Real-time imaging of brain activity in freely moving rats using functional ultrasound

Nature Methods September 2015, Volume 12 No 9 pp795-893

Alan Urban1,2,5, Clara Dussaux1,2,5, Guillaume Martel1, Clément Brunner1–3, Emilie Mace4 & Gabriel Montaldo1,2

1UMRS 894 INSERM Centre de Psychiatrie et Neurosciences, Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.

2Optogenetics and Brain Imaging, Stroke Research Team, Paris, France.

3Sanofi Research and Development, Lead Generation to Candidate Realization, Chilly-Mazarin, France.

4Neural Circuit Laboratories, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.

5These authors contributed equally to this work. Correspondence should be addressed to A.U. (



Innovative imaging methods help to investigate the complex relationship between brain activity and behavior in freely moving animals. Functional ultrasound (fUS) is an imaging modality suitable for recording cerebral blood volume (CBV) dynamics in the whole brain but has so far been used only in head-fixed and anesthetized rodents. We designed a fUS device for tethered brain imaging in freely moving rats based on a miniaturized ultrasound probe and a custom-made ultrasound scanner. We monitored CBV changes in rats during various behavioral states such as quiet rest, after whisker or visual stimulations, and in a food-reinforced operant task. We show that fUS imaging in freely moving rats could efficiently decode brain activity in real time.


Rodent models are valuable for understanding the relationship between brain and behavior1,2. Most functional neuroimaging methods require head fixation and immobilization of the subjects, which suppresses most rodent behavior. Therefore, it is important to develop methods for imaging the entire brain in freely moving rodents.

Spatial patterns of neuronal activity can be studied at a macroscopic level in freely moving rodents using fluorescent, voltage-sensitive dyes and flexible fiber optic bundles connected to the head of the animal3. Single-cell-resolution imaging has been achieved with genetically encoded or loaded fluorescent calcium reporters in combination with head-restrained imaging4 or miniature head-mounted single-photon5 or two-photon6,7 microscopes. These techniques allow imaging at high spatial and temporal resolutions but are limited to the brain surface and typically have a limited field of view (FOV) (~500 µm).

On the other hand, optical techniques to image brain perfusion have a low temporal resolution inherently limited by the slow CBV dynamics but may offer a large FOV up to the entire surface of the brain. These techniques include small head-mounted systems enabling portable one-photon5 or two-photon imaging8, laser speckle imaging9, intrinsic optical imaging10 or diffuse optical tomography11.

fUS in head-fixed and anesthetized rodents can complement existing techniques to image whole-brain sensory-evoked hemodynamic responses in acute12 or chronic conditions13 at a good spatiotemporal resolution (pixel size of 100 µm × 150 µm, 400 ms) and without the need for contrast agents.

Here, we present advances in fUS technology for real-time brain imaging in freely moving rats, allowing studies of the relationship between rat behavior and brain activity. We demonstrate the advantages of freely moving fUS (fm-fUS) by imaging brain activity in rats that were quiet, during manual somatosensory or visual stimulations and during active tasks. We also validated fm-fUS for real-time decoding of brain activity in the cortex and in deep cerebral structure with a low error rate, making a first step toward its application to next-generation brain-computer interfaces (BCIs).



Implant and scanner for fUS imaging in freely moving rats

To reduce the burden on freely moving animals, fm-fUS required the development of a miniature ultrasound probe. We developed a 15-MHz ultrasound probe weighing less than 10 g without the cable (Fig. 1a) by limiting the number of piezoelectric elements to reduce cable and probe sizes and by using a cylindrical geometry avoiding the need for an acoustic lens (Supplementary Note 1).

Furthermore, we designed a magnetic implant allowing on-demand fixation of the ultrasound probe on the head of freely moving rats. The head-fixation system was 2.5 cm in width, 1.5 cm in height and 0.5 cm in depth (Fig. 1b) and was divided into three 3D-printed components: a head plate permanently attached to the skull, a magnetic ultrasound probe holder and a magnetic head shield protecting the brain when the ultrasound probe was not attached (Fig. 1c and Supplementary Data)

To implant the head plate, we performed a thin craniotomy of 2.5 mm by 10 mm along the coronal axis and then affixed the head plate with six stainless steel screws (0.5 mm in diameter and 5 mm in length), which reduces mechanical vibrations. We filled the empty space between the skull surface and the top of the head-plate window with 2% agarose for acoustic coupling; the agarose contained ampicillin to limit infections. The acquired CBV images have a pixel size of 100 µm × 150 µm, a slice thickness of 500 µm and a FOV of 9.6 mm × 9 mm covering approximately two-thirds of an adult rat brain in a coronal plane (Fig. 1d and Supplementary Note 2).

To reduce noise during rat movements, we measured CBV variations with a fUS sequence modified from a previously used power Doppler sequence12,14,15 with the addition of a spatiotemporal filter algorithm. As fUS signals are proportional to CBV15, we refer to the acquired images as ‘CBV images’ and to the corresponding temporal signal as ‘CBV signal’.

