Abstract
In this paper, we present a neural network approach to hierarchical unsupervised clustering of functional magnetic resonance imaging (fMRI) time-sequences of the human brain by self-organized fuzzy minimal free energy vector quantization (VQ). In contrast to conventional model-based fMRI data analysis techniques, this deterministic annealing procedure does not imply presumptive knowledge of expected stimulus-response patterns, and, thus, may be applied to fMRI experiments in which the time course of the stimulus is unknown like in spontaneously occurring events, e.g. hallucinations, epileptic fits, or sleep. Moreover, as minimal free energy VQ represents a hierarchical data analysis strategy implying repetitive cluster splitting, it can provide a natural approach to the subclassification task of activated brain regions on different scales of resolution with respect to fine-grained differences in pixel dynamics.
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Wismüller, A., Dersch, D.R., Lipinski, B., Hahn, K., Auer, D. (2001). Hierarchical Clustering of Functional MRI Time-Series by Deterministic Annealing. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_8
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DOI: https://doi.org/10.1007/3-540-39949-6_8
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