کنفرانس مهندسی برق ایران

صفحه اصلی / سی و سومین کنفرانس بین المللی مهندسی برق

Sensitive RSNs to Schizophrenia; A graph parameter approach

نویسندگان :

Shirin Karimian¹ Farzaneh Keyvanfard² Abbas Nasiraei Moghaddam³

1- Amir 2- K. N Toosi university of technology 3- Amir

کلمات کلیدی :

Schizophrneia،Brain resting state networks،Functional connectivity

چکیده :

Schizophrenia as a debilitating disorder is mainly known by hallucination and delusions symptoms, affecting various aspects of patients’ lives. Schizophrenia disorder is characterized by functional dysconnectivity. Significant hypoconnectivity in key areas of Resting State Networks (RSNs) including vision, somatomotor, attention and limbic networks have been reported. In order to analyze brain as a connected network, graph theory have proposed valuable insights toward functional connectivity alterations due to various disorders. There is strong evidence toward significant changes in graph parameters of brain networks due to Schizophrenia. Hence, in this work we were focused on affected graph parameters in Schizophrenia individuals considering two types of RSN’s connections. Two groups of RSN connectivity matrices were extracted, considering within-network and between network connections. Six graph parameters were examined. The findings indicated that intra-network connections were significantly altered in visual, somatomotor, and ventral attention networks due to Schizophrenia. Moreover, graph-based investigations showed remarkable changes in five key graph parameters for Schizophrenia group in these networks. In addition, in individuals diagnosed with schizophrenia, particular internetwork neural connections were impacted in dorsal attention, limbic and default mode network as well. However, significant changes in graph parameters for specific inter-networks were just limited to somatomotor and ventral attention networks in Schizophrenia group. At the whole-brain level, there was only a minor variation in a single parameter (nodal strength) observed in the brains of individuals with schizophrenia, which is less significant compared to the network-based analysis of functional connectivities.

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