Imaging Genetic Studies Research Articles

Multivariate Imaging Genetics Study of MRI Gray Matter Volume and SNPs Reveals Biological Pathways Correlated with Brain Structural Differences in Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder affecting children, adolescents, and adults. Its etiology is not well understood, but it is increasingly believed to result from diverse pathophysiologies that affect the structure and function of specific brain circuits. Although one of the best-studied neurobiological abnormalities in ADHD is reduced fronto-striatal-cerebellar gray matter (GM) volume, its specific genetic correlates are largely unknown. In this study, T1-weighted MR images of brain structure were collected from 198 adolescents (63 ADHD-diagnosed). A multivariate parallel independent component analysis (Para-ICA) technique-identified imaging genetic relationships between regional GM volume and single nucleotide polymorphism data. Para-ICA analyses extracted 14 components from genetic data and 9 from MR data. An iterative cross-validation using randomly chosen subsamples indicated acceptable stability of these ICA solutions. A series of partial correlation analyses controlling for age, sex, and ethnicity revealed two genotype-phenotype component pairs significantly differed between ADHD and non-ADHD groups, after a Bonferroni correction for multiple comparisons. The brain phenotype component not only included structures frequently found to have abnormally low volume in previous ADHD studies but was also significantly associated with ADHD differences in symptom severity and performance on cognitive tests frequently found to be impaired in patients diagnosed with the disorder. Pathway analysis of the genotype component identified several different biological pathways linked to these structural abnormalities in ADHD. Some of these pathways implicate well-known dopaminergic neurotransmission and neurodevelopment hypothesized to be abnormal in ADHD. Other more recently implicated pathways included glutamatergic and GABA-eric physiological systems; others might reflect sources of shared liability to disturbances commonly found in ADHD, such as sleep abnormalities.

Gray matter maturation and cognition in children with different APOE ε genotypes

The aims of the current study were to determine whether children with the 6 different APOE ε genotypes show differences in gray matter maturation, particularly for those with ε4 and ε2 alleles, which are associated with poorer outcomes in many neurologic disorders. A total of 1,187 healthy children (aged 3-20 years, 52.1% boys, 47.9% girls) with acceptable data from the cross-sectional Pediatric Imaging Neurocognition and Genetics Study were evaluated for the effects of 6 APOE ε genotypes on macroscopic and microscopic cortical and subcortical gray matter structures (measured with 3-tesla MRI and FreeSurfer for automated morphometry) and on cognition (NIH Toolbox). Among APOE ε4 carriers, age-related changes in brain structures and cognition varied depending on genotype, with the smallest hippocampi in ε2ε4 children, the lowest hippocampal fractional anisotropy in younger ε4ε4 children, the largest medial orbitofrontal cortical areas in ε3ε4 children, and age-dependent thinning of the entorhinal cortex in ε4ε4 children. Younger ε4ε4 children had the lowest scores on executive function and working memory, while younger ε2ε4 children performed worse on attention tasks. Larger parietal gyri in the younger ε2ε4 children, and thinner temporal and cingulate isthmus cortices or smaller hippocampi in the younger ε4ε4 children, predicted poorer performance on attention or working memory. Our findings validated and extended prior smaller studies that showed altered brain development in APOE ε4-carrier children. The ε4ε4 and ε2ε4 genotypes may negatively influence brain development and brain aging at the extremes of age. Studying APOE ε polymorphisms in young children may provide the earliest indicators for individuals who might benefit from early interventions or preventive measures for future brain injuries and dementia.

Gene Prioritization for Imaging Genetics Studies Using Gene Ontology and a Stratified False Discovery Rate Approach.

Imaging genetics is an emerging field in which the association between genes and neuroimaging-based quantitative phenotypes are used to explore the functional role of genes in neuroanatomy and neurophysiology in the context of healthy function and neuropsychiatric disorders. The main obstacle for researchers in the field is the high dimensionality of the data in both the imaging phenotypes and the genetic variants commonly typed. In this article, we develop a novel method that utilizes Gene Ontology, an online database, to select and prioritize certain genes, employing a stratified false discovery rate (sFDR) approach to investigate their associations with imaging phenotypes. sFDR has the potential to increase power in genome wide association studies (GWAS), and is quickly gaining traction as a method for multiple testing correction. Our novel approach addresses both the pressing need in genetic research to move beyond candidate gene studies, while not being overburdened with a loss of power due to multiple testing. As an example of our methodology, we perform a GWAS of hippocampal volume using both the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA2) and the Alzheimer's Disease Neuroimaging Initiative datasets. The analysis of ENIGMA2 data yielded a set of SNPs with sFDR values between 10 and 20%. Our approach demonstrates a potential method to prioritize genes based on biological systems impaired in a disease.

