Andrea N. Goldstein-Piekarski

Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial.

Although the cost of poor treatment outcomes of depression is staggering, we do not yet have clinically useful methods for selecting the most effective antidepressant for each depressed person. Emotional brain activation is altered in major depressive disorder (MDD) and implicated in treatment response. Identifying which aspects of emotional brain activation are predictive of general and specific responses to antidepressants may help clinicians and patients when making treatment decisions. We examined whether amygdala activation probed by emotion stimuli is a general or differential predictor of response to three commonly prescribed antidepressants, using functional magnetic resonance imaging (fMRI). A test–retest design was used to assess patients with MDD in an academic setting as part of the International Study to Predict Optimized Treatment in Depression. A total of 80 MDD outpatients were scanned prior to treatment and 8 weeks after randomization to the selective serotonin reuptake inhibitors escitalopram and sertraline and the serotonin–norepinephrine reuptake inhibitor, venlafaxine-extended release (XR). A total of 34 matched controls were scanned at the same timepoints. We quantified the blood oxygen level-dependent signal of the amygdala during subliminal and supraliminal viewing of facial expressions of emotion. Response to treatment was defined by ⩾50% symptom improvement on the 17-item Hamilton Depression Rating Scale. Pre-treatment amygdala hypo-reactivity to subliminal happy and threat was a general predictor of treatment response, regardless of medication type (Cohen’s d effect size 0.63 to 0.77; classification accuracy, 75%). Responders showed hypo-reactivity compared to controls at baseline, and an increase toward ‘normalization’ post-treatment. Pre-treatment amygdala reactivity to subliminal sadness was a differential moderator of non-response to venlafaxine-XR (Cohen’s d effect size 1.5; classification accuracy, 81%). Non-responders to venlafaxine-XR showed pre-treatment hyper-reactivity, which progressed to hypo-reactivity rather than normalization post-treatment, and hypo-reactivity post-treatment was abnormal compared to controls. Impaired amygdala activation has not previously been highlighted in the general vs differential prediction of antidepressant outcomes. Amygdala hypo-reactivity to emotions signaling reward and threat predicts the general capacity to respond to antidepressants. Amygdala hyper-reactivity to sad emotion is involved in a specific non-response to a serotonin–norepinephrine reuptake inhibitor. The findings suggest amygdala probes may help inform the personal selection of antidepressant treatments.

The sleep-deprived human brain

How does a lack of sleep affect our brains? In contrast to the benefits of sleep, frameworks exploring the impact of sleep loss are relatively lacking. Importantly, the effects of sleep deprivation (SD) do not simply reflect the absence of sleep and the benefits attributed to it; rather, they reflect the consequences of several additional factors, including extended wakefulness. With a focus on neuroimaging studies, we review the consequences of SD on attention and working memory, positive and negative emotion, and hippocampal learning. We explore how this evidence informs our mechanistic understanding of the known changes in cognition and emotion associated with SD, and the insights it provides regarding clinical conditions associated with sleep disruption.

Sleep Deprivation Impairs the Human Central and Peripheral Nervous System Discrimination of Social Threat

Facial expressions represent one of the most salient cues in our environment. They communicate the affective state and intent of an individual and, if interpreted correctly, adaptively influence the behavior of others in return. Processing of such affective stimuli is known to require reciprocal signaling between central viscerosensory brain regions and peripheral-autonomic body systems, culminating in accurate emotion discrimination. Despite emerging links between sleep and affective regulation, the impact of sleep loss on the discrimination of complex social emotions within and between the CNS and PNS remains unknown. Here, we demonstrate in humans that sleep deprivation impairs both viscerosensory brain (anterior insula, anterior cingulate cortex, amygdala) and autonomic-cardiac discrimination of threatening from affiliative facial cues. Moreover, sleep deprivation significantly degrades the normally reciprocal associations between these central and peripheral emotion-signaling systems, most prominent at the level of cardiac-amygdala coupling. In addition, REM sleep physiology across the sleep-rested night significantly predicts the next-day success of emotional discrimination within this viscerosensory network across individuals, suggesting a role for REM sleep in affective brain recalibration. Together, these findings establish that sleep deprivation compromises the faithful signaling of, and the “embodied” reciprocity between, viscerosensory brain and peripheral autonomic body processing of complex social signals. Such impairments hold ecological relevance in professional contexts in which the need for accurate interpretation of social cues is paramount yet insufficient sleep is pervasive.

Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants

Significance Amygdala reactivity and early life stress (ELS) are both strongly implicated in the mechanisms of depression in animal and human models. Despite these mechanistic foundations, amygdala reactivity and ELS have not been investigated as biobehavioral targets for predicting functional remission in depression. We addressed this issue by integrating human imaging and ELS measures within a controlled trial of antidepressant outcomes. We demonstrate that the interaction between ELS and amygdala engagement predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. In depressed people exposed to high ELS, a greater likelihood of remission was predicted by amygdala hyperreactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, amygdala hyporeactivity to both rewarding and threat-related stimuli predicted remission. Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person.

Human Hippocampal Structure: A Novel Biomarker Predicting Mnemonic Vulnerability to, and Recovery from, Sleep Deprivation.

Sleep deprivation impairs the formation of new memories. However, marked interindividual variability exists in the degree to which sleep loss compromises learning, the mechanistic reasons for which are unclear. Furthermore, which physiological sleep processes restore learning ability following sleep deprivation are similarly unknown. Here, we demonstrate that the structural morphology of human hippocampal subfields represents one factor determining vulnerability (and conversely, resilience) to the impact of sleep deprivation on memory formation. Moreover, this same measure of brain morphology was further associated with the quality of nonrapid eye movement slow wave oscillations during recovery sleep, and by way of such activity, determined the success of memory restoration. Such findings provide a novel human biomarker of cognitive susceptibility to, and recovery from, sleep deprivation. Moreover, this metric may be of special predictive utility for professions in which memory function is paramount yet insufficient sleep is pervasive (e.g., aviation, military, and medicine). SIGNIFICANCE STATEMENT Sleep deprivation does not impact all people equally. Some individuals show cognitive resilience to the effects of sleep loss, whereas others express striking vulnerability, the reasons for which remain largely unknown. Here, we demonstrate that structural features of the human brain, specifically those within the hippocampus, accurately predict which individuals are susceptible (or conversely, resilient) to memory impairments caused by sleep deprivation. Moreover, this same structural feature determines the success of memory restoration following subsequent recovery sleep. Therefore, structural properties of the human brain represent a novel biomarker predicting individual vulnerability to (and recovery from) the effects of sleep loss, one with occupational relevance in professions where insufficient sleep is pervasive yet memory function is paramount.

A trans-diagnostic review of anxiety disorder comorbidity and the impact of multiple exclusion criteria on studying clinical outcomes in anxiety disorders.

Anxiety disorders are highly comorbid with each other and with other serious mental disorders. As our field progresses, we have the opportunity to pursue treatment study designs that consider these comorbidities. In this perspective review, we first characterized the prevalence of multiple anxiety disorder comorbidity by reanalyzing national survey data, then conducted an English-language PubMed search of studies analyzing the impact of exclusion criteria on treatment outcome data. In the prevalence data, 60% of people with an anxiety disorder had one or more additional anxiety or depression diagnosis. Because our commonly applied exclusion criteria focus on a single diagnosis and do not consider a multiple comorbidity profile, the impact of the criteria may be to exclude up to 92% of anxiety disorder treatment seekers. Moreover, the findings do not suggest a consistent relationship between the number of exclusion criteria and the effect size of treatment outcomes. Thus, future studies might consider a more trans-diagnostic rationale for determining exclusion criteria, one that is generalizable to real-world settings in which multiple diagnoses commonly co-occur. The findings also encourage a more systematic reporting of rationales for the choice of—and the implications of—each exclusion criterion.

