Recent technological advances have led to an unprecedented increase in the volume and detail of neuroscientific data, creating significant challenges in their processing and interpretation. We tackle this challenge via a network-centric approach, which matches the physical structure of the brain.
Progress is essential on three major fronts:
1. Measurement of neural activity
2. Estimation of network structures from this activity, and their analysis
3. Modeling network function, leading to theoretical understanding.
The measurement front spans the range from multi-electrode recordings to whole-brain measurements using imaging. Several basic scientific questions arise. What do we need to measure? Are there theoretical constraints that would dictate this? How do we design our experiments to generate the most meaningful data? How do we record from awake/behaving animals, or even from multiple animals interacting socially?
The analysis front consists of creating network models from the measurements. Some promising techniques explore the estimation of networks using causality. However, several open questions remain: How do we define the fundamental unit within the network? Are these units fixed or evolve dynamically? How do we infer connectivity between network elements? How do we identify functional clustering, based on the individual neuronal features? How do we quantify and interpret the activity of multiple neurons via multi-unit recordings, especially when there is no stimulus-response paradigm?
The modeling front can proceed in several directions. From the extracted network we can identify topological regularities, such as motifs and cycles. An interesting research direction is to analyze the relationship between the structure of the network, as represented by its motifs, and its function. A growing body of work is examining the relationship between network structure and phenomena such as stability and synchrony. For instance, hub neurons in the hippocampus promote synchrony, and cycles may cause instability.
We emphasize that these three fronts are interdependent but must evolve synergestically. The model and theoretical understanding needs to be grounded in constraints produced by the measurement process. Insights derived from modeling can be used to drive novel experiments and measurement techniques. An emerging trend deploys active probing and network manipulation through viral vectors and optogenetic methods.
Finally, we can derive value from our understanding of network function by applying it to brain-related disorders, such as schizophrenia, drug addiction or autism. Differences between default mode networks of ASD (autism spectrum disorder) subjects and normals have been reported. Cortical networks also play a role in establishing and maintaining oscillations, which when properly controlled may lead to better treatment of Parkinson’s disease. Schizophrenics display an impaired ability to synchronize different brain areas during cognitive tasks. Overall, network-based measures better capture the dynamics of brain processes, and provide features with better discriminative power than point-based measures.
Suggested topics include, but are not limited to:
1. Methodologies for network model validation.
2. Interesting emergent properties that may arise from the network, such as synchrony.
3. Multi-scale modeling that bridges gaps between the single neuron level and behavior at higher level.
4. Applications of cortical network modeling to understand disease function.
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