In neurotypical adults, the CEN is negatively correlated – i.e., anticorrelated – with the default mode network , an RSN associated with internal mentation and self-referential processing, whose key nodes include the medial prefrontal cortex. The decoupling of these RSNs has been found to be adaptive: Greater MPFC-DLPFC anticorrelations are associated with superior cognitive control and cognitive performance, such as greater working memory capacity. In addition, there is a selective growth of anticorrelations between the MPFC and DLPFC in typically developing children, which is consistent with the findings that top-down control mechanisms improve markedly over childhood and adolescence. Rs-fMRI studies have shown an association between diminished MPFC-DLPFC anticorrelations and cognitive impairment in Attention Deficit Hyperactivity Disorder. The CEN also plays a role in regulating mood through its interactions with the subgenual anterior cingulate cortex . The sgACC is part of the affective network, which is involved in emotion processing, and has anatomical connections to hypothalamus, amygdala, entorhinal cortex, nucleus accumbens and other limbic structures. There are several lines of evidence showing that top-down modulation of the sgACC is dysregulated in adults with major depressive disorder . Neuroimaging studies have reported decreased metabolism and decreased gray matter volume in patients with MDD, and a decreased number of glia in sgACC. Furthermore, deep brain stimulation of the sgACC results in attenuation of hyperactivation in sgACC and increased activation in previously underactive DLPFC in adults with MDD. In addition,vertical horticulture the left DLPFC region that shows maximal anticorrelation with the sgACC in rs-fMRI has been identified as an optimal target for TMS of MDD.
The sgACC has also been shown to exhibit decreased connectivity with cognitive control regions in children with a history of preschool-depression. Finally, left DLPFC and sgACC exhibit hypoconnectivity in children at familial risk for MDD. In sum, prior research on adult patient populations reveals that atypically strong functional connectivity between DLPFC and MPFC is characteristic of ADHD, whereas atypically weak connectivity between DLPFC and the sgACC is characteristic of MDD. Here, we build on this prior work by asking whether the strength of connectivity between these regions can predict a progression towards attentional or mood disorders in childhood. Rather than comparing children diagnosed with psychiatric disorders and typically developing children, we examined longitudinal data from a community sample of children. Specifically, we tested whether DLPFC-MPFC and DLPFC-sgACC connectivity at age 7 predict scores at age 11 on a questionnaire used to screen children for behavioral problems, the Child Behavior Checklist . The goals of this research are twofold: first, to better understand how changes in brain connectivity over childhood relates to cognitive and affective development, and second, to evaluate the predictive validity of DLPFC-MPFC and DLPFC-sgACC connectivity for future mental health problems in children who have not been identified previously as being at elevated risk for the development of a psychiatric disorder. Numerous studies have demonstrated a high rate of reliability between the CBCL scales and actual psychiatric diagnosis. For example, CBCL Attention Problem scores are highly correlated for the screening of and prediction of ADHD. A sub-threshold elevation on the anxiety/depression sub-scale of the CBCL in preadolescence is a predictor for future development of the diagnosis of MDD. However, neuroimaging measures may, in conjunction with clinical measures, allow us to identify with greater confidence and at an earlier age children at the greatest risk for development of psychiatric disorders. In the present study, then, we investigated whether rs-fMRI data can be used to predict future CBCL scores in a community sample of 54 children. Specifically, we tested whether the individual differences in MPFC-DLPFC connectivity at age 7 predict subsequent change in attention four years later, as measured by the CBCL “Attentional Problems” measure at age 11.
