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.
| Original language | English |
|---|---|
| Pages (from-to) | 292-300 |
| Number of pages | 9 |
| Journal | Behavioural Brain Research |
| Volume | 317 |
| DOIs | |
| Publication status | Published - Jan 15 2017 |
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Keywords
- Connectome
- EEG
- Functional connectivity
- Graph theory
- Resting state networks
ASJC Scopus subject areas
- Behavioral Neuroscience
Cite this
Searching for signs of aging and dementia in EEG through network analysis. / Miraglia, Francesca; Vecchio, Fabrizio; Rossini, Paolo Maria.
In: Behavioural Brain Research, Vol. 317, 15.01.2017, p. 292-300.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Searching for signs of aging and dementia in EEG through network analysis
AU - Miraglia, Francesca
AU - Vecchio, Fabrizio
AU - Rossini, Paolo Maria
PY - 2017/1/15
Y1 - 2017/1/15
N2 - 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.
AB - 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.
KW - Connectome
KW - EEG
KW - Functional connectivity
KW - Graph theory
KW - Resting state networks
UR - http://www.scopus.com/inward/record.url?scp=84991489863&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991489863&partnerID=8YFLogxK
U2 - 10.1016/j.bbr.2016.09.057
DO - 10.1016/j.bbr.2016.09.057
M3 - Review article
AN - SCOPUS:84991489863
VL - 317
SP - 292
EP - 300
JO - Behavioural Brain Research
JF - Behavioural Brain Research
SN - 0166-4328
ER -