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Fleury MN, Binding LP, Taylor P, Xiao F, Giampiccolo D, Caciagli L, Buck S, Winston GP, Thompson PJ, Baxendale S, Koepp MJ, Duncan JS, Sidhu MK. Predictors of long-term memory and network connectivity 10 years after anterior temporal lobe resection. Epilepsia 2024. [PMID: 38990127 DOI: 10.1111/epi.18058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/25/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
OBJECTIVE Anterior temporal lobe resection (ATLR) effectively controls seizures in medically refractory temporal lobe epilepsy but risks significant episodic memory decline. Beyond 1 year postoperatively, the influence of preoperative clinical factors on episodic memory and long-term network plasticity remain underexplored. Ten years post-ATLR, we aimed to determine biomarkers of successful memory network reorganization and establish presurgical features' lasting impact on memory function. METHODS Twenty-five ATLR patients (12 left-sided) and 10 healthy controls underwent a memory-encoding functional magnetic resonance imaging paradigm alongside neuropsychometry 10 years postsurgery. Generalized psychophysiological interaction analyses modeled network functional connectivity of words/faces remembered, seeding from the medial temporal lobes (MTLs). Differences in successful memory connectivity were assessed between controls and left/right ATLR. Multivariate regressions and mixed-effect models probed preoperative phenotypes' effects on long-term memory outcomes. RESULTS Ten years post-ATLR, lower baseline functioning (verbal and performance intelligence quotient) and a focal memory impairment preoperatively predicted worse long-term memory outcomes. Poorer verbal memory was significantly associated with longer epilepsy duration and earlier onset age. Relative to controls, successful word and face encoding involved increased functional connectivity from both or remnant MTL seeds and contralesional parahippocampus/hippocampus after left/right ATLR. Irrespective of surgical laterality, successful memory encoding correlated with increased MTL-seeded connectivity to frontal (bilateral insula, right anterior cingulate), right parahippocampal, and bilateral fusiform gyri. Ten years postsurgery, better memory performance was correlated with contralateral frontal plasticity, which was disrupted with longer epilepsy duration. SIGNIFICANCE Our findings underscore the enduring nature of functional network reorganizations to provide long-term cognitive support. Ten years post-ATLR, successful memory formation featured stronger connections near resected areas and contralateral regions. Preoperative network disruption possibly influenced effectiveness of postoperative plasticity. These findings are crucial for enhancing long-term memory prediction and strategies for lasting memory rehabilitation.
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Affiliation(s)
- Marine N Fleury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
| | - Lawrence P Binding
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
- Department of Computer Science, UCL Centre for Medical Image Computing, London, UK
| | - Peter Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- CNNP Lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, Newcastle, UK
| | - Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- Department of Neurosurgery, Institute of Neurosciences, Cleveland Clinic London, London, UK
| | - Lorenzo Caciagli
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy Center, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sarah Buck
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
- Division of Neurology, Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Pamela J Thompson
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Psychology Department, Epilepsy Society, Buckinghamshire, UK
| | - Sallie Baxendale
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- Psychology Department, Epilepsy Society, Buckinghamshire, UK
| | - Matthias J Koepp
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
| | - Meneka K Sidhu
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
- MRI Unit, Epilepsy Society, Buckinghamshire, UK
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Beck J, Chyl K, Dębska A, Łuniewska M, van Atteveldt N, Jednoróg K. Letter-speech sound integration in typical reading development during the first years of formal education. Child Dev 2024; 95:e236-e252. [PMID: 38396333 DOI: 10.1111/cdev.14080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
This study investigated the neural basis of letter and speech sound (LS) integration in 53 typical readers (35 girls, all White) during the first 2 years of reading education (ages 7-9). Changes in both sensory (multisensory vs unisensory) and linguistic (congruent vs incongruent) aspects of LS integration were examined. The left superior temporal cortex and bilateral inferior frontal cortex showed increasing activation for multisensory over unisensory LS over time, driven by reduced activation to speech sounds. No changes were noted in the congruency effect. However, at age nine, heightened activation to incongruent over congruent LS pairs were observed, correlating with individual differences in reading development. This suggests that the incongruency effect evolves at varying rates depending on reading skills.
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Affiliation(s)
- Joanna Beck
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
- Bioimaging Research Center, Institute of Physiology and Pathology of Hearing, Warsaw, Poland, Kajetany, Mazovia, Poland
| | - Katarzyna Chyl
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
- Educational Research Institute, Warsaw, Poland
| | - Agnieszka Dębska
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
| | - Magdalena Łuniewska
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
- Faculty of Psychology, University of Warsaw, Warsaw, Poland
| | - Nienke van Atteveldt
- Department of Clinical Developmental Psychology & Institute LEARN!, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Katarzyna Jednoróg
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
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3
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Tansey R, Graff K, Rai S, Merrikh D, Godfrey KJ, Vanderwal T, Bray S. Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks. Neurosci Biobehav Rev 2024; 162:105729. [PMID: 38763178 DOI: 10.1016/j.neubiorev.2024.105729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 05/21/2024]
Abstract
Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.
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Affiliation(s)
- Ryann Tansey
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Kirk Graff
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Shefali Rai
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Daria Merrikh
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kate J Godfrey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Signe Bray
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Turesky TK, Escalante E, Loh M, Gaab N. Longitudinal trajectories of brain development from infancy to school age and their relationship to literacy development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.29.601366. [PMID: 39005343 PMCID: PMC11244924 DOI: 10.1101/2024.06.29.601366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Reading is one of the most complex skills that we utilize daily, and it involves the early development and interaction of various lower-level subskills, including phonological processing and oral language. These subskills recruit brain structures, which begin to develop long before the skill manifests and exhibit rapid development during infancy. However, how longitudinal trajectories of early brain development in these structures supports long-term acquisition of literacy subskills and subsequent reading is unclear. Children underwent structural and diffusion MRI scanning at multiple timepoints between infancy and second grade and were tested for literacy subskills in preschool and decoding and word reading in early elementary school. We developed and implemented a reproducible pipeline to generate longitudinal trajectories of early brain development to examine associations between these trajectories and literacy (sub)skills. Furthermore, we examined whether familial risk of reading difficulty and a child's home literacy environment, two common literacy-related covariates, influenced those trajectories. Results showed that individual differences in curve features (e.g., intercepts and slopes) for longitudinal trajectories of volumetric, surface-based, and white matter organization measures in left-hemispheric reading-related regions and tracts were linked directly to phonological processing and indirectly to second-grade decoding and word reading skills via phonological processing. Altogether, these findings suggest that the brain bases of phonological processing, previously identified as the strongest behavioral predictor of reading and decoding skills, may already begin to develop early in infancy but undergo further refinement between birth and preschool. The present study underscores the importance of considering academic skill acquisition from the very beginning of life.
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James C, Müller D, Müller C, Van De Looij Y, Altenmüller E, Kliegel M, Van De Ville D, Marie D. Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living-A 23-year scoping review. Heliyon 2024; 10:e26674. [PMID: 38707392 PMCID: PMC11066598 DOI: 10.1016/j.heliyon.2024.e26674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/28/2024] [Accepted: 02/16/2024] [Indexed: 05/07/2024] Open
Abstract
Little is known about the simultaneous effects of non-pharmacological interventions (NPI) on healthy older adults' behavior and brain plasticity, as measured by psychometric instruments and magnetic resonance imaging (MRI). The purpose of this scoping review was to compile an extensive list of randomized controlled trials published from January 1, 2000, to August 31, 2023, of NPI for mitigating and countervailing age-related physical and cognitive decline and associated cerebral degeneration in healthy elderly populations with a mean age of 55 and over. After inventorying the NPI that met our criteria, we divided them into six classes: single-domain cognitive, multi-domain cognitive, physical aerobic, physical non-aerobic, combined cognitive and physical aerobic, and combined cognitive and physical non-aerobic. The ultimate purpose of these NPI was to enhance individual autonomy and well-being by bolstering functional capacity that might transfer to activities of daily living. The insights from this study can be a starting point for new research and inform social, public health, and economic policies. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist served as the framework for this scoping review, which includes 70 studies. Results indicate that medium- and long-term interventions combining non-aerobic physical exercise and multi-domain cognitive interventions best stimulate neuroplasticity and protect against age-related decline and that outcomes may transfer to activities of daily living.
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Affiliation(s)
- C.E. James
- Geneva Musical Minds Lab (GEMMI Lab), Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland HES-SO, Avenue de Champel 47, 1206, Geneva, Switzerland
- Faculty of Psychology and Educational Sciences, University of Geneva, Boulevard Carl-Vogt 101, 1205, Geneva, Switzerland
| | - D.M. Müller
- Geneva Musical Minds Lab (GEMMI Lab), Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland HES-SO, Avenue de Champel 47, 1206, Geneva, Switzerland
| | - C.A.H. Müller
- Geneva Musical Minds Lab (GEMMI Lab), Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland HES-SO, Avenue de Champel 47, 1206, Geneva, Switzerland
| | - Y. Van De Looij
- Geneva Musical Minds Lab (GEMMI Lab), Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland HES-SO, Avenue de Champel 47, 1206, Geneva, Switzerland
- Division of Child Development and Growth, Department of Pediatrics, School of Medicine, University of Geneva, 6 Rue Willy Donzé, 1205 Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Animal Imaging and Technology Section, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH F1 - Station 6, 1015, Lausanne, Switzerland
| | - E. Altenmüller
- Hannover University of Music, Drama and Media, Institute for Music Physiology and Musicians' Medicine, Neues Haus 1, 30175, Hannover, Germany
- Center for Systems Neuroscience, Bünteweg 2, 30559, Hannover, Germany
| | - M. Kliegel
- Faculty of Psychology and Educational Sciences, University of Geneva, Boulevard Carl-Vogt 101, 1205, Geneva, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Switzerland, Chemin de Pinchat 22, 1207, Carouge, Switzerland
| | - D. Van De Ville
- Ecole polytechnique fédérale de Lausanne (EPFL), Neuro-X Institute, Campus Biotech, 1211 Geneva, Switzerland
- University of Geneva, Department of Radiology and Medical Informatics, Faculty of Medecine, Campus Biotech, 1211 Geneva, Switzerland
| | - D. Marie
- Geneva Musical Minds Lab (GEMMI Lab), Geneva School of Health Sciences, University of Applied Sciences and Arts Western Switzerland HES-SO, Avenue de Champel 47, 1206, Geneva, Switzerland
- CIBM Center for Biomedical Imaging, Cognitive and Affective Neuroimaging Section, University of Geneva, 1211, Geneva, Switzerland
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Zhao Y, Caffo BS, Luo X. Longitudinal regression of covariance matrix outcomes. Biostatistics 2024; 25:385-401. [PMID: 36451549 DOI: 10.1093/biostatistics/kxac045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/25/2022] [Accepted: 11/02/2022] [Indexed: 02/17/2024] Open
Abstract
In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W 10th Street, Indianapolis, IN 46202, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
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Mulder JD, Dobbelaar S, Achterberg M. Behavioral and neural responses to social rejection: Individual differences in developmental trajectories across childhood and adolescence. Dev Cogn Neurosci 2024; 66:101365. [PMID: 38493526 PMCID: PMC10958064 DOI: 10.1016/j.dcn.2024.101365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 02/09/2024] [Accepted: 03/10/2024] [Indexed: 03/19/2024] Open
Abstract
Dealing with social rejection is challenging, especially during childhood when behavioral and neural responses to social rejection are still developing. In the current longitudinal study, we used a Bayesian multilevel growth curve model to describe individual differences in the development of behavioral and neural responses to social rejection in a large sample (n > 500). We found a peak in aggression following negative feedback (compared to neutral feedback) during late childhood, as well as individual differences during this developmental phase, possibly suggesting a sensitive window for dealing with social rejection across late childhood. Moreover, we found evidence for individual differences in the linear development of neural responses to social rejection in our three brain regions of interest: The anterior insula, the medial prefrontal cortex, and the dorsolateral prefrontal cortex. In addition to providing insights in the individual trajectories of dealing with social rejection during childhood, this study also makes a meaningful methodological contribution: Our statistical analysis strategy (and can be found in this study's online supplementary materials at https://jeroendmulder.github.io/social-emotion-regulation/) can be used as an example on how to take into account the many complexities of developmental neuroimaging datasets, while still enabling researchers to answer interesting questions about individual-level relationships.
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Affiliation(s)
- Jeroen D Mulder
- Department of Methodology and Statistics, Faculty of Social and Behavioral Sciences, Utrecht University, the Netherlands
| | - Simone Dobbelaar
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Leiden Consortium Individual Development, Faculty of Social and Behavioral Sciences, Leiden University, the Netherlands
| | - Michelle Achterberg
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Leiden Consortium Individual Development, Faculty of Social and Behavioral Sciences, Leiden University, the Netherlands.
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Bonte M, Brem S. Unraveling individual differences in learning potential: A dynamic framework for the case of reading development. Dev Cogn Neurosci 2024; 66:101362. [PMID: 38447471 PMCID: PMC10925938 DOI: 10.1016/j.dcn.2024.101362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 02/02/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024] Open
Abstract
Children show an enormous capacity to learn during development, but with large individual differences in the time course and trajectory of learning and the achieved skill level. Recent progress in developmental sciences has shown the contribution of a multitude of factors including genetic variation, brain plasticity, socio-cultural context and learning experiences to individual development. These factors interact in a complex manner, producing children's idiosyncratic and heterogeneous learning paths. Despite an increasing recognition of these intricate dynamics, current research on the development of culturally acquired skills such as reading still has a typical focus on snapshots of children's performance at discrete points in time. Here we argue that this 'static' approach is often insufficient and limits advancements in the prediction and mechanistic understanding of individual differences in learning capacity. We present a dynamic framework which highlights the importance of capturing short-term trajectories during learning across multiple stages and processes as a proxy for long-term development on the example of reading. This framework will help explain relevant variability in children's learning paths and outcomes and fosters new perspectives and approaches to study how children develop and learn.
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Affiliation(s)
- Milene Bonte
- Department of Cognitive Neuroscience and Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland; URPP Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland
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9
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Borchers LR, Yuan JP, Leong JK, Jo B, Chahal R, Ryu J, Nam A, Coury SM, Gotlib IH. Sex-Specific Vulnerability to Externalizing Problems: Sensitivity to Early Stress and Nucleus Accumbens Activation Over Adolescence. Biol Psychiatry 2024:S0006-3223(24)00038-6. [PMID: 38272286 PMCID: PMC11266527 DOI: 10.1016/j.biopsych.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/05/2024] [Accepted: 01/11/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND Exposure and sensitivity to early-life stress (ELS) are related to increased risk for psychopathology in adolescence. While cross-sectional studies have reported blunted nucleus accumbens (NAcc) activation in the context of these associations, researchers have not yet assessed the effects of ELS on developmental trajectories of activation. We examined whether trajectories are affected by stress and the moderating role of biological sex in predicting vulnerability to symptoms of psychopathology. METHODS Adolescents (n = 173) completed 3 assessments at 2-year intervals across puberty (ages 9-18 years). At baseline, we assessed objective ELS and stress sensitivity using the Traumatic Events Screening Inventory for Children. At all time points, we assessed NAcc activation using the Monetary Incentive Delay task and externalizing, internalizing, and total problems using the Youth Self-Report. We examined correlations between NAcc trajectories (extracted using linear mixed-effects models) with ELS and stress sensitivity and conducted multivariate regression analysis to examine the interaction of NAcc trajectories and biological sex in predicting symptoms of psychopathology. RESULTS Symptoms increased over adolescence. Stress sensitivity, but not objective ELS, was associated with decreasing trajectories of NAcc activation. Biological sex interacted with NAcc trajectories to predict psychopathology; boys, but not girls, with decreasing NAcc activation had more severe externalizing problems in adolescence. These findings were replicated in the putamen and caudate but not in the medial prefrontal cortex or control brain regions. CONCLUSIONS NAcc activation may be a sex-specific marker of externalizing problems in adolescence. Efforts to reduce stress sensitivity may help to decrease symptoms of psychopathology in adolescent boys.
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Affiliation(s)
- Lauren R Borchers
- Department of Psychology, Stanford University, Stanford, California.
| | - Justin P Yuan
- Department of Psychology, Stanford University, Stanford, California
| | - Josiah K Leong
- Department of Psychological Science, University of Arkansas, Fayetteville, Arkansas
| | - Booil Jo
- Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford, California
| | - Rajpreet Chahal
- Department of Psychology, Stanford University, Stanford, California
| | - Joshua Ryu
- Department of Psychology, Stanford University, Stanford, California
| | - Andrew Nam
- Department of Psychology, Stanford University, Stanford, California
| | - Saché M Coury
- Department of Psychology, Stanford University, Stanford, California
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, California
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10
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Chen DY, Di X, Biswal B. Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: A longitudinal fMRI study. Hum Brain Mapp 2024; 45:e26515. [PMID: 38183372 PMCID: PMC10789211 DOI: 10.1002/hbm.26515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 01/08/2024] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold (BH) task is commonly used to understand cerebrovascular reactivity (CVR) in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's BH data set from the Nathan Kline Institute (NKI) Rockland Sample (aged 6-18 years old at enrollment). A general linear model approach was applied to derive CVR from BH data. To model both the longitudinal and cross-sectional effects of age on BH response, we used mixed-effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased BH BOLD signals in multiple networks across age, in which linear and logarithmic mixed-effects models provided the best fit with the lowest Akaike information criterion scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes that occur with age.
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Affiliation(s)
- Donna Y. Chen
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
- Rutgers Biomedical and Health SciencesRutgers School of Graduate StudiesNewarkNew JerseyUSA
| | - Xin Di
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Bharat Biswal
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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11
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McCormick EM, Byrne ML, Flournoy JC, Mills KL, Pfeifer JH. The Hitchhiker's guide to longitudinal models: A primer on model selection for repeated-measures methods. Dev Cogn Neurosci 2023; 63:101281. [PMID: 37536082 PMCID: PMC10412784 DOI: 10.1016/j.dcn.2023.101281] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 01/30/2023] [Accepted: 07/15/2023] [Indexed: 08/05/2023] Open
Abstract
Longitudinal data are becoming increasingly available in developmental neuroimaging. To maximize the promise of this wealth of information on how biology, behavior, and cognition change over time, there is a need to incorporate broad and rigorous training in longitudinal methods into the repertoire of developmental neuroscientists. Fortunately, these models have an incredibly rich tradition in the broader developmental sciences that we can draw from. Here, we provide a primer on longitudinal models, written in a beginner-friendly (and slightly irreverent) manner, with a particular focus on selecting among different modeling frameworks (e.g., multilevel versus latent curve models) to build the theoretical model of development a researcher wishes to test. Our aims are three-fold: (1) lay out a heuristic framework for longitudinal model selection, (2) build a repository of references that ground each model in its tradition of methodological development and practical implementation with a focus on connecting researchers to resources outside traditional neuroimaging journals, and (3) provide practical resources in the form of a codebook companion demonstrating how to fit these models. These resources together aim to enhance training for the next generation of developmental neuroscientists by providing a solid foundation for future forays into advanced modeling applications.
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Affiliation(s)
- Ethan M McCormick
- Methodology & Statistics Department, Institute of Psychology, Leiden University, Leiden, Netherlands; Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, United States; Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands.
| | - Michelle L Byrne
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia; Department of Psychology, University of Oregon, Eugene, United States
| | - John C Flournoy
- Department of Psychology, Harvard University, Cambridge, United States
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, United States
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12
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Liu J, Chen H, Cornea E, Gilmore JH, Gao W. Longitudinal developmental trajectories of functional connectivity reveal regional distribution of distinct age effects in infancy. Cereb Cortex 2023; 33:10367-10379. [PMID: 37585708 PMCID: PMC10545442 DOI: 10.1093/cercor/bhad288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 07/13/2023] [Indexed: 08/18/2023] Open
Abstract
Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.
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Affiliation(s)
- Janelle Liu
- Department of Biomedical Sciences, and Imaging, Cedars–Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States
| | - Haitao Chen
- Department of Biomedical Sciences, and Imaging, Cedars–Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27514, United States
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27514, United States
| | - Wei Gao
- Department of Biomedical Sciences, and Imaging, Cedars–Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, CA 90048, United States
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, United States
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13
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Chen DY, Di X, Biswal B. Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: a longitudinal fMRI study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522905. [PMID: 36712029 PMCID: PMC9881997 DOI: 10.1101/2023.01.05.522905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold task is commonly used to understand cerebrovascular reactivity in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's breath-hold dataset from the Nathan Kline Institute (NKI) Rockland Sample (ages 6 to 18 years old at enrollment). A general linear model (GLM) approach was applied to derive cerebrovascular reactivity from breath-hold data. To model both the longitudinal and cross-sectional effects of age on breath-hold response, we used mixed effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased breath-hold BOLD signal in multiple networks across age, in which linear and logarithmic mixed effects models provided the best fit with the lowest Akaike Information Criterion (AIC) scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes which occur with age.
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Affiliation(s)
- Donna Y. Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, US
- Rutgers Biomedical and Health Sciences, Rutgers School of Graduate Studies, Newark, NJ, US
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, US
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, US
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14
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Vaisvilaite L, Andersson M, Salami A, Specht K. Time of day dependent longitudinal changes in resting-state fMRI. Front Neurol 2023; 14:1166200. [PMID: 37475742 PMCID: PMC10354550 DOI: 10.3389/fneur.2023.1166200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/13/2023] [Indexed: 07/22/2023] Open
Abstract
Longitudinal studies have become more common in the past years due to their superiority over cross-sectional samples. In light of the ongoing replication crisis, the factors that may introduce variability in resting-state networks have been widely debated. This publication aimed to address the potential sources of variability, namely, time of day, sex, and age, in longitudinal studies within individual resting-state fMRI data. DCM was used to analyze the fMRI time series, extracting EC connectivity measures and parameters that define the BOLD signal. In addition, a two-way ANOVA was used to assess the change in EC and parameters that define the BOLD signal between data collection waves. The results indicate that time of day and gender have significant model evidence for the parameters that define the BOLD signal but not EC. From the ANOVA analysis, findings indicate that there was a significant change in the two nodes of the DMN and their connections with the fronto-parietal network. Overall, these findings suggest that in addition to age and gender, which are commonly accounted for in the fMRI data collection, studies should note the time of day, possibly treating it as a covariate in longitudinal samples.
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Affiliation(s)
- Liucija Vaisvilaite
- ReState Research Group, Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical and Imaging Visualization Centre, Haukel and University Hospital, Bergen, Norway
| | - Micael Andersson
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Alireza Salami
- Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
- Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- Ageing Research Center, Karolinska Institute, Stockholm, Sweden
| | - Karsten Specht
- ReState Research Group, Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical and Imaging Visualization Centre, Haukel and University Hospital, Bergen, Norway
- Department of Education, UiT/The Arctic University of Norway, Tromsø, Norway
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15
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McGuckian TB, Wilson PH, Johnston RD, Rahimi-Golkhandan S, Piek J, Green D, Rogers JM, Maruff P, Steenbergen B, Ruddock S. Development of complex executive function over childhood: Longitudinal growth curve modeling of performance on the Groton Maze Learning Task. Child Dev 2023; 94:648-658. [PMID: 36593650 DOI: 10.1111/cdev.13888] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
This longitudinal study modeled children's complex executive function (EF) development using the Groton Maze Learning Task (GMLT). Using a cohort-sequential design, 147 children (61 males, 5.5-11 years) were recruited from six multicultural primary schools in Melbourne and Perth, Australia. Race/ethnicity data were not available. Children were assessed on the GMLT at 6-month intervals over 2-years between 2010 and 2012. Growth curve models describe age-related change from 5.5 to 12.5 years old. Results showed a quadratic growth trajectory on each measure of error-that is, those that reflect visuospatial memory, executive control (or the ability to apply rules for action), and complex EF. The ability to apply rules for action, while a rate-limiting factor in complex EF, develops rapidly over early-to-mid childhood.
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Affiliation(s)
- Thomas B McGuckian
- Healthy Brain and Mind Research Centre, Australian Catholic University, Melbourne, Victoria, Australia.,School of Behavioural and Health Sciences, Australian Catholic University, Brisbane, Queensland, Australia
| | - Peter H Wilson
- Healthy Brain and Mind Research Centre, Australian Catholic University, Melbourne, Victoria, Australia.,School of Behavioural and Health Sciences, Australian Catholic University, Brisbane, Queensland, Australia
| | - Rich D Johnston
- School of Behavioural and Health Sciences, Australian Catholic University, Brisbane, Queensland, Australia.,Sports Performance, Recovery, Injury and New Technologies (SPRINT) Research Centre, Australian Catholic University, Brisbane, Queensland, Australia.,Carnegie Applied Rugby Research Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, UK
| | - Shahin Rahimi-Golkhandan
- School of Psychology and Wellbeing, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Jan Piek
- Curtin University, Perth, Western Australia, Australia
| | - Dido Green
- Jönköping University, Jönköping, Sweden.,Brunel University, London, UK
| | | | - Paul Maruff
- University of Melbourne, Melbourne, Victoria, Australia
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16
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Corr R, Glier S, Bizzell J, Pelletier-Baldelli A, Campbell A, Killian-Farrell C, Belger A. Stress-related hippocampus activation mediates the association between polyvictimization and trait anxiety in adolescents. Soc Cogn Affect Neurosci 2022; 17:767-776. [PMID: 34850948 PMCID: PMC9340110 DOI: 10.1093/scan/nsab129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 11/17/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022] Open
Abstract
Early life stress exposures are associated with adverse health outcomes and heightened anxiety symptoms in adolescents. Stress-sensitive brain regions like the hippocampus and amygdala are particularly impacted by early life adversities and are also implicated in the development of anxiety disorders. However, to date, no studies have specifically examined the neural correlates of polyvictimization (exposure to multiple categories of victimization) or the contribution of stress-sensitive neural nodes to polyvictimization's impact on mental health. To elucidate these relationships, the current study analyzed associations between polyvictimization, hippocampal and amygdalar activation during an acute stress task and trait anxiety in a sample of 80 children and adolescents aged 9-16 years (33 female participants). Results showed that polyvictimization was associated with higher trait anxiety as well as greater stress-related right hippocampus activation, and this greater hippocampal activity predicted heightened trait anxiety. Robust mediation analyses revealed that stress-related right hippocampus activation partially mediated the relationship between polyvictimization and trait anxiety. Our results expand upon the existing polyvictimization literature by suggesting a possible neurobiological pathway through which polyvictimization is connected to the etiology of mental illness.
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Affiliation(s)
- Rachel Corr
- Correspondence should be addressed to Rachel Corr, Department of Psychiatry, University of North Carolina at Chapel Hill, 101 Manning Drive, CB 7160, Chapel Hill, NC 27514, USA. E-mail:
| | - Sarah Glier
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Joshua Bizzell
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
- Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Andrea Pelletier-Baldelli
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
| | - Alana Campbell
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
| | - Candace Killian-Farrell
- Department of Child and Adolescent Psychiatry & Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
- Duke-UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27510, USA
- Frank Porter Graham Child Development Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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17
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Bloom PA, VanTieghem M, Gabard‐Durnam L, Gee DG, Flannery J, Caldera C, Goff B, Telzer EH, Humphreys KL, Fareri DS, Shapiro M, Algharazi S, Bolger N, Aly M, Tottenham N. Age-related change in task-evoked amygdala-prefrontal circuitry: A multiverse approach with an accelerated longitudinal cohort aged 4-22 years. Hum Brain Mapp 2022; 43:3221-3244. [PMID: 35393752 PMCID: PMC9188973 DOI: 10.1002/hbm.25847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 02/20/2022] [Accepted: 03/15/2022] [Indexed: 12/22/2022] Open
Abstract
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.
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Affiliation(s)
| | | | | | - Dylan G. Gee
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | | | - Christina Caldera
- Department of PsychologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Bonnie Goff
- Department of PsychologyUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Eva H. Telzer
- University of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | | | | | | | - Sameah Algharazi
- Department of PsychologyCity College of New YorkNew YorkNew YorkUSA
| | - Niall Bolger
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
| | - Mariam Aly
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
| | - Nim Tottenham
- Department of PsychologyColumbia UniversityNew YorkNew YorkUSA
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18
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van Duijvenvoorde ACK, Whitmore LB, Westhoff B, Mills KL. A methodological perspective on learning in the developing brain. NPJ SCIENCE OF LEARNING 2022; 7:12. [PMID: 35654860 PMCID: PMC9163171 DOI: 10.1038/s41539-022-00127-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 05/05/2022] [Indexed: 05/07/2023]
Abstract
The brain undergoes profound development across childhood and adolescence, including continuous changes in brain morphology, connectivity, and functioning that are, in part, dependent on one's experiences. These neurobiological changes are accompanied by significant changes in children's and adolescents' cognitive learning. By drawing from studies in the domains of reading, reinforcement learning, and learning difficulties, we present a brief overview of methodological approaches and research designs that bridge brain- and behavioral research on learning. We argue that ultimately these methods and designs may help to unravel questions such as why learning interventions work, what learning computations change across development, and how learning difficulties are distinct between individuals.
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Affiliation(s)
- Anna C K van Duijvenvoorde
- Institute of Psychology, Leiden University, Leiden, The Netherlands.
- Leiden Institute for Brain & Cognition, Leiden University, Leiden, The Netherlands.
| | - Lucy B Whitmore
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Bianca Westhoff
- Institute of Psychology, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain & Cognition, Leiden University, Leiden, The Netherlands
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, OR, USA
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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19
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Mejia AF, Koppelmans V, Jelsone-Swain L, Kalra S, Welsh RC. Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS. Neuroimage 2022; 255:119180. [PMID: 35395402 PMCID: PMC9580623 DOI: 10.1016/j.neuroimage.2022.119180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 03/28/2022] [Accepted: 04/03/2022] [Indexed: 11/13/2022] Open
Abstract
Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans) Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individua anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory o motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package
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Affiliation(s)
- Amanda F Mejia
- Department of Statistics, Indiana University, Bloomington, IN, USA.
| | | | - Laura Jelsone-Swain
- Department of Psychology, University of South Carolina Aiken, Aiken, SC, USA
| | - Sanjay Kalra
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Robert C Welsh
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
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20
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Zhang R, Zhang Y, Liu Y, Guo Y, Shen Y, Deng D, Qiu YJ, Dinov ID. Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data. Neural Comput Appl 2022; 34:6377-6396. [PMID: 35936508 PMCID: PMC9355340 DOI: 10.1007/s00521-021-06789-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 11/21/2021] [Indexed: 11/25/2022]
Abstract
Many modern techniques for analyzing time-varying longitudinal data rely on parametric models to interrogate the time-courses of univariate or multivariate processes. Typical analytic objectives include utilizing retrospective observations to model current trends, predict prospective trajectories, derive categorical traits, or characterize various relations. Among the many mathematical, statistical, and computational strategies for analyzing longitudinal data, tensor-based linear modeling offers a unique algebraic approach that encodes different characterizations of the observed measurements in terms of state indices. This paper introduces a new method of representing, modeling, and analyzing repeated-measurement longitudinal data using a generalization of event order from the positive reals to the complex plane. Using complex time (kime), we transform classical time-varying signals as 2D manifolds called kimesurfaces. This kime characterization extends the classical protocols for analyzing time-series data and offers unique opportunities to design novel inference, prediction, classification, and regression techniques based on the corresponding kimesurface manifolds. We define complex time and illustrate alternative time-series to kimesurface transformations. Using the Laplace transform and its inverse, we demonstrate the bijective mapping between time-series and kimesurfaces. A proposed general tensor regression based linear model is validated using functional Magnetic Resonance Imaging (fMRI) data. This kimesurface representation method can be used with a wide range of machine learning algorithms, artificial intelligence tools, analytical approaches, and inferential techniques to interrogate multivariate, complex-domain, and complex-range longitudinal processes.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yupeng Zhang
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yuyao Liu
- Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yunjie Guo
- Electrical Computer Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yueyang Shen
- Electrical Computer Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daxuan Deng
- Electrical Computer Engineering Division, University of Michigan, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yongkai Joshua Qiu
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ivo D. Dinov
- Statistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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21
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Namiranian R, Rahimi Malakshan S, Abrishami Moghaddam H, Khadem A, Jafari R. Normal development of the brain: a survey of joint structural-functional brain studies. Rev Neurosci 2022; 33:745-765. [PMID: 35304982 DOI: 10.1515/revneuro-2022-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/17/2022] [Indexed: 11/15/2022]
Abstract
Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain's maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain's performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state and cognitive experiments. However, nonsignificant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were partially inconsistent with previous nonjoint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.
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Affiliation(s)
- Roxana Namiranian
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran
| | - Sahar Rahimi Malakshan
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran
| | - Hamid Abrishami Moghaddam
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran.,Inserm UMR 1105, Université de Picardie Jules Verne, 80054 Amiens, France
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran 16317-14191, Iran
| | - Reza Jafari
- Department of Electrical and Computer Engineering, Thompson Engineering Building, University of Western Ontario, London, ON N6A 5B9, Canada
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22
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Teeuw J, Klein M, Mota NR, Brouwer RM, van ‘t Ent D, Al-Hassaan Z, Franke B, Boomsma DI, Hulshoff Pol HE. Multivariate Genetic Structure of Externalizing Behavior and Structural Brain Development in a Longitudinal Adolescent Twin Sample. Int J Mol Sci 2022; 23:ijms23063176. [PMID: 35328598 PMCID: PMC8949114 DOI: 10.3390/ijms23063176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/10/2022] [Accepted: 03/10/2022] [Indexed: 12/10/2022] Open
Abstract
Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers, and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and the brain volumes and white matter integrity (fractional anisotropy [FA] and mean diffusivity [MD]) with magnetic resonance imaging in the BrainSCALE cohort, which consisted of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years for the twins. Genetic covariance modeling based on the classical twin design, extended to also include siblings of twins, showed that genes influence externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to −0.20), with sizeable genetic correlations (FA ra up to +0.42; MD ra up to −0.33). The cortical gray matter (CGM; rph up to −0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genetically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence of the same genetic factor.
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Affiliation(s)
- Jalmar Teeuw
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Correspondence: ; Tel.: +31-(088)-75-53-387
| | - Marieke Klein
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
| | - Rachel M. Brouwer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Dennis van ‘t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (D.v.‘t.E.); (D.I.B.)
| | - Zyneb Al-Hassaan
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (D.v.‘t.E.); (D.I.B.)
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Department of Psychology, Utrecht University, 3584 CS Utrecht, The Netherlands
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Keresztes A, Raffington L, Bender AR, Bögl K, Heim C, Shing YL. Longitudinal Developmental Trajectories Do Not Follow Cross-Sectional Age Associations in Hippocampal Subfield and Memory Development. Dev Cogn Neurosci 2022; 54:101085. [PMID: 35278767 PMCID: PMC8917271 DOI: 10.1016/j.dcn.2022.101085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 11/03/2022] Open
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24
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Sanchez-Alonso S, Aslin RN. Towards a model of language neurobiology in early development. BRAIN AND LANGUAGE 2022; 224:105047. [PMID: 34894429 DOI: 10.1016/j.bandl.2021.105047] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 10/24/2021] [Accepted: 10/27/2021] [Indexed: 06/14/2023]
Abstract
Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.