Impact of PICALM and CLU on hippocampal degeneration.

PICALM and CLU are two major risk genes of late-onset Alzheimer's disease (LOAD), and there is strong molecular evidence suggesting their interaction on amyloid-beta deposition, hence finding functional dependency between their risk genotypes may lead to better understanding of their roles in LOAD development and greater clinical utility. In this study, we mainly investigated interaction effects of risk loci PICALM rs3581179 and CLU rs11136000 on hippocampal degeneration in both young and elderly adults in order to understand their neural mechanism on aging process, which may help identify robust biomarkers for early diagnosis and intervention. Besides volume we also assessed hippocampal shape phenotypes derived from diffeomorphic metric mapping and nonlinear dimensionality reduction. In elderly individuals (75.6 ± 6.7 years) significant interaction effects existed on hippocampal volume (P < 0.001), whereas in young healthy adults (19.4 ± 1.1 years) such effects existed on a shape phenotype (P = 0.01) indicating significant variation at hippocampal head and tail that mirror most AD vulnerable regions. Voxel-wise analysis also pointed to the same regions but lacked statistical power. In both cohorts, PICALM protective genotype AA only exhibited protective effects on hippocampal degeneration and cognitive performance when combined with CLU protective T allele, but adverse effects with CLU risk CC. This study revealed novel PICALM and CLU interaction effects on hippocampal degeneration along aging, and validated effectiveness of diffeomorphometry in imaging genetics study. Hum Brain Mapp 37:2419-2430, 2016. © 2016 Wiley Periodicals, Inc.

Multiple genetic imaging study of the association between cholesterol metabolism and brain functional alterations in individuals with risk factors for Alzheimer's disease

Alzheimer's disease (AD) is a clinically and genetically heterogeneous neurodegenerative disease. Genes involved in cholesterol metabolism may play a role in the pathological changes of AD. However, the imaging genetics-based endophenotypes derived from polymorphisms in multiple functionally related genes are unclear in individuals with risk factors for AD. Forty-three amnestic mild cognitive impairment (aMCI) subjects and 30 healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) measurements of brain topological organization. Thirty-three previously suggested tagging single nucleotide polymorphisms (SNPs) from 12 candidate genes in the cholesterol metabolism pathway were further investigated. A cholesterol metabolism pathway gene-based imaging genetics approach was then utilized to investigate disease-related differences between the groups based on genotype-by-aMCI interactions. The cholesterol metabolism pathway genes exerted widespread effects on the cortico-subcortical-cerebellar spontaneous brain activity. Meanwhile, left lateralization of global brain connectivity was associated with cholesterol metabolism pathway genes. The APOE rs429358 variation significantly influenced the brain network characteristics, affecting the activation of nodes as well as the connectivity of edges in aMCI subjects. The cholesterol metabolism pathway gene-based imaging genetics approach may provide new opportunities to understand the mechanisms underlying AD and suggested that APOE rs429358 is a core genetic variation that is associated with disease-related differences in brain function.

Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics.

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others.

Within- and between-session replicability of cognitive brain processes: An MEG study with an N-back task

