Brain structure and function in Huntington's disease gene carriers far from predicted disease onset

Whilst there are currently no available disease modifying therapies for Huntington’s Disease (HD), recent progress in huntingtin-lowering strategies hold great promise. Initiating therapies early in the disease course will be important and a complete characterisation of the premanifest period will help inform when to initiate disease modifying therapies and the biomarkers that may be useful in such trials. Previous research has characterised the premanifest period up to approximately 15 years from predicted onset, but even at this early stage the disease process is already underway as evidenced by striatal and white matter atrophy, reductions in structural connectivity within brain networks, rising biofluid biomarkers of neuronal dysfunction, elevations in psychiatric symptoms and emerging subtle cognitive impairments. In order to understand how early neurodegeneration can be detected and which measures are most sensitive to the early disease processes, we need to look even earlier in the disease course. This thesis documents the recruitment and analysis of the HD Young Adult Study: a premanifest cohort further from predicted clinical onset than previously studied with an average of 24 years prior to predicted onset. Differences between gene carriers and controls were examined across a range of imaging, cognitive, neuropsychiatric and biofluid measures. The structural and functional brain connectivity in this cohort is then investigated in further detail. By providing a detailed characterisation of brain structure and function in the early premanifest period along with the most sensitive biomarkers at this stage, this work will inform future treatment strategies that may seek to delay the onset of functional impairments in HD

Genetic architecture of the white matter connectome of the human brain

White matter tracts form the structural basis of large-scale functional networks in the human brain. We applied brain-wide tractography to diffusion images from 30,810 adult participants (UK Biobank), and found significant heritability for 90 regional connectivity measures and 851 tract-wise connectivity measures. Multivariate genome- wide association analyses identified 355 independently associated lead SNPs across the genome, of which 77% had not been previously associated with human brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia and neurons. We used the multivariate association profiles of lead SNPs to identify 26 genomic loci implicated in structural connectivity between core regions of the left-hemisphere language network, and also identified 6 loci associated with hemispheric left-right asymmetry of structural connectivity. Polygenic scores for schizophrenia, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, left-handedness, Alzheimer’s disease, amyotrophic lateral sclerosis, and epilepsy showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to the structural connectome of the human brain in the general adult population, highlighting links with polygenic disposition to brain disorders and behavioural traits

Functional and structural substrates of increased dosage of Grik4 gene elucidated using multi-modal MRI

Grik4 is the gene responsible for encoding the high-affinity GluK4 subunit of the kainate receptors. Increased dosage of this subunit in the forebrain was linked to an increased level of anxiety, lack of social communication, and depression. On the synaptic level, abnormal synaptic transmission was also reported. The manifestations of this abnormal expression have not been investigated at the circuit level, nor the correlations between those circuits and the abnormal patterns of the behavior previously reported. In this line of work, we aspired to use different non-invasive magnetic resonance imaging (MRI) modalities to elucidate any disturbance that might stem from the increased dosage of Grik4 and how those changes might explain the abnormal behaviors. MRI offers a noninvasive way to look into the intact brain in vivo. Resting-state functional MRI casts light on how the brain function at rest on the network level and has the capability to detect any anomalies that might occur within or between those networks. On the microstructural level, the diffusion MRI is concerned with the underlying features of the tissues, using the diffusion of water molecules as a proxy for that end. Moving more macroscopically, using structural scans, voxel-based morphometry can detect subtle differences in the morphology of the different brain structures. We recorded videos of our animals performing two tasks that have long been linked to anxiety, the open field and the plus-maze tests before acquiring structural and functional scans. Lastly, we recorded blood-oxygenationlevel dependent (BOLD) signals in a different set of animals during electrical stimulation of specific white matter tracts in order to investigate how neuronal activity propagates. Our analysis showed a vast spectrum of changes in the transgenic group relative to the animals in the control group. On the resting-state networks level, we observed an increase in the within-network strength spanning different structures such as the hippocampus, some regions of the cortex, and the hypothalamus. The increased internal coherence or strength in the networks contrasted with a significant reduction in between-networks connectivity for some regions such as parts of the cortex and the hypothalamus, suggesting long-range network decorrelation. Supporting this idea, major white matter (WM) tracts, such as the corpus callosum and the hippocampal commissure, suffered from substantial changes compatible with an important reduction in myelination and/or a decrease in the mean axonal diameter. Macrostructurally speaking, the overexpression of GluK4 subunit had a bimodal effect, with expansion in some cortical areas in the transgenic animals accompanied by a shrinkage in the subcortical regions. Upon stimulating the brain with an electrical current, we noticed a difference in activity propagation between the two hemispheres. In transgenic animals, the evoked activity remained more confined to the stimulated hemisphere, again consistent with an impaired long-range connectivity. The structural changes both, at the micro and macro level, were in tight correlation with different aspects of the behavior including markers of anxiety such as the time spent in the open arms vs the closed arms in the plus-maze test and the time spent in the center vs the corners in the open field test. Our findings reveal how the disruption of kainate receptors, or more globally the glutamate receptors, and the abnormal synaptic transmission can translate into brain-wide changes in connectivity and alter the functional equilibrium between macro-and mesoscopic networks. The postsynaptic enhancement previously reported in the transgenic animals was here reflected in the BOLD signal and measured as an increase in the within-network strength. Importantly, the correlations between the structural changes and the behavior help to put the developmental changes and their behavioral ramifications into context. RESUMEN Grik4 es el gen responsable de codificar la subunidad GluK4 de alta afinidad de los receptores de kainato. El aumento de la dosis de esta subunidad en el prosencéfalo se relacionó con un mayor nivel de ansiedad, falta de comunicación social y depresión. A nivel sináptico, también se informó una transmisión sináptica anormal. Las manifestaciones de esta expresión anormal no se han investigado a nivel de circuito, ni las correlaciones entre esos circuitos y los patrones anormales de la conducta previamente informada. En esta línea de trabajo, aspiramos a utilizar diferentes modalidades de imágenes por resonancia magnética (MRI) no invasivas para dilucidar cualquier alteración que pudiera derivarse del aumento de la dosis de Grik4 y cómo esos cambios podrían explicar los comportamientos anormales. La resonancia magnética ofrece una forma no invasiva de observar el cerebro intacto in vivo. La resonancia magnética funcional en estado de reposo arroja luz sobre cómo funciona el cerebro en reposo en el nivel de la red y tiene la capacidad de detectar cualquier anomalía que pueda ocurrir dentro o entre esas redes. En el nivel microestructural, la resonancia magnética de difusión se ocupa de las características subyacentes de los tejidos utilizando la difusión de moléculas de agua como un proxy para ese fin. Moviéndose más macroscópicamente, utilizando escaneos estructurales, la morfometría basada en vóxeles puede detectar diferencias sutiles en la morfología de las diferentes estructuras cerebrales. Grabamos videos de nuestros animales realizando dos tareas que durante mucho tiempo se han relacionado con la ansiedad, el campo abierto y las pruebas de laberinto positivo antes de adquirir escaneos estructurales y funcionales. Por último, registramos señales dependientes del nivel de oxigenación de la sangre (BOLD) en un grupo diferente de animales durante la estimulación eléctrica de tractos específicos de materia blanca para investigar cómo se propaga la actividad neuronal. Nuestro análisis mostró un amplio espectro de cambios en el grupo transgénico en relación con los animales en el grupo de control. En el nivel de las redes de estado de reposo, observamos un aumento en la fuerza dentro de la red que abarca diferentes estructuras como el hipocampo, algunas regiones de la corteza y el hipotálamo. La mayor coherencia interna o fuerza en las redes contrastó con una reducción significativa en la conectividad entre redes para algunas regiones como partes de la corteza y el hipotálamo, lo que sugiere una descorrelación de redes de largo alcance. Apoyando esta idea, los grandes tractos de materia blanca (WM), como el cuerpo calloso y la comisura del hipocampo, sufrieron cambios sustanciales compatibles con una importante reducción de la mielinización y / o una disminución del diámetro axonal medio. Macroestructuralmente hablando, la sobreexpresión de la subunidad GluK4 tuvo un efecto bimodal, con expansión en algunas áreas corticales en los animales transgénicos acompañada de una contracción en las regiones subcorticales. Al estimular el cerebro con una corriente eléctrica, notamos una diferencia en la propagación de la actividad entre las dos hemiesferas. En los animales transgénicos, la actividad evocada permaneció más confinada al hemisferio estimulado, de nuevo consistente con una conectividad de largo alcance deteriorada. Los cambios estructurales, tanto a nivel micro como macro, estaban en estrecha correlación con diferentes aspectos de la conducta, incluidos marcadores de ansiedad como el tiempo pasado con los brazos abiertos frente a los brazos cerrados en la prueba del laberinto positivo y el tiempo pasado en el centro vs las esquinas en la prueba de campo abierto. Nuestros hallazgos revelan cómo la interrupción de los receptores de kainato, o más globalmente los receptores de glutamato, y la transmisión sináptica anormal pueden traducirse en cambios de conectividad en todo el cerebro y alterar el equilibrio funcional entre las redes macro y mesoscópicas. La mejora postsináptica informada anteriormente en los animales transgénicos se reflejó aquí en la señal BOLD y se midió como un aumento en la fuerza dentro de la red. Es importante destacar que las correlaciones entre los cambios estructurales y elcomportamiento ayudan a contextualizar los cambios en el desarrollo y sus ramificaciones conductuales

