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.

(1) Figure 1.1, 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.

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

(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 threshold-free 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?

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

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

(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.

(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 ephys. data, it would be really great. In the current form, the DCM and ephys. results seem to be totally unrelated.

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

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.

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/day in a row, or am I mistaken?

Overall, if my concerns are addressed, this is outstanding work, and I congratulate the authors.

Author response:

We appreciate the insightful comments and suggestions, which will significantly improve our work. We will revise the manuscript to address the reviewer’s concerns. Here, we list some of the key aspects of those concerns and our preliminary plans to address them.

Both reviewers pointed out that we did not sufficiently justify the chosen optogenetic stimulation frequencies. We acknowledge and concur with their assessment, and will discuss it more extensively from a biological perspective (e.g., the neural firing rates in the olfactory bulb, OB, anterior olfactory nucleus, AON, and piriform cortex, Pir, under natural odor stimulation and respiration rhythm). Reviewer #1 suggested using beta values (b) rather than the area under the BOLD signal profile (AUC) to quantify the fMRI activations as they are more conventional for general linear model (GLM) analysis. We are aware of b and have used them for quantification of the amplitude of fMRI activations in our previous rodent fMRI studies1-3. However, in this study, we chose to utilize 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)4 and can be constrained by the assumptions and limitations of the GLM analysis, such as the shape of the hemodynamic response function (HRF). AUC provides greater flexibility in capturing different aspects of neural responses across various brain regions, such as transient peaks and sustained responses.

As mentioned by reviewer #1, correlating the adaptation of BOLD and electrophysiology signals at the brain region level would better signify our findings. We will pursue additional analysis to address this in our forthcoming responses. Reviewer #2 would like us to clarify the image and signal quality of our echo planar imaging (EPI)-based fMRI data, especially in the regions close to the air-tissue interface such as OB, Pir, entorhinal cortex and amygdala, and the methodology for some of the experimental protocols implemented in our study. We will show the raw EPI fMRI images from a representative animal and revise the results, discussion, and methods sections of the manuscript to address reviewer #2's concerns.

In our forthcoming detailed responses to the reviewers' comments and recommendations, we will revise the text, figures, and captions accordingly to address and clarify the questions brought up by both reviewers.

References

(1) 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).

(2) Leong, A.T.L., Wong, E.C., Wang, X. & Wu, E.X. Hippocampus Modulates Vocalizations Responses at Early Auditory Centers. Neuroimage 270, 119943 (2023).

(3) 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).

(4) 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).