In the vast majority of electrophysiological studies on cognition, participants are only measured once during a single experimental session. The dearth of studies on test-retest reliability in magnetoencephalography (MEG) within and across experimental sessions is a preventing factor for longitudinal designs, imaging genetics studies, and clinical applications. From the recorded signals, it is not straightforward to draw robust and steady indices of brain activity that could directly be used in exploring behavioral effects or genetic associations. To study the variations in markers associated with cognitive functions, we extracted three event-related field (ERF) features from time-locked global field power (GFP) epochs using MEG while participants were performing a numerical N-back task in four consecutive measurements conducted during two different days separated by two weeks.We demonstrate that the latency of the M170, a neural correlate associated with cognitive functions such as working memory, was a stable parameter and did not show significant variations over time. In addition, the M170 peak amplitude and the mean amplitude of late positive component (LPP) also expressed moderate-to-strong reliability across multiple measures over time over many sensor spaces and between participants. The M170 amplitude varied more significantly between the measurements in some conditions but showed consistency over the participants over time. In addition we demonstrated significant correlation with the M170 and LPP parameters and cognitive load. The results are in line with the literature showing less within-subject fluctuation for the latency parameters and more consistency in between-subject comparisons for amplitude based features. The within-subject consistency was apparent also with longer delays between the measurements. We suggest that with a few limitations the ERF features show sufficient reliability and stability for longitudinal research designs and clinical applications for cognitive functions in single as well as cross-subject designs.

Alcohol and the Human Brain: a Systematic Review of Recent Functional Neuroimaging and Imaging Genetics Findings

New information from neuroimaging studies on alcohol use disorders (AUDs) is available which necessitates an update on our review on alcohol and the human brain published in 2010. Here, we provide an overview on functional Magnetic Resonance Imaging (fMRI) and imaging genetics studies published in the last 5 years. Our systematic search in PubMed yielded a total of 70 fMRI studies out of which 57 were finally considered. We have summarized the study results based on their main research focus as follows: studies on neural correlates of reward processing (alcohol cues (N = 17), non-drug cues (N = 3)), decision making (N = 2), attention (N = 5), impulsivity and cognitive control (N = 1), pharmacological effects/treatment responses (N = 13) and relapse prediction (N = 6). Neuroimaging genetics studies have mainly considered the impact of various functional polymorphisms on alcohol cue processing (N = 10). These results indicate that recent research on AUDs has concentrated on reward processing (mainly alcohol cue reactivity). Possible directions for future research are discussed.

Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies

BackgroundQuantifying associations in neuroscience (and many other scientific disciplines) is often challenged by high-dimensionality, nonlinearity and noisy observations. Many classic methods have either poor power or poor scalability on data sets of the same or different scales such as genetical, physiological and image data. New methodBased on the framework of reproducing kernel Hilbert spaces we proposed a new nonlinear association criteria (NAC) with an efficient numerical algorithm and p-value approximation scheme. We also presented mathematical justification that links the proposed method to related methods such as kernel generalized variance, kernel canonical correlation analysis and Hilbert–Schmidt independence criteria. NAC allows the detection of association between arbitrary input domain as long as a characteristic kernel is defined. A MATLAB package was provided to facilitate applications. ResultsExtensive simulation examples and four real world neuroscience examples including functional MRI causality, Calcium imaging and imaging genetic studies on autism [Brain, 138(5):13821393 (2015)] and alcohol addiction [PNAS, 112(30):E4085–E4093 (2015)] are used to benchmark NAC. It demonstrates the superior performance over the existing procedures we tested and also yields biologically significant results for the real world examples. Comparison with existing method(s)NAC beats its linear counterparts when nonlinearity is presented in the data. It also shows more robustness against different experimental setups compared with its nonlinear counterparts. ConclusionsIn this work we presented a new and robust statistical approach NAC for measuring associations. It could serve as an interesting alternative to the existing methods for datasets where nonlinearity and other confounding factors are present.

Semiparametric Bayes conditional graphical models for imaging genetics applications.

Motivated by the need for understanding neurological disorders, large-scale imaging genetic studies are being increasingly conducted. A salient objective in such studies is to identify important neuroimaging biomarkers such as the brain functional connectivity, as well as genetic biomarkers, which are predictive of disorders. However, typical approaches for estimating the group level brain functional connectivity do not account for potential variation, resulting from demographic and genetic factors, while usual methods for discovering genetic biomarkers do not factor in the influence of the brain network on the imaging phenotype. We propose a novel semiparametric Bayesian conditional graphical model for joint variable selection and graph estimation, which simultaneously estimates the brain network after accounting for heterogeneity, and infers significant genetic biomarkers. The proposed approach specifies priors on the regression coefficients, which clusters brain regions having similar activation patterns depending on covariates, leading to dimension reduction. A novel graphical prior is proposed, which encourages modularity in brain organization by specifying denser and sparse connections within and across clusters, respectively. The posterior computation proceeds via a Markov chain Monte Carlo. We apply the approach to data obtained from the Alzheimer's disease neuroimaging initiative and demonstrate numerical advantages via simulation studies.