Diagnosis of Autism Spectrum Disorder Based on Brain Network Clustering

Developments in magnetic resonance imaging (MRI) provide new non-invasive approach—functional MRI (fMRI)—to study functions of brain. With the help of fMRI, I can build functional brain networks (FBN) to model correlations of brain activities between cortical regions. Studies focused on brain diseases, including autism spectrum disorder (ASD), have been conducted based on analyzing alterations in FBNs of patients. New biomarkers are identified, and new theories and assumptions are proposed on pathology of brain diseases. Considering that traditional clinical ASD diagnosis instruments, which greatly rely on interviews and observations, can yield large variance, recent studies start to combine machine learning methods and FBN to perform auto-classification of ASD. Such studies have achieved relatively good accuracy. However, in most of these studies, features they use are extracted from the whole brain networks thus the dimension of the features can be high. High-dimensional features may yield overfitting issues and increase computational complexity. Therefore, I need a feature selection strategy that effectively reduces feature dimensions while keeping a good classification performance. In this study, I present a new feature selection strategy that extracting features from functional modules but not the whole brain networks. I will show that my strategy not only reduces feature dimensions, but also improve performances of auto-classifications of ASD. The whole study can be separated into 4 stages: building FBNs, identification of functional modules, statistical analysis of modular alterations and, finally, training classifiers with modular features for auto-classification of ASD. I firstly demonstrate the whole procedure to build FBNs from fMRI images. To identify functional module, I propose a new network clustering algorithm based on joint non-negative matrix factorization. Different from traditional brain network clustering algorithms that mostly perform on an average network of all subjects, I design my algorithm to factorize multiple brain networks simultaneously because the clustering results should be valid not only on the average network but also on each individual network. I show the modules I find are more valid in both views. Then I statistically analyze the alterations in functional modules between ASD and typically developed (TD) group to determine from which modules I extract features from. Several indices based on graph theory are calculated to measure modular properties. I find significant alterations in two modules. With features from these two modules, I train several widely-used classifiers and validate the classifiers on a real-world dataset. The performances of classifiers trained by modular features are better than those with whole-brain features, which demonstrates the effectiveness of my feature selection strategy

Imaging mouse models of neurodegeneration using multi-parametric MRI

Alzheimer’s disease (AD) is a devastating condition characterised by significant cognitive impairment and memory loss. Transgenic mouse models are increasingly being used to further our knowledge of the cause and progression of AD, and identify new targets for therapeutic intervention. These mice permit the study of specific pathological hallmarks of the disease, including intracellular deposits of hyperphosphorylated tau protein and extracellular amyloid plaques. In order to characterise these transgenic mice, robust biomarkers are required to evaluate neurodegenerative changes and facilitate preclinical evaluation of emerging therapeutics. In this work, a platform for in vivo structural imaging of the rTg4510 mouse model of tauopathy was developed and optimised. This was combined with a range of other clinically relevant magnetic resonance imaging (MRI) biomarkers including: arterial spin labelling, diffusion tensor imaging and chemical exchange saturation transfer. These techniques were applied in a single time-point study of aged rTg4510 mice, as well as a longitudinal study to serially assess neurodegeneration in the same cohort of animals. Doxycycline was administered to a subset of rTg4510 mice to suppress the tau transgene; this novel intervention strategy permitted the evaluation of the sensitivity of MRI biomarkers to the accumulation and suppression of tau. Follow-up ex vivo scans were acquired in order to assess the sensitivity of in vivo structural MRI to the current preclinical gold standard. High resolution structural MRI, when used in conjunction with advanced computational analysis, yielded high sensitivity to pathological changes occurring in the rTg4510 mouse. Atrophy was reduced in animals treated with doxycycline. All other MRI biomarkers were able to discriminate between doxycycline-treated and untreated rTg4510 mice as well as wildtype controls, and provided insight into complimentary pathological mechanisms occurring within the disease process. In addition, this imaging protocol was applied to the J20 mouse model of familial AD. This mouse exhibits widespread plaque formation, enabling the study of amyloid-specific pathological changes. Atrophy and deficits in cerebral blood flow were observed; however, the changes occurring in this model were markedly less than those observed in the rTg4510 mouse. This study was expanded to investigate the early-onset AD observed in individuals with Down’s syndrome (DS) by breeding the J20 mouse with the Tc1 mouse model of DS, permitting the relationship between genetics and neurodegeneration to be dissected. This thesis demonstrates the application of in vivo multi-parametric MRI to mouse models of neurodegeneration. All techniques were sensitive to pathological changes occurring in the models, and may serve as important biomarkers in clinical studies of AD. In addition, in vivo multi-parametric MRI permits longitudinal studies of the same animal cohort. This experimental design produces more powerful results, whilst contributing to worldwide efforts to reduce animal usage with respect to the 3Rs principles

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

EVALUATING THE MICROBIOME TO BOOST RECOVERY FROM STROKE: THE EMBRS STUDY

Accumulating evidence suggests that gut microbes modulate brain plasticity via the bidirectional gut-brain axis and may play a role in stroke rehabilitation. A severely imbalanced microbial community has been shown to occur following stroke, causing a systemic flood of neuro- and immunomodulatory substances due to increased gut permeability and decreased gut motility. Here we measure post-stroke increased gut dysbiosis and how it correlates with gut permeability and subsequent cognitive impairment. We recruited 12 participants with acute stroke, 12 healthy control participants, and 18 participants who had risk factors for stroke, but had not had a stroke. We measured the gut microbiome with whole shotgun sequencing on stool samples. We measured cognitive and emotional health with MRI imaging and the NIH toolbox. We normalized all variables and used linear regression methods to identify gut microbial levels associations with cognitive and emotional assessments. Beta diversity analysis revealed that the bacteria populations of the stroke group were statistically dissimilar from the risk factors and healthy control groups. Relative abundance analysis revealed notable decreases in butyrate-producing microbial taxa. The stroke group had higher levels of the leaky gut marker alpha-1-antitrypsin than the control groups, and roseburia species were negatively correlated with alpha-1-antitrypsin. Several Actinobacteria species were associated with cerebral blood flow and white matter integrity in areas of the brain responsible for language, learning, and memory. Stroke participants scored lower on the picture vocabulary and list sorting tests than those in the control groups. Stroke participants who had higher levels of roseburia performed better on the picture vocabulary task. We found that microbial communities are disrupted in a stroke population. Many of the disrupted bacteria have previously been reported to have correlates to health and disease. This preparatory study will lay the foundation for the development of therapeutics targeting the gut following stroke

Effects of copy number variations on brain structure and risk for psychiatric illness: large-scale studies from the ENIGMA working groups on CNVs

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.Funding information: European Union's Horizon2020 Research and Innovation Programme, Grant/Award Number: CoMorMent project; Grant #847776; KG Jebsen Stiftelsen; National Institutes of Health, Grant/Award Number: U54 EB020403; Norges Forskningsråd, Grant/Award Number: #223273; South-Eastern Norway Regional Health Authority, Grant/Award Number: #2020060ACKNOWLEDGMENTS: The ENIGMA Consortium is supported by the NIH Big Data to Knowledge (BD2K) program under consortium grant number U54 EB020403 (PI: Thompson). OAA is supported by the Research Council of Norway, South East Norway Health Authority, KG Jebsen Stiftelsen, EU H2020. C. A. has been funded by the Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/ 024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7-4-HEALTH-2009-2.2.1-2-241,909 (Project EU-GEI), FP7- HEALTH-2013-2.2.1-2-603,196 (Project PSYSCAN) and FP7- HEALTH-2013- 2.2.1-2-602,478 (Project METSY); and European Union H2020 Program under the Innovative Medicines Initiative two Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso and Fundación Alicia Koplowitz. R. A-A is funded by a Miguel Servet contract from the Carlos III Health Institute (CP18/00003). G. B. is supported by the Dutch Organization for Health Research and Development ZonMw (grants 91112002 & 91712394). A. S. B. is supported by the Dalglish Family Chair in 22q11.2 Deletion Syndrome, Canadian Institutes of Health Research (CIHR) grants MOP-79518, MOP89066, MOP-97800 and MOP-111238, and NIMH grant number U01 MH101723–01(3/5). C. E. B. is also supported by the National Institute of Mental Health: RO1 MH085953, R01 MH100900 and 1U01MH119736. N. E. B. is granted the KNAW Academy Professor Award (PAH/6635). V. D. C. is supported by NIH R01 MH094524. S. C. is supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3); Helmholtz Initiative and Networking Fund. C. R. K. C. is supported by NIA T32AG058507. E. W. C. C. is supported by the Canadian Institutes of Health Research, Ontario Mental Health Foundation grant MOP-74631 and NIMH grant U01MH101723–01(3/5). S. Ci. has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). M. C. C. is supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. N. A. C. is supported by Agencia Nacional de Investigación y Desarrollo (ANID Chile) PIA ACT192064. GId. Z. is supported by the NHMRC. J. L. D. and D. E. J. L. are supported by the Wellcome Trust. T. B. C. is supported by NICHD grant PO1-HD070454, NIH grant UO1-MH191719, and NIMH grant R01 MH087636-01A1. AMD is supported by U24DA041147. B. D. is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant numbers 32003B_135679, 32003B_159780, 324730_192755 and CRSK3_190185), the Leenaards Foundation and the Roger De Spoelberch Foundation. SE is supported by the NARSAD-Young Investigator Grant “Epigenetic Regulation of Intermediate Phenotypes in Schizophrenia”. B. E. S. is supported by the NIH (NIMH). D. C. G. is supported by NIH grant numbers MH078143, MH083824, AG058464. W. R. K. is supported by NIH/MH R0106824. R. E. G. is supported by NIH/NIMH grant numbers MH087626, MH119737. DMMcD-McG is supported by National Institutes of Mental Health (NIMH), grant numbers MH119737-02; MH191719; and MH087636-01A1. S. E. M. is supported by NHMRC grants APP1103623; APP1158127; APP1172917. TM is supported by Research Council of Norway - grant number 273345. D. G. M. is supported by the National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and S (European Autism Interventions)/EU AIMS-2-TRIALS, a European Innovative Medicines Initiative Joint Undertaking under grant agreements 115300 and 777394. T. N. was supported by Stiftelsen KG Jebsen under grant number SKGJ-MED-021. R. A. O. is supported by NIMH R01 MH090553. S. Y. S. has been funded by the Canadain Institutes of Health Research. M. J. O. is supported by MRC Centre grant MR/L010305/1 and Wellcome Trust grant 100,202/Z/12/Z; Dr. Owen has received research support from Takeda. Z. P. is supported by CIHR, CFI, HSFC. B. G. P. is supported by CIHR FDN 143290 and CAIP Chair. G. M. R. is supported by Fondecyt-Chile #1171014 and ANID-Chile ACT192064. A. Re. was supported by a grant from the Swiss National Science Foundation (31003A_182632). DRR is supported by R01 MH120174 (PI: Roalf). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London (to J. J. R). PSS is supported by NHMRC (Australia) program grant 1093083. J. E. S. is supported by NIH K01-ES026840. S. M. S. is supported by the Epilepsy Society. T. J. S. is supported by NIH grants R01MH107108, R01HD042794, and HDU54079125. I. E. S. is supported by South-Eastern Norway Regional Health Authority (#2020060), European Union's Horizon2020 Research and Innovation Programme (CoMorMent project; grant #847776) and the KG Jebsen Foundation (SKGJ-MED-021). V. M. S. is supported by Research Council of Norway (CoE funding scheme, grant number 223273). D. J. S. is supported by the SA MRC. C. K. T. is supported by Research Council of Norway (#230345, #288083, #223273) and South-Eastern Norway Regional Health Authority (#2019069, #2021070, #500189). D. T.