title: Rich-Club Organization in Effective Connectivity among Cortical Neurons.
abstract: The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a "rich club." We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory. Significance statement: Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.
authors.affiliation: Department of Physics and nigams@indiana.edu.
authors.forename: Sunny
authors.identifier: http://orcid.org/0000-0003-1559-5556
authors.initials: S
authors.lastname: Nigam
authors.affiliation: Department of Physics and.
authors.forename: Masanori
authors.initials: M
authors.lastname: Shimono
authors.affiliation: Santa Cruz Institute for Particle Physics, University of California at Santa Cruz, Santa Cruz, California 95064.
authors.forename: Shinya
authors.initials: S
authors.lastname: Ito
authors.affiliation: Duke-NUS Graduate Medical School Singapore, Department of Neuroscience and Behavioural Disorders, Singapore 169857.
authors.forename: Fang-Chin
authors.initials: FC
authors.lastname: Yeh
authors.affiliation: Department of Physics and.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Timme
authors.affiliation: Program in Neuroscience, Indiana University, Bloomington, Indiana 47405.
authors.forename: Maxym
authors.initials: M
authors.lastname: Myroshnychenko
authors.affiliation: School of Science Institute for Mathematical Modeling and Computational Sciences, Indiana University-Purdue University, Indianapolis, Indianapolis, Indiana 46202.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Lapish
authors.affiliation: School of Informatics and Computing, College of Arts and Sciences, and.
authors.forename: Zachary
authors.initials: Z
authors.lastname: Tosi
authors.affiliation: Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Krakow, Poland, and.
authors.forename: Pawel
authors.initials: P
authors.lastname: Hottowy
authors.affiliation: Department of Neurobiology and.
authors.forename: Wesley C
authors.initials: WC
authors.lastname: Smith
authors.affiliation: California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California 90095.
authors.forename: Sotiris C
authors.initials: SC
authors.lastname: Masmanidis
authors.affiliation: Duke-NUS Graduate Medical School Singapore, Department of Neuroscience and Behavioural Disorders, Singapore 169857.
authors.forename: Alan M
authors.initials: AM
authors.lastname: Litke
authors.affiliation: Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47401.
authors.forename: Olaf
authors.identifier: http://orcid.org/0000-0001-7265-4036
authors.initials: O
authors.lastname: Sporns
authors.affiliation: Department of Physics and.
authors.forename: John M
authors.initials: JM
authors.lastname: Beggs
mesh_terms: D000818:Animals; D000831:Animals, Newborn; D051379:Mice; D008810:Mice, Inbred C57BL; D009415:Nerve Net / Q000166:cytology* / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009924:Organ Culture Techniques; D013003:Somatosensory Cortex / Q000166:cytology* / Q000502:physiology*
keywords: effective connectivity; information transfer; microcircuits; rich club; transfer entropy