Effects of norepinephrine transporter gene variants on NET binding in ADHD and healthy controls investigated by PET.

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous disorder with a strong genetic component. The norepinephrine transporter (NET) is a key target for ADHD treatment and the NET gene has been of high interest as a possible modulator of ADHD pathophysiology. Therefore, we conducted an imaging genetics study to examine possible effects of single nucleotide polymorphisms (SNPs) within the NET gene on NET nondisplaceable binding potential (BPND) in patients with ADHD and healthy controls (HCs). Twenty adult patients with ADHD and 20 HCs underwent (S,S)‐[18F]FMeNER‐D2 positron emission tomography (PET) and were genotyped on a MassARRAY MALDI‐TOF platform using the Sequenom iPLEX assay. Linear mixed models analyses revealed a genotype‐dependent difference in NET BPND between groups in the thalamus and cerebellum. In the thalamus, a functional promoter SNP (−3081 A/T) and a 5′‐untranslated region (5′UTR) SNP (−182 T/C), showed higher binding in ADHD patients compared to HCs depending on the major allele. Furthermore, we detected an effect of genotype in HCs, with major allele carriers having lower binding. In contrast, for two 3′UTR SNPs (*269 T/C, *417 A/T), ADHD subjects had lower binding in the cerebellum compared to HCs depending on the major allele. Additionally, symptoms of hyperactivity and impulsivity correlated with NET BPND in the cerebellum depending on genotype. Symptoms correlated positively with cerebellar NET BPND for the major allele, while symptoms correlated negatively to NET BPND in minor allele carriers. Our findings support the role of genetic influence of the NE system on NET binding to be pertubated in ADHD. Hum Brain Mapp 37:884–895, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Measuring how genetic and epigenetic variants can filter emotion perception.

Emotion perception has been extensively studied in cognitive neurosciences and stands as a promising intermediate phenotype of social cognitive processes and psychopathologies. Exciting imaging genetic studies have recently identified genetic and epigenetic variants affecting brain responses during emotion perception tasks, but characterizing how these variants interact and relate to higher-order cognitive processes remains a challenge. Here, we integrate works in parallel fields and propose a new psychophysical conceptualization to address this issue. This approach proposes to consider genetic variants as 'filters' of perceptual information that can interact to shape different perceptual profiles. Importantly, these perceptual profiles can be precisely described and compared between multivariate genetic groups using a new psychophysical method. Crucially, this approach represents a potentially powerful novel tool to address gene-by-gene and gene-by-environment interactions, and provides a new cognitive perspective to link social perceptive and social cognitive processes in the context of psychiatric disorders.

G-protein genomic association with normal variation in gray matter density.

While detecting genetic variations underlying brain structures helps reveal mechanisms of neural disorders, high data dimensionality poses a major challenge for imaging genomic association studies. In this work, we present the application of a recently proposed approach, parallel independent component analysis with reference (pICA-R), to investigate genomic factors potentially regulating gray matter variation in a healthy population. This approach simultaneously assesses many variables for an aggregate effect and helps to elicit particular features in the data. We applied pICA-R to analyze gray matter density (GMD) images (274,131 voxels) in conjunction with single nucleotide polymorphism (SNP) data (666,019 markers) collected from 1,256 healthy individuals of the Brain Imaging Genetics (BIG) study. Guided by a genetic reference derived from the gene GNA14, pICA-R identified a significant SNP-GMD association (r=-0.16, P=2.34×10(-8)), implying that subjects with specific genotypes have lower localized GMD. The identified components were then projected to an independent dataset from the Mind Clinical Imaging Consortium (MCIC) including 89 healthy individuals, and the obtained loadings again yielded a significant SNP-GMD association (r=-0.25, P=0.02). The imaging component reflected GMD variations in frontal, precuneus, and cingulate regions. The SNP component was enriched in genes with neuronal functions, including synaptic plasticity, axon guidance, molecular signal transduction via PKA and CREB, highlighting the GRM1, PRKCH, GNA12, and CAMK2B genes. Collectively, our findings suggest that GNA12 and GNA14 play a key role in the genetic architecture underlying normal GMD variation in frontal and parietal regions.

