Abstract
| Language | English |
|---|---|
| Pages | 55090-55098 |
| Number of pages | 9 |
| Journal | IEEE Access |
| Volume | 6 |
| Early online date | 14 Sep 2018 |
| DOIs | |
| Publication status | E-pub ahead of print - 14 Sep 2018 |
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Keywords
- EEG phenotypes
- ex-combatants
- functional connectivity
- graph theory
- machine learning
- scalp EEG
- electroencephalography
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Phenotyping ex-combatants from EEG scalp connectivity. / Quintero-Zea, Andrés; López, José D.; Smith, Keith; Trujillo, Natalia; Parra, Mario A.; Escudero, Javier.
In: IEEE Access, Vol. 6, 14.09.2018, p. 55090-55098.Research output: Contribution to journal › Article
TY - JOUR
T1 - Phenotyping ex-combatants from EEG scalp connectivity
AU - Quintero-Zea, Andrés
AU - López, José D.
AU - Smith, Keith
AU - Trujillo, Natalia
AU - Parra, Mario A.
AU - Escudero, Javier
PY - 2018/9/14
Y1 - 2018/9/14
N2 - Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this work, we propose a SVM-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.
AB - Being involved in war experiences may have severe consequences in mental health. This exposure has been associated in Colombian ex-combatants with risk of proactive aggression modulating emotional processing. However, the extent of the cognitive processes underlying aggressive behavior is still an open issue. In this work, we propose a SVM-based processing pipeline to identify different cognitive phenotypes associated with atypical emotional processing, based on canonical correlation analysis of EEG network features, and cognitive and behavioral evaluations. Results show the existence of cognitive phenotypes associated with differences in the mean value of leaf fraction and diameter of EEG networks across groups. The ability of identifying phenotypes in these otherwise healthy subjects opens up the possibility to aid in the development of specific interventions aimed to reduce expression of proactive aggression in ex-combatants and assessing the efficacy of such interventions.
KW - EEG phenotypes
KW - ex-combatants
KW - functional connectivity
KW - graph theory
KW - machine learning
KW - scalp EEG
KW - electroencephalography
U2 - 10.1109/ACCESS.2018.2872765
DO - 10.1109/ACCESS.2018.2872765
M3 - Article
VL - 6
SP - 55090
EP - 55098
JO - IEEE Access
T2 - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -