Reliability map of individual differences reflected in inter-subject correlation in naturalistic imaging

Gao, Junhui; Chen, Gang; Wu, Jinfeng; Wang, Yinshan; Hu, Yang; Xu, Ting; Zuo, Xi‐Nian; Yang, Zhi

doi:10.1016/j.neuroimage.2020.117277

Cited by 26 publications

(27 citation statements)

References 44 publications

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“…9). This is in line with recent studies showing that the way the brain processes complex sensory and social information during NV may be an intrinsic characteristic of individuals [8,28] and associated with brain baseline functional organisation [41]. We note that further work is needed to assess whether our findings here are generalisable across different movie types.…”

Section: Discussionsupporting

confidence: 91%

A topography-based predictive framework for naturalistic viewing fMRI

Friedrich

Patil

et al. 2022

Preprint

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Recent work has shown great interest in understanding individual differences in complex brain function under naturalistic viewing (NV) conditions. However, methods specifically designed for achieving this goal remain limited. Here, we propose a novel approach, called TOpography-based Predictive Framework (TOPF), to investigate individual differences in evoked brain activity on NV fMRI data. Specifically, TOPF identifies individual-specific evoked activity topographies in a data- driven manner and examines their behavioural relevance using a machine learning predictive framework. Our results show that these topographies successfully predict individual phenotypes across cognition, emotion and personality on unseen subjects, and the identified predictive brain regions are neurobiologically interpretable. Further, the prediction accuracy exceeds that of the commonly-used functional connectivity-based features. Conceptually, we highlight the importance of examining multivariate evoked activity patterns for studying brain-behaviour relationships. In summary, we provide a powerful tool for understanding individual differences and brain-behaviour relationships on NV fMRI data.

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Section: Discussionsupporting

confidence: 91%

A topography-based predictive framework for naturalistic viewing fMRI

Friedrich

Patil

et al. 2022

Preprint

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“…Naturalistic movies by definition have a large number of superposed features that, if taken into account, can provide functional localizers for both low-level perceptual features, including low-level retinotopic maps ( Nishimoto et al, 2011 ), and high-level semantic/cognitive features ( Huth et al, 2012 ; Mantini et al, 2013 , 2012a , 2012b ; Russ and Leopold, 2015 ; Sliwa and Freiwald, 2017 ). In fact, the use of naturalistic movies has increased significantly in the last decade as both human and animal research has seen the potential of using these less constrained but more ethologically relevant stimuli ( Gao et al, 2020 ). Just within NHPs, naturalistic movies have been used to localize low-level visual features such as contrast, luminance, and motion responses ( Russ and Leopold, 2015 ), face specific responses ( Russ and Leopold, 2015 ; Sliwa and Freiwald, 2017 ), eye-movement related signals ( Russ et al, 2016 ), cross-species functional homologies ( Mantini et al, 2013 , 2012a ; 2012b ), and social processing ( Sliwa and Freiwald, 2017 ).…”

Section: Existent Localizersmentioning

confidence: 99%

Common functional localizers to enhance NHP & cross-species neuroscience imaging research

et al. 2021

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Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform.

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“…For each ROI we calculate the Pearson correlation of the brain activity between all pairs of participants. To obtain the ISC for each ROI, for each ROI we average the Fisher z-transformed Pearson correlation across all pairs of participants and then inverse Fisher-transform the average, similarly to previous works [44,68,69,70] . These results are shown in Supplementary Fig.…”

Section: Iscmentioning

confidence: 99%

Behavior measures are predicted by how information is encoded in an individual's brain

Williams¹,

Wehbe²

2021

Preprint

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Similar to how differences in the proficiency of the cardiovascular and musculoskeletal system predict an individual's athletic ability, differences in how the same brain region encodes information across individuals may explain their behavior. However, when studying how the brain encodes information, researchers choose different neuroimaging tasks (e.g., language or motor tasks), which can rely on processing different types of information and can modulate different brain regions. We hypothesize that individual differences in how information is encoded in the brain are task-specific and predict different behavior measures. We propose a framework using encoding-models to identify individual differences in brain encoding and test if these differences can predict behavior. We evaluate our framework using task functional magnetic resonance imaging data. Our results indicate that individual differences revealed by encoding-models are a powerful tool for predicting behavior, and that researchers should optimize their choice of task and encoding-model for their behavior of interest.

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Reliability map of individual differences reflected in inter-subject correlation in naturalistic imaging

Cited by 26 publications

References 44 publications

A topography-based predictive framework for naturalistic viewing fMRI

A topography-based predictive framework for naturalistic viewing fMRI

Common functional localizers to enhance NHP & cross-species neuroscience imaging research

Behavior measures are predicted by how information is encoded in an individual's brain

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