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Genetic variants associated with longitudinal changes in brain structure across the lifespan

Authors:
Rachel M Brouwer at Vrije Universiteit Amsterdam
Rachel M Brouwer
Marieke Klein at Radboud University Medical Centre (Radboudumc)
Marieke Klein
Katrina Grasby at Queensland Institute of Medical Research
Katrina Grasby
Hugo G Schnack at University Medical Center Utrecht
Hugo G Schnack
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Abstract and Figures

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging. Human brain structure changes throughout the lifespan. Brouwer et al. identified genetic variants that affect rates of brain growth and atrophy. The genes are linked to early brain development and neurodegeneration and suggest involvement of metabolic processes.
Demographics and analysis Overview of demographics (left). Per cohort, an age distribution is displayed, based on mean and standard deviation of the age at baseline. Cohorts of European ancestry are displayed in green, non-European cohorts are displayed in yellow. On the right, the total number of included subjects is displayed and a pie-chart of the distribution of diagnostic groups (pink) and subjects not belonging to diagnostic groups - often healthy subjects (aqua). Overview of analysis pipeline (right).
Demographics and analysis Overview of demographics (left). Per cohort, an age distribution is displayed, based on mean and standard deviation of the age at baseline. Cohorts of European ancestry are displayed in green, non-European cohorts are displayed in yellow. On the right, the total number of included subjects is displayed and a pie-chart of the distribution of diagnostic groups (pink) and subjects not belonging to diagnostic groups - often healthy subjects (aqua). Overview of analysis pipeline (right).
… 
Correlations between change rates Pearson correlations between rates of change and between baseline intracranial volume and rates of change in the largest adolescent cohort (top, N = 1068) and the largest cohort in older age (bottom, N = 624) in phase 1. The size of the correlations is displayed by color and size of the circles.
Correlations between change rates Pearson correlations between rates of change and between baseline intracranial volume and rates of change in the largest adolescent cohort (top, N = 1068) and the largest cohort in older age (bottom, N = 624) in phase 1. The size of the correlations is displayed by color and size of the circles.
… 
Phenotypic brain changes throughout the lifespan Visualization of growth and decline of brain structures throughout the lifespan. The subcortical structures are shown in exploded view.
Phenotypic brain changes throughout the lifespan Visualization of growth and decline of brain structures throughout the lifespan. The subcortical structures are shown in exploded view.
… 
Annual rates of change Δ per cohort for each structure a-o, The estimated trajectories with 95% confidence intervals (in green) are displayed in the top row. Mean values of individual cohorts are displayed as points, with error bars representing standard errors displayed in gray. The size of the points represents the relative size of the cohorts (total sample size n = 15,640). Means and standard deviations are based on raw data; no covariates were included. Cohorts that were added in phase 2 are displayed in gray. Only cohorts that satisfy n > 75 and mean interval >0.5 years are shown. The estimated trajectories of the volumes themselves are displayed in the bottom row, for all individuals (solid line) and for individuals not part of diagnostic groups (dashed line).
Annual rates of change Δ per cohort for each structure a-o, The estimated trajectories with 95% confidence intervals (in green) are displayed in the top row. Mean values of individual cohorts are displayed as points, with error bars representing standard errors displayed in gray. The size of the points represents the relative size of the cohorts (total sample size n = 15,640). Means and standard deviations are based on raw data; no covariates were included. Cohorts that were added in phase 2 are displayed in gray. Only cohorts that satisfy n > 75 and mean interval >0.5 years are shown. The estimated trajectories of the volumes themselves are displayed in the bottom row, for all individuals (solid line) and for individuals not part of diagnostic groups (dashed line).
… 
Genetic effects on rates of brain changes throughout the lifespan Genome-wide significant SNPs and genes with effects on brain changes at their respective loci across the human genome, from phase 2 (total n = 15,100). This plot was created using PhenoGram (http://visualization.ritchielab.org).
+2
Genetic effects on rates of brain changes throughout the lifespan Genome-wide significant SNPs and genes with effects on brain changes at their respective loci across the human genome, from phase 2 (total n = 15,100). This plot was created using PhenoGram (http://visualization.ritchielab.org).
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Articles
https://doi.org/10.1038/s41593-022-01042-4
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Under the influence of genes and a varying environment, human
brain structure changes throughout the lifespan. Even in adult-
hood, when the brain seems relatively stable, individuals dif-
fer in the profile and rate of brain changes1. Longitudinal studies are
crucial to identify genetic and environmental factors that influence
the rate of these brain changes throughout development2 and aging3.
Inter-individual differences in brain development are associated with
general cognitive function4,5 and risk for psychiatric disorders6,7 and
neurological diseases8,9. Genetic factors involved in brain development
and aging overlap with those for cognition10 and risk for neuropsychi-
atric disorders11. A recent cross-sectional study showed brain age to be
advanced in several brain disorders. Brain age is an estimate of biologi-
cal age based on brain structure, which can deviate from chronological
age. Several shared loci were found between the genome-wide asso-
ciation study (GWAS) summary statistics for advanced brain age and
psychiatric disorders12. However, information is still lacking on which
genetic variants influence an individual’s brain changes throughout
life, because this requires longitudinal data. Discovering genetic fac-
tors that explain variation between individuals in brain structural
changes may reveal key biological pathways that drive normal devel-
opment and aging and may contribute to identifying disease risk and
resilience— a crucial goal given the urgent need for new treatments for
aberrant brain development and aging worldwide.
As part of the Enhancing NeuroImaging Genetics through Meta-
Analysis (ENIGMA) consortium13, the ENIGMA Plasticity
Working Group quantified the overall genetic contribution to
longitudinal brain changes by combining evidence from multiple
twin cohorts across the world14. Most global and subcortical brain
measures showed genetic influences on change over time, with a
higher genetic contribution in the elderly (heritability, 16–42%).
Genetic factors that influence longitudinal changes were partially
independent of those that influence baseline volumes of brain
structures, suggesting that there might be genetic variants that spe-
cifically affect the rate of development or aging. However, the genes
involved in these processes are still not known, with only a single,
small-scale GWAS performed for longitudinal volume change in
gray and white matter of the cerebrum, basal ganglia and cerebel-
lum15. In this study, we set out to find genetic variants that may
influence rates of brain changes over time, using genome-wide
analysis in individuals scanned with magnetic resonance imag-
ing (MRI) on more than one occasion. We also aimed to identify
age-dependent effects of genomic variation on longitudinal brain
changes in mostly healthy populations, but also populations with
neurological and psychiatric disorders.
In our GWAS meta-analysis, we sought genetic loci associated
with annual change rates in eight global and seven subcortical mor-
phological brain measures in a coordinated two-phased analysis
using data from 40 longitudinal cohorts (Extended Data Fig. 1 and
Supplementary Table 1). We extracted global and subcortical brain
measures, and assessed annual change rates, using additive genetic
association analyses to estimate the effects of genetic variants on the
rates of change within each cohort. As brain change is not constant
over age1, and gene expression also changes during development and
aging16, we determined whether the estimated genetic variants were
age dependent—that is, differentially affected rates of brain changes
at different stages of life—by using genome-wide meta-regression
models with linear or quadratic age effects (Methods). It must be
noted that, although the cohorts analyzed in this study together cover
the full lifespan, there is relatively little age overlap between them.
This implies that we cannot rule out that cohort-specific character-
istics other than age could influence our meta-regression findings.
We employed a rolling cumulative meta-analysis and meta-
regression approach17. In phase 1, for which data collection ended
on 1 February 2019, we analyzed the cohorts of European descent
(n = 9,623). We sought replication by adding data from three addi-
tional cohorts that became available after our analysis of phase 1:
one developmental cohort (average age 10 years at baseline) and
two in aging populations (n = 5,477; all of European descent) (total
n = 15,100 in phase 2). For all follow-up analyses, we used results
from phase 2. Finally, we added cohorts of non-European ancestry
(total n = 15,640).
