On the Distinguishability of HRF Models in fMRI
Modelling the Haemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a nonlinear time-invariant dynamic system....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-05-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00054/full |
| _version_ | 1819266808068177920 |
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| author | Paulo Nobre Rosa Patricia eFigueiredo Carlos Jorge Silvestre Carlos Jorge Silvestre |
| author_facet | Paulo Nobre Rosa Patricia eFigueiredo Carlos Jorge Silvestre Carlos Jorge Silvestre |
| author_sort | Paulo Nobre Rosa |
| collection | DOAJ |
| description | Modelling the Haemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a nonlinear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying nonlinear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data. |
| first_indexed | 2024-12-23T21:07:09Z |
| format | Article |
| id | doaj.art-b7708162226b42e7b9104c08ab5f1d7a |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-23T21:07:09Z |
| publishDate | 2015-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-b7708162226b42e7b9104c08ab5f1d7a2022-12-21T17:31:12ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882015-05-01910.3389/fncom.2015.00054110699On the Distinguishability of HRF Models in fMRIPaulo Nobre Rosa0Patricia eFigueiredo1Carlos Jorge Silvestre2Carlos Jorge Silvestre3Deimos Engenharia, Lda.Instituto Superior TecnicoInstituto Superior TecnicoUniversity of MacauModelling the Haemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a nonlinear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying nonlinear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00054/fullfMRIModel selectionBOLD fMRIHRFexperimental paradigmdistinguishability |
| spellingShingle | Paulo Nobre Rosa Patricia eFigueiredo Carlos Jorge Silvestre Carlos Jorge Silvestre On the Distinguishability of HRF Models in fMRI Frontiers in Computational Neuroscience fMRI Model selection BOLD fMRI HRF experimental paradigm distinguishability |
| title | On the Distinguishability of HRF Models in fMRI |
| title_full | On the Distinguishability of HRF Models in fMRI |
| title_fullStr | On the Distinguishability of HRF Models in fMRI |
| title_full_unstemmed | On the Distinguishability of HRF Models in fMRI |
| title_short | On the Distinguishability of HRF Models in fMRI |
| title_sort | on the distinguishability of hrf models in fmri |
| topic | fMRI Model selection BOLD fMRI HRF experimental paradigm distinguishability |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00054/full |
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