Computation and management of weighted activation vectors in support to fMRI analysis of clinical subjects.

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Title: Computation and management of weighted activation vectors in support to fMRI analysis of clinical subjects.
Authors: Binaghi, Elisabetta1,2 elisabetta.binaghi@uninsubria.it, Vergani, Alberto A.1,2, Montalbetti, Andrea3, Minotto, Renzo4, Pedoia, Valentina5, Strocchi, Sabina2,6, Balbi, Sergio3,7
Source: Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation. Sep-Dec2019, Vol. 7 Issue 5/6, p563-582. 20p.
Subjects: Vectors (Calculus), Statistical maps, Brain tumors, Medical decision making, Document clustering
Abstract: In the present work, we investigate the usefulness of a new representation of the results obtained by fMRI data analysis, named weighted activation vector (WAV), built based on statistical parametric mapping. A software package for the generation and management of WAVs is illustrated. It is designed to support single-subject, multi-temporal and collective brain tumour studies. As seen in our experimental context, the combined use of WAVs and statistical parametric maps (SPMs) improves the quality of medical decisions before and after neurosurgical practice. Clustering techniques applied to WAVs can be efficiently analysed and optimised in an attempt to discover relevant properties of collective data. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In the present work, we investigate the usefulness of a new representation of the results obtained by fMRI data analysis, named weighted activation vector (WAV), built based on statistical parametric mapping. A software package for the generation and management of WAVs is illustrated. It is designed to support single-subject, multi-temporal and collective brain tumour studies. As seen in our experimental context, the combined use of WAVs and statistical parametric maps (SPMs) improves the quality of medical decisions before and after neurosurgical practice. Clustering techniques applied to WAVs can be efficiently analysed and optimised in an attempt to discover relevant properties of collective data. [ABSTRACT FROM AUTHOR]
ISSN:21681163
DOI:10.1080/21681163.2018.1501767