Normative structural similarity brain development in early human adolescence
Poster No:
989
Submission Type:
Abstract Submission
Authors:
Sofia Orellana1, Rafael Romero-Garcia2,3, Isaac Sebenius3,4, Lena Dorfschmidt5,6, Richard Bethlehem7, Petra Vértes3, Edward Bullmore3,8
Institutions:
1Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom, 2University of Sevilla, Sevilla, Sevilla, 3Department of Psychiatry, University of Cambridge, Cambridge, Cambridgeshire, 4Department of Computer Science and Technology, University of Cambridge, Cambridge, Cambridgeshire, 5Lifespan Brain Institute, The Children’s Hospital of Philadelphia, Pennsylvania, PA, 6Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA, 7Department of Psychology, University of Cambridge, Cambridge, Cambridgeshire, 8Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, Cambridgeshire
First Author:
Co-Author(s):
Rafael Romero-Garcia
University of Sevilla|Department of Psychiatry, University of Cambridge
Sevilla, Sevilla|Cambridge, Cambridgeshire
University of Sevilla|Department of Psychiatry, University of Cambridge
Sevilla, Sevilla|Cambridge, Cambridgeshire
Isaac Sebenius
Department of Psychiatry, University of Cambridge|Department of Computer Science and Technology, University of Cambridge
Cambridge, Cambridgeshire|Cambridge, Cambridgeshire
Department of Psychiatry, University of Cambridge|Department of Computer Science and Technology, University of Cambridge
Cambridge, Cambridgeshire|Cambridge, Cambridgeshire
Lena Dorfschmidt
Lifespan Brain Institute, The Children’s Hospital of Philadelphia|Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia
Pennsylvania, PA|Philadelphia, PA
Lifespan Brain Institute, The Children’s Hospital of Philadelphia|Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia
Pennsylvania, PA|Philadelphia, PA
Edward Bullmore
Department of Psychiatry, University of Cambridge|Cambridgeshire & Peterborough NHS Foundation Trust
Cambridge, Cambridgeshire|Cambridge, Cambridgeshire
Department of Psychiatry, University of Cambridge|Cambridgeshire & Peterborough NHS Foundation Trust
Cambridge, Cambridgeshire|Cambridge, Cambridgeshire
Introduction:
Structural brain similarity networks (SSNs) are a useful tool for characterizing in-vivo the whole-brain patterning of joint morphometric and microstructural brain organization on the basis of multiple structural imaging features[1]. SSNs have been shown to capture variability in cognition [2], cortical genetic expression[2] and developmental processes [3,4] and purportedly provide an index of coordinated structural development between regions [1]. Using morphometric and microstructural features derived from magnetic resonance (MRI) and diffusion weighted (DWI) imaging respectively, we characterize for the first time brain-wide coordinated developmental change in brain structure during early human adolescence (ages 9-14) in a large sample (N=4802). We show that changing network (weighted) degree across development is primarily driven by opposing trends in cortical thickness and neurite density; that structural networks tend to become, on average, more similar across development and that limbic cytoarchitectonic and functional systems are enriched for regions showing the most differentiation, whilst idiotypic and default mode systems display the strongest increases in similarity.
Methods:
Data was derived from the Adolescent Brain and Cognitive Development (ABCD) study. Participants were scanned three times at ages 9-10 (N=4802; female=2235), 11-12 (N=4802; female=2235), and 13-14 (N=1556; female=715); sample sizes given are after data quality control. For each subject at each scanning session we estimated MRI-derived cortical thickness (CT) and mean curvature (MC), and DWI-derived neurite density index (NDI) and mean diffusivity (MD) for the n=360 regions of the HCP atlas [5]. We also estimated the morphometric inverse divergence (MIND)[6] of these features for each pair of HCP regions, yielding individual whole-brain SSNs. Linear mixed models were used to estimate the linear effects of age (with subjects as random effects) on global and regional feature changes, MIND-network weighted degree (k) changes, and MIND-edge weight (ew) changes.
Results:
Global morphometric features (CT, MC, and MDI) decreased across adolescence, with the exception of NDI, an index of axon and dendrite density, which increased (Pfdr<.05 for all); MIND degree (k) tended to increase with age, indicating greater morphometric similarity across brain regions during adolescent development (Fig.1A) Regional trends, within the HCP atlas parcellation (n=360), generally followed the same pattern: CT and MD displayed brain-wide decreases with age, most prominent in frontal and parietal regional; NDI displayed increases accentuated in temporal regions and MC both trends (Fig.2B). MIND degree (k) tended to increase with age, with some decreases in prefrontal, parietal and motor strip regions (Fig.1C). Changes in MIND degree (k) age were most strongly driven by increasing NDI and decreasing CT (Fig.1D); increasing changes disproportionately fell within regions of idiotypic mesulam zones or the default mode network, and decreases within limbic and paralimbic regions (Fig.1E). We also computed the linear effect of age on MINDedge weight, showing how similarity between regions changes across development. We found that, whilst edge weight (similarity) increases amongst the majority of regions, this effect is strongest between association, premotor, posterior cingulate and opercular cortices (Fig.2) whilst the medial temporal cortex became uniformly dissimilar to other regions.
Conclusions:
We show that early adolescence is characterized by strong brain-wide morphometric and microstructural similarity changes with diverging patterns in limbic, idiotypic and default mode systems.
Lifespan Development:
Early life, Adolescence, Aging 1
Normal Brain Development: Fetus to Adolescence 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Normal Development
Keywords:
Computational Neuroscience
Cortex
Development
Informatics
Modeling
MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Connectomics
1|2Indicates the priority used for review
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[3] Seidlitz, J., Nadig, A., Liu, S., Bethlehem, R. A. I., Vértes, P. E., Morgan, S. E., Váša, F., Romero-Garcia, R., Lalonde, F. M., Clasen, L. S., Blumenthal, J. D., Paquola, C., Bernhardt, B., Wagstyl, K., Polioudakis, D., de la Torre-Ubieta, L., Geschwind, D. H., Han, J. C., Lee, N. R., … Raznahan, A. (2020). Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nature Communications
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