NDLI: On the distinguishability of HRF models in fMRI

Content Provider	IEEE Xplore Digital Library
Author	Silvestre, C. Figueiredo, P. Rosa, P.
Copyright Year	2010
Description	Author affiliation: Institute for Systems and Robotics - Instituto Superior Tecnico, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal (Silvestre, C.; Figueiredo, P.; Rosa, P.)
Abstract	The problem of model falsification or model invalidation appears in several areas where we are interested in distinguishing among an eligible set of dynamic systems. In the context of fMRI studies of brain activity, modeling the haemodynamic response function (HRF) is a critical step. The estimation of the dynamic system describing a biophysical model of the HRF may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model identification task. Therefore, this paper proposes a systematic tool to address the problem of the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input distinguishable systems is introduced and applied to the 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 determined, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to the other. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that should be used in order to maximize the distinguishability of the HRF models under investigation. The proposed approach may be used to assess the performance of HRF model identification techniques from fMRI data.
Starting Page	5677
Ending Page	5680
File Size	581288
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5627891
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Noise Brain models Noise measurement Data models Thigh
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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