Heritability of individualized cortical network topography

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.

Download Full-text

Heritability of individualized cortical network topography

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2016271118 ◽

2021 ◽

Vol 118 (9) ◽

pp. e2016271118

Author(s):

Kevin M. Anderson ◽

Tian Ge ◽

Ru Kong ◽

Lauren M. Patrick ◽

R. Nathan Spreng ◽

...

Keyword(s):

Genetic Control ◽

Large Scale ◽

Posterior Parietal Cortex ◽

Genetic Factors ◽

Population Level ◽

Individual Variability ◽

Cortical Networks ◽

Brain Organization ◽

Human Connectome Project ◽

Sensory Motor

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 is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional 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 = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional 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.

Download Full-text

The Encoding/Retrieval Flip: Interactions between Memory Performance and Memory Stage and Relationship to Intrinsic Cortical Networks

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00366 ◽

2013 ◽

Vol 25 (7) ◽

pp. 1163-1179 ◽

Cited By ~ 32

Author(s):

Willem Huijbers ◽

Aaron P. Schultz ◽

Patrizia Vannini ◽

Donald G. McLaren ◽

Sarah E. Wigman ◽

...

Keyword(s):

Large Scale ◽

Posterior Parietal Cortex ◽

Memory Performance ◽

Resting State Fmri ◽

Brain Regions ◽

Default Network ◽

Cortical Networks ◽

Age Related ◽

Retrieval Processes ◽

Memory Stage

fMRI studies have linked the posteromedial cortex to episodic learning (encoding) and remembering (retrieval) processes. The posteromedial cortex is considered part of the default network and tends to deactivate during encoding but activate during retrieval, a pattern known as the encoding/retrieval flip. Yet, the exact relationship between the neural correlates of memory performance (hit/miss) and memory stage (encoding/retrieval) and the extent of overlap with intrinsic cortical networks remains to be elucidated. Using task-based fMRI, we isolated the pattern of activity associated with memory performance, memory stage, and the interaction between both. Using resting-state fMRI, we identified which intrinsic large-scale functional networks overlapped with regions showing task-induced effects. Our results demonstrated an effect of successful memory performance in regions associated with the control network and an effect of unsuccessful memory performance in the ventral attention network. We found an effect of memory retrieval in brain regions that span the default and control networks. Finally, we found an interaction between memory performance and memory stage in brain regions associated with the default network, including the posteromedial cortex, posterior parietal cortex, and parahippocampal cortex. We discuss these findings in relation to the encoding/retrieval flip. In general, the findings demonstrate that task-induced effects cut across intrinsic cortical networks. Furthermore, regions within the default network display functional dissociations, and this may have implications for the neural underpinnings of age-related memory disorders.

Download Full-text

The HCP 7T Retinotopy Dataset: Description and pRF Analysis

10.1101/308247 ◽

2018 ◽

Cited By ~ 4

Author(s):

Noah C. Benson ◽

Keith W. Jamison ◽

Michael J. Arcaro ◽

An Vu ◽

Matthew F. Glasser ◽

...

Keyword(s):

Visual Processing ◽

Large Scale ◽

Individual Variability ◽

7 Tesla ◽

Individual Subject ◽

Retinotopic Mapping ◽

Human Connectome Project ◽

The Brain ◽

Model Based Analysis

AbstractAbout a quarter of human cerebral cortex is dedicated mainly to visual processing. The large-scale organization of visual cortex can be measured with functional magnetic resonance imaging (fMRI) while subjects view spatially modulated visual stimuli, also known as ‘retinotopic mapping’. One of the datasets collected by the Human Connectome Project (HCP) involved ultra-high-field (7 Tesla) fMRI retinotopic mapping in 181 healthy young adults (1.6-mm resolution), yielding the largest freely available collection of retinotopy data. Here, we describe the experimental paradigm and the results of model-based analysis of the fMRI data. These results provide estimates of population receptive field position and size. Our analyses include both results from individual subjects as well as results obtained by averaging fMRI time-series across subjects at each cortical and subcortical location and then fitting models. Both the group-average and individual-subject results reveal robust signals across much of the brain, including occipital, temporal, parietal, and frontal cortex as well as subcortical areas. The group-average results agree well with previously published parcellations of visual areas. In addition, split-half analyses show strong within-subject reliability, further demonstrating the high quality of the data. We make publicly available the analysis results for individual subjects and the group average, as well as associated stimuli and analysis code. These resources provide an opportunity for studying fine-scale individual variability in cortical and subcortical organization and the properties of high-resolution fMRI. In addition, they provide a set of observations that can be compared with other HCP measures acquired in these same participants.

