Skip to main content
SpringerLink
Log in

4D functional ultrasound imaging of whole-brain activity in rodents

  • Brief Communication
  • Published:

From Nature Methods

View current issue Submit your manuscript

Abstract

We extended the capabilities of functional ultrasound to whole-brain four-dimensional (4D) neuroimaging. Our multiplane-wave transmission scheme on matrix arrays at thousands of frames per second provides volumetric recordings of cerebral blood volume changes at high spatiotemporal resolution. We illustrated the approach in rats while providing multiple sensory stimuli, for 4D functional connectivity and during instantaneous tracking of epileptiform events.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: 4D functional ultrasound imaging during task evoked activity.
Fig. 2: Application of whole rat brain fUS imaging to FC mapping and 4D tracking of epileptiform events.

Similar content being viewed by others

Data availability

All data supporting the findings of this study associated with figures are available upon request. Example data can be downloaded in the Supplementary Information.

Code availability

Example encoding and post-processing codes associated with figures are provided as Supplementary Software. Step-by-step instructions are available in a Readme pdf document to guide the user. A binary executable file for GPU-based 3D beamforming is included to process decoded sample radio frequency data. The low-level beamforming code is available in the framework of an official collaboration between academic institutions.

References

  1. Friston, K. J. Science 326, 399–403 (2009).

    Article  CAS  Google Scholar 

  2. Xia, J. & Wang, L. V. IEEE Trans. Biomed. Eng. 61, 1380–1389 (2014).

    Article  Google Scholar 

  3. Razansky, D., Buehler, A. & Ntziachristos, V. Nat. Protoc. 6, 1121–1129 (2011).

    Article  CAS  Google Scholar 

  4. Gottschalk, S., Felix Fehm, T., Luís Deán-Ben, X. & Razansky, D. J. Cereb. Blood Flow. Metab. 35, 531–535 (2015).

    Article  Google Scholar 

  5. Macé, E. et al. Nat. Methods 8, 662–664 (2011).

    Article  Google Scholar 

  6. Mace, E. et al. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 60, 492–506 (2013).

    Article  Google Scholar 

  7. Deffieux, T., Demene, C., Pernot, M. & Tanter, M. Curr. Opin. Neurobiol. 50, 128–135 (2018).

    Article  CAS  Google Scholar 

  8. Tanter, M. & Fink, M. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 61, 102–119 (2014).

    Article  Google Scholar 

  9. Tiran, E. et al. Phys. Med. Biol. 60, 8549–8566 (2015).

    Article  Google Scholar 

  10. Errico, C. et al. NeuroImage 124, 752–761 (2016).

    Article  Google Scholar 

  11. Osmanski, B.-F., Pezet, S., Ricobaraza, A., Lenkei, Z. & Tanter, M. Nat. Commun. 5, 5023 (2014).

    Article  CAS  Google Scholar 

  12. De Luca, M., Beckmann, C. F., De Stefano, N., Matthews, P. M. & Smith, S. M.NeuroImage 29, 1359–1367 (2006).

    Article  Google Scholar 

  13. van den Heuvel, M. P. & Hulshoff Pol, H. E. Eur. Neuropsychopharmacol. 20, 519–534 (2010).

    Article  Google Scholar 

  14. Ayata, C. & Lauritzen, M. Physiol. Rev. 95, 953–993 (2015).

    Article  CAS  Google Scholar 

  15. Cain, S. M. et al. Proc. Natl Acad. Sci. USA 114, 2401–2406 (2017).

    Article  CAS  Google Scholar 

  16. Dizeux, A. et al. Nat. Commun. 10, 1400 (2019).

    Article  Google Scholar 

  17. Bourdeau, R. W. et al. Nature 553, 86–90 (2018).

    Article  CAS  Google Scholar 

  18. Sieu, L.-A. et al. Nat. Methods 12, 831–834 (2015).

    Article  CAS  Google Scholar 

  19. Demene, C. et al. Sci. Transl. Med. 9, eaah6756 (2017).

    Article  Google Scholar 

  20. Provost, J. et al. Phys. Med. Biol. 59, L1 (2014).

    Article  Google Scholar 

  21. Bercoff, J. et al. IEEE Trans. Ultrason Ferroelectr. Freq. Control 58, 134–147 (2011).

    Article  Google Scholar 

  22. Demene, C. et al. IEEE Trans. Med. Imaging 34, 2271–2285 (2015).

    Article  Google Scholar 

  23. Demené, C. et al. NeuroImage 127, 472–483 (2016).

    Article  Google Scholar 

  24. Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A. & Bjaalie, J. G. NeuroImage 97, 374–386 (2014).