Additionally, we tested whether individual differences in sgACC-DLPFC connectivity at age 7 predicts subsequent change in Anxiety/Depression four years later, as measured by the CBCL “Internalization” and Anxiety/Depressed subscale at age 11. First-level correlation maps were produced by extracting the residual blood oxygen level–dependent time course from each seed and computing Pearson’s correlation coefficients between that time course and the time course of all other voxels. Correlation coefficients were converted to normally distributed z-scores using the Fisher transformation to improve the validity of second-level General Linear Model analyses. Fisher transformed r-maps from each seed were submitted to a second-level analysis of covariance regressing the changes in the CBCL measures onto brain responses, controlling for the effect of initial severity . To create a robust prediction model that could be generalized to new cases, we performed leave-one-out cross-validation, which minimizes potential biases due to voxel-selection in our predictive models. Cross validated scatter plots were computed as follow: 1) for each subject, a second-level analysis looking for voxel-level associations between connectivity with MPFC and change in attentional problems was run entering only the remaining N-1 subjects; 2) the suprathreshold cluster from this analysis was used a subject specific mask of DLPFC in the left-out subject, and functional connectivity between MPFC and DLPFC for this individual subject was computed as the average of the Fisher-transformed correlation coefficients between MPFC and each voxel in this mask; and 3) the previous two steps were repeated for each subject to obtain a list of MPFC-DLPFC connectivity values, which were then plotted against the corresponding subjects’ change in attentional scores. In addition, we implemented a replication analysis wherein we correlated the connectivity between the MPFC seed and an independent DLPFC mask defined from the literature.Independent Component Analyses were used to identify the emotional regulation network , including the sgACC.
Group-level components were estimated using a 64-dimensions subject-level dimensionality reduction step, followed by 40- component group-level dimensionality reduction and fast-ICA with a hyperbolic tangent contrast function. The ERN was identified as the component with highest loading at the sgACC coordinates . ERN subject-level component-score maps were averaged across participants and thresholded using a combination of T>6 voxel-level “height” threshold and a FWE-corrected p<.001 cluster-level threshold. This analysis resulted in a positive cluster including sgACC as well as bilateral amygdala and hippocampus,hydroponic rack system and two negative clusters in bilateral DLPFC areas. Average ICA subject-level component scores over the resulting DLPFC cluster was used in subsequent analyses as a measure of the negative association between the ERN and DLPFC for each subject, specifically between the sgACC and left DLPFC. In order to explore whether individual differences in DLPFC connectivity predicted future negative behavioral outcomes, we performed cross-validated prediction analysis to investigate whether T1 resting state correlations predict progression of subsequent change of CBCL behavioral measures after factoring out T1 CBCL behavior. First, we tested whether stronger MPFC-DLPFC resting state correlations at T1 predict future change in CBCL attentional problems, after controlling for the initial attentional score at T1. Second, we used a data-driven Independent Component Analyses approach as described above to define a component which consisted of the left DLPFC-sgACC. We then tested whether the connectivity of this component predicted worsening of internalization across three sub-scales, and subsequently examined the internalization sub-scales separately: a) anxiety/depression, b) withdrawn behavior, and c) somatic complaints. Here, we used resting state networks to identify the neural mechanisms underlying the emergence of adaptive behavior during middle childhood, and identified neural signatures of maladaptive behavior that could lead to future mental health problems and potentially to psychiatric diagnoses. First, we found that MPFC-DLPFC anticorrelations, known to be related to cognitive functioning, are a positive predictor of attentional development. Diminished DMN-CEN anticorrelations may reflect an attenuation of top-down control mechanisms and an inability to allocate resources away from internal thoughts and feelings and towards external stimuli in order to adaptively perform difficult tasks74,80. Thus, 7-year-olds who exhibit MPFC-DLPFC anticorrelations may have the capacity to toggle between internal and external foci of attention, while those children who have diminished MPFC-DLPFC anticorrelations may not have this ability. The failure to decouple these networks may be an early indicator of attentional problems, or may in fact preclude the development of age-appropriate attentional skills. Second, we found that sgACC – left DLPFC anticorrelations predict the progression of Internalization symptoms related to MDD. Stronger sgACC-left DLPFC anticorrelations at this young age may already reflect an attenuation or failure of top-down control mechanisms that are evident in adult MDD. Thus, the functional connectivity of specific neural systems in middle childhood forecasts individuals’ resilience or vulnerability in cognition and emotion over the ensuing four years of development. Environmental stressors activate descending pain inhibitory systems, which suppress pain by inhibiting the transmission of impulses from nociceptors to the central nervous system. This antinociceptive response, termed stress-induced analgesia , is mediated, in part, by the release of opioid peptides. However, opioid-dependent and opioid-independent forms of SIA can be differentially activated based upon stressor parameters and duration. Recent research in our laboratories has demonstrated that an endocannabinoid signaling system mediates nonopioid SIA induced by continuous foot shock. A role for cannabinoid CB1 receptors in SIA was demonstrated by our observations that competitive CB1 antagonists, administered systemically or locally in the dorsolateral periaqueduc-tal gray , block nonopioid SIA. Furthermore, SIA is attenuated in rats rendered tolerant to cannabinoids, but not in rats rendered tolerant to morphine. In the midbrain PAG, a key structure implicated in the descending control of pain, stress triggers the rapid mobilization of two endocannabinoid lipids—2-arachidonoyl glycerol and anandamide.