Longitudinal trajectories
Brain measures showed differing trajectories of change with age
(Figs. 1 and 2 and Extended Data Video 1)—monotonic increases
(lateral ventricles), monotonic decreases (cortex volume, cerebel-
lar gray matter volume, cortical thickness, surface area and total
brain volume) or increases followed by stabilization and subse-
quently decreases (cerebral and cerebellar white matter, thalamus,
caudate, putamen, nucleus accumbens, pallidum, hippocampus and
amygdala volumes). Each brain structure showed a characteristic
trajectory of change. Within two of our largest cohorts in phase 1
Genetic variants associated with longitudinal
changes in brain structure across the lifespan
Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast
range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants
that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis
of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals
were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE
are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive
functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and
neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help
to determine biological pathways underlying optimal and dysfunctional brain development and aging.
NATURE NEUROSCIENCE | VOL 25 | APRIL 2022 | 421–432 | www.nature.com/natureneuroscience 421
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... These changes are observed in cortical regions, the evolutionarily younger areas of the brain important to focus on goals against distractors and obstacles (Tamnes et al., 2017), and in subcortical regions, the evolutionarily older areas showing greater inter-individual variation in developmental trajectories and important for processing motivational and affective signals (Wierenga et al., 2018). The impact of individual genes on brain development varies across the lifespan, facilitating changes in e.g. the brain's neurotransmission and hormonal systems, sleep regulation, and behavior (Brouwer et al., 2022;Gao et al., 2019;van Soelen et al., 2012). A large-scale study including five twin cohorts across the lifespan (N=861, ages 9-70-years) yielded heritability estimates of brain structure change ranging from 16 % in subcortical regions to 42 % in cortical regions, demonstrating a significant effect of genetic makeup on brain structure change (Brouwer et al., 2017). ...
... Several studies have examined longitudinal trajectories in separate domains, including brain development (Brouwer et al., 2022;Tamnes et al., 2017;Teeuw et al., 2018), social networks (Gremmen et al., 2017), and antisocial behavior (Moffitt, 2018), but very few have integrated these perspectives into a single study design. As insights often emerge at the intersection of scientific disciplines (Park et al., 2023), the GUTS program aims to include diversity across a variety of societal contexts (Dotson and Duarte, 2020) and combine the study of these different domains. ...
... Biological and environmental changes have been examined separately in different fields of research, resulting in a lack of comprehensive understanding of what motivates youth to contribute to individual goals in different societal contexts (Choudhury et al., 2023). We propose in the GUTS program that genetic, hormonal, and brain development are biological opportunities (Brouwer et al., 2022) and that societal experiences (social-economic status, parental support) are social/societal opportunities Keijsers et al., 2022) which together predict future contribution to society. We hypothesize that the development of balanced self-regulatory abilities (goal setting, goal motivation, goal capacity/flexibility) will explain and/or influence the relation between diverse biological and social/societal opportunities and individual contributions to society at academic and social levels (Wesarg-Menzel et al., 2023). ...
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... In this study, we first investigated the causal effects of migraine on four common types of dementia using two-sample MR analyses. Leveraging the recent genome-wide association study (GWAS) summary statistics of longitudinal brain measures, which revealed the genetic influences on brain structural alterations with age using magnetic resonance imaging (MRI) data [28], we then investigated the causal effects of migraine on longitudinal brain changes. Furthermore, we replicated the MR estimates in two migraine subtypes (i.e., MA and MO subtypes). ...
... The diagnostic process integrated clinical features and biomarkers obtained from imaging and cerebrospinal fluid analyses. We collected the GWAS of longitudinal brain measures from a study conducted on 15,100 participants of European ancestry where each participant underwent both baseline and follow-up MRI scans [28]. Participants in this study were recruited from various population-based, case-control, and family-based cohorts through multiple methods, including invitation letters, citizen registries, and the project's website. ...
... The reversed MR analysis did not provide evidence supporting the causal effects of AD on migraine ( Figure S15). Though the remaining four local brain measures provided by Brouwer et al. were not the primary focus of our current investigation [28], we evaluated and displayed their respective causal associations with migraine in Figure S16. ...
Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy
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Full-text available
  • Jun 2024
  • J HEADACHE PAIN
Background Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR). Methods We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer’s disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis. Results The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10− 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm² per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10− 4) and thalamic volume (-9.507 cm³ per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10− 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10− 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm² per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10− 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect. Discussion This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.
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... The brain is neither born mature nor static across the human lifespan [7]. Consequently, regional morphometric changes correspond to chronological human neurodevelopmental changes [8][9][10]. However, the cerebral cortex comprises parallel, segregated organizations of brain regions central to processing distinct information [11]. ...
Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance
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Full-text available
The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain’s organization in a multidimensional connectivity gradient space. However, how the brain’s morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a “fingerprint” of an individual’s brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance.
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... Several genetic studies also support the link between gradients and brain organization. The advent of large neuroimaging-genetics biobanks has brought about unprecedented power for detecting genetic variants that relate to brain structure (81)(82)(83)(84)(85)(86)(87). A common finding across several of these papers (40,41,84,85) is that correlation in genetic determinants of morphometric features across brain regions varies. ...
Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain
Article
Full-text available
  • Jun 2024
  • P NATL ACAD SCI USA
Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning. The interaction of these spatiomolecular gradients i) accurately reconstructs the position of brain tissue samples, ii) delineates known functional territories, and iii) can model the topographical variation of diverse cortical features. The spatiomolecular gradients are distinct from canonical cortical axes differentiating the primary sensory cortex from the association cortex, but radiate in parallel with the axes traversed by local field potentials along the cortex. We replicate all three molecular gradients in three independent human datasets as well as two nonhuman primate datasets and find that each gradient shows a distinct developmental trajectory across the lifespan. The gradients are composed of several well-known transcription factors (e.g., PAX6 and SIX3 ), and a small set of genes shared across gradients are strongly enriched for multiple diseases. Together, these results provide insight into the developmental sculpting of functionally distinct brain regions, governed by three robust transcriptomic axes embedded within brain parenchyma.
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... Four GWAS relating to the change rate of brain, including cortical thickness, were used in our study. They were obtained from the research of Brouwer et al., which has calculated the rate of change in brain structure from longitudinal MRI data of 15,640 individuals in 40 cohorts [28]. It was the first GWAS of brain morphological changes across the lifespan. ...
No genetic causal associations between periodontitis and brain atrophy or cognitive impairment: evidence from a comprehensive bidirectional Mendelian randomization study
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  • May 2024
  • BMC Oral Health
Background Observational studies have explored the relationships of periodontitis with brain atrophy and cognitive impairment, but these findings are limited by reverse causation, confounders and have reported conflicting results. Our study aimed to investigate the causal associations of periodontitis with brain atrophy and cognitive impairment through a comprehensive bidirectional Mendelian randomization (MR) research. Methods We incorporated two distinct genome-wide association study (GWAS) summary datasets as an exploration cohort and a replication cohort for periodontitis. Four and eight metrics were selected for the insightful evaluation of brain atrophy and cognitive impairment, respectively. The former involved cortical thickness and surface area, left and right hippocampal volumes, with the latter covering assessments of cognitive performance, fluid intelligence scores, prospective memory, and reaction time for mild cognitive impairment to Alzheimer's disease (AD), Lewy body dementia, vascular dementia and frontotemporal dementia for severe situations. Furthermore, supplementary analyses were conducted to examine the associations between the longitudinal rates of change in brain atrophy and cognitive function metrics with periodontitis. The main analysis utilized the inverse variance weighting (IVW) method and evaluated the robustness of the results through a series of sensitivity analyses. For multiple tests, associations with p-values < 0.0021 were considered statistically significant, while p-values ≥ 0.0021 and < 0.05 were regarded as suggestive of significance. Results In the exploration cohort, forward and reverse MR results revealed no causal associations between periodontitis and brain atrophy or cognitive impairment, and only a potential causal association was found between AD and periodontitis (IVW: OR = 0.917, 95% CI from 0.845 to 0.995, P = 0.038). Results from the replication cohort similarly corroborated the absence of a causal relationship. In the supplementary analyses, the longitudinal rates of change in brain atrophy and cognitive function were also not found to have causal relationships with periodontitis. Conclusions The MR analyses indicated a lack of substantial evidence for a causal connection between periodontitis and both brain atrophy and cognitive impairment.
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... ICV, a measure of global brain size, was calculated as 1/(determinant of a rotation-translation matrix obtained after affine registration to a common study template and multiplied by the template volume (1,948,105 mm3)) [36]. The GWAS meta-analysis data for brain structure change across the lifespan was also obtained from the ENIGMA Consortium [37]. It comprised the 15 brain structures (total brain, surface area, cortical thickness, amygdala, caudate, cerebellar gray matter, cerebral and cerebellar white matter, cortical gray matter, hippocampus, lateral ventricles, nucleus accumbens, putamen, and thalamus) and the change rates were computed from longitudinal MRI data from 15,640 individuals covering the lifespan. ...
Elucidating the association of obstructive sleep apnea with brain structure and cognitive performance
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  • May 2024
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Background Obstructive sleep apnea (OSA) is a pervasive, chronic sleep-related respiratory condition that causes brain structural alterations and cognitive impairments. However, the causal association of OSA with brain morphology and cognitive performance has not been determined. Methods We conducted a two-sample bidirectional Mendelian randomization (MR) analysis to investigate the causal relationship between OSA and a range of neurocognitive characteristics, including brain cortical structure, brain subcortical structure, brain structural change across the lifespan, and cognitive performance. Summary-level GWAS data for OSA from the FinnGen consortium was used to identify genetically predicted OSA. Data regarding neurocognitive characteristics were obtained from published meta-analysis studies. Linkage disequilibrium score regression analysis was employed to reveal genetic correlations between OSA and related traits. Results Our MR study provided evidence that OSA was found to significantly increase the volume of the hippocampus (IVW β (95% CI) = 158.997 (76.768 to 241.227), P = 1.51e-04), with no heterogeneity and pleiotropy detected. Nominally causal effects of OSA on brain structures, such as the thickness of the temporal pole with or without global weighted, amygdala structure change, and cerebellum white matter change covering lifespan, were observed. Bidirectional causal links were also detected between brain cortical structure, brain subcortical, cognitive performance, and OSA risk. LDSC regression analysis showed no significant correlation between OSA and hippocampus volume. Conclusions Overall, we observed a positive association between genetically predicted OSA and hippocampus volume. These findings may provide new insights into the bidirectional links between OSA and neurocognitive features, including brain morphology and cognitive performance.
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... A delay in the development of gray and white matter in adolescent patients with schizophrenia has also been previously reported [63,64]. Common genetic variants that affect rates of brain growth or atrophy, including the hippocampus, showed genetic overlap with schizophrenia in a recent meta-analysis of changes in brain morphology across the lifespan [65]. The DG newly born granule neurons are thought to be extremely important to the DG circuitry [66,67]. ...
Monozygotic twins discordant for schizophrenia differ in maturation and synaptic transmission
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Schizophrenia affects approximately 1% of the world population. Genetics, epigenetics, and environmental factors are known to play a role in this psychiatric disorder. While there is a high concordance in monozygotic twins, about half of twin pairs are discordant for schizophrenia. To address the question of how and when concordance in monozygotic twins occur, we have obtained fibroblasts from two pairs of schizophrenia discordant twins (one sibling with schizophrenia while the second one is unaffected by schizophrenia) and three pairs of healthy twins (both of the siblings are healthy). We have prepared iPSC models for these 3 groups of patients with schizophrenia, unaffected co-twins, and the healthy twins. When the study started the co-twins were considered healthy and unaffected but both the co-twins were later diagnosed with a depressive disorder. The reprogrammed iPSCs were differentiated into hippocampal neurons to measure the neurophysiological abnormalities in the patients. We found that the neurons derived from the schizophrenia patients were less arborized, were hypoexcitable with immature spike features, and exhibited a significant reduction in synaptic activity with dysregulation in synapse-related genes. Interestingly, the neurons derived from the co-twin siblings who did not have schizophrenia formed another distinct group that was different from the neurons in the group of the affected twin siblings but also different from the neurons in the group of the control twins. Importantly, their synaptic activity was not affected. Our measurements that were obtained from schizophrenia patients and their monozygotic twin and compared also to control healthy twins point to hippocampal synaptic deficits as a central mechanism in schizophrenia.