    Article  Google Scholar 

  25. Hernandez, V. et al. Front. Neuroinform. 5, 26 (2011).

    Google Scholar 

  26. Bowden, D. M., Song, E., Kosheleva, J. & Dubach, M. F. Neuroinform. 10, 97–114 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the European Research Council under the European Union’s Seventh Framework Program (no. FP7/2007-2013)/ERC Advanced grant agreement no. 339244-FUSIMAGINE) and by a funding from the Human Brain Project, Project no. FUSIMICE ANR-15-HBPR-0004. This work was also supported by the AXA Research Fund.

Author information

Author notes

  1. These authors contributed equally: Thomas Deffieux, Mickael Tanter.

Authors and Affiliations

  1. Physics for Medicine Paris, INSERM U1273, ESPCI Paris, CNRS FRE 2031, PSL Research University, Paris, France

    Claire Rabut, Mafalda Correia, Victor Finel, Sophie Pezet, Mathieu Pernot, Thomas Deffieux & Mickael Tanter

Authors
  1. Claire Rabut
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mafalda Correia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor Finel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sophie Pezet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mathieu Pernot
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Thomas Deffieux
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mickael Tanter
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.T. and T.D. conceived the study. C.R., V.F., M.P. and M.C. developed sequence acquisitions, C.R. and S.P. acquired data. C.R., T.D. and M.T. performed data processing. C.R., S.P., T.D. and M.T. interpreted the results. C.R. and M.T. wrote the first draft of the manuscript with substantial contribution from T.D., S.P. and M.P. All authors edited and approved the final version of the manuscript.

Corresponding author

Correspondence to Mickael Tanter.

Ethics declarations

Competing interests

T.D., M.P. and M.T are co-founders and shareholders of Iconeus company commercializing ultrasound neuroimaging scanners.

Additional information

Peer review information Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Subject-level correlation matrices of four animals estimated from 103 common cortical and subcortical regions of interest (ROI) under anesthesia.

Connectivity matrices display the pairwise correlation (Pearson’s product-moment) between all the 103 common ROIs signals. The neocortex regions are well intra-correlated in rats 1, 2 and 3 : [Rat 1: r(intra-neocortex)= 0.37; r(neocortex/others)=0,19], [Rat 2: r(intra-neocortex) = 0.37; r (neocortex/others)=0,09] [Rat 3: r(intra-neocortex) = 0.40; r(neocortex/others)=0,13] [Rat 4: r(intra-neocortex) = 0.24; r (neocortex/others)=0,21] as well as the endbrain regions in all rats: [Rat 1: r(intra-endbrain) = 0.29; r(endbrain/others)=0,21], [Rat 2: r(intra-endbrain) = 0.33; r(endbrain/others)=0,11] [Rat 3: r(intra- endbrain) = 0.28; r(endbrain /others)=0,11] [Rat 4: r(intra- endbrain) = 0.36; r(endbrain /others)=0,21]. Legends: r(intra-neocortex) = mean pairwise Pearson’s product-moment of regions belonging to the neocortex. r(neocortex/others) = mean pairwise Pearson’s product-moment between regions belonging to the neocortex and all the other regions of the brain. r(intra-endbrain) = mean pairwise Pearson’s product-moment of regions belonging to the endbrain. r(endbrain/others) = mean pairwise Pearson’s product-moment between regions belonging to the endbrain and all the other regions of the brain.

Supplementary Figure 2 Spatial mapping of the time of arrival of spreading depression activity.

Power Doppler (a) volume with overlaid volume color-coded map and (b-c) 2D planes with overlaid isochronous maps (one line every 7s) showing the propagation of a cortical depression wave during the seizure presented in Supplementary Video 2 (from time T=40 s to T=120 s) and Supplementary Video 3 (« Left Cortical Spreading Depression Wave »).

Supplementary Figure 3 Propagation of cortical spreading depressions both in ipsi and contralateral hemispheres.

The space-time representation of temporal CBV changes along segments A1-A2, B1-B2, C1-C2, D1-D2 highlights successive propagation patterns. Measurements of the slope in this space-time representation give access to the propagation speed centered around 3.0 +/- 0.3 mm/min.

Supplementary Figure 4 Hadamard decoding process on received Radio-Frequency (RF) data from a 80µm static nylon wire.

