A major challenge in neuroscience is to longitudinally monitor whole brain activity across multiple spatial scales in the same animal. Functional UltraSound (fUS) is an emerging technology that offers images of cerebral blood volume over large brain portions. Here we show for the first time its capability to resolve the functional organization of sensory systems at multiple scales in awake animals, both within small structures by precisely mapping and differentiating sensory responses, and between structures by elucidating the connectivity scheme of top-down projections. We demonstrate that fUS provides stable (over days), yet rapid, highly-resolved 3D tonotopic maps in the auditory pathway of awake ferrets, thus revealing its unprecedented functional resolution (100/300µm). This was performed in four different brain regions, including very small (1–2 mm3 size), deeply situated subcortical (8 mm deep) and previously undescribed structures in the ferret. Furthermore, we used fUS to map long-distance projections from frontal cortex, a key source of sensory response modulation, to auditory cortex.https://doi.org/10.7554/eLife.35028.001
Functional ultrasound imaging (fUS) based on Ultrafast Doppler (UfD) was first introduced in neuroimaging in 2011 (Macé et al., 2011). Using ultrasonic plane wave emissions, this system exhibits a 50-fold enhanced sensitivity to blood volume changes compared to conventional ultrasound Doppler techniques (Mace et al., 2013), with a very high acquisition rate (ms) enabling unambiguous discrimination between blood flow and motion artifacts (breathing motion, tissue pulsatility,...) (Demené et al., 2015). Relative to fMRI, it also presents substantially higher spatial resolution for cerebral blood flow imaging at the expense of non-invasiveness, greater portability and lower cost, and versatility for awake animal imaging. However, most fUS studies thus far have investigated its sensitivity in capturing coarse-grained sensory responses (Tiran et al., 2017; Osmanski et al., 2014a; Gesnik et al., 2017; Urban et al., 2014; Urban et al., 2015), or used it to explore indirect in-plane brain connectivity (Osmanski et al., 2014b; Rideau Batista Novais et al., 2016). Also, while the theoretical spatial resolution of Ultrafast Doppler for high sensitivity mapping of microvascularisation has been shown to be 100 µm for whole brain imaging in rats (Mace et al., 2013; Demené et al., 2016), the ability of the fUS technique to measure independent information on functional brain activity from the cerebral blood volume (CBV) variation maps at such a small scale, that is the truly informative fUS imaging resolution, has remained to date unproven. Here, we demonstrate fUS imaging capability in capturing a fine-grained 3D functional characterization of sensory systems and direct, long-distance connectivity scheme between brain structures. Our first goal was to provide such 3D high-resolution functional mapping in the auditory system. However the limited richness of stimuli previously applied in state-of-the-art fUS imaging together with their long duration (typically 10 to 30 s) constituted an obstacle as they would require several days of acquisitions incompatible with in vivo investigations. Moreover, most studies used physiological stimuli (Macé et al., 2011; Gesnik et al., 2017; Urban et al., 2015) or direct electrical stimulations (Urban et al., 2014) specifically designed to activate at most the entire sensory structures. We therefore drastically reduced the durations and repetitions of presented stimuli while increasing their diversity to push the sensitivity limits of fUS imaging. Consequently we show that this technique can rapidly produce highly-resolved 3D in vivo maps of responses reflecting precise tonotopic organizations of the vascular system in the almost complete auditory pathway of awake ferrets. We further demonstrate that fUS imaging can provide voxel to voxel independent information (with a functional resolution of 100 µm for voxel responsiveness, 300 µm for voxel frequency tuning), indicative of its high sensitivity. These measurements are repeated over several days in small (1–2 mm3 size) and deep nuclei (8 mm below the cortical surface), as well as across various fields of the auditory cortex. On a broader scale, we describe how fUS can be used to assess long distance (out-of-plane) connectivity, with a study of top-down projections from frontal cortex to the auditory cortex. Therefore, fUS can provide a multi-scale functional mapping of a sensory system, from the functional properties of highly-resolved single voxels, to inter-area functional connectivity patterns.
Physiological experiments were conducted in three awake ferrets (Mustela putorius furo, thereafter called V, B and S). After performing craniotomies over the temporal lobe, chronic imaging chambers were installed (both hemispheres in one animal, and right hemispheres in the other two) to access a large portion of both the auditory (middle and posterior ectosylvian gyri - resp. MEG and PEG) and visual cortex (in caudal suprasylvian and lateral gyri) (Figure 1a). The 3D scan of the craniotomy via Ultrafast Doppler Tomography (Demené et al., 2016) revealed the in-depth vasculature of the Auditory Cortex (AC) surrounded by the supra-sylvian sulcus (Figure 1a and b). In addition, we were able to detect and image deep auditory-responsive structures such as the Medial Geniculate Body (MGB), the Inferior Colliculus (IC) and the dorsal nucleus of the Lateral Lemniscus (DNLL), as well as visually-responsive nuclei such as the Lateral Geniculate (LGN) (Figure 1—figure supplement 1).
In order to reveal the tonotopic organization of the auditory structures, we recorded in each voxel the evoked hemodynamic responses to pure tones of 5 different frequencies by computing the %CBV, defined as the percentage of variation in CBV. We then computed the resultant 3-dimensional tonotopic map (Figure 1c–e, Figure 1—figure supplement 2). Within a relatively short time (10 to 15 min per slice), we could accurately reproduce the known tonotopic organization of the primary (A1 and AAF in the middle ectosylvian gyrus) and secondary auditory cortex (PPF and PSF in the posterior ectosylvian gyrus) (Bizley et al., 2005; Mrsic-Flogel et al., 2006; Nelken et al., 2008), with a high- to low-frequency gradient in A1, reversing to a low- to high-frequency gradient in the dorsal PEG (Figure 1c). We note that the fUS enabled us to map within the challenging deep folds of the ferret auditory cortex, such as the supra-sylvian sulcus (sss) and pseudo-sylvian sulcus (pss). Recordings could be performed in the same slice across days, with a high repositioning precision (error <1 slice, 200 µm in that case), which was within the range of the out-of-plane point-spread function for fUS (Figure 1—figure supplement 3). Interestingly we were able to capture inter-individual variability along the transect going from the pss to the sss, consistent with previous work in the ferret (Bizley et al., 2005).
