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1. An fMRI dataset in response to “The Grand Budapest Hotel”, a socially-rich, naturalistic movie

   https://doi.org/10.1038/s41597-020-00735-4  

   Visconti di Oleggio Castello, Matteo; Chauhan, Vassiki; Jiahui, Guo; Gobbini, M. Ida (December 2020, Scientific Data)

   null (Ed.)

   Abstract Naturalistic stimuli evoke strong, consistent, and information-rich patterns of brain activity, and engage large extents of the human brain. They allow researchers to compare highly similar brain responses across subjects, and to study how complex representations are encoded in brain activity. Here, we describe and share a dataset where 25 subjects watched part of the feature film “The Grand Budapest Hotel” by Wes Anderson. The movie has a large cast with many famous actors. Throughout the story, the camera shots highlight faces and expressions, which are fundamental to understand the complex narrative of the movie. This movie was chosen to sample brain activity specifically related to social interactions and face processing. This dataset provides researchers with fMRI data that can be used to explore social cognitive processes and face processing, adding to the existing neuroimaging datasets that sample brain activity with naturalistic movies. 

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2. Naturalistic Audio-Movies reveal common spatial organization across “visual” cortices of different blind individuals

   https://doi.org/10.1093/cercor/bhac048  

   Musz, E.; Loiotile, R.; Chen, J.; Bedny, M. (January 2022, Cerebral cortex)

   Occipital cortices of different sighted people contain analogous maps of visual information (e.g. foveal vs. peripheral). In congenital blindness, “visual” cortices respond to nonvisual stimuli. Do visual cortices of different blind people represent common informational maps? We leverage naturalistic stimuli and inter-subject pattern similarity analysis to address this question. Blindfolded sighted (n = 22) and congenitally blind (n = 22) participants listened to 6 sound clips (5–7 min each): 3 auditory excerpts from movies; a naturalistic spoken narrative; and matched degraded auditory stimuli (Backwards Speech, scrambled sentences), during functional magnetic resonance imaging scanning. We compared the spatial activity patterns evoked by each unique 10-s segment of the different auditory excerpts across blind and sighted people. Segments of meaningful naturalistic stimuli produced distinctive activity patterns in frontotemporal networks that were shared across blind and across sighted individuals. In the blind group only, segment-specific, cross-subject patterns emerged in visual cortex, but only for meaningful naturalistic stimuli and not Backwards Speech. Spatial patterns of activity within visual cortices are sensitive to time-varying information in meaningful naturalistic auditory stimuli in a broadly similar manner across blind individuals. 

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3. The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains

   https://doi.org/10.1162/imag_a_00032  

   Feilong, Ma; Nastase, Samuel A.; Jiahui, Guo; Halchenko, Yaroslav O.; Gobbini, M. Ida; Haxby, James V. (November 2023, Imaging Neuroscience)

   Abstract Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their use in studying fine-grained vertex-level differences. In this work, we present the individualized neural tuning (INT) model, a fine-grained individualized model of brain functional organization. The INT model is designed to have vertex-level granularity, to capture both representational and topographic differences, and to model stimulus-general neural tuning. Through a series of analyses, we demonstrate that (a) our INT model provides a reliable individualized measure of fine-grained brain functional organization, (b) it accurately predicts individualized brain response patterns to new stimuli, and (c) for many benchmarks, it requires only 10–20 minutes of data for good performance. The high reliability, specificity, precision, and generalizability of our INT model affords new opportunities for building brain-based biomarkers based on naturalistic neuroimaging paradigms. 

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4. Improving the accuracy of single-trial fMRI response estimates using GLMsingle

   https://doi.org/10.7554/eLife.77599  

   Prince, Jacob S; Charest, Ian; Kurzawski, Jan W; Pyles, John A; Tarr, Michael J; Kay, Kendrick N (November 2022, eLife)

   Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle , a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses ( glmsingle.org ). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions. 

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5. In-degree centrality in a social network is linked to coordinated neural activity

   https://doi.org/10.1038/s41467-022-28432-3  

   Baek, Elisa C.; Hyon, Ryan; López, Karina; Finn, Emily S.; Porter, Mason A.; Parkinson, Carolyn (December 2022, Nature Communications)

   Abstract Convergent processing of the world may be a factor that contributes to social connectedness. We use neuroimaging and network analysis to investigate the association between the social-network position (as measured by in-degree centrality) of first-year university students and their neural similarity while watching naturalistic audio-visual stimuli (specifically, videos). There were 119 students in the social-network study; 63 of them participated in the neuroimaging study. We show that more central individuals had similar neural responses to their peers and to each other in brain regions that are associated with high-level interpretations and social cognition (e.g., in the default mode network), whereas less-central individuals exhibited more variable responses. Self-reported enjoyment of and interest in stimuli followed a similar pattern, but accounting for these data did not change our main results. These findings show that neural processing of external stimuli is similar in highly-central individuals but is idiosyncratic in less-central individuals. 

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