In a project with investigators from the Nervous System Development and Plasticity Section, NICHD, and the Program on Pediatric Imaging and Tissue Sciences (PPITS), NICHD we use a theoretical framework to predict how the changes in myelin thickness, as well as the increase in the nodal width, affects the propagation of the signals along a myelinated axon. Both of these are regulated by the surrounding glial cells and dependent on the level of activity present in an axon. The theoretical predictions are implemented in Mathematica and are compared with the experimentally observed values, with the ultimate goal of addressing the role of myelinating glia in learning and plasticity. Manuscript describing a detailed experimental findings on the role of astrocytes in modifying the myelinated axon properties is submitted to Neuron. We also study theoretical aspects of myelin plasticity and the consequences of such adaptive time delays. In particular, we studied the effect of adaptive time delays on the stability of the system of coupled oscillators, with implications to the stability of the oscillations and synchrony in the brain. In a manuscript published in Neuroscience, 2014, we showed how the impairment of activity-dependent myelination and the loss of adaptive time delays may contribute to disorders where hyper- and hypo-synchrony of neuronal firing leads to dysfunction (e.g., dyslexia, schizophrenia, epilepsy). This suggests that the myelin plasticity may be necessary to maintain normal oscillatory activity in the developing and adult brain. This work is also to be presented at the Annual Meeting of the Society for Neuroscience, Washington, D.C., in November of 2014. In a project with investigators of the Section on Critical Brain Dynamics in the Intramural Research Program at NIMH, we study weighted complex networks, in particular cortical and brain networks. Our study reveals novel and robust weight organization particularly pronounced in the networks with biological origin (neural, gene), but also in different social and language (word) networks. Additionally, using simulations, we show that such network architecture can be obtained using local learning rules that adjust the weights in the network based on the past interactions between the nodes. We conducted a detailed simulation study of this learning rule, which is reminiscent of temporal delay reinforcement learning. We show that such type of learning increases the accessibility of all paths on the network and allows such complex systems to overcome engrained structure/paths and allows it to adapt to new challenges. This work has been submitted to IEEE Transactions on Network Science and Engineering. We were also involved in two projects that relate to Statistical and Machine Learning, but not reported under the Statistical Learning for Biomedical project (with James Malley as LI). The first involved using machine learning and the large genetic and other data available publicly for the C. elegans worm to investigate to what degree the known neural connectivity of C. elegans can be estimated from the genetic, geometric and other information that is available on various public databases. We showed that even with a limited set of gene expression data that we gathered, accuracy of 86% percent can be achieved. We expect the accuracy to improve as more genetic data become available in the future. This work was presented at the Annual Meeting of the Society for Neuroscience, San Diego, in November of 2013. The second project involved using machine learning to estimate behavioral modes of mouse to be used with The System for Continuous Observation of Rodents in Home-cage Environment (SCORHE). This work was published in Behavior Research Methods in 2014. A project with investigators from the Laboratory of Clinical and Developmental Genomics, NICHD we study neuronal cultures created by reprogramming skin cells from autistic patients as well as normals. It is a part of a larger study addressing the molecular and cellular changes that occur in the autistic brain during the development. In the current study the goal is to identify the changes in the network structure, or in the activity profile of different cells that distinguishes the normal cell cultures from those of autistic patients. In a continuing project with investigators in the Program on Pediatric Imaging and Tissue Sciences (PPITS), NICHD we conduct a theoretical study of the observed skewed and heavy-tailed axon diameter distribution and arrive at a parametric form that optimizes the informative upper bound (IUB) as well as the information capacity of the nerve fascicles. A manuscript describing this work is published in PLOS ONE in 2013. In a follow-up work, this distribution has been implemented and applied to simulated and experimental data, yielding improved measurements of the axon diameter distributions.
