The structure of the brain undergoes continual changes throughout the entire lifespan, with structural brain alterations intimately linking brain development and brain aging (Fjell and Walhovd, 2010; Shaw et al., 2008). Brain aging is a progressive process that often co-occurs with biological aging and declines of cognitive functions (Elliott et al., 2021; Mattson and Arumugam, 2018; Park and Reuter-Lorenz, 2009), which contribute to the onset and acceleration of neurodegenerative (Mariani et al., 2005) and neuropsychiatric disorders (Kaufmann et al., 2019). Studies on healthy brain aging have revealed significant inter-individual heterogeneity in the patterns of neuroanatomical changes (Raz et al., 2010; Raz and Rodrigue, 2006). Therefore, examining the patterns of structural brain aging and its associations with cognitive decline is of paramount importance in understanding the diverse biological mechanisms of age-related neuropsychiatric disorders.

Despite the fact that there exist large differences between brain development and brain aging (Courchesne et al., 2000), a discernible association between these two processes remains evident. Direct comparisons of brain development and brain aging using structural MRI indicated a ‘last in, first out’ mirroring pattern, where brain regions develop relatively late during adolescence demonstrated accelerated degeneration in older ages (McGinnis et al., 2011; Tamnes et al., 2013). In addition, brain regions with strong mirroring effects showed increased vulnerability to neurodegenerative and neuropsychiatric disorders, including Alzheimer’s disease and schizophrenia (Douaud et al., 2014). However, due to the lack of large-scale longitudinal MRI studies during adolescence and mid-to-late adulthood, validation of the ‘last in, first out’ mirroring hypothesis remains unavailable.

Prior investigations have largely focused on regional and cross-sectional changes of brain aging (Raz and Rodrigue, 2006; Douaud et al., 2014; Suzuki et al., 2019), with relatively few studies exploring longitudinal trajectories of brain aging and its associations with brain development (Raz et al., 2010; Fjell et al., 2015; Nyberg et al., 2023). In this article, we present a data-driven approach to examine the population clustering of longitudinal brain aging trajectories using structure MRI data obtained from 37,013 healthy individuals during mid-to-late adulthood (44–82 years), and explore its association with biological aging, cognitive decline and susceptibilities for neuropsychiatric disorders. Further, mirroring patterns between longitudinal brain development and brain aging are investigated by comparing the region-specific aging / developmental trajectories, and manifestation of the mirroring patterns are investigated across the whole-brain and among participants with different brain aging patterns. Genomic analyses are conducted to reveal risk loci and genes associated with accelerated brain aging and delayed brain development.

In this study, we adopted a data-driven approach and revealed two distinct brain aging patterns using large-scale longitudinal neuroimaging data in mid-to-late adulthood. Compared to brain aging pattern 1, brain aging pattern 2 were characterized by a faster rate of GMV decrease, accelerated biological aging, cognitive decline, and genetic susceptibility to neurodevelopmental disorders. By integrating longitudinal neuroimaging data from adult and adolescent cohorts, we demonstrated the ‘last in, first out’ mirroring patterns between structural brain aging and brain development, and showed that the mirroring pattern was manifested in the temporal lobe and among participants with accelerated brain aging. Further, genome-wide association studies identified significant genetic loci contributing to accelerated brain aging, while spatial correlation between whole-brain transcriptomic profiles and structural brain aging / development revealed important gene sets associated with both accelerated brain aging and delayed brain development.

Brain aging is closely related to the onset and progression of neurodegenerative and neuropsychiatric disorders. Both neurodegenerative and neuropsychiatric disorders demonstrate strong inter-individual heterogeneity, which prevents the comprehensive understanding of their neuropathology and neurogenetic basis. Therefore, multidimensional investigation into disease subtyping and population clustering of structural brain aging are crucial in elucidating the sources of heterogeneity and neurophysiological basis related to the disease spectrum (Habes et al., 2020). In the last decades, major developments in the subtyping of Alzheimer’s disease, dementia and Parkinson’s disease, have provided new perspectives regarding their clinical diagnosis, treatment, disease progression and prognostics (Habes et al., 2020; Berg et al., 2021; Ferreira et al., 2020). While previous studies of brain aging mostly focused on the cross-sectional differences between cases and healthy controls, we here delineated the structural brain aging patterns among healthy participants using a novel data-driven approach that captured both cross-sectional and longitudinal trajectories of the whole-brain gray matter volume (Feczko et al., 2019; Poulakis et al., 2022). The two brain aging patterns identified using the above approach showed large differences in the rate of change in medial occipitotemporal gyrus, which is involved in vision, word processing, and scene recognition (Bogousslavsky et al., 1987; Epstein et al., 1999; Mechelli et al., 2000). Significant reduction of the gray matter volume and abnormal changes of the functional connectivity in this region were found in subjects with mild cognitive impairment (MCI) and AD, respectively (Chételat et al., 2005; Yao et al., 2010). Previous research on brainAGE (Elliott et al., 2021; Christman et al., 2020) (the difference between chronological age and the age predicted by the machine learning model of brain imaging data) showed that as a biomarker of accelerated brain aging, people with older brainAGE have accelerated biological aging and early signs of cognitive decline, which is consistent with our discoveries in this study. Our results support the establishment of a network connecting brain aging patterns with biological aging profiles involving multi-organ systems throughout the body (Tian et al., 2023). Since structural brain patterns might manifest and diverge decades before cognitive decline (Aljondi et al., 2019), subtyping of brain aging patterns could aid in the early prediction of cognitive decline and severe neurodegenerative and neuropsychiatric disorders.