We performed experiments using seven adult Sprague-Dawley rats that recovered from surgery in individual cages for 2 d before ultrasound acquisition The FOV of the miniature probe allowed imaging of cortical and subcortical regions including the hippocampus and the thalamus but was limited to one hemisphere at a time (Fig. 1d,e). Nevertheless, images of the entire brain were reconstructed by merging two CBV images after manual displacement of the probe (Fig. 1e).

Figure 1 | Miniaturized device for fUS in freely moving rats. (a) Miniaturized ultrasound probe. (b) Computer-aided design drawing of the magnetic head-fixation system showing the head plate (purple) and the probe holder (brown) with magnets (yellow). (c) Rat at rest (with the implanted head plate) or during fm-fUS, carrying the ultrasound probe. (d) Coronal view of the imaging plane. The dashed red square indicates the field of view of the probe. BF, barrel field cortex; M, motor cortex; TH, thalamus; HPC, hippocampus; D, dorsal; V, ventral; L, left; R, right; B –3.00, 3.00 mm posterior to bregma. (e) CBV images in freely moving conditions, taken with the probe positioned on the left or the right hemisphere and then merged into a single image. Scale bars, 5 mm.

Resting-state imaging

To test the performance of the miniaturized ultrasound probe, we recorded CBV images of four rats in their home cages. These imaging sessions, ranging from 15 to 40 min, were designed to assess the stability of the images and to study brain activity during rest. We tracked and quantified animal movements using a camera under reduced illumination. CBV images were stable while the animals were moving in the cages at velocities up to 0.5 m/s and during natural behaviors such as digging, chewing, feeding and grooming. However, images with high noise levels occurred occasionally (<3% of all images acquired in a session) during vigorous head shaking. We excluded these images when their total intensity exceeded more than three times the s.d. of the baseline (Supplementary Fig. 1).

We analyzed the temporal evolution of the CBV when rats were in resting state. We observed low-frequency oscillations (LFOs) of the CBV signal (Fig. 2a) in the cortex with a broadly distributed spectrum below 0.4 Hz. We also observed LFOs in subcortical areas (Fig. 2b) but with lower amplitude than those seen in cortical LFOs in previous functional magnetic resonance imaging (fMRI) and optical imaging studies16,17,18 (Supplementary Fig. 2a,b). LFOs in the cortex also had smaller amplitudes in rats under isoflurane anesthesia compared to those in awake rats (1.8% ± 0.9% versus 6.8% ± 1.7% (±s.d.), respectively; Supplementary Fig. 2c). We used a standard seed-based approach to study the resting-state functional connectivity by analyzing the temporal correlation of the CBV between different brain regions. The two motor cortices were strongly connected, as confirmed by their high level of correlation and coherence at frequencies below 0.15 Hz (Fig. 2c,d). In addition, the barrel field (BF) cortex showed no spectral correlation with other regions of the imaging plane (Fig. 2c), and we observed no coherent association between BF and motor cortices using cross-spectrum analysis. Similarly, subcortical structures such as the hippocampus and the striatum were not correlated with other cortical areas (data not shown).

Figure 2 | Functional connectivity addressed by fm-fUS imaging. (a) Resting-state functional connectivity map based on seed regions i in the primary somatosensory barrel cortex (BF; magenta) and ii in the motor cortex (M; green). Coronal view shown at bregma –2.00 mm. (b) Spontaneous oscillations of the CBV signal in the motor cortex in resting rats. (c) Frequency distribution of the CBV signal in b showing low-frequency oscillations (<0.4 Hz; vertical dashed line). (d) Cross-spectrum analysis (coherence) between seed regions ii and iii of the opposite motor cortices. Scale bar, 2 mm. Image and plots are representative of n = 4 rats.


Brain activity evoked by manual whisker stimulation

We imaged stimulus-evoked hemodynamic responses in the BF cortex after deflection of all whiskers on the contralateral side with an artist’s paintbrush. We applied two different stimulation paradigms to assess the sensitivity of fm-fUS: a 7-s (long) stimulus (ten deflections) or a 1-s (short) stimulus (two deflections). The short stimulus duration mimics active touch sensing during surface exploration. Brain activation maps of the correlation between the CBV signal and the stimulus pattern revealed significant activation in the contralateral hemisphere in response to single trials of long or short stimuli (P < 0.01, one-tailed Student’s t-test; Fig. 3a and Supplementary Fig. 3). The long stimulus resulted in activation across the BF cortex, whereas the activated area was more restricted for a short stimulus (5.1 ± 0.9 mm2 versus 3.2 ± 0.6 mm2 (±s.d.), respectively). The hemodynamic response was specific, as stimulation of either side of the muzzle led to CBV increases in the contralateral BF cortex (Supplementary Fig. 4).

We analyzed the temporal evolution of the CBV signal upon whisker stimulation in the contralateral BF cortex and, as a control, in the ipsilateral motor cortex (Fig. 3a). We performed five trials of each of long and short stimuli and plotted the CBV response in the BF for each trial and after averaging. The peak amplitude (PA) of the CBV signal increased on average by 26.1% ± 5.6% and 19.7% ± 4.5% (±s.d.) over baseline for long and short stimuli, respectively. CBV responses evoked by long stimuli (Supplementary Fig. 3) were consistently monophasic in shape, with a prolonged and slow decay that remained elevated for several seconds beyond the duration of the stimulus. In contrast, CBV signal rapidly returned to baseline after short stimuli (Fig. 3a).

