Generative models of brain maps
Poster No:
1461
Submission Type:
Abstract Submission
Authors:
Vincent Bazinet1, Zhen-Qi Liu1, Filip Milisav2, Bratislav Misic1
Institutions:
1McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Quebec, 2McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec
First Author:
Vincent Bazinet
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montreal, Quebec
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montreal, Quebec
Co-Author(s):
Zhen-Qi Liu
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montreal, Quebec
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montreal, Quebec
Filip Milisav
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montréal, Quebec
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montréal, Quebec
Bratislav Misic
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montreal, Quebec
McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University
Montreal, Quebec
Introduction:
Technological and data sharing advances have resulted in the proliferation of whole-brain maps of multiple structural and functional features including gene expression, neurotransmitter receptors, synapse and cell types, laminar organization, metabolism and intrinsic dynamics [1]. Brain maps are anchored by global organizational principles constraining and shaping cortico-cortical interactions between neuronal populations [2]. The most important constraint of brain organization is spatial proximity: brain regions that are proximal in space have more similar biological properties than regions that are distant in space. Other constraints include connectivity (structural or functional) and biological similarity (e.g. genetic). Using a generative framework, we evaluate how these different constraints shape the organization of biological features across the brain.
Methods:
In this work, we present a family of generative models that allow the construction of surrogate brain maps constrained by cortico-cortical interactions including cortical connectivity (structural and functional), physical distance (Euclidean and geodesic) and biological similarity (genetic and chemo-architectural) (Fig. 1a). The generative procedure consists in, first, randomly sampling values from a Gaussian distribution, thus generating a random map without any autocorrelation structure. Then, by randomly swapping values on the simulated maps, we iteratively increase the autocorrelation of the maps until the autocorrelation structure of empirical maps is adequately approximated. An interesting feature of our generative models is that multiple types of cortico-cortical interactions can be combined into a single generative model, such that the autocorrelation structure of a brain map, across multiple interaction matrices, is preserved.
Results:
We applied our generative models to the maps openly available in neuromaps, a repository of brain maps developed by our group [1]. A total of 48 empirical brain maps were re-constructed (Fig. 1b). For each map, we first generated a total of 200 individual simulation using generative models that individually rely on each type of interaction matrix. Then, for each map, the six interaction matrices were combined into a single generative model, to generate 200 simulated maps recapitulating the autocorrelation structure of the empirical brain maps. To evaluate the accuracy of our models, we computed the average similarity between the generated maps and the empirical maps (Fig. 2a). Our results highlight important organization principles. Notably, we find that several maps can be accurately re-constructed using the gene similarity matrix. This includes the 5HT1a (Fig. 2b) and MU receptor density maps, the CMRO2 map (Fig. 2c), as well as myelin and cortical thickness maps.
Conclusions:
We present a generative framework to reconstruct empirical brain maps from their autocorrelation structure with respect to biological constraints. Altogether, our work introduces a novel paradigm for the study of brain maps. Indeed, this framework can be applied to several research questions. For instance, it can be used to investigate how different biological constraints shape maps of cortical abnormalities across neurological disorders. It can also be used to investigate individual differences across normal individuals. Furthermore, our generative framework can be used to construct null models and make inferences about cortical organization.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Methods Development
Other Methods 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Neuroanatomy Other
Keywords:
Data analysis
Other - Generative modelling
1|2Indicates the priority used for review
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[2] Huntenburg, J. M. (2018). Large-scale gradients in human cortical organization. Trends in cognitive sciences, 22(1), 21-31.
[3] Van Essen, D. C. (2012). The Human Connectome Project: a data acquisition perspective. Neuroimage, 62(4), 2222-2231.
[4] Hawrylycz, M. J. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391-399.
[5] Hansen, J. Y. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature neuroscience, 25(11), 1569-1581.
[6] Schaefer, A. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.