Mirroring pattern between brain development and brain aging has long been hypothesized by postulating that phylogenetically newer and ontogenetically less precocious brain structures degenerate relatively early (Douaud et al., 2014). Early studies have reported a positive correlation between age-related differences of cortical volumes and precedence of myelination of intracortical fibers (Raz, 2000). Large differences in the patterns of change between adolescent late development and aging in the medial temporal cortex were previously found in studies of brain development and aging patterns (Tamnes et al., 2013). Here, we compared the annual volume change of the whole-brain gray matter during brain development and early / late stages of brain aging, and found that mirroring patterns are predominantly localized to the lateral / medial temporal cortex and the cingulate cortex. These cortical regions characterized by ‘last in, first out’ mirroring patterns showed increased vulnerability to the several neuropsychiatric disorders. For example, regional deficits in the superior temporal gyrus and medial temporal lobe were observed in schizophrenia (Honea et al., 2005), along with morphological abnormalities in the medial occipitotemporal gyrus (Schultz et al., 2010). Children diagnosed with ADHD had lower brain surface area in the frontal, cingulate, and temporal regions (Hoogman et al., 2019). Douaud et al., 2014 revealed a population transmodal network with lifespan trajectories characterized by the mirroring pattern of development and aging. We investigated the genetic susceptibility to individual-level mirroring patterns based on the lasting impact of neurodevelopmental genetic factors on brain (Fjell et al., 2015), demonstrating that those with more rapidly brain aging patterns have a higher risk of delayed development.

Identifying genes contributing to structural brain aging remains a critical step in understanding the molecular changes and biological mechanisms that govern age-related cognitive decline. Several genetic loci have been reported to be associated with brain aging modes and neurocognitive decline, many of which demonstrated global overlap with neuropsychiatric disorders and their related risk factors (Smith et al., 2020; Glahn et al., 2013; Brouwer et al., 2022). Here, we focused on the individual brain aging phenotype by estimating individual deviation from the population averaged total GMV and conducted genome-wide association analysis with this phenotype. Our approach identified six risk SNPs associated with accelerated brain aging, most of which could be further validated by previous studies using population averaged brain aging phenotypes. However, our approach revealed additional genetic signals and demonstrated genetic architecture underlying brain aging patterns overlap with bone density (Estrada et al., 2012; Zheng et al., 2015). In addition, molecular profiling of the aging brain has been thoroughly investigated among patients with neurogenerative diseases, but rarely conducted to shed light on the mirroring patterns among healthy participants. Analysis of the spatial correlation between gene expression profiles and structural brain development / aging further identified genes contributing to delayed brain development and accelerated brain aging. Specifically, expression of gene BDNF-AS was significantly associated with both processes. BDNF-AS is an antisense RNA gene and plays a role in the pathoetiology of non-neoplastic conditions mainly through the mediation of BDNF (Ghafouri-Fard et al., 2021). LGR4 (associated with delayed brain development) and FAM3C (associated with accelerated brain aging) identified in the spatial genetic association analysis also validated our findings in the GWAS.

There are several limitations in the current study that need to be addressed in future research. Firstly, the UK Biobank cohort, which we leveraged to identify population clustering of brain aging patterns, had a limited number of repeated structural MRI scans. Therefore, it remains challenging to obtain robust estimation of the longitudinal whole-brain GMV trajectory at the individual level. As a robustness check, we have calculated both intra-class correlation and variance of both random intercept and age slope to ensure appropriateness of the mixed effect models. Secondly, although aging is driven by numerous hallmarks, we have only investigated the association between brain aging patterns and biological aging in terms of telomere length and blood biochemical markers due to limitations of data access. Other dimensions of aging hallmarks and their relationship with structural brain aging need to be investigated in the future. Thirdly, our genomic analyses were restricted to ‘white British’ participants of European ancestry. The diversity of genomic analyses will continue to improve as the sample sizes of GWAS of non-European ancestry increase. Further, although the gene expression maps from Allen Human Brain Atlas enabled us to gain insights into the spatial coupling between gene expression profiles and mirroring patterns of the brain, the strong inter-individual variation of whole-brain gene expression levels and large temporal span of the human brain samples may lead to the inaccurate correspondence in the observed associations. Finally, we focused on structural MRIs in deriving brain aging patterns in this analysis, future investigations could consider other brain imaging modalities from a multi-dimensional perspective. Nevertheless, our study represents a novel attempt for population clustering of structural brain aging and validated the mirroring pattern hypothesis by leveraging large-scale adolescent and adult cohorts.

Population clustering of structural brain aging and its association with brain development.

Supplementary method

Cognitive assessment

Reaction time

This cognitive function test is based on 12 rounds of the card-game 'Snap'. The participant is shown two cards at a time; if both cards are the same, they press a button-box that is on the table in front of them as quickly as possible. The score used for analysis is mean time to correctly identify matches (UK Biobank data field 20023), which is the mean duration to first press of snap-button summed over rounds in which both cards matched.

Numeric memory

The participant was shown a 2-digit number to remember. The number then disappeared and after a short while they were asked to enter the number onto the screen. The number became one digit longer each time they remembered correctly (up to a maximum of 12 digits). This test is available for a subset of participants. The score used for analysis is maximum digits remembered correctly (UK Biobank data field 4282), which is longest number correctly recalled during the numeric memory test.

Fluid intelligence / reasoning

'Fluid intelligence' is defined as the capacity to solve problems that require logic and reasoning ability, independent of acquired knowledge. The participant has 2 min to complete as many questions as possible from the test. This test was incorporated into the touchscreen towards the end of recruitment. The score used for analysis is fluid intelligence score (UK Biobank data field 20016), which is a simple unweighted sum of the number of correct answers given to the 13 fluid intelligence questions. Participants who did not answer all of the questions within the allotted 2 min limit are scored as zero for each of the unattempted questions.

Trail making

The participant was presented with sets of digits/letters in circles scattered around the screen and asked to click on them sequentially according to a specific algorithm. The scores used for analysis are duration to complete numeric path (trail #1) (UK Biobank data field 6348) and duration to complete alphanumeric path (trail #2) (UK Biobank data field 6350).

Matrix pattern completion

The participant was presented with a series of matrix pattern blocks with an element missing and asked to select the element that best completed the pattern from a range of displayed choices. The score used for analysis is number of puzzles correctly solved (UK Biobank data field 6373).

Symbol digit substitution

The participant was presented with one grid linking symbols to single-digit integers and a second grid containing only the symbols. They were then asked to indicate the numbers attached to each of the symbols in the second grid using the first one as a key. The score used for analysis is number of symbol digit matches made correctly (UK Biobank data field 23324).

