Wavelet-Based Multi-Resolution Statistics for Optical Imaging Signals: Application to Automated Detection of Odour Activated Glomeruli in the Mouse Olfactory Bulb
B. Bathellier, D. Van De Ville, T. Blu, M. Unser, A. Carleton
NeuroImage, vol. 34, no. 3, pp. 1020–1035, February 1, 2007.
Optical imaging techniques offer powerful solutions to capture brain networks processing in animals, especially when activity is distributed in functionally distinct spatial domains. Despite the progress in imaging techniques, the standard analysis procedures and statistical assessments for this type of data are still limited. In this paper, we perform two in vivo non-invasive optical recording techniques in the mouse olfactory bulb, using a genetically expressed activity reporter fluorescent protein (synaptopHfluorin) and intrinsic signals of the brain. For both imaging techniques, we show that the odour-triggered signals can be accurately parameterized using linear models. Fitting the models allows us to extract odour specific signals with a reduced level of noise compared to standard methods. In addition, the models serve to evaluate statistical significance, using a wavelet-based framework that exploits spatial correlation at different scales. We propose an extension of this framework to extract activation patterns at specific wavelet scales. This method is especially interesting to detect the odour inputs that segregate on the olfactory bulb in small spherical structures called glomeruli. Interestingly, with proper selection of wavelet scales, we can isolate significantly activated glomeruli and thus determine the odour map in an automated manner. Comparison against manual detection of glomeruli shows the high accuracy of the proposed method. Therefore, beyond the advantageous alternative to the existing treatments of optical imaging signals in general, our framework propose an interesting procedure to dissect brain activation patterns on multiple scales with statistical control.
Supplementary data
- Supplementary Figure 1 (PDF file) (400 kb). Robustness of LM fitting to occasional interfering signals. (Left) LM fitting extractions of the SpH signal for methyl benzoate (50%) and citral (20%). (Right) Same data but obtained with the “blank” subtraction method. The colour scales span the entire range of the images. “Blank” subtraction images show a strong signal from the blood vessels which cover the olfactory bulb. These blood vessel artefacts almost do not appear on LM derived images.
- Supplementary Figure 2 (PDF file) (376 kb). Concentration-response functions for different glomeruli. (A) Maps of SpH signal obtained after blank subtraction for three concentrations of amyl acetate (1%, 5%, 20%). (B) Corresponding maps obtained with the wavelet statistical analysis (including sub-band selection: J = 2, see Fig. 7). (C) Amplitude of response for 6 concentrations of amyl acetate in the 6 glomeruli selected in (A) and (B). Each point is derived from 4 repetitions of the stimulus presentation. Error bars indicate the observed S.D. Concentration-response curves obtained after the full wavelet analysis (red) were much more regular (notice also the reduced S.D.) than the one obtained after the simple blank subtraction (black).
@ARTICLE(http://bigwww.epfl.ch/publications/bathellier0701.html,
AUTHOR="Bathellier, B. and Van De Ville, D. and Blu, T. and Unser, M.
and Carleton, A.",
TITLE="Wavelet-Based Multi-Resolution Statistics for Optical Imaging
Signals: {A}pplication to Automated Detection of Odour Activated
Glomeruli in the Mouse Olfactory Bulb",
JOURNAL="NeuroImage",
YEAR="2007",
volume="34",
number="3",
pages="1020--1035",
month="February 1,",
note="")