Age-related differences in task-induced brain activation is not task specific: Multivariate pattern generalization between metacognition, cognition and perception

ABSTRACT Adolescence is associated with widespread maturation of brain structures and functional connectivity profiles that shift from local to more distributed and better integrated networks, which are active during a variety of cognitive tasks. Nevertheless, the approach to examine task‐induced developmental brain changes is function‐specific, leaving the question open whether functional maturation is specific to the particular cognitive demands of the task used, or generalizes across different tasks. In the present study we examine the hypothesis that functional brain maturation is driven by global changes in how the brain handles cognitive demands. Multivariate pattern classification analysis (MVPA) was used to examine whether age discriminative task‐induced activation patterns generalize across a wide range of information processing levels. 25 young (13‐years old) and 22 old (17‐years old) adolescents performed three conceptually different tasks of metacognition, cognition and visual processing. MVPA applied within each task indicated that task‐induced brain activation is consistent and reliably different between ages 13 and 17. These age‐discriminative activation patterns proved to be common across the different tasks used, despite the differences in cognitive demands and brain structures engaged by each of the three tasks. MVP classifiers trained to detect age‐discriminative patterns in brain activation during one task were significantly able to decode age from brain activation maps during execution of other tasks with accuracies between 63 and 75%. The results emphasize that age‐specific characteristics of task‐induced brain activation have to be understood at the level of brain‐wide networks that show maturational changes in their organization and processing efficacy during adolescence. HIGHLIGHTSMVPA examination of age‐related brain activity at 3 levels of information processing.MVPA revealed consistent task‐induced activity differences between ages 13 and 17.Age‐distinctive patterns generalize across metacognitive, cognitive and visual tasks.Functional brain maturation is driven by global, not function‐specific brain changes.

[1]  B. Luna,et al.  What has fMRI told us about the Development of Cognitive Control through Adolescence? , 2010, Brain and Cognition.

[2]  Eveline A. Crone,et al.  Structural brain development between childhood and adulthood: Convergence across four longitudinal samples , 2016, NeuroImage.

[3]  J. Duncan.  The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour , 2010, Trends in Cognitive Sciences.

[4]  Jonathan M. Campbell,et al.  Peabody Picture Vocabulary Test , 2010 .

[5]  N. De Stefano,et al.  Longitudinal changes in grey and white matter during adolescence , 2010, NeuroImage.

[6]  Adriana Galván,et al.  At risk of being risky: The relationship between “brain age” under emotional states and risk preference , 2017, Developmental Cognitive Neuroscience.

[7]  V. Menon,et al.  A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks , 2008, Proceedings of the National Academy of Sciences.

[8]  Michael J. Brammer,et al.  Effects of age and gender on neural networks of motor response inhibition: From adolescence to mid-adulthood , 2013, NeuroImage.

[9]  Peter Stiers,et al.  Distributed task coding throughout the multiple demand network of the human frontal–insular cortex , 2010, NeuroImage.

[10]  S. Blakemore,et al.  Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness , 2017, The Journal of Neuroscience.

[11]  G. Šimić,et al.  Extraordinary neoteny of synaptic spines in the human prefrontal cortex , 2011, Proceedings of the National Academy of Sciences.

[12]  Lin Ma,et al.  Alterations of functional connectivities from early to middle adulthood: Clues from multivariate pattern analysis of resting-state fMRI data , 2016, NeuroImage.

[13]  M. Corbetta,et al.  Separating Processes within a Trial in Event-Related Functional MRI II. Analysis , 2001, NeuroImage.

[14]  J. Cañive,et al.  Assessment and quantification of head motion in neuropsychiatric functional imaging research as applied to schizophrenia , 2007, Journal of the International Neuropsychological Society.

[15]  M. Delgado,et al.  Neural Correlates of Expected Risks and Returns in Risky Choice across Development , 2015, The Journal of Neuroscience.

[16]  Alan C. Evans,et al.  Brain development during childhood and adolescence: a longitudinal MRI study , 1999, Nature Neuroscience.

[17]  Stephen José Hanson,et al.  Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals , 2009, Psychological science.

[18]  Rainer Goebel,et al.  Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns , 2008, NeuroImage.

[19]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[20]  Peter Stiers,et al.  Developmental changes between ages 13 and 21years in the extent and magnitude of the BOLD response during decision making , 2011, NeuroImage.