Tower rearranging

The participant was presented with an illustration of three pegs (towers) on which three differently-colored hoops had been placed. The were then asked to indicate how many moves it would take to re-arrange the hoops into another specific position. The score used for analysis is number of puzzles correct (UK Biobank data field 21004).

Paired associate learning

In the paired associate learning test the participants were shown 12 pairs of words (for 30 s in total) then, after an interval (in which they did a different test), presented with the first word of 10 of these pairs and asked to select the matching second word from a choice of four alternatives. The words were presented in the order: huge, happy, tattered, old, long, red, sulking, pretty, tiny and new. The score used for analysis is number of word pairs correctly associated (UK Biobank data field 21097), which is the number of word pairs correctly associated out of 10 attempts.

Prospective memory

Early in the touchscreen cognitive section, the participant is shown the message "At the end of the games we will show you four colored shapes and ask you to touch the Blue Square. However, to test your memory, we want you to actually touch the Orange Circle instead." The score used for analysis is prospective memory result (UK Biobank data field 20018), which condenses the results of the prospective memory test into 3 groups (“0”: instruction not recalled, either skipped or incorrect, “1”: correct recall on first attempt, “2”: correct recall on second attempt). We divided the test results into two groups for simplicity of analysis: “0” indicating no correct recall, and the combination of “1” and “2” indicating correct recall.

Pairs matching

Participants are asked to memorize the position of as many matching pairs of cards as possible. The cards are then turned face down on the screen and the participant is asked to touch as many pairs as possible in the fewest tries. Multiple rounds were conducted. The first round used 3 pairs of cards and the second 6 pairs of cards. In the pilot phase an additional (i.e. third) round was conducted using six pairs of cards. However this was dropped from the main study as the extra set of results were very similar to the second and not felt to add significant new information. The score used for analysis is number of incorrect matches in round 2 (UK Biobank data field 399.2).

Appendix 1—figure 1

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Appendix 1—figure 2

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Appendix 1—figure 3

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Appendix 1—figure 4

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Appendix 1—figure 5

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Appendix 1—figure 6

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Appendix 1—table 1

Condition	Code
Malignant neoplasm	C70, C71
Dementia	F00, F01, F02, F03, F04
Mental and behavioural disorders due to psychoactive substance use	F10-F19
Schizophrenia, schizotypal and delusional disorders	F20-F29
Mood [affective] disorders	F30-F39
Mental retardation	F70-F79
Disorders of psychological development	F80-F89
Hyperkinetic disorders	F90
Inflammatory diseases of the central nervous system	G00-G09
Systemic atrophies primarily affecting the central nervous system	G10, G11, G122, G13
Extrapyramidal and movement disorders	G20, G21, G22, G23
Other degenerative diseases of the nervous system	G30-G32
Demyelinating diseases of the central nervous system	G35-G37
Episodic and paroxysmal disorders	G40, G41, G45, G46
Infantile cerebral palsy	G80
Cerebrovascular diseases	I60-I69
Down’s syndrome	Q90
Intracranial injury	S06

Appendix 1—table 2

Field ID	Condition	Code
20001	Brain cancer/primary malignant brain tumour	1032
	Meningeal cancer/malignant meningioma	1031
20002	Benign neuroma	1683
	Brain abscess/intracranial abscess	1245
	Brain haemorrhage	1491
	Cerebral aneurysm	1425
	Cerebral palsy	1433
	Chronic/degenerative neurological problem	1258
	dementia/alzheimers/cognitive impairment	1263
	Encephalitis	1246
	Epilepsy	1264
	Fracture skull/head	1626
	Head injury	1266
	Ischaemic stroke	1583
	Meningioma/benign meningeal tumour	1659
	Meningitis	1247
	Motor Neurone Disease	1259
	Multiple Sclerosis	1261
	Nervous system infection	1244
	Neurological injury/trauma	1240
	Other demyelinating disease (not Multiple Sclerosis)	1397
	Other neurological problem	1434
	Parkinson’s Disease	1262
	Spina Bifida	1524
	Stroke	1081
	Subarachnoid haemorrhage	1086
	Subdural haemorrhage/haematoma	1083
	Transient ischaemic attack	1082

Appendix 1—table 3

Desikan–Killiany Atlas
bankssts caudal anterior cingulate caudal middle frontal cuneus entorhinal fusiform inferior parietal inferior temporal isthmus cingulate lateral occipital lateral orbitofrontal lingual medial orbitofrontal middle temporal parahippocampal paracentral pars opercularis pars orbitalis pars triangularis pericalcarine postcentral posterior cingulate precentral precuneus rostral anterior cingulate rostral middle frontal superior frontal superior parietal superior temporal supramarginal frontal pole transverse temporal insula
ASEG Atlas
thalamus proper caudate putamen pallidum hippocampus amygdala accumbens area