-G. was supported by the Instituto de Salud Carlos III (PI14/00639 and PI14/00918) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT02534363). Dvd. M. is supported by Research Council of Norway #276082. F. V. R. is supported by the Michael Smith Foundation for Health Research Scholar Award. deCODE genetics has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements' no. 115008 (NEWMEDS) and no. 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (EU-FP7/ 2007–2013). L. T. W. is supported by Research Council of Norway, European Research Council. The IDIVAL neuroimage unit is supported by Instituto de Salud Carlos III PI020499, research funding SCIII-INT13/0014, MICINN research funding SAF2010-20840-C02- 02, SAF2013-46292-R. The TOP/NORMENT study are supported by the Research Council of Norway (#223273). The GOBS study data collection was supported in part by the National Institutes of Health (NIH) grants: R01 MH078143, R01 MH078111, and R01 MH083824 with work conducted in part in facilities constructed under the support of NIH grant number C06 RR020547. The Sydney Memory and Ageing Study has been funded by three National Health & Medical Research Council (NHMRC) Program Grants (ID No. ID350833, ID568969, and APP1093083). We thank the participants and their informants for their time and generosity in contributing to this research. We also acknowledge the MAS research team: https://cheba.unsw.edu.au/researchprojects/sydney-memory-and-ageing-study. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/ project/older-australian-twins-study) to this study. The OATS study has been funded by a National Health & Medical Research Council (NHMRC) and Australian Research Council (ARC) Strategic Award Grant of the Aging Well, Aging Productively Program (ID No. 401162); NHMRC Project (seed) Grants (ID No. 1024224 and 1025243); NHMRC Project Grants (ID No. 1045325 and 1085606); and NHMRC Program Grants (ID No. 568969 and 1093083). We thank the participants for their time and generosity in contributing to this research. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID No. 1079102) from the National Health and Medical Research Council. The NCNG sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the KG Jebsen Foundation, the Research Council of Norway, to S. L. H., V. M. S., A. J. L., and T. E. The authors thank Dr. Eike Wehling for recruiting participants in Bergen, and Professor Jonn-Terje Geitung and Haraldplass Deaconess Hospital for access to the MRI facility. Additional support by RCN grants 177458/V50 and 231286/F20. The Betula study was supported by a Wallenberg Scholar Grant (KAW). The HUNT Study is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU—Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. Research for the GAP cohort was supported by the Department of Health via the National Institute for Health Research (NIHR) Specialist Biomedical Research Center for Mental Health award to South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry at King's College London, London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. S.J. is supported by Calcul Quebec (http:// www.calculquebec.ca), Compute Canada (http://www.computecanada. ca), the Brain Canada Multi investigator research initiative (MIRI), the Institute of Data Valorization (Canada First Research Excellence Fund), CHIR, Canada Research Chairs and the Jeanne et Jean Louis Levesque Foundation. The NTR cohort was supported by the Netherlands Organization for Scientific Research (NWO), MW904-61-193 (de Geus & Boomsma), MaGWnr: 400-07-080 (van 't Ent), MagW 480-04-004 (Boomsma), NWO/SPI 56-464-14,192 (Boomsma), the European Research Council, ERC-230374 (Boomsma), and Amsterdam Neuroscience. Funding for genotyping was obtained from the National Institutes of Health (NIMH U24 MH068457-06; Grand Opportunity grants 1RC2 MH089951, and 1RC2 MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. The Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Boomsma), 51.02.060 (Hilleke Hulshoff Pol), 668.772 (Boomsma & Hulshoff Pol); NWO/SPI 56-464-14192 (Boomsma), the European Research Council (ERC230374) (Boomsma), High Potential Grant Utrecht University (Hulshoff Pol), NWO Brain and Cognition 433-09-220 (Hulshoff Pol). SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide SNP typing in SHIP and MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The ENIGMA-22q11.2 Deletion Syndrome Working Group wishes to acknowledge our dear colleague Dr. Clodagh Murphy, who sadly passed away in April 2020. Open access funding enabled and organized by Projekt DEAL

Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype–phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This “genotype-first” approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior