Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm

Alzheimer's disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing various association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic factors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Generalized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label information into the nonlinear feature extraction process and thus enhancing the performance of association analysis. The application of the DSR-AdaSMDGCCA algorithm on real data sets identified several AD risk regions (such as the hippocampus, parahippocampus, and fusiform gyrus) and risk genes (including VSIG4, NEDD4L, and PINK1), achieving maximum classification accuracy with the fewest selected features compared to baseline algorithms. Molecular biology enrichment analysis revealed that the pathways enriched by these top genes are intimately linked to AD progression, affirming that our algorithm not only improves correlation analysis performance but also identifies biologically significant markers.

Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.

Differences in Intrinsic Gray Matter Connectivity and Their Genomic Underpinnings in Autism Spectrum Disorder

BACKGROUND

Autism is a heterogeneous neurodevelopmental condition accompanied by differences in brain connectivity. Structural connectivity in autism has mainly been investigated within the white matter. However, many genetic variants associated with autism highlight genes related to synaptogenesis and axonal guidance, thus also implicating differences in intrinsic (i.e., gray matter) connections in autism. Intrinsic connections may be assessed in vivo via so-called intrinsic global and local wiring costs.

METHODS

Here, we examined intrinsic global and local wiring costs in the brain of 359 individuals with autism and 279 healthy control participants ages 6 to 30 years from the EU-AIMS LEAP (Longitudinal European Autism Project). FreeSurfer was used to derive surface mesh representations to compute the estimated length of connections required to wire the brain within the gray matter. Vertexwise between-group differences were assessed using a general linear model. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to link neuroanatomical differences to putative underpinnings.

RESULTS

Group differences in global and local wiring costs were predominantly observed in medial and lateral prefrontal brain regions, in inferior temporal regions, and at the left temporoparietal junction. The resulting neuroanatomical patterns were enriched for genes that had been previously implicated in the etiology of autism at genetic and transcriptomic levels.

CONCLUSIONS

Based on intrinsic gray matter connectivity, the current study investigated the complex neuroanatomy of autism and linked between-group differences to putative genomic and/or molecular mechanisms to parse the heterogeneity of autism and provide targets for future subgrouping approaches.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

The effect of Parkin gene S/N 167 polymorphism on resting spontaneous brain functional activity in Parkinson's Disease

BACKGROUND

Genetic susceptibility plays a significant role in Parkinson's disease (PD) development. Carriers of the Parkin S/N167 mutation may have an increased risk of PD and altered spontaneous brain activity.

OBJECTIVE

This study aims to investigate the potential pathogenesis of PD through a comparative analysis of the amplitude of low-frequency fluctuations (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) of subjects with Parkin gene S/N 167 polymorphisms, and to examine the association between spontaneous brain activity and clinical scale scores of PD.

METHODS

A total of 69 PD patients and 84 healthy controls (HC) were included in the study. Each subject was genotyped for the Parkin gene S/N 167 polymorphism and underwent rs-fMRI scans. ALFF analysis was employed to evaluate the relationship among genotypes, interactive brain regions, and clinical symptoms in PD.

RESULTS

PD patients exhibited decreased ALFF values in the right anterior lobe and vermis of the cerebellum compared to HC. No significant interaction was found between the gene's main effect and the "group × genotype" effect on brain ALFF values. One-factor ANOVA revealed no significant difference in ALFF values between PD subgroups; however, the ALFF values in the right anterior lobe and vermis of the cerebellum were lower in the PD-G and PD-GA groups compared to the HC-G and HC-GA groups. Spearman correlation analysis demonstrated that ALFF values in the PD-GG and PD-GA groups were negatively associated with UPDRS-III scores in the bilateral lingual gyrus (Lingual R/L).

CONCLUSION

Parkin gene S/N 167 polymorphisms may influence brain functional activity in specific brain regions, and ALFF values are associated with motor symptoms in PD patients.

Neuroimaging feature extraction using a neural network classifier for imaging genetics

BACKGROUND

Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes.

RESULTS

We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features.

CONCLUSIONS

The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

Deep multimodality-disentangled association analysis network for imaging genetics in neurodegenerative diseases

Imaging genetics is a crucial tool that is applied to explore potentially disease-related biomarkers, particularly for neurodegenerative diseases (NDs). With the development of imaging technology, the association analysis between multimodal imaging data and genetic data is gradually being concerned by a wide range of imaging genetics studies. However, multimodal data are fused first and then correlated with genetic data in traditional methods, which leads to an incomplete exploration of their common and complementary information. In addition, the inaccurate formulation in the complex relationships between imaging and genetic data and information loss caused by missing multimodal data are still open problems in imaging genetics studies. Therefore, in this study, a deep multimodality-disentangled association analysis network (DMAAN) is proposed to solve the aforementioned issues and detect the disease-related biomarkers of NDs simultaneously. First, the imaging data are nonlinearly projected into a latent space and imaging representations can be achieved. The imaging representations are further disentangled into common and specific parts by using a multimodal-disentangled module. Second, the genetic data are encoded to achieve genetic representations, and then, the achieved genetic representations are nonlinearly mapped to the common and specific imaging representations to build nonlinear associations between imaging and genetic data through an association analysis module. Moreover, modality mask vectors are synchronously synthesized to integrate the genetic and imaging data, which helps the following disease diagnosis. Finally, the proposed method achieves reasonable diagnosis performance via a disease diagnosis module and utilizes the label information to detect the disease-related modality-shared and modality-specific biomarkers. Furthermore, the genetic representation can be used to impute the missing multimodal data with our learning strategy. Two publicly available datasets with different NDs are used to demonstrate the effectiveness of the proposed DMAAN. The experimental results show that the proposed DMAAN can identify the disease-related biomarkers, which suggests the proposed DMAAN may provide new insights into the pathological mechanism and early diagnosis of NDs. The codes are publicly available at https://github.com/Meiyan88/DMAAN.

Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients.

METHODS

This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively.

RESULTS

The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity.

LIMITATIONS

Environmental factors and differences in locations of functional areas across individuals were not taken into account.

CONCLUSIONS

This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.

Genetic underpinnings of brain structural connectome for young adults

With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within the genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model and ensure computing feasibility. In the application to the Human Connectome Project Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tracts connecting the hippocampus and two cerebral hemispheres. We also show the superiority of our method in extensive simulations.

Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9-11

Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.

Genetic and environmental influences of variation in diffusion MRI measures of white matter microstructure

Quantitative neuroimaging studies in twin samples can investigate genetic contributions to brain structure and microstructure. Diffusion tensor imaging (DTI) studies with twin samples have shown moderate to high heritability in white matter microstructure. This study investigates the genetic and environmental contributions of another widely used diffusion MRI model not yet applied to twin studies, neurite orientation dispersion and density imaging (NODDI). The NODDI model is a multicompartment model of the diffusion-weighted MRI signal, providing estimates of neurite density (ND) and the orientation dispersion index (ODI). A cohort of monozygotic (MZ) and same-sex dizygotic (DZ) twins (N = 460 individuals) between 13 and 24 years of age were scanned with a multi-shell diffusion weighted imaging protocol. Select white matter (WM) regions of interest (ROI) were extracted. Biometric structural equation modeling estimated the relative contributions from additive genetic (A) and common (C) and unique environmental (E) factors. Genetic factors for the NODDI measures accounted for 91% and 65% of the variation of global ND and ODI, respectively, compared with 83% for FA. We observed higher heritability for ND than both FA and ODI in 25 of 30 discrete white matter regions that we examined, suggesting ND may be more sensitive to underlying genetic sources of variation. This study demonstrated that genetic factors play a key role in the development of white matter microstructure using both DTI and NODDI.

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ~N^2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 10⁵ voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (10^2–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63–0.76, p < 10⁻¹⁰). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.

Associating brain imaging phenotypes and genetic in Alzheimer's disease via JSCCA approach with autocorrelation constraints

Imaging genetics research can explore the potential correlation between imaging and genomics. Most association analysis methods cannot effectively use the prior knowledge of the original data. In this respect, we add the prior knowledge of each original data to mine more effective biomarkers. The study of imaging genetics based on the sparse canonical correlation analysis (SCCA) is helpful to mine the potential biomarkers of neurological diseases. To improve the performance and interpretability of SCCA, we proposed a penalty method based on the autocorrelation matrix for discovering the possible biological mechanism between single nucleotide polymorphisms (SNP) variations and brain regions changes of Alzheimer's disease (AD). The addition of the penalty allows the proposed algorithm to analyze the correlation between different modal features. The proposed algorithm obtains more biologically interpretable ROIs and SNPs that are significantly related to AD, which has better anti-noise performance. Compared with other SCCA-based algorithms (JCB-SCCA, JSNMNMF), the proposed algorithm can still maintain a stronger correlation with ground truth even when the noise is larger. Then, we put the regions of interest (ROI) selected by the three algorithms into the SVM classifier. The proposed algorithm has higher classification accuracy. Also, we use ridge regression with SNPs selected by three algorithms and four AD risk ROIs. The proposed algorithm has a smaller root mean square error (RMSE). It shows that proposed algorithm has a good ability in association recognition and feature selection. Furthermore, it selects important features more stably, improving the clinical diagnosis of new potential biomarkers.

Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.

A systematic review and meta-analysis of imaging genetics studies of specific reading disorder

The imaging genetics of specific reading disabilities (SRD) is an emerging field that aims to characterize the disabilities' neurobiological causes, including atypical brain structure and function and distinct genetic architecture. The present review aimed to summarize current imaging genetics studies of SRD, characterize the effect sizes of reported results by calculating Cohen's d, complete a Fisher's Combined Probability Test for genes featured in multiple studies, and determine areas for future research. Results demonstrate associations between SRD risk genes and reading network brain phenotypes. The Fisher's test revealed promising results for the genes DCDC2, KIAA0319, FOXP2, SLC2A3, and ROBO1. Future research should focus on exploratory approaches to identify previously undiscovered genes. Using comprehensive neuroimaging (e.g., functional and effective connectivity) and genetic (e.g., sequencing and epigenetic) techniques, and using larger samples, diverse stages of development, and longitudinal investigations, would help researchers understand the neurobiological correlates of SRD to improve early identification.

A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits

Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

Imaging genetics reveals the connection between microscopic genetics and macroscopic imaging, enabling the identification of disease biomarkers. In this work, we make full use of prior knowledge that has significant reference value for investigating the correlation between the brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose joint-connectivity-based sparse nonnegative matrix factorization (JCB-SNMF). The algorithm simultaneously projects structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information for each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROIs), SNPs, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a protein-protein interaction (PPI) network, we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also found some significant SNP-ROI and gene-ROI pairs. Among them, two SNPs rs4472239 and rs11918049 and three genes KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.

Effect of NEUROG3 polymorphism rs144643855 on regional spontaneous brain activity in major depressive disorder

PURPOSE

Our previous study identified a significant association between a single nucleotide polymorphism (SNP) located in the neurogenin3 (NEUROG3) gene and post-stroke depression (PSD) in Chinese populations. The present work explores whether polymorphism rs144643855 affects regional brain activity and clinical phenotypes in major depressive disorder (MDD).

METHOD

A total of 182 participants were included: 116 MDD patients and 66 normal controls. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning at baseline. Spontaneous brain activity was assessed using amplitude of low-frequency fluctuation (ALFF). The Hamilton Depression Scale-24 (HAMD-24) and Snaith-Hamilton Pleasure Scale (SHAPS) were used to assess participants at baseline. Two-way analysis of covariance (ANCOVA) was used to explore the interaction between diagnostic groups and NEUROG3 rs144643855 on regional brain activity. We performed correlation analysis to further test the association between these interactive brain regions and clinical manifestations of MDD.

RESULTS

Genotype and disease significantly interacted in the left inferior frontal gyrus (IFG-L), right superior frontal gyrus (SFG-R), and left paracentral lobule (PCL-L) (P < 0.05). ALFF values of the IFG-L were found to be significantly associated with anhedonia in MDD patients.

CONCLUSION

These findings suggest a potential relationship between rs144643855 variations and altered frontal brain activity in MDD. NEUROG3 may play an important role in the neuropathophysiology of MDD.

Dopaminergic modulation of regional cerebral blood flow: An arterial spin labelling study of genetic and pharmacological manipulation of COMT activity

Dopamine has direct and complex vasoactive effects on cerebral circulation. Catechol-O-methyltransferase (COMT) regulates cortical dopamine, and its activity can be influenced both genetically and pharmacologically. COMT activity influences the functional connectivity of the PFC at rest, as well as its activity during task performance, determined using blood oxygen level-dependent (BOLD) fMRI. However, its effects on cerebral perfusion have been relatively unexplored. Here, 76 healthy males, homozygous for the functional COMT Val¹⁵⁸Met polymorphism, were administered either the COMT inhibitor tolcapone or placebo in a double-blind, randomised design. We then assessed regional cerebral blood flow at rest using pulsed arterial spin labelling. Perfusion was affected by both genotype and drug. COMT genotype affected frontal regions (Val¹⁵⁸ > Met¹⁵⁸), whilst tolcapone influenced parietal and temporal regions (placebo > tolcapone). There was no genotype by drug interaction. Our data demonstrate that lower COMT activity is associated with lower cerebral blood flow, although the regions affected differ between those affected by genotype compared with those altered by acute pharmacological inhibition. The results extend the evidence for dopaminergic modulation of cerebral blood flow. Our findings also highlight the importance of considering vascular effects in functional neuroimaging studies, and of exercising caution in ascribing group differences in BOLD signal solely to altered neuronal activity if information about regional perfusion is not available.

Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis

To uncover the genetic underpinnings of brain disorders, brain imaging genomics usually jointly analyzes genetic variations and imaging measurements. Meanwhile, other biomarkers such as proteomic expressions can also carry valuable complementary information. Therefore, it is necessary yet challenging to investigate the underlying relationships among genetic variations, proteomic expressions, and neuroimaging measurements, which stands a chance of gaining new insights into the pathogenesis of brain disorders. Given multiple types of biomarkers, using sparse multi-view canonical correlation analysis (SMCCA) and its variants to identify the multi-way associations is straightforward. However, due to the gradient domination issue caused by the naive fusion of multiple SCCA objectives, SMCCA is suboptimal. In this paper, we proposed two adaptive SMCCA (AdaSMCCA) methods, i.e. the robustness-aware AdaSMCCA and the uncertainty-aware AdaSMCCA, to analyze the complicated associations among genetic, proteomic, and neuroimaging biomarkers. We also imposed a data-driven feature grouping penalty to the genetic data with aim to uncover the joint inheritance of neighboring genetic variations. An efficient optimization algorithm, which is guaranteed to converge, was provided. Using two state-of-the-art SMCCA as benchmarks, we evaluated robustness-aware AdaSMCCA and uncertainty-aware AdaSMCCA on both synthetic data and real neuroimaging, proteomics, and genetic data. Both proposed methods obtained higher associations and cleaner canonical weight profiles than comparison methods, indicating their promising capability for association identification and feature selection. In addition, the subsequent analysis showed that the identified biomarkers were related to Alzheimer's disease, demonstrating the power of our methods in identifying multi-way bi-multivariate associations among multiple heterogeneous biomarkers.

Neuroprotective effect of Val variant of BDNF Val66Met polymorphism on hippocampus is modulated by the severity of menstrual pain

Primary dysmenorrhea (PDM) refers to menstrual pain of which the pathological cause(s) are unknown. This study examined the associations among BDNF Val66Met polymorphisms, menstrual pain severity, and hippocampal volume among young PDM subjects. We recruited 115 PDM subjects, including severe cases (n = 66) and moderate cases (n = 44), and 117 young females (aged 20-30 years) as a control group (CON) for BDNF Val66Met genotyping and MRI examination. The assessment of hippocampal volume involved analysis at various anatomical resolutions, i.e., whole hippocampal volume, hippocampal subfields, and voxel-based morphometry (VBM) volumetric analysis. Two-way ANOVA analyses with planned contrasts and Bonferroni correction were conducted for the assessment of hippocampal volume. Linear regression was used to test for BDNF Val66Met Val allele dosage-dependent effects. We observed no main effects of group, genotype, or group-genotype interactions on bilateral whole hippocampal volumes. Significant interactions between PDM severity and BDNF Val66Met genotype were observed in the right whole hippocampus, subiculum, and molecular layer. Post-hoc analysis revealed that the average hippocampal volume of Val/Val moderate PDM subjects was greater than that of Val/Val severe PDM subjects. Note that right hippocampal volume was greater in the Val/Val group than in the Met/Met group, particularly in the right posterior hippocampal region. Dosage effect analysis revealed a positive dosage-dependent relationship between the Val allele and volume of the right whole hippocampus, subiculum, molecular layer, and VBM-defined right posterior hippocampal region in the moderate PDM subgroup only. These findings indicate that Val/Val PDM subjects are resistant to intermittent moderate pain-related stress, whereas Met carrier PDM subjects are susceptible. When confronted with years of repeated PDM stress, the hippocampus can undergo differential structural changes in accordance with the BDNF genotype and pain severity. This triad study on PDM (i.e., combining genotype with endophenotype imaging results and clinical phenotypes), underscores the potential neurobiological consequences of PDM, which may prefigure in neuroimaging abnormalities associated with various chronic pain disorders. Our results provide evidence for Val allele dosage-dependent protective effects on the hippocampal structure; however, in cases of the Val variant, these effects were modulated in accordance with the severity of menstrual pain.

A novel CERNNE approach for predicting Parkinson's Disease-associated genes and brain regions based on multimodal imaging genetics data

The detection and pathogenic factors analysis of Parkinson's disease (PD) has a practical significance for its diagnosis and treatment. However, the traditional research paradigms are commonly based on single neural imaging data, which is easy to ignore the complementarity between multimodal imaging genetics data. The existing researches also pay little attention to the comprehensive framework of patient detection and pathogenic factors analysis for PD. Based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data, a novel brain disease multimodal data analysis model is proposed in this paper. Firstly, according to the complementarity between the two types of data, the classical correlation analysis method is used to construct the fusion feature of subjects. Secondly, based on the artificial neural network, the fusion feature analysis tool named clustering evolutionary random neural network ensemble (CERNNE) is designed. This method integrates multiple neural networks constructed randomly, and uses clustering evolution strategy to optimize the ensemble learner by adaptive selective integration, selecting the discriminative features for PD analysis and ensuring the generalization performance of the ensemble model. By combining with data fusion scheme, the CERNNE is applied to forming a multi-task analysis framework, recognizing PD patients and predicting PD-associated brain regions and genes. In the multimodal data experiment, the proposed framework shows better classification performance and pathogenic factors predicting ability, which provides a new perspective for the diagnosis of PD.

Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes

Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer's Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.

Effect of BDNF Val66Met on hippocampal subfields volumes and compensatory interaction with APOE-ε4 in middle-age cognitively unimpaired individuals from the ALFA study

Background

Current evidence supports the involvement of brain-derived neurotrophic factor (BDNF) Val66Met polymorphism, and the ε4 allele of APOE gene in hippocampal-dependent functions. Previous studies on the association of Val66Met with whole hippocampal volume included patients of a variety of disorders. However, it remains to be elucidated whether there is an impact of BDNF Val66Met polymorphism on the volumes of the hippocampal subfield volumes (HSv) in cognitively unimpaired (CU) individuals, and the interactive effect with the APOE-ε4 status.

Methods

BDNF Val66Met and APOE genotypes were determined in a sample of 430 CU late/middle-aged participants from the ALFA study (ALzheimer and FAmilies). Participants underwent a brain 3D-T1-weighted MRI scan, and volumes of the HSv were determined using Freesurfer (v6.0). The effects of the BDNF Val66Met genotype on the HSv were assessed using general linear models corrected by age, gender, education, number of APOE-ε4 alleles and total intracranial volume. We also investigated whether the association between APOE-ε4 allele and HSv were modified by BDNF Val66Met genotypes.

Results

BDNF Val66Met carriers showed larger bilateral volumes of the subiculum subfield. In addition, HSv reductions associated with APOE-ε4 allele were significantly moderated by BDNF Val66Met status. BDNF Met carriers who were also APOE-ε4 homozygous showed patterns of higher HSv than BDNF Val carriers.

Conclusion

To our knowledge, the present study is the first to show that carrying the BDNF Val66Met polymorphisms partially compensates the decreased on HSv associated with APOE-ε4 in middle-age cognitively unimpaired individuals.

Electronic supplementary material

The online version of this article (10.1007/s00429-020-02125-3) contains supplementary material, which is available to authorized users.

Independent Multiple Factor Association Analysis for Multiblock Data in Imaging Genetics

Multivariate methods have the potential to better capture complex relationships that may exist between different biological levels. Multiple Factor Analysis (MFA) is one of the most popular methods to obtain factor scores and measures of discrepancy between data sets. However, singular value decomposition in MFA is based on PCA, which is adequate only if the data is normally distributed, linear or stationary. In addition, including strongly correlated variables can overemphasize the contribution of the estimated components. In this work, we introduced a novel method referred as Independent Multifactorial Analysis (ICA-MFA) to derive relevant features from multiscale data. This method is an extended implementation of MFA, where the component value decomposition is based on Independent Component Analysis. In addition, ICA-MFA incorporates a predictive step based on an Independent Component Regression. We evaluated and compared the performance of ICA-MFA with both, the MFA method and traditional univariate analyses, in a simulation study. We showed how ICA-MFA explained up to 10-fold more variance than MFA and univariate methods. We applied the proposed algorithm in a study of 4057 individuals belonging to the population-based Rotterdam Study with available genetic and neuroimaging data, as well as information about executive cognitive functioning. Specifically, we used ICA-MFA to detect relevant genetic features related to structural brain regions, which in turn were involved, in the mechanisms of executive cognitive function. The proposed strategy makes it possible to determine the degree to which the whole set of genetic and/or neuroimaging markers contribute to the variability of the symptomatology jointly, rather than individually. While univariate results and MFA combinations only explained a limited proportion of variance (less than 2%), our method increased the explained variance (10%) and allowed the identification of significant components that maximize the variance explained in the model. The potential application of the ICA-MFA algorithm constitutes an important aspect of integrating multivariate multiscale data, specifically in the field of Neurogenetics.

Neural correlates of NOS1 ex1f-VNTR allelic variation in panic disorder and agoraphobia during fear conditioning and extinction in fMRI

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NOS1 ex1f-VNTR is associated with neural correlates during fear extinction learning.

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Differential effects are prominent in amygdala and hippocampus.

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Patients with panic disorder and agoraphobia differ from healthy controls.

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Genotype associated effects were not altered after cognitive behavioral therapy.

Neuronal nitric oxide synthase (NOS-I) impacts on fear/anxiety-like behavior in animals. In humans, the short (S) allele of a functional promotor polymorphism of NOS1 (NOS1 ex1f-VNTR) has been shown to be associated with higher anxiety and altered fear conditioning in healthy subjects in the amygdala and hippocampus (AMY/HIPP). Here, we explore the role of NOS1 ex1f-VNTR as a pathophysiological correlate of panic disorder and agoraphobia (PD/AG). In a sub-sample of a multicenter cognitive behavioral therapy (CBT) randomized controlled trial in patients with PD/AG (n = 48: S/S-genotype n=15, S/L-genotype n=21, L/L-genotype n=12) and healthy control subjects, HS (n = 34: S/S-genotype n=7, S/L-genotype n=17, L/L-genotype=10), a differential fear conditioning and extinction fMRI-paradigm was used to investigate how NOS1 ex1f-VNTR genotypes are associated with differential neural activation in AMY/HIPP. Prior to CBT, L/L-allele carriers showed higher activation than S/S-allele carriers in AMY/HIPP. A genotype × diagnosis interaction revealed that the S-allele in HS was associated with a pronounced deactivation in AMY/HIPP, while patients showed contrary effects. The interaction of genotype × stimulus type (CS+, conditioned stimulus associated with an aversive stimulus vs. CS-, unassociated) showed effects on differential learning in AMY/HIPP. All effects were predominately found during extinction. Genotype associated effects in patients were not altered after CBT. Low statistical power due to small sample size in each subgroup is a major limitation. However, our findings provide first preliminary evidence for dysfunctional neural fear conditioning/extinction associated with NOS1 ex1f-VNTR genotype in the context of PD/AG, shedding new light on the complex interaction between genetic risk, current psychopathology and treatment-related effects.

DEEP NETWORK-BASED FEATURE SELECTION FOR IMAGING GENETICS: APPLICATION TO IDENTIFYING BIOMARKERS FOR PARKINSON'S DISEASE

Imaging genetics is a methodology for discovering associations between imaging and genetic variables. Many studies adopted sparse models such as sparse canonical correlation analysis (SCCA) for imaging genetics. These methods are limited to modeling the linear imaging genetics relationship and cannot capture the non-linear high-level relationship between the explored variables. Deep learning approaches are underexplored in imaging genetics, compared to their great successes in many other biomedical domains such as image segmentation and disease classification. In this work, we proposed a deep learning model to select genetic features that can explain the imaging features well. Our empirical study on simulated and real datasets demonstrated that our method outperformed the widely used SCCA method and was able to select important genetic features in a robust fashion. These promising results indicate our deep learning model has the potential to reveal new biomarkers to improve mechanistic understanding of the studied brain disorders.

Allelic variant in SLC6A3 rs393795 affects cerebral regional homogeneity and gait dysfunction in patients with Parkinson's disease

Aims

We sought to explore the role of the SLC6A3rs393795 allelic variant in cerebral spontaneous activity and clinical features in Parkinson’s disease (PD) via imaging genetic approach.

Methods

Our study recruited 50 PD and 45 healthy control (HC) participants to provide clinical, genetic, and resting state functional magnetic resonance imaging (rs-fMRI) data. All subjects were separated into 16 PD-AA, 34 PD-CA/CC, 14 HC-AA, and 31 HC-CA/CC four subgroups according to SLC6A3rs393795 genotyping. Afterwards, main effects and interactions of groups (PD versus HC) and genotypes (AA versus CA/CC) on cerebral function reflected by regional homogeneity (ReHo) were explored using two-way analysis of covariance (ANCOVA) after controlling age and gender. Finally, Spearman’ s correlations were employed to investigate the relationships between significantly interactive brain regions and clinical manifestations in PD subgroups.

Results

Compared with HC subjects, PD patients exhibited increased ReHo signals in left middle temporal gyrus and decreased ReHo signals in left pallidum. Compared with CA/CC carriers, AA genotype individuals showed abnormal increased ReHo signals in right inferior frontal gyrus (IFG) and supplementary motor area (SMA). Moreover, significant interactions (affected by both disease factor and allelic variation) were detected in right inferior temporal gyrus (ITG). Furthermore, aberrant increased ReHo signals in right ITG were observed in PD-AA in comparison with PD-CA/CC. Notably, ReHo values in right ITG were negatively associated with Tinetti Mobility Test (TMT) gait subscale scores and positively related to Freezing of Gait Questionnaire (FOG-Q) scores in PD-AA subgroup.

Conclusions

Our findings suggested that SLC6A3rs393795 allelic variation might have a trend to aggravate the severity of gait disorders in PD patients by altering right SMA and IFG function, and ultimately result in compensatory activation of right ITG. It could provide us with a new perspective for exploring deeply genetic mechanisms of gait disturbances in PD.

Association of NPSR1 gene variation and neural activity in patients with panic disorder and agoraphobia and healthy controls

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Higher amygdala activation in T risk allele carriers during the perception of agoraphobia-specific stimuli

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Amygdala activation correlated negatively trendwise with the trait neuroticism in healthy controls carrying A/A genotype

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A diagnosis x genotype interaction as a trend in the inferior orbitofrontal cortex during the perception of agoraphobia-specific stimuli

Introduction

The neurobiological mechanisms behind panic disorder with agoraphobia (PD/AG) are not completely explored. The functional A/T single nucleotide polymorphism (SNP) rs324981 in the neuropeptide S receptor gene (NPSR1) has repeatedly been associated with panic disorder and might partly drive function respectively dysfunction of the neural “fear network”. We aimed to investigate whether the NPSR1 T risk allele was associated with malfunctioning in a fronto-limbic network during the anticipation and perception of agoraphobia-specific stimuli.

Method

121 patients with PD/AG and 77 healthy controls (HC) underwent functional magnetic resonance imaging (fMRI) using the disorder specific “Westphal-Paradigm”. It consists of neutral and agoraphobia-specific pictures, half of the pictures were cued to induce anticipatory anxiety.

Results

Risk allele carriers showed significantly higher amygdala activation during the perception of agoraphobia-specific stimuli than A/A homozygotes. A linear group x genotype interaction during the perception of agoraphobia-specific stimuli showed a strong trend towards significance. Patients with the one or two T alleles displayed the highest and HC with the A/A genotype the lowest activation in the inferior orbitofrontal cortex (iOFC).

Discussion

The study demonstrates an association of the NPSR1rs324981 genotype and the perception of agoraphobia-specific stimuli. These results support the assumption of a fronto-limbic dysfunction as an intermediate phenotype of PD/AG.

Association of rs7688285 allelic variation coding for GLRB with fear reactivity and exposure-based therapy in patients with panic disorder and agoraphobia

The gene coding for glycine receptor β subunits (GLRB) has been found to be related to panic disorder and agoraphobia (PD/AG) and to be associated with altered insular BOLD activation during fear conditioning, as an intermediate phenotype of defensive system reactivity in healthy subjects. In a multicenter clinical trial on PD/AG patients we investigated in three sub-samples whether GLRB allelic variation (A/G; A-allele identified as «risk») in the single nucleotide polymorphism rs7688285 was associated with autonomic (behavioral avoidance test BAT; n = 267 patients) and neural (differential fear conditioning; n = 49 patients, n = 38 controls) measures, and furthermore with responding towards exposure-based cognitive behavioral therapy (CBT, n = 184 patients). An interaction of genotype with current PD/AG diagnosis (PD/AG vs. controls; fMRI data only) and their modification after CBT was tested as well. Exploratory fMRI results prior to CBT, revealed A-allele carriers irrespective of diagnostic status to show overall higher BOLD activation in the hippocampus, motor cortex (MC) and insula. Differential activation in the MC, anterior cingulate cortex (ACC) and insula was found in the interaction genotype X diagnosis. Differential activation in ACC and hippocampus was present in differential fear learning. ACC activation was modified after treatment, while no overall rs7688285 dependent effect on clinical outcomes was found. On the behavioral level, A-allele carriers showed pronounced fear reactivity prior to CBT which partially normalized afterwards. In sum, rs7688285 variation interacts in a complex manner with PD/AG on a functional systems level and might be involved in the development of PD/AG but not in their treatment.

SNCA rs11931074 polymorphism correlates with spontaneous brain activity and motor symptoms in Chinese patients with Parkinson's disease

The α-synuclein (SNCA) gene is thought to be involved in levels of α-synuclein and influence the susceptibility for the development of Parkinson's disease (PD). The aim of the present study is to explore the association among SNCA rs1193074 polymorphism, spontaneous brain activity and clinical symptoms in PD patients. 62 PD patients and 47 healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. Also blood sample of each participant was genotyped for rs11931074 polymorphism (PD: TT = 19, GT = 32, GG = 11; HC: TT = 10, GT = 25, GG = 12) and then examined to ascertain the influence of different genotypes on regional brain activity with amplitude low-frequency fluctuation analysis (ALFF). Furthermore, we evaluated the relationship among genotypes, interactive brain region and clinical symptoms in PD. Compared with HC subjects, PD patients showed decreased ALFF values in right lingual gyrus and increased ALFF values in right cerebellum posterior lobe. Significant interaction of ''groups × genotypes'' was found in the right angular gyrus, where there were higher ALFF values in TT genotype than in GT or GG genotype in the PD group and there was a contrary trend in the HC group. And further Spearman's correlative analyses revealed that ALFF values in right angular gyrus were negatively associated with unified Parkinson's disease rating scale (UPDRS) III score in PD-TT genotype. Our study shows for the first time that SNCA rs11931074 polymorphism might modulate brain functional alterations and correlate with motor symptoms in Chinese PD patients.

Interactive effects of genetic polymorphisms and childhood adversity on brain morphologic changes in depression

The etiology of depression is characterized by the interplay of genetic and environmental factors and brain structural alteration. Childhood adversity is a major contributing factor in the development of depression. Interactions between childhood adversity and candidate genes for depression could affect brain morphology via the modulation of neurotrophic factors, serotonergic neurotransmission, or the hypothalamus-pituitary-adrenal (HPA) axis, and this pathway may explain the subsequent onset of depression. Childhood adversity is associated with structural changes in the hippocampus, amygdala, anterior cingulate cortex (ACC), and prefrontal cortex (PFC), as well as white matter tracts such as the corpus callosum, cingulum, and uncinate fasciculus. Childhood adversity showed an interaction with the brain-derived neurotrophic factor (BDNF) gene Val66Met polymorphism, serotonin transporter-linked promoter region (5-HTTLPR), and FK506 binding protein 51 (FKBP5) gene rs1360780 in brain morphologic changes in patients with depression and in a non-clinical population. Individuals with the Met allele of BDNF Val66Met and a history of childhood adversity had reduced volume in the hippocampus and its subfields, amygdala, and PFC and thinner rostral ACC in a study of depressed patients and healthy controls. The S allele of 5-HTTLPR combined with exposure to childhood adversity or a poorer parenting environment was associated with a smaller hippocampal volume and subsequent onset of depression. The FKBP5 gene rs160780 had a significant interaction with childhood adversity in the white matter integrity of brain regions involved in emotion processing. This review identified that imaging genetic studies on childhood adversity may deepen our understanding on the neurobiological background of depression by scrutinizing complicated pathways of genetic factors, early psychosocial environments, and the accompanying morphologic changes in emotion-processing neural circuitry.

The oxytocin receptor gene predicts brain activity during an emotion recognition task in autism

Background

Autism is a highly varied and heritable neurodevelopmental condition, and common variants explain approximately 50% of the genetic variance of autism. One of the genes implicated in autism is the oxytocin receptor (OXTR). The current study combined genetic and brain imaging (fMRI) data to examine the moderating effect of genotype on the association between diagnosis and brain activity in response to a test of cognitive empathy.

Methods

Participants were adolescents (mean age = 14.7 ± 1.7) who were genotyped for single nucleotide polymorphisms (SNPs) within the OXTR and underwent functional brain imaging while completing the adolescent version of the ‘Reading the Mind in the Eyes’ Test (Eyes Test).

Results

Two (rs2254298, rs53576) of the five OXTR SNPs examined were significantly associated with brain activity during the Eyes Test, and three of the SNPs (rs2254298, rs53576, rs2268491) interacted with diagnostic status to predict brain activity. All of the effects localized to the right supramarginal gyrus (rSMG) and an overlap analysis revealed a large overlap of the effects. An exploratory analysis showed that activity within an anatomically defined rSMG and genotype can predict diagnostic status with reasonable accuracy.

Conclusions

This is one of the first studies to investigate OXTR and brain function in autism. The findings suggest a neurogenetic mechanism by which OXTR-dependent activity within the rSMG is related to the aetiology of autism.

Electronic supplementary material

The online version of this article (10.1186/s13229-019-0258-4) contains supplementary material, which is available to authorized users.

eQTL of KCNK2 regionally influences the brain sulcal widening: evidence from 15,597 UK Biobank participants with neuroimaging data

The grey and white matter volumes are known to reduce with age. This cortical shrinkage is visible on magnetic resonance images and is conveniently identified by the increased volume of cerebrospinal fluid in the sulci between two gyri. Here, we replicated this finding using the UK Biobank dataset and studied the genetic influence on these cortical features of aging. We divided all individuals genetically confirmed of British ancestry into two sub-cohorts (12,162 and 3435 subjects for discovery and replication samples, respectively). We found that the heritability of the sulcal opening ranges from 15 to 45% (SE = 4.8%). We identified 4 new loci that contribute to this opening, including one that also affects the sulci grey matter thickness. We identified the most significant variant (rs864736) on this locus as being an expression quantitative trait locus (eQTL) for the KCNK2 gene. This gene regulates the immune-cell into the central nervous system (CNS) and controls the CNS inflammation, which is implicated in cortical atrophy and cognitive decline. These results expand our knowledge of the genetic contribution to cortical shrinking and promote further investigation into these variants and genes in pathological context such as Alzheimer’s disease in which brain shrinkage is a key biomarker.

A structural equation model for imaging genetics using spatial transcriptomics

Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer’s Disease Neuroimaging Initiative.

Replication in Imaging Genetics: The Case of Threat-Related Amygdala Reactivity

BACKGROUND

Low replication rates are a concern in most, if not all, scientific disciplines. In psychiatric genetics specifically, targeting intermediate brain phenotypes, which are more closely associated with putative genetic effects, was touted as a strategy leading to increased power and replicability. In the current study, we attempted to replicate previously published associations between single nucleotide polymorphisms and threat-related amygdala reactivity, which represents a robust brain phenotype not only implicated in the pathophysiology of multiple disorders, but also used as a biomarker of future risk.

METHODS

We conducted a literature search for published associations between single nucleotide polymorphisms and threat-related amygdala reactivity and found 37 unique findings. Our replication sample consisted of 1117 young adult volunteers (629 women, mean age 19.72 ± 1.25 years) for whom both genetic and functional magnetic resonance imaging data were available.

RESULTS

Of the 37 unique associations identified, only three replicated as previously reported. When exploratory analyses were conducted with different model parameters compared to the original findings, significant associations were identified for 28 additional studies: eight of these were for a different contrast/laterality; five for a different gender and/or race/ethnicity; and 15 in the opposite direction and for a different contrast, laterality, gender, and/or race/ethnicity. No significant associations, regardless of model parameters, were detected for six studies. Notably, none of the significant associations survived correction for multiple comparisons.

CONCLUSIONS

We discuss these patterns of poor replication with regard to the general strategy of targeting intermediate brain phenotypes in genetic association studies and the growing importance of advancing the replicability of imaging genetics findings.

Strategies for integrated analysis in imaging genetics studies

Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Using coordinate-based meta-analyses to explore structural imaging genetics

Imaging genetics has become a highly popular approach in the field of schizophrenia research. A frequently reported finding is that effects from common genetic variation are associated with a schizophrenia-related structural endophenotype. Genetic contributions to a structural endophenotype may be easier to delineate, when referring to biological rather than diagnostic criteria. We used coordinate-based meta-analyses, namely the anatomical likelihood estimation (ALE) algorithm on 30 schizophrenia-related imaging genetics studies, representing 44 single-nucleotide polymorphisms at 26 gene loci investigated in 4682 subjects. To test whether analyses based on biological information would improve the convergence of results, gene ontology (GO) terms were used to group the findings from the published studies. We did not find any significant results for the main contrast. However, our analysis enrolling studies on genotype × diagnosis interaction yielded two clusters in the left temporal lobe and the medial orbitofrontal cortex. All other subanalyses did not yield any significant results. To gain insight into possible biological relationships between the genes implicated by these clusters, we mapped five of them to GO terms of the category "biological process" (AKT1, CNNM2, DISC1, DTNBP1, VAV3), then five to "cellular component" terms (AKT1, CNNM2, DISC1, DTNBP1, VAV3), and three to "molecular function" terms (AKT1, VAV3, ZNF804A). A subsequent cluster analysis identified representative, non-redundant subsets of semantically similar terms that aided a further interpretation. We regard this approach as a new option to systematically explore the richness of the literature in imaging genetics.

The influence of MIR137 on white matter fractional anisotropy and cortical surface area in individuals with familial risk for psychosis

The rs1625579 variant near the microRNA-137 (MIR137) gene is one of the best-supported schizophrenia variants in genome-wide association studies (GWAS), and microRNA-137 functionally regulates other GWAS identified schizophrenia risk variants. Schizophrenia patients with the MIR137 rs1625579 risk genotype (homozygous for the schizophrenia risk variant) also have aberrant brain structure. It is unclear if the effect of MIR137 among schizophrenia patients is due to potential epistasis with genetic risk for schizophrenia or other factors of the disorder. Here, we investigated the effect of MIR137 genotype on white matter fractional anisotropy (FA), cortical thickness (CT), and surface area (SA) in a sample comprising healthy control subjects, and individuals with familial risk for psychosis (first-degree relatives of patients with schizophrenia or bipolar disorder; N=426). In voxel-wise analyses of FA, we observed a significant genotype-by-group interaction (P_FWE<0.05). The familial risk group with risk genotype had lower FA (P_FWE<0.05), but there was no genetic association in controls. In vertex-wise analyses of SA, we also observed a significant genotype-by-group interaction (P_FWE<0.05). Relatives with MIR137 risk genotype had lower SA, however the risk genotype was associated with higher SA in the controls (all P_FWE<0.05). These results show that MIR137 risk genotype is associated with lower FA in psychosis relatives that is similar to previous imaging-genetics findings in patients with schizophrenia. Furthermore, MIR137 genotype may also be a risk factor in a subclinical population with wide reductions in white matter FA and cortical SA.

ZNF804A Variation May Affect Hippocampal-Prefrontal Resting-State Functional Connectivity in Schizophrenic and Healthy Individuals

The ZNF804A variant rs1344706 has consistently been associated with schizophrenia and plays a role in hippocampal-prefrontal functional connectivity during working memory. Whether the effect exists in the resting state and in patients with schizophrenia remains unclear. In this study, we investigated the ZNF804A polymorphism at rs1344706 in 92 schizophrenic patients and 99 healthy controls of Han Chinese descent, and used resting-state functional magnetic resonance imaging to explore the functional connectivity in the participants. We found a significant main effect of genotype on the resting-state functional connectivity (RSFC) between the hippocampus and the dorsolateral prefrontal cortex (DLPFC) in both schizophrenic patients and healthy controls. The homozygous ZNF804A rs1344706 genotype (AA) conferred a high risk of schizophrenia, and also exhibited significantly decreased resting functional coupling between the left hippocampus and right DLPFC (F(2,165) = 13.43, P < 0.001). The RSFC strength was also correlated with cognitive performance and the severity of psychosis in schizophrenia. The current findings identified the neural impact of the ZNF804A rs1344706 on hippocampal-prefrontal RSFC associated with schizophrenia.

Common variation in ZNF804A (rs1344706) is not associated with brain morphometry in schizophrenia or healthy participants

BACKGROUND

The single nucleotide polymorphism (SNP) rs1344706 [A>C] within intron 2 of the zinc finger protein 804A gene (ZNF804A) is associated with schizophrenia at the genome-wide level, but its function in relation to the development of psychotic disorders, including its influence on brain morphology remains unclear.

METHODS

Using both univariate (voxel-based morphometry, VBM; cortical thickness) and multivariate (source-based morphometry, SBM) approaches, we examined the effects of variation of the rs1344706 polymorphism on grey matter integrity in 214 Caucasian schizophrenia cases and 94 Caucasian healthy individuals selected from the Australian Schizophrenia Research Bank.

RESULTS

Neither univariate nor multivariate analyses showed any associations between indices of grey matter and rs1344706 variation in schizophrenia or healthy participants. This was revealed in the context of the typical pattern of decreased grey matter integrity in schizophrenia compared to healthy individuals, including: (1) large grey matter volume reductions in the orbitofrontal and anterior cingulate cortices and the left fusiform/inferior temporal gyri; (2) decreased cortical thickness in the left inferior temporal and fusiform gyri, the left orbitofrontal gyrus, as well as in the right pars opercularis/precentral gyrus; and (3) decreased covariation of grey matter concentration in frontal and limbic brain regions emerging from the SBM analyses.

CONCLUSIONS

Contrary to some - but not all - previous findings, this study of a large sample of schizophrenia cases and healthy controls reveals no evidence for association between grey matter alterations and variation in rs1344706 (ZNF804A). Differences in sample sizes and ethnicities may account for discrepant findings between the present and previous studies.

Imaging genetics in autism spectrum disorders: Linking genetics and brain imaging in the pursuit of the underlying neurobiological mechanisms

Autism spectrum disorders (ASD) include a wide range of heterogeneous neurodevelopmental conditions that affect an individual in several aspects of social communication and behavior. Recent advances in molecular genetic technologies have dramatically increased our understanding of ASD etiology through the identification of several autism risk genes, most of which serve important functions in synaptic plasticity and protein synthesis. However, despite significant progress in this field of research, the characterization of the neurobiological mechanisms by which common genetic risk variants might operate to give rise to ASD symptomatology has proven to be far more difficult than expected. The imaging genetics approach holds great promise for advancing our understanding of ASD etiology by bridging the gap between genetic variations and their resultant biological effects on the brain. This paper provides a conceptual overview of the contribution of genetics in ASD and discusses key findings from the emerging field of imaging genetics.

Polygenic risk for depression and the neural correlates of working memory in healthy subjects

INTRODUCTION

Major depressive disorder (MDD) patients show impairments of cognitive functioning such as working memory (WM), and furthermore alterations during WM-fMRI tasks especially in frontal and parietal brain regions. The calculation of a polygenic risk score (PRS) can be used to describe the genetic influence on MDD, hence imaging genetic studies aspire to combine both genetics and neuroimaging data to identify the influence of genetic factors on brain functioning. We aimed to detect the effect of MDD-PRS on brain activation during a WM task measured with fMRI and expect healthy individuals with a higher PRS to be more resembling to MDD patients.

METHOD

In total, n=137 (80 men, 57 women, aged 34.5, SD=10.4years) healthy subjects performed a WM n-back task [0-back (baseline), 2-back and 3-back condition] in a 3T-MRI-tomograph. The sample was genotyped using the Infinium PsychArray BeadChip and a polygenic risk score was calculated for MDD using PGC MDD GWAS results.

RESULTS

A lower MDD risk score was associated with increased activation in the bilateral middle occipital gyri (MOG), the bilateral middle frontal gyri (MFG) and the right precentral gyrus (PCG) during the 2-back vs. baseline condition. Moreover, a lower PRS was associated with increased brain activation during the 3-back vs. baseline condition in the bilateral cerebellum, the right MFG and the left inferior parietal lobule. A higher polygenic risk score was associated with hyperactivation in brain regions comprising the right MFG and the right supplementary motor area during the 3-back vs. 2-back condition.

DISCUSSION

The results suggest that part of the WM-related brain activation patterns might be explained by genetic variants captured by the MDD-PRS. Furthermore we were able to detect MDD-associated activation patterns in healthy individuals depending on the MDD-PRS and the task complexity. Additional gene loci could contribute to these task-dependent brain activation patterns.

Neuroimaging genetic analyses of novel candidate genes associated with reading and language

Neuroimaging measures provide useful endophenotypes for tracing genetic effects on reading and language. A recent Genome-Wide Association Scan Meta-Analysis (GWASMA) of reading and language skills (N=1862) identified strongest associations with the genes CCDC136/FLNC and RBFOX2. Here, we follow up the top findings from this GWASMA, through neuroimaging genetics in an independent sample of 1275 healthy adults. To minimize multiple-testing, we used a multivariate approach, focusing on cortical regions consistently implicated in prior literature on developmental dyslexia and language impairment. Specifically, we investigated grey matter surface area and thickness of five regions selected a priori: middle temporal gyrus (MTG); pars opercularis and pars triangularis in the inferior frontal gyrus (IFG-PO and IFG-PT); postcentral parietal gyrus (PPG) and superior temporal gyrus (STG). First, we analysed the top associated polymorphisms from the reading/language GWASMA: rs59197085 (CCDC136/FLNC) and rs5995177 (RBFOX2). There was significant multivariate association of rs5995177 with cortical thickness, driven by effects on left PPG, right MTG, right IFG (both PO and PT), and STG bilaterally. The minor allele, previously associated with reduced reading-language performance, showed negative effects on grey matter thickness. Next, we performed exploratory gene-wide analysis of CCDC136/FLNC and RBFOX2; no other associations surpassed significance thresholds. RBFOX2 encodes an important neuronal regulator of alternative splicing. Thus, the prior reported association of rs5995177 with reading/language performance could potentially be mediated by reduced thickness in associated cortical regions. In future, this hypothesis could be tested using sufficiently large samples containing both neuroimaging data and quantitative reading/language scores from the same individuals.

Mapping the effect of the X chromosome on the human brain: Neuroimaging evidence from Turner syndrome

In addition to determining sex, the X chromosome has long been considered to play a crucial role in brain development and intelligence. Turner syndrome (TS) is caused by the congenital absence of all or part of one of the X chromosomes in females. Thus, Turner syndrome provides a unique "knock-out model" for investigating how the X chromosome influences the human brain in vivo. Numerous cutting-edge neuroimaging techniques and analyses have been applied to investigate various brain phenotypes in women with TS, which have yielded valuable evidence toward elucidating the causal relationship between the X chromosome and human brain structure and function. In this review, we comprehensively summarize the recent progress made in TS-related neuroimaging studies and emphasize how these findings have enhanced our understanding of X chromosome function with respect to the human brain. Future investigations are encouraged to address the issues of previous TS neuroimaging studies and to further identify the biological mechanisms that underlie the function of specific X-linked genes in the human brain.

Brain transcriptome atlases: a computational perspective

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.

Functional neuroanatomy in panic disorder: Status quo of the research

AIM

To provide an overview of the current research in the functional neuroanatomy of panic disorder.

METHODS

Panic disorder (PD) is a frequent psychiatric disease. Gorman et al (1989; 2000) proposed a comprehensive neuroanatomical model of PD, which suggested that fear- and anxiety-related responses are mediated by a so-called “fear network” which is centered in the amygdala and includes the hippocampus, thalamus, hypothalamus, periaqueductal gray region, locus coeruleus and other brainstem sites. We performed a systematic search by the electronic database PubMed. Thereby, the main focus was laid on recent neurofunctional, neurostructural, and neurochemical studies (from the period between January 2012 and April 2016). Within this frame, special attention was given to the emerging field of imaging genetics.

RESULTS

We noted that many neuroimaging studies have reinforced the role of the “fear network” regions in the pathophysiology of panic disorder. However, recent functional studies suggest abnormal activation mainly in an extended fear network comprising brainstem, anterior and midcingulate cortex (ACC and MCC), insula, and lateral as well as medial parts of the prefrontal cortex. Interestingly, differences in the amygdala activation were not as consistently reported as one would predict from the hypothesis of Gorman et al (2000). Indeed, amygdala hyperactivation seems to strongly depend on stimuli and experimental paradigms, sample heterogeneity and size, as well as on limitations of neuroimaging techniques. Advanced neurochemical studies have substantiated the major role of serotonergic, noradrenergic and glutamatergic neurotransmission in the pathophysiology of PD. However, alterations of GABAergic function in PD are still a matter of debate and also their specificity remains questionable. A promising new research approach is “imaging genetics”. Imaging genetic studies are designed to evaluate the impact of genetic variations (polymorphisms) on cerebral function in regions critical for PD. Most recently, imaging genetic studies have not only confirmed the importance of serotonergic and noradrenergic transmission in the etiology of PD but also indicated the significance of neuropeptide S receptor, CRH receptor, human TransMEMbrane protein (TMEM123D), and amiloride-sensitive cation channel 2 (ACCN2) genes.

CONCLUSION

In light of these findings it is conceivable that in the near future this research will lead to the development of clinically useful tools like predictive biomarkers or novel treatment options.

Variation at NRG1 genotype related to modulation of small-world properties of the functional cortical network

Functional brain networks possess significant small-world (SW) properties. Genetic variation relevant to both inhibitory and excitatory transmission may contribute to modulate these properties. In healthy controls, genotypic variation in Neuregulin 1 (NRG1) related to the risk of psychosis (risk alleles) would contribute to functional SW modulation of the cortical network. Electroencephalographic activity during an odd-ball task was recorded in 144 healthy controls. Then, small-worldness (SWn) was calculated in five frequency bands (i.e., theta, alpha, beta1, beta2 and gamma) for baseline (from -300 to the stimulus onset) and response (150-450 ms post-target stimulus) windows. The SWn modulation was defined as the difference in SWn between both windows. Association between SWn modulation and carrying the risk allele for three single nucleotide polymorphisms (SNP) of NRG1 (i.e., rs6468119, rs6994992 and rs7005606) was assessed. A significant association between three SNPs of NRG1 and the SWn modulation was found, specifically: NRG1 rs6468119 in alpha and beta1 bands; NRG1 rs6994992 in theta band; and NRG1 rs7005606 in theta and beta1 bands. Genetic variation at NRG1 may influence functional brain connectivity through the modulation of SWn properties of the cortical network.

Oxytocin and Anxiety Disorders

In the present chapter, we review the literature focusing on oxytocin (OT)-centered research in anxiety spectrum conditions, comprising separation anxiety disorder, specific phobias, social anxiety disorder (SAD), panic disorder, generalized anxiety disorder, and anxiety-related endophenotypes (e.g., trust behavior, behavioral inhibition, neuroticism, and state/trait anxiety). OT receptor gene (OXTR) polymorphisms have been implicated in gene-environment interactions with attachment style and childhood maltreatment and to influence clinical outcomes, including SAD intensity and limbic responsiveness. Epigenetic OXTR DNA methylation patterns have emerged as a link between categorical, dimensional, neuroendocrinological, and neuroimaging SAD correlates, highlighting them as potential peripheral surrogates of the central oxytocinergic tone. A pathophysiological framework of OT integrating the dynamic nature of epigenetic biomarkers and the summarized genetic and peripheral evidence is proposed. Finally, we emphasize opportunities and challenges of OT as a key network node of social interaction and fear learning in social contexts. In conjunction with multi-level investigations incorporating a dimensional understanding of social affiliation and avoidance in anxiety spectrum disorders, these concepts will help to promote research for diagnostic, state, and treatment response biomarkers of the OT system, advancing towards indicated preventive interventions and personalized treatment approaches.