Age-Related Developmental and Individual Differences in the Influence of Social and Non-social Distractors on Cognitive Performance.

This study sought to examine age-related differences in the influences of social (neutral, emotional faces) and non-social/non-emotional (shapes) distractor stimuli in children, adolescents, and adults. To assess the degree to which distractor, or task-irrelevant, stimuli of varying social and emotional salience interfere with cognitive performance, children (N = 12; 8-12y), adolescents (N = 17; 13-17y), and adults (N = 17; 18-52y) completed the Emotional Identification and Dynamic Faces (EIDF) task. This task included three types of dynamically-changing distractors: (1) neutral-social (neutral face changing into another face); (2) emotional-social (face changing from 0% emotional to 100% emotional); and (3) non-social/non-emotional (shapes changing from small to large) to index the influence of task-irrelevant social and emotional information on cognition. Results yielded no age-related differences in accuracy but showed an age-related linear reduction in correct reaction times across distractor conditions. An age-related effect in interference was observed, such that children and adults showed slower response times on correct trials with socially-salient distractors; whereas adolescents exhibited faster responses on trials with distractors that included faces rather than shapes. A secondary study goal was to explore individual differences in cognitive interference. Results suggested that regardless of age, low trait anxiety and high effortful control were associated with interference to angry faces. Implications for developmental differences in affective processing, notably the importance of considering the contexts in which purportedly irrelevant social and emotional information might impair, vs. improve cognitive control, are discussed.NIMH R24 Research Network grant (MH67346, PI Ronald Dahl)

What does brain response to neutral faces tell us about major depression? Evidence from machine learning and fMRI

Introduction A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. Methods Two independent depressed samples: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier’s predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Results Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Conclusions Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients

Pediatric functional magnetic resonance neuroimaging: tactics for encouraging task compliance

<p>Abstract</p> <p>Background</p> <p>Neuroimaging technology has afforded advances in our understanding of normal and pathological brain function and development in children and adolescents. However, noncompliance involving the inability to remain in the magnetic resonance imaging (MRI) scanner to complete tasks is one common and significant problem. Task noncompliance is an especially significant problem in pediatric functional magnetic resonance imaging (fMRI) research because increases in noncompliance produces a greater risk that a study sample will not be representative of the study population.</p> <p>Method</p> <p>In this preliminary investigation, we describe the development and application of an approach for increasing the number of fMRI tasks children complete during neuroimaging. Twenty-eight healthy children ages 9-13 years participated. Generalization of the approach was examined in additional fMRI and event-related potential investigations with children at risk for depression, children with anxiety and children with depression (N = 120). Essential features of the approach include a preference assessment for identifying multiple individualized rewards, increasing reinforcement rates during imaging by pairing tasks with chosen rewards and presenting a visual 'road map' listing tasks, rewards and current progress.</p> <p>Results</p> <p>Our results showing a higher percentage of fMRI task completion by healthy children provides proof of concept data for the recommended tactics. Additional support was provided by results showing our approach generalized to several additional fMRI and event-related potential investigations and clinical populations.</p> <p>Discussion</p> <p>We proposed that some forms of task noncompliance may emerge from less than optimal reward protocols. While our findings may not directly support the effectiveness of the multiple reward compliance protocol, increased attention to how rewards are selected and delivered may aid cooperation with completing fMRI tasks</p> <p>Conclusion</p> <p>The proposed approach contributes to the pediatric neuroimaging literature by providing a useful way to conceptualize and measure task noncompliance and by providing simple cost effective tactics for improving the effectiveness of common reward-based protocols.</p

Altered development of white matter in youth at high familial risk for bipolar disorder: a diffusion tensor imaging study

