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Penalized model-based clustering of fMRI data
Functional magnetic resonance imaging (fMRI) data have become increasing...
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CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Spatial Independent Component Analysis (ICA) is an increasingly used dat...
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Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
With the wide adoption of functional magnetic resonance imaging (fMRI) b...
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Multi-scale Mining of fMRI data with Hierarchical Structured Sparsity
Inverse inference, or "brain reading", is a recent paradigm for analyzin...
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Mapping individual differences in cortical architecture using multi-view representation learning
In neuroscience, understanding inter-individual differences has recently...
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A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynam...
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Fast shared response model for fMRI data
The shared response model provides a simple but effective framework toan...
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Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
Functional magnetic resonance imaging (fMRI) is a crucial technology for gaining insights into cognitive processes in humans. Data amassed from fMRI measurements result in volumetric data sets that vary over time. However, analysing such data presents a challenge due to the large degree of noise and person-to-person variation in how information is represented in the brain. To address this challenge, we present a novel topological approach that encodes each time point in an fMRI data set as a persistence diagram of topological features, i.e. high-dimensional voids present in the data. This representation naturally does not rely on voxel-by-voxel correspondence and is robust towards noise. We show that these time-varying persistence diagrams can be clustered to find meaningful groupings between participants, and that they are also useful in studying within-subject brain state trajectories of subjects performing a particular task. Here, we apply both clustering and trajectory analysis techniques to a group of participants watching the movie 'Partly Cloudy'. We observe significant differences in both brain state trajectories and overall topological activity between adults and children watching the same movie.
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