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- Baba, Akiyasu, et al.
(författare)
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Autoantibodies against M2-muscarinic acetylcholine receptors: new upstream targets in atrial fibrillation in patients with dilated cardiomyopathy.
- 2004
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Ingår i: European heart journal. - : Oxford University Press (OUP). - 0195-668X. ; 25:13, s. 1108-15
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Tidskriftsartikel (refereegranskat)abstract
- AIM: To characterise the clinical significance of M2-muscarinic acetylcholine receptor autoantibodies (M2-AAB) in patients with dilated cardiomyopathy (DCM). METHODS AND RESULTS: Sera from 104 patients with DCM, age-matched with 104 patients with idiopathic atrial fibrillation (Af) and 104 healthy control subjects, were screened for M2-AAB by enzyme-linked immunosorbent assay (ELISA). IgG purified by Protein-A column was also used as a primary antibody in ELISA. In DCM, M2-AAB were detected in 40% of patients using whole sera and in 36% of patients using purified IgG. M2-AAB were also found in several patients with idiopathic Af (23%, 23%), and these frequencies were significantly higher than those in healthy subjects (8%, 8%). Af was more common in AAB-positive than in AAB-negative patients with DCM. Multivariable analysis confirmed that M2-AAB were independent predictors of the presence of Af in such patients. We determined electrophysiological changes by adding patient purified M2-AAB to chick embryos. Purified IgG from both Af and DCM patients exhibited negative chronotropic effects and induced supraventricular arrhythmias. CONCLUSION: M2-AAB may play a role in mediating the development of Af in patients with DCM.
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| 2. |
- Haugg, Amelie, et al.
(författare)
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Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?
- 2020
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Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 41:14, s. 3839-3854
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Tidskriftsartikel (refereegranskat)abstract
- Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.
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| 3. |
- Haugg, Amelie, et al.
(författare)
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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
- 2021
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Ingår i: NeuroImage. - : Elsevier. - 1053-8119 .- 1095-9572. ; 237
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Tidskriftsartikel (refereegranskat)abstract
- Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
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