Outlier images were modeled as nuisance covariates. Each outlier image was represented by a single regressor in the GLM, with a 1 for the outlier time point and 0s elsewhere. Time series of all the voxels within each seed were averaged, and first-level correlation maps were produced by extracting the residual BOLD 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 allow for second-level General Linear Model analyses. DMN connectivity was calculated from the averages of the time series from MPFC and PCC seeds , given their similar connectivity patterns. Functional connectivity of left and right DLPFC were analyzed separately, as were left and right amygdala due to evidence of differential roles in emotion processing . First-level connectivity maps for each participant were entered into a between-group t-test to determine connectivity differences for each seed between groups. Clusters-level threshold was set at p < .05 using false discovery rate correction for multiple comparisons , with voxel-wise t-value threshold of 2.42 . Bonferroni correction was applied to the FDR-corrected cluster-level p-values to correct for multiple comparisons of the five a priori seeds tested . Regions that showed significant connectivity differences between groups were further examined for their connectivity values using one sample t-tests in each group. Based on prior evidence of DMN-sgACC hyperconnectivity in MDD and its implication in depressive rumination ,vertical grow racks for sale we examined the within group correlations between DMN-sgACC connectivity values and CBCL scores. Given the higher CBCL total score in the at-risk group, we re-tested group differences by including CBCL total scores as a covariate. Classification models of at-risk children and controls discrimination.
We trained two linear classification models using logistic regression, implemented in machine learning software Weka , in order to categorize individual participants to the at-risk or control groups based on their rs-fMRI or behavioral data. To create robust prediction models that can be generalized to new cases, we performed leave-one-out cross-validation so that each individual was classified on the basis of data from the other individuals. Specifically, data from all participants except one were used as the training set to build a classification model, and the remaining participant was classified with the model and used as the validation case. This procedure was iterated for each participant and used to estimate specificity/sensitivity from the out-of-sample predictions. In the first model, we used anatomically defined regions-of-interest that were independent from the regions that showed between-group connectivity differences. Connectivity values between the five a priori seeds and 116 clusters defined by the AAL atlas were estimated and used in the prediction model. We constructed a second model based on CBCL scores , to compare with classification accuracies from the model based on rs-fMRI data in anatomically defined ROIs. We found differential intrinsic functional connectivity patterns in unaffected children with familial risk for MDD compared to children without such familial risk in the DMN, the cognitive control network, and the amygdala. At-risk children showed hyperconnectivity between the DMN and the sgACC/OF. Furthermore, although none of the at-risk children was clinically depressed, DMN-sgACC/OFC connectivity was positively correlated with individual CBCL scores among those children. At-risk children also showed hypoconnectivity within the cognitive control networ k, lacked the typical anticorrelation between the DMN and the right parietal region, and exhibited lower connectivity between left DLPFC and sgACC. In addition, at-risk children showed hyperconnectivity between amygdala and the right IFG. Finally, classification between at-risk children and controls based on resting-state connectivity yielded high sensitivity and specificity. These findings appear to identify trait neurobiological underpinnings of risk for major depression in the absence of the state of depression.
Increased connectivity between DMN and sgACC in at-risk children, and the positive correlation between DMN-sgACC connectivity and current symptom scores, are consistent with findings reported in adult and pediatric patients with MDD. The fact that these findings were observed in unaffected children at familial risk for MDD suggests that hyperconnectivity with sgACC is not a consequence or manifestation of MDD, but instead may be a biomarker of predisposed risk for MDD.In line with our finding, stimulation of the sgACC resulted in attenuation of hyperactivation in sgACC and increased activation in previouslyunderactive DLPFC in adults with MDD . The left DLPFC region that showed maximum anticorrelation with the sgACC has been identified as a target for TMS treatment of MDD . A prospective study would be needed to determine if atypical sgACC connectivity at this age predicts later development of MDD. The lack of typical anticorrelation between the DMN and supramarginal gyrus / inferior parietal lobule, an important attention control region , in at-risk children is consistent with cognitive control deficits in depressed adult patients and reduced DMN deactivation during an emotional identification task in depressed adolescents . Greater anticorrelation between DMN and cognitive control networks in healthy adults has been linked to better performance in cognitive control and working memory tasks and may reflect an individual’s capacity to switch between internally and externally focused attention . This dynamic interplay between DMN and cognitive control networks in MDD was examined in a task-based connectivity study. During an external attention condition, adults with MDD exhibited increased DMN connectivity and decreased cognitive control network connectivity . The present study suggests that an imbalance between DMN and cognitive-control networks is a developmental risk factor for MDD. With regards to decreased connectivity within the cognitive control regions in at-risk children, a previous study of adolescents with familial risk for depression also reported reduced connectivity between cognitive control regions . In that study, lower connectivity in the control network was associated with more severe parental depression symptoms. These results in at-risk children and adolescents are consistent with findings from depressed adults of reduced connectivity in attention control regions including the DLPFC .
