How babies see the world is a mystery. They cannot share their experiences, and adults cannot recall this time. Clever experimental methods are needed to understand sensory processing in babies' brains and how variations from adults could cause them to have different experiences.

However, finding ways to study infant brain structure and function has challenged scientists. Babies cannot complete many cognitive tasks used to assess adult brain activity. It can also be difficult to use imaging tools like magnetic resonance imaging (MRI) that require individuals to lay still for extended periods, which can be challenging for infants who are often wiggly and have short attention spans. As a result, many questions remain unanswered about infant brain organization and function.

Recent technological advances have made it easier to study infant brain activity. Scientists have developed approaches allowing infants to watch a movie while being comfortably positioned in an MRI machine. Infants and toddlers will often happily watch a film for minutes at a time, enabling scientists to observe how their brains respond to what they see on the screen.

Ellis et al. used this approach to assess the organization of the visual system in the brains of 15 infants while they watched movies during functional MRI. The researchers compared the infant scans with scans of adult brains who watched the same film, which revealed that babies’ brain activity is surprisingly structured and similar to that of adults. Moreover, the organization of the adult brain could predict the organization of the infant brain.

Ellis et al. show that scanning infants while they watch movies can be a valuable way to study their brain activity. The experiments reveal important similarities in adult and infant visual processing, helping to identify the foundation on which visual development rests. The movie-watching experiments may also provide a model for scientists to study other types of infant perception and cognition. Movies can help scientists compare brain activity in typically developing infants to those with neurodevelopmental conditions, which could one day help clinicians create new avenues for diagnosis or treatment.

Studying the function and organization of the youngest human brains remains a challenge. Despite the recent growth in infant fMRI (Biagi et al., 2015; Biagi et al., 2023; Cabral et al., 2022; Deen et al., 2017; Kosakowski et al., 2022; Truzzi and Cusack, 2023), one of the most important obstacles facing this research is that infants are unable to maintain focus for long periods of time and struggle to complete traditional cognitive tasks (Ellis et al., 2020a). Movies can be a useful tool for studying the developing mind (Vanderwal et al., 2019), as has been shown in older children (Vanderwal et al., 2015; Richardson et al., 2018; Alexander et al., 2017). The dynamic, continuous, and content-rich nature of movie stimuli (Nastase et al., 2020; Finn et al., 2022) make them effective at capturing infant attention (Franchak et al., 2016; Tran et al., 2017). Here, we examine what can be revealed about the functional organization of the infant brain during movie-watching.

We focus on visual cortex because its organization at multiple spatial scales is well understood from traditional, task-based fMRI. The mammalian visual cortex is divided into multiple areas with partially distinct functional roles between areas (Brodmann, 1909; Felleman and Van Essen, 1991; Ungerleider and Mishkin, 1982). Within visual areas, there are orderly, topographic representations, or maps, of visual space (Kaas, 1997; White and Fitzpatrick, 2007). These maps capture information about the location and spatial extent of visual stimuli with respect to fixation. Thus, maps reflect sensitivity to polar angle, measured via alternations between horizontal and vertical meridians that define area boundaries (Fox et al., 1987; Schneider et al., 1993), and sensitivity to spatial frequency, reflected in gradients of sensitivity to high and low spatial frequencies from foveal to peripheral vision, respectively (Henriksson et al., 2008). Previously, we reported that these maps could be revealed by a retinotopy task in infants as young as 5 months of age (Ellis et al., 2021). However, it remains unclear whether these maps are evoked by more naturalistic task designs.

The primary goal of the current study is to investigate whether movie-watching data recapitulates the organization of visual cortex. Movies drive strong and naturalistic responses in sensory regions while minimizing task demands (Loiotile et al., 2019; Nastase et al., 2020; Finn et al., 2022) and thus are a proxy for typical experience. In adults, movies and resting-state data have been used to characterize the visual cortex in a data-driven fashion (Knapen, 2021; Lu et al., 2017; Guntupalli et al., 2016). Movies have been useful in awake infant fMRI for studying event segmentation (Yates et al., 2022), functional alignment (Turek et al., 2018), and brain networks (Yates et al., 2023). However, this past work did not address the granularity and specificity of cortical organization that movies evoke. For example, movies evoke similar activity across infants in anatomically aligned visual areas (Yates et al., 2022), but it remains unclear whether responses to movie content differ between visual areas (e.g. is there more similarity of function within visual areas than between [Li et al., 2022]). Moreover, it is unknown whether structure within visual areas, namely visual maps, contributes substantially to visual-evoked activity. Additionally, we wish to test whether methods for functional alignment can be used with infants. Functional alignment finds a mapping between participants using functional activity – rather than anatomy – and in adults can improve signal-to-noise, enhance across participant prediction, and enable unique analyses (Busch et al., 2021; Guntupalli et al., 2016; Chen et al., 2015; Kumar et al., 2020b).

