The broad, long-term objective of this grant is to advance a graph theoretical framework to identify core-nodes in a Brain Network of Networks to develop a software tool that will allow end-users from the broad neuroscience community to identify and analyze the most influential nodes in the brain in various disease states.
Specific Aim #1 : Develop Network of Networks (NoN) graph theoretical tools to identify ?core nodes? for network vulnerability, which can be used as a tool to analyze disorders of the brain. The project will advance the theoretical models from a single network to multiple networks and will create a tool based on this theory to be used by other neuroscience researchers and clinicians.
Specific Aim #2 : Use the graph theoretical NoN analysis to understanding how patients with brain tumors are able to retain function in certain instances due to plasticity and the reorganization of functional networks. This grant will address a central problem in neuroscience: how plasticity allows the brain to recover function after an insult: specifically, how perturbations of the neural network (the growth of a tumor in the brain) affects the stability of the brain NoN and the ability of the network to avoid catastrophic collapse (how the brain adjusts and continues to function notwithstanding the presence of a brain tumor). Hypothesis 1: Involvement of core NoN nodes by tumor necrosis or Gd-enhancement leads to functional deficits, whereas involvement of core nodes by FLAIR abnormality leads to cortical reorganization and preservation of function. Hypothesis 2: Patients with preservation of language/motor function will have connectivity maps that are significantly different from patients with loss of neurological function and normal controls, including the development of core nodes, which is theorized to represent cortical reorganization. Hypothesis 3: The NoN method of analysis will be better able to discriminate loss of neurological function than other methods of analysis.
Specific Aim #3 (Resource sharing plan): The software and tools developed as a result of this grant will be optimized for usability by neuroscience clinicians and researchers for non-profit users in the larger scientific community. Health relatedness and long term goals: The results of the present study should lead to improved planning of brain tumor surgery. Once completed, we trust that the tools developed by this project will be able to be used by the larger neuroscience community to study, diagnose and develop treatment strategies for other brain disorders thought to be due to disruptions of brain connectivity (e.g. Alzheimer's disease, ADHD, stokes or traumatic brain injury). The development and testing (in a clinical situation) of the theories of organization and responses to perturbations of brain networks should lead to the inference of general principles regarding network organization applicable to areas outside of the neurosciences.
Our main goal is to develop a software tool that will allow end-users from the broad neuroscience community to identify and analyze the most influential parts of the brain in various disease states. The tools developed by this project will aid in the understanding, diagnosis, and therapy of brain disorders thought to be due to disruptions of brain connectivity (e.g. brain tumors, Alzheimer's disease, ADHD, stokes or traumatic brain injury).
| Babino, Andrés; Makse, Hernán A; DiTella, Rafael et al. (2018) Maintaining trust when agents can engage in self-deception. Proc Natl Acad Sci U S A 115:8728-8733 |
| Del Ferraro, Gino; Moreno, Andrea; Min, Byungjoon et al. (2018) Finding influential nodes for integration in brain networks using optimal percolation theory. Nat Commun 9:2274 |
| Burleson-Lesser, Kate; Morone, Flaviano; DeGuzman, Paul et al. (2017) Collective Behaviour in Video Viewing: A Thermodynamic Analysis of Gaze Position. PLoS One 12:e0168995 |
| Roth, Kevin; Morone, Flaviano; Min, Byungjoon et al. (2017) Emergence of robustness in networks of networks. Phys Rev E 95:062308 |
| Pei, Sen; Teng, Xian; Shaman, Jeffrey et al. (2017) Efficient collective influence maximization in cascading processes with first-order transitions. Sci Rep 7:45240 |
| Luo, Shaojun; Morone, Flaviano; Sarraute, Carlos et al. (2017) Inferring personal economic status from social network location. Nat Commun 8:15227 |
| Morone, Flaviano; Roth, Kevin; Min, Byungjoon et al. (2017) Model of brain activation predicts the neural collective influence map of the brain. Proc Natl Acad Sci U S A 114:3849-3854 |
| Morone, Flaviano; Min, Byungjoon; Bo, Lin et al. (2016) Collective Influence Algorithm to find influencers via optimal percolation in massively large social media. Sci Rep 6:30062 |
| Teng, Xian; Pei, Sen; Morone, Flaviano et al. (2016) Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks. Sci Rep 6:36043 |