Large-scale, 3D functional maps were also recorded in the deep and smaller structures of the auditory thalamus (MGB, Figure 1d), the inferior colliculus (IC, Figure 1e) and the DNLL (Figure 1e). The 3D views obtained in fUS allowed us to describe for the first time the tonotopical organizations of the ferret ventral division of the MGB and DNLL. This is particularly remarkable in the latter structure in which we characterize a precise tonotopic map despite its small size (~1 mm-long) and subcortical position (8 mm deep below brain surface). Moreover, such a large field of view allows one to measure simultaneously the functional organization of any coplanar structure (such as A1 and the MGB here), thus opening the door to precise, frequency specific (thalamo-cortical) connectivity studies. In this respect, future development of high frequency fUS matrix-probes for 3D UfD imaging (Provost et al., 2015) will extend this capability to any brain structure.
Single-trial analysis is essential for understanding brain dynamics and behavioral variability. However, it remains a challenge as it necessitates to record high-quality signal from a large number of neurons/voxels at the same time. In order to estimate the reliability and selectivity of fUS single-trial responses, we used MultiVoxel Pattern Analysis (MVPA) to decode the stimulus frequency from the hemodynamic signal. Using a simple linear decoder, we attained high decoding accuracy in the auditory cortex (from 0.46 to 0.63 probability, with chance at 0.2) which was even more striking in the IC and DNLL (from 0.72 to 0.98), despite their smaller size and subcortical location (Figure 2a). These results suggest that single trials show reliable and significant activity across all structures.
On a different scale, we sought to demonstrate whether fUS could also reveal encoding differences across cortical layers. We focused on imaging the small vessels in the cortex (keeping only data corresponding to an axial projection of blood flow lower than 3.1 mm/s) and defined cortical layers using an unfolding algorithm providing a flattened version of the AC (Figure 2—figure supplement 1). A linear decoder yielded a significantly higher decoding accuracy when using only measurements at intermediate cortical depths (p<1e-3), peaking around 400–500 µm below the surface (up to 0.83, mean 0.67), consistent with it being granular. As a control, we note that baseline blood volume and response magnitude did not show a similar depth-dependent profile (Figure 2—figure supplement 1), suggesting that the observed decoding accuracy may be due to variations in capillaries structure within cortical layers (Adams et al., 2015). An alternative explanation would be that the improved accuracy at the intermediate depths reflects the underlying neuronal activity, and more specifically the sharper frequency tuning observed in granular layers (Guo et al., 2012). Importantly, all these results could be confirmed in single slice recordings, and over several days (Figure 2—figure supplement 2), showing that the hemodynamic signal imaged in fUS is reliable enough to decode brain activity on a single-trial basis within a single experiment.
Next, we took a closer look at the tonotopic organization in different structures to examine how tuning curves in neighboring voxels change abruptly. This finding exemplifies the ability of fUS imaging to measure independent information at a very small spatial scale. To quantify the minimal functional spatial resolution of the technique, we defined a discriminability index between voxels, and focused on sharp transition areas (Figure 2b left panels). We found that fUS can discriminate responsiveness of neighboring voxels, with a functional resolution as fine as 100 µm (Figure 2b). Furthermore, we were able to discriminate voxels based on their tuning curves within a distance of 300 µm in as little as 10 repetitions per frequency (Figure 2b and Figure 2—figure supplement 3). Importantly, this is a conservative measure of functional resolution, since it largely depends on the smoothness of the underlying functional organization itself (tonotopy) and of the number of trials. The functional resolution described here is thus a lower limit, and could be improved by increasing, for example, the trial number. These results suggest that fUS can be useful to assess the fine organization of vascular domains within brain structures and to better understand the functional coupling between local neuronal activity and the dynamics of surrounding blood vessels, two important questions for hemodynamic-based techniques (O'Herron et al., 2016; Harrison et al., 2002).
Another fundamental view of brain function and functional organization is revealed by mapping brain connectivity among various structures. Localizing and quantifying such connections in awake animals, however, remains technically challenging since tracer injections are not an option, and fMRI gives only access to indirect, spatially diffuse measures of connectivity strength. Here, we demonstrate that fUS can be used to probe the functional connectivity between two brain structures that are far apart: the frontal and the auditory cortices. The frontal cortex (FC) is a region that has been shown to be involved in top-down modulation of early sensory areas, and in particular of the auditory cortex (Fritz et al., 2003; Winkowski et al., 2013). To reveal its potential links to the auditory areas, we electrically stimulated at different points within the FC while recording evoked hemodynamic responses in the auditory cortex of an awake (slightly sedated) animal (Figure 3a). Importantly, this technique does not require any precise priors on the location and nature of the terminal projections. By imaging widely in the auditory cortex, we observed evoked activity in the insular cortex of the pseudosylvian sulcus (PSSC/insula), which was maximal for a certain depth and position of the stimulating electrode (Figure 3b, Figure 3—figure supplement 1). By contrast, there was no evoked activity recorded in secondary auditory areas such as the PEG (Figure 3—figure supplement 2). We also observed a decrease in blood volume in the MEG, possibly originating from polysynaptic connections between FC and A1 (Logothetis et al., 2010; Klink et al., 2017). From these recordings, we cannot disentangle orthodromic versus antidromic activation. We therefore anatomically confirmed the existence of such descending projections from FC to PSSC/insula with independent anterograde virus injections in FC. These injections revealed monosynaptic projections that targeted the PSSC/insula (Figure 3c–d), consistent with a contribution of direct projections from FC to A1 to the functional connectivity pattern revealed by the fUS approach. We also observed FC projections in the Claustrum (Cl in Figure 3c), ventro-medial with respect to the PSSC/insula. Because the neighboring regions have been reported to be multimodal (Bizley et al., 2007; Bizley and King, 2008), we subsequently explored the responsiveness of the FC-targeted PSSC/insula to acoustic and visual stimuli. We found this region to be less responsive to broadband noise than A1 (~5% instead of 15%), and not driven by visual stimuli (Figure 3—figure supplement 3). Altogether, this experiment offers a proof-of-concept of how fUS can serve as a tool to characterize large-scale functional connectivity without sacrificing any resolution. We can point out two key applications building up on such experiments. First, one may explore connectivity changes in animals, for example during different brain states (e.g., sleep vs. awake), or during the course of learning. Second, and maybe even more importantly, the use of optogenetics can allow a precise mapping between brain structures, targeting for example specific neuronal subpopulations, or projection patterns. The development of such tools has just started, but has been so far limited to fMRI (Lee et al., 2010).
