Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), and the results point towards the conclusion that FC exhibits dynamic changes. The existing approaches modeling dynamic connectivity have primarily been based on time-windowing the data and k-means clustering. We propose a non-parametric generative model for dynamic FC in fMRI that does not rely on specifying window lengths and number of dynamic states. Rooted in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find that the number of states extracted are driven by subject variability and preprocessing differences while the individual states are almost purely defined by either task or rest. This questions how we in general interpret dynamic FC and points to the need for more research on what drives dynamic FC.Comment: 8 pages, 1 figure. Presented at the Machine Learning and Interpretation in Neuroimaging Workshop (MLINI-2015), 2015 (arXiv:1605.04435

Scalable Group Level Probabilistic Sparse Factor Analysis

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a group level scalable probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex noise models than the presently considered.Comment: 10 pages plus 5 pages appendix, Submitted to ICASSP 1

Danish Foundation Models

Large language models, sometimes referred to as foundation models, have transformed multiple fields of research. However, smaller languages risk falling behind due to high training costs and small incentives for large companies to train these models. To combat this, the Danish Foundation Models project seeks to provide and maintain open, well-documented, and high-quality foundation models for the Danish language. This is achieved through broad cooperation with public and private institutions, to ensure high data quality and applicability of the trained models. We present the motivation of the project, the current status, and future perspectives.Comment: 4 pages, 2 table

Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan

CD4 positive T helper cells control many aspects of specific immunity. These cells are specific for peptides derived from protein antigens and presented by molecules of the extremely polymorphic major histocompatibility complex (MHC) class II system. The identification of peptides that bind to MHC class II molecules is therefore of pivotal importance for rational discovery of immune epitopes. HLA-DR is a prominent example of a human MHC class II. Here, we present a method, NetMHCIIpan, that allows for pan-specific predictions of peptide binding to any HLA-DR molecule of known sequence. The method is derived from a large compilation of quantitative HLA-DR binding events covering 14 of the more than 500 known HLA-DR alleles. Taking both peptide and HLA sequence information into account, the method can generalize and predict peptide binding also for HLA-DR molecules where experimental data is absent. Validation of the method includes identification of endogenously derived HLA class II ligands, cross-validation, leave-one-molecule-out, and binding motif identification for hitherto uncharacterized HLA-DR molecules. The validation shows that the method can successfully predict binding for HLA-DR molecules-even in the absence of specific data for the particular molecule in question. Moreover, when compared to TEPITOPE, currently the only other publicly available prediction method aiming at providing broad HLA-DR allelic coverage, NetMHCIIpan performs equivalently for alleles included in the training of TEPITOPE while outperforming TEPITOPE on novel alleles. We propose that the method can be used to identify those hitherto uncharacterized alleles, which should be addressed experimentally in future updates of the method to cover the polymorphism of HLA-DR most efficiently. We thus conclude that the presented method meets the challenge of keeping up with the MHC polymorphism discovery rate and that it can be used to sample the MHC "space," enabling a highly efficient iterative process for improving MHC class II binding predictions

Satellite Cells Derived from Obese Humans with Type 2 Diabetes and Differentiated into Myocytes In Vitro Exhibit Abnormal Response to IL-6

Obesity and type 2 diabetes are associated with chronically elevated systemic levels of IL-6, a pro-inflammatory cytokine with a role in skeletal muscle metabolism that signals through the IL-6 receptor (IL-6Rα). We hypothesized that skeletal muscle in obesity-associated type 2 diabetes develops a resistance to IL-6. By utilizing western blot analysis, we demonstrate that IL-6Rα protein was down regulated in skeletal muscle biopsies from obese persons with and without type 2 diabetes. To further investigate the status of IL-6 signaling in skeletal muscle in obesity-associated type 2 diabetes, we isolated satellite cells from skeletal muscle of people that were healthy (He), obese (Ob) or were obese and had type 2 diabetes (DM), and differentiated them in vitro into myocytes. Down-regulation of IL-6Rα was conserved in Ob myocytes. In addition, acute IL-6 administration for 30, 60 and 120 minutes, resulted in a down-regulation of IL-6Rα protein in Ob myocytes compared to both He myocytes (P<0.05) and DM myocytes (P<0.05). Interestingly, there was a strong time-dependent regulation of IL-6Rα protein in response to IL-6 (P<0.001) in He myocytes, not present in the other groups. Assessing downstream signaling, DM, but not Ob myocytes demonstrated a trend towards an increased protein phosphorylation of STAT3 in DM myocytes (P = 0.067) accompanied by a reduced SOCS3 protein induction (P<0.05), in response to IL-6 administration. Despite this loss of negative control, IL-6 failed to increase AMPKα2 activity and IL-6 mRNA expression in DM myocytes. There was no difference in fusion capacity of myocytes between cell groups. Our data suggest that negative control of IL-6 signaling is increased in myocytes in obesity, whereas a dysfunctional IL-6 signaling is established further downstream of IL-6Rα in DM myocytes, possibly representing a novel mechanism by which skeletal muscle function is compromised in type 2 diabetes