We compared the temporal characteristics of the CBV signal for PA, time at half-maximum (THM) and full-width at half-maximum (FWHM) reported as mean values and s.d. for each stimulation paradigm (Supplementary Fig. 5a). We observed a significant difference for PA but not for THM (P = 0.0051 for PA, P = 0.1644 for THM, ANOVA; Supplementary Fig. 5b). FWHM was significantly increased for long stimuli compared to short stimuli (9.5 ± 3.3 s versus 2.2 ± 0.4, respectively, P < 0.0001, Kruskal-Wallis test). Moreover, CBV signals remained stable in the control motor cortex during whisker stimulation (Fig. 3a).


Functional imaging in subcortical brain structures

We assessed the ability of fm-fUS to detect brain activity in subcortical structures by analyzing CBV signal variations in deep brain regions during manual stimulation of the whiskers. Whisker stimulations led to an increase of hemodynamic activity in the BF cortex and in the contralateral ventral posterior medial (VPM) nucleus of the thalamus (Fig. 3b). CBV responses were lower in the VPM than in the cortex for both short (data not shown) and long stimuli such that the biphasic shape of the hemodynamic response function could be detected only by averaging five trials. Nevertheless, we measured an averaged PA of 9.0% ± 3.2% in the VPM thalamic nucleus.

In a separate set of experiments, we recorded hemodynamic signals in the lateral geniculate nucleus (LGN), the main thalamic relay conveying visual inputs from the retina to the primary visual cortex19. Long (7-s) or short (2-s) flashes of blue light were randomly presented to freely moving rats in their cages. Both stimuli elicited a significant increase of the CBV signal in the LGN that we detected in real time by fm-fUS without temporal averaging (P < 0.01, one-tailed Student’s t-test; Fig. 3c and Supplementary Fig. 6).

Figure 3 | Spatial and temporal evolution of the CBV signal in response to whisker or visual stimulation. (a) Left, brain activation map of a single 1-s short manual stimulus applied to the whiskers. Center, temporal evolution of the CBV signal in the primary somatosensory barrel (BF) cortex for five single trials. Right, average CBV signal measured in activated (solid line) and control (dashed line) ROIs. Image and plots are representative of n = 4 rats. (b) Left, brain activation map of five 7-s long manual stimulation of the whiskers. Center, temporal evolution of the CBV signal in the ventral posterior medial (VPM) thalamic nucleus for five single trials. Right, average CBV signal measured in activated and control ROIs. Image and plots are representative of n = 4 rats. (c) Left, brain activation map of a single 2-s short visual stimulus. Center, temporal evolution of the CBV signal in the lateral geniculate nucleus (LGN) for five single trials. Right, average CBV signal measured in activated and control ROIs. Image and plots are representative of n = 2 rats. Arrows and solid boxes correspond to activated ROIs; dashed boxes correspond to control ROIs. Vertical bars (red) indicate stimulus duration. M, motor cortex; V2, secondary visual cortex. Scale bars, 2 mm.

Behavioral neuroimaging

We monitored CBV signals during an operant food-reinforced conditioning procedure using four rats that were food restricted and trained to perform a task consisting of a round trip between a starting zone and a reward zone (reinforced round trip, RRT) on an elevated corridor. We placed the reward between vertical sticks to stimulate the animals’ whiskers (Fig. 4a and Online Methods). Sessions were quantified by videography and lasted up to 20 min, during which rats performed from 5 to 15 RRTs (Fig. 4a). We implanted the fm-fUS head plates after a training period of 10 d, and fm-fUS recordings started 2 d after recovery from surgery. Even though the fm-fUS device did not impose an excessive burden, we measured a slight but significant reduction of the running velocity of the rats after connection of the probe compared to values measured before surgery or after surgery without the probe (P < 0.0001, ANOVA; Supplementary Fig. 7). Nevertheless, we did not observe a significant difference between conditions when measuring the ‘residency time’ during which the whiskers were touching the vertical sticks (P = 0.1257, Kruskal-Wallis test; Supplementary Fig. 7).

In a typical behavioral session, a conditioned rat completed 13 RRTs in 8.5 min (Fig. 4b) that were consistently associated with a large increase of CBV in the BF. We averaged 55 RRTs, from eight independent imaging sessions in four rats, to study the characteristics of the CBV in the reward zone (Fig. 4c). We compared these data to those obtained by manual stimulation. During the behavioral task, the PA of the hemodynamic response was not significantly different from the signal observed for a manually evoked 1-s activity (P = 0.0907, 14.7% ± 5.4% versus 19.7% ± 4.5%, respectively, ANOVA; Supplementary Fig. 5a). Both the FWHM (4.0 ± 0.9 s) and residency time (2.4 ± 1.8 s) values during the behavioral task were intermediate between those for 1-s (short) and 7-s (long) manual stimuli (Supplementary Figs. 5 and 7). Finally, the THM during active tasks (2.1 ± 0.4 s) was not significantly different from the THM for both long and short manual stimuli, hence confirming that 2 s are required for decoding CBV signal in the BF after stimulation of the whiskers (P = 0.0668 for long versus short, P = 0.4972 for long versus task, P = 0.1855 for short versus task, Student’s t-test; Supplementary Fig. 7).

