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Task-based functional magnetic resonance imaging (task fMRI) is a non-invasive technique that allows identifying brain regions whose activity changes when individuals are asked to perform a given task. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. Task fMRI experiments from high-resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to measurements of brain activity over each voxel of the brain along the duration of the experiment. In this context, we propose some visualization techniques for high dimensional functional data relying on depth-based notions that allow for computationally efficient 2-dim representations of tfMRI data and that shed light on sample composition, outlier presence and individual variability. We believe that this step is crucial previously to any inferential approach willing to identify neuroscientific patterns across individuals, tasks and brain regions. We illustrate the proposed technique through a simulation study and demonstrate its application on a motor and language task fMRI experiment.
We propose an alternative to $k$-nearest neighbors for functional data whereby the approximating neighboring curves are piecewise functions built from a functional sample. Using a locally defined distance function that satisfies stabilization criteri
High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classical unsupervised clustering strategy concerns primarily the identificat
Aggregation of large databases in a specific format is a frequently used process to make the data easily manageable. Interval-valued data is one of the data types that is generated by such an aggregation process. Using traditional methods to analyze
We propose a new method for clustering of functional data using a $k$-means framework. We work within the elastic functional data analysis framework, which allows for decomposition of the overall variation in functional data into amplitude and phase
Smart metering infrastructures collect data almost continuously in the form of fine-grained long time series. These massive time series often have common daily patterns that are repeated between similar days or seasons and shared between grouped mete