Abstract
Participant-specific, functionally-defined brain areas are usually mapped with functional localizers and estimated by making contrasts between responses to single categories of input. Naturalistic stimuli engage multiple brain systems in parallel, provide more ecologically plausible estimates of real-world statistics, and are friendly to special populations. It is often important to tailor the movie to meet the specific needs of participants in different experiments, and participants are often scanned with different parameters from one experiment to another, at different institutes across the world, and with different scanner models. As a result, it is important to demonstrate that individualized predictions can be archived with high fidelity in spite of the differences above. Here, we used connectivity hyperalignment (CHA), a method that affords the calculation of transformation matrices using stimuli that are not the same for normative and index participants, to map category-selective functional topographies. We analyzed four different data sets collected with three different movies, three different scanners, and two different types of functional localizers that used dynamic or static stimuli. We first demonstrate 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. Then, critically, we show that robust, individualized estimates can be obtained even when participants watched different movies, were scanned with different parameters/scanners, and were sampled from different institutes across the world. Our results create a foundation for future studies that allow researchers to estimate a broad range of functional topographies based on naturalistic movies and a normative database, making it possible to integrate high-level cognitive functions across datasets from laboratories worldwide.