To conclude, we have shown that fUS imaging can serve as a technique to record in awake animals a very stable (over days), high-resolution and simultaneous tonotopic mapping of various brain regions, be they large, small, superficial, or deep. This was done over multiple scales, from functional tuning of individual voxels to large-scale connectivity between brain regions. The amplitude of the fUS responses (~20% in the ferret, and close to 50% in neonates [Demene et al., 2017]) is quite large compared to typical auditory cortex BOLD responses in fMRI (~5%). This makes mapping both rapid, compared to the electrophysiological approach with multiple penetrations (Bizley et al., 2005; Mrsic-Flogel et al., 2006), and precise, as illustrated by the ease with which single-trial information can be decoded from its high-sensitivity signal, a key feature when it comes to recording in behaving animals. Furthermore, fUS can be a valuable tool in acquiring broad, yet accurate views of the functional organization of unmapped brain regions and their connectivity with the rest of the brain. Finally, fUS imaging can be readily adapted to mobile and highly stable configurations (Sieu et al., 2015), which will make it ideally suited for behavioral cognitive neuroscience studies requiring extended observations, as in the characterization of the neural correlates of learning.
Experiments were approved by the French Ministry of Agriculture (protocol authorization: 01236.02) and strictly comply with the European directives on the protection of animals used for scientific purposes (2010/63/EU). To secure stability during imaging, a stainless steel headpost was surgically implanted on the skull and stereotaxis locations of the dorsolateral frontal cortex (FC) and the auditory cortex (AC) were marked (Atiani et al., 2014). Under anaesthesia (isoflurane 1%), four craniotomies above the auditory cortex were performed on three ferrets (Vright and Vleft, Bright, and Sright), using a surgical micro drill, yielding a ~ 15×10 mm window over the brain. After clean-up and antibiotic application, the hole was sealed with an ultrasound-transparent TPX cover, embedded in an implant of dental cement (Sieu et al., 2015). Animals could then recover for one week, with unrestricted access to food, water and environmental enrichment.
For fUS imaging, animals were habituated to stay in a head-fixed contention tube. The ultrasonic probe was then inserted in the implant and acoustic coupling was assured via degassed ultrasound gel. Experiments were conducted in a double-walled sound attenuation chamber. All sounds were synthesized using a 100 kHz sampling rate, and presented through Sennheiser IE800 earphones (HDVA 600 amplifier) that was equalized to achieve a flat gain. Stimulus presentation were controlled by custom software written in Matlab (MathWorks) and available on a bitbucket repository at this link: https://bitbucket.org/abcng/baphy/branch/abcng (Boubenec, 2018; copy archived at https://github.com/elifesciences-publications/baphy-branch-abcng/).
We used a custom miniaturized probe (15 MHz central frequency, 70% bandwidth, 0.110 mm pitch, 128 elements) inserted in a four degree-of-freedom motorized setup. The probe was driven using a custom fully-programmable ultrasonic research platform (PI electronics) and dedicated Matlab software. Ultrasound codes are all are available within the framework of research collaboration agreements between academic institutions.
Vascular anatomy of the brain portion accessible from the craniotomy was imaged in 3D using the Ultrafast Doppler Tomography (UFD-T) strategy described in (Demené et al., 2016). Briefly, this method acquires 2D Ultrafast Power Doppler (UfD) images at a frame rate of 500 Hz. Each frame is a compound frame built with 11 tilted plane wave emissions (−10° to 10° with 2° steps) fired at a PRF of 5500 Hz, combined with mechanical translation and rotation, and then post-processed via a Wiener deconvolution to correct for the intrinsic out-of-plane loss of resolution, so that we ultimately recover an isotropic 100 µm 3D resolution. In the end, a 3D (14 × 14 × 20 mm) blood volume reconstruction of the vasculature is obtained (voxel size: 50 µm, isotropic resolution 100 µm). This 3D vascular imaging was performed on each craniotomy, and was used as a local reference framework, specific to the craniotomy, where recording planes could be repositioned over days using correlation methods.
fUS imaging relies on rapid acquisition (every 1 s) of ultrasensitive 2D Power UfD images of the ferret brain. For each Power image, 300 frames are acquired at a 500 Hz frame rate (covering 600ms, that is one to two ferret cardiac cycles), each frame being a compound frame acquired via 11 tilted plane wave emissions (-10° to 10° with 2° steps) fired at a PRF of 5500 Hz. Image reconstruction is performed using an in-house GPU-parallelized delay-and-sum beamforming. Those 300 frames at 500 Hz are filtered to discard global tissue motion from the signal using a dedicated spatio-temporal clutter filter (Demené et al., 2015) based on a singular value decomposition of the spatio-temporal raw data. Although the ultrafast 2ms temporal resolution is available for the CBV image generation, they are in fact averaged into one CBV image every second to capture the dynamics of the cerebral blood physiological response. Nevertheless, it should be noted that this rapid sampling rate is a key asset to unambiguously cancel any respiratory or tissue pulsatility artifacts (Demené et al., 2015) in the final averaged images. Blood signal energy (called Power UfD) is then computed for each voxel (100 x 100 x ~400 µm, the latter dimension, called elevation, being slightly dependent of depth) by taking the integral over the 300 time points (Mace et al., 2013). This power Doppler is known to be proportional to blood volume (Rubin et al., 1994). A certain band of Doppler frequencies can be chosen before computation of the power using a bandpass filter (in our case a fifth order low-pass Butterworth filter), enabling the selection of a particular range of axial blood flow speeds, that is roughly discriminating between capillaries and arterioles (slow blood flow) and big vessels (fast blood flow). In our study, we set the filtering to better focus on small vessels with axial velocity lower than 3.1mm.s−1 when indicated in the text. Power UfD signal was normalized towards the baseline to monitor changes in Cerebral Blood Volume (%CBV).
Auditory responses were studied by playing different sounds through animal earphones during recording of the brain activity via fUS imaging. The protocol for sound presentation is as follows: 10 s of silence (baseline), then 3 s of sound followed by 8 s of silence (return to baseline). Trials were following each other with only a little random jitter in time of about 1 to 3 s, and fUS acquisitions were synchronize with the beginning of each trial.
Visual responses were obtained by playing a flickering red-light stimulus instead of sound, with the same durations of different epochs.
In order to find the boundaries of the auditory structures in the imaged portion of the brain, white noise sound was played (70 dB).
Auditory structures are known to exhibit tonotopic organization based on extensive physiological and structural studies (in the ferret, see [Bizley et al., 2005; Moore et al., 1983; Pallas et al., 1990; Versnel et al., 2002; Nelken et al., 2004]). To image these tonotopic maps, we played unmodulated pure tones while recording fUS images at five equally spaced frequencies on a logarithmic scale (602 Hz, 1430 Hz, 3400 Hz, 8087 Hz, 19234 Hz, covering the auditory hearing spectrum of the ferret, at 65 dBSPL). The tones were played in random order, 10 trials/frequency (20 in the animal S.). To obtain the whole tonotopic organization in a 3D volume, this process was repeated in different slices in order to build a 3D stack from successive 2D slices (spaced by 300 µm). Each slice was acquired in ~15 min, thus allowing us to map in 3D the whole auditory cortex within a few hours.
We note that these tone stimuli elicited large and reliable responses in the whole auditory tract despite being unmodulated. This suggests that a variety of other auditory stimuli (such as natural sounds) can be used to elicit stronger responses and hence reveal more organizational properties.
Frontal cortex (FC) electric stimulations were adapted from previously described protocols (Logothetis et al., 2010; Tolias et al., 2005). Platinium-iridium stimulation electrodes (impedance 200-400kOhms, FHC) were positioned in the region in between the anterior part of the anterior sigmoid gyrus and the posterior part of the proreal gyrus using stereotaxic coordinates, obtained from functional recordings in behaving animals (AP: 25.5–28.5 mm (0 to 3 mm on Figure 2d) from caudal crest, caudal crest antero-posterior position being defined at 5 mm lateral from the medial crest/ML: 2 mm (Radtke-Schuller, 2018)). Each trial consisted of 10 s of baseline, then 6 s of monophasic stimulation at 100 Hz and 200 µA (2 ms pulses, 200ms-long train, repeated at 2 Hz), after a return to baseline of 10 s. The %CBV was computed as the mean response between 3 and 6 s after stimulation onset. 30 trials were performed for each A-P position of the electrodes. In these connectivity experiments, the animal was slightly sedated using a small dose of medetomidine (Domitor 0.02 mL at 0.08 mg.kg−1) to reduce movement artifacts. Stimulation experiments were performed in one ferret, and each of the four experiments presented (Figure 3 and its figures supplements) was done once, on different days.
A one year old female ferret weighing 620 g received a 2 µl injection of pAAV2.5-CaMKIIa-hChR2(H134R)-EYFP (PennCore) as anterograde tracer into left FC. Six months later the animal was perfused and the brain was cryoprotected, shock frozen and cut on a cryostat into 50 µm thick frontal sections into parallel series of which one was counterstained with neutral red. For overview images, combined brightfield and fluorescence images were taken with a Hamamatsu slide scanner 2.0HT (Institut de la Vision) (Figure 2e, left). For details, fluorescence images were taken with a virtual slide microscope (VS120 S1, Olympus BX61VST) at 10× magnification (Figure 2e, right). Anatomical structures were reconstructed in accord with the ferret brain atlas (Radtke-Schuller, 2018).
Power UfD signal normalized towards the baseline was used to monitor changes in Cerebral Blood Volume (%CBV). The %CBV varied after stimulus presentation (Figure 1c) and we quantified voxel responses with the mean of %CBV in a time-window 3 to 5 s after sound onset. Tonotopy of the imaged structures was mapped as follows: for each voxel this mean vascular response across the five tested frequencies was used to determine its best frequency (BF). Statistical differences of the responses to different frequencies in an individual voxel (Figure 1c, tuning curve) were assessed using a Wilcoxon rank sum test (post-hoc test after significant ANOVA p<1e-3). For visualization purpose, maps were thresholded by showing only voxels that had (i) a minimal 15% response and (ii) a mean response at their BF highly correlated (p<1e-3) with the mean hemodynamic response. This thresholding method was used to highlight sound-responsive voxels (disregarding of frequency tuning), and thus allows for the display of zones that were poorly tonotopic (such as AEG). Note here that this thresholding was used only for visualization purposes. Maps constructed with a threshold based on frequency tuning gave similar qualitative results. The mean hemodynamic response was used to approximate the typical vascular response to stimulus (as the Hemodynamic Response Function does for fMRI) and was computed in each structure as the average response over all the voxels showing a response to sound with z-score >3. Note that thresholds could be adjusted depending on the overall responsiveness of different structures and different animals, for illustration purpose. Intriguingly, two additional ferrets did not show any reliable response to sound (responses below 10 %CBV), for unknown reasons. They were not used in the experiments.
Last, maps were spatially smoothed with a 3 × 3 × 1 voxel gaussian filter (std = 0.5), and a 3D median filter (3 × 3 × 3) was applied to the significance map to remove isolated voxels. The view of the brain surface (Figure 1c) was computed as the mean BF averaged from 5 to 10 voxels from the auditory cortex surface delimited manually. For 3D reconstructions of the cortex only, manually adjusted masks were used in order to show only tonotopic regions, and avoid crowdy representations caused by voxel transparency in the 3D visualization. Cortical depths were obtained by manually tracing the surface (just below the pia’s blood vessels) and depth limits of the cortex. The 10 different depths were then automatically extracted by a custom-made algorithm (Figure 2a and Figure 2—figure supplements 1 and 2). The number of voxels at each depth was then equalized for the decoding analysis.
For the single slice analysis presented in Figure 2—figure supplement 2, the protocol was designed to speed up tone-responses acquisition (2 s tone, and random interval of 4 to 6 s - uniformly distributed - between two tone presentations). We then used a General Linear Model (GLM) to compute impulse responses of individual voxels to each tone frequency, without any predefined hemodynamic response function. This allowed us to present more stimuli (75 per frequency) in a relatively shorter time (~45 min).
Frequency selectivity of the auditory cortex was assessed using a 5-class linear classifier and a leave-one out strategy: for each frequency pair, vascular responses of the two frequencies (%CBV averaged over 4 to 5 s after sound onset) were separated in a voxel-based space via a linear boundary optimized on 9 of the 10 trials in a learning set. No thresholding procedure was used in this analysis. Overall, pseudo-populations were built by grouping, across all slices recorded within the same structure, trials with identical frequency labels. The decoder was run over 100 shuffles of these pseudo-populations, where train and test sets were randomly chosen. In single slice analysis (Figure 2—figure supplement 2), we used a Fisher decoder (normalized by covariance) in order to take into account the noise correlation between voxels in decoding analysis. This was doable thanks to the higher number of tone presentations that allowed us to have a stable estimation of the covariance matrix.
In order to prove the significance of the obtained accuracy, we used a permutation procedure in which we shuffled the labels (i.e., which frequency was played during each trial) across trials, and performed the same decoding analysis, thus obtaining the chance distribution for decoding accuracies. We used 100 permutations, and considered that the real decoding accuracy was significantly out of the chance distribution (trial frequency labels shuffled) when above the 95th percentile. All the actual decoding accuracies were above the chance decoding accuracies. Our p-value resolution is limited by the number of permutations (100) and therefore our obtained p-values are all below 0.01.
To evaluate whether cortical depth had an effect on decoding accuracy (Figure 2a), we performed a one-way repeated-measure ANOVA over the four different craniotomies, with depth as the factor.
In order to quantify the minimal spatial scale at which fUS can provide independent information from two neighbouring voxels, we focused on sharp edges of functional transition and performed 2-way (voxel and frequency as factors) ANOVA on the tuning curves (%CBV averaged over 4 to 5 s after sound onset) of each pair of voxels within a certain contour (example transect and contour shown in Figure 2b, left panel). The voxel factor quantified the dissimilarity in the average responses for two voxels, being thus representative of an overall responsiveness dissimilarity when significant. The interaction term (frequency x voxel) quantified how dissimilar the tuning curves were for two different voxels, independently of their overall responsiveness. This term therefore represented our ability to discriminate between different functional voxel tuning. Pairs of voxels were considered to be ‘dissimilar’ (in responsiveness or tuning) when the associated p-value was <5.10−2. Importantly, these values depend on the smoothness of the underlying functional neuronal map (the sharper the better) and on the number of trials used in each experiments (the higher the better). Here, we show that using only 10 trials per frequency, we could go down to a functional resolution comparable to the voxel size (100 µm) for the overall responsiveness, and of 300 µm for the tuning.
We randomized 50 times the responses over all voxels and all frequencies and performed the same analysis to find the average distribution expected by chance for both responsiveness and tuning dissimilarity percentages. We determined the spatial resolution as the shortest distance between two voxels at which the actual number of dissimilar pairs was above the 95th percentile of the randomized distribution. Distance between voxels defined by coordinates (x1,y1) and (x2,y2) was computed as the rounding of .
Finally, we performed this analysis in different regions (AC and IC) and different animals (Bright, Vleft, Vright, Sright) in order to generalize this result (Figure 2—figure supplement 3).
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Andrew J KingReviewing Editor; University of Oxford, United Kingdom
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
Thank you for submitting your work entitled "Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret" for consideration by eLife. Your article has been reviewed by four peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Victoria Bajo Lorenzana (Reviewer #1).
Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.
As you will see from the comments included below, the four reviewers differed in their opinions on your paper. Although these differences were not fully resolved in the ensuing discussion, it was agreed that previous studies (e.g. Gesnik et al., 2017) have demonstrated the feasibility of using functional ultrasound imaging to measure responses in cortical and subcortical structures. We recognize that there are clear advances over that study in the present work, including imaging at higher spatial resolution and the use of awake animals. However, other studies have employed functional ultrasound imaging in awake animals, so the question of novelty for a methods paper is something that we have to consider carefully. A Tools and Resources paper not only needs to describe a significant methodological advance, but also to do so in a way that would allow others to adopt the technique in their own work. All the reviewers agreed that your paper does not achieve this and that there are numerous areas where the experimental and/or analytical details provided are inadequate. To a large degree, these points are potentially addressable, but other concerns raised included the variable maps obtained from different animals, some inconsistencies with previous electrophysiological or intrinsic imaging studies of ferret auditory cortex, the time course over which the signals were obtained, and the effects of electrical stimulation of frontal cortex, which were regarded as not particularly convincing. Although comparisons are drawn with published data obtained using other methods, it was felt that independent validation of the frequency tuning using, e.g. microelectrode recording would be desirable.
On the basis of the reviews, we will unfortunately not be able to publish your work as a methods paper in the Tools and Resources section of eLife. However, we recognize the value of this interesting approach for measuring activity in different brain regions of the same animal and would therefore welcome future submissions in which you use functional ultrasound imaging to investigate specific questions in the auditory system.
The authors use Functional Ultrasound (fUS) to image the auditory brain of awake ferrets with milliseconds temporal resolution and 100 µm spatial resolution. fUS is based on ultrafast Doppler but using ultrasonic plane wave emissions claiming a 50 fold enhanced sensitivity to blood volume changes.
Results in three animals (one imaging both hemispheres) show tonotopy in the auditory cortex (MEG, PEG), auditory thalamus (MGB), inferior colliculus (IC) and lateral lemniscus (LL). They also show blood volume changes in the auditory cortex following electrical stimulation of the frontal cortex in a single experiment.
