Olfactory cortical outputs recruit and shape distinct brain-wide spatiotemporal networks

Reviewer #1 (Public review):

Summary:

This manuscript combined rat fMRI, optogenetics and electrophysiology to examine the large-scale functional network of the olfactory system as well as its alteration in an aged rat model.

Strengths:

Overall methodology is very solid and the results provided an interesting perspective on large-scale functional network perturbation of the olfactory system.

Weaknesses:

The biological relevance and validation of the current results can be improved.

Comment on revised version.

Authors made satisfactory revision and I have no further comments.

Reviewer #2 (Public review):

Summary:

Ma and colleagues presented a study on the characterization of brain-wide spatio-temporal impact of olfactory cortical outputs. They take advantage of multi-modal techniques on rats: fMRI, optogenetics and electrophysiology. In addition, they used cutting-edge analytical techniques and modeling to support and interpret their data. The main findings of the study are:

(1) Neurons in Olfactory Bulb (OB) predominantly activate primary olfactory network regions, while stimulation of OB afferents in Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) primarily orthodromically activates hippocampal/striatal and limbic networks, respectively.
(2) Non-specified adaptation or habituation mechanisms may play a significant role in modulating olfactory outputs over subsequent fMRI sessions.

(3) Artificially induced aging in rats induces profound modification in the functional interaction between olfactory cortices and multiple brain regions.

The results on AON are of particular interest because of the lack of functional information on this region, despite its recognized importance in shaping OB output and behavior (odor localization tasks).

Strengths:

The manuscript is very accurate. The figures are well-crafted, clear and provide much information with the most appropriate plots and graphics. The study's amount and data quality are remarkable, and the experimental size adequately addresses the scientific questions. I particularly appreciated the details in the description of the methods regarding the missing data and the size of the different animal groups. The supplementary data complete the leading figures and provide information at a single animal level.

Weaknesses:

(1) One of the main reasons the Piriform Cx is understudied in rodents is because of the proximity to air, which creates artifacts in fMRI images. This issue becomes more critical at ultra-high magnetic fields, but I would expect it also at 7T. One main achievement of this study is, indeed, the acquisition of fMRI data from Piriform, and this point should be highlighted by showing raw functional data from a rat. The best would be if an fMRI data sample for a rat, no matter which stimulation, is shared on a public repository, like Zenodo or similar. I am curious to check the quality of the BOLD data from such an 'enormous' field of view, particularly in the OB, with a single-shot sequence. Also, the visual inspection of raw data is essential to appreciate how many 0.5 x 0.5 x 1 mm voxels fit into AON, and others analyzed small brain structures, like the amygdala, etc. Was the amygdala entirely visible in BOLD, or did the air in the ear channel make an artifact partially shadowing it?

(2) Surprisingly, the only information missing in the methods is the post-surgery period and the time between two consecutive fMRI sessions. How much time was accorded to rats to recover from the surgeries, and what time interval between two scans? This information is crucial for interpreting the decrease in most BOLD responses in subsequent recordings. The supposed adaptation should fit into the known time frames for odor adaptation. Usually, fast adaptation does not last for days (and it should be measured within a single experiment: is it the case?), while for long-lasting adaptation the stimulus (odor or opto) should be maintained constantly ON. This does not seem to be the case in this study. The hypothesis, alternative to adaptation, of a less efficient light activation, for example, due to gliosis around the fiber tips, should be discarded with more evidence than the preservation of OB > Pir responses or acknowledged in the manuscript.

(3) The D-galactose experiments were conducted only after administering the aging molecule, with no baseline/reference data on the same animals. Then, comparisons were made with healthy rats, but the two groups not only can be discriminated with respect to D-galactose administration but also with age (10 VS 18 weeks). A control group for 18-weeks-old rats with no D-galactose treatment would better compare the D-galactose effect and avoid any potential bias from group comparisons of rats at different ages. Do you confirm that D-galactose was injected into each rat 56 times/days in a raw, or am I mistaken?

The updated version of the manuscript partially addresses the flaws of the original submission. Here are my general concerns:

(1) Overall, the revised version comes with a few modifications/additions and no new data. Apart from a new correlation analysis, the improvements are mainly discursive, often non-convincing, justifications of the authors' choices. This may reflect a lack of interest in a publication that, in the meantime, lost its original peer-review value. However, it should be acknowledged that the Authors made an effort to partially address the concerns raised by the reviewers.

(2) My main concern was the quality of fMRI recordings. In the revised version, the Authors provided a new figure with an example single-mouse fMRI data. However, the depicted regions of interest (ROIs) mostly cover the brain spots that I expected to be the most impacted by the BOLD artifacts caused by the proximity of the air and the big field-of-view. In addition, these ROIs do not appear to match the mouse anatomy shown above the functional data. As an example, the EPI images in the OB are almost entirely covered by the colored mask. The feeling is that the fMRI data was indeed poor, as I worried, and the lack of any public repository of raw data reinforces that feeling. To make this point clear: I do not think the findings are not true, but poor fMRI data quality might have hidden more insightful results and does not foster the use of fMRI to monitor the olfactory pathway, which lowers the impact of this article.

Author response:

The following is the authors’ response to the original reviews

eLife Assessment

This important study partially fills the gap in the knowledge of olfaction at the level of the Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) with functional magnetic resonance imaging, electrophysiology, and modeling. The methods used are convincing. Some of the findings confirm ongoing hypotheses, such as the behavioral importance of AON for odor source discrimination. Other results shed light on the dynamics of the connection between the olfactory system and the rest of the brain.

We sincerely thank the editors and reviewers for the thorough review of our manuscript. We appreciate the insightful comments, which have significantly contributed to improving our work. In this revision, we addressed all the concerns posed by the reviewers, including conducting additional analyses and providing the data generated and codes used in this study.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript combined rat fMRI, optogenetics, and electrophysiology to examine the large-scale functional network of the olfactory system as well as its alteration in an aged rat model.

Strengths:

Overall methodology is very solid and the results provided an interesting perspective on large-scale functional network perturbation of the olfactory system.

Weaknesses:

The biological relevance and validation of the current results can be improved.

We thank the reviewer for the comments and suggestions regarding our manuscript. They have been invaluable and instrumental in enhancing our work. Please see R1-1 to R1-8 below for our corresponding responses and revisions.

Comments:

(R1-1) Figure 1A, on the top of the figure, ChR2 may be replaced by ChR2-mCherry, as only mCherry is fluorescent. And also, it’s somewhat surprising that in AON and Pir regions (where only axon fibers should be labelled as red), most fluorescence appeared dot-like and looked more similar to cell body instead of typical fiber. The authors may want to double-check this.

(a) Thank you for pointing out the missing fluorescent marker for ChR2 expression in the label of Figure 1A. We distinguished the transfection of ChR2 in olfactory bulb (OB) neurons from axonal terminals in AON and Pir by identifying the expression patterns in these three regions (see Figure 1). In OB, the cellular nucleus (i.e., labelled in blue by DAPI) was surrounded evenly by ChR2-mCherry expression (red fluorescent, indicated by green arrows in Figure 1), which clearly traced the neuronal cell body shape. Meanwhile, the expression of mCherry in AON and Pir consisted of concentrated red fluorescent dots that were sparsely assembled near the cell nucleus, indicating the presence of synaptic boutons at the axonal terminals. Further, the patterns of expression here did not trace the shape of the neuronal cell body.

(b) In this revision, we amended the label of Fig. 1A to “ChR2-mCherry”.

In this revision, changes were made to the confocal images of AON and Pir, based on Figure 1, for clarity and the figure caption was also edited accordingly.

(R1-2) The authors primarily presented 1 Hz stimulation results. What is the most biologically relevant frequency (e.g., perhaps firing frequency under natural odor stimulation) among all frequencies that were used?

(a) There are various firing rate and oscillatory frequency of neural activities within the olfactory system (i.e., OB, Pir) in rodents during olfaction. For example in OB, neural oscillations ranging from 1 to 12 Hz (i.e., slow to theta) are driven by sensory stimulation and are closely linked to respiration [1]. Specifically, odor-evoked responsive excitatory bursts of mitral and tufted cells in OB are coupled with the low-frequency respiration rhythm (1-4 Hz) [2,3]. Such coupling has also been documented during light anaesthesia [4]. Meanwhile, higher frequencies such as beta oscillations (15 ~ 30 Hz) have been associated with odor learning and sensitization [5,6], and gamma oscillations (40 ~ 80 Hz) evoked by sensory stimulation are associated with fine olfactory discrimination and odor learning [6-8].

The piriform cortex (Pir) also exhibits natural oscillatory activity across various frequency bands, including slow, theta, beta, and gamma oscillations [9-11]. As in OB mitral and tufted cells, slow oscillations (< 1.5 Hz) in Pir are also correlated with the respiratory rhythm, indicating the intrinsic respiration-related oscillatory properties within the olfactory system [12]. However, while it has been shown that the primary burst of firing in the anterior Pir is locked to respiration, its coding strategies differed from OB during olfaction [13].

Despite not being able to entirely mimic the evoked neural activities under natural odor stimulation due to the synchronizations induced by our optogenetic stimulations, our frequencies were chosen to be within the range of firing rates of neurons in the olfactory system under natural circumstances. Our selection of 1 Hz frequency for optogenetic stimulation was based on the balance of experimental simplicity of inducing neural excitation and the biological significance of low-frequency neural oscillations in OB, Pir and AON. The robustness of evoked brain-wide long-range activations is one of our criteria for the selection of 1 Hz stimulation frequency at OB, considering that the goal of this study is to examine long-range olfactory networks. Furthermore, we also examined other frequencies of stimulation covering theta, beta and gamma frequency bands, which provided insights into the response characteristics within the olfactory networks.

(b) In this revision, we added a brief statement in the Results section that 1 Hz was the most biologically relevant frequency based on discussions in R1-2a above.

(R1-3) In Figure 2, the statistical thresholding is confusing: in the figure legend, it was stated that “t > 3.1 corresponding to P < 0.001” but later “further corrected for multiple comparisons with thresholdfree cluster enhancement with family-wise error rate (TFCE-FWE) at P < 0.05”? Regardless of the statistical thresholding, such BOLD activation seemed to be widespread (almost whole-brain activation). Does such activation remain specific to the optogenetic stimulation, or something more general (e.g., arousal level change)? Furthermore, how those results (I assume they are group-level results) were obtained was not described very clearly. Is it just a simple average of individual-level results, or (more conventionally) second-level analysis?

(a) We thank the reviewer for drawing our attention to clarity issues regarding the generation of group-level BOLD activation maps and statistical thresholding methods used.

