Abstract
Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color–word Stroop task. Time–frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal–parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4348-4356 |
| Number of pages | 9 |
| Journal | Journal of Neuroscience |
| Volume | 38 |
| Issue number | 18 |
| DOIs | |
| State | Published - May 2 2018 |
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Keywords
- Alpha
- EEG
- Granger causality
- Inhibitory control
- Machine learning
- Neuronal oscillations
- Theta
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
Time course of brain network reconfiguration supporting inhibitory control. / Popov, Tzvetan; Westner, Britta U.; Silton, Rebecca L.; Sass, Sarah M.; Spielberg, Jeffrey M.; Rockstroh, Brigitte; Heller, Wendy; Miller, Gregory A.
In: Journal of Neuroscience, Vol. 38, No. 18, 02.05.2018, p. 4348-4356.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Time course of brain network reconfiguration supporting inhibitory control
AU - Popov, Tzvetan
AU - Westner, Britta U.
AU - Silton, Rebecca L.
AU - Sass, Sarah M.
AU - Spielberg, Jeffrey M.
AU - Rockstroh, Brigitte
AU - Heller, Wendy
AU - Miller, Gregory A.
PY - 2018/5/2
Y1 - 2018/5/2
N2 - Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color–word Stroop task. Time–frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal–parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation.
AB - Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color–word Stroop task. Time–frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal–parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation.
KW - Alpha
KW - EEG
KW - Granger causality
KW - Inhibitory control
KW - Machine learning
KW - Neuronal oscillations
KW - Theta
UR - http://www.scopus.com/inward/record.url?scp=85050911098&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050911098&partnerID=8YFLogxK
U2 - 10.1523/JNEUROSCI.2639-17.2018
DO - 10.1523/JNEUROSCI.2639-17.2018
M3 - Article
C2 - 29636394
AN - SCOPUS:85050911098
VL - 38
SP - 4348
EP - 4356
JO - Journal of Neuroscience
JF - Journal of Neuroscience
SN - 0270-6474
IS - 18
ER -