Gene and Environment Interactions in the Brain: A Pathway to ADHD?

Defining how interactions between an individual’s genotype andenvironment (“gene-byenvironment”) actupon thebrain to result in mental illness is a vital but challenging endeavor. The study by van derMeer et al. (1) in this issue of the Journal takes one of the most widely studied of all gene-by environment interactions into the brain in an effort tomap out neural pathways to attention deficit hyperactivity disorder (ADHD). The “gene” under study is a genetic variant, a variable number tandem repeat polymorphism, in the promoter region of the serotonin transporter referred to as 5-HTTLPR. It has a short (“S”-allele) and a long (“L”-allele) form, and the variation affects serotonergic signaling (2). The “environment” is an amalgam of chronic social stressors and major adverse life events. It has been reported that carriers of the short, S-allele are more vulnerable to depression when exposed to adverse events (3–6). Here, the authors find that the same gene-byenvironment interaction may extend to ADHD. They show that young adults with one or two copies of the S-allele when exposedtosocial stressorshavemoresymptomsofADHDthan thosewith two copies of the L-allele. They proceed to askwhy and look to the brain for an answer. The study’s sample is enriched for ADHD, with 291 young, affected adults, 78 individuals with subthreshold ADHD, and 332healthy comparison subjects;many of the participantswere siblings. All had magnetic resonance neuroanatomic scans to allow the definition of gray matter volume with an exquisite level of spatial resolution (around half-a-million voxels, or tiny “volumes”). The authors askwhether change in brain structure is the mechanism through which the 5-HTTLPR/stress interaction affects the severity of ADHD symptoms. Mediation analysesarewidelyused inmappingsuchpossiblecausalchains. The authors start by defining a term that reflects the interaction ofthegeneandstressfulenvironments.Theyshowthatthis term is significantly associated with the volume of several prefrontal cortical regions. Next, they find that some of these prefrontal regions are further associated with the severity of ADHD symptoms. Combined, this demonstrates that variation in prefrontal cortical structuremediates, or statistically accounts for, the stronger correlation seen in S-allele carriers between social stress and ADHD symptoms. Specifically, decreased volumes of the left frontal pole and right anterior cingulate cortex emerge as possible contributors to the increasedADHD symptom severity seen in S-allele carriers when exposed to stress. It has been intensely debated whether this interaction between 5-HTTLPR and social stress plays a role in altering risk for psychopathology. Most studies have examined vulnerability to depressionwithmeta-analytic evidence both for andagainst a link (4–6).Theproposal by vanderMeer et al. that this gene-by-environment interaction is also linked to ADHD is much less studied. One prior study of 110 adults with ADHD found that the L-allele (but not the S-allele) interacted with childhood stressors in altering ADHD severity (7, 8). A second study of 184 boys with conduct problems found that S-allele carriershadfewerADHDsymptomswhenunder lowstress,but not more symptoms when under high stress. Despite the differences, in combination with the van derMeer et al. study, the findingssuggest that it isworthwhile toexplore furtherwhether this gene-by-environment interaction affects ADHD. If replicated, this would add the 5-HTTLPR to the list of genes that alter the risk of a wide range of psychiatric disorders, either directly or through interaction with key environments. It also raises the question of whether this gene-by-environment interaction operates upon a behavioral dimension that might underpinmultipledisorders.Theauthorsreasonablysuggestthatthe regulation of attention and emotion could be such a dimension. While the gene-by-environmentfield has had its fair share of nonreplications, three features bolster confidence in the present study. Firstly, the sample size of 701 is very impressive by neuroimaging standards. It afforded the detection of a gene-byenvironment interaction that accounts for between 0.1% and 0.8% of the variance in key brain regions. For a phenotype as complexasbrainmorphology,explainingthisamountofvariance is in line with expectations. To appreciate the scale, the largest genetic imaging study to date of brain structure examined 30,717 individuals and found that the most strongly associated single common genetic variant explained 0.52% of the variation in the size of the caudate (9). Secondly, the authors carefully consider potential confounding variables. They demonstrate that the pattern of results holds across the two magnetic resonance scanners used. Importantly, they show that age—which is Decreased volumes of the left frontal pole and right anterior cingulate cortex emerge as possible contributors to the increased ADHD symptom severity seen in S-allele carriers when exposed to stress.