Nonetheless, there are several reasons for skepticism that movies could evoke detailed, retinotopic organization: Movies may not fully sample the stimulus parameters (e.g. spatial frequencies) or visual functions needed to find topographic maps and areas in visual cortex. Even if movies contain the necessary visual properties, they may unfold at a faster rate than can be detected by fMRI. Additionally, naturalistic stimuli may not drive visual responses as robustly as experimenter-defined stimuli that are designed for retinotopic mapping with discrete onsets and high contrast. Finally, the complexity of movie stimuli may result in variable attention between participants, impeding discovery of reliable visual structure across individuals. If movies do show the fine-grained organization of the infant visual cortex, this suggests that this structure (e.g. visual maps) scaffolds the processing of ongoing visual information.

We conducted several analyses to probe different kinds of visual granularity in infant movie-watching fMRI data. First, we asked whether distinct areas of the infant visual cortex have different functional profiles. Second, we asked whether the topographic organization of visual areas can be recovered within participants. Third, we asked whether this within-area organization is aligned across participants. These three analyses assess key indicators of the mature visual system: functional specialization between areas, organization within areas, and consistency between individuals.

We performed fMRI in awake, behaving infants and toddlers using a protocol described previously (Ellis et al., 2020a). The dataset consisted of 15 sessions of infant participants (4.8–23.1 months of age) who had both movie-watching data and retinotopic mapping data collected in the same session (Appendix 1—table 1). All available movies from each session were included (Appendix 1—table 2), with an average duration of 540.7 s (range: 186–1116 s).

The retinotopic-mapping data from the same infants (Ellis et al., 2021) allowed us to generate infant-specific meridian maps (horizontal versus vertical stimulation) and spatial frequency maps (high versus low stimulation). The meridian maps were used to define regions of interest (ROIs) for visual areas V1, V2, V3, V4, and V3A/B.

As a proof of concept that the analyses we use with infants can identify fine-grained visual organization, we ran the main analyses on an adult sample. These adults (8 participants) had both retinotopic mapping data and movie-watching data. Figure 1—figure supplement 1, Figure 2—figure supplement 1, Figure 4—figure supplement 1, and Figure 6—figure supplement 1 demonstrate that applying these analyses to adult movie data reveals similar structure to what we find in infants.

Evidence of area organization with homotopic similarity

To determine what movies can reveal about the organization of areas in visual cortex, we compared activity across left and right hemispheres. Although these analyses cannot define visual maps, they test whether visual areas have different functional signatures. Namely, we correlated time courses of movie-related BOLD activity between retinotopically defined, participant-specific ROIs (7.3 regions per participant per hemisphere, range: 6–8) (Arcaro and Livingstone, 2017; Butt et al., 2015; Li et al., 2022). Higher correlations between the same (i.e. homotopic) areas than different areas indicate differentiation of function between areas. Moreover, other than V1, homotopic visual areas are anatomically separated across the hemispheres, so similar responses are unlikely to be attributable to spatial autocorrelation.

Homotopic areas (e.g. left ventral V1 and right ventral V1; diagonal of Figure 1A) were highly correlated (mean [M]=0.88, range of area means: 0.85–0.90), and more correlated than non-homotopic areas, such as the same visual area across streams (e.g. left ventral V1 and right dorsal V1; Figure 1B; Δ_{Fisher Z} M=0.42, p<0.001). To clarify, we use the term ‘stream’ to liberally distinguish visual regions that are more dorsal or more ventral, as opposed to the functional definition used in reference to the ‘what’ and ‘where’ streams (Ungerleider and Mishkin, 1982). We found no evidence that the variability in movie duration per participant correlated with this difference (r=0.08, p=0.700). Within stream (Figure 1C), homotopic areas were more correlated than adjacent areas in the visual hierarchy (e.g. left ventral V1 and right ventral V2; Δ_{Fisher Z} M=0.09, p<0.001), and adjacent areas were more correlated than distal areas (e.g. left ventral V1 and right ventral V4; Δ_{Fisher Z} M=0.20, p<0.001). There was no correlation between movie duration and effect (Same>Adjacent: r=−0.01, p=0.965, Adjacent>Distal: r=−0.09, p=0.740). Additionally, if we control for motion in the correlation between areas – in case motion transients drive consistent activity across areas – then the effects described here are negligibly different (Figure 1—figure supplement 2). Hence, movies elicit distinct processing dynamics across areas of infant visual cortex defined independently using retinotopic mapping.

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We previously found (Ellis et al., 2021) that an anatomical segmentation of visual cortex (Wang et al., 2015) could identify these same areas reasonably well. Indeed, the results above were replicated when using visual areas defined anatomically (Figure 1—figure supplement 3). However, a key advantage of anatomical segmentation is that it can define visual areas not mapped by a functional retinotopy task. This could help address limitations of the analyses above, namely that there was a variable number of retinotopic areas identified across infants and these areas covered only part of visually responsive cortex. Focusing on broader areas that include portions of the ventral and dorsal stream in the adult visual cortex (Ungerleider and Mishkin, 1982; Dale et al., 1999), we tested for functional differentiation of these streams in infants. We applied multi-dimensional scaling (MDS) – a data-driven method for assessing the clustering of data – to the average cross-correlation matrix across participants (Figure 1—figure supplement 3; Haak and Beckmann, 2018; Arcaro and Livingstone, 2017). The stress of fitting these data with a two-dimensional MDS was in the acceptable range (0.076). Clear organization was present (Figure 2): areas in the adult-defined ventral stream (e.g. VO, PHC) differentiated from areas in the adult-defined dorsal stream (e.g. V3A/B). Indeed, we see a slight separation between canonical dorsal areas and the recently defined lateral pathway (Weiner and Gomez, 2021) (e.g. LO1, hMT), although more evidence is needed to substantiate this distinction. This separation between streams is striking when considering that it happens despite differences in visual field representations across areas: while dorsal V1 and ventral V1 represent the lower and upper visual field, respectively, V3A/B and hV4 both have full visual field maps. These visual field representations can be detected in adults (Haak et al., 2013), however, they are often not the primary driver of function (Haak and Beckmann, 2018). We see that in infants too: hV4 and V3A/B represent the same visual space yet have distinct functional profiles. Again, this organization cannot be attributed to mere spatial autocorrelation within stream because analyses were conducted across hemispheres (at significant anatomical distance) and this pattern is preserved when accounting for motion (Figure 1—figure supplement 2). These results thus provide evidence of a dissociation in the functional profile of anatomically defined ventral and dorsal streams during infant movie-watching.

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Evidence of within-area organization with independent component analysis

We next explored whether movies can reveal fine-grained organization within visual areas by using independent component analysis (ICA) to propose visual maps in individual infant brains (Arcaro and Livingstone, 2017; Beckmann et al., 2005; Knapen, 2021; Lu et al., 2017; Moeller et al., 2009). ICA is a method for decomposing a source into constituent signals by finding components that account for independent variance. When applied to fMRI data (using MELODIC in FSL), these components have spatial structure that varies in strength over time. Many of these components reflect noise (e.g. motion, breathing) or task-related signals (e.g. face responses), while other components reflect the functional architecture of the brain (e.g. topographic maps) (Arcaro and Livingstone, 2017; Beckmann et al., 2005; Knapen, 2021; Lu et al., 2017; Moeller et al., 2009). We visually inspected each component and categorized it as a potential spatial frequency map, a potential meridian map, or neither. This process was blind to the ground truth of what the visual maps look like for that participant from the retinotopic mapping task, simulating what would be possible if retinotopy data from the participants were unavailable. Success in this process requires that (1) retinotopic organization accounts for sufficient variance in visual activity to be identified by ICA and (2) experimenters can accurately identify these components.

