Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

被引:3
作者
Bitra, Veera Raghavulu [1 ]
Challa, Siva Reddy [2 ,4 ]
Adiukwu, Paul C. [1 ]
Rapaka, Deepthi [3 ]
机构
[1] Univ Botswana, Fac Hlth Sci, Sch Pharm, P Bag 0022, Gaborone, Botswana
[2] Univ Illinois, Coll Med, Dept Canc Biol & Pharmacol, Peoria, IL 61614 USA
[3] DDT Coll Med, Pharmacol Div, Gaborone, Botswana
[4] KVSR Siddartha Coll Pharmaceut Sci, Vijayawada, Andhra Pradesh, India
关键词
Tau; Connectome; Hubs; Connectivity; STATE FUNCTIONAL CONNECTIVITY; GRAPH-THEORETICAL ANALYSIS; NETWORK DIFFUSION-MODEL; BRAIN NETWORKS; EPISODIC MEMORY; DEFAULT MODE; PATHOLOGICAL PROCESS; SEMANTIC DEMENTIA; COGNITIVE DECLINE; TEMPORAL-LOBE;
D O I
10.1016/j.brainresbull.2023.110777
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.
引用
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页数:14
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