Appendix 1—table 4

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15
Cortical
bankssts	0.12	–0.25	0.01	0.30	–0.13	0.01	0.09	0.34	0.04	0.05	–0.10	0.16	–0.09	–0.20	0.02
caudal.anterior.cingulate	0.12	–0.06	–0.02	0.13	0.39	0.28	0.13	–0.09	–0.37	0.24	0.05	0.03	–0.04	–0.13	0.07
caudal.middle.frontal	0.15	0.01	–0.10	–0.11	0.34	–0.21	0.10	0.06	0.03	–0.28	–0.23	–0.20	0.24	0.00	0.25
cuneus	0.13	0.47	–0.01	0.06	–0.01	0.00	0.07	0.12	0.01	0.07	0.12	–0.05	–0.09	0.08	–0.07
entorhinal	0.09	0.07	0.13	0.08	0.10	0.17	–0.53	–0.18	0.06	0.04	–0.19	–0.10	0.13	0.01	–0.04
fusiform	0.18	–0.03	0.01	0.13	–0.02	0.02	–0.20	–0.02	0.15	0.30	–0.01	–0.28	–0.23	0.08	0.21
inferior.parietal	0.15	–0.18	0.01	0.39	–0.09	0.01	0.14	–0.04	0.16	0.05	–0.20	–0.02	–0.15	–0.02	0.02
inferior.temporal	0.16	–0.13	0.01	0.26	–0.01	0.15	–0.04	–0.10	0.14	–0.08	–0.08	–0.25	0.20	0.29	–0.01
isthmus.cingulate	0.15	0.23	0.02	0.11	–0.14	–0.03	0.08	–0.03	–0.23	–0.07	–0.22	0.16	0.43	0.04	0.11
lateral.occipital	0.16	0.30	0.00	0.12	–0.02	0.02	0.05	0.10	0.16	0.18	0.06	–0.23	–0.23	0.12	0.17
lateral.orbitofrontal	0.22	–0.04	–0.08	–0.17	–0.05	0.15	–0.02	–0.15	0.09	–0.02	–0.01	0.40	–0.01	0.22	0.17
lingual	0.12	0.43	0.03	0.14	–0.01	–0.02	0.07	0.11	–0.01	–0.03	–0.10	0.16	0.02	–0.09	–0.08
medial.orbitofrontal	0.21	–0.06	–0.06	–0.06	–0.05	0.10	–0.01	–0.13	0.18	–0.06	0.13	0.17	0.05	0.01	–0.25
middle.temporal	0.17	–0.17	–0.01	0.27	–0.14	0.10	0.21	0.25	0.09	–0.15	–0.09	0.04	0.07	0.05	–0.11
parahippocampal	0.12	0.09	0.13	0.05	0.06	0.14	–0.46	0.02	–0.02	0.09	–0.22	0.09	0.03	–0.32	0.02
paracentral	0.19	–0.05	–0.10	–0.07	0.18	–0.23	–0.11	–0.06	0.01	0.03	–0.02	0.06	–0.35	–0.01	–0.35
pars.opercularis	0.16	–0.02	–0.06	–0.23	–0.19	0.10	0.14	0.01	–0.16	–0.13	–0.43	–0.29	–0.16	–0.06	0.00
pars.orbitalis	0.18	0.02	–0.05	–0.17	–0.11	0.24	0.03	–0.17	0.23	0.08	0.01	0.41	–0.14	0.05	0.24
pars.triangularis	0.16	0.02	–0.03	–0.35	–0.26	0.23	0.13	–0.05	–0.06	–0.06	–0.25	–0.17	–0.21	–0.08	–0.05
pericalcarine	0.10	0.47	–0.01	0.08	–0.01	0.01	0.09	0.13	–0.02	–0.01	0.02	0.08	–0.03	0.03	–0.17
postcentral	0.20	–0.04	–0.11	0.06	0.16	–0.29	–0.09	0.12	0.08	0.03	–0.09	0.10	0.04	0.01	0.04
posterior.cingulate	0.18	–0.08	–0.04	0.02	0.24	0.09	0.08	–0.02	–0.30	0.01	–0.07	0.10	–0.05	–0.12	–0.31
precentral	0.21	–0.01	–0.10	–0.08	0.28	–0.29	–0.09	0.08	0.15	–0.05	–0.15	0.13	–0.06	0.05	0.12
precuneus	0.20	0.02	–0.02	0.13	–0.24	–0.26	0.01	–0.35	–0.20	–0.04	0.06	–0.04	0.00	0.03	0.07
rostral.anterior.cingulate	0.15	–0.07	–0.07	0.05	0.31	0.21	0.15	–0.05	–0.30	0.13	0.11	–0.06	0.00	0.18	0.15
rostral.middle.frontal	0.20	0.02	–0.05	–0.05	0.10	0.14	0.22	–0.11	0.17	–0.11	0.24	–0.14	0.19	–0.22	0.14
superior.frontal	0.22	–0.03	–0.09	–0.21	0.14	–0.13	–0.01	0.06	0.20	–0.18	0.06	–0.11	0.02	0.00	–0.16
superior.parietal	0.17	0.02	–0.05	0.14	–0.18	–0.29	0.01	–0.45	–0.16	0.00	0.14	–0.08	–0.08	–0.06	0.04
superior.temporal	0.21	–0.13	–0.02	–0.06	–0.10	0.01	–0.07	0.40	–0.07	0.13	0.24	–0.05	0.02	–0.06	0.08
supramarginal	0.17	–0.15	–0.08	0.06	–0.24	–0.23	–0.13	0.03	–0.21	0.04	0.11	0.10	0.14	–0.08	–0.06
frontal.pole	0.16	0.03	–0.10	0.01	0.00	0.16	0.03	–0.13	0.28	0.05	0.28	–0.16	0.26	–0.38	–0.22
transverse.temporal	0.15	–0.01	–0.10	–0.27	–0.17	–0.10	–0.16	0.25	–0.22	0.19	0.24	–0.10	0.09	0.04	0.16
insula	0.19	–0.08	0.01	–0.22	–0.12	0.10	–0.07	0.11	–0.06	0.15	–0.08	0.00	0.19	0.16	–0.23
Subcortical
thalamus.proper	0.09	–0.02	0.31	–0.08	0.03	–0.08	0.08	0.00	0.02	–0.21	0.11	0.10	–0.19	–0.37	0.33
caudate	0.06	–0.03	0.32	–0.12	0.02	–0.10	0.15	–0.02	0.11	0.31	–0.11	0.09	0.27	0.14	0.12
putamen	0.08	–0.03	0.42	–0.12	0.03	–0.13	0.13	–0.03	0.05	0.22	–0.05	–0.14	0.07	–0.02	–0.17
pallidum	0.03	–0.03	0.42	–0.07	0.00	–0.13	0.16	–0.06	0.04	0.20	–0.07	0.00	0.04	–0.20	–0.06
hippocampus	0.12	0.01	0.33	0.04	–0.02	0.11	–0.20	0.08	–0.16	–0.39	0.09	0.03	–0.11	–0.03	0.05
amygdala	0.13	–0.03	0.31	0.05	–0.01	0.12	–0.13	0.09	–0.07	–0.35	0.25	–0.09	–0.02	0.24	–0.01
accumbens.area	0.11	–0.05	0.31	–0.02	0.12	–0.07	0.11	–0.04	0.00	–0.03	0.10	0.09	–0.14	0.34	–0.18

