Category-selective functional topographies are a prominent and consistent feature of lateral occipital, ventral temporal, and lateral temporal visual cortices (Downing et al., 2001; Epstein et al., 1999; Grill-Spector and Weiner, 2014; Kanwisher et al., 1997). Category-selective topographies are mostly similar across individuals but are idiosyncratic in terms of their precise conformation and location (Zhen et al., 2015; Zhen et al., 2017). Because of these idiosyncrasies, category-selective topographies and areas are typically mapped in each individual using a functional localizer fMRI scan (Fedorenko et al., 2010; Saxe et al., 2006). Functional localizers map individualized topographies with simple contrasts between responses to different categories, such as contrasting responses to faces versus objects to localize face-selective areas.

We reported an alternative approach to map category-selective topographies using fMRI data collected while participants view a naturalistic movie (Guntupalli et al., 2016; Haxby et al., 2011; Jiahui et al., 2020). With this approach, movie-viewing and functional localizer data are collected in a normative sample, and new participants need only be scanned during movie viewing. Movie data are used to calculate transformation matrices using hyperalignment (Guntupalli et al., 2016; Haxby et al., 2011; Jiahui et al., 2020; Feilong et al., 2018; Feilong et al., 2021; Feilong et al., 2021; Guntupalli et al., 2018) that afford projecting the localizer data from the normative sample into the idiosyncratic cortical topography of new participants. Using this hyperalignment procedure, we can estimate the idiosyncratic details of individual topographies with high fidelity based on localizer data from the normative sample. Unlike functional localizers, naturalistic stimuli (e.g., movies) evoke a rich variety of brain states and engage multiple brain systems in parallel. This makes it possible to efficiently map multiple functional topographies using data from a single movie and avoid the time and cost of running multiple localizers. Compared to controlled localizers, movies better simulate real-world cognition and better engage participants’ attention (Vanderwal et al., 2015; Vanderwal et al., 2017; Vanderwal et al., 2019), contributing to more ecologically valid and higher-quality maps. In addition, movies are more friendly and engaging for special populations, such as young children.

In previous work, we used response hyperalignment (RHA) to predict functional topographies in new participants. RHA requires that all participants watch the same movie to obtain time-locked responses to the same stimuli. It is often important, however, to tailor the movie to meet the specific needs of participants in different experiments. For example, participants from different countries may prefer movies that reflect their diverse backgrounds and are in their native languages (Hanke et al., 2016; Sengupta et al., 2016); movies for infants and young children are differently structured from those for adults (Vanderwal et al., 2015). Thus, it is unrealistic to limit all participants from diverse populations and backgrounds to watch the same movie. Additionally, experimenters may need to shorten or edit the stimuli to fit their data collection schedule. Finally, participants are often scanned with different parameters from one experiment to another, at different institutes across the world, and with different scanner models. Due to these factors, it is impractical to expect two laboratories to acquire the same movie scans across individuals.

Here, we test whether connectivity hyperalignment (CHA) (Guntupalli et al., 2018) can be used to map category-selective functional topographies. CHA, in contrast to RHA, affords calculation of transformation matrices using stimuli that are not the same for normative and index participants. We analyzed four different datasets collected with three different movies, three different scanners, and two different types of functional localizers that used dynamic or static stimuli. We first demonstrated that CHA based on participants’ connectomes that were calculated using their responses to movies was able to generate high-fidelity maps of category-selective topographies within datasets that were equivalent to maps estimated using RHA. Then, critically, we showed that cross-dataset predictions that used connectomes calculated from different movies for the normative and index brains were as good as those from participants in the same dataset. This means that different laboratories can use different movies to derive functional topographies from a normative sample.

In summary, we demonstrate that a target participant’s individualized category-selective topography can be accurately estimated using CHA, regardless of whether different movies are used to calculate the connectome and regardless of other data collection parameters. Movies engage multiple cognitive domains in parallel, such as visual perception, audition, language comprehension, theory of mind, and social interaction. In addition to estimating different functional topographies from a single movie, our approach allows us to estimate topographies from different movies. We provide a novel alternative for future data collection that can save time and money using rich and efficient movie scans.