Imaging of tonotopic arrangement of different auditory structures seems convincing, whereas the activation of the PSSC/insula by electrical stimulation of the frontal cortex looks vague. Details about how many cases, what place in the frontal cortex and electrical settings were stimulated most effectively must be added. The effect of electric stimulation of frontal cortex in the Claustrum needs to be reported. It has not been reported when the connection is clear (Figure 3C), but not a change in blood volume unless the increase observed in Figure 3B includes both PSSC and Claustrum together. In fact, the increase is observed also dorsal to the PSSC and a mix of increase and decrease deeper than that (Figure 3—figure supplement 1). Whether a change in blood volume after far away electrical stimulation is indicative of connectivity or otherwise need to be clarified. Results and Discussion section. Please state where exactly was the stimulating electrode; to say that the evoked activity in the PSSC/insula was maximal for a certain depth and position of the stimulating electrode is imprecise. The decrease of blood volume in MEG is indicating some kind of polysynaptic inhibition? If PSSC/insula area is not driven by sensory stimulation (broadband noise or visual stimulation), is suggested that is not a sensory structure?
Comparison in tonotopy is tricky across cases. Figure 1 is the best example but high frequencies in MEG are not only in the tip of the gyrus but also in between A1 and AAF. The other two cases were very different with few high frequencies in Vright and AAF full of high frequencies in Bright case (Figure 1—figure supplement 2). Also, in these two cases the tonotopy in IC and DNLL look less convincing than in Figure 1. The authors should explain in detail the different cases and discuss the differences between cases.
The paper will improve if further details are added. For example, how many repetitive days of imaging is not clear until Figure 1—figure supplement 3 (even here it is difficult to read the y-axes legend). Where exactly was the electric stimulation in the frontal cortex was applied: anterior sigmoid gyrus, lateral part, at what depth? LL is related to lateral lemniscus fibres, lateral lemniscus nuclei, only the dorsal nucleus of the lateral lemniscus as suggested in one of the figures; the patterns of sensory and electrical stimulation are not clear; 2 s stimulation every (8 + 10 s) of silence or only 8 s of silence? In this 2 seconds of stimulation, how long is the stimulus? The% CBV as percentage of cerebral blood volume is not explained until subsection “Signal processing, analysis & statistics”.
The time resolution of the technique seems to be more in the range of seconds than milliseconds when the quantified voxel responses are taken in a time-window 3-5 seconds after sound onset. Could the authors explain it?
Red and blue color code in figures is extremely confused. It can apply to increase and decrease in blood volume, to auditory cortex and visual cortex, or even high frequency and low frequency of auditory stimulation. I suggest the use of different colors when possible and always add a color code scale to each panel.
The decoder accuracy is very poor, at least in the auditory cortex and MGB (Figure 2). It would be good to add a plausible explanation about the fact that is better in the middle layers of the cortex (about 500 microns).
This technique could be a great complement to another imaging and recording techniques used in parallel with behavior in awake preparation. It would add value to include a new section of future potential applications where limitations were also discussed, for example having the head fixed or the need for sedation.
1) As the key novelty of this paper critically relies on the multivoxel pattern analysis/decoding accuracy and discriminability/resolution index calculations, these analysis procedures need to be described much more carefully and the numerous ad hoc thresholds currently described in the analysis section appropriately justified. How the comparison for multiple corrections is done also needs clarifying. If this work is meant to speak to the neuroimaging community at large, it needs to employ standard neuroimaging analysis as well as provide a link between its output and the metrics currently employed.
2) Given that both spatial coverage and resolution reported critically depend on the extensive skull removal, the limitations of this technique as a means of studying across-region connectivity needs to be carefully qualified. This also makes current comments on how the technique compares to fMRI resolution ill grounded: if invasiveness is allowed, then implantation (even on pial surface!) of RF coils also affords much higher spatial resolution than what the authors currently quote.
3) The rigor of figure creation is subpar: all of the images that have colored overlays need to have a color bar alongside with numbers reflecting the mapping of those colors to actual values of physiologically interpretable quantities. The reported CBV changes are very high compared to the literature, even in the awake mammals, so a discussion on this topic is warranted as well.
The paper titled "Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret" – submitted as a Tools and Resources article in eLife – reports imaging of tonotopic maps in the awake ferret and imaging of activity evoked in auditory areas by electrical stimulation of the frontal cortex in the sedated ferret, with functional ultrasound imaging. The authors present these results as a new method for recording brain activity at multiple spatial scales at higher resolution in awake animals, broadly applicable for other neuroscience questions. The imaging of tonotopic maps could be of interest for the auditory field, particularly in deeper structures difficult to access. However, there are major problems concerning both the novelty and the relevance of these experiments to justify the claim made by the authors in term of methodological advances. For this reason, I do not recommend the publication of this article as a Tool and Resources article in eLife.
1) There is no significant technological or methodological advance compared to previous work. The authors claim that they did for the first time (a) in an "awake" animals (b) "multi-scale mapping" (c) at "high-resolution". These three points have been shown before with the same technique.
a) There were three papers describing functional ultrasound imaging, including two papers in awake freely-moving rats (Macé et al., 2011; Urban et al., 2015 and Sieu et al., 2015) (one was not cited by the authors).
b) The possibility to record at "multiple spatial scales" (defined by the authors as the possibility to image mesoscale patterns of activity within a brain region and across brain regions) has been shown previously. For example, a single barrel was mapped in the rat brain, as well as large scale activity during epileptic seizures (Macé et al., 2011). Odor topographic maps have been imaged within the rat olfactory bulb (different glomeruli) and piriform cortex (Osmanski et al., 2014). Large-scale connectivity was explored previously in the awake rat (Osmanski et al., 2014). Imaging of sensory responses in small deep subcortical nuclei were shown in freely moving rats (Urban et al., 2015). A recent paper by the authors reported mapping of the visual responses in the rat brain at large-scale as well as local scale (for different retinotopic positions) in visual cortex (Gesnik et al., 2013).
c) The resolution of the technique (100 µm) has been demonstrated experimentally (point spread function) and theoretically in a previous paper (Macé et al., 2017). Therefore, the title "high-resolution functional ultrasound", as well as claims about spatial resolution made in the article are misleading, given that no improvement was made. In fact, the ultrasound acquisition and data processing used in this work is the same as in previous papers (for example in (Gesnik et al., 2013)).
d) Concerning the temporal resolution, the authors claim in the introduction that functional ultrasound imaging has "ms resolution compared to fMRI" (Introduction) and use the term "rapid" multiple times in the manuscript. However, they acquire one image of one brain slice in 1 second (subsection “fUS imaging”), and the whole tonotopic map in several hours (subsection “Protocol for sensory response acquisition”). Although the spatial resolution of fUS is better than fMRI, this is not the case for the temporal resolution.
