Non-linear dynamic state-space network modeling for decoding neurodegeneration
Venkata C.Chirumamilla1
Chi Wang Ip2
Martin Reich2
Robert Peach2
Jens Volkmann2
Bahman Nasseroleslami3
Muthuraman Muthuraman2
1.Prenatal Pediatrics Institute,Children's National Hospital,Washington,DC,USA2.Neural Engineering with Signal Analytics and Artificial Intelligence,Department of Neurology,University of Würzburg,Würzburg,Germany3.Academic Unit of Neurology,Trinity Biomedical Sciences Institute,Trinity College Dublin,University of Dublin,Dublin,Ireland
摘要：Neurodegenerative disorders represent a pervasive global health challenge,yet therapeutic options remain conspicuously limited.These disorders are inherently dynamic processes within the central nervous system,unfolding across distinct sub-stages:initial structural neuronal alterations(sub-stage 1),functional impairment(sub-stage 2),and culminating in neuronal death(sub-stage 3).Previous studies have revealed shared pathological features between amyotrophic lateral sclerosis(ALS)and Parkinson's disease(PD)(van Rheenen et al.,2021;Mantle and Hargreaves,2022)including common genetic risk factors identified through genome-wide association studies(van Rheenen et al.,2021).Both disorders manifest similar neurodegenerative mechanisms,such as oligomer formation,aberrant protein accumulation,and protein misfolding-specifically,superoxide dismutase 1 in ALS and α-synuclein in PD.Mitochondrial dysfunction further serves as a common denominator in the pathogenesis of ALS and PD(Mantle and Hargreaves,2022).
机标关键词：modelingneurodnetworkdynamicdecodingdegenerationnon-linearstate-space
论文发表日期：2024-09-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：2( 1879-1880 )