Altered Resting-State Functional Connectivity of Basolateral and Centromedial Amygdala Complexes in Posttraumatic Stress Disorder

Abstract

The amygdala is a major structure that orchestrates defensive reactions to environmental threats and is implicated in hypervigilance and symptoms of heightened arousal in posttraumatic stress disorder (PTSD). The basolateral and centromedial amygdala (CMA) complexes are functionally heterogeneous, with distinct roles in learning and expressing fear behaviors. PTSD differences in amygdala-complex function and functional connectivity with cortical and subcortical structures remain unclear. Recent military veterans with PTSD (n ¼ 20) and matched trauma-exposed controls (n ¼ 22) underwent a resting-state fMRI scan to measure task-free synchronous blood-oxygen level dependent activity. Whole-brain voxel-wise functional connectivity of basolateral and CMA seeds was compared between groups. The PTSD group had stronger functional connectivity of the basolateral amygdala (BLA) complex with the pregenual anterior cingulate cortex (ACC), dorsomedial prefrontal cortex, and dorsal ACC than the trauma-exposed control group (po0.05; corrected). The trauma-exposed control group had stronger functional connectivity of the BLA complex with the left inferior frontal gyrus than the PTSD group (po0.05; corrected). The CMA complex lacked connectivity differences between groups. We found PTSD modulates BLA complex connectivity with prefrontal cortical targets implicated in cognitive control of emotional information, which are central to explanations of core PTSD symptoms. PTSD differences in resting-state connectivity of BLA complex could be biasing processes in target regions that support behaviors central to prevailing laboratory models of PTSD such as associative fear learning. Further research is needed to investigate how differences in functional connectivity of amygdala complexes affect target regions that govern behavior, cognition, and affect in PTSD.

Dissociable relations between amygdala subregional networks and psychopathy trait dimensions in conduct‐disordered juvenile offenders

“…That is, trait‐specific variance in amygdalar iFC over and above what can be explained by the other traits. Following earlier work on amygdala subregional iFC patterns [Aghajani et al, 2016; Brown et al, 2014; Roy et al, 2009; Singh et al, 2015], the resulting statistical maps were all corrected for multiple comparisons using cluster‐based correction with initial cluster forming threshold of Z  > 2.3 and cluster extent threshold of P < 0.05, which tends to adequately balance the propagation of false positives and false negatives [Bennett et al, 2009; Jenkinson et al, 2012; Lieberman and Cunningham, 2009]. …”

“…Given apparent lateralization effects in amygdalar functionality and subregional connectivity [Aghajani et al 2016, 2016; Baas et al, 2004; Brown et al, 2014; Gabard‐Durnam et al, 2014; Qin et al, 2014; Sergerie et al, 2008; Styliadis et al, 2014], and in line with recent work [Aghajani et al, 2016; Qin et al, 2012, 2014], BLA and CMA region of interest (ROI) masks were created in each hemisphere (see Supporting Information), and used to extract individual participant's mean time series within BLA and CMA complexes. Seed‐based whole‐brain iFC analysis implemented within FSL FEAT [Jenkinson et al, 2012], was then employed to reveal BLA and CMA connectivity patterns [Fox and Raichle, 2007].…”

“…Seed‐based whole‐brain iFC analysis implemented within FSL FEAT [Jenkinson et al, 2012], was then employed to reveal BLA and CMA connectivity patterns [Fox and Raichle, 2007]. Specifically, for each participant, and in each hemisphere, a general linear model (GLM) was created that included individual participant's mean time series of BLA and CMA complexes as predictors [Aghajani et al, 2016; Brown et al, 2014; Roy et al, 2013]. Temporally filtered signal from the white matter and cerebrospinal fluid (see Supporting Information for details), as well as six motion parameters and parameters obtained from the motion censoring procedure (see Supporting Information for details), were also included in this model as covariates of no interest to correct for physiological and motion‐related variance [Aghajani et al, 2016].…”

“…Temporally filtered signal from the white matter and cerebrospinal fluid (see Supporting Information for details), as well as six motion parameters and parameters obtained from the motion censoring procedure (see Supporting Information for details), were also included in this model as covariates of no interest to correct for physiological and motion‐related variance [Aghajani et al, 2016]. This resulted in individual subject–level connectivity maps comprising voxels throughout the brain that exhibited iFC (i.e., temporal partial correlations) with each amygdala subregion, accounting for the relationships with the other subregion (i.e., unique subregion‐specific connectivity maps) [Aghajani et al, 2016; Brown et al, 2014; Roy et al, 2013]. Subject‐level iFC maps of BLA and CMA subregions were then fed into a group‐level mixed‐effects GLM analysis implemented in FEAT (with FLAME and automatic outlier de‐weighting), in order to average these subject‐level maps and create group‐level connectivity maps of BLA and CMA complexes [Aghajani et al, 2016; Brown et al, 2014; Roy et al, 2013].…”

“…This resulted in individual subject–level connectivity maps comprising voxels throughout the brain that exhibited iFC (i.e., temporal partial correlations) with each amygdala subregion, accounting for the relationships with the other subregion (i.e., unique subregion‐specific connectivity maps) [Aghajani et al, 2016; Brown et al, 2014; Roy et al, 2013]. Subject‐level iFC maps of BLA and CMA subregions were then fed into a group‐level mixed‐effects GLM analysis implemented in FEAT (with FLAME and automatic outlier de‐weighting), in order to average these subject‐level maps and create group‐level connectivity maps of BLA and CMA complexes [Aghajani et al, 2016; Brown et al, 2014; Roy et al, 2013]. Individual participant's affective, interpersonal, and behavioral trait scorings were simultaneously entered in this group‐level GLM analysis as predictors (with age and IQ as covariates), in order to reveal how individual variations in psychopathic traits relate to iFC profiles of BLA and CMA subregions.…”