The BF cortex was specifically activated when the whiskers were stimulated during collection of the reward (Fig. 4d). Furthermore, LFOs were present in all cortical regions when rats were running toward or away from the reward zone during reward collection (Fig. 4e). In control experiments, the BF cortex was not activated during the same task when whiskers were trimmed and vertical sticks were removed from the reward zone (Supplementary Fig. 8).

Figure 4 | fm-fUS imaging during a food-reinforced operant task. (a) Schematic top view of the experimental setup. The blue line indicates the trajectory of the rat during a typical imaging session. The right subpanel (red box) shows a magnified view of the reward zone. (b) Position of the rat along the corridor during a typical task and the corresponding temporal evolution of CBV signal in the BF. (c) Average CBV signal of 13 tasks presented in b. Grey shaded area represents s.d. The vertical bar (red) corresponds to the average residency time in the reward zone. (d) Average brain activation map from 55 tasks in eight independent imaging sessions from four rats. Scale bar, 2 mm. (e) Spatiotemporal diagram of the CBV signal in the cortex. Image and plots are representative of n = 4 rats. M, motor cortex; HL, hindlimb cortex; RSD, retrosplenial dysgranular cortex.

Real-time decoding of brain activity

To analyze CBV images in real time during tasks, we devised a specific framework to demonstrate additional capabilities of fm-fUS not reported for other imaging modalities such as fMRI or positron emission tomography20. Our real-time analysis tool recorded and analyzed the CBV signal in a specific ROI corresponding to all voxels in the BF in response to a 1-s manual stimulation (Fig. 5a). The text message “Active” was displayed on the screen when the CBV signal exceeded a defined threshold (Supplementary Video 1). We determined the threshold value in a set of ten recording sessions in four different animals using a peak detection and classification algorithm after normalization of the CBV signal. In this data set, 1,022 peaks segregated in two significantly different populations of 75 ‘active’ and 947 ‘inactive’ peaks (P < 0.001, Student’s t-test; Fig. 5b). We evaluated the detection performance of CBV activity in the BF associated with reward collection by receiver operating characteristic (ROC) curve analysis21. fm-fUS efficiently decoded brain activation with a sensitivity of 93% and a specificity of 94% using a normalized CBV threshold value of 2 (Fig. 5c).

Figure 5 | Real-time decoding of brain activity in freely moving rats. (a) Schematic workflow for decoding brain activity via analysis of the CBV signal in an ROI located in the BF (white dashed line) during reward collection. The arrowhead and horizontal line (black) show the selected threshold value. A dedicated algorithm can classify ‘active’ (red) and ‘inactive’ (blue) peaks in real time. (b) Distribution of active and inactive peaks in all experiments. The arrowhead and vertical line (black) show the selected threshold value as in a. (c) Receiver-operating characteristic (ROC) curve analysis of the distribution presented in b showing the threshold value (arrowhead) that was chosen during brain decoding experiments. Scale bar, 2 mm. Image and plots are representative of n = 4 rats.

We observed a 3.5-s delay (seven frames) between the beginning of the whisker deflection and the detection of hemodynamic activity in the activated ROI (Supplementary Video 1). This delay corresponds to 1.3-s processing time plus a 2-s THM related to slow hemodynamic response. In a separate set of experiments, fm-fUS demonstrated similar levels of sensitivity (93%) and specificity (95%) during brain activity decoding in the subcortical LGN of the thalamus in response to 2-s visual stimuli that were randomly presented to freely moving rats in their home cage (80 active and 454 inactive peaks; Supplementary Fig. 9).

In short, fm-fUS was suitable for rapid classification of active and inactive peaks without requiring any assumption about the shape of the signal for validity.



fm-fUS is an imaging modality that allows detection of brain activity at the mesoscopic scale in freely moving rats, both in cortical and in subcortical areas not accessible to optical methods13. fm-fUS is translatable to other species including primates by adapting the frequency of the ultrasonic probe. A drawback is that fm-fUS, like fMRI, can only measure brain hemodynamics, which are secondary physiological correlates of neural activity, and with a temporal resolution inherently limited by slow changes in blood supply (~1 s). Nevertheless, fUS is compatible with electrophysiological recording at high temporal resolution12,13 to obtain complementary information on neuronal activity.

fm-fUS is currently limited to a single imaging plane at a time. Optical imaging technologies such as intrinsic optical imaging22 or laser speckle imaging23have advantages for applications requiring large FOVs, such as resting-state functional connectivity studies. However, fm-fUS has not yet reached full maturity, and miniature two-dimensional (2D) matrix array transducers or fast-scanning strategies currently under development will enable 3D imaging of the whole brain. Moreover, the weight of the probe can be further reduced to less than 1 g by using transducer technologies similar to those used in intravascular catheters for humans24. This approach would extend fm-fUS to mouse models.