We show here ultrasonic raw data recorded on one set of 32 channels from the 32x32 matrix array probe. a) Reception of the eight echoes from the wire after the emission of eight successive tilted polarized plane waves. The polarizations of the wavefronts coming back from the wire are shown. The +1/-1 emitted polarizations correspond to the coefficients of the Hadamard matrix H8. b) Coherent summations (following the coefficient of H8) of the eight receptions. In the first combination, the contributions of the echoes from reception#2 to reception#8 disappear thanks to the multiplicative factors applied, whereas the contributions of the first echo are added ((+1)+(+1)+ +(+1)=8). Therefore, we obtain the same image as if only one individual transmit was sent with eight-fold amplitude. The time delay τn corresponding to the time before the emission of the wave #n is then compensated and the eight resulting volumes are coherently summed c) To obtain the final power volume, we square the imaging volume. d)-e) Axial and lateral resolution calculated with the point-spread function of the power image of the static nylon wire of 80µm. The spatial resolution is quantified as the full width at half maximum. We calculated Δz=180µm and Δx=240µm. The same experiment was performed with the wire rotated 90 degrees and a similar lateral resolution was found(Δy=240µm). f) On the edge of the volume, the axial resolution does not change but lateral resolution increases up to 320 µm.

Supplementary Figure 5 Virtual sources emitting a multiplane waves sequence.

Acquisitions are defined by virtual ultrasonic sources provided on a virtual array located far behind the probe (60m). For each individual source, delays are computed and tilted plane waves are obtained.

Supplementary Figure 6 Comparison between Conventional Compounding and Multiplane Transmit Compounding.

Typical example of two coronal imaging planes in a trepanned rat, both acquired with a)-c): the conventional compounding method using successive transmission/receptions of tilted plane waves or b)-d): the multiplane wave transmit method using simultaneous transmission of multiple tilted plane waves. To be comparable, data are normalized and displayed in logarithmic scale (−40dB to 0dB). Those two coronal planes are representative of all imaging planes in the imaged volume.

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and Tables 1 and 2.

Reporting Summary

Supplementary Video 1

4D functional ultrasound imaging of whole-brain activity in rat brain. Time, 1–>10 s. Moving view of a cerebral vascular doppler volume of a whole rat brain embedded in a brain atlas. The Doppler volume is acquired in 350 ms thanks to the emission of successive plane waves with the matrix array probe that allows the insonification of the whole volume at each shot. From time = 11 s: functional activity of the rat brain following ON–OFF stimulations. Baseline signal (when no stimulation) is indicated in gray, and all voxels with an increase of the CBV above 15% of their baseline over time are overlaid in orange. Whiskers were stimulated with a cotton swab, followed by a visual stimulation.

Supplementary Video 2

Different views of the propagation of cortical depression waves in a single rat during an ictal event Right: four static views. Left, the camera moves around the brain. Cortical depression waves after injection of 1 µl of 4-aminopyridine (4AP) (15 mmol l−1) at T = 7 s.

Supplementary Video 3

3D time-of-arrival map showing a power Doppler cerebral volume with overlaid color-coded 3D map Time of arrival color-coded map of the first and third cortical depression waves presented in Supplementary Video 1. The location of the onset of the spreading depression is shown in in dark blue.

Supplementary Software and Code

Hadamard coding (Generate multiplane wave coded emissions that can be used in a transmit sequence in a 3D research scanner) and decoding (decode the received RF buffer using the multiplane wave scheme.). Beamforming of decoded RF buffer data to obtain a beamformed IQ data volume. Clutter filter to remove signals coming from tissue and compute the 3D power Doppler volume. Computation of the normalized correlation between one Doppler movie and one stimulus signal (whisker stimulation) Computation of the connectivity matrix from one Doppler movie and associated 3D mask of regions of interest In vivo data provided from functional imaging experiments on a trepanned rat. Step-by step instructions are provided in the Readme pdf document.

Dataset 1

Coded_emissions.mat and Encoded_RF_buffer.mat : input data for Script 1_Encode_Emissions.m - RF_decoded.mat: input data for Script3_Beamform_decoded_RF.m -stim_tot: : input data for Script5_Create_Activation_maps.m -ROI_3D.mat: input data for Script6_Create_connectivity_matrix.m

Dataset 2a

IQ_part1.mat: first part of the input data for Script4_Apply_Clutter_Filter3D.m.

Dataset 2b

IQ_part2.mat: second part of the input data for Script4_Apply_Clutter_Filter3D.m.

Dataset 2c

IQ_part3.mat: third part of the input data for Script4_Apply_Clutter_Filter3D.m.

Dataset 3

Doppler_acq_stim.mat: input data for Script5_Create_Activation_maps.m.

Dataset 4a

PW_connectivity_part1.mat: first part of the input data for Script6_Create_connectivity_matrix.m.

Dataset 4b

PW_connectivity_part2.mat: second part of the input data for Script6_Create_connectivity_matrix.m.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rabut, C., Correia, M., Finel, V. et al. 4D functional ultrasound imaging of whole-brain activity in rodents. Nat Methods 16, 994–997 (2019). https://doi.org/10.1038/s41592-019-0572-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-019-0572-y

  • Springer Nature America, Inc.

This article is cited by

Search

Navigation