Behr syndrome with homozygous C19ORF12 mutation

ObjectiveBehr syndrome, first described in 1909 by the ophthalmologist Carl Behr, is a clinical entity characterised by a progressive optic atrophy, ataxia, pyramidal signs and mental retardation. Some reported cases have been found to carry mutations in the OPA1, OPA3 or C12ORF65 genes which are known causes of pure optic atrophy or optic atrophy complicated by movement disorder. MethodsWe present the long-term observation of two Turkish sisters with Behr syndrome. We performed neurophysiological, imaging and molecular genetic studies to identify the underlying genetic cause in our patients. ResultsMagnetic resonance imaging of the brain showed bilateral hypointense signals in the basal ganglia which prompted us to consider neurodegeneration with brain iron accumulation (NBIA) as a differential diagnosis. Molecular genetic studies revealed a homozygous mutation in the C19ORF12 gene which has been previously reported in patients with a subtype of NBIA, mitochondrial membrane protein-associated neurodegeneration (MPAN). ConclusionWe expand the spectrum of genetic causes of Behr syndrome. Genetic testing of patients presenting with Behr syndrome should include C19ORF12 mutation screening.

Effects of an opioid (proenkephalin) polymorphism on neural response to errors in health and cocaine use disorder

Chronic exposure to drugs of abuse perturbs the endogenous opioid system, which plays a critical role in the development and maintenance of addictive disorders. Opioid genetics may therefore play an important modulatory role in the expression of substance use disorders, but these genes have not been extensively characterized, especially in humans. In the current imaging genetics study, we investigated a single nucleotide polymorphism (SNP) of the protein-coding proenkephalin gene (PENK: rs2609997, recently shown to be associated with cannabis dependence) in 55 individuals with cocaine use disorder and 37 healthy controls. Analyses tested for PENK associations with fMRI response to error (during a classical color-word Stroop task) and gray matter volume (voxel-based morphometry) as a function of Diagnosis (cocaine, control). Results revealed whole-brain Diagnosis×PENK interactions on the neural response to errors (fMRI error>correct contrast) in the right putamen, left rostral anterior cingulate cortex/medial orbitofrontal cortex, and right inferior frontal gyrus; there was also a significant Diagnosis×PENK interaction on right inferior frontal gyrus gray matter volume. These interactions were driven by differences between individuals with cocaine use disorders and controls that were accentuated in individuals carrying the higher-risk PENK C-allele. Taken together, the PENK polymorphism—and potentially opioid neurotransmission more generally—modulates functioning and structural integrity of brain regions previously implicated in error-related processing. PENK could potentially render a subgroup of individuals with cocaine use disorder (i.e., C-allele carriers) more sensitive to mistakes or other related challenges; in future studies, these results could contribute to the development of individualized genetics-informed treatments.

A Population-Based Imaging Genetics Study of Inattention/Hyperactivity: Basal Ganglia and Genetic Pathways

Although attention-deficit/hyperactivity disorder (ADHD) is 1 of the most common neurodevelopmental disorders, little is known about the neurobiology. Clinical studies suggest basal ganglia morphology plays a role. Furthermore, hyperactivity/impulsivity symptoms have recently been linked to genetic pathways involved in dopamine/norepinephrine and serotonin neurotransmission and neuritic outgrowth. We aimed to assess the association between ADHD symptoms, basal ganglia volume, and the 3 proposed genetic pathways in a pediatric population-based sample. With this, we aimed to investigate the generalizability of earlier clinical findings to the general population. This study included a population-based sample of 1,871 children with data on ADHD symptoms and genetic data, and 344 children with additional neuroimaging data. Regression analyses between ADHD symptom severity and volumetric data of the basal ganglia were performed. Also, gene-set analyses investigating the association between both ADHD symptom severity and basal ganglia volume with the dopamine/norepinephrine, serotonin, and neuritic outgrowth pathways were performed. More inattention and hyperactivity/impulsivity symptoms were associated with a smaller volume of the putamen (β = -0.13, p = .034), which was regarded as trend-level after correction for multiple testing. Stratified analyses showed a stronger putamen-hyperactivity association in children with clinical scores, although a similar trend was visible in the nonclinical subsample. The genetic pathways were not related to either ADHD symptoms or basal ganglia volume. ADHD symptoms were marginally related to putamen volume in our population-based sample. We found no evidence for a role of dopamine/norepinephrine, serotonin, or neuritic outgrowth genetic pathways in ADHD symptom severity.

Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children

Phonological awareness is the best-validated predictor of reading and spelling skill and therefore highly relevant for developmental dyslexia. Prior imaging genetics studies link several dyslexia risk genes to either brain-functional or brain-structural factors of phonological deficits. However, coherent evidence for genetic associations with both functional and structural neural phenotypes underlying variation in phonological awareness has not yet been provided. Here we demonstrate that rs11100040, a reported modifier of SLC2A3, is related to the functional connectivity of left fronto-temporal phonological processing areas at resting state in a sample of 9- to 12-year-old children. Furthermore, we provide evidence that rs11100040 is related to the fractional anisotropy of the arcuate fasciculus, which forms the structural connection between these areas. This structural connectivity phenotype is associated with phonological awareness, which is in turn associated with the individual retrospective risk scores in an early dyslexia screening as well as to spelling. These results suggest a link between a dyslexia risk genotype and a functional as well as a structural neural phenotype, which is associated with a phonological awareness phenotype. The present study goes beyond previous work by integrating genetic, brain-functional and brain-structural aspects of phonological awareness within a single approach. These combined findings might be another step towards a multimodal biomarker for developmental dyslexia.

FVGWAS: Fast voxelwise genome wide association analysis of large-scale imaging genetic data

More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing genome-wide (NC>12 million known variants) associations with signals at millions of locations (NV~106) in the brain from thousands of subjects (n~103). The aim of this paper is to develop a Fast Voxelwise Genome Wide Association analysiS (FVGWAS) framework to efficiently carry out whole-genome analyses of whole-brain data. FVGWAS consists of three components including a heteroscedastic linear model, a global sure independence screening (GSIS) procedure, and a detection procedure based on wild bootstrap methods. Specifically, for standard linear association, the computational complexity is O (nNVNC) for voxelwise genome wide association analysis (VGWAS) method compared with O ((NC+NV)n2) for FVGWAS. Simulation studies show that FVGWAS is an efficient method of searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. Finally, we have successfully applied FVGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 193,275voxels in RAVENS maps, and 501,584 SNPs, and the total processing time was 203,645s for a single CPU. Our FVGWAS may be a valuable statistical toolbox for large-scale imaging genetic analysis as the field is rapidly advancing with ultra-high-resolution imaging and whole-genome sequencing.

The OPTIMISE data project: toward improving multiple sclerosis treatment

BIOGRAPHY Paul Matthews speaks to Ellen Clarke, Commissioning Editor: Paul Matthews, OBE, MD, DPhil, FRCP, FMedSci is Head of the new Division of Brain Sciences at Imperial College, London. He was the founding Director of two leading research imaging centers, the University of Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB) and of GlaxoSmithKline's Clinical Imaging Centre. From 2005 to 2014 he was Vice President of GlaxoSmithKline Medicines Discovery and Development. Professor Matthews’ research has extended applications of advanced imaging methods to answer a new spectrum of clinical research questions. He worked with Oxford colleagues in applications developments for advanced structural and functional brain imaging incorporated into the open access FSL software distributed by the FMRIB Centre, now one of the two most widely available image analysis software ‘toolboxes’ worldwide. While at Imperial and GSK, his group piloted approaches extending these methods for the first controlled, prospectively designed imaging genetics studies. He has been Chair of the Imaging Working Group for UK Biobank over the last 5 years, which is pioneering an ambitious program for very large population imaging as part of the UK Biobank. MRI scanning of the brain, heart and body, along with DEXA and 3D carotid ultrasound, was initiated in an imaging center at UK Biobank's Cheadle site in May 2014. A focus has been to address the challenge of neurodegeneration in multiple sclerosis and enhancing intrinsic brain repair and plasticity for functional recovery. More recent collaborative work with Imanova Ltd builds on studies of the genetics and pharmacology of the latest generation of PET microglial imaging agents to relate microglial activation and neurodegeneration in vivo. Professor Matthews received an OBE in 2008 for services to neuroscience. He was elected to the Academy of Medical Sciences in 2014.