Download Full-text

Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns

10.1101/2021.06.20.448984 ◽

2021 ◽

Author(s):

Taylor S Bolt ◽

Jason Nomi ◽

Danilo Bzdok ◽

Catie Chang ◽

B.T. Thomas Yeo ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Low Frequency ◽

Resting State Fmri ◽

Spatiotemporal Patterns ◽

Brain Organization ◽

Functional Brain ◽

Temporal Properties ◽

Human Connectome Project ◽

Functional Brain Organization

The characterization of intrinsic functional brain organization has been approached from a multitude of analytic techniques and methods. We are still at a loss of a unifying conceptual framework for capturing common insights across this patchwork of empirical findings. By analyzing resting-state fMRI data from the Human Connectome Project using a large number of popular analytic techniques, we find that all results can be seamlessly reconciled by three fundamental low-frequency spatiotemporal patterns that we have identified via a novel time-varying complex pattern analysis. Overall, these three spatiotemporal patterns account for a wide variety of previously observed phenomena in the resting-state fMRI literature including the task-positive/task-negative anticorrelation, the global signal, the primary functional connectivity gradient and the network community structure of the functional connectome. The shared spatial and temporal properties of these three canonical patterns suggest that they arise from a single hemodynamic mechanism.

Download Full-text

Robust prediction of individual personality from brain functional connectome

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsaa044 ◽

2020 ◽

Vol 15 (3) ◽

pp. 359-369 ◽

Cited By ~ 1

Author(s):

Huanhuan Cai ◽

Jiajia Zhu ◽

Yongqiang Yu

Keyword(s):

Personality Traits ◽

Large Scale ◽

Five Factor Model ◽

Brain Regions ◽

Individual Variability ◽

Personality Factors ◽

Imaging Features ◽

Imaging Data ◽

Functional Connectome ◽

Human Connectome Project

Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

Download Full-text

Large-Scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

Cerebral Cortex ◽

10.1093/cercor/bhab075 ◽

2021 ◽

Author(s):

Xiang-Zhen Kong ◽

Merel Postema ◽

Dick Schijven ◽

Amaia Carrión Castillo ◽

Antonietta Pepe ◽

...

Keyword(s):

Large Scale ◽

Right Hemisphere ◽

Association Studies ◽

Genomic Analysis ◽

Population Level ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Imaging Data ◽

Physical And Mental Health ◽

Human Connectome Project

Abstract The human cerebral hemispheres show a left–right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here, we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, UK Biobank (N = 39 678), Human Connectome Project (N = 1113), and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional gray and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed single nucleotide polymorphisms-based heritabilities of 4–13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in genome-wide association studies for either skew. There was evidence for a significant genetic correlation between horizontal brain skew and autism, which requires future replication. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

Download Full-text

Escape from X-inactivation in twins exhibits intra- and inter-individual variability across tissues and is heritable

10.1101/2021.10.15.463586 ◽

2021 ◽

Author(s):

Antonino Zito ◽

Amy L Roberts ◽

Alessia Visconti ◽

Niccolo' Rossi ◽

Rosa Andres-Ejarque ◽

...

Keyword(s):

X Chromosome ◽

Large Scale ◽

Genetic Factors ◽

Immune Cell ◽

Cell Types ◽

Individual Variability ◽

Healthy Population ◽

Cell Type ◽

Cell Type Specificity ◽

Escape From X Inactivation

X-chromosome inactivation (XCI) silences one X-chromosome in female cells to balance sex-differences in X-dosage. A subset of X-linked genes escape XCI, but the extent to which this phenomenon occurs and how it varies across tissues and in a population is as yet unclear. In order to characterize the incidence and variability of escape across individuals and tissues, we conducted a large scale transcriptomic study of XCI escape in adipose, skin, lymphoblastoid cell lines (LCLs) and immune cells in 248 twins drawn from a healthy population cohort. We identify 159 X-linked genes with detectable escape, of which 54 genes, including 19 lncRNAs, were not previously known to escape XCI. Across tissues we find a range of tissue-specificity, with 11% of genes escaping XCI constitutively across tissues and 24% demonstrating tissue-restricted escape, including genes with cell-type specific escape between immune cell types (B, T-CD4+, T-CD8+ and NK cells) of the same individual. Escape genes interact with autosomal-encoded proteins and are involved in varied biological processes such as gene regulation. We find substantial variability in escape between individuals. 49% of genes show inter-individual variability in escape, indicating escape from XCI is an under-appreciated source of gene expression differences. We utilized twin models to investigate the role of genetics in variable escape. Overall, monozygotic (MZ) twin pairs share more similar escape than dizygotic twin pairs, indicating that genetic factors underlie differences in escape across individuals. However, we also identify instances of discordant XCI within MZ co-twin pairs, suggesting that environmental factors also influence escape. Thus, XCI escape may be shaped by an interplay of genetic factors with tissue- and cell type-specificity, and environment. These results illuminate an intricate phenotype whose characterization aids understanding the basis of variable trait expressivity in females.

Download Full-text

Large-scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew

10.1101/756395 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiang-Zhen Kong ◽

Merel Postema ◽

Amaia Carrión Castillo ◽

Antonietta Pepe ◽

Fabrice Crivello ◽

...

Keyword(s):

Large Scale ◽

Right Hemisphere ◽

Genomic Analysis ◽

Population Level ◽

Sociodemographic Factors ◽

Autism Spectrum ◽

Imaging Data ◽

Physical And Mental Health ◽

Human Connectome Project ◽

The Uk

AbstractThe human cerebral hemispheres show a left-right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, the UK Biobank (N = 39,678), Human Connectome Project (N = 1,113) and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional grey and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed SNP-based heritabilities of 4-13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in GWAS for either skew. There was evidence for a significant genetic correlation (rg=−0.40, p=0.0075) between horizontal brain skew and Autism Spectrum Disorder. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.

Download Full-text

Innate Organization of the Human Brain

10.1101/2021.03.24.436788 ◽

2021 ◽

Author(s):

M. Fiona Molloy ◽

Zeynep M. Saygin

Keyword(s):

Individual Variability ◽

Adult Brain ◽

Brain Organization ◽

Attention Networks ◽

Functional Brain ◽

Human Connectome Project ◽

Differential Gene ◽

Functional Brain Organization ◽

High Level ◽

Genetically Determined

The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We also quantified neonate individual variability, finding low variability for sensorimotor, but high for ventral attention networks. These neonate networks resembled adult networks (Yeo et al., 2011), but frontoparietal and limbic networks found in adults were indiscernible in neonates. Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks. Our results reveal the basic proto-organization of cortex at birth, but indicate a role for maturation and experience in developing adult-like functional brain organization.

Download Full-text

The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

10.1101/266635 ◽

2018 ◽

Cited By ~ 3

Author(s):

J.M. Shine ◽

M. Breakspear ◽

P.T. Bell ◽

K. Ehgoetz Martens ◽

R. Shine ◽

...

Keyword(s):

Human Brain ◽

Neural Activity ◽

Large Scale ◽

System Level ◽

Dimensional Manifold ◽

Brain Organization ◽

Human Connectome Project ◽

Functional Brain Organization ◽

Low Dimensional ◽

Dynamic Execution

AbstractThe human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of diverse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function.One Sentence SummaryA diverse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by diverse cognitive demands

Download Full-text