Default Mode Network Moderates the Relationship Between Lifestyle Changes and Natural Improvements in Clinical Symptoms Over Time in Untreated Participants
ELSEVIER SCIENCE INC. 2021: S111
View details for Web of Science ID 000645683800263
Reduced functional connectivity of default mode network subsystems in depression: Meta-analytic evidence and relationship with trait rumination.
2021; 30: 102570
Resting-state functional connectivity changes in the default mode network (DMN) of patients with major depressive disorder (MDD) have been linked to rumination. The DMN is divided into three subsystems: a midline Core, a dorsal medial prefrontal cortex (DMPFC) subsystem, and a medial temporal lobe (MTL) subsystem. We examined resting-state functional connectivity within and between DMN subsystems in MDD and its association with rumination. First, we conducted a meta-analysis on a large multi-site dataset of 618 MDD and 683 controls to quantify the differences in DMN subsystem functional connectivity between MDD and controls. Second, we tested the association of DMN subsystem functional connectivity and rumination in a sample of 115 unmedicated participants with symptoms of anxiety/depression and 48 controls. In our meta-analysis, only functional connectivity in the DMN Core was significantly reduced in MDD compared to controls (g = -0.246, CI = [-0.417; -0.074], pFDR = 0.048). Functional connectivity in the DMPFC subsystem and between the Core and DMPFC subsystems was slightly reduced but not significantly (g = -0.162, CI = [-0.310; -0.013], pFDR = 0.096; g = -0.249, CI = [-0.464; -0.034], pFDR = 0.084). Results were heterogeneous across sites for connectivity in the Core and between Core and DMPFC (I2 = 0.348 and I2 = 0.576 respectively). Prediction intervals consistently encompassed 0. In the independent sample we collected, functional connectivity within the DMN Core, DMPFC and between Core and DMPFC was not reduced in MDD compared to controls (all pFDR > 0.05). Trait rumination did not predict connectivity within and between DMN subsystems (all pFDR > 0.05). We conclude that MDD as a diagnostic category shows slightly reduced functional connectivity within the DMN Core, independent of illness duration, treatment, symptoms and trait rumination. However, this effect is small, highly variable and heterogeneous across samples, so that we could only detect it at the meta-analytic level, with a sample size of several hundreds. Our results indicate that reduced Core DMN connectivity has significant limitations as a potential clinical or prognostic marker for the diagnosis of MDD and might be more relevant to consider as a characteristic distinguishing a subgroup of individuals within this diagnostic category.
View details for DOI 10.1016/j.nicl.2021.102570
View details for PubMedID 33540370
Stress Markers for Mental States and Biotypes of Depression and Anxiety: A Scoping Review and Preliminary Illustrative Analysis.
Chronic stress (Thousand Oaks, Calif.)
2021; 5: 24705470211000338
Depression and anxiety disrupt daily function and their effects can be long-lasting and devastating, yet there are no established physiological indicators that can be used to predict onset, diagnose, or target treatments. In this review, we conceptualize depression and anxiety as maladaptive responses to repetitive stress. We provide an overview of the role of chronic stress in depression and anxiety and a review of current knowledge on objective stress indicators of depression and anxiety. We focused on cortisol, heart rate variability and skin conductance that have been well studied in depression and anxiety and implicated in clinical emotional states. A targeted PubMed search was undertaken prioritizing meta-analyses that have linked depression and anxiety to cortisol, heart rate variability and skin conductance. Consistent findings include reduced heart rate variability across depression and anxiety, reduced tonic and phasic skin conductance in depression, and elevated cortisol at different times of day and across the day in depression. We then provide a brief overview of neural circuit disruptions that characterize particular types of depression and anxiety. We also include an illustrative analysis using predictive models to determine how stress markers contribute to specific subgroups of symptoms and how neural circuits add meaningfully to this prediction. For this, we implemented a tree-based multi-class classification model with physiological markers of heart rate variability as predictors and four symptom subtypes, including normative mood, as target variables. We achieved 40% accuracy on the validation set. We then added the neural circuit measures into our predictor set to identify the combination of neural circuit dysfunctions and physiological markers that accurately predict each symptom subtype. Achieving 54% accuracy suggested a strong relationship between those neural-physiological predictors and the mental states that characterize each subtype. Further work to elucidate the complex relationships between physiological markers, neural circuit dysfunction and resulting symptoms would advance our understanding of the pathophysiological pathways underlying depression and anxiety.
View details for DOI 10.1177/24705470211000338
View details for PubMedID 33997582
The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression.
Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest ("Emotion", "Gambling" and "Continuous Performance" tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.
View details for DOI 10.1016/j.neuroimage.2020.116715
View details for PubMedID 32147367