Our work demonstrates that fm-fUS can detect brain activity in cortical or subcortical regions during active tasks. A potential application for fm-fUS lies in BCIs. An important issue with fm-fUS decoding is the delay of 3.5 s between task initiation and detection of the CBV signal. Although this delay appears long, it is comparable to delays observed in steady-state visually evoked potential–based BCIs, which have the highest bit rate among electroencephalogram-based methods (3.4 s; ref. 25). Nevertheless, fm-fUS accuracy and throughput may be increased by analyzing many brain areas simultaneously (multiplexing) and by reducing the processing time.

Considering its spatial and temporal resolutions, penetration depth, real-time processing and functionality, fm-fUS is a promising tool to help understand brain function and may support the development of new types of BCIs.



Supplementary Figures


Methods and any associated references are available in the online version of the paper.

Note: Any Supplementary Information and Source Data files are available in the online version of the paper.


We thank the Ecole Normale Supérieure de Lyon for it’s financial support of the 4th year study project of C. Dussaux. We thank L. Zamfirov and S. Raja for computer-assisted design and technical help with the fm-fUS implant. We also thank the Phenobrain platform of the “Centre de Psychiatrie & Neuroscience” for animal care. This work was supported by a grant from Agence Nationale de la Recherche, Paris.


A.U. and C.D. contributed equally to this work. A.U. and G. Montaldo designed the experiments. C.D. and G. Martel performed behavioral experiments. A.U. designed the head implant and associated surgical procedures. G. Montaldo programmed the ultrasound system. A.U., C.D, C.B. and G. Montaldo performed the experiments. E.M. designed the visual experiments. A.U., C.D. and G. Montaldo analyzed the results. A.U. and G. Montaldo wrote the manuscript, which was edited by all authors.


The authors declare no competing financial interests.

Reprints and permissions information is available online at

1. Hartmann, D.A., Underly, R.G., Watson, A.N. & Shih, A.Y. A murine toolbox for imaging the neurovascular unit. Microcirculation 22, 168–182 (2015). Medline

2. Picciotto, M.R. & Wickman, K. Using knockout and transgenic mice to study neurophysiology and behavior. Physiol. Rev. 78, 1131–1163 (1998). Medline

3. Ferezou, I., Bolea, S. & Petersen, C.C. Visualizing the cortical representation of whisker touch: voltage-sensitive dye imaging in freely moving mice. Neuron 50, 617–629 (2006). Medline

4. Dombeck, D.A., Khabbaz, A.N., Collman, F., Adelman, T.L. & Tank, D.W. Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56, 43–57 (2007). Medline

5. Flusberg, B.A. et al. High-speed, miniaturized fluorescence microscopy in freely moving mice. Nat. Methods 5, 935–938 (2008). Medline

6. Sawinski, J. et al. Visually evoked activity in cortical cells imaged in freely moving animals. Proc. Natl. Acad. Sci. USA 106, 19557–19562 (2009). Medline

7. Sullivan, M.R., Nimmerjahn, A., Sarkisov, D.V., Helmchen, F. & Wang, S.S. In vivo calcium imaging of circuit activity in cerebellar cortex. J. Neurophysiol. 94, 1636–1644 (2005). Medline

8. Helmchen, F., Fee, M.S., Tank, D.W. & Denk, W. A miniature head-mounted two-photon microscope. high-resolution brain imaging in freely moving animals. Neuron 31, 903–912 (2001).Medline

9. Miao, P., Lu, H., Liu, Q., Li, Y. & Tong, S. Laser speckle contrast imaging of cerebral blood flow in freely moving animals. J. Biomed. Opt. 16, 090502 (2011). Medline

10. Levy, H., Ringuette, D. & Levi, O. Rapid monitoring of cerebral ischemia dynamics using laser-based optical imaging of blood oxygenation and flow. Biomed. Opt. Express 3, 777–791 (2012). Medline

11. Holzer, M. et al. in Conf. Proc. IEEE Eng. Med. Biol. Soc. 29–32 (IEEE, 2006).

12.  Macé, E. et al. Functional ultrasound imaging of the brain. Nat. Methods 8, 662–664 (2011). Medline

13. Urban, A. et al. Chronic assessment of cerebral hemodynamics during rat forepaw electrical stimulation using functional ultrasound imaging. Neuroimage 101, 138–149 (2014). Medline

14. Montaldo, G., Tanter, M., Bercoff, J., Benech, N. & Fink, M. Coherent plane-wave compounding for very high frame rate ultrasonography and transient elastography. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 56, 489–506 (2009). Medline

15. Mace, E. et al. Functional ultrasound imaging of the brain: theory and basic principles. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 60, 492–506 (2013). Medline

16. Liu, J.V. et al. fMRI in the awake marmoset: somatosensory-evoked responses, functional connectivity, and comparison with propofol anesthesia. Neuroimage 78, 186–195 (2013).Medline

17. Rayshubskiy, A. et al. Direct, intraoperative observation of ~0.1 Hz hemodynamic oscillations in awake human cortex: implications for fMRI. Neuroimage 87, 323–331 (2014). Medline

18. Du, C., Volkow, N.D., Koretsky, A.P. & Pan, Y. Low-frequency calcium oscillations accompany deoxyhemoglobin oscillations in rat somatosensory cortex. Proc. Natl. Acad. Sci. USA 111, E4677–E4686 (2014). Medline

19. Lund, R.D., Lund, J.S. & Wise, R.P. The organization of the retinal projection to the dorsal lateral geniculate nucleus in pigmented and albino rats. J. Comp. Neurol. 158, 383–403 (1974).Medline</jrn>

20. Schulz, D. et al. Simultaneous assessment of rodent behavior and neurochemistry using a miniature positron emission tomograph. Nat. Methods 8, 347–352 (2011). Medline

21. Metz, C.E. Basic principles of ROC analysis. Semin. Nucl. Med. 8, 283–298 (1978). Medline

22.  Mayhew, J.E. et al. Cerebral vasomotion: a 0.1-Hz oscillation in reflected light imaging of neural activity. Neuroimage 4, 183–193 (1996). Medline

23. Bergonzi, K.M., Bauer, A.Q., Wright, P.W. & Culver, J.P. Mapping functional connectivity using cerebral blood flow in the mouse brain. J. Cereb. Blood Flow Metab. 35, 367–370 (2015).

24. Alves, K.Z. et al. In vivo endoluminal ultrasound biomicroscopic imaging in a mouse model of colorectal cancer. Acad. Radiol. 20, 90–98 (2013). Medline

25. Bakardjian, H., Tanaka, T. & Cichocki, A. Optimization of SSVEP brain responses with application to eight-command Brain-Computer Interface. Neurosci. Lett. 469, 34–38 (2010). Medline



The investigation was performed in accordance with the US National Institutes of Health Guide for Care and Use of Laboratory Animals. The protocol was approved by the Local Animal Ethics Committee of Paris 5 (CEEA 34) and conducted in accordance with Directive 2010/63/EU of the European Parliament and with the ARRIVE guidelines. Seven adult male Sprague-Dawley rats of 8 weeks (Janvier Labs) were used in this study. Animals were kept in a dedicated behavioral room starting 1 week before the beginning of the training for habituation to human and to the environment. Rats were initially kept in group cages in a 12-h dark-light cycle environment at a constant temperature of 23 °C. One week before the beginning of the behavioral training, rats were individually housed with ad libitum access to food and water and were handled daily.

Miniature ultrasound probe.

The ultrasound probe was optimized to achieve minimum size and weight while preserving image quality. Hence, the number of channels was limited to 64 so that we could use a smaller and more flexible cable between the probe and the imaging station, allowing freely moving imaging without providing an excessive burden on the head of the rat. As a consequence, the FOV was restricted to two-thirds of the size brain in a coronal plane (9.6 mm). Moreover, the inter-element distance (pitch) was set to 150 µm to maximize the lateral resolution that is linked to the numerical aperture of the array. The lateral resolution in a point (x, z) of the image can be calculated as:


where n is the maximal numerical aperture of the array, L is the dimension of the array, lambda is the ultrasound wavelength and Dr, Dl are right and left aperture of the array, respectively. A 150-µm pitch enables a numerical aperture of 1.5. Therefore, the lateral resolution of the image is 150 µm in the central part of the FOV and down to 300 µm at the border of the image. Note that the resolution in depth has a constant value of 100 µm as determined by the duration and frequency of ultrasound pulses (Supplementary Note 2).

Behavioral tasks.

Four rats were trained to perform a food-reinforced conditioned operant task in a 1.2-m-long straight-alley maze (Fig. 4a) placed 70 cm above the floor over 10 d. During the training, the amount of food ration was limited to 5 g per day to maintain rats at around 80% of their initial weight.

During the task, animals were placed at one extremity of the maze (starting point) and had to reach the other extremity to obtain a reward (AIN-76A Rodent Tablet 45 mg, Sandown Scientific). Then rats had to come back to the starting point and perform the task again to obtain another reward (reinforced round trip, RRT). The reward was placed between six vertical sticks deflecting the whiskers during reward collection (Fig. 4b). All tasks were video monitored, allowing off-line tracking of animal movements. The preconditioning period lasted between 5 d and 1 week, after which the animals were well habituated to the setup and showed no sign of stress throughout an entire training session. When rats were able to successfully perform the task, they received ad libitum access to food and water during 48 h before the surgery to implant the head plate. After 2 d of recovery from surgery, rats performed their tasks with the magnetic probe on the head without the need for habituation and with moderate decline in performance compared to control rats (Supplementary Fig. 7). Food restriction was maintained from day 2 to day 7 as described previously.

Surgical implantation of the head plate.

All components of the magnetic ultrasound probe holder were CAD designed with Sketchup software (Google) and 3D printed in a biocompatible polylactate (Sculpteo). Rats were deeply anesthetized with isoflurane (5% for induction, 2% for maintenance) in 100% O2 and then head fixed in a stereotaxic frame. The eyes of the rats were covered with Vaseline to prevent them from drying out. Prior to skin incision, lidocaine was injected subcutaneously (1 mL per kg body weight), and then a small cranial window of 2 mm × 10 mm was created in the coronal plane (from AP –1.5 mm to – 3.5 mm and L ± 5 mm) over the center of the BF cortex. Then pre-holes were made with a small drill burr without damaging the dura, and the head plate was screwed with six stainless screws (Stoelting) to the bone (Fig. 1a). The brain was covered by low-melting 2% agarose containing ampicillin (100 mg/L, Sigma-Aldrich) to limit infections. Agarose gel ensured proper acoustic coupling required for optimal transmission of the ultrasonic beam in the brain tissue. Rats were placed in a warm cage directly after surgery and monitored periodically until wake up. To reduce pain, we used buprenorphine (i.p. 30 mL per 100 g body weight, Buprecare) immediately after the surgery, and a second injection was performed 12 h later. Finally, the magnetic head shield was placed to protect the brain between each imaging session.

Visual stimulation.

Two rats were used for visual experiments. The surgical implantation of the head plate was performed at 4.5 mm posterior to bregma over the LGN of the thalamus. The rats were returned to their home cage following the surgery and were kept under reduced illumination during visual experiments, which started at day 0 after the complete waking of animals and lasted up to 10 d. Two blue (470 nm, 500 mW) LED strobe lights (OptoLed Lite, Cairn Research) were placed 20 cm from the cage on either side, and the stimulus paradigm consisted of a rest period followed by a period during which the LEDs were blinking at 3.3 Hz (10-ms pulse width, 50% output power) for either 7.3 s (24 flashes) or 2.1 s (7 flashes). The light stimulus frequency was selected to elicit strong binocular visual responses26, and each fm-fUS imaging session lasted up to 1 h, during which period the stimulus was presented many times with a random interstimulus duration ranging from 5 s to 1 min.

Animal tracking in quiet rest and during operant tasks.

We used two HD webcams (Logitech) associated with a free video analysis and modeling tool built on the Open Source Physics Java framework (Tracker 4.87) to track the position of the rat during tasks. In a first set of experiments, we quantified the influence of the fixation of the ultrasound probe on the head of the rat by measuring the time to perform a food-reinforced operant task before surgery or after surgery without or with the probe. We measured the time that was necessary for the rat to move from a starting zone and to reach a reward zone. This delay was measured with 33-ms resolution and allows calculation of the running velocity. We also quantified the residency time in the reward zone as defined by the time to collect the reward. The stimulation pattern, A(t), was considered equal to 1 when the nose was between the sticks, and 0 otherwise. This pattern was used to compute brain activation maps.

Ultrasound sequence and computation of CBV images.

We used a fast sequence for functional imaging that we adapted from the µDoppler sequence described previously27 with the following parameters: five angles (–6°, –3°, 0°, 3°, 6°) averaged three times, 7.5-kHz firing frequency, 500-Hz frame rate, 200 images, 0.4-s acquisition time, and impulse of two cycles at 15 MHz.

This ultrasound sequence triggers emission of a set of 5 × 200 plane waves. We recorded a set of 5 × 200 signals for each channel of the ultrasound array. Each CBV image was calculated using a three-step process: (i) a GPU beamforming step to get a set of 200 ultrasound images a(x, z, t), where x is the lateral position, z the depth and t the time (200 points); (ii) a spatiotemporal filtering step to obtain the blood signal aB(x, z, t) (see “Spatiotemporal filtering” below); and (iii) a computation step of the CBV signal intensity as I(x, z) = ∫aB2(x, z, t)dt. Steps (ii) and (iii) are performed by the workstation.

As a final result, we obtained an image I(x, z) proportional to the CBV signal that we termed ‘CBV image’ for simplification. For detection of hemodynamic variations, we captured CBV images every 0.7 s (1.42-Hz frame rate) during the entire task to obtain a time-dependent signal of CBV in each pixel I(x, z, t).

Spatiotemporal filtering.

We developed a specific antivibration filter to minimize the effect of mechanical vibrations during functional imaging in free-movement conditions.

After beamforming of the echoes data, each CBV image was obtained from a set of 200 ultrasound images a(x, z, t) comprising a tissue and a blood component as a(x, z, t) = aT(x, z, t) + aB(x, z, t). We used both temporal and spatial differences between those signals to extract the CBV signal.

The temporal difference was based on the slower velocity of the movement in the tissue compared to blood. Therefore, the Doppler shift of the tissue had a low-frequency signal. The temporal filter was a third-order high-pass Butterworth filter with 50-Hz cutoff frequency. In case of excessive vibration, the temporal filter was not sufficient to separate CBV signal from noise; therefore, we also applied a spatial filter.

Vibration generates a coherent movement in the tissue. In contrast, the blood signal coming from moving red blood cells (RBCs) flows randomly inside the vessel and generates a signal that is uncorrelated between two different voxels. The spatial filter used this difference to dissociate the tissue and RBCs by computing the spatially coherent signal.