2) Beyond the problem of novelty, there is a major issue with the claim about the "awake" state in this paper. The second part of the paper, about long-distance connectivity, was done in sedated ferret (subsection “FC stimulation”). This is not clear to the reader except in the Materials and methods section. The paragraph on connectivity experiments starts with: (Results and Discussion section: "Localizing and quantifying such connection in awake animals, remains technically challenging […] Here we show that fUS can be used to probe functional connectivity between two brain structures") leading the reader to think this is done awake. The choice of this experiment, in the context of demonstrating the interest of a new tool for imaging awake ferrets, is disputable. Whereas only tonotopic maps were recorded in awake (restrained) animals, the awake state is emphasized at multiple instances, including in the title and abstract. Moreover, obtaining tonotopic maps do not require awake state (some examples under anesthesia: (Nelken et al., 2008; Bizley et al., 2005 and Mrsic-Flogel, Versnel and King 2006)). A key advantage of functional ultrasound is to be applicable to awake, behaving and freely-moving animals, as shown previously (Urban et al., 2015; Sieu et al., 2015). Although many interesting biological insights can be obtained under sedation, this limits the interest of this work compared what was previously published, in particular for a methodology paper.
3) As an addition to the point 2, the authors report in the Materials and methods section that "the ferret was sedated to avoid movement artefacts". Previous studies were done in freely moving animals without motion problems (Urban et al., 2015; Sieu et al., 2015). Why is motion a problem in connectivity experiments compared to tonotopic experiments is unclear and not discussed. The claim in the conclusion that this method is "readily adapted to mobile and highly stable configurations" is undermined by the fact that the connectivity study was done in sedated animals.
4) Functional ultrasound imaging technique is applied here in a different animal model, the ferret. However little effort is done to provide readers with a protocol for reproducing the work. For example, identification of brain regions is an important step for other users with different questions. Identifying brain regions anatomically on ultrasound images is not easy compared to, for example, MRI. This step is not clearly explained in the article or in the Materials and methods section. It was apparently done manually and/or based on the auditory responses (which is not translatable to other behaviors). The usability for other neuroscientists to study other questions in the ferret, for example to reveal new regions implicated in a given behavior, is therefore limited. Availability of the ultrasound codes for other users is not stated.
5) The connectivity experiment, used to demonstrate top-down projections from the frontal cortex to the auditory system, is conceptually problematic. Electrical stimulation is known to evoke antidromic activity; therefore, it is not possible to discriminate between top-down and feed-forward connections if reciprocal connections exist. Optogenetic activation would have helped resolving this issue (and would have broadened the applicability for other neuroscience questions). Compared to simple tracer injections, it is unclear what we learned from this experiment, in part because the functional data presented are not discussed and all the focus is put on the method.
Comments on Methodology:
- Tonotopic maps (Materials and methods section). The authors display voxels in tonotopic maps if the max response is above a certain threshold (15%) and if the max response is correlated with the average hemodynamic response function (p<1e-3). Then they indicate: "these thresholds are adjusted for different structures and different animals". First, the same thresholding parameters should be used for the three structures of the three animals. Second, the frequency tuning is not taken into account in this thresholding method, only amplitude. Any voxel responsive to sound, regardless of whether it is tuned or not, would pass this threshold. A statistical test should be used on each voxel to determine significant tuning, and only tuned voxels should appear on the maps. Third, the authors applied a mask manually to show only voxels in auditory structures (subsection “Signal processing, analysis & statistics”). This biases the results and is misleading. It is not possible to assess the quality of their thresholding method outside of the auditory regions.
- Significance of the frequency tuning is tested only for one single voxel of one animal (Figure 1C, subsection “Signal processing, analysis & statistics”). This is uninformative. Average tuning with respect to preferred frequency of all voxels would have been the right analysis to do. This should be done for all regions separately, compared to control regions of the same size and compared across animals.
- Displaying an average tonotopy map for each brain region would be beneficial. The organization in each region is not clear from the different examples. In particular, in Figure 1—figure supplement 2, maps of the third animal are different from the other two animals (for example, in PEG). No comparison is made with the known tonotopic maps from the literature (such as: axis of the tonotopic map, reversals/boundaries of the maps) (Nelken et al., 2008; Bizley., 2005 and Mrsic-Flogel, Versnel and King 2006).
- The interest of the multivoxel pattern analysis decoding is disputable. The analysis shows that, when pooling all voxels from one structure, single trial responses carry significant information about the frequency tuning. First, that does not mean that a tonotopic map can be reconstructed at the single-voxel level from a single trial. Second, this kind of analysis is usually used to determine if a property (i.e. sound frequency tuning here) is encoded in a brain region when the spatial resolution is too low or if responses are intermingled. Here it is clear that frequency tuning exists in auditory areas. The claim that " the hemodynamic signal imaged in fUS is reliable enough to decode brain activity on a single-trial basis within a single experiment" (Results and Discussion section) is trivial. It could have been demonstrated in a much simpler way – for example by averaging single-trial tuning curves of all voxels of the tonotopic map in a specific region.
- The quantification of the spatial resolution was made based on 6 voxels hand-picked in one animal and one structure (Figure 2B). It is not reported where (in which brain structure? which animal?) and these voxels are not put in the context of a larger map. This is not sufficient to sustain this claim. Such quantification should be made on more voxels, in different structures and compared across the three animals.
- Statements about the multimodal responsiveness of brain areas (Results and Discussion section, Figure 3—figure supplement 3) are not sufficiently supported. The authors report the data obtained from only one animal. Generalization on the multimodal aspect of these regions would require a statistical test across different animals. Moreover, as mentioned before, the anatomical identification of brain regions should be standardized based on an atlas and not manually picked after the functional recordings. For example: responses in PSSC seem restricted to the upper voxels of the delimited region (Figure 3—figure supplement 3Figure). This could be due to a misalignment of the region boundaries, thus misattributing voxels from the neighboring auditory responsive regions to PSSC.
