Specified-resolution wavelet analysis of activation patterns from BOLD contrast fMRI

Abstract

Functional magnetic resonance (MR) MR imaging (fMRI) with blood-oxygenation-level-dependent (BOLD) contrast localizes neuronal processing of cognitive paradigms. As magnetic resonance signal responses are small, functional mapping requires statistical analysis of temporally averaged image data. Although voxels activating at the paradigm frequency can be identified from the Fourier power spectrum, such analyses collapse the temporal information that is useful to establish consistency of responses during the paradigm. The design of a set of nonorthogonal wavelets of specified frequency resolution within the power spectrum was investigated for extracting desired frequency responses from the noisy signal intensity of individual voxels. These wavelets separate the low-frequency cognitive response to the paradigm from the respiratory and cardiac responses at higher frequencies. The retention of the temporal information, possible by wavelet analysis, allows the MR signal changes to be compared to changes in behavioral responses over the duration of an entire paradigm. The amplitude and time delay of the wavelet specified by the paradigm identify quantitatively the size of the MR signal change and the temporal delay of the hemodynamic BOLD response, respectively. This specified-resolution wavelet analysis was demonstrated for individual voxels and maps through the frontal eye fields using a visually guided saccade paradigm.

Emotional task-dependent low-frequency fluctuations and methylphenidate: Wavelet scaling analysis of 1/f-type fluctuations in fMRI of the cerebellar vermis

“…The method utilized long (11-min), continuous sequences of images for processing, which were much longer than both the 10-60 s blocks typically used in standard fMRI experiments and the data sets studied with other wavelet processing methods (Basar et al, 1999;Brammer, 1998;Breakspear et al, 2004;Bullmore et al, 2001Bullmore et al, , 2003Bullmore, 2002, 2004;Long et al, 2004;Muller et al, 2003;Ruttimann et al, 1998;Shimizu et al, 2004;von Tscharner and Thulborn, 2001). As such, it is doubtful that subjects would remain in a single stationary mental state for the entire scan period.…”

Detection and quantification of a wide range of fMRI temporal responses using a physiologically‐motivated basis set

“…Whether other approaches with the potential for identifying responses with a range of dynamics (e.g., wavelet analysis, fuzzy clustering, or principle component analysis) would also perform well on auditory cortical data is an open question. For the most part, these techniques have been tested on less challenging data, typically sustained responses, either simulated or measured [Baumgartner et al, 1998; Brammer, 1998; Chuang et al, 1999; Fadili et al, 2000; Golay et al, 1998; Sychra et al, 1994; von Tscharner and Thulborn, 2001]. Applying these techniques to a broader range of waveforms, such as those found in auditory cortex, would provide a far stronger test of their detection capabilities.…”

“…Of the various approaches with the potential to detect a range of response waveshapes, we identified the general linear model (GLM) as having, in principle, the characteristics needed to meet our goals, and rejected several other alternatives because they did not satisfy our requirements. In theory, a signal decomposition based on wavelet analysis may support the detection of responses with dynamics that vary over different temporal scales [Brammer, 1998; von Tscharner and Thulborn, 2001]. However, a wavelet transformation does not necessarily facilitate the extraction of directly pertinent, interpretable information concerning the temporal dynamics of activation.…”

Wavelet-based interpolation algorithm for MRI images

“…Its capacity for obtaining images from soft tissues has made it a valuable aid in the diagnosis of several injuries and diseases [4]. A relatively new application of MRI images is the generation of a 3D model of a certain section of the body.…”