State rumination predicts inhibitory control failures and dysregulation of default, salience, and cognitive control networks in youth at risk of depressive relapse: Findings from the RuMeChange trial.

Background: Trait rumination is a habitual response to negative experiences that can emerge during adolescence, increasing risk of depression. Trait rumination is correlated with poor inhibitory control (IC) and altered default mode network (DMN) and cognitive control network (CCN) engagement. Provoking state rumination in high ruminating youth permits investigation of rumination and IC at the neural level, highlighting potential treatment targets. Methods: Fifty-three high-ruminating youth were cued with an unresolved goal that provoked state rumination, then completed a modified Sustained Attention to Response Task (SART) that measures IC (commissions on no-go trials) in a functional MRI study. Thought probes measured state rumination about that unresolved goal and task-focused thoughts during the SART. Results: Greater state rumination during the SART was correlated with more IC failures. CCN engagement increased during rumination (relative to task-focus), including left dorsolateral prefrontal cortex and dorsalmedial prefrontal cortex. Relative to successful response suppression, DMN engagement increased during IC failures amongst individuals with higher state and trait rumination. Exploratory analyzes suggested more bothersome unresolved goals predicted higher left DLPFC activation during rumination. Limitations: The correlational research design did not permit a direct contrast of causal accounts of the relationship between rumination and IC. Conclusions: State rumination was associated with impaired IC and disrupted modulation of DMN and CCN. Increased CCN engagement during rumination suggested effortful suppression of negative thoughts, and this was greater for more bothersome unresolved goals. Relative task disengagement was observed during rumination-related errors. DMN-CCN dysregulation in high-ruminating youth may be an important treatment target.

Human emotion processing accuracy, negative biases, and fMRI activation are associated with childhood trauma.

Introduction: Emerging literature suggests that childhood trauma may influence facial emotion perception (FEP), with the potential to negatively bias both emotion perception and reactions to emotion-related inputs. Negative emotion perception biases are associated with a range of psychiatric and behavioral problems, potentially due or as a result of difficult social interactions. Unfortunately, there is a poor understanding of whether observed negative biases are related to childhood trauma history, depression history, or processes common to (and potentially causative of) both experiences. Methods: The present cross-sectional study examines the relation between FEP and neural activation during FEP with retrospectively reported childhood trauma in young adult participants with remitted major depressive disorder (rMDD, n = 41) and without psychiatric histories (healthy controls [HC], n = 34). Accuracy of emotion categorization and negative bias errors during FEP and brain activation were each measured during exposure to fearful, angry, happy, sad, and neutral faces. We examined participant behavioral and neural responses in relation to total reported severity of childhood abuse and neglect (assessed with the Childhood Trauma Questionnaire, CTQ). Results: Results corrected for multiple comparisons indicate that higher trauma scores were associated with greater likelihood of miscategorizing happy faces as angry. Activation in the right middle frontal gyrus (MFG) positively correlated with trauma scores when participants viewed faces that they correctly categorized as angry, fearful, sad, and happy. Discussion: Identifying the neural mechanisms by which childhood trauma and MDD may change facial emotion perception could inform targeted prevention efforts for MDD or related interpersonal difficulties.

Rumination Induction Task in fMRI: Test-Retest Reliability in Youth and Potential Mechanisms of Change with Intervention.

Background: Rumination is a transdiagnostic problem that is common in major depressive disorder (MDD). Rumination Focused Cognitive Behavioral Therapy (RF-CBT) explicitly targets the ruminative habit. This study examined changes in brain activation during a rumination induction task in adolescents with remitted MDD following RF-CBT. We also evaluated the reliability of the rumination task among adolescents who received treatment as usual (TAU). Method: Fifty-five adolescents ages 14-17 completed a self-relevant rumination induction fMRI task and were then randomized to either RF-CBT (n = 30) or TAU (n = 25). Participants completed the task a second time either following 10-14 sessions of RF-CBT or the equivalent time delay for the TAU group. We assessed activation change in the RF-CBT group using paired-samples t-tests and reliability by calculating intraclass correlation coefficients (ICCs) of five rumination-related ROIs during each of three blocks for the TAU and RF-CBT groups separately (Rumination Instruction, Rumination Prompt, and Distraction). Results: Following treatment, participants in the RF-CBT group demonstrated an increase in activation of the left precuneus during Rumination Instruction and the left angular and superior temporal gyri during Rumination Prompt ( p < .01). The TAU group demonstrated fair to excellent reliability ( M = .52, range = .27-.86) across most ROIs and task blocks. In contrast, the RF-CBT group demonstrated poor reliability across most ROIs and task blocks ( M = .21, range = -.19-.69). Conclusion: RF-CBT appears to lead to rumination-related brain change. We demonstrated that the rumination induction task has fair to excellent reliability among individuals who do not receive an intervention that explicitly targets the ruminative habit, whereas reliability of this task is largely poor in the context of RF-CBT. This has meaningful implications in longitudinal and intervention studies, particularly when conceptualizing it as an important target for intervention. It also suggests one of many possible mechanisms for why fMRI test-retest reliability can be low that appears unrelated to the methodology itself.

The Rapid Evolution of Crisis Mental Health Services in Utah: Opportunities and Challenges as a Result of the Global Pandemic.

Our country is facing a resurgence of behavioral health crises from over the past 30 years, further illuminated and exacerbated by the global COVID-19 pandemic. Increasing suicide crises among youths over recent decades, untreated anxiety and depression, and serious mental illness are signs of the need for improvements in accessible, affordable, timely, and comprehensive behavioral health services. Against the backdrop of high suicide rates and low behavioral health services in Utah, statewide collaborators aligned with a common goal: deliver crisis services to anyone, anytime, and anywhere. After its initiation in 2011, the integrated behavioral health crisis response system continued to expand and excel, ultimately improving access and referral to services, flattening suicide rates, and reducing stigma. The global pandemic further motivated the expansion of Utah's crisis response system. This review focuses on the unique experiences of the Huntsman Mental Health Institute as a catalyst and partner in these changes. Our goals are to: inform about unique Utah partnerships and actions in the crisis mental health space, describe initial steps and outcomes, highlight continuing challenges, discuss pandemic-specific barriers and opportunities, and explore the long-term vision to improve quality and access to mental health resources.

Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM.

Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. In this paper, machine learning based classifiers are used to reveal anatomical features as responders for distinguishing MDD patients who have received treatment from those who never received any treatment. The features are drawn from two sets of measurements: 1) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and 2) topological measurements from anatomical networks. Feature selection was performed based on p-value and minimum redundancy maximum relevance (mRMR) method to achieve improved classification accuracy. The classification performance is evaluated with a leave-one-out cross-validation method using 37 treated and 15 untreated subjects. The proposed methodology achieves 73% accuracy, 100% specificity, and 100% precision for 52 subjects. The most distinguishing features are the strength of the right hippocampus of the mean diffusivity (MD) network at 18% density and of the track-count (TR) network, the participation coefficient of the left middle temporal gyrus of the radial diffusivity (RD) network at 20% density, the axial diffusivity (AD) connectivity between right middle temporal gyrus and right supramarginal gyrus, the betweenness centrality of the right hippocampus of the TR network at 11% density, the apparent diffusion coefficient (ADC) connectivity between the left pars opercularis and the left rostral anterior cingulate cortex, the clustering coefficient of the middle anterior corpus callosum of the TR network at 11% density, and the AD connectivity between the left pars opercularis and the left rostral anterior cingulate cortex.

Biomarkers for Adolescent MDD from Anatomical Connectivity and Network Topology Using Diffusion MRI.

Due to the high resistance (35%) to the current treatment methods in adolescent Major Depressive Disorder (MDD) and its tragic outcomes, the discovery of treatmentrelated responders is critical to developing effective treatments. In this paper, the permutation test is performed to identify statistically significant changes in anatomical characteristics during pairwise comparisons among the control group (n=27), treated MDD group (n=37), and untreated MDD group (n=15). The anatomical characteristics include: 1) anatomical connectivity defined using DTI metrics between a pair of brain regions, and 2) topological measurements of anatomical networks. With the Bonferroni correction for multiple-comparison, significant alterations in community structure and local topology were identified as the p-value < 5%, which include: 1) a reduced nodal centrality (degree and strength) on right hippocampus for treated compared to untreated group, 2) an elevated clustering coefficient and local efficiency on right lateral orbitofrontal cortex for untreated compared to the combination of control and treated groups, 3) an increased participation coefficient for untreated patients on left insula cortex in the meandiffusivity network compared to the combination of control and treated groups, and 4) a degraded module degree z-score on right caudate nucleus for all the patients compared to the control group. Two connections, hippocampus-insula in the right hemisphere and parahippocampal-insula in the left hemisphere, were found significantly altered in TR, AD, and FA due to MDD.

Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier.

Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks.