Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises–Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data. </jats:p

Study on the effect of 40 Hz non-invasive light therapy system. A protocol for a randomized, double-blinded, placebo-controlled clinical trial

IntroductionWith no cure or effective treatment, the prevalence of patients with Alzheimer’s disease (AD) is expected to intensify, thereby increasing the social and financial burden on society. Light-based 40 Hz brain stimulation is considered a novel treatment strategy for patients with AD that may alleviate some of this burden. The clinical trial ALZLIGHT will utilize a novel Light Therapy System (LTS). The LTS uses Invisible Spectral Flicker for non-invasive induction of 40 Hz neural activity. This protocol describes a trial evaluating the efficacy and safety of a light-based 40 Hz brain stimulation in patients with mild-to-moderate AD.Methods62 patients with mild-to-moderate AD will participate in a randomized, double-blinded, placebo-controlled, parallel-group, and single-center trial. The participants will partake in an enrollment period of 1 month, an intervention period of 6 months, and a 1.5-month post-interventional follow-up period. Prior to the baseline measurement (week 0), the patients will be randomized to either active or placebo intervention from baseline (week 0) to post-intervention follow-up (week 26).DiscussionThis protocol describes a randomized, double-blinded, placebo-controlled clinical trial that may increase the understanding of the effect of gamma oscillations in the human brain and how it could be utilized as a novel and important tool for the treatment of AD. The effect is measured through a large, multidisciplinary assessment battery.Clinical trial registration:www.ClinicalTrials.gov, (NCT05260177). Registered on March 2, 2022

Association of common genetic variants related to atrial fibrillation and the risk of ventricular fibrillation in the setting of first ST-elevation myocardial infarction

Background: Cohort studies have revealed an increased risk for ventricular fibrillation (VF) and sudden cardiac death (SCD) in patients with atrial fibrillation (AF). In this study, we hypothesized that single nucleotide polymorphisms (SNP) previously associated with AF may be associated with the risk of VF caused by first ST-segment elevation myocardial infarction (STEMI). Methods: We investigated association of 24 AF-associated SNPs with VF in the prospectively assembled case–control study among first STEMI-patients of Danish ancestry. Results: We included 257 cases (STEMI with VF) and 537 controls (STEMI without VF). The median age at index infarction was 60 years for the cases and 61 years for the controls (p = 0.100). Compared to the control group, the case group was more likely to be male (86% vs. 75%, p = 0.001), have a history of AF (7% vs. 2%, p = 0.006) or hypercholesterolemia (39% vs. 31%, p = 0.023), and a family history of sudden death (40% vs. 25%, p < 0.001). All 24 selected SNPs have previously been associated with AF. None of the 24 SNPs were associated with the risk of VF after adjustment for age and sex under additive genetic model of inheritance in the logistic regression model. Conclusion: In this study, we found that the 24 AF-associated SNPs may not be involved in increasing the risk of VF. Larger VF cohorts and use of new next generation sequencing and epigenetic may in future identify additional AF and VF risk loci and improve our understanding of genetic pathways behind the two arrhythmias