Magnetic Resonance Imaging Studies on Acupuncture Therapy in Depression: A Systematic Review.
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ABSTRACT:
SUBMITTER: Zhang J
PROVIDER: S-EPMC8417590 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMajor depressive disorder (MDD) has been related to structural brain characteristics that are correlated with the severity of disease. However, the correlation of these structural changes is less well clarified in treatment-resistant depression (TRD).AimsTo summarise the existing literature on structural brain characteristics in TRD to create an overview of known abnormalities of the brain in patients with MDD, to form hypotheses about the absence or existence of a common pathophysiology of MDD and TRD.MethodA systematic search of PubMed and the Cochrane Library for studies published between 1998 and August of 2016 investigating structural brain changes in patients with TRD compared with healthy controls or patients with MDD.ResultsFourteen articles are included in this review. Lower grey matter volume (GMV) in the anterior cingulate cortex, right cerebellum, caudate nucleus, superior/medial frontal gyrus and hippocampus does not seem to differentiate TRD from milder forms of MDD. However, lower GMV in the putamen, inferior frontal gyrus, precentral gyrus, angular- and post-central gyri together with specific mainly parietal white matter tract changes seem to be more specific structural characteristics of TRD.ConclusionsThe currently available data on structural brain changes in patients with TRD compared with milder forms of MDD and healthy controls cannot sufficiently distinguish between a 'shared continuum hypothesis' and a 'different entity hypothesis'. Our review clearly suggests that although there is some overlap in affected brain regions between milder forms of MDD and TRD, TRD also comes with specific alterations in mainly the putamen and parietal white matter tracts.Declaration of interestNone.
| S-EPMC6737518 | biostudies-literature
Project description:The neural mechanisms of acupuncture are not well-understood. Over the past decades, an increasing number of studies have used MRI to investigate the response of the brain to acupuncture. The current review aims to provide an update on acupuncture therapy in disease. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched from inception to January 31, 2021. Article selection and data extraction were conducted by two review authors. A total of 107 publications about MRI in acupuncture were included, the collective findings of which were as follows: (1) stroke and GB34 (Yanglingquan) are the most studied disease and acupoint. Related studies suggested that the mechanism of acupuncture treatment for stroke may associate with structural and functional plasticity, left and right hemispheres balance, and activation of brain areas related to movement and cognition. GB34 is mainly used in stroke and Parkinson's disease, which mainly activates brain response in the premotor cortex, the supplementary motor area, and the supramarginal gyrus; (2) resting-state functional MRI (rs-fMRI) and functional connectivity (FC) analysis are the most frequently used approaches; (3) estimates of efficacy and brain response to acupuncture depend on the type of sham acupuncture (SA) used for comparison. Brain processing after acupuncture differs between patients and health controls (HC) and occurs mainly in disorder-related areas. Factors that influence the effect of acupuncture include depth of needling, number and locations of acupoints, and deqi and expectation effect, each contributing to the brain response. While studies using MRI have increased understanding of the mechanism underlying the effects of acupuncture, there is scope for development in this field. Due to the small sample sizes, heterogeneous study designs, and analytical methods, the results were inconsistent. Further studies with larger sample sizes, careful experimental design, multimodal neuroimaging techniques, and standardized methods should be conducted to better explain the efficacy and specificity of acupuncture, and to prepare for accurate efficacy prediction in the future.
| S-EPMC8417610 | biostudies-literature
Project description:IntroductionComplete reporting assists readers in confirming the methodological rigor and validity of findings and allows replication. The reporting quality of observational functional magnetic resonance imaging (fMRI) studies involving clinical participants is unclear.ObjectivesWe sought to determine the quality of reporting in observational fMRI studies involving clinical participants.MethodsWe searched OVID MEDLINE for fMRI studies in six leading journals between January 2010 and December 2011.Three independent reviewers abstracted data from articles using an 83-item checklist adapted from the guidelines proposed by Poldrack et al. (Neuroimage 2008; 40: 409-14). We calculated the percentage of articles reporting each item of the checklist and the percentage of reported items per article.ResultsA random sample of 100 eligible articles was included in the study. Thirty-one items were reported by fewer than 50% of the articles and 13 items were reported by fewer than 20% of the articles. The median percentage of reported items per article was 51% (ranging from 30% to 78%). Although most articles reported statistical methods for within-subject modeling (92%) and for between-subject group modeling (97%), none of the articles reported observed effect sizes for any negative finding (0%). Few articles reported justifications for fixed-effect inferences used for group modeling (3%) and temporal autocorrelations used to account for within-subject variances and correlations (18%). Other under-reported areas included whether and how the task design was optimized for efficiency (22%) and distributions of inter-trial intervals (23%).ConclusionsThis study indicates that substantial improvement in the reporting of observational clinical fMRI studies is required. Poldrack et al.'s guidelines provide a means of improving overall reporting quality. Nonetheless, these guidelines are lengthy and may be at odds with strict word limits for publication; creation of a shortened-version of Poldrack's checklist that contains the most relevant items may be useful in this regard.
| S-EPMC3995931 | biostudies-literature
Project description:Longitudinal comorbidity of depression and cognitive impairment has been reported by number of epidemiological studies but the underlying mechanisms explaining the link between affective problems and cognitive decline are not very well understood. Imaging studies have typically investigated patients with major depressive disorder (MDD) and mild cognitive impairment (MCI) separately and thus have not identified a structural brain signature common to these conditions that may illuminate potentially targetable shared biological mechanisms. We performed a meta-analysis of. 48 voxel-based morphometry (VBM) studies of individuals with MDD, MCI, and age-matched controls and demonstrated that MDD and MCI patients had shared volumetric reductions in a number of regions including the insula, superior temporal gyrus (STG), inferior frontal gyrus, amygdala, hippocampus, and thalamus. We suggest that the shared volumetric reductions in the insula and STG might reflect communication deficits and infrequent participation in mentally or socially stimulating activities, which have been described as risk factors for both MCI and MDD. We also suggest that the disease-specific structural changes might reflect the disease-specific symptoms such as poor integration of emotional information, feelings of helplessness and worthlessness, and anhedonia in MDD. These findings could contribute to better understanding of the origins of MDD-MCI comorbidity and facilitate development of early interventions.
| S-EPMC8473769 | biostudies-literature
Project description:(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.
| S-EPMC8321322 | biostudies-literature
Project description:Cerebrovascular reactivity (CVR) magnetic resonance imaging (MRI) probes cerebral haemodynamic changes in response to a vasodilatory stimulus. CVR closely relates to the health of the vasculature and is therefore a key parameter for studying cerebrovascular diseases such as stroke, small vessel disease and dementias. MRI allows in vivo measurement of CVR but several different methods have been presented in the literature, differing in pulse sequence, hardware requirements, stimulus and image processing technique. We systematically reviewed publications measuring CVR using MRI up to June 2020, identifying 235 relevant papers. We summarised the acquisition methods, experimental parameters, hardware and CVR quantification approaches used, clinical populations investigated, and corresponding summary CVR measures. CVR was investigated in many pathologies such as steno-occlusive diseases, dementia and small vessel disease and is generally lower in patients than in healthy controls. Blood oxygen level dependent (BOLD) acquisitions with fixed inspired CO2 gas or end-tidal CO2 forcing stimulus are the most commonly used methods. General linear modelling of the MRI signal with end-tidal CO2 as the regressor is the most frequently used method to compute CVR. Our survey of CVR measurement approaches and applications will help researchers to identify good practice and provide objective information to inform the development of future consensus recommendations.
| S-EPMC7947694 | biostudies-literature
Project description:PURPOSE:We aimed to assess the role of radiologists, cardiologists, and other medical and non-medical figures in cardiac magnetic resonance imaging (MRI) research in the last 34 years, focusing on first and last authorship, number of published studies, and journal impact factors (IF). METHODS:Articles in the field of cardiac MRI were considered in this systematic review and retrospective bibliometric analysis. For included studies, the first and last authors were categorized as cardiologists, radiologists/nuclear medicine physicians, medical doctors (MD) with specialties in both cardiology and radiology/nuclear medicine, and other MD and non-MD. Differences in the number of papers published overall and by year and institution location for the first and last author category were assessed. Mean IF differences between author categories were also investigated. RESULTS:A total of 2053 articles were included in the final analysis. For the first authors (n = 2011), 52% were cardiologists, 22% radiologists/nuclear medicine physicians, 16% other MD, 10% other non-MD, and 1% both cardiologists and radiologists/nuclear medicine physicians. Similarly, the last authors (n = 2029) resulted 54% cardiologists, 22% radiologists/nuclear medicine physicians, 15% other MD, 8% other non-MD, and 2% both cardiologists and radiologists/nuclear medicine physicians. No significant differences due to institution location in the first and last authorship proportions were found. Average journal IF was significantly higher for cardiologist first and last authors when compared to that of radiologists/nuclear medicine physicians (both p < 0.0001). CONCLUSION:Over 50% of studies in the field of cardiac MRI published in the last 34 years are conducted by cardiologists.
| S-EPMC7046856 | biostudies-literature
Project description:MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.
| S-EPMC8493836 | biostudies-literature
Project description:BACKGROUND:Depression is commonly treated with anti-depressant medication and/or psychological interventions. Patients with depression are common users of complementary therapies, such as acupuncture, either as a replacement for, or adjunct to, their conventional treatments. This systematic review and meta-analysis examined the effectiveness of acupuncture in major depressive disorder. METHODS:A search of English (Medline, PsychINFO, Google Scholar, and CINAL), Chinese (China National Knowledge Infrastructure Database (CNKI) and Wanfang Database), and Korean databases was undertaken from 1980 to November 2018 for clinical trials using manual, electro, or laser acupuncture. RESULTS:Twenty-nine studies including 2268 participants were eligible and included in the meta-analysis. Twenty-two trials were undertaken in China and seven outside of China. Acupuncture showed clinically significant reductions in the severity of depression compared to usual care (Hedges (g) = 0.41, 95% confidence interval (CI) 0.18 to 0.63), sham acupuncture (g = 0.55, 95% CI 0.31 to 0.79), and as an adjunct to anti-depressant medication (g = 0.84, 95% CI 0.61 to 1.07). A significant correlation between an increase in the number of acupuncture treatments delivered and reduction in the severity of depression (p = 0.015) was found. LIMITATIONS:The majority of the included trials were at a high risk of bias for performance blinding. The applicability of findings in Chinese populations to other populations is unclear, due to the use of a higher treatment frequency and number of treatments in China. The majority of trials did not report any post-trial follow-up and safety reporting was poor. CONCLUSIONS:Acupuncture may be a suitable adjunct to usual care and standard anti-depressant medication.
| S-EPMC6722678 | biostudies-other
Project description:BackgroundThere is growing interest in understanding the neurobiology of major depressive disorder (MDD) in youth, particularly in the context of neuroimaging studies. This systematic review provides a timely comprehensive account of the available functional magnetic resonance imaging (fMRI) literature in youth MDD.MethodsA literature search was conducted using PubMED, PsycINFO and Science Direct databases, to identify fMRI studies in younger and older youth with MDD, spanning 13-18 and 19-25 years of age, respectively.ResultsTwenty-eight studies focusing on 5 functional imaging domains were identified, namely emotion processing, cognitive control, affective cognition, reward processing and resting-state functional connectivity. Elevated activity in "extended medial network" regions including the anterior cingulate, ventromedial and orbitofrontal cortices, as well as the amygdala was most consistently implicated across these five domains. For the most part, findings in younger adolescents did not differ from those in older youth; however a general comparison of findings in both groups compared to adults indicated differences in the domains of cognitive control and affective cognition.ConclusionsYouth MDD is characterized by abnormal activations in ventromedial frontal regions, the anterior cingulate and amygdala, which are broadly consistent with the implicated role of medial network regions in the pathophysiology of depression. Future longitudinal studies examining the effects of neurodevelopmental changes and pubertal maturation on brain systems implicated in youth MDD will provide a more comprehensive neurobiological model of youth depression.
| S-EPMC3895619 | biostudies-literature