In this study, using four datasets that contain three different movies, two different types of functional localizers, and collected with three different scanners, we showed that individualized category-selective topographies can be estimated with high fidelity using CHA. Unlike RHA, which requires the same ‘time-locked’ response time series in the normative sample and new participants, CHA affords the calculation of transformation matrices based on responses to completely different movies. By showing that CHA based on participants’ connectomes calculated using their responses to different movies generated high-fidelity mappings that were as good as those using RHA with participants in the same dataset, we demonstrated that CHA is able to effectively predict topographies across diverse situations. This study opens new possibilities connecting independent public and in-lab datasets for future data analysis so that researchers can derive multiple topographies at once for each individual with excellent performance based on the naturalistic movie data and the localizer data from another normative dataset. Our results also provide a novel alternative for new data collection to take better advantage of naturalistic stimuli.

We used a new, enhanced CHA in this study that optimized our previous CHA algorithm with iterative steps. In each step, transformation matrices to each index brain were calculated from other participants’ brains and the matrices were applied to both the movie and the localizer data. Because using dense connectivity targets (e.g., using all vertices as connectivity targets) with anatomically alignment data often leads to suboptimal alignment across participants (Hanke et al., 2014), we started with coarse connectivity targets and gradually increased the number of connectivity targets to form a denser representation of connectivity profiles. The iterations improved the prediction performance step by step, and at the final step (step 6, all vertices were used as connectivity targets) in this analysis, the enhanced CHA generated comparable performance with RHA (Figure 4—figure supplement 4). We investigated the influence of naturalistic movie length and the size of the training group on the prediction accuracy of individualized functional topographies. By incrementally increasing both the number of movie runs in the training and target dataset and the participants in the training group in the Budapest and Sraiders dataset, we observed enhanced prediction accuracy (Figure 4—figure supplement 5). Notably, even with just one movie run in the training or target dataset, or with a mere five participants in the training group, our prediction performance (Pearson r) ranged from about 0.6 to 0.7. This accuracy significantly outperformed results obtained using surface-based alignment. In addition, this study is based on the new optimized 1-step hyperalignment procedure (Jiahui et al., 2020). The classic hyperalignment method (2-step), builds a common information model space at the initial step that is based on all normative group participants, then projects information encoded in idiosyncratic representational spaces to the common model space, and lastly projects the information back to the individual participant’s space based on the transpose of the transformation matrices from the former step. Different from the 2-step method, the 1-step method directly projects the data for each normative sample brain to the index participant’s space without the intermediate step of building a common information model space. This method requires fewer steps and is free from the accumulation of errors across steps. The 1-step method consistently improved the prediction performances across all conditions and datasets (Figure 4—figure supplement 6). This method is particularly useful for estimating information encoded in each individual’s brain space. Our original algorithm is designed to apply transformation matrices to the time series of localizer data of training participants before generating contrast maps. To explore whether directly applying these matrices to pre-calculated contrast maps yields comparable results, we conducted an additional analysis across the four categories. Our findings indicate that the prediction outcomes were indeed quite similar between the two approaches for both the within- and across-datasets predictions (Figure 4—figure supplement 7). However, it is worth noting that the improvements observed with enhanced CHA were not as pronounced when applied directly to the contrast maps as opposed to the time series. In our study, we used fine-scale connectomes, noting that some participants are more similar to the target participant in specific searchlights. It is an interesting question whether predictions could be enhanced by exclusively selecting those more similar participants for the target participant. To explore this option, we examined a searchlight in the right ventral temporal cortex that was roughly at the location of the posterior fusiform area using the top and bottom nine participants similar to each target participant measured by their fine-scale connectome similarities in the budapest dataset. Generally, using all or part of the participants for the prediction generated similar results (Figure 4—figure supplement 8). Compared to using all the participants, using only the top nine participants who are the most similar to the target participants did not significantly improve the prediction (Tukey test, z=–0.09, p=0.996), but using only the bottom nine participants generated significantly lower prediction accuracies (Tukey test, z=2.492, p=0.034). This suggests a trade-off between the number of participants included in the prediction and the similarity of the participants. Future studies are needed to explore the optimal threshold for the number of participants included for each searchlight to refine the algorithm.