In brief, we first generate the BOLD activation map of individual animal through conventional general linear model (GLM) analysis. These individual activation maps then underwent a two-step statistical thresholding method to generate the group-level results. Firstly, uncorrected one-sample t-tests were conducted with a threshold of P < 0.001. Secondly, these thresholded activation maps were further corrected using nonparametric inference with threshold-free cluster enhancement multiple comparison correction of family-wise error rate (TFCE-FWE, P < 0.05) before they were averaged. Hence, the BOLD activation maps presented in our manuscript were the result of group-level analysis instead of individual-level analysis.

(b) We note the reviewer’s concern about whether the observed widespread BOLD activations were caused by the animal’s general brain state (e.g., arousal levels) rather than the specificity of optogenetic stimulation. First, such widespread activations were only specific to 1 Hz stimulation of OB neurons and OB afferents at AON (Figs. 2A, B and 3A, B) whereas activations were localized to regions in the primary olfactory network with increasing stimulation frequencies. Furthermore, varied neural activity adaptation properties were observed (Fig. 3A, B) following repeated 1 Hz optogenetic excitation of OB neurons and OB afferents at AON indicating that such widespread propagation of evoked neural activity was not driven by the animal’s general brain state, which would be relatively random across animals as we interleaved the presentation of each stimulation frequencies. While we cannot discount that certain characteristics of brain states differ under anaesthetized and awake conditions, we showed that light (1.0% isoflurane) anaesthesia minimally affects the BOLD fMRI activations and/or the propagation of optogenetically-evoked neural activity across thalamo-cortical, hippocampal-cortical and vestibulo-cortical networks [14-20].

Second, the neural representations in OB have been shown to be brain state-independent to ensure the high fidelity of olfactory inputs to higher olfactory cortices, despite differences in spontaneous baseline activity across states [21-24]. In fact, OB neural activity (i.e., slow to delta oscillations) remains highly coupled to respiration rhythms under low anaesthesia as in awake conditions [4]. Further, low-dose isoflurane (i.e., 1% as in the present study) does not affect cortical gamma oscillations (40 Hz) [25-28]. Although odorant-evoked responses in olfactory cortices (e.g., Pir and olfactory tubercle, Tu) can be modulated by overall brain state, various interactions between such cortical circuits to process odor inputs persist under anaesthesia [29-31].

(c) In this revision, we clarified the two-step statistical thresholding method that was applied to generate the group-level BOLD activation maps, as discussed in R1-3a above, in the captions of Fig. 2 and the Methods section of the manuscript.

In this revision, we also included a statement in the Discussion section that the widespread BOLD fMRI activations were specific to the 1 Hz optogenetic stimulation and not the animal’s general brain state, as discussed in R1-3b above.

(R1-4) In Figure 2, why use AUC to quantify the activation, not the more conventional beta value in the GLM analysis?

(a) We thank the reviewer for raising this concern. We are aware of the more conventional beta value, b, in GLM analysis and have used them for quantification and comparison of the amplitudes of fMRI activations in our previous rodent fMRI studies [18,32,33]. However, in this study, we chose to utilize area under curve (AUC) as it offers a more comprehensive measure of BOLD signal change over time, including shape, duration, and magnitude, thereby capturing the bulk of neural activities and their dynamics throughout the stimulation period. b primarily represents the peak amplitude of BOLD responses (i.e., the % BOLD signal change) [34] and can be constrained by the assumptions and limitations of the GLM analysis, such as the shape of the canonical hemodynamic response function (HRF). Therefore, AUC provides greater accuracy in capturing different aspects of neural responses across various brain regions, such as transient peaks and/or sustained responses.

(b) In this revision, we have included the justifications for using AUC over the more conventional b value to quantify BOLD fMRI activations, as in R1-4a above, in the Methods section.

(R1-5) For Figure 2D, the way that it was quantified can be better described as “relative” activation within one condition, and I don’t know how to interpret the comparison among the relative fraction of activated regions. Perhaps comparison using percentage change (i.e., beta values) is more straightforward.

(a) We thank the reviewer’s comment and suggestion here. “BOLD activation strength” is the AUCs normalized to the respective sum of BOLD activations of their respective stimulation target. We are of the opinion that “strength” can accurately reflect our purpose for conducting this comparison, which is to distinguish the brain networks that were predominantly recruited by AON- or Pir-driven neural activities compared to OB stimulation. 

(b) We conducted a comparison using relative fraction for fair comparison considering the differences in absolute BOLD signal amplitudes upon different stimulation targets. As we found the overall amplitudes of activations evoked by Pir stimulation were appreciably weaker, our normalization step ensures that the contribution of Pir is not underrepresented. Without this normalization process, the Pir stimulation seems not to activate most of the downstream targets based on the relatively weak activations. As discussed earlier in R1-4a above, comparison using percentage change/b value would be skewed towards the absolute amplitude of BOLD responses and would be erroneous for subsequent interpretation of brain networks that are primarily recruited by OB vs. AON vs. Pir.

(b) In this revision, we chose not to include additional statements as we have already described in the Results section the reasons for normalizing AUC to reflect and subsequently compare the BOLD activation strength across various networks that were recruited by optogenetic stimulation of three distinct olfactory regions (i.e., OB, AON and Pir). 

(R1-6) For Figure 3, it may be more convenient for readers to include the results of 1st activation for direct comparison. The current layout makes it difficult to make direct, visual comparisons among all 3 activations. Again, I think using beta values (instead of AUC) may be more conventional.

(a) We agree that it will be more convenient for readers if we include the 1st activation maps in Figure 3 for direct comparison. Please refer to R1-4 for the usage of AUC rather than beta values.

(b) In this revision, we modified Fig. 3 to include activation maps from the 1st fMRI session for easy comparison with the 2nd and 3rd sessions.

(R1-7) Can the DCM results (at least part of it) be verified using the current electrophysiological data? For example, the long-range inhibitory effective connectivity of AON is rather intriguing. If that can be verified using the electrophysiology data, it would be really great. In the current form, the DCM and electrophysiology results seem to be totally unrelated.

(a) We thank the reviewer for raising this concern, and it’s a great suggestion to causally link the outcomes from dynamic causal modeling (DCM) analysis and electrophysiology findings.

(b) In principle, the recorded local field potentials (LFPs) can be treated as the neural dynamic component in DCM analysis under specific conditions. They are as follows:

(1) LFPs are recorded at the identical brain state as the optogenetic fMRI experiments with identical stimulation paradigms.

(2) The electrode locations of LFP recordings must match the nodes of the a priori matrix defined for the existing DCM analysis (Fig. 4A).

However, in the present study, we did not have LFP recordings at the entorhinal cortex (Ent), which will likely influence the modeling of effective connectivity strength as one of the nodes defined is dropped from the analysis. Previous studies have showed that changes in the a priori matrix will result in different connectivity estimations [35-38]. We chose to model the connectivity involving Ent due to its documented interactions with the primary olfactory cortices and hippocampal regions [39,40]. Hence, it would be incomplete without modelling the contributions from Ent.

(c) In this revision, we decided not to redefine the a priori matrix by removing Ent as a node in the DCM analysis to estimate new effective connectivities. Instead, we acknowledge the absence of Ent recordings.

(R1-8) In Figure 6, it would be great if the adaptation of BOLD and electrophysiology signals can be correlated at the brain region level. The current figure only demonstrated there is adaptation in the electrophysiology recording, but did not show if such adaptation is related to the BOLD adaptation.

(a) We thank the reviewer for pointing out that we should associate the electrophysiology and fMRI data to validate the olfactory adaptation. As such, we conducted additional correlation analysis between LFP power and BOLD signal profiles. The method for computing the correlation coefficient is described below:

(1) For each 1 Hz optogenetic stimulation target (i.e., OB, AON, and Pir), regions of interest (ROIs) that have both LFP recordings and BOLD activations were selected. These ROIs were AON, Pir, ventral caudate putamen (vCPu), visual cortex (V1), and ventral hippocampus (vHP) for OB stimulation; OB, Pir, vCPu, V1, amygdala (Amg), and vHP for AON stimulation; and OB, AON, vCPu, V1, Amg, and vHP for Pir stimulation. Note that we excluded the respective stimulated region as a ROI because of BOLD fMRI signal dropout caused by the implanted optical fibre.

(2) We first calculate the individual animal AUC differences of 2nd stimulation session vs. 1st session and 3rd session vs. 1st session in LFP power and BOLD signal profiles, respectively.

Subsequently, the mean of the differences for each ROI across animals was then calculated.

(3) Correlation coefficients and P values of AUC differences between LFP power and BOLD signal profiles were computed using Spearman nonparametric correlation.

(b) The relationship between the differences of LFP power and BOLD signal profiles showing neural adaptation was described by the correlation coefficient, r, and the corresponding P value. The r and P values upon the three distinct stimulations were OB: r = 0.77 (P = 0.01), AON: r = 0.46 (P = 0.13), and Pir: r = - 0.06 (P = 0.85), respectively. This indicates that the decrease of LFP power is highly correlated with the decrease of BOLD activations upon OB and AON stimulation compared to those upon Pir stimulation.

(c) In this revision, we added the description of the correlation analysis conducted, as in R1-8a above, in the Methods section.

In this revision, we also added several statements in the Results section to indicate that neural adaptation observed is significantly related to the decreased BOLD activations when stimulating OB excitatory neurons or OB afferents at AON, as discussed in R1-8b above. Additionally, we also included the outcomes of the correlation analysis as a new Supplementary Fig. 7.

Reviewer #2 (Public review):

Summary:

Ma and colleagues presented a study on the characterization of brain-wide spatio-temporal impact of olfactory cortical outputs. They take advantage of multi-modal techniques on rats: fMRI, optogenetics, and electrophysiology. In addition, they used cutting-edge analytical techniques and modeling to support and interpret their data. The main findings of the study are:

(1) The neurons in the Olfactory Bulb (OB) predominantly activate primary olfactory network regions, while stimulation of OB afferents in Anterior Olfactory Nucleus (AON) and Piriform Cortex (Pir) primarily orthodromically activates hippocampal/striatal and limbic networks, respectively.

(2) Non-specified adaptation or habituation mechanisms may play a significant role in modulating olfactory outputs over subsequent fMRI sessions.

(3) Artificially induced aging in rats induces profound modification in the functional interaction between olfactory cortices and multiple brain regions.

The results on AON are of particular interest because of the lack of functional information on this region, despite its recognized importance in shaping OB output and behavior (odor localization tasks).

Strengths:

The manuscript is very accurate. The figures are well-crafted, and clear and provide much information with the most appropriate plots and graphics. The study’s amount and data quality are remarkable, and the experimental size adequately addresses the scientific questions. I particularly appreciated the details in the description of the methods regarding the missing data and the size of the different animal groups. The supplementary data complete the leading figures and provide information at a single animal level.

We are grateful for the reviewer’s appreciation of our study’s breadth and depth, and the quality and quantity of our data. The recognition of the strengths in our methods and the inclusion of single-animal-level data in the supplementary information is highly encouraging. We are also appreciative for the reviewer’s constructive comments below, which has help us to address specific weaknesses of the study and thereby improving the manuscript text and flow. We have read through the comments carefully and have made the necessary corrections. We hope that our responses to R2-1 to R2-11 below has sufficiently addressed all the reviewer’s concerns.

Weaknesses:

(R2-1) One of the main reasons the Piriform Cx is understudied in rodents is because of the proximity to air, which creates artifacts in fMRI images. This issue becomes more critical at ultra-high magnetic fields, but I would expect it also at 7T. One main achievement of this study is, indeed, the acquisition of fMRI data from Piriform, and this point should be highlighted by showing raw functional data from a rat. The best would be if an fMRI data sample for a rat, no matter which stimulation, is shared on a public repository, like Zenodo or similar. I am curious to check the quality of the BOLD data from such an ‘enormous’ field of view, particularly in the OB, with a single-shot sequence. Also, the visual inspection of raw data is essential to appreciate how many 0.5 x 0.5 x 1 mm voxels fit into AON, and others analyzed small brain structures, like the amygdala, etc. Was the amygdala entirely visible in BOLD, or did the air in the ear channel make an artifact partially shadowing it?

(a) As per the reviewer’s suggestion, we displayed the representative EPI images after preprocessing at a matrix size of 128 x 128 with a pixel size of 0.25 x 0.25 mm in Supplementary Figure 2. Up-sampling was not applied out-of-plane. Note that the atlas-based region-of-interest (ROIs) used to extract BOLD signal profiles (i.e., indicated by colored overlays in Supplementary Figure 2) were drawn based on the up-sampled EPI images (from the acquired 64 x 64 to 128 x 128). Notably, the piriform cortex (Pir) and amygdala (Amg) are visible with no appreciable signal dropout at these regions. To clarify, the Pir in our study represented the anterior Pir.

Signal dropout is pronounced in the posterior ventral regions of the brain (e.g., Bregma- 4 mm, below the Ent) and as expected in a localized region where the implanted optical fiber region that targeted the AON.

(b) In this revision, we included as a new Supplementary Figure 8 and added a statement in the Results section to describe the absence of appreciable signal dropouts at OB, Pir and Amg, which could affect subsequent quantitative analyses.

(R2-2) Surprisingly, the only information missing in the methods is the post-surgery period and the time between two consecutive fMRI sessions. How much time was accorded to rats to recover from the surgeries, and what time interval between two scans? This information is crucial for interpreting the decrease in most BOLD responses in subsequent recordings. The supposed adaptation should fit into the known time frames for odor adaptation. Usually, fast adaptation does not last for days (and it should be measured within a single experiment: is it the case?), while for long-lasting adaptation the stimulus (odor or opto) should be maintained constantly ON. This does not seem to be the case in this study. The hypothesis, alternative to adaptation, of a less efficient light activation, for example, due to gliosis around the fiber tips, should be discarded with more evidence than the preservation of OB > Pir responses or acknowledged in the manuscript.

(a) We thank the reviewer for drawing our attention to the missing information regarding the timeline of each optogenetic fMRI experiment. We conducted fMRI experiments immediately after the surgical procedure for implanting the optical fiber cannula. Such an implantation procedure typically last for 45mins under 1.2-2.0% isoflurane. The animal is then moved from the surgical table to the magnet to begin the optogenetic fMRI experiment. Intervals between two fMRI scans were within one minute. As the stimulation frequencies (i.e., 1 Hz, 5 Hz, 10 Hz, 20 Hz and 40 Hz) were pseudorandomized, the interval between any two identical stimulation frequency was on average 30 minutes. In this case, we are measuring fast adaptation (on the order of tens of minutes) rather than long-lasting adaptation. Further, as the fMRI experiment was conducted immediately following fiber implantation, the risk of gliosis around the fiber tips is expected to be low.

(b) The main representation of olfactory adaptation is the attenuation of neural responses upon continuous or repeated stimuli [41,42]. Several fMRI studies in rodents have reported that olfactory adaptation was detected in primary olfactory cortices (i.e., OB, AON, and Pir) upon repeated odor stimulation [43,44]. Specifically, these studies showed robust decreased responses in primary olfactory cortices under repeated odor stimulation with an odor interval of 30 minutes, which were comparable to those observed in our study upon repeated 1 Hz optogenetic stimulation. As such, the pronounced neural adaptation that we demonstrated when stimulating OB excitatory neurons or OB afferents at AON is unlikely to be caused by less efficient light activation.

(c) In this revision, we added statements in the Methods section, as discussed in R2-2a above, to clarify the timeline of our optogenetic fMRI experiment.

(R2-3) The D-galactose experiments were conducted only after administering the aging molecule, with no baseline/reference data on the same animals. Then, comparisons were made with healthy rats, but the two groups not only can be discriminated with respect to D-galactose administration but also with age (10 VS 18 weeks). A control group for 18-weeks-old rats with no D-galactose treatment would better compare the D-galactose effect and avoid any potential bias from group comparisons of rats at different ages. Do you confirm that D-galactose was injected into each rat 56 times/day in a row, or am I mistaken?

(a) We appreciate the reviewer’s comment here and understand the concerns regarding the absence of an age-matched control group with saline administration instead of D-galactose. We acknowledge that the inclusion of a control group would have provided a more direct comparison to evaluate the differences between healthy and aged animals. However, we were unable to include this group in the present study due to logistical constraints.

