Organization, development and function of complex brain networks

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Organization, development and function of complex
brain networks

• Olaf Sporns, Dante R. Chialvo, Marcus Kaiser and
  Claus C. Hilgetag
• Presented by Aysegül Tüysüz

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Motivation

• structural properties of large-scale anatomical
  and functional brain networks
• how they might arise in the course of network
  growth and rewiring
• the relationship between the structural substrate
  of neuroanatomy and more dynamic functional and
  effective connectivity patterns

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Outline

• Lattice-like connections in random networks,
  small-world networks and scale-free networks
• Brain Connectivity Structural, Functional and
  Effective
• Structural organization of brain networks
• Brain network growth and development
• Functional brain networks
• Relationship between structural connectivity and
  functional dynamics

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Lattice-like networks
All networks have 24 nodes and 86 connections
with nodes arranged on a circle.
For comparison, an ideal lattice with 24 nodes
and 86 connections has L1.96 and C0.64
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Neuron Connections

• Neurons maintain thousands of input and output
  connections with other neurons, forming a dense
  network of connectivity
• The human cerebral cortex contains approximately
  8.3x109 neurons and 6.7x1013 connections
• The length of all connections within a single
  human brain is estimated between 100,000 and
  10,000,000 km
• Neural connections are formed through
  developmental processes that are dependent upon
  neural activity

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Brain Connectivity Structural, Functional and
Effective

• Anatomical (structural) connectivity
• the set of physical or structural (synaptic)
  connections linking neuronal units at a given
  time
• static at shorter time scales (seconds to
  minutes)
• dynamic at longer time scales (hours to days)

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Brain Connectivity Structural, Functional and
Effective (cont.)

• Functional connectivity
• The correlations between spatially remote
  neurophysiologic events
• The pattern of temporal correlations (or,more
  generally, deviations from statistical
  independence) that exists between distinct
  neuronal units
• Time-dependent (hundreds of milliseconds)
• Model-free

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Brain Connectivity Structural, Functional and
Effective (cont.)

• Effective connectivity
• The influence one neural system exerts over
  another
• Time-dependent
• Not model-free

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Brain Connectivity Structural, Functional and
Effective (cont.)

• Relation between different brain connectivity
• Structural connectivity is a major constraint on
  the kinds of patterns of functional or effective
  connectivity that can be generated
• Structural inputs and outputs of a given cortical
  region are major determinants of its functional
  properties

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Structural organization of brain networks

• Most structural analyses are on datasets
  describing the large-scale connection patterns of
  the cerebral cortex of rat, cat, and monkey
• Structural connection data for the human brain is
  largely missing
• Cerebral cortical areas in mammalian brains are
  neither completely connected with each other nor
  randomly linked instead, their interconnections
  show a specific and intricate organization

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Structural organization of brain networks (cont.)

• At the local circuit level
• focus on the pattern of synaptic connections
  between individual neurons.
• area's in-degree and out-degree, and its
  transmission coefficient are measured
• identification of highly connected nodes (hubs)
  and provide an initial functional
  characterization of areas as either (mainly
  sending) broadcasters or (mainly receiving)
  integrators of signals.

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Structural organization of brain networks (cont.)

• At the next higher level
• Analyses of intra-areal patterns of connections
  would involve connection bundles or synaptic
  patches linking local neuronal populations
  (neuronal groups or columns)
• Neural circuits linking small sets of connected
  brain areas

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Structural organization of brain networks (cont.)

• Large-Scale Connection Patterns
•  Analyses of large scale connection patterns
  would focus on connection pathways linking
  segregated areas of the brain.
• All large-scale cortical connection patterns
  (adjacency matrices) examined so far exhibit
  small-world attributes with short path lengths
  and high clustering coefficients

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Structural organization of brain networks (cont.)

• Characteristic path length and clustering
  coefficient (C) for the large-scale connection
  matrix of the macaque visual cortex (red). For
  comparison, 10 000 examples of equivalent random
  and lattice networks are also shown (blue).

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Structural organization of brain networks (cont.)

• In-degree and out-degree specify the amount of
  functional convergence and divergence of a given
  region
• the clustering coefficient measures the degree to
  which the area is part of a local collective of
  functionally related regions
• the path length between two brain regions
  captures their potential functional proximity
• If no path exists, no functional interaction can
  take place.

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Structural organization of brain networks (cont.)

• A computational approach based on evolutionary
  optimization was proposed to identify the
  clusters which are indicated by the high
  clustering coefficients of cortical networks
• This optimization method delineated a small
  number of distinctive clusters in global cortical
  networks of cat and macaque
• The algorithm could be steered to identify
  clusters that no longer contained any known
  absent connections, and thus produced maximally
  interconnected sets of areas

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Structural organization of brain networks (cont.)

• Cluster structure of cat corticocortical
  connectivity. Bars indicate borders between nodes
  in separate clusters. Cortical areas were
  arranged around a circle by evolutionary
  optimization, so that highly inter-linked areas
  were placed close to each other. The ordering
  agrees with the functional and anatomical
  similarity of visual, auditory,
  somatosensory-motor and frontolimbic cortices.
• Visualized using Pajek

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Structural organization of brain networks (cont.)

• In networks composed of multiple distributed
  clusters, inter-cluster connections occur most
  frequently in all shortest paths linking areas
  with one another
• This is important for the structural stability
  and efficient working of cortical networks
• The degree of connectedness of neural structures
  may affect the functional impact of local and
  remote network lesions
• The cortical networks of cat and macaque are
  vulnerable towards the damage of the few highly
  connected nodes in a similar way as scale-free
  networks react to the elimination of hubs

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Brain network growth and development

• Brain structures are shaped by evolution,
  ontogenetic development, experience-dependent
  refinement, and finally degradation as a result
  of brain injury or disease.
• growth mechanisms for the large classes of
  small-world and scale-free networks are not
  biologically realistic and do not represent good
  models for the development of cortical networks
• Alternative developmental algorithms were
  proposed recently that acknowledge spatial
  constraints in biological systems, while also
  yielding different types of scale-free and
  small-world networks

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Brain network growth and development (cont.)

• Local Spatial Growth Rules
• algorithms for the generation of random and
  scale-free networks ignore the fact that cortical
  networks develop in space
• Preferential attachment, for instance, would
  establish links to hubs independent of their
  distance
• In biological networks, however, long-distance
  connections are rare, in part because the
  concentration of diffusible signaling and growth
  factors decays with distance

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Brain network growth and development (cont.)

• Local Spatial Growth Rules
• spatial growth model was presented in which
  growth starts with two nodes, and a new node is
  added at each step.
• The establishment of connections from a new node
  u to one of the existing nodes v depends on the
  distance d(u,v) between nodes, that is,
• P(u,v) ? e-? d(u,v)

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Brain network growth and development (cont.)

• Local Spatial Growth Rules
• This spatial growth mechanism can lead to
  networks with similar clustering coefficients and
  characteristic path lengths as in cortical
  networks when growth limits are present, such as
  extrinsic limits imposed by volume constraints
• Lower clustering results if the developing model
  network does not reach the spatial borders and
  path lengths among areas increase
• By comparison, a preferential attachment model
  may yield similar global properties, but fails to
  generate multiple clusters, as found in cortical
  networks.

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Brain network growth and development (cont.)

• and Global Network Design
• In addition to similar global properties, defined
  by clustering coefficient and characteristic path
  length, the generated networks also exhibit
  wiring properties similar to the macaque cortex,
  whose network and wiring distribution is shown in
  the following figure.

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Brain network growth and development (cont.)

1. Macaque cortex with associated long-range
   connectivity among areas.
2. Distribution of approximate fiber length as
   calculated by the direct Euclidian distance
   between the average spatial positions of brain
   areas.

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Brain network growth and development (cont.)

• and Global Network Design
• This figure supports the idea that the likelihood
  of long-range connections among cortical areas of
  the macaque decreases with distance
• The (few) long-range connections existing in the
  biological networks may constitute shortcuts,
  ensuring short average paths with only few
  intermediate nodes.

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Functional Brain Networks

• A methodology used to extract functional brain
  networks
• Using functional magnetic resonance imaging
  (fMRI) in humans, analyzed with graph theory to
  reveal brain functional connectivity
• Image voxels form nodes of a graph, their
  temporal correlation matrix forms the weight
  matrix of the edges between the nodes.
• Thus a network can be implemented based entirely
  on fMRI data, defining as connected those
  voxels that are functionally linked, that is
  correlated beyond a certain threshold rc

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Functional Brain Networks (cont.)
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Functional Brain Networks (cont.)

• A typical functional brain network extracted from
  human fMRI data
• Nodes are colored according to degree (yellow1,
  green2, red3,blue4, black gt 4)

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Functional Brain Networks (cont.)

• Degree distribution for two correlation
  thresholds. The inset depicts the degree
  distribution for an equivalent random network
• If rc is too small, then the majority of the
  voxels will appear to be connected to one another
• if rc is too high, then voxels will appear
  isolated.

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Functional Brain Networks (cont.)

• Their degree distribution and the probability of
  finding a link versus metric distance both decay
  as a power law.
• Their characteristic path length is short
  (similar to that of equivalent random networks),
  while the clustering coefficient is several
  orders of magnitude larger.
• Scaling and small-world properties persisted
  across different tasks and within different
  locations of the brain.

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Structural Connectivity and Functional Dynamics

• Different connection topologies generated
  different modes of neuronal dynamics
• Locally clustered connections with a small
  admixture of long-range connections exhibited
• robust small-world attributes,
• conserving wiring length,
• gave rise to functional connectivity of high
  complexity with spatially and temporally highly
  organized patterns.

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Conclusion

• Small-world attributes and the occurrence of
  highly clustered connection patterns appear to
  represent a general organizational principle
  found throughout many large-scale cortical
  networks.
• Clustering implies short path lengths between
  cluster components.

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Conclusion (cont.)

• But path lengths between any two cortical areas
  are already very short so it is not immediately
  clear why direct connections between areas within
  a cluster provide additional benefits
• the short way of signal transformations that are
  carried out by cortical areas is important to
  eliminate noise
• Failures of nodes and edges can be compensated
  for more easily

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Conclusion (cont.)

• 3 main purposes of clustered organization of
  cortical networks
• Creating a balance between functional segregation
  and integration, resulting in functional
  connectivity of high complexity with short wiring
  length,
• Supporting efficient recurrent processing,
• Supporting synchronous processing or efficient
  information exchange.

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QUESTIONS?

• ?