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Novel early-onset Alzheimer-associated genes influence risk through dysregulation of glutamate, immune activation, and intracell signaling pathways
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Alzheimer Disease (AD) is a highly polygenic disease that presents with relatively earlier onset (≤70yo; EOAD) in about 5% of cases. Around 90% of these EOAD cases remain unexplained by pathogenic mutations. Using data from EOAD cases and controls, we performed a genome-wide association study (GWAS) and trans-ancestry meta-analysis on non-Hispanic Whites (NHW, NCase=6,282, NControl=13,386), African Americans (AA NCase=782, NControl=3,663) and East Asians (NCase=375, NControl=838 CO). We identified eight novel significant loci: six in the ancestry-specific analyses and two in the trans-ancestry analysis. By integrating gene-based analysis, eQTL, pQTL and functional annotations, we nominate four novel genes that are involved in microglia activation, glutamate production, and signaling pathways. These results indicate that EOAD, although sharing many genes with LOAD, harbors unique genes and pathways that could be used to create better prediction models or target identification for this type of AD
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Advantages and Disadvantages of Current Therapeutics and Phytochemicals for Age-Related Brain Disorders
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Age-related brain disorders are a group of conditions characterised by the progressive loss of structure and function of neurons in the brain. These disorders primarily affect older individuals and include conditions such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, etc. Age-related brain disorders present significant challenges to individuals and healthcare professionals worldwide. Recent advancements in current therapeutics and phytochemicals play a crucial role in managing these ageing disorders of the brain. Current therapeutics have several advantages, like symptom management, modifying the underlying disease progression, established efficacy, and targeted treatment. On the other hand, it has several disadvantages, which include side effects, cost and accessibility, treatment complexity, etc. Similarly, the advantages of phytochemical usage in age-related brain disorders include that phytochemicals are derived from natural sources, they have significant antioxidant and anti-inflammatory properties, they exhibit potential neuroprotective effects, and they serve as a potential adjunct therapy. However, there is no universal standardisation or quality control, and they can interact with other medications, potentially affecting their efficacy or safety. Thus, considering the higher advantage when compared to the disadvantages regarding the current therapeutics and phytochemicals, it can be concluded that recent advancements in the treatment of age-related brain disorders provide promise for improved health care in the future. Despite these limitations, recent advancements in both current therapeutics and phytochemical research offer promise for improved management of age-related brain disorders in the future. Further studies are crucial to fully understand the mechanisms of action of phytochemicals, establish optimal dosages, and develop standardized extracts to ensure consistent clinical outcomes.
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The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan
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Development and aging of the cerebral cortex show similar topographic organization and are governed by the same genes. It is unclear whether the same is true for subcortical regions, which follow fundamentally different ontogenetic and phylogenetic principles. We tested the hypothesis that genetically governed neurodevelopmental processes can be traced throughout life by assessing to which degree brain regions that develop together continue to change together through life. Analyzing over 6000 longitudinal MRIs of the brain, we used graph theory to identify five clusters of coordinated development, indexed as patterns of correlated volumetric change in brain structures. The clusters tended to follow placement along the cranial axis in embryonic brain development, suggesting continuity from prenatal stages, and correlated with cognition. Across independent longitudinal datasets, we demonstrated that developmental clusters were conserved through life. Twin-based genetic correlations revealed distinct sets of genes governing change in each cluster. Single nucleotide polymorphisms-based analyses of 38127 cross-sectional MRIs showed a similar pattern of genetic volume-volume correlations. In conclusion, coordination of subcortical change adheres to fundamental principles of lifespan continuity and genetic organization.
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Orphan G protein Coupled Receptors (GPCRs) present attractive targets both for understanding neuropsychiatric diseases and for development of novel therapeutics. GPR139 is an orphan GPCR expressed in select brain circuits involved in controlling movement, motivation and reward. It has been linked to the opioid and dopamine neuromodulatory systems; however, its role in animal behavior and neuropsychiatric processes is poorly understood. Here we present a comprehensive behavioral characterization of a mouse model with a GPR139 null mutation. We show that loss of GPR139 in mice results in delayed onset hyperactivity and prominent neuropsychiatric manifestations including elevated stereotypy, increased anxiety-related traits, delayed acquisition of operant responsiveness, disruption of cued fear conditioning and social interaction deficits. Furthermore, mice lacking GPR139 exhibited complete loss of pre-pulse inhibition and developed spontaneous ‘hallucinogenic’ head-twitches, altogether suggesting schizophrenia-like symptomatology. Remarkably, a number of these behavioral deficits could be rescued by the administration of μ-opioid and D2 dopamine receptor (D2R) antagonists: naltrexone and haloperidol, respectively, suggesting that loss of neuropsychiatric manifestations in mice lacking GPR139 are driven by opioidergic and dopaminergic hyper-functionality. The inhibitory influence of GPR139 on D2R signaling was confirmed in cell-based functional assays. These observations define the role of GPR139 in controlling behavior and implicate in vivo actions of this receptor in the neuropsychiatric process with schizophrenia-like pathology.
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ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries
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Full-text available
  • Dec 2020
Abstract This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
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Genetic architecture of subcortical brain structures in 38,851 individuals
Article
Full-text available
  • Nov 2019
  • Nat Genet
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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Common brain disorders are associated with heritable patterns of apparent aging of the brain
Article
Full-text available
  • Oct 2019
  • NAT NEUROSCI
Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3–96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders. Using structural MRI data from 45,615 individuals aged 3–96 years, Kaufmann and colleagues reveal that common brain disorders are associated with heritable patterns of apparent aging of the brain.
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Structural Brain Development During Childhood and Adolescence
Chapter
  • Oct 2014
The fifth edition of a work that defines the field of cognitive neuroscience, with entirely new material that reflects recent advances in the field. Each edition of this classic reference has proved to be a benchmark in the developing field of cognitive neuroscience. The fifth edition of The Cognitive Neurosciences continues to chart new directions in the study of the biological underpinnings of complex cognition—the relationship between the structural and physiological mechanisms of the nervous system and the psychological reality of the mind. It offers entirely new material, reflecting recent advances in the field. Many of the developments in cognitive neuroscience have been shaped by the introduction of novel tools and methodologies, and a new section is devoted to methods that promise to guide the field into the future—from sophisticated models of causality in brain function to the application of network theory to massive data sets. Another new section treats neuroscience and society, considering some of the moral and political quandaries posed by current neuroscientific methods. Other sections describe, among other things, new research that draws on developmental imaging to study the changing structure and function of the brain over the lifespan; progress in establishing increasingly precise models of memory; research that confirms the study of emotion and social cognition as a core area in cognitive neuroscience; and new findings that cast doubt on the so-called neural correlates of consciousness.
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How (not) to measure replication
Article
  • Jun 2021
The replicability crisis refers to the apparent failures to replicate both important and typical positive experimental claims in psychological science and biomedicine, failures which have gained increasing attention in the past decade. In order to provide evidence that there is a replicability crisis in the first place, scientists have developed various measures of replication that help quantify or “count” whether one study replicates another. In this nontechnical essay, I critically examine five types of replication measures used in the landmark article “Estimating the reproducibility of psychological science” (Open Science Collaboration, Science, 349, ac4716, 2015) based on the following techniques: subjective assessment, null hypothesis significance testing, comparing effect sizes, comparing the original effect size with the replication confidence interval, and meta-analysis. The first four, I argue, remain unsatisfactory for a variety of conceptual or formal reasons, even taking into account various improvements. By contrast, at least one version of the meta-analytic measure does not suffer from these problems. It differs from the others in rejecting dichotomous conclusions, the assumption that one study replicates another or not simpliciter. I defend it from other recent criticisms, concluding however that it is not a panacea for all the multifarious problems that the crisis has highlighted.
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The genetic architecture of the human cerebral cortex
Article
  • Mar 2020
Genetic determination of cortex structure The human cerebral cortex is important for cognition, and it is of interest to see how genetic variants affect its structure. Grasby et al. combined genetic data with brain magnetic resonance imaging from more than 50,000 people to generate a genome-wide analysis of how human genetic variation influences human cortical surface area and thickness. From this analysis, they identified variants associated with cortical structure, some of which affect signaling and gene expression. They observed overlap between genetic loci affecting cortical structure, brain development, and neuropsychiatric disease, and the correlation between these phenotypes is of interest for further study. Science, this issue p. eaay6690
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An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans
Article
  • Nov 2017
To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (P < 5 × 10−8), including variants near the GLP2R, GIP, and HLA-DQA1 genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action–associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.
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