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Affiliation(s)
| | - Richard N Aslin
- Haskins Laboratories, New Haven, CT, USA; Department of Psychology, Yale University, New Haven, CT, USA; Child Study Center, Yale University, New Haven, CT, USA.
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25
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Wu H, Zhao L, Guo Y, Lei W, Guo C. Neural Correlates of Academic Self-concept and the Association with Academic Achievement in Older Children. Neuroscience 2021; 482:53-63. [PMID: 34923040 DOI: 10.1016/j.neuroscience.2021.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022]
Abstract
Academic self-concept, which can be defined as one's beliefs about their academic ability, plays an important role in students' future academic achievement. Here, we examined the neuroanatomical substrates underlying academic self-concept in 92 school-aged children (9.90 ± 0.85 years, 41 girls) using voxel-based morphometry of images obtained by structural magnetic resonance imaging. Our results revealed a significant positive correlation between academic self-concept and achievement 1 year after assessment. Whole-brain regression analyses found that gray matter volume in the right dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) was negatively associated with academic self-concept. Region of interest analyses further showed that regional gray matter volume in the right DLPFC could significantly predict achievement 1 year after assessment. Notably, mediation analyses suggested that regional gray matter volume in the right DLPFC mediated the effect of academic self-concept on students' future academic achievement.
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Affiliation(s)
- Huimin Wu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Le Zhao
- Faculty of Psychology, Beijing Normal University, Zhuhai, China
| | - Yiqun Guo
- School of Innovation and Entrepreneurship Education, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wei Lei
- Department of Psychiatry, the Affiliated Hospital of Southwest Medical University, Luzhou, China; Laboratory of Neurological Diseases and Brain Function, the Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Cheng Guo
- Faculty of Psychology, Southwest University, Chongqing, China.
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26
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Similarity and stability of face network across populations and throughout adolescence and adulthood. Neuroimage 2021; 244:118587. [PMID: 34560271 DOI: 10.1016/j.neuroimage.2021.118587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/20/2022] Open
Abstract
The ability to extract cues from faces is fundamental for social animals, including humans. An individual's profile of functional connectivity across a face network can be shaped by common organizing principles, stable individual traits, and time-varying mental states. In the present study, we used data obtained with functional magnetic resonance imaging in two cohorts, IMAGEN (N = 534) and ALSPAC (N = 465), to investigate - both at group and individual levels - the consistency of the regional profile of functional connectivity across populations (IMAGEN, ALSPAC) and time (Visits 1 to 3 in IMAGEN; age 14 to 22 years). At the group level, we found a robust canonical profile of connectivity both across populations and time. At the individual level, connectivity profiles deviated from the canonical profile, and the magnitude of this deviation related to the presence of psychopathology. These findings suggest that the brain processes faces in a highly stereotypical manner, and that the deviations from this normative pattern may be related to the risk of mental illness.
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Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. Neuroimage 2021; 244:118579. [PMID: 34536537 DOI: 10.1016/j.neuroimage.2021.118579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
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28
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Pas P, Hulshoff Pol HE, Raemaekers M, Vink M. Self-regulation in the pre-adolescent brain. Dev Cogn Neurosci 2021; 51:101012. [PMID: 34530249 PMCID: PMC8450202 DOI: 10.1016/j.dcn.2021.101012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/21/2021] [Accepted: 09/08/2021] [Indexed: 01/09/2023] Open
Abstract
Self-regulation refers to the ability to monitor and modulate emotions, behavior, and cognition, which in turn allows us to achieve goals and adapt to ever changing circumstances. This trait develops from early infancy well into adulthood, and features both low-level executive functions such as reactive inhibition, as well as higher level executive functions such as proactive inhibition. Development of self-regulation is linked to brain maturation in adolescence and adulthood. However, how self-regulation in daily life relates to brain functioning in pre-adolescent children is not known. To this aim, we have analyzed data from 640 children aged 8–11, who performed a stop-signal anticipation task combined with functional magnetic resonance imaging, in addition to questionnaire data on self-regulation. We find that pre-adolescent boys and girls who display higher levels of self-regulation, are better able to employ proactive inhibitory control strategies, exhibit stronger frontal activation and more functional coupling between cortical and subcortical areas of the brain. Furthermore, we demonstrate that pre-adolescent children show significant activation in areas of the brain that were previously only associated with reactive and proactive inhibition in adults and adolescents. Thus, already in pre-adolescent children, frontal-striatal brain areas are active during self-regulatory behavior. Children with higher levels of self-regulation employ more proactive inhibition. During proactive inhibition, children aged 8–11 show activation in frontal-cortical areas. Children higher in self-regulation exhibit more cortical-subcortical coupling. Children aged 8–11 show similar brain activation as adults during inhibition.
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Affiliation(s)
- P Pas
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands; Experimental Psychology, Utrecht University, Utrecht, The Netherlands.
| | - H E Hulshoff Pol
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - M Raemaekers
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - M Vink
- Developmental Psychology, Utrecht University, Utrecht, The Netherlands
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29
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McCormick EM. Multi-Level Multi-Growth Models: New opportunities for addressing developmental theory using advanced longitudinal designs with planned missingness. Dev Cogn Neurosci 2021; 51:101001. [PMID: 34391004 PMCID: PMC8363832 DOI: 10.1016/j.dcn.2021.101001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 05/09/2021] [Accepted: 08/04/2021] [Indexed: 11/03/2022] Open
Abstract
Longitudinal models have become increasingly popular in recent years because of their power to test theoretically derived hypotheses by modeling within-person processes with repeated measures. Growth models constitute a flexible framework for modeling a range of complex trajectories across time in outcomes of interest, including non-linearities and time-varying covariates. However, these models can be expanded to include the effects of multiple growth processes at once on a single outcome. Here, I outline such an extension, showing how multiple growth processes can be modeled as a specific case of the general ability to include time-varying covariates in growth models. I show that this extension of growth models cannot be accomplished by statistical models alone, and that study design plays a crucial role in allowing for proper parameter recovery. I demonstrate these principles through simulations to mimic important theoretical conditions where modeling the effects of multiple growth processes can address developmental theory including, disaggregating the effects of age and practice or treatment in repeated assessments and modeling age- and puberty-related effects during adolescence. I compare how these models behave in two common longitudinal designs, cohort and accelerated, and how planned missingness in observations is key to parameter recovery. I conclude with directions for future substantive research using the method outlined here.
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Affiliation(s)
- Ethan M McCormick
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, 27599, United States; Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands.
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30
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The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood. Neuroimage 2021; 242:118448. [PMID: 34358659 DOI: 10.1016/j.neuroimage.2021.118448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 07/03/2021] [Accepted: 08/02/2021] [Indexed: 10/20/2022] Open
Abstract
Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.
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31
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Chen G, Nash TA, Cole KM, Kohn PD, Wei SM, Gregory MD, Eisenberg DP, Cox RW, Berman KF, Shane Kippenhan J. Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies. Neuroimage 2021; 233:117891. [PMID: 33667672 PMCID: PMC8284193 DOI: 10.1016/j.neuroimage.2021.117891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 02/14/2021] [Accepted: 02/15/2021] [Indexed: 12/03/2022] Open
Abstract
The ubiquitous adoption of linearity for quantitative predictors in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variable's effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative predictor for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of a predictor across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.
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Affiliation(s)
- Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA.
| | - Tiffany A Nash
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Katherine M Cole
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA; Section on Behavioral Endocrinology, National Institute of Mental Health, USA
| | - Philip D Kohn
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Shau-Ming Wei
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA; Section on Behavioral Endocrinology, National Institute of Mental Health, USA
| | - Michael D Gregory
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Daniel P Eisenberg
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - Robert W Cox
- Scientific and Statistical Computing Core, National Institute of Mental Health, USA
| | - Karen F Berman
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
| | - J Shane Kippenhan
- Section on Integrative Neuroimaging, Clinical and Translational Neuroscience Branch, National Institute of Mental Health, USA
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32
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Baranger DAA, Lindenmuth M, Nance M, Guyer AE, Keenan K, Hipwell AE, Shaw DS, Forbes EE. The longitudinal stability of fMRI activation during reward processing in adolescents and young adults. Neuroimage 2021; 232:117872. [PMID: 33609668 PMCID: PMC8238413 DOI: 10.1016/j.neuroimage.2021.117872] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 02/12/2021] [Accepted: 02/13/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The use of functional neuroimaging has been an extremely fruitful avenue for investigating the neural basis of human reward function. This approach has included identification of potential neurobiological mechanisms of psychiatric disease and examination of environmental, experiential, and biological factors that may contribute to disease risk via effects on the reward system. However, a central and largely unexamined assumption of much of this research is that neural reward function is an individual difference characteristic that is relatively stable and trait-like over time. METHODS In two independent samples of adolescents and young adults studied longitudinally (Ns = 145 & 139, 100% female and 100% male, ages 15-21 and 20-22, 2-4 scans and 2 scans respectively), we tested within-person stability of reward-task BOLD activation, with a median of 1 and 2 years between scans. We examined multiple commonly used contrasts of active states and baseline in both the anticipation and feedback phases of a card-guessing reward task. We examined the effects of cortical parcellation resolution, contrast, network (reward regions and resting-state networks), region-size, and activation strength and variability on the stability of reward-related activation. RESULTS In both samples, contrasts of an active state relative to a baseline were more stable (ICC: intra-class correlation; e.g., Win>Baseline; mean ICC = 0.13 - 0.33) than contrasts of two active states (e.g., Win>Loss; mean ICC = 0.048 - 0.05). Additionally, activation in reward regions was less stable than in many non-task networks (e.g., dorsal attention), and activation in regions with greater between-subject variability showed higher stability in both samples. CONCLUSIONS These results show that some contrasts from functional neuroimaging activation during a card guessing reward task have partially trait-like properties in adolescent and young adult samples over 1-2 years. Notably, results suggest that contrasts intended to map cognitive function and show robust group-level effects (i.e. Win > Loss) may be less effective in studies of individual differences and disease risk. The robustness of group-level activation should be weighed against other factors when selecting regions of interest in individual difference fMRI studies.