Studies consistently show that the DLPFC is under-activated in depressed adults , which might contribute to their difficulty in cognitive control and emotion regulation . It is possible that children at-risk for depression have an underconnected control network that is also a developmental risk factor for MDD. There was increased connectivity between the right amygdala and the right IFG and supramarginal gyrus in at-risk children. The right IFG is a key region in emotion regulation . The top-down IFG-amygdala circuitry is disrupted during emotion regulation in adults with mood disorders . A study of children with MDD and children of mothers with MDD also reported reduced negative correlation between the amygdala and lateral parietal regions including the supramarginal gyrus . The atypically high level of connectivity between amygdala and emotion regulation and cognitive-control regions might reflect emotion dysregulation in MDD. To test whether intrinsic functional organization of the brain, as measured by rs-fMRI,rolling hydro tables can be a potential biomarker for risk for depression in children, we performed a classification analysis to discriminate children in the at-risk group and control group based their resting-state functional connectivity data. This classification based on functional connectivity yielded high accuracy, sensitivity, and specificity in discriminating between children at risk for MDD and controls compared to classification based on CBCL scores. Importantly, the rs-fMRI classification was based on analyses that, at the level of each individual child, were independent of the group differences in functional connectivity. Such generalizable and individually robust classification is important if brain measures are to be used for early identification . Future prospective and longitudinal studies can determine whether such biomarkers predict which high-risk children progress to MDD and whether early intervention reduces the likelihood of developing MDD. Also, perhaps such biomarkers may be helpful in identifying children at risk for developing depression independent of parental histories of depression. Our findings need to be viewed in light of some methodological limitations. First, we did not exclude children born prematurely, and premature births can lead to neurological complications. However, we did exclude children with known developmental delays such as autism and intellectual disability. Second, because parental MDD confers a spectrum of risk to offspring , the at risk children were also at risk for anxiety and other disorders. Parents with MDD also have higher rates of comorbid anxiety than the general population. Thus we cannot rule out that the brain differences we found were due to the children being at risk for anxiety and other disorders. Third, although our sample size of at-risk children was moderate, the control group was small .
Lastly, our resting-state scans were acquired with a repetition time of 6 seconds, which is longer than most resting state fMRI studies so that we could acquire high-resolution whole brain data without the use of parallel imaging. A previous study found there was no significant difference in correlation strengths within and between resting-state functional networks when comparing TR = 2.5 and 5 seconds resting scans, and that correlation strengths stabilized with acquisition time of 5 min . In the current and previous studies using the same acquisition parameters , we observed the typical resting-state network patterns observed in other studies. Nonetheless, an additional issue of the long TR is that cognitive and emotional processes internally initiated at the beginning and the end of each scan can be different. We cannot rule out the possibility that the group difference observed here might be in part due to systematic differences in chronometry between the two groups. The present study consisted of a sample of pre-adolescent children who were at familial risk for depression but not currently affected with depression and therefore functional connectivity differences cannot reflect an expression of depression as could be the case in patients with ongoing MDD. Rather, the differences in intrinsic functional brain architecture likely reflect neural traits that predispose children towards MDD or related disorders. Importantly, we demonstrated that discrimination between at-risk and control children occurred with high sensitivity and specificity based on resting-state functional connectivity. Future studies that track the development of children at familial risk for MDD and determines which children develop MDD or other mood and anxiety disorders are needed to build predictive models based on findings from the present study so as to identify high-risk individuals for early intervention.The regulation of both cognition and emotion is thought to depend upon top-down modulation of multiple neural circuits by prefrontal cortex, and in particular the dorsolateral prefrontal cortex. Because prefrontal-dependent cognitive control mechanisms regulate the focus of attention and regulate mood, it stands to reason that they play a key role in mental health. There is indeed ample evidence that adult psychiatric patients exhibit an attenuation or failure of top down control mechanisms in depression, anxiety, and Attention Deficit Hyperactivity Disorder 14. Given that these prevalent mental health problems tend to emerge during childhood and adolescence, it is important to know whether dysregulated top-down control can be detected even before behavioral symptoms are evident. The strength of coupling between regions involved in top-down control and their targets can be measured with resting state fMRI . Regions of the brain that are highly temporally correlated during rest form resting state profiles which are intrinsic, spontaneous, low-frequency fluctuations in the fMRI blood-oxygen-level dependent signal that define specific networks of the brain in the absence of any task. There is great heterogeneity in the functional organization of the brain that is captured by RSNs. In fact, they may be considered “fingerprints” of the human brain, as they can accurately identify individual subjects from a large group of individuals. Furthermore, RSN profiles are known to be robust and reliable. RSNs are particularly relevant to studying psychiatric and pediatric populations because 1) they are task-independent, so individual differences in task performance cannot explain differences observed in the BOLD data, 2) they are easy and fast to acquire which make them more accessible to a wide variety of subjects including young children and a wide range of clinical populations, and 3) they are plastic and have been shown to change during typical development and can be modulated by behavioral or pharmacological interventions. An RSN that is particularly relevant for mental health is the Central Executive Network , of which the DLPFC is a key node.