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

Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Norway
Department of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Spain
Center for Lifespan Psychology, Max Planck Institute for Human Development, Germany
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Germany
Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, United States
Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Norway
Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Norway
MRC Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Netherlands
Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, United States
Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Germany
Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Sweden
Department of Psychiatry, University of California, San Diego, United States

Jun 28, 2021

https://doi.org/10.7554/eLife.66466

Open access
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Figures
Tables
Additional files

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.

Sample	N	N longitudinal	Observations	Age Mean (range)	Sex Female/Male	Interval years Mean (range)
LCBC	974	635	1633	25.8 (4.1–88.5)	508/466	2.3 (0.2–6.6)
VETSA*	331	331	662	56.3 (2.6)	0/331	5.5 (0.5)
Lifebrain^x	756	756	1512	59.0 (19.3–89.0)	330/426	2.2 (0.3–4.6)
UKB	38,127	na	38,127	63.6 (44–81)	20,026/18,101	na
UKB long	1337	1337	2674	62.5 (46–80)	663/674	2.3 (2–3)

*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 structure	Cluster	Embryonic development			Cranial vertical axis
Third ventricle	1	Prosencephalon (posterior)	Diencephalon
Fourth ventricle	1	Rhombencephalon
Lat ventricle	1	Prosencephalon (anterior)	Telencephalon
Inf lateral ventricle	1
Brainstem (medulla oblongata)	2	Rhombencephalon	Myelencephalon		0
Cerebellum cortex	2	Rhombencephalon	Metencephalon		1
Cerebellum WM	2	Rhombencephalon	Metencephalon		1
Thalamus	2	Prosencephalon (posterior)	Diencephalon		2
Hippocampus	2	Prosencephalon (anterior)	Telencephalon (dorsal)	Pallium (medial)	4
Cortical WM	2	Prosencephalon (anterior)	Forebrain WM
Caudate	4	Prosencephalon (anterior)	Telencephalon (ventral)	Subpallium	3
Pallidum	5	Prosencephalon (anterior)	Telencephalon (ventral)	Subpallium	3
Putamen	3	Prosencephalon (anterior)	Telencephalon (ventral)	Subpallium	3
Accumbens	3	Prosencephalon (anterior)	Telencephalon (ventral)	Subpallium	3
Amygdala	3	Prosencephalon (anterior)	Telencephalon (dorsal)	Pallium (lateral)	4
Cortex	3	Prosencephalon (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.

	Development			Adulthood and aging			Lifespan
	Edf	F	p	Edf	F	p	Edf	F	p
Cluster 1	1.1	51.0	0.23e⁻¹²	6.0	67.1	2e⁻¹⁶	7.5	176.6	2e⁻¹⁶
Cluster 2	6.5	363.5	2e⁻¹⁶	6.8	16.0	2e⁻¹⁶	8.7	214.3	2e⁻¹⁶
Cluster 3	5.4	37.4	2e⁻¹⁶	6.7	85.5	2e⁻¹⁶	8.6	206.7	2e⁻¹⁶
Cluster 4	5.7	18.1	2e⁻¹⁶	1.0	79.4	2e⁻¹⁶	8.3	45.9	2e⁻¹⁶
Cluster 5	3.7	16.9	3.33e⁻¹²	3.8	15.8	1.23e⁻¹¹	7.9	171.0	2e⁻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.

	AIC		BIC		Effect of sex
	Age	S(Age)	Age	S(Age)	p
Accumbens	18,324	18,335	18,357	18,367	0.57
Amygdala	20,360	20,048	20,392	20,081	0.13
Brainstem	27,514	26,805	27,546	26,837	0.11
Caudate	22,636	23,558	22,669	23,591	0.75
Cerebellum cortex	30,079	29,946	30,111	29,979	0.94
Cerebellum WM	27,430	27,017	27,463	27,050	0.16
Cortex	37,525	37,895	37,557	37,927	0.72
Cortical WM	36847	36,258	36,879	36,290	0.31
Hippocampus	22,494	22,235	22,526	22,268	0.22
Pallidum	20,937	20,741	20,969	20,774	0.17
Thalamus	23,841	23,669	23,874	23,701	0.06
Total GM	38,130	37,849	38,163	37,881	0.78
Lateral ventricles	30,276	30,147	30,308	30,174	0.33
In flat vent	21,081	20,931	21,113	20,964	0.23

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 (r_i) was larger than the mean correlation between the variables in the cluster and the variables outside the cluster (r_e). Note that the clusters were defined in the developmental sample, which is independent from the other four samples.

	Cluster 1			Cluster 2			Cluster 3
Dataset	r_i	r_e	p<	r_i	r_e	p<	r_i	r_e	p<
UKB cross-sectional	0.52	−0.10	1e⁻⁸	0.30	0.05	1e⁻⁷	0.28	0.06	9e⁻⁷
VETSA (TWIN heritability)	0.57	−0.11	1e⁻⁸	0.40	−0.05	3e⁻⁷	0.16	−0.02	0.17
Lifebrain (Aging)	0.47	−0.16	1e⁻⁸	0.14	−0.05	0.0002	0.14	−0.01	0.004
LCBC (Aging)	0.45	−0.20	1e⁻⁸	0.30	−0.06	1e⁻⁸	0.28	0.05	1e⁻⁷

r_i: intra-cluster correlation. r_e: extra-cluster correlation.

Table 6

Scanner and acquisition parameters.

Sample	Scanner	Field strength (Tesla)	Sequence parameters
LCBC	Avanto Siemens	1.5	TR: 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 Siemens	1.5	TR: 2400 ms, TE = 3.79 ms, TI = 1000 ms, flip angle = 8, slice thickness: 1.2 mm, FoV: 240 × 240 mm, 160 slices
Barcelona	Tim Trio Siemens	3.0	TR: 2300 ms, TE: 2.98, TI: 900 ms, slice thickness 1 mm, flip angle: 9°, FoV: 256 × 256 mm, 240 slices
BASE-II	Tim Trio Siemens	3.0	TR: 2500 ms, TE: 4.77 ms, TI: 1100 ms, flip angle: 7°, slice thickness: 1.0 mm, FoV: 256 × 256 mm, 176 slices
Betula	Discovery GE	3.0	TR: 8.19 ms, TE: 3.2 ms, TI: 450 ms, flip angle: 12°, slice thickness: 1 mm, FoV: 250 × 250 mm, 180 slices
Cam-CAN	Tim Trio Siemens	3.0	TR: 2250 ms, TE: 2.98 ms, TI: 900 ms, flip angle: 9°, slice thickness 1 mm, FoV: 256 × 240 mm, 192 slices
UKB	Skyra Siemens	3.0	TR: 2000 ms, TI: 880 ms, slice thickness: 1 mm, FoV: 208 × 256 mm, 256 slices, iPAT = 2
VETSA baseline	Siemens	1.5	TR = 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 Trio	3.0	TE = 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 750x	3.0	TE = 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.