Overall the study looks impressive and interesting to me, and I have just a few statistical questions.
The primary voxelwise activation response mapping was thresholded both in terms of minimal% signal change and statistically. However, the latter does not appear to have corrected for multiple comparisons across voxels and frequencies. This needs to be done for clarity and transparency. However, I'm not saying that any potential lack of full family-wise-error significance in the initial voxelwise mapping would necessarily cause concern for the later multivariate analyses or overall story.
The exact calculations being made for the resolution quantification need to be written out a little more explicitly and clearly.
For any timeseries analysis involving temporal correlation (whether for external-sensory or electrical stimulation) please describe whether/how temporal autocorrelation was adjusted for in the statistical analyses.
Figure 1A caption – presumably the authors mean structural MRI not fMRI?
[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]
Thank you for resubmitting your work entitled "Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret" for further consideration at eLife. Your revised article has been evaluated by Andrew King as the Reviewing and Senior Editor and three reviewers.
Although the manuscript is improved, the reviewers had widely differing opinions regarding the extent to which the manuscript has been adequately revised, which we were not able to resolve in the subsequent consultation. In particular, reviewer 3 continued to raise a number of concerns. The key elements of those concerns, along with the minor comments provided by the other reviewers, are summarized in the following. We are hoping that you will be able to address these in a further (and final) revision.
The main argument of the authors to justify novelty is that the auditory stimulation (5 tones, 3 s presentation) "pushes the limits of fUS sensitivity". There is no conceptual difference between this paradigm and previous studies that already used fUS to record sensory-evoked activity in rats or other models. The authors argue for shorter stimulation time (3 s), compared to previous studies, as a novel aspect of their paradigm. However, (Urban et al., 2014) already recorded the response to forepaw stimulation as short as 200 µs (see graphical abstract and Figure 4, for example). Yet, this article is not cited. Another claim made by the authors is that they show, for the first time, activations in very small and deep structures. A highly relevant paper (Urban et al., 2015) contains a dedicated section about "Functional imaging in subcortical brain structures", showing, for example, activation in small and deep thalamic nuclei in freely moving rats. However, although this paper was brought to the attention of the authors, they did not add this reference to the manuscript or address these concerns about novelty.
Reviewer 3 stated that you should emphasize that both ultrasound sequence and data processing used in this study are identical to previous studies, and thus, the imaging resolution remains the same. Indeed, a resolution of 100 µm (the physical resolution of the method) was claimed in previous papers, so it remains unclear why this resolution is presented as "unprecedented". Because the same fUS sequence was used here as in previous studies, the apparent improvements in sensitivity and resolution most likely relate to differences in experimental protocol, including the stimulation strategy used. The revised version of the paper addressed some of these points, but not to the satisfaction of reviewer 3. Please take another look at this and ensure that all relevant key studies are cited.
Another concern that has not been well addressed by the authors is their comparison with fMRI. The authors insist on the millisecond acquisition rate of fUS. While the acquisition of a compound ultrasound image (~2 ms) is fast, the relevant information about neural activity is derived from – much slower – images of blood volume (~ 1 s) that are computed from hundreds of ultrasonic images. Thus, the relevant temporal resolution of fUS is on the order of seconds and not few milliseconds. Also, fMRI is not slow compared to fUS; the acquisition of a single slice is usually faster. As an example, (Leaver and Rauschecker, 2016) acquired tonotopic maps using 6 tones of 2 s duration. Each brain slice was acquired in 250 ms and the full volume (28 brain slices) in 7 s. By comparison, a single brain slice is acquired in 1 s with fUS. The advantages of fUS compared to fMRI reside in the higher spatial resolution and ease-of-use, but not in the temporal resolution. Hence, insisting on a superior temporal resolution of the technique is not justified.
Reviewer 3 continues to have a concern about the way the tonotopic maps were constructed, insisting that this should be based the preferred frequency of pixels modulated by the sound frequency. We believe that – for the reasons that you outlined in the response letter – this is not necessary, but please ensure that the manuscript text fully justifies the thresholding procedures used. We do not think it necessary (or desirable) to use the approach illustrated in the response letter, where a threshold of 10% was used. The reviewer proposed that you should average over more trials or use more stimuli to improve sensitivity to modulation. If you have additional data along these lines, that would clearly help to address this concern, but we believe it is useful to show non-tonopically organized regions too and to focus on the MVPA for quantification of the reliability and sensitivity of the responses (albeit with a little more explanation).
[Editors’ note: the author responses to the first round of peer review follow.]https://doi.org/10.7554/eLife.35028.020
- Célian Bimbard
- Constantin Girard
- Susanne Radtke-Schuller
- Shihab Shamma
- Yves Boubenec
- Célian Bimbard
- Constantin Girard
- Shihab Shamma
- Yves Boubenec
- Célian Bimbard
- Charlie Demene
- Constantin Girard
- Susanne Radtke-Schuller
- Shihab Shamma
- Mickael Tanter
- Yves Boubenec
- Charlie Demene
- Mickael Tanter
- Shihab Shamma
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank Marc Gesnik from the Institut Langevin for his valuable inputs for the ultrasound sequence programming, Roberto Toro for the ferret fMRI scan, and Kishore Kuchibhotla for careful reading of the manuscript. This work was supported by ANR-10-LABX-0087 IEC et ANR-10-IDEX-0001–02 PSL* and research grants from the European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013)/ERC Advanced grant agreement n° 339244-FUSIMAGINE and ERC Advanced grant agreement n° ADG_20110406-ADAM and R01-DC005779 (SS). The project received the technical support of the INSERM Technology Research Accelerator in Biomedical Ultrasound.
Animal experimentation: Experiments were approved by the French Ministry of Agriculture (protocol authorization: 01236.02) and strictly comply with the European directives on the protection of animals used for scientific purposes (2010/63/EU). All surgery was performed under anaesthesia (isoflurane 1%), and every effort was made to minimise suffering.
- Andrew J King, Reviewing Editor, University of Oxford, United Kingdom
© 2018, Bimbard et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.