Curve Clustering for Brain Functional Activity and Synchronization
Functional Magnetic Resonance Imaging (fMRI) has become one of the leading methods for brain mapping in neuroscience and it is an important tool in modern neuroscience investigation. Moreover, the recent advances in fMRI analysis are widely used to define the default state of brain activity, functional connectivity and basal activity. Signal processing schemes have been suggested to analyze the resting state Blood-Oxygenation-Level-Dependent (BOLD) signal from simple correlations to spectral decomposition. Our goal is to determine which brain areas behave similarly in the time domain. To address this question, we apply functional curve clustering methods. We carry out an exploratory study using classical functional clustering of fMRI time series. The analysis confirms the hypothesis of a possible spatial influence on the results and therefore suggests the development of spatial curve clustering methods for brain data.
KeywordsCurve clustering fMRI Functional boxplot Smoothing
We are grateful to Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who graciously pre-processed the raw DTI and R-fMRI imaging data available at http://fcon_1000.projects.nitrc.org/indi/CoRR/html/nki_1.html, using the pipelines ndmg and C-PAC and to the reviewers and The Scientific Committee of StartUp Research for all the suggestions aimed at improving this paper.
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