Objective: To study white matter (WM) development in youth at high familial risk for bipolar disorder (BD). WM alterations are reported in youth and adults with BD. WM undergoes important maturational changes in adolescence. Age-related changes in WM microstructure using diffusion tensor imaging with tract-based spatial statistics in healthy offspring having a parent with BD were compared with those in healthy controls. Method: A total of 45 offspring participated, including 20 healthy offspring with a parent diagnosed with BD (HBO) and 25 healthy control offspring of healthy parents (CONT). All were free of medical and psychiatric disorders. Mean fractional anisotropy (FA), radial diffusivity (RD), and longitudinal diffusivity were examined using whole-brain analyses, co-varying for age. Results: Group-by-age interactions showed a linear increase in FA and a linear decrease in RD in CONT in the left corpus callosum and right inferior longitudinal fasciculus. In HBO, there was a linear decrease in FA and an increase in RD with age in the left corpus callosum and no relation between FA or RD and age in the right inferior longitudinal fasciculus. Curve fitting confirmed linear and showed nonlinear relations between FA and RD and age in these regions in CONT and HBO. Conclusions: This is the first study to examine WM in healthy offspring at high familial risk for BD. Results from this cross-sectional study suggest altered development of WM in HBO compared with CONT in the corpus callosum and temporal associative tracts, which may represent vulnerability markers for future BD and other psychiatric disorders in HBO. J. Am. Acad. Child Adolesc. Psychiatry, J. Am. Acad. Child Adolesc. Psychiatry, 2010; 49(12):1249 -1259. Key words: bipolar disorder, familial risk, white matter, diffusion tensor imaging, neurodevelopment B ipolar disorder (BD) is a serious psychiatric illness affecting 1% to 3% of the adult population and remains a leading cause of morbidity, functional impairment, and completed suicide. 1 BD is characterized by difficulties in the regulation of emotions and behavior, as indicated by episodes of mania and depression. BD is highly heritable: the risk of BD is much greater in first-degree relatives of individuals diagnosed with BD. 2,3 Recent evidence has indicated that offspring of parents with BD are at increased risk for BD and other psychiatric disorders, including BD spectrum disorder, anxiety, and depression disorders. 2 Although genetic and environmental factors and their interactions are important in the development of BD, abnormalities of brain structure and function that most likely mediate these effects have yet to be elucidated. Converging evidence from epidemiologic, genetic, and neuroimaging studies has suggested that abnormalities in the development of white matter (WM) may play an important role in the neuropathophysiology of BD

Neural Correlates of Treatment in Adolescents with Bipolar Depression During Response Inhibition

Abstract Objective: Abnormal prefrontal and subcortical activity during cognitive control tasks is identified in non-depressed adolescents with bipolar disorder (BD); however, little is known about the neural correlates of bipolar adolescents in a depressed state (BDd). We aimed to investigate baseline versus after-treatment patterns of neural activity underlying motor response and response inhibition in adolescents with BDd. Methods: In this functional magnetic resonance imaging (fMRI) study, 10 adolescents with BDd relative to 10 age-and sexmatched healthy controls (HC) completed a well-validated go/no go block-design cognitive control task at baseline and after 6 weeks of naturalistic treatment. We used whole-brain analysis and controlled our results for multiple comparisons. Results: There was significant improvement in depression scores (mean change: 57% -28). There was no behavioral difference in BDd baseline versus HC and after treatment. BDd adolescents relative to HC had higher baseline cortical, but not subcortical, neural activity (e.g., bilateral ventrolateral prefrontal during both the go [motor control] and the no go [response inhibition] conditions, and left superior temporal during the no go condition). However, after-treatment activity relative to baseline neural activity during response inhibition was significantly increased in subcortical (e.g., right hippocampus and left thalamus), but not cortical, regions. In addition, at baseline, lower left thalamus activity was correlated with higher depression scores. Conclusions: Adolescents with BDd had baseline prefrontal and temporal hyperactivity underlying motor control and response inhibition that did not change after treatment in contrast to relatively decreased baseline subcortical activity underlying response inhibition associated with the depressive state that was increased after the treatment

A Researcher’s Guide to the Measurement and Modeling of Puberty in the ABCD Study® at Baseline

9 pagesThe Adolescent Brain Cognitive Development℠ (ABCD) Study is an ongoing, diverse, longitudinal, and multi-site study of 11,880 adolescents in the United States. The ABCD Study provides open access to data about pubertal development at a large scale, and this article is a researcher’s guide that both describes its pubertal variables and outlines recommendations for use. These considerations are contextualized with reference to cross-sectional empirical analyses of pubertal measures within the baseline ABCD dataset by Herting, Uban, and colleagues (2021). We discuss strategies to capitalize on strengths, mitigate weaknesses, and appropriately interpret study limitations for researchers using pubertal variables within the ABCD dataset, with the aim of building toward a robust science of adolescent development.This project was conceptualized at the ABCD Workshop 2019, which was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R25MH120869. Author TC was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1TR002371. Author CL was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number MH099007. Author MLB was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number K01MH111951. Author MH was supported by the National Institute of Mental Health under Award Number: K01 MH10876. Author SW was supported by the National Health and Medical Research Council under award number 1125504. Author KU was supported by the National Institute on Alcohol Abuse and Alcoholism under Award Number: K01 AA026889. Author JP was supported by the National Institute of Mental Health under award number MH174108. To prepare this article, we examine and present details about measures administered in the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multi-site, longitudinal study designed to recruit more than 10,000 children ages 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/scientists/workgroups/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or ABCD consortium investigators. Author TC was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1TR002371 and by the National Institute of Mental Health under award number 1F31MH124353

What Does Brain Response to Neutral Faces Tell Us about Major Depression? Evidence from Machine Learning and fMRI

Introduction: A considerable number of previous studies have shown abnormalities in the processing of emotional faces in major depression. Fewer studies, however, have focused specifically on abnormal processing of neutral faces despite evidence that depressed patients are slow and less accurate at recognizing neutral expressions in comparison with healthy controls. The current study aimed to investigate whether this misclassification described behaviourally for neutral faces also occurred when classifying patterns of brain activation to neutral faces for these patients. Methods: Two independent depressed samples: (1) Nineteen medication-free patients with depression and 19 healthy volunteers and (2) Eighteen depressed individuals and 18 age and gender-ratio-matched healthy volunteers viewed emotional faces (sad/neutral; happy/neutral) during an fMRI experiment. We used a new pattern recognition framework: first, we trained the classifier to discriminate between two brain states (e.g. viewing happy faces vs. viewing neutral faces) using data only from healthy controls (HC). Second, we tested the classifier using patterns of brain activation of a patient and a healthy control for the same stimuli. Finally, we tested if the classifier's predictions (predictive probabilities) for emotional and neutral face classification were different for healthy controls and depressed patients. Results: Predictive probabilities to patterns of brain activation to neutral faces in both groups of patients were significantly lower in comparison to the healthy controls. This difference was specific to neutral faces. There were no significant differences in predictive probabilities to patterns of brain activation to sad faces (sample 1) and happy faces (samples 2) between depressed patients and healthy controls. Conclusions: Our results suggest that the pattern of brain activation to neutral faces in depressed patients is not consistent with the pattern observed in healthy controls subject to the same stimuli. This difference in brain activation might underlie the behavioural misinterpretation of the neutral faces content by the depressed patients. © 2013 Oliveira et al

Pattern recognition and functional neuroimaging help to discriminate healthy adolescents at risk for mood disorders from low risk adolescents.

There are no known biological measures that accurately predict future development of psychiatric disorders in individual at-risk adolescents. We investigated whether machine learning and fMRI could help to: 1. differentiate healthy adolescents genetically at-risk for bipolar disorder and other Axis I psychiatric disorders from healthy adolescents at low risk of developing these disorders; 2. identify those healthy genetically at-risk adolescents who were most likely to develop future Axis I disorders

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms