A network analysis based approach to characterizing periodic sharp wave complexes in electroencephalograms of patients with sporadic CJD. (January 2019)

• Record Type: Journal Article
• Authors: LoLo Giudice, Paolo; Ursino, Domenico; Mammone, Nadia; Morabito, Francesco Carlo; Aguglia, Umberto; Cianci, Vittoria; Ferlazzo, Edoardo; Gasparini, Sara
• Is Part Of: International journal of medical informatics. Volume 121(2019)
• Page Start: 19
• Page End: 29
• Publication Date: 2019-01
• Subjects: Network analysis -- Connection coefficient -- Network motifs -- Sporadic Creutzfeldt–Jacob disease -- Periodic sharp wave complexes -- Electroencephalograms; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2018.11.003
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: A coefficient characterizing the presence of PSWCs in EEGs of patients with CJD. Some network motifs characterizing the presence of PSWCs in an EEG tracing. Some network motifs denoting the most active brain areas in presence of PSWCs. Abstract: Creutzfeldt–Jacob disease (CJD) is a rapidly progressive, uniformly fatal transmissible spongiform encephalopathy. Sporadic CJD (sCJD) is the most common form of CJD. Electroencephalography (EEG) is one of the main methods to perform clinical diagnosis of CJD, mainly because of periodic sharp wave complexes (PSWCs). In this paper, we propose a network analysis based approach to characterizing PSWCs in EEGs of patients with sCJD. Our approach associates a network with each EEG at disposal and defines a new numerical coefficient and some network motifs, which characterize the presence of PSWCs in an EEG tracing. The new coefficient, called connection coefficient, and the detected network motifs are capable of characterizing the EEG tracing segments with PSWCs. Furthermore, network motifs are able to detect what are the most active and/or connected brain areas in the tracing segments with PSWCs. The results obtained show that, analogously to what happens for other neurological diseases, network analysis can be successfully exploited to investigate sCJD.