دانلود رایگان مقاله جستجو برای نشانه های پیری و زوال عقل در EEG از طریق آنالیز شبکه

ABSTRACT

Graph theory applications had spread widely in understanding how human cognitive functions are linked to dynamics of neuronal network structure, providing a conceptual frame that can reduce the analytical brain complexity. This review summarizes methodological advances in this field. Electroencephalographic functional network studies in pathophysiological aging will be presented, focusing on neurodegenerative disease −such Alzheimer’s disease-aiming to discuss whether network science is changing the traditional concept of brain disease and how network topology knowledge could help in modeling resilience and vulnerability of diseases. Aim of this work is to open discussion on how network model could better describe brain architecture

4. Conclusions

Brain networks’ characterization by graph theory provides a significant advantage in structural description allowing efficient computation and comparison of brain topologies within a common theoretical context [9]. Evidences from this review confirm the utility to adopt a mathematical approach in the study of relevant neurological features in brain networks. The graph analysis applications described in this review represent an interesting probe to analyze the distinctive features of physiological and pathological aging through a focus on functional connectivity networks. This technique, applied to patient data, could add information to the pathophysiological processes of brain disconnection and might aid in monitoring the impact of eventual pharmacological and rehabilitative treatments. The methodological choice of EEG data was nicely integrating information from MRI investigation for a large scale recruitment and disease progression analysis due to the fact that EEG is a low cost, largely diffuse and non invasive technique allowing a cost-effective large population screening. Using network analysis in neuroimaging research could help understanding how human cognitive functions are linked to neuronal network structure and −most important- how they deal with time-varying networks dynamics providing an abstraction that can reduce the analytical complexity. Aim of this review has been to look globally at this innovative methodological approach in order to discuss whether network science might change the traditional views of brain disease mechanism and in which way networks’ topology knowledge can be adopted to model and characterize vulnerability of diseases and dysfunctions.