Sex differences modulating serotonergic polymorphisms implicated in the mechanistic pathways of risk for depression and related disorders

Despite consistent observations of sex differences in depression and related emotional disorders, we do not yet know how these sex differences modulate the effects of genetic polymorphisms implicated in risk for these disorders. This Mini‐Review focuses on genetic polymorphisms of the serotonergic system to illustrate how sex differences might modulate the neurobiological pathways involved in the development of depression. We consider the interacting role of environmental factors such as early‐life stress. Given limited current knowledge about this topic, we highlight methodological considerations, challenges, and guidelines for future research.

Quantifying person-level brain network functioning to facilitate clinical translation

Although advances in neuroimaging have yielded insights into the intrinsic organization of human brain networks and their relevance to psychiatric and neurological disorders, there has been no translation of these insights into clinical practice. One necessary step toward clinical translation is identifying a summary metric of network function that is reproducible, reliable, and has known normative data, analogous to normed neuropsychological tests. Our aim was therefore to establish the proof of principle for such a metric, focusing on the default mode network (DMN). We compared three candidate summary metrics: global clustering coefficient, characteristic path length, and average connectivity. Across three samples totaling 322 healthy, mostly Caucasian adults, average connectivity performed best, with good internal consistency (Cronbach’s α=0.69–0.70) and adequate eight-week test–retest reliability (intra-class coefficient=0.62 in a subsample N=65). We therefore present normative data for average connectivity of the DMN and its sub-networks. These proof of principle results are an important first step for the translation of neuroimaging to clinical practice. Ultimately, a normed summary metric will allow a single patient’s DMN function to be quantified and interpreted relative to normative peers.

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders

Importance The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices. Objective To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample. Design, Setting, and Participants This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017. Main Outcomes and Measures We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference. Results Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, &eegr;p2 = 0.063), working memory (F5,401 = 3.29, P = .006, &eegr;p2 = 0.039), electroencephalography-recorded &bgr; power in a resting paradigm (F5,357 = 3.84, P = .002, &eegr;p2 = 0.051), electroencephalography-recorded &bgr; power in an emotional paradigm (F5,365 = 3.56, P = .004, &eegr;p2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, &eegr;p2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, &eegr;p2 = 0.171). Conclusions and Relevance These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

Antidepressant Outcomes Predicted by Genetic Variation in Corticotropin-Releasing Hormone Binding Protein

OBJECTIVE Genetic variation within the hypothalamic-pituitary-adrenal (HPA) axis has been linked to risk for depression and antidepressant response. However, these associations have yet to produce clinical gains that inform treatment decisions. The authors investigated whether variation within HPA axis genes predicts antidepressant outcomes within two large clinical trials. METHOD The test sample comprised 636 patients from the International Study to Predict Optimized Treatment in Depression (iSPOT-D) who completed baseline and 8-week follow-up visits and for whom complete genotyping data were available. The authors tested the relationship between genotype at 16 candidate HPA axis single-nucleotide polymorphisms (SNPs) and treatment outcomes for three commonly used antidepressants (escitalopram, sertraline, and extended-release venlafaxine), using multivariable linear and logistic regression with Bonferroni correction. Response and remission were defined using the Hamilton Depression Rating Scale. Findings were then validated using the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study of outcome predictors in treatment-naive patients with major depression. RESULTS The authors found that the rs28365143 variant within the corticotropin-releasing hormone binding protein (CRHBP) gene predicted antidepressant outcomes for remission, response, and symptom change. Patients homozygous for the G allele of rs28365143 had greater remission rates, response rates, and symptom reductions. These effects were specific to drug class. Patients homozygous for the G allele responded significantly better to the selective serotonin reuptake inhibitors escitalopram and sertraline than did A allele carriers. In contrast, rs28365143 genotype was not associated with treatment outcomes for the serotonin norepinephrine reuptake inhibitor venlafaxine. When patients were stratified by race, the overall effect of genotype on treatment response remained. In the validation sample, the GG genotype was again associated with favorable antidepressant outcomes, with comparable effect sizes. CONCLUSIONS These findings suggest that a specific CRHBP SNP, rs28365143, may have a role in predicting which patients will improve with antidepressants and which type of antidepressant may be most effective. The results add to the foundational knowledge needed to advance a precision approach to personalized antidepressant choices.