Appendix 1—table 5

	Total (n=37,013)	Pattern 1 (n=18,929)Pattern 2 (n=18,084)
Age (years), mean (SD)	63.9 (7.63)	63.9 (7.64)	63.8 (7.63)
Female, n (%)	19,958 (53.9)	10,117 (53.4)	9,841 (54.4)
Ethnicity, n (%)
White	34,219 (92.5)	17,509 (92.5)	16,710 (92.4)
Mixed	1,137 (3.1)	573 (3.0)	564 (3.1)
Asian or Asian British	1,210 (3.3)	612 (3.2)	598 (3.3)
Other	447 (1.2)	235 (1.2)	212 (1.2)
Smoking status, n (%)*
Never smoker	23,633 (64.4)	12,269 (65.4)	11,364 (63.4)
Previous smoker	12,213 (33.3)	6,085 (32.4)	6,128 (34.2)
Current smoker	833 (2.3)	414 (2.2)	419 (2.3)
TDI, mean (SD)†	–1.94 (2.69)	–1.97 (2.66)	–1.90 (2.71)
BMI (kg/m2), mean (SD)‡	26.4 (4.32)	26.3 (4.17)	26.5 (4.46)
Years of Schooling, mean (SD)§	16.8 (4.32)	16.9 (4.29)	16.8 (4,35)

1. TDI = Townsend Deprivation Index, BMI = Body Mass Index.

2. *

   Missing 334

3. †

   Missing 36

4. ‡

   Missing 1937

5. §

   Missing 337

Appendix 1—table 6

Biological aging biomarkers	Brain aging pattern 1		Brain aging pattern 2
	N	Mean(SD)	N	Mean(SD)
LTL	17,691	0.083 (0.98)	16,876	0.055 (0.97)
PhenoAge	15,228	41.35 (8.17)	14,323	41.58 (8.32)
Biological aging biomarkers	Unadjusted			Adjusted
	Cohen’s d (95% CI)	p	P.Bonferroni	Cohen’s d (95% CI)	p	P.Bonferroni
LTL	–0.028 (-0.049,–0.007)	0.009	0	–0.030 (-0.051,–0.009)	0.006	0.011
PhenoAge	0.027 (0.004, 0.050)	0.019	0	0.092 (0.070, 0.116)	3.05E-15	6.11E-15

Appendix 1—table 7

Cognitive functions	Brain aging pattern 1		Brain aging pattern 2
	N	Mean(SD)	N	Mean(SD)
Reaction time	17,749	–594.70 (108.15)	16,831	–594.68 (110.50)
Numeric memory	13,350	6.82 (1.26)	12,346	6.72 (1.27)
Fluid intelligence	17,580	6.73 (2.06)	16,612	6.53 (2.04)
Trail making A	13,052	–224.50 (84.82)	12,036	–227.56 (84.85)
Trail making B	12,743	–563.06 (246.74)	11,753	–576.55 (260.75)
Matrix pattern completion	13,064	8.07 (2.11)	12,064	7.91 (2.14)
Symbol digit substitution	13,077	19.08 (5.15)	12,056	18.87 (5.35)
Tower rearranging	12,952	9.96 (3.20)	11,958	9.83 (3.23)
Paired associate learning	13,184	7.01 (2.59)	12,198	6.88 (2.65)
Prospective memory	17,831	N/A	16,949	N/A
Pairs matching	17,840	–3.58 (2.90)	16,956	–3.67 (2.94)
Cognitive functions	Unadjusted			Adjusted
	Cohen’s d (95% CI)	P	P.FDR	Cohen’s d (95% CI)	P	P.FDR
Reaction time	0.000 (-0.021, 0.021)	0.99	0.99	0.006 (-0.016, 0.028)	0.61	0.61
Numeric memory	–0.082 (-0.106,–0.057)	5.97E-11	3.28E-10	–0.080 (-0.106,–0.055)	8.99E-10	4.95E-09
Fluid intelligence	–0.102 (-0.123,–0.080)	5.94E-21	6.54E-20	–0.99 (-0.121,–0.077)	3.30E-18	3.63E-17
Trail making A	–0.036 (-0.061,–0.011)	0.004	0.006	–0.050 (-0.074,–0.024)	1.46E-04	2.29E-04
Trail making B	–0.053 (-0.078,–0.028)	3.16E-05	8.68E-05	–0.067 (-0.093,–0.041)	6.16E-07	1.69E-06
Matrix pattern completion	–0.076 (-0.101,–0.051)	1.84E-09	6.74E-09	–0.078 (-0.104,–0.052)	3.67E-09	1.35E-08
Symbol digit substitution	–0.040 (-0.065,–0.015)	0.002	0.002	–0.053 (-0.079,–0.027)	6.15E-05	1.13E-04
Tower rearranging	–0.041 (-0.066,–0.016)	0.001	0.002	–0.049 (-0.075,–0.023)	2.18E-04	3.00E-04
Paired associate learning	–0.051 (-0.076,–0.027)	4.64E-05	1.02E-04	–0.054 (-0.079,–0.028)	4.89E-05	1.08E-04
Prospective memory	OR: 0.943 (0.891, 0.999)	0.047	0.052	OR: 0.940 (0.883, 1.000)	0.052	0.057
Pairs matching	–0.029 (-0.050,–0.008)	0.006	0.008	–0.033 (-0.055,–0.011)	0.003	0.004

Appendix 1—table 8

Study	Number cases	Number controls	Number of genome-wide independently significant loci	Download link
Attention deficit hyperactivity disorder Demontis et al., 2019	20,183	35,191	12	https://figshare.com/ndownloader/files/28169253
Autism spectrum disorder Grove et al., 2019	18,381	27,969	5	https://figshare.com/ndownloader/files/28169292
Alzheimer’s disease Jansen et al., 2019	71,880	383,378	25	https://ctg.cncr.nl/software/summary_statistics
Parkinson’s disease Nalls et al., 2019	37,688, and 18,618 (proxy-cases)	1,417,791	90	https://drive.google.com/file/d/1FZ9UL99LAqyWnyNBxxlx6qOUlfAnublN/view?usp=sharing
Bipolar disorder Mullins et al., 2021	41,917	371,549	64	https://figshare.com/ndownloader/files/40036705
Major depressive disorder Wray et al., 2018	135,458	344,901	44	https://figshare.com/ndownloader/files/39504667
Schizophrenia Trubetskoy et al., 2022	76,755	243,649	287	https://figshare.com/ndownloader/files/34517828
Delayed Brain Development Shi et al., 2023	7662 (proxy phenotype, continuous)		1	https://delayedneurodevelopment.page.link/amTC

Appendix 1—table 9

Trait	n1	n2	statistic	p	p.adjust
AAM	18,429	17,586	–2.218	0.027	0.080
AMD	18,429	17,586	1.753	0.080	0.169
AD	18,429	17,586	0.735	0.462	0.616
AST	18,429	17,586	–0.861	0.389	0.543
AF	18,429	17,586	–0.100	0.920	0.945
BD	18,429	17,586	3.557	3.75E-04	0.002
BMI	18,429	17,586	–3.309	0.001	0.005
CRC	18,429	17,586	–0.544	0.586	0.703
BC	18,429	17,586	–3.140	0.002	0.008
CVD	18,429	17,586	–2.104	0.035	0.091
CED	18,429	17,586	1.046	0.296	0.484
CAD	18,429	17,586	–1.588	0.112	0.202
CD	18,429	17,586	–0.094	0.925	0.945
EOC	18,429	17,586	–2.183	0.029	0.080
EBMDT	18,429	17,586	–11.343	<1.00E-20	<1.00E-20
HBA1C_DF	18,429	17,586	–2.948	0.003	0.013
HEIGHT	18,429	17,586	6.658	2.81E-11	3.37E-10
HDL	18,429	17,586	0.884	0.377	0.543
HT	18,429	17,586	–3.539	4.02E-04	0.002
IOP	18,429	17,586	–1.605	0.109	0.202
ISS	18,429	17,586	–2.383	0.017	0.056
LDL_SF	18,429	17,586	–0.686	0.492	0.627
MEL	18,429	17,586	2.025	0.043	0.103
MS	18,429	17,586	–0.069	0.945	0.945
OP	18,429	17,586	12.029	<1.00E-20	<1.00E-20
PD	18,429	17,586	1.456	0.145	0.249
POAG	18,429	17,586	–0.856	0.392	0.543
PC	18,429	17,586	–0.240	0.810	0.941
PSO	18,429	17,586	–1.781	0.075	0.169
RA	18,429	17,586	–2.437	0.015	0.053
SCZ	18,429	17,586	0.158	0.874	0.945
SLE	18,429	17,586	1.695	0.090	0.180
T1D	18,429	17,586	0.666	0.505	0.627
T2D	18,429	17,586	–5.523	3.35E-08	3.02E-07
UC	18,429	17,586	0.883	0.377	0.543
VTE	18,429	17,586	–0.170	0.865	0.945

Appendix 1—table 10

Trait	n1	n2	statistic	p	p.adjust
AAM	3,407	3,409	–1.708	0.088	0.344
AMD	3,407	3,409	0.547	0.584	0.931
AD	3,407	3,409	0.756	0.450	0.820
APOEA	3,407	3,409	0.023	0.982	0.993
APOEB	3,407	3,409	0.119	0.905	0.968
AST	3,407	3,409	0.112	0.911	0.968
AF	3,407	3,409	1.306	0.192	0.600
BD	3,407	3,409	0.561	0.575	0.931
BMI	3,407	3,409	–0.976	0.329	0.730
CRC	3,407	3,409	0.984	0.325	0.730
BC	3,407	3,409	–0.995	0.320	0.730
CAL	3,407	3,409	–1.786	0.074	0.326
CVD	3,407	3,409	–0.009	0.993	0.993
CED	3,407	3,409	1.280	0.200	0.600
CAD	3,407	3,409	0.231	0.818	0.961
DOA	3,407	3,409	–0.326	0.745	0.961
EOC	3,407	3,409	–2.167	0.030	0.155
EBMDT	3,407	3,409	–6.111	1.04E-09	2.65E-08
EGCR	3,407	3,409	0.413	0.680	0.961
EGCY	3,407	3,409	–0.210	0.834	0.961
HBA1C_DF	3,407	3,409	0.130	0.896	0.968
HEIGHT	3,407	3,409	4.351	1.38E-05	2.35E-04
HDL	3,407	3,409	0.294	0.769	0.961
HT	3,407	3,409	–0.884	0.377	0.743
IOP	3,407	3,409	–2.366	0.018	0.151
ISS	3,407	3,409	0.066	0.947	0.986
LDL_SF	3,407	3,409	–0.193	0.847	0.961
MEL	3,407	3,409	2.659	0.008	0.080
MS	3,407	3,409	–2.293	0.022	0.151
OTFA	3,407	3,409	0.318	0.750	0.961
OSFA	3,407	3,409	0.770	0.441	0.820
OP	3,407	3,409	6.484	9.54E-11	4.87E-09
PD	3,407	3,409	1.041	0.298	0.730
PDCL	3,407	3,409	0.663	0.507	0.862
PHG	3,407	3,409	0.392	0.695	0.961
PFA	3,407	3,409	0.495	0.621	0.932
POAG	3,407	3,409	–1.083	0.279	0.730
PC	3,407	3,409	0.675	0.500	0.862
PSO	3,407	3,409	–2.234	0.026	0.151
RMNC	3,407	3,409	0.501	0.617	0.932
RHR	3,407	3,409	–2.865	0.004	0.053
RA	3,407	3,409	0.297	0.766	0.961
SCZ	3,407	3,409	–0.880	0.379	0.743
SGM	3,407	3,409	–0.224	0.823	0.961
SLE	3,407	3,409	1.458	0.145	0.493
TCH	3,407	3,409	0.191	0.848	0.961
TFA	3,407	3,409	0.892	0.372	0.743
TTG	3,407	3,409	1.212	0.226	0.640
T1D	3,407	3,409	1.771	0.077	0.326
T2D	3,407	3,409	–2.218	0.027	0.151
VTE	3,407	3,409	–1.613	0.107	0.390

