as it not only helps to decipher different signaling pathway modulation, but also helps to explore novel therapeutic strategies. On the basis of the beneficial effect of hypoxia preconditioning of cellular organelles, many therapeutic investigations are ongoing as a promising disease management strategy in near future. Hence, the present review discusses about the effects of hypoxia on different cellular organelles, mechanisms and their involvement in the progression of different diseases.

Artificial intelligence in acupuncture: A bibliometric study

This study aimed to provide a panorama of artificial intelligence (AI) in acupuncture by characterizing and visualizing the knowledge structure, hotspots and trends in global scientific publications. Publications were extracted from the Web of Science. Analyses on the number of publications, countries, institutions, authors, co-authorship, co-citation and co-occurrence were conducted. The USA had the highest volume of publications. Harvard University had the most publications among institutions. Dey P was the most productive author, while lczkowski KA was the most referenced author. The Journal of Alternative and Complementary Medicine was the most active journal. The primary topics in this field concerned the use of AI in various aspects of acupuncture. "Machine learning" and "deep learning" were speculated to be potential hotspots in acupuncture-related AI research. In conclusion, research on AI in acupuncture has advanced significantly over the last two decades. The USA and China both contribute significantly to this field. Current research efforts are concentrated on the application of AI in acupuncture. Our findings imply that the use of deep learning and machine learning in acupuncture will remain a focus of research in the coming years.

Acupuncture-Neuroimaging Research Trends over Past Two Decades: A Bibliometric Analysis

OBJECTIVE

To identify topics attracting growing research attention as well as frontier trends of acupuncture-neuroimaging research over the past two decades.

METHODS

This paper reviewed data in the published literature on acupuncture neuroimaging from 2000 to 2020, which was retrieved from the Web of Science database. CiteSpace was used to analyze the publication years, countries, institutions, authors, keywords, co-citation of authors, journals, and references.

RESULTS

A total of 981 publications were included in the final review. The number of publications has increased in the recent 20 years accompanied by some fluctuations. Notably, the most productive country was China, while Harvard University ranked first among institutions in this field. The most productive author was Tian J with the highest number of articles (50), whereas the most co-cited author was Hui KKS (325). Evidence-Based Complementary and Alternative Medicine (92) was the most prolific journal, while Neuroimage was the most co-cited journal (538). An article written by Hui KKS (2005) exhibited the highest co-citation number (112). The keywords "acupuncture" (475) and "electroacupuncture" (0.10) had the highest frequency and centrality, respectively. Functional magnetic resonance imaging (fMRI) ranked first with the highest citation burst (6.76).

CONCLUSION

The most active research topics in the field of acupuncture-neuroimaging over the past two decades included research type, acupoint specificity, neuroimaging methods, brain regions, acupuncture modality, acupoint specificity, diseases and symptoms treated, and research type. Whilst research frontier topics were "nerve regeneration", "functional connectivity", "neural regeneration", "brain network", "fMRI" and "manual acupuncture".

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.

Artificial intelligence-directed acupuncture: a review

Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.

Neuroplasticity of Acupuncture for Stroke: An Evidence-Based Review of MRI

Acupuncture is widely recognized as a potentially effective treatment for stroke rehabilitation. Researchers in this area are actively investigating its therapeutic mechanisms. Magnetic resonance imaging (MRI), as a noninvasive, high anatomical resolution technique, has been employed to investigate neuroplasticity on acupuncture in stroke patients from a system level. However, there is no review on the mechanism of acupuncture treatment for stroke based on MRI. Therefore, we aim to summarize the current evidence about this aspect and provide useful information for future research. After searching PubMed, Web of Science, and Embase databases, 24 human and five animal studies were identified. This review focuses on the evidence on the possible mechanisms underlying mechanisms of acupuncture therapy in treating stroke by regulating brain plasticity. We found that acupuncture reorganizes not only motor-related network, including primary motor cortex (M1), premotor cortex, supplementary motor area (SMA), frontoparietal network (LFPN and RFPN), and sensorimotor network (SMN), as well as default mode network (aDMN and pDMN), but also language-related brain areas including inferior frontal gyrus frontal, temporal, parietal, and occipital lobes, as well as cognition-related brain regions. In addition, acupuncture therapy can modulate the function and structural plasticity of post-stroke, which may be linked to the mechanism effect of acupuncture.