These compounds are hydrolyzed in vivo by distinct serine hydrolases. Anandamide is degraded by fatty-acid amide hydrolase, whereas 2-AG is hydrolyzed by monoacylglycerol lipase. Inhibition of either FAAH or MGL in the PAG also enhances SIA in a CB1-dependent manner, further supporting a role for endocannabinoids in regulating expression of SIA at the supraspinal level. The distribution of CB1 receptors in the brain suggests several anatomical regions where endocannabinoid actions could modulate SIA. One such region is the amygdala, an area of the limbic forebrain implicated in both fear conditioning and affective dimensions of pain. An ascending spino-pontoamygdaloid circuit has been postulated as an “affective” nociceptive pathway. CB1 immunoreactivity is dense in the basolateral nucleus of the amygdala, but is reportedly absent in the central nucleus of the amygdala . CB1 immunoreactivity is associated with a distinct sub-population of GABAergic interneurons in the BLA, corresponding to large cholecystokinin-positive cells. The distribution of FAAH and MGL at this site also correlates well with the distribution of CB1 receptors. The anatomical localization of CB1 in the BLA is consistent with electrophysiological data demonstrating that activation of these receptors presynaptically modulates GABAergic transmission. Endocannabinoids may act as retrograde messengers to control neuronal signaling in the BLA. For example, a form of short-term synaptic plasticity – depolarization-induced suppression of inhibition – in the BLA is blocked by CB1 antagonists. BLA efferents innervate the CeA, the main amygdaloid output nucleus, which sends projections to the PAG and other regions. Thus, an endocannabinoid-mediated reduction of GABA release would disinhibit principal neurons innervating the CeA, to control information processing in the amygdala. Unilateral micro-injection of cannabinoid agonists into the amygdala also induces antinociception in the tail- flick test, supporting a role for this structure in modulation of pain sensitivity. Furthermore, micro-injections of the GABA agonist muscimol in the CeA attenuates cannabinoid antinociception.Unilateral or bilateral lesions of the CeA also suppress the antinociceptive effects elicited by both systemic cannabinoids and diverse environmental challenges. Endocannabinoid signaling in the BLA also mediates extinction of aversive memories, suggesting that endocannabinoids modulate multiple responses to stress via actions in the amygdala. In the present study, we investigated the role of cannabinoid CB1 receptors in the BLA and CeA in nonopioid SIA in rats. First, the CB1-selective antagonist/inverse agonist rimonabant was micro-injected into the BLA and the CeA to examine the contribution of these sites to endocannabinoid-mediated SIA. Based upon the distributions of CB1 receptors in these sites, we hypothesized that pharmacological blockade of CB1 receptors in the BLA, but not the CeA, would suppress nonopioid SIA. To examine the contribution of endocannabinoids in the amygdala to SIA, we administered selective pharmacological inhibitors of FAAH and MGL locally in the BLA, at doses that enhanced nonopioid SIA following micro-injection into the midbrain PAG. To this end, we used two pharmacological inhibitors that selectively target either FAAH or MGL. The FAAH inhibitor URB597 increases brain accumulation of anandamide but not 2- AG and enhances SIA in a CB1-dependent manner . On the other hand, the MGL inhibitor URB602 increases levels of 2-AG, but not anandamide, in the midbrain PAG and enhances CB1-mediated SIA when micro-injected into this structure.