[21]  Damien A. Fair,et al.  Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature , 2017, NeuroImage.

[22]  Alain Pitiot,et al.  Growth of White Matter in the Adolescent Brain: Role of Testosterone and Androgen Receptor , 2008, The Journal of Neuroscience.

[23]  Karl J. Friston,et al.  Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.

[24]  M. Stevens.  The contributions of resting state and task-based functional connectivity studies to our understanding of adolescent brain network maturation , 2016, Neuroscience & Biobehavioral Reviews.

[25]  A M Dale,et al.  Optimal experimental design for event‐related fMRI , 1999, Human brain mapping.

[26]  R. Poldrack.  Inferring Mental States from Neuroimaging Data: From Reverse Inference to Large-Scale Decoding , 2011, Neuron.

[27]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[28]  Andrew Simmons,et al.  Pattern classification of response inhibition in ADHD: Toward the development of neurobiological markers for ADHD , 2013, Human brain mapping.

[29]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

[30]  D. Margulies,et al.  Development of anterior cingulate functional connectivity from late childhood to early adulthood. , 2009, Cerebral cortex.

[31]  Thomas F. Nugent,et al.  Dynamic mapping of human cortical development during childhood through early adulthood. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Paul Mazaika.  - 1-Percent Signal Change for fMRI calculations , 2009 .

[33]  P. Huttenlocher,et al.  Regional differences in synaptogenesis in human cerebral cortex , 1997, The Journal of comparative neurology.

[34]  M. Ernst.  The triadic model perspective for the study of adolescent motivated behavior , 2014, Brain and Cognition.

[35]  Tomáš Paus,et al.  Growth of white matter in the adolescent brain: Myelin or axon? , 2010, Brain and Cognition.

[36]  R. Dahl.  Adolescent Brain Development: A Period of Vulnerabilities and Opportunities. Keynote Address , 2004, Annals of the New York Academy of Sciences.

[37]  Russell A. Poldrack,et al.  Analyses of regional-average activation and multivoxel pattern information tell complementary stories , 2012, Neuropsychologia.

[38]  Daniel L. Schacter,et al.  Neural Correlates of Metamemory: A Comparison of Feeling-of-Knowing and Retrospective Confidence Judgments , 2009, Journal of Cognitive Neuroscience.

[39]  J. Raven,et al.  Manual for Raven's progressive matrices and vocabulary scales , 1962 .

[40]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[41]  Russell A. Poldrack,et al.  Decoding Developmental Differences and Individual Variability in Response Inhibition Through Predictive Analyses Across Individuals , 2010, Front. Hum. Neurosci..

[42]  Ashley R. Smith,et al.  The dual systems model: Review, reappraisal, and reaffirmation , 2015, Developmental Cognitive Neuroscience.

[43]  Justin L. Vincent,et al.  Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.

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

[45]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[46]  Ronald R. Peeters,et al.  Mapping multiple visual areas in the human brain with a short fMRI sequence , 2006, NeuroImage.

[47]  Peter Stiers,et al.  Maturation of task-induced brain activation and long range functional connectivity in adolescence revealed by multivariate pattern classification , 2012, NeuroImage.

[48]  Erich Novak,et al.  Special issue , 2006, J. Complex..

[49]  Bart Larsen,et al.  An integrative model of the maturation of cognitive control. , 2015, Annual review of neuroscience.

[50]  Jonathan D. Power,et al.  Development of the Brain’s Functional Network Architecture , 2010, Neuropsychology Review.

[51]  J. Duncan,et al.  Common regions of the human frontal lobe recruited by diverse cognitive demands , 2000, Trends in Neurosciences.

[52]  Tamara van Gog,et al.  Reverse inference of memory retrieval processes underlying metacognitive monitoring of learning using multivariate pattern analysis , 2016, NeuroImage.

[53]  Seung-Schik Yoo,et al.  Head motion analysis during cognitive fMRI examination: Application in patients with schizophrenia , 2005, Neuroscience Research.

[54]  Geraint Rees,et al.  The development of metacognitive ability in adolescence , 2013, Consciousness and Cognition.

[55]  Marisa M. Silveri,et al.  Special Issue on the Adolescent Brain , 2016, Neuroscience & Biobehavioral Reviews.