Multiple maps could be identified per participant because there were more than one candidate that the experimenter thought was a suitable map. Across infant participants, we identified an average of 2.4 (range: 0–5) components as potential spatial frequency maps and 1.1 (range: 0–4) components as potential meridian maps. To evaluate the quality of these maps, we compared them to the ground truth of that participant’s task-evoked maps (Figure 3). Spatial frequency and meridian maps are defined by their systematic gradients of intensity across the cortical surface (Arcaro et al., 2009). Lines drawn parallel to area boundaries show monotonic gradients on spatial frequency maps, with stronger responses to high spatial frequency at the fovea, and stronger responses to low spatial frequencies in the periphery (Figure 4—figure supplement 2). By contrast, lines drawn perpendicular to the area boundaries show oscillations in sensitivity to horizontal and vertical meridians on meridian maps (Figure 4—figure supplement 3). Using the same manually traced lines from the retinotopy task, we measured the intensity gradients in each component from the movie-watching data. We can then use the gradients of intensity in the retinotopy task-defined maps as a benchmark for comparison with the ICA-derived maps.

Figure 3

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To assess the selected component maps, we correlated the gradients (described above) of the task-evoked and component maps. This test uses independent data: the components were defined based on movie data and validated against task-evoked retinotopic maps. Figure 4A shows the absolute correlations between the task-evoked maps and the manually identified spatial frequency components (M=0.52, range: 0.23–0.85). To evaluate whether movies are a viable method for defining retinotopic maps, we tested whether the task-evoked retinotopic maps were more similar to manually identified components than other components. We identified the best component in 6 of 13 participants (Figure 4B). The percentile of the average manually identified component was high (M=63.8 percentile, range: 26.7–98.1) and significantly above chance (ΔM=13.8, CI=[3.3–24.0], p=0.011). This illustrates that the manually identified components derived from movie-watching data are similar to the spatial frequency maps derived from retinotopic mapping. The fact that this can work also indicates the underlying architecture of the infant visual system influences how movies are processed.

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We performed the same analyses on the meridian maps. As noted above, the lines were now traced perpendicular to the boundaries. Figure 4C shows the correlation between the task-evoked meridian maps and the manually identified components (M=0.46, range: 0.03–0.81). Compared to all possible components identified by ICA, the best possible component was identified for 1 out of 9 participants (Figure 4D). Although the percentile of the average manually identified component was numerically high (M=67.6 percentile, range: 3.0–100.0), it was not significantly above chance (ΔM=17.6, CI=[–1.8–33.0], p=0.074). This difference in performance compared to spatial frequency is also evident in the fact that fewer components were identified as potential meridian maps, and that several participants had no such maps. Even so, some participants have components that are highly similar to the meridian maps (e.g. s8037_1_2 or s6687_1_5 in Figure 4—figure supplement 3). Because it is possible, albeit less likely, to identify meridian maps from ICA, the structure may be present in the data but more susceptible to noise or gaze variability. Spatial frequency maps have a coarser structure than meridian maps, and are more invariant to fixation, which may explain why they are easier to identify. Equivalent analyses of adult data (Figure 4—figure supplement 1) support this conclusion: meridian maps are found in fewer adult participants.

Despite the similarity of the identified components to the retinotopic maps, it is possible that the components are noise and this similarity arose by chance. Indeed, given enough patterns of spatially smooth noise, some will resemble retinotopic organization. To test how often components derived from noise are misidentified, we made a version of each component in which the functional data were misaligned with respect to the anatomical data while preserving spatial smoothness. We then intermixed an equal number of these ‘rolled’ components among the original components and randomized the order such that a coder would be blind as to whether any given component was rolled or original. The blind coder manually categorized each component as a spatial frequency component, a meridian component, or neither (identical to the steps above). It was not possible to make the coder blind for some participants whose rolled data contained visible clues because of partial voluming. In the 6 participants without such clues, only 1 of the 14 components labeled as spatial frequency or meridian, from 920 total components, was a rolled component. The fact that 13 of 14 selected components (93%) were original was extremely unlikely to have occurred by chance (binomial test: p=0.002). Thus, our selection procedure rarely identified components as retinotopic in realistic noise.