To clarify, our experiments were conducted on healthy rats at the median age of 14 weeks to ensure the animals had reached adulthood. The median age of the D-galactose injected animal was 18 weeks. In terms of the lifespan of rats, they reach sexual maturity at approximately 6 weeks of age [45], at which point we conducted the optogenetic viral vector injection. The age of rats at 14 and 18 weeks can both be regarded as teenage adult [45,46]. The primary purpose of the experiments on the aged animal model and corresponding analyses was to provide some potential clues into the dysfunction of olfactory networks at the system level as animals aged. Despite the age difference between the two groups, we believe that the comparison between healthy and aged rats still offers valuable insights into the ageing-related dysfunctions within the olfactory system. 

(b) Note that D-galactose was injected into each rat 56 times in total over the whole experiment (i.e., one injection per day over the course of 8 weeks).

(c) In this revision, we acknowledge that the comparisons made were not with age-matched healthy controls in the Discussion section.

In this revision, we also clarified that D-galactose was administered once daily for 8 weeks (i.e., a total of 56 injections) in the Methods section.

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

Minor:

(R2-4) The look of the activation maps will greatly improve if the displayed brain slices are coronal (as in the supplementary figures) with no angle.

We thank the reviewer for this suggestion. The purpose of such a display was to maintain the consistency of displaying the overlay of BOLD fMRI activation maps on the 3D renders of rat brain anatomical images. In addition, such 3D display can better impress readers that the optogenetically evoked BOLD activations were indeed long-range and brain-wide. 

As such, in this revision, we decided to maintain the existing displays of BOLD activation maps in the main figures.

(R2-5) The choice of testing different stimulation frequencies deserves more justification. Why was it performed in the OB, which is naturally activated on the breathing rhythm?

(a) We thank the reviewer’s comment here to seek further clarification. OB is widely recognized as the first stage of olfactory information processing in the brain. By activating the OB excitatory neurons, we can ensure that our optogenetically-evoked activations are olfactory-related.

The choice of various frequencies ranging from low (i.e., 1 Hz) to high (e.g., 40 Hz) was made to cover a range of firing rate and oscillatory frequency of neural activities neural oscillations that exist within the rodent olfactory system during olfaction. For example in OB, neural oscillations ranging from 1 to 12 Hz (i.e., slow to theta) are driven by sensory stimulation and are closely linked to respiration [1]. Specifically, odor-evoked responsive excitatory bursts of mitral and tufted cells in OB are coupled with the low-frequency respiration rhythm (1-4 Hz) [2,3]. Such coupling has also been documented during light anaesthesia [4]. Meanwhile, higher frequencies such as beta oscillations (15 ~ 30 Hz) have been associated with odor learning and sensitization [5,6], and gamma oscillations (40 ~ 80 Hz) evoked by sensory stimulation are associated with fine olfactory discrimination and odor learning [6-8].

(b) In this revision, we added a statement in the Results section to further describe our justifications for testing various stimulation frequencies.

(R2-6) The absence of natural (odor) stimulation should be acknowledged as a limitation for the findings of this study in the Discussion.

We thank the reviewer for the suggestion. In this revision, we made the acknowledgement in the Discussion section. 

(R2-7) Line 46: overstatement, the role of the Piriform cortex at the system level is well known and was not discovered by this study. (see Gordon Shepherd’s book: The Synaptic Organization of the Brain, Figure 10.6)

As per the reviewer’s suggestion, we decided to use “distinguishes” rather than “uncovers” for an accurate description of our study’s findings, which demonstrated the differences between the piriform cortex and anterior olfactory nucleus in driving the downstream activations of olfactory and non-olfactory targets.

(R2-8) Line 75: please rephrase for an easier understanding.

As per the reviewer’s suggestion, we edited this statement to “Further, the need to present a multitude of odor combinations also makes it challenging to efficiently and reliably interrogate long-range olfactory networks and their properties”.

(R2-9) Line 313: the intermediary region between the Piriform and Entorhinal cortices is the Perirhinal Cortex. No doubt about that.

As per the reviewer’s comment, we modified the corresponding statement by adding “such as the perirhinal cortex” and cited the appropriate references [47,48].

(R2-10) Line 391: there is no evidence from this study to exclude that the downstream targets would be less activated because of a decreased OB activation in favor of broad inhibition.

As per the reviewer’s suggestion, we modified the statement by replacing “rather than” with “in addition to” for a more precise statement.

(R2-11) Line 585: which was/were the regressor/s used in GLM? Any convolution with common HRFs?

We regarded the block-designed optogenetic stimulation as a task. The regressors are then obtained by convolving the expected task-evoked neural activity (i.e., boxcar function for block design) with the canonical hemodynamic response function, HRF (SPM12, Wellcome Department of Imaging Neuroscience, University College London, UK).

In this revision, we added a statement in the Methods section to provide clarification on the regressors used in GLM and the convolution step with canonical HRF.

References

(1) Kay, L. M. & Stopfer, M. Information processing in the olfactory systems of insects and vertebrates. Semin Cell Dev Biol 17, 433-442 (2006). https://doi.org/10.1016/j.semcdb.2006.04.012

(2) Cang, J. & Isaacson, J. S. In vivo whole-cell recording of odor-evoked synaptic transmission in the rat olfactory bulb. J Neurosci 23, 4108-4116 (2003). 

