Open reviews
February 23, 2025 — Open review of Gruskin and colleagues:
Gruskin, D. C., Vieira, D. J., Lee, J. K., & Patel, G. H. (2024). Heritability of movie-evoked brain activity and connectivity. bioRxiv. DOI
Gruskin and colleagues use twin data from a movie-watching fMRI paradigm to show how genetic control of cortical function intersects with the processing of naturalistic audiovisual stimuli. This is a very thorough paper that tackles this question from several different angles. I love the use of hyperalignment to dissect heritability into the heritability of functional-topographic organization versus function proper: “how stimuli are processed and where stimuli are processed”. The finding that heritability is strongest at slower-evolving neural time scales is also quite interesting. In general, I don’t have many complaints after a couple reads through the manuscript; most of my “major” comments are relatively minor suggestions and points of clarification.
Major comments:
On page 16, you compare heritability in functional connectivity (FC) and response time series, and find that the heritability effect is larger in FC. In general, I agree with your diagnosis that this is in large part due to the fact that FC captures the covariance structure across parcels, whereas response time series only diverge in terms of univariate time-point-by-time-point differences. Another important factor here is that (within-subject) FC can be driven by intrinsic fluctuations that occur with idiosyncratic timing across subjects and are unrelated to the stimulus (whereas time-locked metrics like ISC and time-series differences cannot, by definition). This makes me wonder how this connectivity result would change if you used intersubject functional connectivity (ISFC) analysis to specifically isolate the stimulus-driven components of functional connectivity (Simony et al., 2016). This, to me, would provide a closer comparison to the ISC and response time series results, and could allow the authors to quantify how much of the heritability in FC is intrinsic versus stimulus-driven. I’m not asking that the authors actually perform this analysis, as I don’t think it’s critical for the message of the manuscript—but it could be an interesting future direction. As the authors discuss on page 17, I also suspect there’s something fundamentally shared between response time series and connectivity as they relate to functional topography (Busch et al., 2021) that drives part of the heritability effect.
The observation that regions with intermediate ISC have the largest differences between MZ, DZ, and UR is very interesting, but it’s kind of hard to see in Figure 1B. Is there any other way to plot this that might make the effect more obvious? For example, I could imagine three scatter plots where the x- and y-axes are, e.g., MZ ISC and UR ISC, and each data point is a parcel. In this kind of plot, I would expect to see the middle values lifted visibly off the diagonal/unity line toward MZ. You could even color the data points according to networks like in Figure 3C. (You also might not need to scale the ISC axis all the way to r = 1, which would make the differences more visible.)
On page 9, if I understand correctly, you regress the vector of ISC values across parcels out of the vector of heritability values across parcels, and then plot the residual heritability values. Do you center the heritability values (or include some kind of intercept) in the process? I’m trying to understand why the heritability values go from all positive (Figure 2A) to roughly balanced between positive and negative (Figure 2B). Important question for me: How should we interpret negative values in this plot? Can you explain this explicitly in the text? (I also wonder if there’s a more intuitive way to control for ISC. For example, instead of regressing out ISC at the parcel/map level, could you go into a single parcel and then regress the subject-level pairwise ISC values out when computing the heritability score?)
On page 4 (line 155), you say “we shuffled dyad labels”—is this equivalent to shuffling rows and columns of the pairwise subject-by-subject matrix combined across groups? I’m trying to make sure your approach here is consistent with recommendations by Chen et al., 2016. Is this the same kind of shuffling used for the kinship matrix mentioned at line 189?
Do the authors have any ideas why we see this hotspot of heritability in pMTG/LOTC? It really jumps out in Figure 1A and Figure 2. The more posterior sensory MT+ area seems to drop when regressing out ISC in Figure 2B, but this pMTG area stays hot. Is there anything special about this kind of multimodal biological motion / action observation / social perception area (Pitcher & Ungerleider, 2021)? I don’t think this is necessary to discuss in the manuscript, but I’m curious if the authors have any speculation.
I found panel A in Figure 4 to be a little bit misleading because your parcel-wise approach to hyperalignment won’t actually resolve topographic idiosyncrasies across a large cortical distance like what’s depicted in the illustration (at the scale of the parcels you’re performing hyperalignment within). Maybe just move the green and purple brain areas a bit closer to each other so they could feasibly be “aligned” within a large parcel. Worth keeping in mind when writing that hyperalignment is also not actually going to yield a one-to-one mapping of functionally homologous voxels across individuals: it’s effectively going to model any given voxel time series as a linear combination of time series across other voxels in the parcel.
Just to confirm: The subjects watched all different movies across the two days, right? For a moment I was wondering “are Day 1 and Day 2 repetitions of the same movies?” Given that Day 1 and Day 2 are an organizational feature of several figures, it might be worth making this very explicit in the Methods and reminding the reader in the Results section.
Minor comments:
Page 3, line 127: “More information on these clips”—it might be worth saying a little bit more here just to make sure people understand that these are audiovisual clips, they include language, they’re long enough to convey meaningful social and narrative information, etc.
Figure 1 caption: can you add a sentence reminding readers what’s going on with Day 1 and Day 2?
Page 9, line 379: “although these more associative parcels do not encode a substantial amount of stimulus-specific information”—is this really true? I suspect these association areas still have decent ISCs, even if they’re many processing stages downstream of the raw stimulus.
Page 9, line 417: Can you unpack a bit more what you mean by “supra-BOLD frequency band”?
Page 18, line 695: This discussion of how attention and gaze might partly shape response time series reminded me of recent work by Borovska & de Haas, 2024—might be worth citing.
Page 19, line 755: I’m not sure I’d describe the hyperalignment results here as a “deleterious effects [on] heritability”—my reading was that hyperalignment allows you to say something more specific about heritability of function by allowing you to effectively factor out heritability effects that reduce to individual differences cortical topography; this seems like a good thing!
I would love to see a ventral view in some of these plots! Not asking you to recreate the figures, but ventral temporal cortex is an area of interest for many folks in the movie fMRI space (e.g., Haxby et al., 2011).
References:
Borovska, P., & de Haas, B. (2024). Individual gaze shapes diverging neural representations. Proceedings of the National Academy of Sciences, 121(36), e2405602121. DOI
Busch, E. L., Slipski, L., Feilong, M., Guntupalli, J. S., di Oleggio Castello, M. V., Huckins, J. F., Nastase, S. A., Gobbini, M. I., Wager, T. D., & Haxby, J. V. (2021). Hybrid hyperalignment: a single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity. NeuroImage, 233, 117975. DOI
Chen, G., Shin, Y. W., Taylor, P. A., Glen, D. R., Reynolds, R. C., Israel, R. B., & Cox, R. W. (2016). Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. NeuroImage, 142, 248–259. DOI
Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M., & Ramadge, P. J. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72(2), 404–416. DOI
Pitcher, D., & Ungerleider, L. G. (2021). Evidence for a third visual pathway specialized for social perception. Trends in Cognitive Sciences, 25(2), 100–110. DOI
Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nature Communications, 7, 12141. DOI