Appendix 1—table 11

SNP	A1	A2	p-value	β	β SE	Location (chr:bp)	Gene symbol	Position relative to gene
rs10835187	C	T	1.70e-14	–0.02558	0.003333	11:27505677	LGR4, LIN7C	intergenic
rs7776725	C	T	4.47e-13	–0.02640	0.003644	7:121033121	FAM3C	intronic
rs779233904	AAC	A	1.57e-09	0.02083	0.003449	6:151910404	CCDC170	intronic
rs2504071	T	C	3.34e-09	0.01959	0.003311	6:152084862	ESR1	intronic
17:43553496:A:AAT	A	AAT	6.65e-09	–0.02472	0.004261	17:43553496	PLEKHM1	intronic
10:104227791:G:GA	GA	G	1.48e-08	–0.01889	0.003334	10:104227791	TMEM180	intronic

Appendix 1—table 12

Gene Symbol	Spearman's ρ	p value	P.permutation
ACTR1A	0.096	0.440	0.328
ARHGAP27	0.051	0.684	0.445
ARL17B	0.047	0.705	0.452
BDNF-AS	0.256	0.038	0.038
CCDC170	0.268	0.030	0.069
ESR1	0.021	0.870	0.483
FAM3C	–0.096	0.444	0.356
KANSL1	–0.262	0.034	0.073
KANSL1-AS1	0.067	0.594	0.313
LGR4	0.558	1.78E-06	2.50E-04
LIN7C	0.036	0.775	0.464
LRRC37A4P	–0.272	0.027	0.148
MAPT	0.024	0.846	0.405
PLEKHM1	–0.276	0.025	0.109
SPPL2C	0.116	0.351	0.189
STH	–0.147	0.238	0.277
SUFU	0.407	0.001	0.028

Appendix 1—table 13

Gene Symbol	Spearman's ρ	Pvalue	P.permutation
ACTR1A	–0.235	0.058	0.052
ARHGAP27	0.486	4.48E-05	5.50E-04
ARL17B	0.090	0.473	0.240
BDNF-AS	0.490	3.68E-05	1.50E-04
CCDC170	0.206	0.098	0.075
ESR1	0.532	6.02E-06	1.50E-04
FAM3C	–0.366	0.003	0.005
KANSL1	0.213	0.086	0.078
KANSL1-AS1	–0.262	0.034	0.059
LGR4	0.070	0.576	0.348
LIN7C	0.177	0.154	0.120
LRRC37A4P	0.143	0.250	0.165
MAPT	–0.287	0.020	0.022
PLEKHM1	0.202	0.104	0.080
SPPL2C	0.211	0.089	0.059
STH	–0.001	0.997	0.490
SUFU	–0.036	0.773	0.373

