Petra E Vértes
Profile Url: petra-e-vrtes
Researcher at The Alan Turing Institute
The emergence of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms govern the diversity of these developmental processes? There are many existing descriptive theories, but to date none are computationally formalized. We provide a mathematical framework that specifies the growth of a brain network over developmental time. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over development. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the developmental simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity of childhood brain development, capable of integrating different levels of analysis - from genes to cognition. ### Competing Interest Statement Edward Bullmore is a member of the Scientific Advisory Board for Sosei Heptares. Other authors declare no competing financial interests.
Network Neuroscience, 2018-01-16
We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC corresponds to the small-world axis and correlates strongly with the networks' global efficiency. There is also some evidence that PC1 correlates with the average length of the 5% of longest edges in the network. Hence this axis represents the trade-off between the cost of long distance edges and their topological benefits. The second PC correlates with the networks' assortativity. Finally, we show that the economical clustering generative model proposed by Vértes et al. can approximately reproduce the motif PC space of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualise the relationships between network properties and to study the driving forces behind the topology of fMRI brain networks.
Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.
Biological Psychiatry, 2019-12-13
Background Genetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behaviour are unclear. Methods We assessed schizotypy using a self-reported questionnaire, and measured magnetization transfer (MT), as a putative micro-structural MRI marker of intra-cortical myelination, in 68 brain regions, in 248 healthy young people (aged 14-25 years). We used normative adult brain gene expression data, and partial least squares (PLS) analysis, to find the weighted gene expression pattern that was most co-located with the cortical map of schizotypy-related magnetization (SRM). Results Magnetization was significantly correlated with schizotypy in bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy also in medial prefrontal cortex; all FDR-corrected P < 0.05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole genome expression map co-located with SRM were enriched for genes that were significantly down-regulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally up-regulated in schizophrenia. Positively weighted (down-regulated) genes were enriched for neuronal, specifically inter-neuronal, affiliations and coded a network of proteins comprising a few highly interactive “hubs” such as parvalbumin and calmodulin. Conclusions Microstructural MRI maps of intracortical magnetization can be linked to both the behavioural traits of schizotypy and to prior histological data on dysregulated gene expression in schizophrenia.
Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled. TBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.
Adolescent changes in human brain function are not entirely understood. Here we used multi-echo functional magnetic resonance imaging (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in N=298 healthy adolescents. Participants were aged 14-26 years and were scanned on two or more occasions at least 6 months apart. We found two distinct modes of age-related change in FC: "conservative'' and "disruptive''. Conservative development was characteristic of primary cortex, which was strongly connected at 14 years and became even more connected in the period 14-26 years. Disruptive development was characteristic of association cortex, hippocampus and amygdala, which were not strongly connected at 14 years but became more strongly connected during adolescence. We defined the maturational index (MI) as the signed coefficient of the linear relationship between baseline FC (at 14 years, FC14) and adolescent change in FC (ΔFC14-26). Disruptive systems (with negative MI) were functionally specialised for social cognition and autobiographical memory and were significantly co-located with prior maps of aerobic glycolysis (AG), AG-related gene expression, post-natal expansion of cortical surface area, and adolescent shrinkage of cortical depth. We conclude that human brain organization is disrupted during adolescence by the emergence of strong functional connectivity of subcortical nuclei and association cortical areas, representing metabolically expensive re-modelling of synaptic connectivity between brain regions that were not strongly connected in childhood. We suggest that this re-modelling process may support emergence of social skills and self-awareness during healthy human adolescence.