Wavelet decomposition analysis for ultra-high temporal resolution fMRI time series

Xu Feng, Zibonele A. Valdez-Jasso, Lu Hanzhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Functional magnetic resonance imaging (fMRI) is a powerful tool for human brain mapping. Previously, it has primarily been applied at low temporal resolution, i.e. repetition time >500ms, and cannot resolve rapid neuronal and vascular function/dysfunction. Here we aim to achieve a ten-fold improvement in temporal resolution by localizing the brain coverage (i.e. single-slice) in combination with optimized MR acquisition schemes, e.g. using parallel imaging, reducing flip angle and reducing echo-time. A new challenge is that, at this resolution, physiologic noises become more pronounced and may mix with the true brain activation signals. We therefore applied wavelet decomposition to separate the MRI time-course into four components: fMRI signal, cardiac pulsation signal, respiratory fluctuation signal, and residual noise. In vivo experiments using flashing checkerboard visual stimulation revealed hemodynamic responses that are consistent with previous low-resolution data but with more detailed temporal features. Time-to-peak of the fMRI signal was determined in six healthy subjects and one patient with possible Alzheimer's disease. Measurement reproducibility of the proposed method was also evaluated in three of the subjects.

Original languageEnglish (US)
Title of host publication2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS
Pages86-89
Number of pages4
DOIs
StatePublished - 2007
Event2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS - Richardson, TX, United States
Duration: Nov 11 2007Nov 12 2007

Other

Other2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS
CountryUnited States
CityRichardson, TX
Period11/11/0711/12/07

Fingerprint

Wavelet Analysis
Wavelet decomposition
Time series
Magnetic Resonance Imaging
Brain
Brain mapping
Hemodynamics
Magnetic resonance imaging
Chemical activation
Noise
Imaging techniques
Brain Mapping
Photic Stimulation
Blood Vessels
Experiments
Healthy Volunteers
Alzheimer Disease

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Feng, X., Valdez-Jasso, Z. A., & Hanzhang, L. (2007). Wavelet decomposition analysis for ultra-high temporal resolution fMRI time series. In 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS (pp. 86-89). [4454180] https://doi.org/10.1109/EMBSW.2007.4454180

Wavelet decomposition analysis for ultra-high temporal resolution fMRI time series. / Feng, Xu; Valdez-Jasso, Zibonele A.; Hanzhang, Lu.

2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS. 2007. p. 86-89 4454180.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Feng, X, Valdez-Jasso, ZA & Hanzhang, L 2007, Wavelet decomposition analysis for ultra-high temporal resolution fMRI time series. in 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS., 4454180, pp. 86-89, 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS, Richardson, TX, United States, 11/11/07. https://doi.org/10.1109/EMBSW.2007.4454180
Feng X, Valdez-Jasso ZA, Hanzhang L. Wavelet decomposition analysis for ultra-high temporal resolution fMRI time series. In 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS. 2007. p. 86-89. 4454180 https://doi.org/10.1109/EMBSW.2007.4454180
Feng, Xu ; Valdez-Jasso, Zibonele A. ; Hanzhang, Lu. / Wavelet decomposition analysis for ultra-high temporal resolution fMRI time series. 2007 IEEE Dallas Engineering in Medicine and Biology Workshop, DEMBS. 2007. pp. 86-89
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