(3) Margrie, T. W. & Schaefer, A. T. Theta oscillation coupled spike latencies yield computational vigour in a mammalian sensory system. J Physiol 546, 363-374 (2003). https://doi.org/10.1113/jphysiol.2002.031245

(4) Fontanini, A. & Bower, J. M. Variable coupling between olfactory system activity and respiration in ketamine/xylazine anesthetized rats. Journal of Neurophysiology 93, 3573-3581 (2005). https://doi.org/10.1152/jn.01320.2004

(5) Kay, L. M. et al. Olfactory oscillations: the what, how and what for. Trends in Neurosciences 32, 207-214 (2009). https://doi.org/10.1016/j.tins.2008.11.008

(6) Gervais, R., Buonviso, N., Martin, C. & Ravel, N. What do electrophysiological studies tell us about processing at the olfactory bulb level? Journal of physiology, Paris 101, 40-45 (2007). https://doi.org/10.1016/j.jphysparis.2007.10.006

(7) Beshel, J., Kopell, N. & Kay, L. M. Olfactory bulb gamma oscillations are enhanced with task demands. J Neurosci 27, 8358-8365 (2007). https://doi.org/10.1523/JNEUROSCI.119907.2007

(8) Martin, C., Beshel, J. & Kay, L. M. An olfacto-hippocampal network is dynamically involved in odor-discrimination learning. J Neurophysiol 98, 2196-2205 (2007). https://doi.org/10.1152/jn.00524.2007

(9) Kay, L. M. Theta oscillations and sensorimotor performance. Proc Natl Acad Sci U S A 102, 3863-3868 (2005). https://doi.org/10.1073/pnas.0407920102

(10) Lowry, C. A. & Kay, L. M. Chemical factors determine olfactory system beta oscillations in waking rats. J Neurophysiol 98, 394-404 (2007). https://doi.org/10.1152/jn.00124.2007

(11) Vanderwolf, C. H. & Zibrowski, E. M. Pyriform cortex beta-waves: odor-specific sensitization following repeated olfactory stimulation. Brain Res 892, 301-308 (2001). https://doi.org/10.1016/s0006-8993(00)03263-7

(12) Fontanini, A., Spano, P. & Bower, J. M. Ketamine-xylazine-induced slow (< 1.5 Hz) oscillations in the rat piriform (olfactory) cortex are functionally correlated with respiration. J Neurosci 23, 7993-8001 (2003). https://doi.org/10.1523/JNEUROSCI.23-22-07993.2003

(13) Miura, K., Mainen, Z. F. & Uchida, N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74, 1087-1098 (2012). https://doi.org/10.1016/j.neuron.2012.04.021

(14) Leong, A. T. et al. Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile. Proc Natl Acad Sci U S A 113, E8306-E8315 (2016). https://doi.org/10.1073/pnas.1616361113

(15) Chan, R. W. et al. Low-frequency hippocampal–cortical activity drives brain-wide resting-state functional MRI connectivity. Proc Natl Acad Sci U S A 114, E6972-E6981 (2017). https://doi.org/10.1073/pnas.1703309114

(16) Leong, A. T. L. et al. Optogenetic fMRI interrogation of brain-wide central vestibular pathways. Proceedings of the National Academy of Sciences 116, 10122-10129 (2019). https://doi.org/10.1073/pnas.1812453116

(17) Leong, A. T. L., Wang, X., Wong, E. C., Dong, C. M. & Wu, E. X. Neural activity temporal pattern dictates long-range propagation targets. Neuroimage 235, 118032 (2021). https://doi.org/10.1016/j.neuroimage.2021.118032

(18) Leong, A. T. L., Wong, E. C., Wang, X. & Wu, E. X. Hippocampus Modulates Vocalizations Responses at Early Auditory Centers. Neuroimage 270, 119943 (2023). https://doi.org/10.1016/j.neuroimage.2023.119943

(19) Wang, X. et al. Functional MRI reveals brain-wide actions of thalamically-initiated oscillatory activities on associative memory consolidation. Nat Commun 14, 2195 (2023). https://doi.org/10.1038/s41467-023-37682-8

(20) Xie, L. et al. Brain-wide resting-state fMRI network dynamics elicited by activation of single thalamic input. Nat Commun 16, 11247 (2025). https://doi.org/10.1038/s41467-025-66104-0

(21) Li, A., Gong, L. & Xu, F. Brain-state-independent neural representation of peripheral stimulation in rat olfactory bulb. Proc Natl Acad Sci U S A 108, 5087-5092 (2011). https://doi.org/10.1073/pnas.1013814108

(22) Lang, J. et al. Odor representation in the olfactory bulb under different brain states revealed by intrinsic optical signals imaging. Neuroscience 243, 54-63 (2013). https://doi.org/10.1016/j.neuroscience.2013.03.057

(23) Chery, R., Gurden, H. & Martin, C. Anesthetic regimes modulate the temporal dynamics of local field potential in the mouse olfactory bulb. J Neurophysiol 111, 908-917 (2014). https://doi.org/10.1152/jn.00261.2013

(24) Wachowiak, M. et al. Optical dissection of odor information processing in vivo using GCaMPs expressed in specified cell types of the olfactory bulb. J Neurosci 33, 5285-5300 (2013). https://doi.org/10.1523/JNEUROSCI.4824-12.2013

(25) Hudetz, A. G., Vizuete, J. A. & Pillay, S. Differential effects of isoflurane on high-frequency and low-frequency gamma oscillations in the cerebral cortex and hippocampus in freely moving rats. Anesthesiology 114, 588-595 (2011). https://doi.org/10.1097/ALN.0b013e31820ad3f9

(26) Joliot, M., Ribary, U. & Llinas, R. Human oscillatory brain activity near 40 Hz coexists with cognitive temporal binding. Proc Natl Acad Sci U S A 91, 11748-11751 (1994). https://doi.org/10.1073/pnas.91.24.11748

