1,834 research outputs found
Influence of wiring cost on the large-scale architecture of human cortical connectivity
In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain
A geometric network model of intrinsic grey-matter connectivity of the human brain
Network science provides a general framework for analysing the large-scale brain networks that naturally arise from modern neuroimaging studies, and a key goal in theoretical neuro- science is to understand the extent to which these neural architectures influence the dynamical processes they sustain. To date, brain network modelling has largely been conducted at the macroscale level (i.e. white-matter tracts), despite growing evidence of the role that local grey matter architecture plays in a variety of brain disorders. Here, we present a new model of intrinsic grey matter connectivity of the human connectome. Importantly, the new model incorporates detailed information on cortical geometry to construct ‘shortcuts’ through the thickness of the cortex, thus enabling spatially distant brain regions, as measured along the cortical surface, to communicate. Our study indicates that structures based on human brain surface information differ significantly, both in terms of their topological network characteristics and activity propagation properties, when compared against a variety of alternative geometries and generative algorithms. In particular, this might help explain histological patterns of grey matter connectivity, highlighting that observed connection distances may have arisen to maximise information processing ability, and that such gains are consistent with (and enhanced by) the presence of short-cut connections
The specificity and robustness of long-distance connections in weighted, interareal connectomes
Brain areas' functional repertoires are shaped by their incoming and outgoing
structural connections. In empirically measured networks, most connections are
short, reflecting spatial and energetic constraints. Nonetheless, a small
number of connections span long distances, consistent with the notion that the
functionality of these connections must outweigh their cost. While the precise
function of these long-distance connections is not known, the leading
hypothesis is that they act to reduce the topological distance between brain
areas and facilitate efficient interareal communication. However, this
hypothesis implies a non-specificity of long-distance connections that we
contend is unlikely. Instead, we propose that long-distance connections serve
to diversify brain areas' inputs and outputs, thereby promoting complex
dynamics. Through analysis of five interareal network datasets, we show that
long-distance connections play only minor roles in reducing average interareal
topological distance. In contrast, areas' long-distance and short-range
neighbors exhibit marked differences in their connectivity profiles, suggesting
that long-distance connections enhance dissimilarity between regional inputs
and outputs. Next, we show that -- in isolation -- areas' long-distance
connectivity profiles exhibit non-random levels of similarity, suggesting that
the communication pathways formed by long connections exhibit redundancies that
may serve to promote robustness. Finally, we use a linearization of
Wilson-Cowan dynamics to simulate the covariance structure of neural activity
and show that in the absence of long-distance connections, a common measure of
functional diversity decreases. Collectively, our findings suggest that
long-distance connections are necessary for supporting diverse and complex
brain dynamics.Comment: 18 pages, 8 figure
Generative models of the human connectome
The human connectome represents a network map of the brain's wiring diagram
and the pattern into which its connections are organized is thought to play an
important role in cognitive function. The generative rules that shape the
topology of the human connectome remain incompletely understood. Earlier work
in model organisms has suggested that wiring rules based on geometric
relationships (distance) can account for many but likely not all topological
features. Here we systematically explore a family of generative models of the
human connectome that yield synthetic networks designed according to different
wiring rules combining geometric and a broad range of topological factors. We
find that a combination of geometric constraints with a homophilic attachment
mechanism can create synthetic networks that closely match many topological
characteristics of individual human connectomes, including features that were
not included in the optimization of the generative model itself. We use these
models to investigate a lifespan dataset and show that, with age, the model
parameters undergo progressive changes, suggesting a rebalancing of the
generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl
From Caenorhabditis elegans to the Human Connectome: A Specific Modular Organisation Increases Metabolic, Functional, and Developmental Efficiency
The connectome, or the entire connectivity of a neural system represented by
network, ranges various scales from synaptic connections between individual
neurons to fibre tract connections between brain regions. Although the
modularity they commonly show has been extensively studied, it is unclear
whether connection specificity of such networks can already be fully explained
by the modularity alone. To answer this question, we study two networks, the
neuronal network of C. elegans and the fibre tract network of human brains
yielded through diffusion spectrum imaging (DSI). We compare them to their
respective benchmark networks with varying modularities, which are generated by
link swapping to have desired modularity values but otherwise maximally random.
We find several network properties that are specific to the neural networks and
cannot be fully explained by the modularity alone. First, the clustering
coefficient and the characteristic path length of C. elegans and human
connectomes are both higher than those of the benchmark networks with similar
modularity. High clustering coefficient indicates efficient local information
distribution and high characteristic path length suggests reduced global
integration. Second, the total wiring length is smaller than for the
alternative configurations with similar modularity. This is due to lower
dispersion of connections, which means each neuron in C. elegans connectome or
each region of interest (ROI) in human connectome reaches fewer ganglia or
cortical areas, respectively. Third, both neural networks show lower
algorithmic entropy compared to the alternative arrangements. This implies that
fewer rules are needed to encode for the organisation of neural systems
A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale
In this era of complete genomes, our knowledge of neuroanatomical circuitry
remains surprisingly sparse. Such knowledge is however critical both for basic
and clinical research into brain function. Here we advocate for a concerted
effort to fill this gap, through systematic, experimental mapping of neural
circuits at a mesoscopic scale of resolution suitable for comprehensive,
brain-wide coverage, using injections of tracers or viral vectors. We detail
the scientific and medical rationale and briefly review existing knowledge and
experimental techniques. We define a set of desiderata, including brain-wide
coverage; validated and extensible experimental techniques suitable for
standardization and automation; centralized, open access data repository;
compatibility with existing resources, and tractability with current
informatics technology. We discuss a hypothetical but tractable plan for mouse,
additional efforts for the macaque, and technique development for human. We
estimate that the mouse connectivity project could be completed within five
years with a comparatively modest budget.Comment: 41 page
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