Intrinsic cortical geometry captures individual differences in local functional organization
Poster No:
1707
Submission Type:
Abstract Submission
Authors:
Francesco Alberti1,2, Pierre-Louis Bazin3, R. Austin Benn4,2, Wei Wei5,2, Robert Scholz4,2,6,7, Victoria Shevchenko4,8,9,10, Ulysse Klatzmann4,2, Carla Pallavicini4,2,11,12, Alexander Holmes13,1, Robert Leech14, Daniel Margulies15,2
Institutions:
1Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS, Paris, France, 2Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 3Full brain picture Analytics, Leiden, Leiden, 4Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS, Paris, Île-de-France, 5Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS, Paris , Île-de-France, 6Wilhelm Wundt Institute for Psychology, Leipzig University, Leipzig, Germany, 7Max Planck School of Cognition, Leipzig, Germany, 8Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 9MIND Team, Inria Saclay, Université Paris-Saclay, Palaiseau, France, 10Neurospin, CEA, Gif-sur-Yvette, France, 11National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina, 12Cognitive Neuroscience Center, University of San Andres, Buenos Aires, Argentina, 13Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences,, Oxford, Oxfordshire, 14Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, Greater London, 15Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS, Paris, France, Paris, Île-de-France
First Author:
Francesco Alberti
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, France|Oxford, United Kingdom
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, France|Oxford, United Kingdom
Co-Author(s):
R. Austin Benn
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, Île-de-France|Oxford, United Kingdom
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, Île-de-France|Oxford, United Kingdom
Wei Wei
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris , Île-de-France|Oxford, United Kingdom
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris , Île-de-France|Oxford, United Kingdom
Robert Scholz
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford|Wilhelm Wundt Institute for Psychology, Leipzig University|Max Planck School of Cognition
Paris, Île-de-France|Oxford, United Kingdom|Leipzig, Germany|Leipzig, Germany
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford|Wilhelm Wundt Institute for Psychology, Leipzig University|Max Planck School of Cognition
Paris, Île-de-France|Oxford, United Kingdom|Leipzig, Germany|Leipzig, Germany
Victoria Shevchenko
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford|MIND Team, Inria Saclay, Université Paris-Saclay|Neurospin, CEA
Paris, Île-de-France|Oxford, United Kingdom|Palaiseau, France|Gif-sur-Yvette, France
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford|MIND Team, Inria Saclay, Université Paris-Saclay|Neurospin, CEA
Paris, Île-de-France|Oxford, United Kingdom|Palaiseau, France|Gif-sur-Yvette, France
Ulysse Klatzmann
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, Île-de-France|Oxford, United Kingdom
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, Île-de-France|Oxford, United Kingdom
Carla Pallavicini
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford|National Scientific and Technical Research Council (CONICET)|Cognitive Neuroscience Center, University of San Andres
Paris, Île-de-France|Oxford, United Kingdom|Buenos Aires, Argentina|Buenos Aires, Argentina
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford|National Scientific and Technical Research Council (CONICET)|Cognitive Neuroscience Center, University of San Andres
Paris, Île-de-France|Oxford, United Kingdom|Buenos Aires, Argentina|Buenos Aires, Argentina
Alexander Holmes
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences,|Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS
Oxford, Oxfordshire|Paris, France
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences,|Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS
Oxford, Oxfordshire|Paris, France
Robert Leech
Institute of Psychiatry, Psychology & Neuroscience, King’s College London
London, Greater London
Institute of Psychiatry, Psychology & Neuroscience, King’s College London
London, Greater London
Daniel Margulies
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS, Paris, France|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, Île-de-France|Oxford, United Kingdom
Cognitive Neuroanatomy Lab, Université Paris Cité, INCC UMR 8002, CNRS, Paris, France|Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
Paris, Île-de-France|Oxford, United Kingdom
Introduction:
The functional organization of the cortex is characterized by a stable hierarchical structure with a variable spatial distribution across individuals (Mueller et al., 2013; Yeo et al., 2011). Intrinsic cortical geometry, as measured by interregional geodesic distance, is associated with this variability (Bijsterbosch et al., 2019). Cortical geometry can also explain spatio-temporal patterns of neural activity and functional organization (Leech et al., 2024; Pang et al., 2023). The spatial autocorrelation present in functional connectivity (FC) (Leech et al., 2023) implies that distances between cortical regions may contribute to the emergence of local functional hierarchy and specialization (Margulies et al., 2016; Pang et al., 2023). In this study, we investigate if cortical geometry contributes to interindividual differences in function through its relationship with the functional similarity of regions.