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Affiliation(s)
- David A A Baranger
- University of Pittsburgh School of Medicine, Department of Psychiatry, 121 Meyran Avenue, Pittsburgh, PA 15213, United States.
| | - Morgan Lindenmuth
- University of Pittsburgh School of Medicine, Department of Psychiatry, 121 Meyran Avenue, Pittsburgh, PA 15213, United States
| | - Melissa Nance
- University of Pittsburgh School of Medicine, Department of Psychiatry, 121 Meyran Avenue, Pittsburgh, PA 15213, United States
| | - Amanda E Guyer
- Center for Mind and Brain, University of California Davis, Davis, CA, United States; Department of Human Ecology, University of California Davis, Davis, CA, United States
| | - Kate Keenan
- University of Chicago, Department of Psychiatry and Behavioral Neuroscience, Chicago, IL, United States
| | - Alison E Hipwell
- University of Pittsburgh School of Medicine, Department of Psychiatry, 121 Meyran Avenue, Pittsburgh, PA 15213, United States
| | - Daniel S Shaw
- University of Pittsburgh, Department of Psychology, Pittsburgh, PA, United States
| | - Erika E Forbes
- University of Pittsburgh School of Medicine, Department of Psychiatry, 121 Meyran Avenue, Pittsburgh, PA 15213, United States; University of Pittsburgh, Department of Psychology, Pittsburgh, PA, United States
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McCormick EM, Peters S, Crone EA, Telzer EH. Longitudinal network re-organization across learning and development. Neuroimage 2021; 229:117784. [PMID: 33503482 PMCID: PMC7994295 DOI: 10.1016/j.neuroimage.2021.117784] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/07/2021] [Accepted: 01/11/2021] [Indexed: 12/15/2022] Open
Abstract
While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8-29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naïve subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals.
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Affiliation(s)
- Ethan M McCormick
- Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC 27599, United States.
| | - Sabine Peters
- Department of Developmental and Educational Psychology, Leiden University, 2333AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, 2333ZA Leiden, the Netherlands
| | - Eveline A Crone
- Department of Developmental and Educational Psychology, Leiden University, 2333AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, 2333ZA Leiden, the Netherlands; School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Eva H Telzer
- Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC 27599, United States
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Qu Y, Jorgensen NA, Telzer EH. A Call for Greater Attention to Culture in the Study of Brain and Development. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2021; 16:275-293. [PMID: 32813984 PMCID: PMC9356540 DOI: 10.1177/1745691620931461] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Despite growing research on neurobiological development, little attention has been paid to cultural and ethnic variation in neurodevelopmental processes. We present an overview of the current state of developmental cognitive neuroscience with respect to its attention to cultural issues. Analyses based on 80 publications represented in five recent meta-analyses related to adolescent developmental neuroscience show that 99% of the publications used samples in Western countries. Only 22% of studies provided a detailed description of participants' racial/ethnic background, and only 18% provided for socioeconomic status. Results reveal a trend in developmental cognitive neuroscience research: The body of research is derived not only mostly from Western samples but also from participants whose race/ethnicity is unknown. To achieve a holistic perspective on brain development in different cultural contexts, we propose and highlight an emerging interdisciplinary approach-developmental cultural neuroscience-the intersection of developmental psychology, cultural psychology, and cognitive neuroscience. Developmental cultural neuroscience aims to elucidate cultural similarities and differences in neural processing across the life span. We call attention to the importance of incorporating culture into the empirical investigation of neurodevelopment.
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Affiliation(s)
- Yang Qu
- School of Education and Social Policy, Northwestern University, USA
| | - Nathan A. Jorgensen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, USA
| | - Eva H. Telzer
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, USA
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35
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Sommer CJ, Schäbitz WR. Principles and requirements for stroke recovery science. J Cereb Blood Flow Metab 2021; 41:471-485. [PMID: 33175596 PMCID: PMC7907998 DOI: 10.1177/0271678x20970048] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 10/03/2020] [Accepted: 10/05/2020] [Indexed: 12/11/2022]
Abstract
The disappointing results in bench-to-bedside translation of neuroprotective strategies caused a certain shift in stroke research towards enhancing the endogenous recovery potential of the brain. One reason for this focus on recovery is the much wider time window for therapeutic interventions which is open for at least several months. Since recently two large clinical studies using d-amphetamine or fluoxetine, respectively, to enhance post-stroke neurological outcome failed again it is a good time for a critical reflection on principles and requirements for stroke recovery science. In principal, stroke recovery science deals with all events from the molecular up to the functional and behavioral level occurring after brain ischemia eventually ending up with any measurable improvement of various clinical parameters. A detailed knowledge of the spontaneously occurring post-ischemic regeneration processes is the indispensable prerequisite for any therapeutic approaches aiming to modify these responses to enhance post-stroke recovery. This review will briefly illuminate the molecular mechanisms of post-ischemic regeneration and the principle possibilities to foster post-stroke recovery. In this context, recent translational approaches are analyzed. Finally, the principal and specific requirements and pitfalls in stroke recovery research as well as potential explanations for translational failures will be discussed.
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Affiliation(s)
- Clemens J Sommer
- Institute of Neuropathology, University Medical Center of the
Johannes Gutenberg-University Mainz, Mainz, Germany
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Klapwijk ET, van den Bos W, Tamnes CK, Raschle NM, Mills KL. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev Cogn Neurosci 2021; 47:100902. [PMID: 33383554 PMCID: PMC7779745 DOI: 10.1016/j.dcn.2020.100902] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 11/19/2020] [Accepted: 12/08/2020] [Indexed: 01/08/2023] Open
Abstract
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
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Affiliation(s)
- Eduard T Klapwijk
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora M Raschle
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland
| | - Kathryn L Mills
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychology, University of Oregon, Eugene, OR, USA
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37
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Abstract
Longitudinal data collection is a time-consuming and cost-intensive part of developmental research. Wu et al. (2016) discussed planned missing (PM) designs that are similar in efficiency to complete designs but require fewer observations per person. The authors reported optimal PM designs for linear latent growth curve models based on extensive Monte Carlo simulations. They called for further formal investigation of the question as to how much the proposed PM mechanisms influence study design efficiency to arrive at a better understanding of PM designs. Here, we propose an approximate solution to the design problem by comparing the asymptotic effective errors of PM designs. Effective error was previously used to find optimal longitudinal study designs for complete data designs; here, we extend the approach to planned missing designs. We show how effective error is a metric for comparing the efficiency of study designs with both planned and unplanned missing data, and how earlier simulation-based results for PM designs can be explained by an asymptotic solution. Our approach is computationally more efficient than Wu et al.’s approach and leads to a better understanding of how various design factors, such as the number of measurement occasions, their temporal arrangement, attrition rates, and PM design patterns interact and how they conjointly determine design efficiency. We provide R scripts to calculate effective errors in various scenarios of PM designs.
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38
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Li Q, Xiao M, Song S, Huang Y, Chen X, Liu Y, Chen H. The personality dispositions and resting-state neural correlates associated with aggressive children. Soc Cogn Affect Neurosci 2020; 15:1004-1016. [PMID: 32991698 PMCID: PMC7647379 DOI: 10.1093/scan/nsaa134] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 09/03/2020] [Accepted: 09/24/2020] [Indexed: 11/16/2022] Open
Abstract
Despite aggression being detrimental to children's physical health, mental health and social development, the dispositional and neurological antecedents of aggression in the child are poorly understood. Here we examined the relationship between trait aggression as measured by Buss and Warren's Aggression Questionnaire and personality traits measured with Big Five Questionnaire for Children in 77 primary-school children and recorded resting-state brain activity (fractional amplitude of low-frequency fluctuations [fALFF]) and resting-state functional connectivity (rsFC) using functional magnetic resonance imaging. The present results showed that trait aggression was negatively correlated with agreeableness and positively correlated with neuroticism. The brain analyses showed that children with a higher propensity for aggression had a lower fALFF mainly in the left superior temporal gyrus, right parahippocampal gyrus and left supramarginal gyrus. Physical and total aggressions were negatively associated with rsFC between the right parahippocampal gyrus and the right putamen. Further analysis revealed that this rsFC could moderate the influence of neuroticism on total aggression. Moreover, the results suggest the presence of a sex difference in the neurodevelopmental mechanisms underlying aggression in middle childhood. Overall, our findings indicate that aggressive children have lower agreeableness and higher neuroticism, and the underlying neural systems are mainly implicated in social judgment and empathy.
