Alejandro Lopez-Rincon1, Cesar Cantu2, Gibran Etcheverry3, Rogelio Soto2, Shingo Shimoda4. 1. Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, David de Wied building Universiteitsweg 99, code3584 CG Utrecht, The Netherlands. Electronic address: alejandro.lopezrn@hotmail.com. 2. Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Tecnológico, code64849 Monterrey, N.L. México. 3. UDLAP, Ex-Hacienda Santa Catarina Mártir S/N, codeCP. 72810, San Andrés Cholula, Puebla, México. 4. RIKEN Center of Brain Science, CBS-TOYOTA Collaboration Center, code2271-130 Anagahora, Shimoshidami, Moriyama-ku, Nagoya, Aichi 463-0003, Japan.
Abstract
BACKGROUND: Several studies have shown that post-stroke patients develop divergent activity in the sensorimotor areas of the affected hemisphere of the brain compared to healthy people during motor tasks. Proper mathematical models will help us understand this activity and clarify the associated underlying mechanisms. New Method. This research describes an anatomically based brain computer model in post-stroke patients. We simulate an ischemic region for arm motion using the bidomain approach. Two scenarios are considered: a healthy subject and a post-stroke patient with motion impairment. Next, we limit the volume of propagation considering only the sensorimotor area of the brain. Comparison with existing methods. In comparison to existing methods, we combine the use of the bidomain for modeling the propagation of the electrical activity across the brain volume with functional information to limit the volume of propagation and the position of the expected stimuli, given a specific task. Whereas just using the bidomain without limiting the functional volume, propagates the electrical activity into non-expected areas. RESULTS: To validate the simulation, we compare the activity with patient measurements using functional near-infrared spectroscopy during arm motion (n=5) against controls (n=3). The results are consistent with empirical measurements and previous research and show that there is a disparity between position and number of spikes in post-stroke patients in contrast to healthy subjects. CONCLUSIONS: These results hold promise in improving the understanding of brain deterioration in stroke patients and the re-arrangement of brain networks. Furthermore, shows the use of functionality based brain modeling.
BACKGROUND: Several studies have shown that post-strokepatients develop divergent activity in the sensorimotor areas of the affected hemisphere of the brain compared to healthy people during motor tasks. Proper mathematical models will help us understand this activity and clarify the associated underlying mechanisms. New Method. This research describes an anatomically based brain computer model in post-strokepatients. We simulate an ischemic region for arm motion using the bidomain approach. Two scenarios are considered: a healthy subject and a post-strokepatient with motion impairment. Next, we limit the volume of propagation considering only the sensorimotor area of the brain. Comparison with existing methods. In comparison to existing methods, we combine the use of the bidomain for modeling the propagation of the electrical activity across the brain volume with functional information to limit the volume of propagation and the position of the expected stimuli, given a specific task. Whereas just using the bidomain without limiting the functional volume, propagates the electrical activity into non-expected areas. RESULTS: To validate the simulation, we compare the activity with patient measurements using functional near-infrared spectroscopy during arm motion (n=5) against controls (n=3). The results are consistent with empirical measurements and previous research and show that there is a disparity between position and number of spikes in post-strokepatients in contrast to healthy subjects. CONCLUSIONS: These results hold promise in improving the understanding of brain deterioration in strokepatients and the re-arrangement of brain networks. Furthermore, shows the use of functionality based brain modeling.