522 research outputs found
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 morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
Computing Scalable Multivariate Glocal Invariants of Large (Brain-) Graphs
Graphs are quickly emerging as a leading abstraction for the representation
of data. One important application domain originates from an emerging
discipline called "connectomics". Connectomics studies the brain as a graph;
vertices correspond to neurons (or collections thereof) and edges correspond to
structural or functional connections between them. To explore the variability
of connectomes---to address both basic science questions regarding the
structure of the brain, and medical health questions about psychiatry and
neurology---one can study the topological properties of these brain-graphs. We
define multivariate glocal graph invariants: these are features of the graph
that capture various local and global topological properties of the graphs. We
show that the collection of features can collectively be computed via a
combination of daisy-chaining, sparse matrix representation and computations,
and efficient approximations. Our custom open-source Python package serves as a
back-end to a Web-service that we have created to enable researchers to upload
graphs, and download the corresponding invariants in a number of different
formats. Moreover, we built this package to support distributed processing on
multicore machines. This is therefore an enabling technology for network
science, lowering the barrier of entry by providing tools to biologists and
analysts who otherwise lack these capabilities. As a demonstration, we run our
code on 120 brain-graphs, each with approximately 16M vertices and up to 90M
edges.Comment: Published as part of 2013 IEEE GlobalSIP conferenc
Mapping hybrid functional-structural connectivity traits in the human connectome
One of the crucial questions in neuroscience is how a rich functional
repertoire of brain states relates to its underlying structural organization.
How to study the associations between these structural and functional layers is
an open problem that involves novel conceptual ways of tackling this question.
We here propose an extension of the Connectivity Independent Component Analysis
(connICA) framework, to identify joint structural-functional connectivity
traits. Here, we extend connICA to integrate structural and functional
connectomes by merging them into common hybrid connectivity patterns that
represent the connectivity fingerprint of a subject. We test this extended
approach on the 100 unrelated subjects from the Human Connectome Project. The
method is able to extract main independent structural-functional connectivity
patterns from the entire cohort that are sensitive to the realization of
different tasks. The hybrid connICA extracted two main task-sensitive hybrid
traits. The first, encompassing the within and between connections of dorsal
attentional and visual areas, as well as fronto-parietal circuits. The second,
mainly encompassing the connectivity between visual, attentional, DMN and
subcortical networks. Overall, these findings confirms the potential ofthe
hybrid connICA for the compression of structural/functional connectomes into
integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5
figure
Reading the Book of Memory: Sparse Sampling versus Dense Mapping of Connectomes
Many theories of neural networks assume rules of connection between pairs of neurons that are based on their cell types or functional properties. It is finally becoming feasible to test such pairwise models of connectivity, due to emerging advances in neuroanatomical techniques. One method will be to measure the functional properties of connected pairs of neurons, sparsely sampling pairs from many specimens. Another method will be to find a āconnectome,ā a dense map of all connections in a single specimen, and infer functional properties of neurons through computational analysis. For the latter method, the most exciting prospect would be to decode the memories that are hypothesized to be stored in connectomes
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