Age‐related change in task‐evoked amygdala—prefrontal circuitry: A multiverse approach with an accelerated longitudinal cohort aged 4–22 years

The amygdala and its connections with medial prefrontal cortex (mPFC) play central roles in the development of emotional processes. While several studies have suggested that this circuitry exhibits functional changes across the first two decades of life, findings have been mixed – perhaps resulting from differences in analytic choices across studies. Here we used multiverse analyses to examine the robustness of task-based amygdala–mPFC function findings to analytic choices within the context of an accelerated longitudinal design (4-22 years- old; N=98; 183 scans; 1-3 scans/participant). Participants, recruited from the greater Los Angeles area, completed an event-related emotional face (fear, neutral) task. Parallel analyses varying in preprocessing and modeling choices found that age-related change estimates for amygdala reactivity were more robust than task-evoked amygdala–mPFC functional connectivity to varied analytical choices. Specification curves indicated evidence for age-related decreases in amygdala reactivity to faces, though within-participant changes in amygdala reactivity could not be differentiated from between-participant differences. In contrast, amygdala—mPFC functional connectivity results varied across methods much more, and evidence for age-related change in amygdala–mPFC connectivity was not consistent. Generalized psychophysiological interaction (gPPI) measurements of connectivity were especially sensitive to whether a deconvolution step was applied. Our findings demonstrate the importance of assessing the robustness of findings to analysis choices, although the age-related changes in our current work cannot be overinterpreted given low test-retest reliability. Together, these findings highlight both the challenges in estimating developmental change in longitudinal cohorts and the value of multiverse approaches in developmental neuroimaging for assessing robustness of results. (Preprint: https://www.biorxiv.org/content/10.1101/2021.10.08.463601v1). Key Points Multiverse analyses applied to fMRI data are valuable for determining the robustness of findings to varied analytical choices In the current study, age-related change estimates for amygdala reactivity were relatively robust to analytical decisions, though gPPI functional connectivity analyses were much more sensitive, leading some estimates to flip sign Both test-retest reliability and robustness to analytical choices are important considerations for developmental research

[1]  E. Leibenluft,et al.  Reliability of task‐evoked neural activation during face‐emotion paradigms: Effects of scanner and psychological processes , 2022, Human brain mapping.

[2]  Eric W. Bridgeford,et al.  A Guide for Quantifying and Optimizing Measurement Reliability for the Study of Individual Differences , 2022, bioRxiv.

[3]  D. Barch,et al.  Reliability and stability challenges in ABCD task fMRI data , 2021, NeuroImage.

[4]  J. Vogelstein,et al.  Moving Beyond Processing and Analysis-Related Variation in Neuroscience , 2021, bioRxiv.

[5]  Yong He,et al.  Developmental Sex Differences in Negative Emotion Decision-Making Dynamics: Computational Evidence and Amygdala-Prefrontal Pathways. , 2021, Cerebral cortex.

[6]  G. Perrault Bureau , 2021, La boussole du confiné.

[7]  Patrick Mair,et al.  Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity , 2021, NeuroImage.

[8]  Indrajeet Patil,et al.  performance: An R Package for Assessment, Comparison and Testing of Statistical Models , 2021, J. Open Source Softw..

[9]  T. Wager,et al.  Functional MRI Can Be Highly Reliable, but It Depends on What You Measure: A Commentary on Elliott et al. (2020) , 2021, Psychological science.

[10]  M. Del Giudice,et al.  A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions , 2021, Advances in Methods and Practices in Psychological Science.

[11]  S. Whittle,et al.  Neural Correlates of Emotion Regulation in Adolescents and Emerging Adults: A Meta-analytic Study , 2020, Biological Psychiatry.

[12]  B. Luna,et al.  Considerations When Characterizing Adolescent Neurocognitive Development , 2020, Biological Psychiatry.

[13]  K. Mills,et al.  Opportunities for increased reproducibility and replicability of developmental neuroimaging , 2019, Developmental Cognitive Neuroscience.

[14]  Eva H. Telzer,et al.  Longitudinal changes in amygdala, hippocampus and cortisol development following early caregiving adversity , 2019, Developmental Cognitive Neuroscience.

[15]  Ricardo Pio Monti,et al.  Neuroimaging: into the Multiverse , 2020, bioRxiv.

[16]  C. Grady,et al.  Influence of sample size and analytic approach on stability and interpretation of brain‐behavior correlations in task‐related fMRI data , 2020, Human brain mapping.

