Development of subcortical volumes across adolescence in males and females: a multisample study of longitudinal changes
Open accessPublished version of a preprint
Abstract
The developmental patterns of subcortical brain volumes in males and females observed in previous studies have been inconsistent. To help resolve these discrepancies, we examined developmental trajectories using three independent longitudinal samples of participants in the age-span of 8-22 years (total 216 participants and 467 scans). These datasets, including Pittsburgh (PIT; University of Pittsburgh, USA), NeuroCognitive Development (NCD; University of Oslo, Norway), and Orygen Adolescent Development Study (OADS; The University of Melbourne, Australia), span three countries and were analyzed together and in parallel using mixed-effects modeling with both generalized additive models and general linear models. For all regions and across all samples, males were found to have significantly larger volumes as compared to females, and significant sex differences were seen in age trajectories over time. However, direct comparison of sample trajectories and sex differences identified within samples were not consistent. The trajectories for the amygdala, putamen, and nucleus accumbens were most consistent between the three samples. Our results suggest that even after using similar preprocessing and analytic techniques, additional factors, such as image acquisition or sample composition may contribute to some of the discrepancies in sex specific patterns in subcortical brain changes across adolescence, and highlight region-specific variations in congruency of developmental trajectories.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
On average, subcortical regions involved in emotion and reward processing develop earlier than prefrontal regions involved in decision making and self-regulation, although this pattern differs between individuals.
“For example, on average, subcortical regions involved in emotion and reward processing develop earlier than prefrontal regions involved in decision making and selfregulation, although this pattern differs between individuals. 129,130”
From The Lancet Psychiatry Commission on youth mental health (2024) · cites this paper as [129]
The Discussion (§4.1; page unavailable) reports average developmental trajectories for subcortical volumes, and the Conclusions (§5; page unavailable) acknowledge differences at the group and individual levels. But the paper does not compare those trajectories with prefrontal development or establish that emotion- and reward-related regions develop earlier than decision-making and self-regulation regions. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 51 · Cited by 185
Research works printed but not linked: 42
- Backhausen LL Herting MM Buse J Roessner V Smolka MN Vetter NC 2016 Quality Control of Structural MRI Images Applied Using FreeSurfer-A Hands-On Workflow to Rate Motion Artifacts Front Neurosci 10 558 27999528 PMC5138230
- Chakravarty MM Steadman P van Eede MC Calcott RD Gu V Shaw P Raznahan A Collins DL Lerch JP 2013 Performing label-fusion-based segmentation using multiple automatically generated templates Hum Brain Mapp 34 10 2635 2654 22611030 PMC4896505
- Chepkoech JL Walhovd KB Grydeland H Fjell AM I. Alzheimer’s Disease Neuroimaging 2016 Effects of change in FreeSurfer version on classification accuracy of patients with Alzheimer’s disease and mild cognitive impairment Hum Brain Mapp 37 5 1831 1841 27018380 PMC6867543
- Choudhury S Blakemore SJ Charman T 2006 Social cognitive development during adolescence Soc Cogn Affect Neurosci 1 3 165 174 18985103 PMC2555426
- Crone EA Elzinga BM 2015 Changing brains: how longitudinal functional magnetic resonance imaging studies can inform us about cognitive and social-affective growth trajectories Wiley Interdiscip Rev Cogn Sci 6 1 53 63 26262928
- Dennison M Whittle S Yucel M Vijayakumar N Kline A Simmons J Allen NB 2013 Mapping subcortical brain maturation during adolescence: evidence of hemisphere-and sex-specific longitudinal changes Dev Sci 16 5 772 791 24033581
- Ducharme S Albaugh MD Nguyen TV Hudziak JJ Mateos-Perez JM Labbe A Evans AC Karama S G. Brain Development Cooperative 2016 Trajectories of cortical thickness maturation in normal brain development–The importance of quality control procedures Neuroimage 125 267 279 26463175 PMC4691414
- Giedd JN Vaituzis AC Hamburger SD Lange N Rajapakse JC Kaysen D Vauss YC Rapoport JL 1996 Quantitative MRI of the temporal lobe, amygdala, and hippocampus in normal human development: ages 4–18 years J Comp Neurol 366 2 223 230 8698883
- Goddings AL Mills KL Clasen LS Giedd JN Viner RM Blakemore SJ 2014 The influence of puberty on subcortical brain development Neuroimage 88 242 251 24121203 PMC3991320
- Gogtay N Thompson PM 2010 Mapping gray matter development: implications for typical development and vulnerability to psychopathology Brain Cogn 72 1 6 15 19796863 PMC2815268
- Gur RE Gur RC 2016 Sex differences in brain and behavior in adolescence: Findings from the Philadelphia Neurodevelopmental Cohort Neurosci Biobehav Rev 70 159 170 27498084 PMC5098398
- Herting MM Gautam P Spielberg JM Kan E Dahl RE Sowell ER 2014 The role of testosterone and estradiol in brain volume changes across adolescence: a longitudinal structural MRI study Hum Brain Mapp 35 11 5633 5645 24977395 PMC4452029
- Jones K Almond S 1992 Moving out of the Linear Rut: The Possibilities of Generalized Additive Models Transactions of the Institute of British Geographers 17 4 434 447
- Kessler RC Berglund P Demler O Jin R Merikangas KR Walters EE 2005 Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication Arch Gen Psychiatry 62 6 593 602 15939837
- Koolschijn PC Crone EA 2013 Sex differences and structural brain maturation from childhood to early adulthood Dev Cogn Neurosci 5 106 118 23500670 PMC6987760
- Kuhn C 2015 Emergence of sex differences in the development of substance use and abuse during adolescence Pharmacol Ther 153 55 78 26049025 PMC4527891
- Lenroot RK Gogtay N Greenstein DK Wells EM Wallace GL Clasen LS Blumenthal JD Lerch J Zijdenbos AP Evans AC Thompson PM Giedd JN 2007 Sexual dimorphism of brain developmental trajectories during childhood and adolescence Neuroimage 36 4 1065 1073 17513132 PMC2040300
- LeWinn KZ Sheridan MA Keyes KM Hamilton A McLaughlin KA 2017 Sample composition alters associations between age and brain structure Nat Commun 8 1 874 29026076 PMC5638928
- Makowski C Beland S Kostopoulos P Bhagwat N Devenyi GA Malla AK Joober R Lepage M Chakravarty MM 2017 Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation Neuroimage 28259781
- Morey RA Selgrade ES Wagner HR 2nd Huettel SA Wang L McCarthy G 2010 Scan-rescan reliability of subcortical brain volumes derived from automated segmentation Hum Brain Mapp 31 11 1751 1762 20162602 PMC3782252
- Narvacan K Treit S Camicioli R Martin W Beaulieu C 2017 Evolution of deep gray matter volume across the human lifespan Hum Brain Mapp 38 8 3771 3790 28548250 PMC6867004
- Ostby Y Tamnes CK Fjell AM Westlye LT Due-Tonnessen P Walhovd KB 2009 Heterogeneity in subcortical brain development: A structural magnetic resonance imaging study of brain maturation from 8 to 30 years J Neurosci 29 38 11772 11782 19776264 PMC6666647
- Paus T Keshavan M Giedd JN 2008 Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci 9 12 947 957 19002191 PMC2762785
- Raznahan A Shaw PW Lerch JP Clasen LS Greenstein D Berman R Pipitone J Chakravarty MM Giedd JN 2014 Longitudinal four-dimensional mapping of subcortical anatomy in human development Proc Natl Acad Sci U S A 111 4 1592 1597 24474784 PMC3910572
- Reardon PK Clasen L Giedd JN Blumenthal J Lerch JP Chakravarty MM Raznahan A 2016 An Allometric Analysis of Sex and Sex Chromosome Dosage Effects on Subcortical Anatomy in Humans J Neurosci 36 8 2438 2448 26911691 PMC4764663
- Reuter M Schmansky NJ Rosas HD Fischl B 2012 Within-subject template estimation for unbiased longitudinal image analysis Neuroimage 61 4 1402 1418 22430496 PMC3389460
- Reuter M Tisdall MD Qureshi A Buckner RL van der Kouwe AJ Fischl B 2015 Head motion during MRI acquisition reduces gray matter volume and thickness estimates Neuroimage 107 107 115 25498430 PMC4300248
- Roalf DR Gur RE Ruparel K Calkins ME Satterthwaite TD Bilker WB Hakonarson H Harris LJ Gur RC 2014 Within-individual variability in neurocognitive performance: age- and sex-related differences in children and youths from ages 8 to 21 Neuropsychology 28 4 506 518 24773417 PMC4175525
- Ruigrok AN Salimi-Khorshidi G Lai MC Baron-Cohen S Lombardo MV Tait RJ Suckling J 2014 A meta-analysis of sex differences in human brain structure Neurosci Biobehav Rev 39 34 50 24374381 PMC3969295
- Sanfilipo MP Benedict RH Zivadinov R Bakshi R 2004 Correction for intracranial volume in analysis of whole brain atrophy in multiple sclerosis: the proportion vs. residual method Neuroimage 22 4 1732 1743 15275929
- Schoemaker D Buss C Head K Sandman CA Davis EP Chakravarty MM Gauthier S Pruessner JC 2016 Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation Neuroimage 129 1 14 26824403 PMC7243960
- Shaw P Gogtay N Rapoport J 2010 Childhood psychiatric disorders as anomalies in neurodevelopmental trajectories Hum Brain Mapp 31 6 917 925 20496382 PMC6870870
- Sowell ER Thompson PM Toga AW 2004 Mapping changes in the human cortex throughout the span of life Neuroscientist 10 4 372 392 15271264
- Sowell ER Trauner DA Gamst A Jernigan TL 2002 Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study Developmental Medicine and Child Neurology 44 1 4 16 11811649
- Tamnes CK Herting MM Goddings AL Meuwese R Blakemore SJ Dahl RE Guroglu B Raznahan A Sowell ER Crone EA Mills KL 2017 Development of the Cerebral Cortex across Adolescence: A Multisample Study of Inter-Related Longitudinal Changes in Cortical Volume, Surface Area, and Thickness J Neurosci 37 12 3402 3412 28242797 PMC5373125
- Tamnes CK Walhovd KB Dale AM Ostby Y Grydeland H Richardson G Westlye LT Roddey JC Hagler DJ Jr Due-Tonnessen P Holland D Fjell AM I. Alzheimer’s Disease Neuroimaging 2013 Brain development and aging: overlapping and unique patterns of change Neuroimage 68 63 74 23246860 PMC5378867
- Vijayakumar N Mills KL Alexander-Bloch A Tamnes CK Whittle S Accepted Structural brain development: a review of methodological approaches and best practices PMC5963981 29221915
- Wierenga LM Sexton JA Laake P Giedd JN Tamnes CK N. the Pediatric Imaging, and Genetics Study 2017 A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls Cerebral Cortex 1 11 PMC6041809 28981610
- Wonderlick JS Ziegler DA Hosseini-Varnamkhasti P Locascio JJ Bakkour A van der Kouwe A Triantafyllou C Corkin S Dickerson BC 2009 Reliability of MRI-derived cortical and subcortical morphometric measures: effects of pulse sequence, voxel geometry, and parallel imaging Neuroimage 44 4 1324 1333 19038349 PMC2739882
- Yap MB Allen NB O’Shea M di Parsia P Simmons JG Sheeber L 2011 Early adolescents’ temperament, emotion regulation during mother-child interactions, and depressive symptomatology Dev Psychopathol 23 1 267 282 21262053
- Zhang Y Brady M Smith S 2001 Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm IEEE Trans Med Imaging 20 1 45 57 11293691
- Zijdenbos AP Forghani R Evans AC 2002 Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis IEEE Trans Med Imaging 21 10 1280 1291 12585710
- Authors
- Herting, M. M., Johnson, C., Mills, K. L., Vijayakumar, N., Dennison, M., Liu, C., Goddings, A.-L., Dahl, R. E., Sowell, E. R., Whittle, S., Allen, N. B., & Tamnes, C. K.
- Affiliations
- University of Southern California (US)
- University of Oregon (US)
- The University of Melbourne (AU)
- University College London (GB)
- University of California, Berkeley (US)
- University of Oslo (NO)
- Corresponding author
- Herting, Megan M.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 172
- Pages
- 194-205
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC5910239
- OpenAlex
- W2952639684
- Cited by
- 185
- References
- 51
- Citations by year
- 2026: 16 · 2025: 16 · 2024: 29 · 2023: 29 · 2022: 16 · 2021: 28 · 2020: 25 · 2019: 21 · 2018: 5
- FWCI
- 11.76
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Health and Medical Research Council (SW, 1007716, 350241)
- Economic Research Service (K01 MH1087610, R01 MH107418, F32 HD0708084)
- Division of Arctic Sciences (DP0878136)
- National Institutes of Health (R01 MH087563, R01 HD053893, R01 DA018910)
- Australian Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Clinical Psychology › Child and Adolescent Psychosocial and Emotional Development
- Keywords
- subcortical volume, brain development, adolescence, sex differences, developmental trajectories, amygdala, putamen, nucleus accumbens, longitudinal neuroimaging
- MeSH terms
- Adolescent, Brain, Female, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Sex Characteristics, Adolescent Development, Neuroimaging, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
