FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Improved multimodal prediction of progression from MCI to Alzheimer's disease combining genetics with quantitative brain MRI and cognitive measures.

INTRODUCTION: There is a pressing need for non-invasive, cost-effective tools for early detection of Alzheimer's disease (AD). METHODS: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. RESULTS: The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. DISCUSSION: The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. HIGHLIGHTS: A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory. The MHS predicted time to conversion from mild cognitive impairment to dementia. MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%. A polygenic hazard score predicted age of onset of AD neuropathology.

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.

Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.

Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology.

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI Using Reduced-Field-of-View Echo-Planar Imaging and Enhanced Reversed Polarity Gradient Method.

BACKGROUND: Diffusion-weighted (DW) echo-planar imaging (EPI) is prone to geometric distortions due to B0 inhomogeneities. Both prospective and retrospective approaches have been developed to decrease and correct such distortions. PURPOSE: The purpose of this work was to evaluate the performance of reduced-field-of-view (FOV) acquisition and retrospective distortion correction methods in decreasing distortion artifacts for breast imaging. Coverage of the axilla in reduced-FOV DW magnetic resonance imaging (MRI) and residual distortion were also assessed. STUDY TYPE: Retrospective. POPULATION/PHANTOM: Breast phantom and 169 women (52.4 ± 13.4 years old) undergoing clinical breast MRI. FIELD STRENGTH/SEQUENCE: A 3.0 T/ full- and reduced-FOV DW gradient-echo EPI sequence. ASSESSMENT: Performance of reversed polarity gradient (RPG) and FSL topup in correcting breast full- and reduced-FOV EPI data was evaluated using the mutual information (MI) metric between EPI and anatomical images. Two independent breast radiologists determined if coverage on both EPI data sets was adequate to evaluate axillary nodes and identified residual nipple distortion artifacts. STATISTICAL TESTS: Two-way repeated-measures analyses of variance and post hoc tests were used to identify differences between EPI modality and distortion correction method. Generalized linear mixed effects models were used to evaluate differences in axillary coverage and residual nipple distortion. RESULTS: In a breast phantom, residual distortions were 0.16 ± 0.07 cm and 0.22 ± 0.13 cm in reduced- and full-FOV EPI with both methods, respectively. In patients, MI significantly increased after distortion correction of full-FOV (11 ± 5% and 18 ± 9%, RPG and topup) and reduced-FOV (8 ± 4% both) EPI data. Axillary nodes were observed in 99% and 69% of the cases in full- and reduced-FOV EPI images. Residual distortion was observed in 93% and 0% of the cases in full- and reduced-FOV images. DATA CONCLUSION: Minimal distortion was achieved with RPG applied to reduced-FOV EPI data. RPG improved distortions for full-FOV images but with more modest improvements and limited correction near the nipple. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

The genetic architecture of human complex phenotypes is modulated by linkage disequilibrium and heterozygosity.

We propose an extended Gaussian mixture model for the distribution of causal effects of common single nucleotide polymorphisms (SNPs) for human complex phenotypes that depends on linkage disequilibrium (LD) and heterozygosity (H), while also allowing for independent components for small and large effects. Using a precise methodology showing how genome-wide association studies (GWASs) summary statistics (z-scores) arise through LD with underlying causal SNPs, we applied the model to GWAS of multiple human phenotypes. Our findings indicated that causal effects are distributed with dependence on total LD and H, whereby SNPs with lower total LD and H are more likely to be causal with larger effects; this dependence is consistent with models of the influence of negative pressure from natural selection. Compared with the basic Gaussian mixture model it is built on, the extended model-primarily through quantification of selection pressure-reproduces with greater accuracy the empirical distributions of z-scores, thus providing better estimates of genetic quantities, such as polygenicity and heritability, that arise from the distribution of causal effects.

Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

BACKGROUND: Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. PURPOSE: To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. STUDY TYPE: Retrospective. SUBJECTS: Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. FIELD STRENGTH/SEQUENCE: 3T multishell diffusion-weighted sequence. ASSESSMENT: Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps. STATISTICAL TESTS: Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models. RESULTS: The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order. DATA CONCLUSION: The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.

Author Correction: Understanding the genetic determinants of the brain with MOSTest.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Understanding the genetic determinants of the brain with MOSTest.

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10-8, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.

Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR.

MOTIVATION: Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. RESULTS: Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. AVAILABILITY AND IMPLEMENTATION: The software is available at: https://github.com/precimed/mixer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model.

Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.

Discovery of shared genomic loci using the conditional false discovery rate approach.

In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.

Modifying the minimum criteria for diagnosing amnestic MCI to improve prediction of brain atrophy and progression to Alzheimer's disease.

Mild cognitive impairment (MCI) is a heterogeneous condition with variable outcomes. Improving diagnosis to increase the likelihood that MCI reliably reflects prodromal Alzheimer's Disease (AD) would be of great benefit for clinical practice and intervention trials. In 230 cognitively normal (CN) and 394 MCI individuals from the Alzheimer's Disease Neuroimaging Initiative, we studied whether an MCI diagnostic requirement of impairment on at least two episodic memory tests improves 3-year prediction of medial temporal lobe atrophy and progression to AD. Based on external age-adjusted norms for delayed free recall on the Rey Auditory Verbal Learning Test (AVLT), MCI participants were further classified as having normal (AVLT+, above -1 SD, n = 121) or impaired (AVLT -, -1 SD or below, n = 273) AVLT performance. CN, AVLT+, and AVLT- groups differed significantly on baseline brain (hippocampus, entorhinal cortex) and cerebrospinal fluid (amyloid, tau, p-tau) biomarkers, with the AVLT- group being most abnormal. The AVLT- group had significantly more medial temporal atrophy and a substantially higher AD progression rate than the AVLT+ group (51% vs. 16%, p < 0.001). The AVLT+ group had similar medial temporal trajectories compared to CN individuals. Results were similar even when restricted to individuals with above average (based on the CN group mean) baseline medial temporal volume/thickness. Requiring impairment on at least two memory tests for MCI diagnosis can markedly improve prediction of medial temporal atrophy and conversion to AD, even in the absence of baseline medial temporal atrophy. This modification constitutes a practical and cost-effective approach for clinical and research settings.

Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation.

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.

Awake Mouse Imaging: From Two-Photon Microscopy to Blood Oxygen Level-Dependent Functional Magnetic Resonance Imaging.

BACKGROUND: Functional magnetic resonance imaging (fMRI) in awake behaving mice is well positioned to bridge the detailed cellular-level view of brain activity, which has become available owing to recent advances in microscopic optical imaging and genetics, to the macroscopic scale of human noninvasive observables. However, though microscopic (e.g., two-photon imaging) studies in behaving mice have become a reality in many laboratories, awake mouse fMRI remains a challenge. Owing to variability in behavior among animals, performing all types of measurements within the same subject is highly desirable and can lead to higher scientific rigor. METHODS: We demonstrated blood oxygenation level-dependent fMRI in awake mice implanted with long-term cranial windows that allowed optical access for microscopic imaging modalities and optogenetic stimulation. We started with two-photon imaging of single-vessel diameter changes (n = 1). Next, we implemented intrinsic optical imaging of blood oxygenation and flow combined with laser speckle imaging of blood flow obtaining a mesoscopic picture of the hemodynamic response (n = 16). Then we obtained corresponding blood oxygenation level-dependent fMRI data (n = 5). All measurements could be performed in the same mice in response to identical sensory and optogenetic stimuli. RESULTS: The cranial window did not deteriorate the quality of fMRI and allowed alternation between imaging modalities in each subject. CONCLUSIONS: This report provides a proof of feasibility for multiscale imaging approaches in awake mice. In the future, this protocol could be extended to include complex cognitive behaviors translatable to humans, such as sensory discrimination or attention.

Polygenic hazard score, amyloid deposition and Alzheimer's neurodegeneration.

Mounting evidence indicates that the polygenic basis of late-onset Alzheimer's disease can be harnessed to identify individuals at greatest risk for cognitive decline. We have previously developed and validated a polygenic hazard score comprising of 31 single nucleotide polymorphisms for predicting Alzheimer's disease dementia age of onset. In this study, we examined whether polygenic hazard scores are associated with: (i) regional tracer uptake using amyloid PET; (ii) regional volume loss using longitudinal MRI; (iii) post-mortem regional amyloid-ß protein and tau associated neurofibrillary tangles; and (iv) four common non-Alzheimer's pathologies. Even after accounting for APOE, we found a strong association between polygenic hazard scores and amyloid PET standard uptake volume ratio with the largest effects within frontal cortical regions in 980 older individuals across the disease spectrum, and longitudinal MRI volume loss within the entorhinal cortex in 607 older individuals across the disease spectrum. We also found that higher polygenic hazard scores were associated with greater rates of cognitive and clinical decline in 632 non-demented older individuals, even after controlling for APOE status, frontal amyloid PET and entorhinal cortex volume. In addition, the combined model that included polygenic hazard scores, frontal amyloid PET and entorhinal cortex volume resulted in a better fit compared to a model with only imaging markers. Neuropathologically, we found that polygenic hazard scores were associated with regional post-mortem amyloid load and neuronal neurofibrillary tangles, even after accounting for APOE, validating our imaging findings. Lastly, polygenic hazard scores were associated with Lewy body and cerebrovascular pathology. Beyond APOE, we show that in living subjects, polygenic hazard scores were associated with amyloid deposition and neurodegeneration in susceptible brain regions. Polygenic hazard scores may also be useful for the identification of individuals at the highest risk for developing multi-aetiological dementia.

Insulin-Like Growth Factor Binding Protein 2 Is Associated With Biomarkers of Alzheimer's Disease Pathology and Shows Differential Expression in Transgenic Mice.

There is increasing evidence that metabolic dysfunction plays an important role in Alzheimer's disease (AD). Brain insulin resistance and subsequent impairment of insulin and insulin-like growth factor (IGF) signaling are associated with the neurodegenerative and clinical features of AD. Nevertheless, how the brain insulin/IGF signaling system is altered in AD and the effects of these changes on AD pathobiology are not well understood. IGF binding protein 2 (IGFBP-2) is an abundant cerebral IGF signaling protein and there is early evidence suggesting it associates with AD biomarkers. We evaluated the relationship between protein levels of IGFBP-2 with cerebrospinal fluid (CSF) biomarkers and neuroimaging markers of AD progression in 300 individuals from across the AD spectrum. CSF IGFBP-2 levels were correlated with CSF tau levels and brain atrophy in non-hippocampal regions. To further explore the role of IGFBP2 in tau pathobiology, we evaluated the expression of IGFBP2 in different human and mouse brain cell types and brain tissue from two transgenic mouse models: the P301L-tau model of tauopathy and TASTPM model of AD. We observed significant differential expression of IGFBP2 in both transgenic mouse models relative to wild-type mice in cortex but not in hippocampus. In both humans and mice, IGFBP2 is most highly expressed in astrocytes. Taken together, our findings suggest that IGFBP-2 may be linked to tau pathology and provides further evidence for a relationship between metabolic dysregulation and neurodegeneration. Our results also raise the possibility that this relationship may extend beyond neurons.