Evidence of within-area organization with shared response modeling

Finally, we investigated whether the organization of visual cortex in one infant can be predicted from movie-watching data in other participants using functional alignment (Guntupalli et al., 2016). For such functional alignment to work, stimulus-driven responses to the movie must be shared across participants. These analyses also benefit from greater amounts of data, so we expanded the sample in two ways (Appendix 1—table 3): First, we added 71 movie-watching datasets from additional infants who saw the same movies but did not have usable retinotopy data (and thus were not included in the analyses above that compared movie and retinotopy data within participant). Second, we used data from adult participants, including 8 participants who completed the retinotopy task and saw a subset of the movies we showed infants, and 41 datasets from adults who had seen the movies shown to infants but did not have retinotopy data.

With this expanded dataset, we used shared response modeling (SRM) (Chen et al., 2015) to predict visual maps from other participants (Figure 5). Specifically, we held out one participant for testing purposes and used SRM to learn a low-dimensional, shared feature space from the movie-watching data of the remaining participants in a mask of occipital cortex. This shared space represented the responses to that movie in visual cortex that were shared across participants, agnostic to the precise localization of these responses across voxels in each individual (Figure 5A). The number of features in the shared space (K=10) was determined via a cross-validation procedure on movie-watching data in adults (Figure 5—figure supplement 1). The task-evoked retinotopic maps from all but the held-out participant were transformed into this shared space and averaged, separately for each map type (Figure 5B). We then mapped the held-out participant’s movie data into the learned shared space without changing the shared space (Figure 5C). In other words, the shared response model was learned and frozen before the held-out participant’s data was considered. This approach has been used and validated in prior SRM studies (Yates et al., 2021). Taking the inverse of the held-out participant’s mapping allowed us to transform the averaged shared space representation of visual maps into the held-out participant’s brain space (Figure 5D).

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This predicted visual organization was compared to the participant’s actual visual map from the retinotopy task using the same methods as for ICA. In other words, the manually traced lines were used to measure the intensity gradients in the predicted maps, and these gradients were compared to the ground truth. Critically, predicting the retinotopic maps used no retinotopy data from the held-out participant. Moreover, it is completely unconstrained anatomically (except for a liberal occipital lobe mask). Hence, the similarity of the SRM-predicted map to the task-evoked map is due to representations of visual space in other participants being mapped into the shared space.

We trained SRMs on two populations to predict a held-out infant’s maps: (1) other infants and (2) adults. There may be advantages to either approach: infants are likely more similar to each other than adults in terms of how they respond to the movie; however, their data is more contaminated by motion. When using the infants to predict a held-out infant, the spatial frequency map (Figure 6A) and meridian map (Figure 6C) predictions are moderately correlated with task-evoked retinotopy data (spatial frequency: M=0.46, range: –0.06 to 0.78; meridian: M=0.24, range: –0.12 to 0.78). Some participants were fit well using SRM (e.g. s2077_1_1, and s6687_1_5 for Figure 6—figure supplement 2, Figure 6—figure supplement 3).

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To evaluate whether success was due to fitting the shared response, we flipped the held-out participant’s movie data (i.e. the first timepoint became the last timepoint and vice versa) so that an appropriate fit is not be learnable. The vertical lines for each movie in Figure 6 indicate the change in performance for this baseline. Indeed, flipping significantly worsened prediction of the spatial frequency map (Δ_{Fisher Z} M=0.52, CI=[0.24–0.80], p<0.001) and the meridian map (Δ_{Fisher Z} M=0.24, CI=[0.02–0.49], p=0.034). Hence, the movie-evoked response enables the mapping of other infants’ retinotopic maps into a held-out infant.

Using adult data to predict infant data also results in maps similar to task-evoked spatial frequency maps (Figure 6B; M=0.56, range: 0.17–0.79) and meridian maps (Figure 6D; M=0.34, range: –0.27–0.64). Some participants were well predicted by these methods (e.g. s8037_1_2, and s6687_1_4 for Figure 6—figure supplement 4, Figure 6—figure supplement 5). Again, flipping the held-out participants movie data significantly worsened prediction of the held-out participant’s spatial frequency map (Δ_{Fisher Z} M=0.40, CI=[0.17–0.65], p<0.001) and meridian map (Δ_{Fisher Z} M=0.33, CI=[0.12–0.55], p=0.002). There was no significant difference in SRM performance when using adults versus infants as the training set (spatial frequency: Δ_{Fisher Z} M=0.14, CI=[–0.00–0.27], p=0.054; meridian: Δ_{Fisher Z} M=0.11, CI=[–0.05–0.28], p=0.179). In sum, SRM could be used to predict visual maps with moderate accuracy. This indicates that functional alignment methods like SRM can partially capture the retinotopic organization of visual cortex from infant movie-watching data.

We performed an anatomical alignment analog of the functional alignment (SRM) approach. This analysis serves as a benchmark for predicting visual maps using task-based data, rather than movie data, from other participants. For each infant participant, we aggregated all other infant or adult participants as a reference. The retinotopic maps from these reference participants were anatomically aligned to the standard surface template, and then averaged. These averages served as predictions of the maps in the test participant, akin to SRM, and were analyzed equivalently (i.e. correlating the gradients in the predicted map with the gradients in the task-based map). These correlations (Appendix 1—table 4) are significantly higher than for functional alignment (using infants to predict spatial frequency, anatomical alignment<functional alignment: Δ_{Fisher Z} M=0.44, CI=[0.32–0.58], p<0.001; using infants to predict meridians, anatomical alignment<functional alignment: Δ_{Fisher Z} M=0.61, CI=[0.47–0.74], p<0.001; using adults to predict spatial frequency, anatomical alignment<functional alignment: Δ_{Fisher Z} M=0.31, CI=[0.21–0.42], p<0.001; using adults to predict meridians, anatomical alignment<functional alignment: Δ_{Fisher Z} M=0.49, CI=[0.39–0.60], p<0.001). This suggests that even if SRM shows that movies can be used to produce retinotopic maps that are significantly similar to a participant, these maps are not as good as those that can be produced by anatomical alignment of the maps from other participants without any movie data.

We present evidence that movies can reveal the organization of infant visual cortex at different spatial scales. We found that movies evoke differential function across areas, topographic organization of function within areas, and this topographic organization is shared across participants.

We show that the movie-evoked response in a visual area is more similar to the same area in the other hemisphere than to different areas in the other hemisphere. This suggests that visual areas are functionally differentiated in infancy and that this function is shared across hemispheres (Li et al., 2022). By comparing across anatomically distant hemispheres, we reduced the impact of spatial autocorrelation and isolated the stimulus-driven signals in the brain activity (Arcaro and Livingstone, 2017; Li et al., 2022; Smyser et al., 2010). The greater across-hemisphere similarity for same versus different areas provides some of the first evidence that visual areas and streams are functionally differentiated in infants as young as 5 months of age. Previous work suggests that functions of the dorsal and ventral streams are detectable in young infants (Wattam-Bell et al., 2010) but that the localization of these functions is immature (Braddick and Atkinson, 2011). Despite this, we find that the areas of infant visual cortex that will mature into the dorsal and ventral streams have distinct activity profiles during movie-watching.

Not only do movies evoke differentiated activity in the infant visual cortex between areas, but movies also evoke fine-grained information about the organization of maps within areas. We used a data-driven approach (ICA) to discover maps that are similar to retinotopic maps in the infant visual cortex. We observed components that were highly similar to a spatial frequency map obtained from the same infant in a retinotopy task. This was also true for the meridian maps, to a lesser degree. This means that the retinotopic organization of the infant brain accounts for a detectable amount of variance in visual activity, otherwise components resembling these maps would not be discoverable. Importantly, the components could be identified without knowledge of these ground-truth maps; however, their moderate similarity to the task-defined maps makes them a poor replacement. One caveat for interpreting these results is that although some of the components are similar to a spatial frequency map or meridian map, they could reflect a different kind of visual map. For instance, the spatial frequency map is highly correlated with the eccentricity map (Henriksson et al., 2008; Smith et al., 2001; Srihasam et al., 2014; Tolhurst and Thompson, 1981, which itself is related to receptive field size). This means it is inappropriate to make strong claims about the underlying function of the components based on their similarity to visual maps alone. Another limitation is that ICA does not provide a scale to the variation: although we find a correlation between gradients of spatial frequency in the ground truth and the selected component, we cannot use the component alone to infer the spatial frequency selectivity of any part of cortex. In other words, we cannot infer units of spatial frequency sensitivity from the components alone. Nonetheless, these results do show that it is possible to discover approximations of visual maps in infants and toddlers with movie-watching data and ICA.

