A biomechanics comparison of grasping in locomotion and feeding with the mouse lemur (Microcebus murinus, Primate): a study case
| Autor: | E. Reghem, E. Pouydebat, P. Gorce, V. Bels |
|---|---|
| Přispěvatelé: | Laboratoire de Biomodélisation et Ingénierie des Handicaps - EA 4322 (HANDIBIO), Université de Toulon (UTLN), Mécanismes adaptatifs : des organismes aux communautés, Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Biomedical Engineering
Phase (waves) Bioengineering 03 medical and health sciences symbols.namesake 0302 clinical medicine medicine 0501 psychology and cognitive sciences 050102 behavioral science & comparative psychology Time domain Entropy (energy dispersal) ComputingMilieux_MISCELLANEOUS Mathematics General linear model Series (mathematics) medicine.diagnostic_test business.industry 05 social sciences Mathematical analysis Pattern recognition General Medicine Computer Science Applications Human-Computer Interaction symbols Phase entropy Hilbert transform Artificial intelligence business Functional magnetic resonance imaging 030217 neurology & neurosurgery [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology |
| Zdroj: | Computer Methods in Biomechanics and Biomedical Engineering Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis, 2010, 13 (sup1), pp.121--123. ⟨10.1080/10255842.2010.495593⟩ |
| ISSN: | 1025-5842 1476-8259 |
| DOI: | 10.1080/10255842.2010.495593⟩ |
| Popis: | Functional magnetic resonance imaging (fMRI) data-processing methods in the time domain include correlation analysis and the general linear model, among others. Virtually, many fMRI processing strategies utilise temporal information and ignore or pay little attention to phase information, resulting in an unnecessary loss of efficiency. We proposed a novel method named Hilbert phase entropy imaging (HPEI) that used the discrete Hilbert transform of the magnitude time series to detect brain functional activation. The data from two simulation studies and two in vivo fMRI studies that both contained block-design and event-related experiments revealed that the HPEI method enabled the effective detection of brain functional activation and the distinction of different response patterns. Our results demonstrate that this method is useful as a complementary analysis, but hypothesis-constrained, in revealing additional information regarding the complex nature of fMRI time series. |
| Databáze: | OpenAIRE |
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