(27) Murthy, V. N. & Fetz, E. E. Oscillatory activity in sensorimotor cortex of awake monkeys: synchronization of local field potentials and relation to behavior. J Neurophysiol 76, 3949-3967 (1996). https://doi.org/10.1152/jn.1996.76.6.3949

(28) Tallon-Baudry, C., Bertrand, O., Delpuech, C. & Pernier, J. Stimulus specificity of phaselocked and non-phase-locked 40 Hz visual responses in human. J Neurosci 16, 4240-4249 (1996). https://doi.org/10.1523/JNEUROSCI.16-13-04240.1996

(29) Murakami, M., Kashiwadani, H., Kirino, Y. & Mori, K. State-dependent sensory gating in olfactory cortex. Neuron 46, 285-296 (2005). https://doi.org/10.1016/j.neuron.2005.02.025

(30) Wilson, D. A. & Yan, X. Sleep-like states modulate functional connectivity in the rat olfactory system. J Neurophysiol 104, 3231-3239 (2010). https://doi.org/10.1152/jn.00711.2010

(31) Schreck, M. R. et al. State-dependent olfactory processing in freely behaving mice. Cell Rep 38, 110450 (2022). https://doi.org/10.1016/j.celrep.2022.110450

(32) Gao, P. P., Zhang, J. W., Chan, R. W., Leong, A. T. L. & Wu, E. X. BOLD fMRI study of ultrahigh frequency encoding in the inferior colliculus. Neuroimage 114, 427-437 (2015). https://doi.org/10.1016/j.neuroimage.2015.04.007

(33) Gao, P. P., Zhang, J. W., Fan, S. J., Sanes, D. H. & Wu, E. X. Auditory midbrain processing is differentially modulated by auditory and visual cortices: An auditory fMRI study. Neuroimage 123, 22-32 (2015). https://doi.org/10.1016/j.neuroimage.2015.08.040

(34) Goddard, E. & Mullen, K. T. fMRI representational similarity analysis reveals graded preferences for chromatic and achromatic stimulus contrast across human visual cortex. Neuroimage 215, 116780 (2020). https://doi.org/10.1016/j.neuroimage.2020.116780

(35) Friston, K. J., Harrison, L. & Penny, W. Dynamic causal modelling. Neuroimage 19, 12731302 (2003). https://doi.org/10.1016/s1053-8119(03)00202-7

(36) Friston, K. J., Kahan, J., Biswal, B. & Razi, A. A DCM for resting state fMRI. Neuroimage 94, 396-407 (2014). https://doi.org/10.1016/j.neuroimage.2013.12.009

(37) Bernal-Casas, D., Lee, H. J., Weitz, A. J. & Lee, J. H. Studying Brain Circuit Function with Dynamic Causal Modeling for Optogenetic fMRI. Neuron 93, 522-532 e525 (2017). https://doi.org/10.1016/j.neuron.2016.12.035

(38) Zeidman, P. et al. A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI. Neuroimage 200, 174-190 (2019). https://doi.org/10.1016/j.neuroimage.2019.06.031

(39) Salimi, M. et al. Disrupted connectivity in the olfactory bulb-entorhinal cortex-dorsal hippocampus circuit is associated with recognition memory deficit in Alzheimer's disease model. Sci Rep 12, 4394 (2022). https://doi.org/10.1038/s41598-022-08528-y

(40) Chen, Y. N., Kostka, J. K., Bitzenhofer, S. H. & Hanganu-Opatz, I. L. Olfactory bulb activity shapes the development of entorhinal-hippocampal coupling and associated cognitive abilities. Curr Biol 33, 4353-4366 e4355 (2023). https://doi.org/10.1016/j.cub.2023.08.072

(41) Pellegrino, R., Sinding, C., de Wijk, R. A. & Hummel, T. Habituation and adaptation to odors in humans. Physiol Behav 177, 13-19 (2017). https://doi.org/10.1016/j.physbeh.2017.04.006

(42) Sinding, C. et al. New determinants of olfactory habituation. Sci Rep 7, 41047 (2017). https://doi.org/10.1038/srep41047

(43) Zhao, F. et al. fMRI study of olfaction in the olfactory bulb and high olfactory structures of rats: Insight into their roles in habituation. Neuroimage 127, 445-455 (2016). https://doi.org/10.1016/j.neuroimage.2015.10.080

(44) Zhao, F. et al. fMRI study of the role of glutamate NMDA receptor in the olfactory adaptation in rats: Insights into cellular and molecular mechanisms of olfactory adaptation. Neuroimage 149, 348-360 (2017). https://doi.org/10.1016/j.neuroimage.2017.01.068

(45) Sengupta, P. The Laboratory Rat: Relating Its Age With Human's. Int J Prev Med 4, 624-630 (2013). 

(46) Quinn, R. Comparing rat’s to human’s age: How old is my rat in people years? Nutrition 21, 775-777 (2005). https://doi.org/10.1016/j.nut.2005.04.002

(47) Burwell, R. D. & Amaral, D. G. Perirhinal and postrhinal cortices of the rat: interconnectivity and connections with the entorhinal cortex. J Comp Neurol 391, 293-321 (1998).

(48) Kajiwara, R., Takashima, I., Mimura, Y., Witter, M. P. & Iijima, T. Amygdala input promotes spread of excitatory neural activity from perirhinal cortex to the entorhinal-hippocampal circuit. J Neurophysiol 89, 2176-2184 (2003). https://doi.org/10.1152/jn.01033.2002