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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]
Copyright of Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Computation and management of weighted activation vectors in support to fMRI analysis of clinical subjects.
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  Data: <searchLink fieldCode="DE" term="%22Vectors+%28Calculus%29%22">Vectors (Calculus)</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+maps%22">Statistical maps</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+tumors%22">Brain tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+decision+making%22">Medical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Document+clustering%22">Document clustering</searchLink>
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  Data: 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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  Data: <i>Copyright of Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/21681163.2018.1501767
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        Text: English
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              Text: Sep-Dec2019
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