Fusiform Gyrus Dysfunction is Associated with Perceptual Processing Efficiency to Emotional Faces in Adolescent Depression: A Model-Based Approach

While the extant literature has focused on major depressive disorder (MDD) as being characterized by abnormalities in processing affective stimuli (e.g., facial expressions), little is known regarding which specific aspects of cognition influence the evaluation of affective stimuli, and what are the underlying neural correlates. To investigate these issues, we assessed 26 adolescents diagnosed with MDD and 37 well-matched healthy controls (HCL) who completed an emotion identification task of dynamically morphing faces during functional magnetic resonance imaging (fMRI). We analyzed the behavioral data using a sequential sampling model of response time (RT) commonly used to elucidate aspects of cognition in binary perceptual decision making tasks: the Linear Ballistic Accumulator (LBA) model. Using a hierarchical Bayesian estimation method, we obtained group-level and individual-level estimates of LBA parameters on the facial emotion identification task. While the MDD and HCL groups did not differ in mean RT, accuracy, or group-level estimates of perceptual processing efficiency (i.e., drift rate parameter of the LBA), the MDD group showed significantly reduced responses in left fusiform gyrus compared to the HCL group during the facial emotion identification task. Furthermore, within the MDD group, fMRI signal in the left fusiform gyrus during affective face processing was significantly associated with greater individual-level estimates of perceptual processing efficiency. Our results therefore suggest that affective processing biases in adolescents with MDD are characterized by greater perceptual processing efficiency of affective visual information in sensory brain regions responsible for the early processing of visual information. The theoretical, methodological, and clinical implications of our results are discussed

The neural mechanisms of perceptual decision making

Perceptual decision making (PDM) involves choosing one option among several on the basis of sensory evidence and is a highly adaptive mechanism for organisms to successfully interact with their environments. Such a choice requires integrating and interpreting sensory information for the purpose of guiding subsequent behavior (e.g., seeing a ball move rightward and veering accordingly to catch it). Typical single-unit recording studies examining PDM utilize simple sensorimotor tasks (e.g., a macaque views a noisy array of dots moving in one of two possible directions and deploys a saccade in the chosen - and presumably, perceived - direction) in order to parse various aspects of PDM. With the aid of mathematical models, these experiments have found that the activity of individual neurons involved in motor response generation comprises perceptual decisions, and that PDM can be formalized as an accumulation of sensory evidence towards a particular choice (as represented by an increase in neuronal firing rate) until some threshold is reached. Explaining the mechanisms of PDM at the level of neural populations and linking ensemble patterns of neural activity to perception, however, still remains unclear. With a combination of visual psychophysics, neuroimaging, and modeling, I present a set of studies that examines the neural correlates subserving PDM in human cortex (Experiment 1), clarifies the relationship between sensory representations in visual cortex and perceptual performance (Experiment 2), and tests the behavioral predictions derived from single-cell recordings (Experiment 3). These findings both challenge and confirm some of the previous neurophysiological work: Experiment 1 provides evidence of a neural mechanism of PDM not based purely on oculomotor regions, Experiment 2 shows that the optimality of activation patterns in visual cortex predicts task performance, and Experiment 3 illustrates that attentional manipulations influence perception in a manner consistent with the enhancement and suppression of distinct neural populations predicted from single-unit recordings. Furthermore, these studies demonstrate the utility of model-based cognitive neuroscience in quantifying psychological processes of interest for each individual and relating between-subject differences with corresponding brain measurement

Early Life Stress Predicts Depressive Symptoms in Adolescents During the COVID-19 Pandemic: The Mediating Role of Perceived Stress

Background: Exposure to early life stress (ELS) is alarmingly prevalent, and has been linked to the high rates of depression documented in adolescence. Researchers have theorized that ELS may increase adolescents’ vulnerability or reactivity to the effects of subsequent stressors, placing them at higher risk for developing symptoms of depression. Methods: We tested this formulation in a longitudinal study by assessing levels of stress and depression during the COVID-19 pandemic in a sample of adolescents from the San Francisco Bay Area (N=100; 43 male; ages 13-20 years) who had been characterized 4-7 years earlier (M=5.27, SD=0.75 years) with respect to exposure to ELS and symptoms of depression. Results: As expected, severity of ELS predicted levels of depressive symptoms during the pandemic (r(98)=0.25, p=.012), which were higher in females than in males (t(98)=-3.36, p=.001). Importantly, the association between ELS and depression was mediated by adolescents’ reported levels of stress, even after controlling for demographic and other COVID-19-related variables. Conclusions: These findings underscore the importance of monitoring the mental health of vulnerable children and adolescents during this pandemic and targeting perceived stress and isolation in high-risk youth

ENIGMA and Global Neuroscience: A Decade of Large-Scale Studies of the Brain in Health and Disease across more than 40 Countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1,400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive and psychosocial factors