3 citations · 5 across the 5 of their papers we have counts for
5 papers
Stacked Regression using Off-the-shelf, Stimulus-tuned and Fine-tuned Neural Networks for Predicting fMRI Brain Responses to Movies (Algonauts 2025 Report)
Robert Scholz, Kunal Bagga, Christine Ahrends +1
We present our submission to the Algonauts 2025 Challenge, where the goal is to predict fMRI brain responses to movie stimuli. Our approach integrates multimodal representations fr…
BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation
Yiran Huang, Amirhossein Nouranizadeh, Christine Ahrends +1
Functional Magnetic Resonance Imaging (fMRI) is an imaging technique widely used to study human brain activity. fMRI signals in areas across the brain transiently synchronise and d…
A comprehensive framework for statistical testing of brain dynamics
Nick Yao Larsen, Laura Paulsen, Christine Ahrends +2
Neural activity data can be associated with behavioral and physiological variables by analyzing their changes in the temporal domain. However, such relationships are often difficul…
The Gaussian-Linear Hidden Markov model: a Python package
Diego Vidaurre, Laura Masaracchia, Nick Y. Larsen +4
We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework wh…
Dynamic Functional Connectivity
Christine Ahrends, Diego Vidaurre
Most generally, dynamic functional connectivity (FC) refers to the non-instantaneous couplings across timeseries from a set of brain areas, here as measured by fMRI. This is in con…