Appendix 1—table 14

model	AIC	BIC	lrtest	ICC_adjusted	ICC_unadjusted
lmer_intr_thalamus.proper	79444.24	79591.29	6.45E-15	0.8875	0.3958
lmer_slope_thalamus.proper	79382.89	79547.24	6.45E-15	0.8889	0.3986
lmer_intr_caudate	95845.48	95992.54	2.68E-71	0.9543	0.6824
lmer_slope_caudate	95524.49	95688.84	2.68E-71	0.9564	0.6817
lmer_intr_putamen	92106.06	92253.12	2.40E-42	0.9398	0.5946
lmer_slope_putamen	91918.4	92082.75	2.40E-42	0.9424	0.5933
lmer_intr_pallidum	90500.3	90647.36	4.21E-42	0.8859	0.5116
lmer_slope_pallidum	90313.76	90478.12	4.21E-42	0.8862	0.5093
lmer_intr_hippocampus	89833.92	89980.97	1.37E-08	0.9309	0.5505
lmer_slope_hippocampus	89801.71	89966.06	1.37E-08	0.9329	0.5505
lmer_intr_amygdala	89976.98	90124.03	4.01E-06	0.8697	0.4898
lmer_slope_amygdala	89956.13	90120.48	4.01E-06	0.8713	0.4897
lmer_intr_accumbens.area	96936.19	97083.24	1.69E-14	0.8228	0.5295
lmer_slope_accumbens.area	96876.77	97041.12	1.69E-14	0.8233	0.5310
lmer_intr_bankssts	97981.96	98129.01	0.001818	0.9339	0.6798
lmer_slope_bankssts	97973.34	98137.69	0.001818	0.9354	0.6814
lmer_intr_caudal.anterior.cingulate	109133.2	109280.3	0.131507	0.8638	0.7653
lmer_slope_caudal.anterior.cingulate	109133.2	109297.5	0.131507	0.8644	0.7659
lmer_intr_caudal.middle.frontal	93118.13	93265.19	8.31E-06	0.9227	0.5929
lmer_slope_caudal.middle.frontal	93098.74	93263.09	8.31E-06	0.9246	0.5949
lmer_intr_cuneus	101801.4	101948.5	0.014429	0.9242	0.7256
lmer_slope_cuneus	101796.9	101961.3	0.014429	0.9245	0.7262
lmer_intr_entorhinal	109404.9	109551.9	1.63E-14	0.8013	0.6908
lmer_slope_entorhinal	109345.4	109509.7	1.63E-14	0.8037	0.6928
lmer_intr_fusiform	87545.88	87692.93	9.17E-05	0.9139	0.5062
lmer_slope_fusiform	87531.28	87695.63	9.17E-05	0.9163	0.5074
lmer_intr_inferior.parietal	89044.43	89191.48	5.89E-14	0.9374	0.5564
lmer_slope_inferior.parietal	88987.51	89151.86	5.89E-14	0.9419	0.5603
lmer_intr_inferior.temporal	84066.47	84213.52	5.73E-07	0.9384	0.4956
lmer_slope_inferior.temporal	84041.73	84206.08	5.73E-07	0.9405	0.4977
lmer_intr_isthmus.cingulate	92442.12	92589.17	0.127191	0.9275	0.5862
lmer_slope_isthmus.cingulate	92442	92606.35	0.127191	0.9284	0.5869
lmer_intr_lateral.occipital	89550.4	89697.45	0.003943	0.9121	0.5273
lmer_slope_lateral.occipital	89543.33	89707.68	0.003943	0.9129	0.5282
lmer_intr_lateral.orbitofrontal	86224.13	86371.18	1.29E-08	0.8466	0.4323
lmer_slope_lateral.orbitofrontal	86191.79	86356.14	1.29E-08	0.8497	0.4345
lmer_intr_lingual	102605.2	102752.2	0.041242	0.9181	0.7268
lmer_slope_lingual	102602.8	102767.2	0.041242	0.9181	0.7272
lmer_intr_medial.orbitofrontal	89954.36	90101.41	2.25E-06	0.7832	0.4219
lmer_slope_medial.orbitofrontal	89932.35	90096.7	2.25E-06	0.7872	0.4241
lmer_intr_middle.temporal	83331.01	83478.06	0.0019	0.9177	0.4615
lmer_slope_middle.temporal	83322.48	83486.83	0.0019	0.9195	0.4629
lmer_intr_parahippocampal	108997.1	109144.2	4.99E-05	0.8686	0.7639
lmer_slope_parahippocampal	108981.3	109145.6	4.99E-05	0.8690	0.7639
lmer_intr_paracentral	98058.63	98205.68	0.015686	0.8695	0.5958
lmer_slope_paracentral	98054.32	98218.67	0.015686	0.8705	0.5960
lmer_intr_pars.opercularis	96829.05	96976.1	1.20E-12	0.9354	0.6658
lmer_slope_pars.opercularis	96778.15	96942.5	1.20E-12	0.9396	0.6698
lmer_intr_pars.orbitalis	96989.61	97136.67	1.39E-05	0.8785	0.5892
lmer_slope_pars.orbitalis	96971.25	97135.6	1.39E-05	0.8805	0.5912
lmer_intr_pars.triangularis	96637.58	96784.63	4.55E-18	0.9402	0.6710
lmer_slope_pars.triangularis	96561.72	96726.07	4.55E-18	0.9439	0.6748
lmer_intr_pericalcarine	105115.9	105263	0.022841	0.9429	0.8190
lmer_slope_pericalcarine	105112.4	105276.7	0.022841	0.9441	0.8200
lmer_intr_postcentral	91605.6	91752.65	0.000549	0.8764	0.5189
lmer_slope_postcentral	91594.59	91758.94	0.000549	0.8789	0.5203
lmer_intr_posterior.cingulate	96853.73	97000.78	2.21E-19	0.8913	0.6014
lmer_slope_posterior.cingulate	96771.82	96936.17	2.21E-19	0.8949	0.6022
lmer_intr_precentral	91186.59	91333.64	2.61E-12	0.8478	0.4864
lmer_slope_precentral	91137.25	91301.6	2.61E-12	0.8504	0.4864
lmer_intr_precuneus	84734.58	84881.63	1.23E-08	0.9088	0.4708
lmer_slope_precuneus	84702.16	84866.51	1.23E-08	0.9126	0.4730
lmer_intr_rostral.anterior.cingulate	95688.68	95835.73	2.64E-12	0.9093	0.6097
lmer_slope_rostral.anterior.cingulate	95639.36	95803.72	2.64E-12	0.9122	0.6129
lmer_intr_rostral.middle.frontal	80873.84	81020.89	2.57E-17	0.9137	0.4354
lmer_slope_rostral.middle.frontal	80801.44	80965.79	2.57E-17	0.9191	0.4395
lmer_intr_superior.frontal	79730.6	79877.65	1.25E-11	0.8921	0.4038
lmer_slope_superior.frontal	79684.38	79848.73	1.25E-11	0.8972	0.4060
lmer_intr_superior.parietal	92895.95	93043	8.55E-07	0.8899	0.5487
lmer_slope_superior.parietal	92872.01	93036.36	8.55E-07	0.8934	0.5512
lmer_intr_superior.temporal	86495.24	86642.29	0.003021	0.9148	0.4959
lmer_slope_superior.temporal	86487.64	86651.99	0.003021	0.9168	0.4971
lmer_intr_supramarginal	87390.39	87537.44	3.67E-13	0.9263	0.5221
lmer_slope_supramarginal	87337.12	87501.47	3.67E-13	0.9320	0.5258
lmer_intr_frontal.pole	110426.3	110573.4	1.95E-65	0.6907	0.5868
lmer_slope_frontal.pole	110132.3	110296.7	1.95E-65	0.6957	0.5882
lmer_intr_transverse.temporal	102753.2	102900.2	0.032413	0.9130	0.7247
lmer_slope_transverse.temporal	102750.3	102914.7	0.032413	0.9144	0.7258
lmer_intr_insula	88693.2	88840.26	7.61E-14	0.8263	0.4416
lmer_slope_insula	88636.79	88801.14	7.61E-14	0.8290	0.4420

The summary statistics of GWAS for individual deviations of total GMV is available at https://doi.org/10.5061/dryad.jh9w0vtmn. All the UK Biobank data used in the study are available at https://www.ukbiobank.ac.uk. The IMAGEN project data are available at https://imagen-project.org (Schumann et al., 2010). GWAS summary statistics used to calculate the PRS are available in the Appendix 1—table 8. Human gene expression data are available in the Allen Human Brain Atlas dataset: https://human.brainmap.org (Hawrylycz et al., 2012). Source data files have been provided for Figures 2—5, 7 and 8, which contain the numerical data used to generate the figures. Summary statistics of the GWAS for delayed brain development by Shi et al are available at https://delayedneurodevelopment.page.link/amTC.

1. 1. Duan H
   2. Shi R
   3. Kang J

   (2024) Dryad

   The summary statistics of GWAS for individual deviations of total GMV.

   https://doi.org/10.5061/dryad.jh9w0vtmn

1. 1. Nalls MA
   2. Blauwendraat C
   3. Vallerga CL

   (2019) GWAS Catalog

   ID GCST009325. Parkinson’s disease or first degree relation to individual with Parkinson’s disease.

   https://www.ebi.ac.uk/gwas/studies/GCST009325
2. 1. Sullivan P

   (2021) figshare

   adhd2019.

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