1. Hang-Rai Kim AA Department of Bio and Brain Engineering
Program of Brain and Cognitive Engineering
KI for Health Science and Technology
Lab. for Cognitive Neuroscience and NeuroImage

2. Introduction
Social and natural systems are generally organized as complex networks of interconnected elements.
From cellular level to social level
Some networks are virtual
But many systems are defined spatially as well as topologically
Brain is also a complex network
On cellular level, the topology of network interactions between proteins is critical.
On social level, the topology of network interactions between people is also important.
Some networks are virtual meaning they do not occupy physical space. Example would be interaction between stock prices and stock exchange.
However many social and natural systems are clearly defined spatially as well as topologically.
Brain is also a complex network and obviously not virtual.

3. ? We do not know how is the complex brain network is organized.
Many aspects can be explained by the economic trade-off between minimizing costs and maximizing its value
Till now we do not fully understand how the complex brain network is organized.
But interestingly many aspects of this organization can be explained by the economic trade-off between minimizing costs and maximizing its value. ?

4. Cost Value Brain is expensive to build and to run
Doing a good business in brain is important
Therefore brain network should be organized in the way that maximize its efficiency
Evolutionary or developmental selection acts as a customer in the market
Cost
Value
In good business, producer will look to maximize their profit by controlling their cost base and finding new ways to add value for their customers
So good business is not how to cut cost but how to optimize a trade-off between the costs of production and market value of the products.
Since brain is expensive to build and expensive to run it is crucial to do a good business in brain. Therefore-

5. What are the cost and the value of brain network?
In this review...
What are the cost and the value of brain network?
What are the evidences for this economical model of brain organization?
What are the application of this model to brain network disorders
In this presentation, we will explore –
And what are=

6. Network wiring cost
In networks, we need wires that are long and large to work in a far distance with high velocity.
Cost is the space that elements of networks take
Cost of network is fundamentally derived from the fact that brain networks are spatially embedded. Since networks need –
It is inevitable that these neural elements are constrained in a 3 D space.

7. Larger brains have more synapses per neuron
Larger brains are metabolically more expensive
As this graph indicates, large-
If we think in a opposite way, then we can notice that to have more synapse, we need larger brain and this larger brain is more expensive. Therefore

8. The metabolic costs of brain increase in proportional to total surface area of neuronal membrane (axonal length and diameter)
In addition to the cost of building an brain network, we need to consider the costs of running it.
Much of the brain’s metabolic cost is attributable to the active maintenance of electrochemical gradients across neuronal membranes.
They increase in proportion to the total surface area of neuronal membrane and these costs are pushed down by myelination and pushed up as a function of axonal length and diameter.

9. Minimize the costs
In order to minimize the wiring cost, in microscopic and macroscopic scale, nodes that are in short distance are likely to be connected.
Functional connectivity decays as an inverse square of physical distances
Sulco-gyral folding of cerebral surface could minimize the distance
Therefore in order to minimize the cost of networks, the distance between nodes that are connected should be as close as possible.
This is supported by animal studies where in mammalian neocortex, greater probability of synaptic connections between cell that are spatially close was found. Also in fMRI, strength of functional connectivity between brain regions decays as an inverse square of physical distance.
Interestingly, cortical folding which has long been thought to increase cortical surface area while conserving axonal volume is also evidence of minimizing the network cost.
By folding cortex, wiring length is decreased. And some animal studies had demonstrated that brain regions are more connected within cortical gyri than across sulci. X

10. Network value
However the cost minimization is the only factor driving brain organization.
Segregation and integration
Brain deliver both segregated and integrated information
Segregated process (ex. Visual input) benefit from highly clustered connections (module) whereas integrated process (ex. Executive function) benefit from information transfer across the networks as a whole.
However instead of minimizing the cost alone, brain need to maximize its value.
Brain need to deliver both segregated and intergrated-
Segregated process (ex. Visual input) benefit from highly clustered connections between topological neighbours whereas integrated such as executive functions would benefit from high global efficiency of information transfer across the network as a whole.

11. Signal between single synapse is faster than that for the polysynaptic connection, separated by the same physical distance “shorter path length”
Therefore there is a functional advantage to direct, monosynaptic connection between nodes even if they are located far apart in space.

12. If C.elegans is reduced further in silico by a rewiring algorithm that minimize wiring cost by simulated annealing, the total path-length is greater than that of actual C.elegans network.

13. Computational model of neuronal placement in C
Computational model of neuronal placement in C.elegans have shown that predictions based on competition between wiring (path length) minimization and topological efficiency maximization.
Topological efficiency
Wiring (Path length) minimization
Computational model of
C. elegans ~ ~

14. A small-world architecture is a efficient topology from the economic trade-off.
This reminds us the efficiency of small-world architecture in the economic trade-off.
It is well studied that human brain also has small-world architecture and is well suit with the formentioned economic model.
Red score: parietal, cingulate, super. Frontal cortex.

15. Economical reconfiguration of networks in millisecond
Brain functional connectivity is dynamic in response to changing task dements.
However it is important to understand that this connectivity properties change dynamically. In the MEG study by Manfred, they demonstrated that when cognitive demend are low like 0-back test, network immediately reconfigured with higher clustering, higher modularity and a smaller proportion of long-distance, inter-modular edges and when cognitive dement increase, network changes to higher efficiency and higher proportion of long-distance, inter-modular edges.
Relaxing
Cognitive processing
Manfred G. Kitzibicher et al

16. Economical reconfiguration of networks in decade
Changes in anatomical and functional brain networks have also been measured over aging
This changes in anatomical and functional brain networks have also been measured over the much longer time period of postnatal development like lifespan.
From child to adolescent, network changes to higher global networks efficiency and lower clustering of anatomincal networks.
MEG
B. Cluster coefficiency
C. Global efficiency
P. Hagmann et al

17. Value drivers of economical brain organization is its greatest risk
Hubs and long-distance connections
Hubs in Alzheimer’s disease
Long-distance connections in Multiple sclerosis
Probability that a specific tract will affected by a focal lesion is proportional to its volume
Value drivers of economical brain organization is its greatest risk
To understatnd the brain netowkr in the economic model, it is known that Value drivers of economical brain organization is its greatest risk.
In brain networks, metabolically high area are more subject to oxidative stress therefore we might expect the most metabolically expensive nodes and edges are to be sensitive to functional disruption. These high-cost components are hub and long-distance connections.
Pathology involving high-degree hubs is well shown in AD. In AD patients, amyldoi beta initially deposits in medial posterior parietal cortex which is a high-degree hub in brain network.
Pathology involving long-distance connections is consitatnly identified in Multiple sclerosis. MS is autoimmune disease where Ab attack myelin fiber in CNS. The probability that a specific tract will be affected by a focal lesion is proportional to its volume, redning longer tracts more vulnerable.

18. Patients with schizophrenia have abnormal shift in terms of how it has negotiated an economical trade- off between topological and physical networks properties.
Other way to understand the netowkr disorder by economic model is in the patients with schizophrenia.
Previous studies have shown contradictory changes in network properties such as clustering, modularity and global netowkr efficiency.
Theses results indicates that patietns with SZP might have abnormal shift -

19. Summary
Brain organization is shaped by an economic trade-off between minimizing cost and maximizing its value.
This process of negotiating and renegotiating continues over long (decades) and short (millisecond) timescales.
Brain network disorder can be accounted for by abnormal trade-offs and is preferential to the most costly components of networks.