By leveraging transformation matrices obtained from hyperaligning participants based on movie-viewing data, we successfully mapped these relationships to the training participants’ localizer data, enabling robust predictions. Prior work employing diffusion-weighted imaging has underscored the link between anatomical connectivity and category selectivity across diverse visual fields (Osher et al., 2016; Saygin et al., 2012) and has established a notable congruence between structural and functional connectivities (Hermundstad et al., 2013). These findings suggest that the unique anatomical connectivity patterns of individuals may serve as a foundational mechanism, contributing to the stable fine-scale functional connectome that underpins our approach. The connectivity-based shared response model (cSRM) proposed by Nastase et al., 2020, used connectivity to functionally align individuals similar to the CHA algorithm. While both approaches share overarching goals, they diverge considerably in implementation and application. First and most important, cSRM used inter-subject functional connectivity rather than within-subject functional connectivity to initially estimate the connectome. As a result, cSRM requires participants to have time-locked fMRI time series. Therefore, unlike our algorithm, the cSRM approach does not support cross-content applications and also is not suitable for use with resting-state data. Second, cSRM is implemented based on a predefined cortical parcellation rather than the overlapping, regularly spaced cortical searchlights applied in our method which are not constrained by areal borders. For the application, cSRM has mainly been used to do ROI analysis rather than the estimation of the whole-brain topography that requires broader coverage of the cortex with a searchlight analysis. Third, our method is specifically designed to work in each individual’s space, while cSRM decomposes data across subjects into shared and subject-specific transformations, focusing on a communal connectivity space. In summary, although cSRM presents a promising alternative for similar aims, its current implementation precludes it from fulfilling the range of applications for which our method is optimized.

The within-movie and cross-movie CHA predictions generated highly similar topographies (Figure 3). This result raises a fascinating question of whether different movie inputs estimate similar fine-grained connectivity profiles in the brain. Previous studies reported that the coarse-grained connectome (based on coarse parcellations) varies across separate cognitive tasks (Shine et al., 2016; Telesford et al., 2016), and that naturalistic movies yield the most condition-specific functional atlases among other classic cognitive tasks (Salehi et al., 2020). In the Budapest and Sraiders datasets, the same group of participants watched the Grand Budapest Hotel and Raiders of the Lost Ark in different sessions in the same 3 T scanner. We built connectivity profiles for each participant separately for the two movies and correlated the two fine-grained connectomes in each searchlight. Results showed that the two fine-grained connectomes based on different movies were very similar in most of the brain regions (r>0.8, Figure 4—figure supplement 9A, B). We split each movie into two halves (Run 1–3/Run 4–5 for Budapest; Run 1–2/Run 3–4 for Sraiders) and averaged the connectome similarities across split halves over searchlights and participants. We found that the across-movie connectome similarities for split halves were high (r>0.74), and the within-movie similarities were even higher in both datasets (r>0.85, Figure 4—figure supplement 9C). Our analysis showed that although the fine-grained connectome was affected by the input naturalistic stimulus content, it was nonetheless highly stable. This result suggested the brain may undergo shared cognitive processes across different movie free-viewing tasks. It could be because featured movies sample a broad range of real-life statistics, and the rich information elicits overall similar representations and connectivities when the entire time series is considered. Studies comparing movie-viewing and resting-state functional connectivity have shown that both paradigms yield overlapping macroscale cortical organizations (Samara et al., 2023), though naturalistic viewing introduces unique modality-specific hierarchical gradients. However, there remains a gap in research comparing the fine-scaled connectomes of naturalistic and resting-state paradigms. Guntupalli et al., 2018, revealed a shared fine-scale structure that coexists with the coarse-scale structure, and CHA successfully improved intersubject correlations across a wide variety of tasks. Feilong et al., 2021, noted that the fine-scaled connectivity profiles in both resting and task states are highly predictive of general intelligence. This suggests a reliable and biologically relevant fine-scale resting-state connectivity structure among individuals. Therefore, it is plausible that individualized functional topography could be effectively estimated using resting-state functional connectivity, expanding the applicability of our approach. Future studies are needed to explore this direction.

