When the brain is injured during development, there is an effect not only on immediate brain function but also on the future ability to learn and acquire skills. We propose to create and simulate multi-scale computational models of the effect of early structural injury on future motor function of the cortex and spinal cord. We will use programmable chips (FPGA's) and the new mathematical theory of """"""""Likelihood Calculus"""""""" to build simulations of 300,000 neurons that run 500 times faster than real-time. The model will be fitted to electromyographic and kinematic data from children at two visits spaced one year apart, and predictions will be tested by comparison to a third visit one year later. High speed simulation of the effect of early injury has the potential to revolutionize the treatment of developmental neurological deficits because it can, in one week, simulate 10 years of future change. In doing so, it allows prediction of disease progression, prediction of the future effects of treatments, and detailed understanding of the interaction between brain injury and resulting disorders of movement, perception, and behavior. Because of these predictions, we can intervene much earlier with treatments customized to each patient's disease profile. With early intervention, it may be possible to attenuate or block the natural progression of their disease. With over 750,000 US children and adults affected by developmental brain disorders, early acquired brain injury, or childhood progressive brain disease, the applicability and potential impact of an early intervention and disease prediction technique are significant.
With over 4.5% of the child and adult US population affected by developmental brain disorders, acquired brain injury, and progressive brain disease, the applicability and potential impact of an early intervention and multi-scale disease simulation and prediction technique are significant.
|Jalaleddini, Kian; Minos Niu, Chuanxin; Chakravarthi Raja, Suraj et al. (2017) Neuromorphic meets neuromechanics, part II: the role of fusimotor drive. J Neural Eng 14:025002|
|Niu, Chuanxin M; Jalaleddini, Kian; Sohn, Won Joon et al. (2017) Neuromorphic meets neuromechanics, part I: the methodology and implementation. J Neural Eng 14:025001|
|Dunning, Amber; Ghoreyshi, Atiyeh; Bertucco, Matteo et al. (2015) The tuning of human motor response to risk in a dynamic environment task. PLoS One 10:e0125461|
|Ghoreyshi, Atiyeh; Sanger, Terence D (2015) A Nonlinear Stochastic Filter for Continuous-Time State Estimation. IEEE Trans Automat Contr 60:2161-2165|
|Sohn, Won J; Niu, Chuanxin M; Sanger, Terence D (2015) Increased long-latency reflex activity as a sufficient explanation for childhood hypertonic dystonia: a neuromorphic emulation study. J Neural Eng 12:036010|
|Sanger, Terence D (2014) Risk-aware control. Neural Comput 26:2669-91|
|Young, Scott J; van Doornik, Johan; Sanger, Terence D (2011) Finger muscle control in children with dystonia. Mov Disord 26:1290-6|
|Sanger, Terence D (2011) Distributed control of uncertain systems using superpositions of linear operators. Neural Comput 23:1911-34|
|Sanger, Terence D (2010) Neuro-mechanical control using differential stochastic operators. Conf Proc IEEE Eng Med Biol Soc 2010:4494-7|
|Sanger, Terence D (2010) Controlling variability. J Mot Behav 42:401-7|