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Affiliation(s)
- Qingqing Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | | | | | | | | | | | - Hong Chen
- Correspondence should be addressed to: Hong Chen, Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Tiansheng Road No.2, Beibei District, Chongqing, China. E-mail:
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39
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Buimer EEL, Pas P, Brouwer RM, Froeling M, Hoogduin H, Leemans A, Luijten P, van Nierop BJ, Raemaekers M, Schnack HG, Teeuw J, Vink M, Visser F, Hulshoff Pol HE, Mandl RCW. The YOUth cohort study: MRI protocol and test-retest reliability in adults. Dev Cogn Neurosci 2020; 45:100816. [PMID: 33040972 PMCID: PMC7365929 DOI: 10.1016/j.dcn.2020.100816] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 06/09/2020] [Accepted: 07/02/2020] [Indexed: 11/30/2022] Open
Abstract
The YOUth cohort study is a unique longitudinal study on brain development in the general population. As part of the YOUth study, 2000 children will be included at 8, 9 or 10 years of age and planned to return every three years during adolescence. Magnetic resonance imaging (MRI) brain scans are collected, including structural T1-weighted imaging, diffusion-weighted imaging (DWI), resting-state functional MRI and task-based functional MRI. Here, we provide a comprehensive report of the MR acquisition in YOUth Child & Adolescent including the test-retest reliability of brain measures derived from each type of scan. To measure test-retest reliability, 17 adults were scanned twice with a week between sessions using the full YOUth MRI protocol. Intraclass correlation coefficients were calculated to quantify reliability. Global brain measures derived from structural T1-weighted and DWI scans were reliable. Resting-state functional connectivity was moderately reliable, as well as functional brain measures for both the inhibition task (stop versus go) and the emotion task (face versus house). Our results complement previous studies by presenting reliability results of regional brain measures collected with different MRI modalities. YOUth facilitates data sharing and aims for reliable and high-quality data. Here we show that using the state-of-the art YOUth MRI protocol brain measures can be estimated reliably.
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Affiliation(s)
- Elizabeth E L Buimer
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Pascal Pas
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Rachel M Brouwer
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Martijn Froeling
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Hans Hoogduin
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Peter Luijten
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Bastiaan J van Nierop
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mathijs Raemaekers
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Hugo G Schnack
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Jalmar Teeuw
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Matthijs Vink
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands; Department of Psychology, Utrecht University, Utrecht, the Netherlands
| | | | - Hilleke E Hulshoff Pol
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - René C W Mandl
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands.
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40
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Sanchez-Alonso S, Aslin RN. Predictive modeling of neurobehavioral state and trait variation across development. Dev Cogn Neurosci 2020; 45:100855. [PMID: 32942148 PMCID: PMC7501421 DOI: 10.1016/j.dcn.2020.100855] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 08/26/2020] [Accepted: 09/04/2020] [Indexed: 11/24/2022] Open
Abstract
A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.
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41
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Kim P, Tribble R, Olsavsky AK, Dufford AJ, Erhart A, Hansen M, Grande L, Gonzalez DM. Associations between stress exposure and new mothers' brain responses to infant cry sounds. Neuroimage 2020; 223:117360. [PMID: 32927083 PMCID: PMC8291268 DOI: 10.1016/j.neuroimage.2020.117360] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/24/2020] [Accepted: 09/03/2020] [Indexed: 01/16/2023] Open
Abstract
Exposure to severe stress has been linked to negative postpartum outcomes among new mothers including mood disorders and harsh parenting. Non-human animal studies show that stress exposure disrupts the normative adaptation of the maternal brain, thus identifying a neurobiological mechanism by which stress can lead to negative maternal outcomes. However, little is known about the impact of stress exposure on the maternal brain response to infant cues in human mothers. We examined the association of stress exposure with brain response to infant cries and maternal behaviors, in a socioeconomically diverse (low- and middle-income) sample of first-time mothers (N=53). Exposure to stress across socioeconomic, environmental, and psychosocial domains was associated with reduced brain response to infant cry sounds in several regions, including the right insula/inferior frontal gyrus and superior temporal gyrus. Reduced activation in these regions was further associated with lower maternal sensitivity observed during a mother-infant interaction. The findings demonstrate that higher levels of stress exposure may be associated with reduced brain response to an infant’s cry in regions that are important for emotional and social information processing, and that reduced brain responses may further be associated with increased difficulties in developing positive mother-infant relationships.
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Affiliation(s)
- Pilyoung Kim
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States.
| | - Rebekah Tribble
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States
| | - Aviva K Olsavsky
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States; University of Colorado Anschutz School of Medicine/Children's Hospital Colorado, 13123 E. 16th Avenue, CO 80045, United States
| | - Alexander J Dufford
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States
| | - Andrew Erhart
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States
| | - Melissa Hansen
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States
| | - Leah Grande
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States
| | - Daniel M Gonzalez
- Department of Psychology, University of Denver, Denver, 2155 South Race Street, Denver, CO 80208-3500, United States; Harvard Medical School, Boston, 25 Shattuck St, Boston, MA 02115, United States
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42
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Pfeifer JH, Weston SJ. Developmental cognitive neuroscience initiatives for advancements in methodological approaches: Registered Reports and Next-Generation Tools. Dev Cogn Neurosci 2020; 44:100755. [PMID: 32716846 PMCID: PMC7374242 DOI: 10.1016/j.dcn.2020.100755] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
| | - Sara J Weston
- Department of Psychology, University of Oregon, United States
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43
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Becht AI, Mills KL. Modeling Individual Differences in Brain Development. Biol Psychiatry 2020; 88:63-69. [PMID: 32245576 PMCID: PMC7305975 DOI: 10.1016/j.biopsych.2020.01.027] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 12/18/2019] [Accepted: 01/24/2020] [Indexed: 12/21/2022]
Abstract
Within the field of developmental cognitive neuroscience, there is an increasing interest in studying individual differences in human brain development in order to predict mental health outcomes. So far, however, most longitudinal neuroimaging studies focus on group-level estimates. In this review, we highlight longitudinal neuroimaging studies that have moved beyond group-level estimates to illustrate the heterogeneity in patterns of brain development. We provide practical methodological recommendations on how longitudinal neuroimaging datasets can be used to understand heterogeneity in human brain development. Finally, we address how taking an individual-differences approach in developmental neuroimaging studies could advance our understanding of why some individuals develop mental health disorders.
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Affiliation(s)
- Andrik I Becht
- Brain and Development Research Center, Developmental and Educational Psychology Unit, Leiden University, Leiden; Adolescent Development Research Center, Utrecht University, Utrecht, the Netherlands; Department of Psychology, Education and Child Studies, Erasmus University, Rotterdam, the Netherlands
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, Eugene, Oregon.
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44
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Spaans JP, Peters S, Crone EA. Neural reward related-reactions to monetar gains for self and charity are associated with donating behavior in adolescence. Soc Cogn Affect Neurosci 2020; 15:151-163. [PMID: 32163162 PMCID: PMC7304510 DOI: 10.1093/scan/nsaa027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 01/18/2020] [Accepted: 03/04/2020] [Indexed: 01/12/2023] Open
Abstract
The aim of the current study was to examine neural signatures of gaining money for self and charity in adolescence. Participants (N = 160, aged 11–21) underwent functional magnetic resonance imaging-scanning while performing a zero-sum vicarious reward task in which they could either earn money for themselves at the expense of charity, for a self-chosen charity at the expense of themselves, or for both parties. Afterwards, they could donate money to charity, which we used as a behavioral index of giving. Gaining for self and for both parties resulted in activity in the ventral striatum (specifically in the NAcc), but not gaining for charity. Interestingly, striatal activity when gaining for charity was positively related to individual differences in donation behavior and perspective taking. Dorsal anterior cingulate cortex, insula and precentral gyrus were active when gaining only for self, and temporal-parietal junction when gaining only for charity, relative to gaining for both parties (i.e. under equity deviation). Taken together, these findings show that striatal activity during vicarious gaining for charity depends on levels of perspective taking and predicts future acts of giving to charity. These findings provide insight in the individual differences in the subjective value of prosocial outcomes.
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Affiliation(s)
- Jochem P Spaans
- Department of Developmental and Educational Psychology, Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Sabine Peters
- Department of Developmental and Educational Psychology, Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Eveline A Crone
- Department of Developmental and Educational Psychology, Institute of Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
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45
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Failla MD, Bryant LK, Heflin BH, Mash LE, Schauder K, Davis S, Gerdes MB, Weitlauf A, Rogers BP, Cascio CJ. Neural Correlates of Cardiac Interoceptive Focus Across Development: Implications for Social Symptoms in Autism Spectrum Disorder. Autism Res 2020; 13:908-920. [PMID: 32133784 DOI: 10.1002/aur.2289] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 11/12/2019] [Accepted: 02/17/2020] [Indexed: 12/12/2022]
Abstract
Interoception involves the processing of sensory information relevant to physiological functioning and is integral to building self-awareness, emotional states, and modulating social behaviors. With the role of interoception in emotional processing and social functioning, there is growing interest in characterizing interoception in autism spectrum disorder (ASD), yet, there are mixed results regarding cardiac interoceptive accuracy in ASD. In this study, we explored the neural basis of cardiac interoception using an fMRI heartbeat-counting task in order to assess neural correlates of primary interoception. We predicted that interoceptive-specific response in the insula, a "hub" for interoception, would be related to ASD symptomatology. We investigated the relationship of insula responses during cardiac interoceptive focus and a self/caregiver-reported autism-related symptom scale (Social Responsiveness Scale-2 (SRS)). Participants included 46 individuals with autism spectrum disorder (ASD) (age 8-54, mean = 19.43 ± 10.68 years) and 54 individuals with typical development for comparison (TC, age 8-53, mean = 21.43 ± 10.41 years). We found no significant difference in cardiac interoceptive accuracy or neural response to cardiac interoception focus in ASD. Several insula subdivisions had a curvilinear relationship to age, peaking in early adulthood. Interoceptive-specific insula response was associated with adult self-report SRS scores; this association differed by diagnostic group and was not present for caregiver-reported scores. This work suggests that (a) there is no global deficit in cardiac interoception in ASD, but integrating interoceptive cues with social information may distinguish individuals with ASD, and (b) there is a developmental trajectory for interoceptive processing in the insula that may be relevant for socio-emotional health. Autism Res 2020, 13: 908-920. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: We use internal sensory information from the body, such as signals from the heart, to understand our emotional response to the external world. We measured how accurately people with autism feel their heartbeat and how the brain responds to this type of information. We found no differences between the autism and comparison groups in how the brain senses heartbeats, or in how accurately people feel their heartbeats. However, for people with autism, brain responses while sensing heartbeats were related to social difficulties. This work suggests people with autism may use internal and external information in a different way.
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Affiliation(s)
- Michelle D Failla
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Lauren K Bryant
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Brynna H Heflin
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Florida International University Doctoral Program in Clinical Science, Miami, Florida, USA
| | - Lisa E Mash
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California, USA
| | - Kimberly Schauder
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Clinical and Social Sciences in Psychology, University of Rochester, Rochester, New York, USA
| | - Samona Davis
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Madison B Gerdes
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Amy Weitlauf
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Baxter P Rogers
- Vanderbilt University Imaging Institute, Nashville, Tennessee, USA
| | - Carissa J Cascio
- Department of Psychiatry and Behavioral Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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46
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Seghier ML, Fahim MA, Habak C. Educational fMRI: From the Lab to the Classroom. Front Psychol 2019; 10:2769. [PMID: 31866920 PMCID: PMC6909003 DOI: 10.3389/fpsyg.2019.02769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022] Open
Abstract
Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.
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Affiliation(s)
- Mohamed L Seghier
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Mohamed A Fahim
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
| | - Claudine Habak
- Cognitive Neuroimaging Unit, Emirates College for Advanced Education (ECAE), Abu Dhabi, United Arab Emirates
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47
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Crofts A, Kelly ME, Gibson CL. Imaging Functional Recovery Following Ischemic Stroke: Clinical and Preclinical fMRI Studies. J Neuroimaging 2019; 30:5-14. [PMID: 31608550 PMCID: PMC7003729 DOI: 10.1111/jon.12668] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 09/12/2019] [Accepted: 09/25/2019] [Indexed: 12/18/2022] Open
Abstract
Disability and effectiveness of physical therapy are highly variable following ischemic stroke due to different brain regions being affected. Functional magnetic resonance imaging (fMRI) studies of patients in the months and years following stroke have given some insight into how the brain recovers lost functions. Initially, new pathways are recruited to compensate for the lost region, showing as a brighter blood oxygen‐level‐dependent (BOLD) signal over a larger area during a task than in healthy controls. Subsequently, activity is reduced to baseline levels as pathways become more efficient, mimicking the process of learning typically seen during development. Preclinical models of ischemic stroke aim to enhance understanding of the biology underlying recovery following stroke. However, the pattern of recruitment and focusing seen in humans has not been observed in preclinical fMRI studies that are highly variable methodologically. Resting‐state fMRI studies show more consistency; however, there are still confounding factors to address. Anesthesia and method of stroke induction are the two main sources of variability in preclinical studies; improvements here can reduce variability and increase the intensity and reproducibility of the BOLD response detected by fMRI. Differences in task or stimulus and differences in analysis method also present a source of variability. This review compares clinical and preclinical fMRI studies of recovery following stroke and focuses on how refinement of preclinical models and MRI methods may obtain more representative fMRI data in relation to human studies.
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Affiliation(s)
- Andrew Crofts
- Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
| | - Michael E Kelly
- Preclinical Imaging Facility, Core Biotechnology Services, University of Leicester, Leicester, UK
| | - Claire L Gibson
- School of Psychology, University of Nottingham, Nottingham, UK
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48
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Aksman LM, Scelsi MA, Marquand AF, Alexander DC, Ourselin S, Altmann A. Modeling longitudinal imaging biomarkers with parametric Bayesian multi-task learning. Hum Brain Mapp 2019; 40:3982-4000. [PMID: 31168892 PMCID: PMC6679792 DOI: 10.1002/hbm.24682] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 05/03/2019] [Accepted: 05/19/2019] [Indexed: 01/09/2023] Open
Abstract
Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.
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Affiliation(s)
- Leon M. Aksman
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Marzia A. Scelsi
- Centre for Medical Image ComputingUniversity College LondonLondonUK
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | | | - Sebastien Ourselin
- Centre for Medical Image ComputingUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging SciencesSt Thomas' Hospital, King's College LondonLondonUK
| | - Andre Altmann
- Centre for Medical Image ComputingUniversity College LondonLondonUK
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Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls. Neuroimage 2019; 202:116073. [PMID: 31386921 DOI: 10.1016/j.neuroimage.2019.116073] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 06/27/2019] [Accepted: 08/02/2019] [Indexed: 12/11/2022] Open
Abstract
The human brain is active during rest and hierarchically organized into intrinsic functional networks. These functional networks are largely established early in development, with reports of a shift from a local to more distributed organization during childhood and adolescence. It remains unknown to what extent genetic and environmental influences on functional connectivity change throughout adolescent development. We measured functional connectivity within and between eight cortical networks in a longitudinal resting-state fMRI study of adolescent twins and their older siblings on two occasions (mean ages 13 and 18 years). We modelled the reliability for these inherently noisy and head-motion sensitive measurements by analyzing data from split-half sessions. Functional connectivity between resting-state networks decreased with age whereas functional connectivity within resting-state networks generally increased with age, independent of general cognitive functioning. Sex effects were sparse, with stronger functional connectivity in the default mode network for girls compared to boys, and stronger functional connectivity in the salience network for boys compared to girls. Heritability explained up to 53% of the variation in functional connectivity within and between resting-state networks, and common environment explained up to 33%. Genetic influences on functional connectivity remained stable during adolescent development. In conclusion, longitudinal age-related changes in functional connectivity within and between cortical resting-state networks are subtle but wide-spread throughout adolescence. Genes play a considerable role in explaining individual variation in functional connectivity with mostly stable influences throughout adolescence.
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Abstract
Developmental cognitive neuroscience is flourishing but there are new challenges and new questions to be asked. I argue that we need a bigger picture and an evolutionary framework. This brings some challenges, such as the need to rewrite the old story of nature and nurture, and the need to systematically investigate innate predispositions. While brain imaging has provided some splendid insights and new puzzles to solve, its limitations must not be ignored. Can they help us to find out more about the extent to which the infant brain already configures the adult brain? Can we find out why neurodevelopmental disorders often have severe consequences on cognition and behaviour, despite the mitigating force of brain plasticity? I wish to encourage researchers of the future to take risks by letting their imagination inspire theories to pursue hard questions. I end with a wish list of topics, from start-up kits to abstract reasoning, that I hope can be tackled afresh. However, collecting physiological and behavioural data is not enough. We need a deeper understanding of the mechanisms of cognitive development.
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Affiliation(s)
- Uta Frith
- UCL Institute of Cognitive Neuroscience, United Kingdom.
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