[17]  Timothy O. Laumann,et al.  Towards Reproducible Brain-Wide Association Studies , 2020, bioRxiv.

[18]  Leif D. Nelson,et al.  Specification curve analysis , 2020, Nature Human Behaviour.

[19]  Richard B. Lopez,et al.  Neural indicators of food cue reactivity, regulation, and valuation and their associations with body composition and daily eating behavior , 2020, Social cognitive and affective neuroscience.

[20]  Ruth Seurinck,et al.  The empirical replicability of task-based fMRI as a function of sample size , 2020, NeuroImage.

[21]  Christian Windischberger,et al.  Reproducibility of amygdala activation in facial emotion processing at 7T , 2020, NeuroImage.

[22]  Carey E. Priebe,et al.  Big Data Reproducibility: Applications in Brain Imaging and Genomics , 2020 .

[23]  Timothy O. Laumann,et al.  Removal of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity , 2019, NeuroImage.

[24]  Jennifer H. Pfeifer,et al.  Improving practices and inferences in developmental cognitive neuroscience , 2019, Developmental Cognitive Neuroscience.

[25]  Anders M. Dale,et al.  Correction of respiratory artifacts in MRI head motion estimates , 2018, bioRxiv.

[26]  Luca Turella,et al.  Variability in the analysis of a single neuroimaging dataset by many teams , 2019, Nature.

[27]  B. Biswal,et al.  Understanding psychophysiological interaction and its relations to beta series correlation , 2019, bioRxiv.

[28]  Carey E. Priebe,et al.  Eliminating accidental deviations to minimize generalization error: applications in connectomics and genomics , 2019, bioRxiv.

[29]  V. Menon,et al.  Development of Human Emotion Circuits Investigated Using a Big-Data Analytic Approach: Stability, Reliability, and Robustness , 2019, The Journal of Neuroscience.

[30]  M. Milham,et al.  Harnessing reliability for neuroscience research , 2019, Nature Human Behaviour.

[31]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[32]  M. Achterberg,et al.  Genetic and environmental influences on MRI scan quantity and quality , 2019, Developmental Cognitive Neuroscience.

[33]  Jonathan D. Power,et al.  Distinctions among real and apparent respiratory motions in human fMRI data , 2019, NeuroImage.

[34]  Nicholas J. Buser,et al.  Variations in structural MRI quality significantly impact commonly used measures of brain anatomy , 2019, bioRxiv.

[35]  Nicholas J. Buser,et al.  Variations in Structural MRI Quality Significantly Impact Commonly-Used Measures of Brain Anatomy , 2019, bioRxiv.

[36]  Heidi C. Meyer,et al.  Translating Developmental Neuroscience to Understand Risk for Psychiatric Disorders. , 2019, The American journal of psychiatry.

[37]  Jennifer H. Pfeifer,et al.  Affective reactivity during adolescence: Associations with age, puberty and testosterone , 2019, Cortex.

[38]  Andrew K. Przybylski,et al.  The association between adolescent well-being and digital technology use , 2019, Nature Human Behaviour.

[39]  Derek Evan Nee,et al.  Correspondence: fMRI replicability depends upon sufficient individual-level data , 2018 .

[40]  Moriah E. Thomason,et al.  Amygdala habituation and uncinate fasciculus connectivity in adolescence: A multi-modal approach , 2018, NeuroImage.

[41]  Annemarie van der Linden,et al.  Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic Patterns and illustrates their relationship with the global signal , 2018, NeuroImage.

[42]  Shinpei Yoshimura,et al.  Affect Labeling disrupts amygdala activity in response to affective stimuli , 2018, The Proceedings of the Annual Convention of the Japanese Psychological Association.

[43]  Roberto Viviani,et al.  Repeated fMRI in measuring the activation of the amygdala without habituation when viewing faces displaying negative emotions , 2018, PloS one.

[44]  Colin L. Sauder,et al.  Robust is not necessarily reliable: From within-subjects fMRI contrasts to between-subjects comparisons , 2018, NeuroImage.

[45]  Thomas E. Nichols,et al.  Exploring the impact of analysis software on task fMRI results , 2018, bioRxiv.

[46]  Eva H. Telzer,et al.  Methodological considerations for developmental longitudinal fMRI research , 2018, Developmental Cognitive Neuroscience.

[47]  Stephen M. Smith,et al.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data , 2017, NeuroImage.

[48]  K. Mills,et al.  Longitudinal modeling in developmental neuroimaging research: Common challenges, and solutions from developmental psychology , 2017, Developmental Cognitive Neuroscience.

[49]  Jennifer H. Pfeifer,et al.  Current methods and limitations for longitudinal fMRI analysis across development , 2017, Developmental Cognitive Neuroscience.

[50]  Behnaz Yousefi,et al.  Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal , 2017, NeuroImage.

[51]  Annchen R. Knodt,et al.  The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences , 2017, Behavior research methods.

[52]  Bart Larsen,et al.  Development of White Matter Microstructure and Intrinsic Functional Connectivity Between the Amygdala and Ventromedial Prefrontal Cortex: Associations With Anxiety and Depression , 2017, Biological Psychiatry.

[53]  Jonathan P. Roiser,et al.  Unreliability of putative fMRI biomarkers during emotional face processing , 2017, NeuroImage.

[54]  Nora C. Vetter,et al.  Test-retest reliability of longitudinal task-based fMRI: Implications for developmental studies , 2017, Developmental Cognitive Neuroscience.

[55]  Christos Davatzikos,et al.  Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity , 2017, NeuroImage.

[56]  Bharat B. Biswal,et al.  Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution , 2017, bioRxiv.

[57]  Xin Di,et al.  Imperfect (de)convolution may introduce spurious psychophysiological interactions and how to avoid it , 2017, Human brain mapping.

[58]  Jiqiang Guo,et al.  Stan: A Probabilistic Programming Language. , 2017, Journal of statistical software.

[59]  H. Schielzeth,et al.  The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded , 2016, bioRxiv.

[60]  Rebecca E. Martin,et al.  The transition from childhood to adolescence is marked by a general decrease in amygdala reactivity and an affect-specific ventral-to-dorsal shift in medial prefrontal recruitment , 2016, Developmental Cognitive Neuroscience.

[61]  B. J. Casey,et al.  vlPFC–vmPFC–Amygdala Interactions Underlie Age-Related Differences in Cognitive Regulation of Emotion , 2016, Cerebral cortex.

[62]  Francis Tuerlinckx,et al.  Increasing Transparency Through a Multiverse Analysis , 2016, Perspectives on psychological science : a journal of the Association for Psychological Science.

[63]  Lorenzo Beretta,et al.  Nearest neighbor imputation algorithms: a critical evaluation , 2016, BMC Medical Informatics and Decision Making.

[64]  Christopher S. Monk,et al.  Altered Development of Amygdala-Anterior Cingulate Cortex Connectivity in Anxious Youth and Young Adults. , 2016, Biological psychiatry. Cognitive neuroscience and neuroimaging.

[65]  Mitchell H. Murdock,et al.  Dynamic changes in neural circuitry during adolescence are associated with persistent attenuation of fear memories , 2016, Nature Communications.

[66]  Christopher S. Monk,et al.  Age‐related changes in amygdala–frontal connectivity during emotional face processing from childhood into young adulthood , 2016, Human brain mapping.

[67]  Paul-Christian Buerkner,et al.  Bayesian Regression Models using Stan , 2016 .

[68]  Richard N. A. Henson,et al.  Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis , 2016, NeuroImage.

[69]  R. Mendes R: The R Project for Statistical Computing , 2016 .

[70]  Leif D. Nelson,et al.  Specification Curve: Descriptive and Inferential Statistics on All Reasonable Specifications , 2015 .

[71]  A. Gelman,et al.  Stan , 2015 .

[72]  R. Sullivan,et al.  Mechanisms and functional implications of social buffering in infants: Lessons from animal models , 2015, Social neuroscience.

[73]  Eva H. Telzer,et al.  “The Cooties Effect”: Amygdala Reactivity to Opposite- versus Same-sex Faces Declines from Childhood to Adolescence , 2015, Journal of Cognitive Neuroscience.

[74]  Kevin N Ochsner,et al.  Concurrent and lasting effects of emotion regulation on amygdala response in adolescence and young adulthood. , 2015, Developmental science.

[75]  Jonathan D. Clark,et al.  Typical and Atypical Neurodevelopment for Face Specialization: An fMRI Study , 2015, Journal of autism and developmental disorders.

[76]  Roland N. Boubela,et al.  fMRI measurements of amygdala activation are confounded by stimulus correlated signal fluctuation in nearby veins draining distant brain regions , 2015, Scientific Reports.

[77]  K. Peacock,et al.  The developmental examination , 2015, Journal of paediatrics and child health.

[78]  K. Pelphrey,et al.  Preschool Anxiety Disorders Predict Different Patterns of Amygdala-Prefrontal Connectivity at School-Age , 2015, PloS one.

[79]  E. Crone,et al.  Changing brains: how longitudinal functional magnetic resonance imaging studies can inform us about cognitive and social-affective growth trajectories. , 2015, Wiley interdisciplinary reviews. Cognitive science.

[80]  Peter Kirsch,et al.  Amygdala habituation: A reliable fMRI phenotype , 2014, NeuroImage.

[81]  J. Carlin,et al.  Beyond Power Calculations , 2014, Perspectives on psychological science : a journal of the Association for Psychological Science.

[82]  M. Petrides,et al.  Architecture and morphology of the human ventromedial prefrontal cortex , 2014, The European journal of neuroscience.

[83]  Moors Pieter,et al.  Test-retest reliability. , 2014 .

[84]  Christina B. Young,et al.  Amygdala Subregional Structure and Intrinsic Functional Connectivity Predicts Individual Differences in Anxiety During Early Childhood , 2014, Biological Psychiatry.

[85]  René S. Kahn,et al.  Functional differences in emotion processing during adolescence and early adulthood , 2014, NeuroImage.

[86]  Moriah E. Thomason,et al.  Age-related changes in the structure and function of prefrontal cortex–amygdala circuitry in children and adolescents: A multi-modal imaging approach , 2014, NeuroImage.

[87]  Cyril R. Pernet,et al.  Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers , 2014, Front. Neurosci..

[88]  Keith Bush,et al.  A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI , 2014, NeuroImage.

[89]  Andrew Gelman,et al.  Data-dependent analysis—a "garden of forking paths"— explains why many statistically significant comparisons don't hold up. , 2014 .

[90]  A. Gelman,et al.  The statistical crisis in science , 2014 .

[91]  Christos Davatzikos,et al.  Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth , 2013, NeuroImage.

[92]  Mike Angstadt,et al.  Test-retest reliability of amygdala response to emotional faces. , 2013, Psychophysiology.

[93]  Chris Beeley,et al.  Web Application Development with R Using Shiny , 2013 .

[94]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[95]  Todd A. Hare,et al.  A Developmental Shift from Positive to Negative Connectivity in Human Amygdala–Prefrontal Circuitry , 2013, The Journal of Neuroscience.

[96]  Eva H. Telzer,et al.  Amygdala Sensitivity to Race Is Not Present in Childhood but Emerges over Adolescence , 2013, Journal of Cognitive Neuroscience.

[97]  Li Qingyang,et al.  Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC) , 2013 .

[98]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[99]  Sterling C. Johnson,et al.  A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches , 2012, NeuroImage.

[100]  Chris Rorden,et al.  Age-specific CT and MRI templates for spatial normalization , 2012, NeuroImage.

[101]  Timothy E. J. Behrens,et al.  Tools of the trade: psychophysiological interactions and functional connectivity. , 2012, Social cognitive and affective neuroscience.

[102]  Mark A. Elliott,et al.  Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.

[103]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[104]  Katherine D. Kinzler,et al.  The contribution of emotion and cognition to moral sensitivity: a neurodevelopmental study. , 2012, Cerebral cortex.

[105]  Neal D Ryan,et al.  Neural Systems of Threat Processing in Adolescents: Role of Pubertal Maturation and Relation to Measures of Negative Affect , 2011, Developmental neuropsychology.

[106]  Carrie L. Masten,et al.  Entering Adolescence: Resistance to Peer Influence, Risky Behavior, and Neural Changes in Emotion Reactivity , 2011, Neuron.

[107]  Susan B Perlman,et al.  Developing connections for affective regulation: age-related changes in emotional brain connectivity. , 2011, Journal of experimental child psychology.

[108]  L. Terhorst,et al.  Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders in a rural high school population. , 2011, Journal of child and adolescent psychiatric nursing : official publication of the Association of Child and Adolescent Psychiatric Nurses, Inc.

[109]  Margot J. Taylor,et al.  The changing face of emotion: age-related patterns of amygdala activation to salient faces. , 2011, Social cognitive and affective neuroscience.

[110]  Patrick J Curran,et al.  Twelve Frequently Asked Questions About Growth Curve Modeling , 2010, Journal of cognition and development : official journal of the Cognitive Development Society.

[111]  Alessandra M Passarotti,et al.  Neural correlates of incidental and directed facial emotion processing in adolescents and adults. , 2009, Social cognitive and affective neuroscience.

[112]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[113]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[114]  Karine Sergerie,et al.  The role of the amygdala in emotional processing: A quantitative meta-analysis of functional neuroimaging studies , 2008, Neuroscience & Biobehavioral Reviews.

[115]  Ellen Leibenluft,et al.  A Developmental Examination of Amygdala Response to Facial Expressions , 2008, Journal of Cognitive Neuroscience.

[116]  G. Glover,et al.  Biological Substrates of Emotional Reactivity and Regulation in Adolescence During an Emotional Go-Nogo Task , 2008, Biological Psychiatry.

[117]  A. Baird,et al.  Eye-Gaze Direction Modulates Race-Related Amygdala Activity , 2008 .

[118]  Ralph Adolphs,et al.  Fear, faces, and the human amygdala , 2008, Current Opinion in Neurobiology.

[119]  D. Lundqvist,et al.  Facial expressions of emotion (KDEF): Identification under different display-duration conditions , 2008, Behavior research methods.

[120]  K. Berridge Faculty Opinions recommendation of Putting feelings into words: affect labeling disrupts amygdala activity in response to affective stimuli. , 2007 .

[121]  Matthew D. Lieberman,et al.  Putting Feelings Into Words , 2007, Psychological science.

[122]  William D S Killgore,et al.  Unconscious processing of facial affect in children and adolescents , 2007, Social neuroscience.

[123]  Jae-Hun Kim,et al.  Spatial accuracy of fMRI activation influenced by volume- and surface-based spatial smoothing techniques , 2007, NeuroImage.

[124]  D. Yurgelun-Todd,et al.  Fear-related activity in the prefrontal cortex increases with age during adolescence: A preliminary fMRI study , 2006, Neuroscience Letters.

[125]  S. Moriceau,et al.  Maternal presence serves as a switch between learning fear and attraction in infancy , 2006, Nature Neuroscience.

[126]  Tom Johnstone,et al.  Stability of amygdala BOLD response to fearful faces over multiple scan sessions , 2005, NeuroImage.

[127]  N. Duan,et al.  Sample designs and sampling methods for the Collaborative Psychiatric Epidemiology Studies (CPES) , 2004, International journal of methods in psychiatric research.

[128]  N. Lazar,et al.  Maturation of cognitive processes from late childhood to adulthood. , 2004, Child development.

[129]  L. Jonkman,et al.  Developmental differences in behavioral and event-related brain responses associated with response preparation and inhibition in a go/nogo task. , 2003, Psychophysiology.

[130]  Karl J. Friston,et al.  Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution , 2003, NeuroImage.

[131]  Thomas E. Nichols,et al.  Optimization of experimental design in fMRI: a general framework using a genetic algorithm , 2003, NeuroImage.

[132]  N. Glenn Distinguishing Age, Period, and Cohort Effects , 2003 .

[133]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[134]  Francesco Fera,et al.  The Amygdala Response to Emotional Stimuli: A Comparison of Faces and Scenes , 2002, NeuroImage.

[135]  Jennifer H Barrett,et al.  Association studies. , 2002, Methods in molecular biology.

[136]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[137]  E Zarahn,et al.  Cortical brain regions engaged by masked emotional faces in adolescents and adults: an fMRI study. , 2001, Emotion.

[138]  G. Glover,et al.  Error‐related brain activation during a Go/NoGo response inhibition task , 2001, Human brain mapping.

[139]  William D S Killgore,et al.  Sex-specific developmental changes in amygdala responses to affective faces , 2001, Neuroreport.

[140]  Bruce W. Dearstyne,et al.  Methodological considerations , 1989 .

[141]  B. Chorpita,et al.  Assessment of symptoms of DSM-IV anxiety and depression in children: a revised child anxiety and depression scale. , 2000, Behaviour research and therapy.

[142]  B. Birmaher,et al.  Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): a replication study. , 1999, Journal of the American Academy of Child and Adolescent Psychiatry.

[143]  Nirbhay N. Singh,et al.  Facial Expressions of Emotion , 1998 .

[144]  Karl J. Friston,et al.  Functional MRI , 1997 .

[145]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[146]  T. Achenbach Integrative Guide for the 1991 CBCL/4-18, Ysr, and Trf Profiles , 1991 .

[147]  D. Cicchetti,et al.  Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. , 1981, American journal of mental deficiency.

[148]  A W Blackler,et al.  Developmental differences. , 1980, Science.

[149]  Sevilla [The transition from childhood to adolescence]. , 1965, Maroc medical.