1. Neuroscience

The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan

  1. Anders Martin Fjell  Is a corresponding author
  2. Hakon Grydeland
  3. Yunpeng Wang
  4. Inge K Amlien
  5. David Bartres-Faz
  6. Andreas M Brandmaier
  7. Sandra Düzel
  8. Jeremy Elman
  9. Carol E Franz
  10. Asta K Håberg
  11. Tim C Kietzmann
  12. Rogier Andrew Kievit
  13. William S Kremen
  14. Stine K Krogsrud
  15. Simone Kühn
  16. Ulman Lindenberger
  17. Didac Macía
  18. Athanasia Monika Mowinckel
  19. Lars Nyberg
  20. Matthew S Panizzon
  21. Cristina Solé-Padullés
  22. Øystein Sørensen
  23. Rene Westerhausen
  24. Kristine Beate Walhovd
  1. Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway
  2. Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
  3. Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Spain
  4. Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
  5. Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Germany
  6. Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, United States
  7. Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Norway
  8. Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway
  9. MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom
  10. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Netherlands
  11. Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, United States
  12. Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Germany
  13. Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Sweden
  14. Department of Psychiatry, University of California, San Diego, United States
https://doi.org/10.7554/eLife.66466
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Cite this article
  1. Anders Martin Fjell
  2. Hakon Grydeland
  3. Yunpeng Wang
  4. Inge K Amlien
  5. David Bartres-Faz
  6. Andreas M Brandmaier
  7. Sandra Düzel
  8. Jeremy Elman
  9. Carol E Franz
  10. Asta K Håberg
  11. Tim C Kietzmann
  12. Rogier Andrew Kievit
  13. William S Kremen
  14. Stine K Krogsrud
  15. Simone Kühn
  16. Ulman Lindenberger
  17. Didac Macía
  18. Athanasia Monika Mowinckel
  19. Lars Nyberg
  20. Matthew S Panizzon
  21. Cristina Solé-Padullés
  22. Øystein Sørensen
  23. Rene Westerhausen
  24. Kristine Beate Walhovd
(2021)
The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan
eLife 10:e66466.
https://doi.org/10.7554/eLife.66466
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5 figures, 6 tables and 3 additional files

Figures

Figure 1
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Volumetric change–change relationships.

Heatmaps represent pairwise correlation coefficients between volume change (annualized percent change) of the brain structures in development in the LCBC sample (left), aging in the LCBC sample (middle), and aging in the Lifebrain replication sample (right). The five clusters, delineated by the black lines, were derived from the developmental sample.

Figure 2
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Genetic correlations.

Left: Change–change correlations in development used to generate clusters. Middle: Genetic change–change correlations, i.e.the genetic contribution to the relationships between change among any two structures, based on twin analysis (VETSA). Right: SNP genetic correlations from the UKB cross-sectional data. The five clusters, delineated by the black lines, were derived from the developmental sample.

Figure 3
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Correspondence between SNP heritability and embryonic brain development.

Clustering of the non-ventricular structures was used to test how shared genetic variance were organized in the UKB sample, and the clusters were compared to the main organization of embryonic brain development. The heatmap shows the pairwise genetic correlations. The flow chart shows the main features of embryonic brain development and how the genetic clusters obtained from middle-aged and older adults follow the same organization.

Figure 4
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Cluster age trajectories for each cluster, for development (left), adulthood (middle), and the full lifespan (right).

The trajectories are fitted with GAMM, and the shaded areas represent 95% CI. Note that the y-axes scales vary for easier viewing. The trajectories were estimated for development and adulthood separately to ensure that the analyses were fully independent.

Figure 5
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Lifespan trajectories of brain volumes.

Age on the x-axis, volume in units of milliliters on the y-axis. The trajectories are fitted with GAMM, using both longitudinal and cross-sectional data, and the shaded areas represent 95% CI. Y-axis is in units of 1000 mm3. Ventricular volumes not shown.

Tables

Table 1
Sample overview.
SampleNN longitudinalObservationsAge
Mean (range)		Sex
Female/Male		Interval years
Mean (range)
LCBC974635163325.8 (4.1–88.5)508/4662.3 (0.2–6.6)
VETSA*33133166256.3 (2.6)0/3315.5 (0.5)
Lifebrainx756756151259.0 (19.3–89.0)330/4262.2 (0.3–4.6)
UKB38,127na38,12763.6 (44–81)20,026/18,101na
UKB long13371337267462.5 (46–80)663/6742.3 (2–3)

  1. *75 complete monozygote (MZ)/53 complete dizygote (DZ) pairs of male twins.

    xNot including LCBC.

Table 2
The embryonic origins of the clusters and placement along the cranial vertical axis.
Brain structureClusterEmbryonic developmentCranial vertical axis
Third ventricle1Prosencephalon (posterior)Diencephalon
Fourth ventricle1Rhombencephalon
Lat ventricle1Prosencephalon (anterior)Telencephalon
Inf lateral ventricle1
Brainstem (medulla oblongata)2RhombencephalonMyelencephalon0
Cerebellum cortex2RhombencephalonMetencephalon1
Cerebellum WM2RhombencephalonMetencephalon1
Thalamus2Prosencephalon (posterior)Diencephalon2
Hippocampus2Prosencephalon (anterior)Telencephalon (dorsal)Pallium (medial)4
Cortical WM2Prosencephalon (anterior)Forebrain WM
Caudate4Prosencephalon (anterior)Telencephalon (ventral)Subpallium3
Pallidum5Prosencephalon (anterior)Telencephalon (ventral)Subpallium3
Putamen3Prosencephalon (anterior)Telencephalon (ventral)Subpallium3
Accumbens3Prosencephalon (anterior)Telencephalon (ventral)Subpallium3
Amygdala3Prosencephalon (anterior)Telencephalon (dorsal)Pallium (lateral)4
Cortex3Prosencephalon (anterior)Telencephalon (dorsal)Pallium (dorsal)4
Table 3
Cluster age trajectories.

Numeric results for the trajectory analyses in Figure 4. Edf: effective degrees of freedom (signifying the complexity of the trajectory, where the value two approximates a quadratic shape, 3 a cubic shape, etc). The p-value is associated with the null hypothesis that there is no relationship to age.

DevelopmentAdulthood and agingLifespan
EdfFpEdfFpEdfFp
Cluster 11.151.00.23e−126.067.12e−167.5176.62e−16
Cluster 26.5363.52e−166.816.02e−168.7214.32e−16
Cluster 35.437.42e−166.785.52e−168.6206.72e−16
Cluster 45.718.12e−161.079.42e−168.345.92e−16
Cluster 53.716.93.33e−123.815.81.23e−117.9171.02e−16
Table 4
Generalized additive mixed model fits LCBC lifespan.

Generalized additive mixed models (GAMM) were run with each neuroanatomical volume as dependent variable, and age, estimated total intracranial volume, and sex as covariates. Separate models were run with a linear age (age) term or a slope function (s(Age)). Except for cortex and caudate, the slope function yielded the lowest IC values. GM: Gray matter. WM: White matter. AIC: Akaike information criterion. BIC: Bayesian information criterion.

AICBICEffect of sex
AgeS(Age)AgeS(Age)p
Accumbens18,32418,33518,35718,3670.57
Amygdala20,36020,04820,39220,0810.13
Brainstem27,51426,80527,54626,8370.11
Caudate22,63623,55822,66923,5910.75
Cerebellum cortex30,07929,94630,11129,9790.94
Cerebellum WM27,43027,01727,46327,0500.16
Cortex37,52537,89537,55737,9270.72
Cortical WM3684736,25836,87936,2900.31
Hippocampus22,49422,23522,52622,2680.22
Pallidum20,93720,74120,96920,7740.17
Thalamus23,84123,66923,87423,7010.06
Total GM38,13037,84938,16337,8810.78
Lateral ventricles30,27630,14730,30830,1740.33
In flat vent21,08120,93121,11320,9640.23
Table 5
Within- vs. outside cluster correlations.

Two-sample Student’s t-tests were run to test whether the mean correlation within the developmentally defined clusters (ri) was larger than the mean correlation between the variables in the cluster and the variables outside the cluster (re). Note that the clusters were defined in the developmental sample, which is independent from the other four samples.

Cluster 1Cluster 2Cluster 3
Datasetrirep<rirep<rirep<
UKB cross-sectional0.52−0.101e−80.300.051e−70.280.069e−7
VETSA (TWIN heritability)0.57−0.111e−80.40−0.053e−70.16−0.020.17
Lifebrain (Aging)0.47−0.161e−80.14−0.050.00020.14−0.010.004
LCBC (Aging)0.45−0.201e−80.30−0.061e−80.280.051e−7

  1. ri: intra-cluster correlation. re: extra-cluster correlation.

Table 6
Scanner and acquisition parameters.
SampleScannerField strength (Tesla)Sequence parameters
LCBCAvanto Siemens1.5TR: 2400 ms, TE: 3.61 ms, TI: 1000 ms, flip angle: 8°, slice thickness: 1.2 mm, FoV: 240 × 240 m, 160 slices, iPat = 2
Avanto Siemens1.5TR: 2400 ms, TE = 3.79 ms, TI = 1000 ms, flip angle = 8, slice thickness: 1.2 mm, FoV: 240 × 240 mm, 160 slices
BarcelonaTim Trio Siemens3.0TR: 2300 ms, TE: 2.98, TI: 900 ms, slice thickness 1 mm, flip angle: 9°, FoV: 256 × 256 mm, 240 slices
BASE-IITim Trio Siemens3.0TR: 2500 ms, TE: 4.77 ms, TI: 1100 ms, flip angle: 7°, slice thickness: 1.0 mm, FoV: 256 × 256 mm, 176 slices
BetulaDiscovery GE3.0TR: 8.19 ms, TE: 3.2 ms, TI: 450 ms, flip angle: 12°, slice thickness: 1 mm, FoV: 250 × 250 mm, 180 slices
Cam-CANTim Trio
Siemens		3.0TR: 2250 ms, TE: 2.98 ms, TI: 900 ms, flip angle: 9°, slice thickness 1 mm, FoV: 256 × 240 mm, 192 slices
UKBSkyra
Siemens		3.0TR: 2000 ms, TI: 880 ms, slice thickness: 1 mm, FoV: 208 × 256 mm, 256 slices, iPAT = 2
VETSA baselineSiemens1.5TR = 2730ms, TI = 1000 ms, TE = 3.31ms, slice thickness = 1.33 mm, flip angle = 7°, voxel size 1.3 × 1.0 × 1.3 mm. Acquisition in Boston and San Diego.
VETSA follow-up (Boston)Siemens Tim Trio3.0TE = 4.33 ms, TR = 2170 ms, TI = 1100 ms, flip angle = 7°, pixel bandwidth = 140, number of slices = 160, slice thickness = 1.2 mm. Acquisition in Boston.
VETSA follow-up (San Diego)GE Discovery 750x3.0TE = 3.164 ms, TR = 8.084 ms, TI = 600 ms, flip angle = 8°, pixel bandwidth = 244.141, FoV = 24 cm, frequency = 256, phase = 192, number of slices = 172, slice thickness = 1.2 mm. Acquisition in San Diego.

  1. TR: Repetition time, TE: Echo time, TI: Inversion time, FoV: Field of View, iPat: in-plane acceleration.

Additional files

Source code 1

Statistical source code.

https://cdn.elifesciences.org/articles/66466/elife-66466-code1-v2.zip
Supplementary file 1

Supplementary results.

https://cdn.elifesciences.org/articles/66466/elife-66466-supp1-v2.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/66466/elife-66466-transrepform-v2.docx

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  1. Anders Martin Fjell
  2. Hakon Grydeland
  3. Yunpeng Wang
  4. Inge K Amlien
  5. David Bartres-Faz
  6. Andreas M Brandmaier
  7. Sandra Düzel
  8. Jeremy Elman
  9. Carol E Franz
  10. Asta K Håberg
  11. Tim C Kietzmann
  12. Rogier Andrew Kievit
  13. William S Kremen
  14. Stine K Krogsrud
  15. Simone Kühn
  16. Ulman Lindenberger
  17. Didac Macía
  18. Athanasia Monika Mowinckel
  19. Lars Nyberg
  20. Matthew S Panizzon
  21. Cristina Solé-Padullés
  22. Øystein Sørensen
  23. Rene Westerhausen
  24. Kristine Beate Walhovd
(2021)
The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan
eLife 10:e66466.
https://doi.org/10.7554/eLife.66466