Homotopic correlations between retinotopic areas. (A) Average correlation of the time-course of activity evoked during movie watching for all areas. This is done for the left and right hemisphere separately, creating a matrix that is not diagonally symmetric. The color triangles overlaid on the corners of the matrix cells indicate which cells contributed to the summary data of different comparisons in subpanels B and C. (B) Across-hemisphere similarity of the same visual area from the same stream (e.g., left ventral V1 and right ventral V1) and from different streams (e.g., left ventral V1 and right dorsal V1). (C) Across-hemisphere similarity in the same stream when matching the same area (e.g., left ventral V1 and right ventral V1), matching to an adjacent area (e.g., left ventral V1 and right ventral V2), or matching to a distal area (e.g., left ventral V1 and right ventral V4). Grey lines represent individual participants. *** = p<0.001 from bootstrap resampling

Multi-dimensional scaling (MDS) of movie-evoked activity in visual cortex. A) Anatomically defined areas25 used for this analysis, separated into dorsal (red) and ventral (blue) visual cortex, overlaid on a flatmap of visual cortex. B) The timecourse of functional activity for each area was extracted and compared across hemispheres (e.g., left V1 was correlated with right V1). This matrix was averaged across participants and used to create a Euclidean dissimilarity matrix. MDS captured the structure of this matrix in two dimensions with suitably low stress. The plot shows a projection that emphasizes the similarity to the brain’s organization.

Example retintopic task vs. ICA-based spatial frequency maps. A) Spatial frequency map of a 17.1 month old infant. The retinotopic task data are from a prior study17. The view is of the flattened occipital cortex with visual areas traced in black. B) Component captured by ICA of movie data from the same participant. This component was chosen as a spatial frequency map in this participant. The sign of ICA is arbitrary so it was flipped here for visualization. C) Gradients in spatial frequency within-area from the task-evoked map in subpanel A. Lines parallel to the area boundaries (emanating from fovea to periphery) were manually traced and used to capture the changes in response to high versus low spatial frequency stimulation. D) Gradients in the component map. These gradients are similar to those found in the task-evoked spatial frequency map, confirming that this is an appropriate component.

Similarity between visual maps from the retinotopy task and ICA applied to movies. Absolute correlation between the task-evoked and component spatial frequency maps (absolute values used because sign of ICA maps is arbitrary). Each dot is a manually identified component. At least one component was identified in 13 out of 15 participants. The bar plot is the average across participants. The error bar is the standard error across participants. B) Ranked correlations for the manually identified spatial frequency components relative to all components identified by ICA. Bar plot is same as A. C) Same as A but for meridian maps. At least one component was identified in 9 out of 15 participants. D) Same as B but for meridian maps.

Pipeline for predicting visual maps from movie data. All participants watched the same movie, and one participant’s data were held out. The remaining participants were mapped into a lower-dimensional feature space using shared response modeling (SRM)31. The visual maps from these participants were transformed into the shared space and averaged. By mapping the held-out participant into the shared space (with only their movie data), the average visual maps in shared space could be transformed into their brain space, resulting in a predicted retinotopic map that can be validated against their real map from the retinotopy task.

Similarity of SRM-predicted maps and task-evoked retinotopic maps. Correlation between the gradients of the A) spatial frequency maps and C) meridian maps predicted with SRM from other infants and task-evoked retinotopy maps. B, D) Same as A, except using adult participants to train the SRM and predict maps. Dot color indicates the movie used for fitting the SRM. The end of the line indicates the correlation of the task-evoked retinotopy map and the predicted map when using flipped training data for SRM. Hence, lines extending below the dot indicate that the true performance was higher than a baseline fit. The bar plot is the average across participants. The error bar is the standard error across participants.

Demographic and dataset information for infant participants in the study. ‘Age’ is recorded in months. ‘Sex’ is the assigned sex at birth. ‘Retinotopy areas’ is the number of areas segmented from task-evoked retinotopy, averaged across hemispheres. Information about the movie data is separated based on analysis type: whereas all movie data is used for homotopy and ICA analyses, a subset of data is used for SRM. ‘Num.’ is the number of movies used. ‘Length’ is the duration in seconds of the run used for these analyses (includes both movie and rest periods). ‘Drops’ is the number of movies that include dropped periods. Runs’ says how many runs or pseudoruns of movie data there were. ‘Gaze’ is the percentage of the data where the participants were looking at the movie.

Number of participants per movie. The first column is the movie name, where ‘Drop-’ indicates that it was a movie containing alternating epochs of blank screens. ‘SRM’ indicates whether the movie is used in SRM analyses. The movies that are not included in SRM are used for homotopy and ICA. ‘Ret. infants’ and ‘Ret. adults’ refers to the number of participants with retinotopy data that saw this movie. ‘Infant SRM’ and ‘Adult SRM’ refer to the number of additional participants available to use for training the SRM but who did not have retinotopy data. ‘Infant Ages’ is the average age in months of the infant participants included in the SRM, with the range of ages included in parentheses.

Details for each movie used in this study.‘Name’ specifies the movie name. ‘Duration’ specifies the duration of the movie in seconds. Movies were edited to standardize length and remove inappropriate content. ‘Sound’ is whether sound was played during the movie. These sounds include background music, animal noises, and sound effects, but no language. ‘Description’ gives a brief description of the movie, as well as a current link to it when appropriate. All movies are provided in the data release.

Homotopic correlations between anatomically defined areas. A) Average correlation of the time course of activity evoked during movie watching for ventral and dorsal areas in an anatomical segmentation25. This is done for the left and right hemispheres separately, which is why the matrix is not diagonally symmetric. The triangles overlaid on the matrix corner highlights the area-wise comparisons used in B and C. Only areas that we were able to retinotopically mapped (i.e., those that overlap with Figure 1) were used for this analysis. B) Correlation of the same area and same stream (e.g., left ventral V1 and right ventral V1) versus the same area and different stream (e.g., left ventral V1 and right dorsal V1). Difference with bootstrap resampling: ΔFisher Z M=0.37, p<0.001. C) Correlation within the same stream between the same areas, adjacent areas (e.g., left ventral V1 and right ventral V2), or distal areas (e.g., left ventral V1 and right ventral V4). Difference with bootstrap resampling: Same > Adjacent ΔFisher Z M=0.09, p<0.001; Adjacent > Distal ΔFisher Z M=0.18, p<0.001. Grey lines represent individual participants. *** = p<0.001 from bootstrap resampling

Cross-validation of the number of features in SRM. The movie data from all adult participants (Table S2) was split in half, with a 10 TR buffer between sets. The data were masked only to include occipital lobe voxels. The first half of the movie was used for training the SRM in all but one participant. The number of features learned by the SRM was varied across analyses from 1–25. The second half of the movie was then used to generate a shared response (i.e., the activity time course in each feature). To test the SRM, the held-out participant’s first half of data is used to learn a mapping of that participant into the SRM space (this mapping does not change the features learned and is not based on the second half of data). The second half of the held-out participant’s data is then mapped into the shared response space, like the other participants. Time-segment matching was performed on the shared response20, 31. In brief, time-segment matching tests whether a segment of the data (10 TRs) in the held-out participant can be matched to its correct time point based on the other participants. This tests whether the SRM succeeds in making the held-out participant similar to the others. This analysis was performed on each participant and movie separately (each has a line). The dashed line is chance for time-segment matching, averaged across all movies and participants. The black solid line at features=10 reflects the number of features chosen.