The four datasets in our study included two types of category-selective localizers (dynamic and static). The dynamic localizer used short video clips for each category and the traditional static localizer used still images. For all categories, the dynamic localizer elicited stronger and broader category-selective activations than the static localizer, and the searchlight analysis showed that the dynamic localizer had higher reliabilities across the cortex, especially in regions that were selectively responsive to the target category. Due to differences between topographies activated by the dynamic and the static localizers, predictions across localizer types generated lower correlations than those within localizer types. For example, for the face-selective topographies, the dynamic localizer activated more areas than the static localizer (e.g., in superior temporal and frontal cortices). In the ventral temporal cortex, especially in the right hemisphere, both dynamic and static localizers performed well in the cross-localizer-type predictions. But in cortical areas where the static localizer did not match the dynamic localizer, predictions from the same dynamic localizer always outperformed the predictions from a different static localizer (Figure 4—figure supplement 1, Figure 4—figure supplement 3, and Figure 4—figure supplement 10). The low correlations were not because the prediction method failed but reflected the difference in the topographies activated by different types of localizers.

This study successfully illustrated that accurate individualized predictions are both robust and applicable across a variety of conditions, including movie types, languages, scanning parameters, and scanner models. Importantly, the intricate connectivity profiles remain consistent even when participants view entirely different movies, as evidenced by Figure 4—figure supplement 9, reinforcing the prediction’s stability in various scenarios. However, all four datasets in this study only included typical participants with anatomically intact brains. An unanswered question is whether individualized topographies of neuropsychological populations with atypical cortical function (e.g., developmental prosopagnosics) or with lesioned brains (e.g., acquired prosopagnosics) could also be accurately predicted using the hyperalignment-based methods. Up to now, as far as we know, no previous literature has investigated this question. Beyond neuropsychological groups, it is also valuable to investigate how well the predictions will be across a wide range of age, from infants to the elderly. Future research is essential to adapt our algorithms to diverse populations.

In summary, our study demonstrated that accurate predictions of individualized category-selective topographies can be achieved with high fidelity using CHA across different naturalistic movie contents, across different scanners, and across different scanning parameters. Compared to traditional functional localizers, naturalistic stimuli are more ecologically valid, engaging multiple cognitive systems in parallel, and more friendly to participants. Our method not only can be applied directly to current public and in-lab datasets, but has the important potential to allow researchers to derive a broad range of topographies based on naturalistic movies and a normative database in the future. By building such a database that comprises various high-quality topographies and naturalistic stimuli, our study opens the gate to new research possibilities that could integrate high-level cognitive functions across datasets from laboratories worldwide.

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data and materials that support the findings of this study can be found at https://github.com/GUO-Jiahui/CHA_Cross-Movie_Prediction; (copy archived at Jiahui, 2023).

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Author details

1. Guo Jiahui

   Center for Cognitive Neuroscience, Dartmouth College, Hanover, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1528-9025

2. Ma Feilong

   Center for Cognitive Neuroscience, Dartmouth College, Hanover, United States

   Contribution

   Writing – original draft, Writing – review and editing, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Samuel A Nastase

   Princeton Neuroscience Institute, Princeton University, Princeton, United States

   Contribution

   Writing – original draft, Methodology

   Competing interests

   No competing interests declared

4. James V Haxby

   Center for Cognitive Neuroscience, Dartmouth College, Hanover, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6558-3118

5. M Ida Gobbini

   1. Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
   2. IRCCS, Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Methodology

   For correspondence

   mariaida.gobbini@unibo.it

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6727-7934

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