Mahsasadat Seyedbarhagh
Papers
1
Total Citations
5
H-Index
1
About
Mahsasadat Seyedbarhagh is a rising researcher at the intersection of computational neuroscience and biomedical engineering, with a primary focus on the digital modeling of neurodegenerative disease mechanisms. Her most cited work, "Deep Brain Stimulation on Ca2+ Signalling and Neuro-Glial Network: A Digital Implementation" (2023, 5 citations), pioneers a novel digital framework to investigate how deep brain stimulation (DBS) modulates calcium signaling and neuro-glial interactions. In this study, Seyedbarhagh addresses the dyshomeostasis of intracellular Ca²⁺ triggered by the anomalous accumulation of amyloid-beta (Aβ) peptides—a hallmark of Alzheimer’s pathology—by simulating the effects of DBS on restoring network-level ionic balance. Her contribution lies in translating complex biological signaling into a tractable computational model, offering a non-invasive platform to test therapeutic interventions. While early in her career, this work has already garnered attention for its innovative integration of digital implementation with neuropathological modeling, positioning her as a promising voice in the quest to understand and mitigate synaptic dysfunction in Alzheimer’s disease. Seyedbarhagh’s research holds significant potential for advancing closed-loop neuromodulation strategies.
Research Focus
Key Achievements
Top Papers
- 1