TY - JOUR
T1 - Resting-state fMRI dynamic functional network connectivity and associations with psychopathy traits
AU - Espinoza, Flor A.
AU - Anderson, Nathaniel E.
AU - Vergara, Victor M.
AU - Harenski, Carla L.
AU - Decety, Jean
AU - Rachakonda, Srinivas
AU - Damaraju, Eswar
AU - Koenigs, Michael
AU - Kosson, David S.
AU - Harenski, Keith
AU - Calhoun, Vince D.
AU - Kiehl, Kent A.
PY - 2019
Y1 - 2019
N2 - Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample. We then examined relationships between psychopathic traits (PCL-R total scores, Factor 1 and Factor 2 scores) and FNC states obtained from dFNC analysis. FNC over the scan duration was better represented by five states rather than one state previously shown in static FNC analysis. Consistent with prior findings, psychopathy was associated with networks from paralimbic regions (amygdala and insula). In addition, whole-brain FNC identified 15 networks from nine functional domains (subcortical, auditory, sensorimotor, cerebellar, visual, salience, default mode network, executive control and attentional) related to psychopathy traits (Factor 1 and PCL-R scores). Results also showed that individuals with higher Factor 1 scores (affective and interpersonal traits) spend more time in a state with weaker connectivity overall, and changed states less frequently compared to those with lower Factor 1 scores. On the other hand, individuals with higher Factor 2 scores (impulsive and antisocial behaviors) showed more dynamism (changes to and from different states) than those with lower scores.
AB - Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample. We then examined relationships between psychopathic traits (PCL-R total scores, Factor 1 and Factor 2 scores) and FNC states obtained from dFNC analysis. FNC over the scan duration was better represented by five states rather than one state previously shown in static FNC analysis. Consistent with prior findings, psychopathy was associated with networks from paralimbic regions (amygdala and insula). In addition, whole-brain FNC identified 15 networks from nine functional domains (subcortical, auditory, sensorimotor, cerebellar, visual, salience, default mode network, executive control and attentional) related to psychopathy traits (Factor 1 and PCL-R scores). Results also showed that individuals with higher Factor 1 scores (affective and interpersonal traits) spend more time in a state with weaker connectivity overall, and changed states less frequently compared to those with lower Factor 1 scores. On the other hand, individuals with higher Factor 2 scores (impulsive and antisocial behaviors) showed more dynamism (changes to and from different states) than those with lower scores.
KW - Dynamic functional network connectivity
KW - Group independent component analysis
KW - K-means clustering
KW - Psychopathy
KW - Resting-state fMRI
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UR - http://www.scopus.com/inward/citedby.url?scp=85071401380&partnerID=8YFLogxK
U2 - 10.1016/j.nicl.2019.101970
DO - 10.1016/j.nicl.2019.101970
M3 - Article
C2 - 31473543
AN - SCOPUS:85071401380
VL - 24
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
SN - 2213-1582
M1 - 101970
ER -