Methods:
We modeled vertex-level functional organization of the cortex as a gaussian process in a geometric latent space shared across individuals. We analyzed structural and functional MRI data from 500 participants of the Human Connectome Project (Van Essen et al., 2012). To describe functional organization, we used the dominant latent dimension of resting-state FC-the principal gradient-obtained by jointly applying diffusion-map embedding to individual and group-average FC matrices (Fig. 1A; Nenning et al., 2020). This method ensures alignment and comparability of individual embeddings, so that differences in the principal gradient reflect functional dissimilarity between vertices across individuals. The same approach was applied to geodesic distance matrices to project individual vertices into a common geometric latent space. Distance in this three-dimensional space reflects the similarity of vertex locations on the cortex of different individuals (Fig. 1B).
We used lattice kriging to model the individual principal gradients as a gaussian process in the geometric latent space (Fig. 1C; Nychka et al., 2015). Lattice kriging constructs an initial spatial model by placing radial basis functions (RBF) on a three-dimensional grid. Coefficients are then estimated for each RBF to reconstruct the spatial process underlying the original data (the principal gradient). We used a searchlight approach applying kriging to cubic sections of the latent geometric space centered on average vertex locations. Within each searchlight individual gradient variations were modeled twice. Once only using spatial features (unadjusted model) and once correcting for the individual average gradient within the searchlight (adjusted model).
We used lattice kriging to model the individual principal gradients as a gaussian process in the geometric latent space (Fig. 1C; Nychka et al., 2015). Lattice kriging constructs an initial spatial model by placing radial basis functions (RBF) on a three-dimensional grid. Coefficients are then estimated for each RBF to reconstruct the spatial process underlying the original data (the principal gradient). We used a searchlight approach applying kriging to cubic sections of the latent geometric space centered on average vertex locations. Within each searchlight individual gradient variations were modeled twice. Once only using spatial features (unadjusted model) and once correcting for the individual average gradient within the searchlight (adjusted model).
·Figure 1 - Methods
Results:
To assess model accuracy, within each cubic section, we computed the variance explained by the predictions of principal gradient values (Fig. 2). While the unadjusted models performed poorly (R2train=.35(.14), R2test=.39(.15)) the ones adjusted by individual local average gradients were able to reconstruct most of the variance in the data (R2train=.91(.10), R2test=.89(.10)).
Importantly, the variance explained by individual local averages (R2train=.66(.19), R2test=.66(.19)) was far lower than that achieved by the full adjusted models (Fig. 2B). The same was true for R2 scores of the group averaged gradient (R2train=.32(.25), R2test=.31(.26)). These results show that a relevant part of interindividual functional variability is linked to vertex location in the individual cortical geometry.
Importantly, the variance explained by individual local averages (R2train=.66(.19), R2test=.66(.19)) was far lower than that achieved by the full adjusted models (Fig. 2B). The same was true for R2 scores of the group averaged gradient (R2train=.32(.25), R2test=.31(.26)). These results show that a relevant part of interindividual functional variability is linked to vertex location in the individual cortical geometry.
·Figure 2 - Variance explained by models and average gradients
Conclusions:
Overall, the results suggest that individual cortical geometry accounts for a sizable amount of additional variance. Specifically, the direction and magnitude of principal gradient differences between vertices across individuals are linked to their Euclidean distance in the latent geometric space. Despite a portion of interindividual variability depending on other factors, these results corroborate the hypothesis that spatial relationships between regions on the cortical surface contribute to the emergence of individual functional organization.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Multivariate Approaches
Task-Independent and Resting-State Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 1
Cortical Anatomy and Brain Mapping
Keywords:
Cortex
Multivariate
Open Data
Open-Source Code
Other - Geometric eigenmodes
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Leech, R. (2024). The spatial layout of antagonistic brain regions is explicable based on geometric principles. In bioRxiv.
Leech, R. (2023). Variation in spatial dependencies across the cortical mantle discriminates the functional behaviour of primary and association cortex. Nature Communications, 14(1), 5656.
Margulies, D. S. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences of the United States of America, 113(44), 12574–12579.
Mueller, S. (2013). Individual variability in functional connectivity architecture of the human brain. Neuron, 77(3), 586–595.
Nenning, K.-H., Xu, T., Schwartz, E., Arroyo, J., Woehrer, A., Franco, A. R., Vogelstein, J. T., Margulies, D. S. (2020). Joint embedding: A scalable alignment to compare individuals in a connectivity space. NeuroImage, 222, 117232.
Nychka, D. (2015). A multiresolution Gaussian process model for the analysis of large spatial datasets. Journal of Computational and Graphical Statistics: A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 24(2), 579–599.
Pang, J. C. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566–574.
Van Essen, D. C. (2012). The Human Connectome Project: a data acquisition perspective. NeuroImage, 62(4), 2222–2231.
Yeo, B. T. T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.