142 citations · 165 across the 9 of their papers we have counts for
10 papers
Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy
Qiang Li, Shujian Yu, Kristoffer H Madsen +2
Pairwise metrics are often employed to estimate statistical dependencies between brain regions, however they do not capture higher-order information interactions. It is critical to…
Probabilistic Spatial Transformer Networks
Pola Schwöbel, Frederik Warburg, Martin Jørgensen +2
Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image. However, STNs are hard to trai…
Accessibility of Cortical Regions to Focal TES: Dependence on Spatial Position, Safety, and Practical Constraints
Guilherme Bicalho Saturnino, Hartwig Roman Siebner, Axel Thielscher +1
Transcranial electric stimulation (TES) can modulate intrinsic neural activity in the brain by injecting weak currents through electrodes attached to the scalp. TES has been widely…
Ergodicity-breaking reveals time optimal decision making in humans
David Meder, Finn Rabe, Tobias Morville +5
Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how in…
Probabilistic PARAFAC2
Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich +3
The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of difference…
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals…
Adaptive Smoothing in fMRI Data Processing Neural Networks
Albert Vilamala, Kristoffer Hougaard Madsen, Lars Kai Hansen
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothin…
Scalable Group Level Probabilistic Sparse Factor Analysis
Jesper L. Hinrich, Søren F. V. Nielsen, Nicolai A. B. Riis +6
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.…
Towards end-to-end optimisation of functional image analysis pipelines
Albert Vilamala, Kristoffer Hougaard Madsen, Lars Kai Hansen
The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pi…
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
Søren F. V. Nielsen, Kristoffer H. Madsen, Rasmus Røge +2
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 res…