Heritability of individualized cortical network topography

TLDR: Using a novel non-linear multi-dimensional estimation of heritability, evidence is provided that individual variability in the size and topographic organization of cortical networks are under genetic control and individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

Abstract: Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging. Significance The widespread use of population-average cortical parcellations has provided important insights into broad properties of human brain organization. However, the size, location, and spatial arrangement of regions comprising functional brain networks can vary substantially across individuals. Here, we demonstrate considerable heritability in both the size and spatial organization of individual-specific network topography across cortex. Genetic factors had a regionally variable influence on brain organization, such that heritability in network size, but not topography, was greater in unimodal relative to heteromodal cortices. These data suggest individual-specific network parcellations may provide an avenue to understand the genetic basis of variation in human cognition and behavior.

Figures

Figure 2. Heritability of individualized network size is greater in unimodal relative to heteromodal networks. (a) Heritability of individual network size (normalized for total surface area) was estimated across 17 canonical functional networks using SOLAR47, separately for each hemisphere. Error bars reflect 95% C.I. (b) The amount of variance explained by genetics (h2) for each network was consistent across hemispheres, as revealed by a correlation of leftand right hemisphere h2 values (r=0.74, p=7.5e-4). Each dot in the correlation plot is a functional network. (c) Heritability of normalized individual network size was higher among unimodal/sensory networks relative to heteromodal association networks (p=5.52e-05). Each dot represents one of 17 cortical networks, split by hemisphere (n=34). See Fig. 1 legend for explanation of abbreviations.

Figure 1. Individualized network size is more variable in heteromodal relative to unimodal cortex. (a) Individualized parcellations are composed of 17 canonical functional networks present in all HCP individuals, as defined by Kong and colleagues13. (b) The relative size of individualized networks was calculated for each participant, expressed as a fraction of total cortical surface area. The ridge plot shows distributions of network size across all individuals, separated by hemisphere (top ridge=right hemisphere, bottom ridge=left hemisphere). (c) Variability of individualized network size across all participants, measured with coefficient of

Figure 4. Local Heritability of Individualized Network Topography. (a) Multi-dimensional heritability of network topography estimated for every vertex, using an ROI at each point on the cortical sheet (radius=10 vertices). Individualized network labels in each ROI were evaluated to compute a participant-to-participant Dice similarity matrix, reflecting the similarity of network assignments within a given cortical area. Warmer colors indicate higher heritability of network assignments, for instance reflecting greater similarity among twins and siblings than unrelated individuals. (b) Example participant pairs with high and low Dice overlap of network labels for an ROI in the precuneus. Dice similarity was higher between MZ twins, relative to DZ, sibling, and unrelated participant pairs. MZ, monozygotic; DZ, dizygotic; SIB, sibling; UNR, unrelated; LH, left hemisphere; RH, right hemisphere.

Figure 3. Individualized network topography is heritable across all networks. (a) The ridge plot displays distributions of inter-individual Dice coefficients across each network. Higher Dice coefficients reflect higher spatial overlap of a network for a given pair of individuals. Topography of unimodal networks were overall more similar across individuals, relative to

Table 1: Demographics of HCP participant groups. Heritability estimates were conducted on 1,023 HCP participants, composed of 137 MZ twins (n=274), 80 DZ twins (n=160), non-twin siblings (n=482), and unrelated singletons (n=107). Although groups were nominally wellmatched demographically, ANOVAs revealed significant differences of age and sex. All heritability analyses included age and sex as covariates.

Full text

1
Title: Heritability of individualized cortical network topography
Authors: Kevin M. Anderson
1*
, Tian Ge
2,3,11
, Ru Kong
4,8
, Lauren M. Patrick
1
, R. Nathan
Spreng
5
, Mert R. Sabuncu
6, 7
, B.T. Thomas Yeo
4,7,8,9
, Avram J. Holmes
1,10,11
1
Department of Psychology, Yale University, New Haven, CT, USA
2
Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine,
Massachusetts General Hospital, Boston, MA, USA
3
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge,
MA, USA
4
Department of Electrical and Computer Engineering, Centre for Sleep and Cognition & Centre
for Translational Magnetic Resonance Research, National University of Singapore, Singapore
5
Montreal Neurological Institute, Department of Neurology and Neurosurgery
McGill University, Montreal, Canada & McConnell Brain Imaging Centre, McGill University,
Montreal, Canada
6
School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering,
Cornell University, Ithaca, NY, USA
7
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA,
USA
8
N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of
Singapore, Singapore
9
NUS Graduate School for Integrative Sciences and Engineering, National University of
Singapore, Singapore
10
Department of Psychiatry, Yale University, New Haven, Connecticut 06520, USA
11
Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School,
Boston, MA 02114, USA
*Correspondence: kevin.anderson@yale.edu
Author Contributions: All authors designed the research. TGE created the multi-dimensional
heritability method. RK derived individualized parcellations. KMA conducted analyses and made
figures. All authors contributed during writing. All authors edited the paper.
Keywords: Heritability, individualized parcellation, resting-state, functional brain networks,
functional connectome
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2
Abstract: 228, Significance: 103, Introduction: 744, Results: 1,443, Discussion: 1,480
Figures: 4, References: 80
.CC-BY-NC 4.0 International licenseavailable under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted July 30, 2020. ; https://doi.org/10.1101/2020.07.30.229427doi: bioRxiv preprint

3
Abstract
Human cortex is patterned by a complex and interdigitated web of large-scale functional
networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial
topography of cortical networks across individuals. While spatial network organization emerges
across development, is stable over time, and predictive of behavior, it is not yet clear to what
extent genetic factors underlie inter-individual differences in network topography. Here,
leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence
that individual variability in the size and topographic organization of cortical networks are under
genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we
find increased variability and reduced heritability in the size of heteromodal association
networks (h
2
: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h
2
: M=0.44,
SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by
genetics, using our multi-dimensional estimation of heritability (h
2
-multi; M=0.14, SD=0.015).
However, topographic heritability did not differ between heteromodal and unimodal networks.
Genetic factors had a regionally variable influence on brain organization, such that the
heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal
cortex. Taken together, these data are consistent with relaxed genetic control of association
cortices relative to primary sensory/motor regions, and have implications for understanding
population-level variability in brain functioning, guiding both individualized prediction and the
interpretation of analyses that integrate genetics and neuroimaging.
.CC-BY-NC 4.0 International licenseavailable under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted July 30, 2020. ; https://doi.org/10.1101/2020.07.30.229427doi: bioRxiv preprint

4
Significance
The widespread use of population-average cortical parcellations has provided important
insights into broad properties of human brain organization. However, the size, location, and
spatial arrangement of regions comprising functional brain networks can vary substantially
across individuals. Here, we demonstrate considerable heritability in both the size and spatial
organization of individual-specific network topography across cortex. Genetic factors had a
regionally variable influence on brain organization, such that heritability in network size, but not
topography, was greater in unimodal relative to heteromodal cortices. These data suggest
individual-specific network parcellations may provide an avenue to understand the genetic basis
of variation in human cognition and behavior.
.CC-BY-NC 4.0 International licenseavailable under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted July 30, 2020. ; https://doi.org/10.1101/2020.07.30.229427doi: bioRxiv preprint

5
Introduction
The cerebral cortex is organized into a tightly interdigitated set of large-scale functional
networks. Seminal tract-tracing work in non-human primates first revealed the structural
properties underlying the distributed and parallel organization of cortical networks
1
. Subsequent
resting-state functional connectivity magnetic resonance imaging (fcMRI) analyses leveraged
correlation patterns of intrinsic fMRI signal fluctuations in humans
2
to establish a canonical
network architecture that is broadly shared across the population
3–8
. Yet, many individual-
specific properties of brain network organization are lost when central tendencies are examined
across large groups. The use of population-average network topographies has accelerated
psychological and neuroscientific discovery, however there is growing recognition that the
human brain is characterized by striking functional variability across individuals
9–15
. As
individualized approaches become increasingly popular for the study of human behavior and
psychopathology
13,16–18
, there is growing need to quantify the heritable bases of population-level
variability in functional network size and topography. Despite the fact that individual differences
result from the convergence of both genetic and environmental influences, the extent to which
the size and spatial patterning of cortical networks may reflect heritable features of brain
function has not yet been systematically investigated.
Population-based neuroimaging studies have revealed core principles that govern the
evolution
19
, development
20
, and organization
7,8
of large-scale brain networks. In particular, fcMRI
has been widely utilized to generate group-average network templates through the joint
analyses of data across vast numbers of individuals. The topography of these population-based
network solutions are closely coupled to cognitive function, and a strong correspondence has
been observed linking the spatial structure of intrinsic (fcMRI) and extrinsic (task-evoked)
networks of the human brain
21–23
. Consistent with these observations, various connectivity
patterns track behavioral variability in the general population
24–26
and symptom expression in
patients with psychiatric illness
27
. Suggesting genetic factors may influence the functioning of
large-scale brain networks, patterns of intrinsic connectivity within population-average defined
network templates are heritable
28–30
and act as a trait-like fingerprint that can accurately identify
specific people from a larger group
31,32
. These data have provided the empirical scaffolding
necessary to examine how genetic, molecular, and cellular mechanisms shape human brain
function
33–35
. Critically however, the use of population-based network templates can obscure
individual-specific features of brain organization
9
, and there is growing evidence for substantial
inter-individual variability in the size, location, and topographic arrangement of regions
comprising spatially distributed functional networks across the cortical sheet.
.CC-BY-NC 4.0 International licenseavailable under a
was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint (whichthis version posted July 30, 2020. ; https://doi.org/10.1101/2020.07.30.229427doi: bioRxiv preprint

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