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Maltreatment during childhood is a major risk factor for anxiety and depression, which are major public health problems. However, the underlying brain mechanism linking maltreatment and internalizing disorders remains poorly understood. Maltreatment may alter the activation of fear circuitry, but little is known about its impact on the connectivity of this circuitry in adolescence and whether such brain changes actually lead to internalizing symptoms. We examined the associations between experiences of maltreatment during childhood, resting-state functional brain connectivity (rs-FC) of the amygdala and hippocampus, and internalizing symptoms in 64 adolescents participating in a longitudinal community study. Childhood experiences of maltreatment were associated with lower hippocampus–subgenual cingulate rs-FC in both adolescent females and males and lower amygdala–subgenual cingulate rs-FC in females only. Furthermore, rs-FC mediated the association of maltreatment during childhood with adolescent internalizing symptoms. Thus, maltreatment in childhood, even at the lower severity levels found in a community sample, may alter the regulatory capacity of the brain’s fear circuit, leading to increased internalizing symptoms by late adolescence. These findings highlight the importance of fronto–hippocampal connectivity for both sexes in internalizing symptoms following maltreatment in childhood. Furthermore, the impact of maltreatment during childhood on both fronto–amygdala and –hippocampal connectivity in females may help explain their higher risk for internalizing disorders such as anxiety and depression.
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We investigated the reliability and validity of a video-based method of measuring the magnitude of children's emotion-modulated startle response when electromyographic (EMG) measurement is not feasible. Thirty-one children between the ages of 4 and 7 years were videotaped while watching short video clips designed to elicit happiness or fear. Embedded in the audio track of the video clips were acoustic startle probes. A coding system was developed to quantify from the video record the strength of the eye-blink startle response to the probes. EMG measurement of the eye blink was obtained simultaneously. Intercoder reliability for the video coding was high (Cohen's kappa = .90). The average within-subjects probe-by-probe correlation between the EMG- and video-based methods was .84. Group-level correlations between the methods were also strong, and there was some evidence of emotion modulation of the startle response with both the EMG- and the video-derived data. Although the video method cannot be used to assess the latency, probability, or duration of startle blinks, the findings indicate that it can serve as a valid proxy of EMG in the assessment of the magnitude of emotion-modulated startle in studies of children conducted outside of a laboratory setting, where traditional psychophysiological methods are not feasible.
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Early life stress (ELS) and function of the hypothalamic-pituitary-adrenal axis predict later psychopathology. Animal studies and cross-sectional human studies suggest that this process might operate through amygdala-ventromedial prefrontal cortex (vmPFC) circuitry implicated in the regulation of emotion. Here we prospectively investigated the roles of ELS and childhood basal cortisol amounts in the development of adolescent resting-state functional connectivity (rs-FC), assessed by functional connectivity magnetic resonance imaging (fcMRI), in the amygdala-PFC circuit. In females only, greater ELS predicted increased childhood cortisol levels, which predicted decreased amygdala-vmPFC rs-FC 14 years later. For females, adolescent amygdala-vmPFC functional connectivity was inversely correlated with concurrent anxiety symptoms but positively associated with depressive symptoms, suggesting differing pathways from childhood cortisol levels function through adolescent amygdala-vmPFC functional connectivity to anxiety and depression. These data highlight that, for females, the effects of ELS and early HPA-axis function may be detected much later in the intrinsic processing of emotion-related brain circuits.
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Neuroanatomists posit that the central nucleus of the amygdala (Ce) and bed nucleus of the stria terminalis (BST) comprise two major nodes of a macrostructural forebrain entity termed the extended amygdala. The extended amygdala is thought to play a critical role in adaptive motivational behavior and is implicated in the pathophysiology of maladaptive fear and anxiety. Resting functional connectivity of the Ce was examined in 107 young anesthetized rhesus monkeys and 105 young humans using standard resting-state functional magnetic resonance imaging (fMRI) methods to assess temporal correlations across the brain. The data expand the neuroanatomical concept of the extended amygdala by finding, in both species, highly significant functional coupling between the Ce and the BST. These results support the use of in vivo functional imaging methods in nonhuman and human primates to probe the functional anatomy of major brain networks such as the extended amygdala.
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Here, we describe a novel method for volumetric segmentation of the amygdala from MRI images collected from 35 human subjects. This approach is adapted from open-source techniques employed previously with the hippocampus (Suh et al., 2011; Wang et al., 2011a,b). Using multi-atlas segmentation and machine learning-based correction, we were able to produce automated amygdala segments with high Dice (Mean = 0.918 for the left amygdala; 0.916 for the right amygdala) and Jaccard coefficients (Mean = 0.850 for the left; 0.846 for the right) compared to rigorously hand-traced volumes. This automated routine also produced amygdala segments with high intra-class correlations (consistency = 0.830, absolute agreement = 0.819 for the left; consistency = 0.786, absolute agreement = 0.783 for the right) and bivariate (r = 0.831 for the left; r = 0.797 for the right) compared to hand-drawn amygdala. Our results are discussed in relation to other cutting-edge segmentation techniques, as well as commonly available approaches to amygdala segmentation (e.g., Freesurfer). We believe this new technique has broad application to research with large sample sizes for which amygdala quantification might be needed.
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