TY - JOUR
T1 - A longitudinal EEG study of Alzheimer's disease progression based on a complex network approach
AU - Morabito, Francesco Carlo
AU - Campolo, Maurizio
AU - Labate, Domenico
AU - Morabito, Giuseppe
AU - Bonanno, Lilla
AU - Bramanti, Alessia
AU - De Salvo, Simona
AU - Marra, Angela
AU - Bramanti, Placido
PY - 2015/3/25
Y1 - 2015/3/25
N2 - A complex network approach is combined with time dynamics in order to conduct a space-time analysis applicable to longitudinal studies aimed to characterize the progression of Alzheimer's disease (AD) in individual patients. The network analysis reveals how patient-specific patterns are associated with disease progression, also capturing the widespread effect of local disruptions. This longitudinal study is carried out on resting electroence phalography (EEGs) of seven AD patients. The test is repeated after a three months' period. The proposed methodology allows to extract some averaged information and regularities on the patients' cohort and to quantify concisely the disease evolution. From the functional viewpoint, the progression of AD is shown to be characterized by a loss of connected areas here measured in terms of network parameters (characteristic path length, clustering coefficient, global efficiency, degree of connectivity and connectivity density). The differences found between baseline and at follow-up are statistically significant. Finally, an original topographic multiscale approach is proposed that yields additional results.
AB - A complex network approach is combined with time dynamics in order to conduct a space-time analysis applicable to longitudinal studies aimed to characterize the progression of Alzheimer's disease (AD) in individual patients. The network analysis reveals how patient-specific patterns are associated with disease progression, also capturing the widespread effect of local disruptions. This longitudinal study is carried out on resting electroence phalography (EEGs) of seven AD patients. The test is repeated after a three months' period. The proposed methodology allows to extract some averaged information and regularities on the patients' cohort and to quantify concisely the disease evolution. From the functional viewpoint, the progression of AD is shown to be characterized by a loss of connected areas here measured in terms of network parameters (characteristic path length, clustering coefficient, global efficiency, degree of connectivity and connectivity density). The differences found between baseline and at follow-up are statistically significant. Finally, an original topographic multiscale approach is proposed that yields additional results.
KW - Alzheimer's disease
KW - complex networks
KW - complexity
KW - longitudinal EEG database
KW - multiscale temporal analysis
KW - mutual information
UR - http://www.scopus.com/inward/record.url?scp=84928388922&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928388922&partnerID=8YFLogxK
U2 - 10.1142/S0129065715500057
DO - 10.1142/S0129065715500057
M3 - Article
C2 - 25655033
AN - SCOPUS:84928388922
VL - 25
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
SN - 0129-0657
IS - 2
M1 - 1550005
ER -