We decomposed the spatially coherent part of the signal using a singular value decomposition (SVD) with


In a spatially coherent signal, mi(x, z) is widespread in the whole image, whereas the incoherent blood signal is present in only some pixels. We removed the spatially coherent components where mi(x, z) was widespread. The blood signal became


with Ne = 5 is the number of eliminated singular values.

Functional images of brain activity.

The functional images were computed as the correlation between the CBV images I(x, z, t) and the temporal stimulus pattern A(t).




A z score was calculated using a Fisher’s transform:


A pixel was considered activated when z > 2.5 (P < 0.01 for one-tailed test). Spatial correlation maps displayed all significant pixels superimposed on a reference CBV image.

Resting-state functional connectivity and coherence.

The functional connectivity was established by a seed-based approach3. An ROI of 5 × 5 pixels was averaged and compared to a set of CBV images I(x, z, t) to obtain the CBV signal A(t). The connectivity map was computed by correlating this CBV signal in this ROI with the signal in other pixels.

We used equation (3) with A(t) representing the CBV signal in the ROI.

The spectral coherence Cab(w) between two regions “a” and “b” was computed as


where G is the cross-spectral density defined as


The á ñ indicated an average over different trials, Sa and Sb are the Fourier transform of the CBV signal in regions a and b respectively. We averaged ten recordings of 3 min to obtain the coherence signal.

Real-time data processing.

The ultrasound imaging system is composed of four main devices including the ultrasound electronics (Vantage, Verasonics), the PCI-Express bus, the graphics processing unit (GPU) and the CPU. A pipeline strategy was used to process CBV images in real time in which all parts of the hardware are working simultaneously (Supplementary Fig. 10) according to the following workflow.

1. The ultrasound electronics performed the acquisition of one ultrasound sequence in 0.4 s and stored echo data corresponding to 1,000 plane wave illuminations.

2. The PCI-Express bus transferred data from the electronics to the computer in 0.1 s.

3. The GPU computed the ultrasound images (beamforming) in 0.6 s, and the final output of 200 ultrasound images was transferred to the CPU.

4. The CPU performed the spatiotemporal filtering to extract the hemodynamic information of a single CBV image in 0.2 s.

This strategy allowed parallel processing of images n and n + 1.

An initial delay was needed for the data to enter in the pipeline (1.3 s). Moreover, the frame rate is limited by the slowest step of the pipeline. In our case, the slowest process was the beamforming, which takes 0.6 s. The fUS imaging frame period was fixed to 0.7 s, including 0.1 s to avoid buffer overflow. General-purpose processing on GPUs was of great importance to accelerate data processing in our imaging station. Indeed, the beamforming step used an algorithm that can be easily parallelized to thousands of threads. Moreover, GPU reduced usage of CPU that was devoted to real-time decoding of brain activity (Supplementary Software).

Decoding algorithm of brain activity.

Real-time decoding of brain activity was based on the same algorithm used previously but with an additional processing composed of four steps, including the following.

 Elimination of images with high levels of noise due to rapid animal head movement. We computed the total CBV of the image as a set of values In where n is the number of images. Then we computed the mean Ī̄ and the s.d. sI of the set of values In. Image i was eliminated if Ii exceeded more than three times the s.d. (Ii > Ī̄ + 3sI).

 Computation of CBV images in the BF cortex. We computed the CBV in the BF region and stored values in a vector sn. The ROI in the BF cortex was defined on the basis of the most-activated voxels for a 1-s short manual stimulus.

Filtering and normalization of the data set. We applied a fourth-order Butterworth high-pass filter with 0.05-Hz cutoff frequency to eliminate very low–frequency oscillations of the CBV. The filter signal was normalized to a mean value of 0, and the s.d. was normalized to 1 as sni = (si)/ss to compare CBV signal between all rats and sessions.

Threshold detection. When sni was greater than threshold, the brain was considered active and a text message was displayed on the screen (Supplementary Video 1). A threshold of 2 allowed detection of 93% of the events with only 6% of false positives (Fig. 5). As application of the decoding algorithm is a fast processing step (<1 ms), the final processing delay between each frame was about 1.3 s as previously defined (Supplementary Fig. 10).

Statistical analysis.

All data used for statistical evaluations were checked for normality of the distributions. The Kruskal-Wallis test was used to assess the relationship between two variables when the assumption of normality was not respected followed by Dunn’s post hoc test for multiple comparisons. The ANOVA test was used for paired samples followed by Tukey’s post hoc test for multiple comparisons of repeated measures. P < 0.05 (two-tailed) was considered significant.

We chose the sample size for experiments (n = 4 rats) on the basis of literature in the field and national guidelines from the local animal ethics committee. No animals were excluded from analysis, and no randomization or blinding was performed.

Code availability.

Custom code used for GPU processing of fm-fUS images is provided as Supplementary Software.

26. Van Camp, N., Verhoye, M., De Zeeuw, C.I. & Van der Linden, A. Light stimulus frequency dependence of activity in the rat visual system as studied with high-resolution BOLD fMRI. J. Neurophysiol. 95, 3164–3170 (2006). Medline

27. Fox, M.D. & Raichle, M.E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007). Medline