We also asked whether functional alignment (Chen et al., 2015; Turek et al., 2018) could be used to detect visual maps in infants. Using a shared response model (Chen et al., 2015) trained on movie-watching data of infants or adults, we transformed the visual maps of other individuals into a held-out infant’s brain to evaluate the fit to visual maps from a retinotopy task (Guntupalli et al., 2016). Like ICA, this was more successful for the spatial frequency maps, but it was still possible in some cases with the meridian maps. This is remarkable because the complex pattern of brain activity underlying these visual maps could be ‘compressed’ by SRM into only 10 dimensions in the shared space (i.e. the visual maps were summarized by a vector of 10 values). The weight matrix that ‘decompressed’ visual maps from this low-dimensional space into the held-out infant was learned from their movie-watching data alone. Hence, success with this approach means that visual maps are engaged during infant movie-watching. Furthermore, this result shows that functional alignment is practical for studies in awake infants that produce small amounts of data (Ellis and Turk-Browne, 2018). This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults (Busch et al., 2021; Chen et al., 2015; Guntupalli et al., 2016), or revealing changing function over development (Yates et al., 2021), which may prove especially useful for infant fMRI (Ellis and Turk-Browne, 2018). In sum, movies evoke sufficiently reliable activity across infants and adults to find a shared response, and this shared response contains information about the organization of infant visual cortex.

To be clear, we are not suggesting that movies work well enough to replace a retinotopy task when accurate maps are needed. For instance, even though ICA found components that were highly correlated with the spatial frequency map, we also selected some components that turned out to have lower correlations. Without knowing the ground truth from a retinotopy task, there would be no way to weed these out. Additionally, anatomical alignment (i.e. averaging the maps from other participants and anatomically aligning them to a held-out participant) resulted in maps that were highly similar to the ground truth. Indeed, we previously Ellis et al., 2021 found that adult-defined visual areas were moderately similar to infants. While functional alignment with adults can outperform anatomical alignment methods in similar analyses (Guntupalli et al., 2016), here we find that functional alignment is inferior to anatomical alignment. Thus, if the goal is to define visual areas in an infant that lacks task-based retinotopy, anatomical alignment of other participants’ retinotopic maps is superior to using movie-based analyses, at least as we tested it.

In conclusion, movies evoke activity in infants and toddlers that recapitulate the organization of the visual cortex. This activity is differentiated across visual areas and contains information about the visual maps at the foundation of visual processing. The work presented here is another demonstration of the power of content-rich, dynamic, and naturalistic stimuli to reveal insights in cognitive neuroscience.

Our experiment display code can be found here: https://github.com/ntblab/experiment_menu/tree/Movies/ and https://github.com/ntblab/experiment_menu/tree/retinotopy/ (Ellis et al., 2020b). The code used to perform the data analyses is available at https://github.com/ntblab/infant_neuropipe/tree/predict_retinotopy/, (Ellis et al., 2020c) this code uses tools from the Brain Imaging Analysis Kit (Kumar et al., 2020a); https://brainiak.org/docs/. Raw and preprocessed functional and anatomical data is available on Dryad.

1. 1. Ellis CT
   2. Yates T
   3. Arcaro M
   4. Turk-Browne N

   (2024) Dryad Digital Repository

   Data from: Movies reveal the fine-grained organization of infant visual cortex.

   https://doi.org/10.5061/dryad.jm63xsjm3

1. 1. Ellis CT
   2. Yates T
   3. Skalaban L
   4. Bejjanki V
   5. Arcaro M
   6. Turk-Browne N

   (2021) Dryad Digital Repository

   Retinotopic organization of visual cortex in human infants.

   https://doi.org/10.5061/dryad.7h44j0ztm

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Author details

1. Cameron T Ellis

   Department of Psychology, Stanford University, Palo Alto, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   cte@stanford.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3254-5940

2. Tristan S Yates

   Department of Psychology, Columbia University, New York, United States

   Contribution

   Data curation, Software, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Michael J Arcaro

   Department of Psychology, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4612-9921

4. Nicholas Turk-Browne

   1. Department of Psychology, Yale University, New Haven, United States
   2. Wu Tsai Institute, Yale University, New Haven, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7519-3001

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