A review of functional magnetic resonance imaging for Brainnetome.
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ABSTRACT:
SUBMITTER: Song M
PROVIDER: S-EPMC5560259 | biostudies-other | 2012 Aug
REPOSITORIES: biostudies-other
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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:Genome wide DNA methylation profiling of normal and tumour prostate samples. The Illumina Infinium MethylationEPIC Human DNA methylation oligonucleotide beads was used to obtain DNA methylation profiles across approximately 850,000 CpGs. Comparative assessment was carried out.
2018-12-01 | GSE101908 | GEO
Project description:The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.
| S-EPMC8210649 | biostudies-literature
Project description:BackgroundPrevious functional magnetic resonance imaging (fMRI) sleep studies have been hampered by the difficulty of obtaining extended amounts of sleep in the sleep-adverse environment of the scanner and often have resorted to manipulations such as sleep depriving subjects before scanning. These manipulations limit the generalizability of the results.New methodThe current study is a methodological validation of procedures aimed at obtaining all-night fMRI data in sleeping subjects with minimal exposure to experimentally induced sleep deprivation. Specifically, subjects slept in the scanner on two consecutive nights, allowing the first night to serve as an adaptation night.Results/comparison with existing method(s)Sleep scoring results from simultaneously acquired electroencephalography data on Night 2 indicate that subjects (n = 12) reached the full spectrum of sleep stages including slow-wave (M = 52.1 min, SD = 26.5 min) and rapid eye movement (REM, M = 45.2 min, SD = 27.9 min) sleep and exhibited a mean of 2.1 (SD = 1.1) nonREM-REM sleep cycles.ConclusionsIt was found that by diligently applying fundamental principles and methodologies of sleep and neuroimaging science, performing all-night fMRI sleep studies is feasible. However, because the two nights of the study were performed consecutively, some sleep deprivation from Night 1 as a cause of the Night 2 results is likely, so consideration should be given to replicating the current study with a washout period. It is envisioned that other laboratories can adopt the core features of this protocol to obtain similar results.
| S-EPMC6524535 | biostudies-literature
Project description:Advances in imaging technologies have allowed for the analysis of functional magnetic resonance imaging data in real-time (rtfMRI), leading to the development of neurofeedback (nf) training. This rtfMRI-nf training utilizes functional magnetic resonance imaging (fMRI) tomographic localization capacity to allow a person to see and regulate the localized hemodynamic signal from his or her own brain. In this review, we summarize the results of several studies that have developed and applied neurofeedback training to healthy and depressed individuals with the amygdala as the neurofeedback target and the goal to increase the hemodynamic response during positive autobiographical memory recall. We review these studies and highlight some of the challenges and advances in developing an rtfMRI-nf paradigm for broader use in psychiatric populations. The work described focuses on our line of research aiming to develop the rtfMRI-nf into an intervention, and includes a discussion of the selection of a region of interest for feedback, selecting a control condition, behavioral and cognitive effects of training, and predicting which participants are most likely to respond well to training. While the results of these studies are encouraging and suggest the clinical potential of amygdala rtfMRI-nf in alleviating symptoms of major depressive disorder, larger studies are warranted to confirm its efficacy.
| S-EPMC6035103 | biostudies-literature
Project description:Despite maximally-safe resection of the MRI-defined contrast-enhanced (CE) central tumor area and chemo-radiotherapy, most glioblastoma patients relapse within one year in peritumor FLAIR regions. Spectroscopic MRI (MRSI) can discriminate metabolic tumor areas with higher recurrence potential as CNI+ regions (Choline/N-acetyl-aspartate Index>2) can predict relapse sites. As relapses are mainly imputed to glioblastoma stem-like cells (GSC), CNI+ areas might be GSC-enriched. We conducted a prospective trial in 16 GBM patients subjected to preoperative MRSI/MRI and surgery/chemo-radiotherapy to investigate GSC content in CNI+ versus CNI- biopsies from CE/FLAIR. Biopsy characterization by RNAseq revealed that FLAIR/CNI+ areas were enriched in stem-related gene signature, but also in pathways related to DNA repair, adhesion/migration and mitochondrial bioenergetics.
2023-11-03 | GSE239379 | GEO
Project description:BackgroundMagnetic resonance (MR) imaging evaluation of the painful failed shoulder arthroplasty is a useful imaging modality due to advancements in metal artifact reduction techniques, which allow assessment of the integrity of the supporting soft-tissue envelope and the implant.Questions/purposesThe focus of this pictorial review is to illustrate the benefits of MR imaging, whether used alone or as an adjunct to other imaging modalities, in aiding the clinician in the complex decision making process.MethodsA PubMed (MEDLINE) search focusing on the complications and imaging assessment of shoulder arthroplasty was performed. Articles were selected for review based on their pertinence to the aforementioned topics.ResultsWe discuss the ability of MR imaging to identify why a patient's arthroplasty may have failed. Specific causes including component loosening and implant failure, rotator cuff and deltoid integrity, infection, subtle fractures, and nerve pathology are reviewed, with illustrative sample images.ConclusionMRI is a valuable tool in the assessment for pathology in the shoulder following arthroplasty.
| S-EPMC4171449 | biostudies-literature
Project description:Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.
| S-EPMC3235557 | biostudies-other
Project description:IntroductionLifestyle factors may influence brain health in midlife. Functional magnetic resonance imaging is a widely used tool to investigate early changes in brain health, including neurodegeneration. In this systematic review, we evaluate the relationship between lifestyle factors and neurodegeneration in midlife, as expressed using functional magnetic resonance imaging.MethodsWe searched MEDLINE, EMBASE, and PsycINFO combining subject headings and free text terms adapted for each database. Articles were screened, and their quality was assessed independently by two reviewers before final inclusion in the review.ResultsWe screened 4116 studies and included 29 in the review. Seven lifestyle factors, such as alcohol, cognitive training, excessive internet use, fasting, physical training, smoking, and substance misuse, were identified in this review.DiscussionCognitive and physical trainings appear to be associated with a neuroprotective effect, whereas alcohol misuse, smoking, and substance misuse appear to be associated with neurodegeneration. Further research is required into the effects of excessive internet use and fasting.
| S-EPMC6021545 | biostudies-literature
Project description:Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.
| S-EPMC10469689 | biostudies-literature