Structural Neuroimaging Genetics Interactions in Alzheimer's Disease.

This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer's disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferior temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.

Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment.

BACKGROUND AND PURPOSE: This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers. METHODS: Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses. RESULTS: The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction. CONCLUSIONS: These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.

Gene interactions and structural brain change in early-onset Alzheimer's disease subjects using the pipeline environment.

OBJECTIVE: This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers. METHODS: Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers. RESULTS: We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area). CONCLUSION: We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.

The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools.

The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.

Irritable bowel syndrome in female patients is associated with alterations in structural brain networks.

Alterations in gray matter (GM) density/volume and cortical thickness (CT) have been demonstrated in small and heterogeneous samples of subjects with differing chronic pain syndromes, including irritable bowel syndrome (IBS). Aggregating across 7 structural neuroimaging studies conducted at University of California, Los Angeles, Los Angeles, CA, USA, between August 2006 and April 2011, we examined group differences in regional GM volume in 201 predominantly premenopausal female subjects (82 IBS, mean age: 32±10 SD, 119 healthy controls [HCs], 30±10 SD). Applying graph theoretical methods and controlling for total brain volume, global and regional properties of large-scale structural brain networks were compared between the group with IBS and the HC group. Relative to HCs, the IBS group had lower volumes in the bilateral superior frontal gyrus, bilateral insula, bilateral amygdala, bilateral hippocampus, bilateral middle orbital frontal gyrus, left cingulate, left gyrus rectus, brainstem, and left putamen. Higher volume was found in the left postcentral gyrus. Group differences were no longer significant for most regions when controlling for the Early Trauma Inventory global score, with the exception of the right amygdala and the left postcentral gyrus. No group differences were found for measures of global and local network organization. Compared to HCs, in patients with IBS, the right cingulate gyrus and right thalamus were identified as being significantly more critical for information flow. Regions involved in endogenous pain modulation and central sensory amplification were identified as network hubs in IBS. Overall, evidence for central alterations in patients with IBS was found in the form of regional GM volume differences and altered global and regional properties of brain volumetric networks.

Sex-related differences of cortical thickness in patients with chronic abdominal pain.

BACKGROUND & AIMS: Regional reductions in gray matter (GM) have been reported in several chronic somatic and visceral pain conditions, including irritable bowel syndrome (IBS) and chronic pancreatitis. Reported GM reductions include insular and anterior cingulate cortices, even though subregions are generally not specified. The majority of published studies suffer from limited sample size, heterogeneity of populations, and lack of analyses for sex differences. We aimed to characterize regional changes in cortical thickness (CT) in a large number of well phenotyped IBS patients, taking into account the role of sex related differences. METHODS: Cortical GM thickness was determined in 266 subjects (90 IBS [70 predominantly premenopausal female] and 176 healthy controls (HC) [155 predominantly premenopausal female]) using the Laboratory of Neuro Imaging (LONI) Pipeline. A combined region of interest (ROI) and whole brain approach was used to detect any sub-regional and vertex-level differences after removing effects of age and total GM volume. Correlation analyses were performed on behavioral data. RESULTS: While IBS as a group did not show significant differences in CT compared to HCs, sex related differences were observed both within the IBS and the HC groups. When female IBS patients were compared to female HCs, whole brain analysis showed significant CT increase in somatosensory and primary motor cortex, as well as CT decrease in bilateral subgenual anterior cingulate cortex (sgACC). The ROI analysis showed significant regional CT decrease in bilateral subregions of insular cortex, while CT decrease in cingulate was limited to left sgACC, accounting for the effect of age and GM volume. Several measures of IBS symptom severity showed significant correlation with CT changes in female IBS patients. CONCLUSIONS: Significant, sex related differences in CT are present in both HCs and in IBS patients. The biphasic neuroplastic changes in female IBS patients are related to symptom severity.

Spatial-temporal atlas of human fetal brain development during the early second trimester.

During the second trimester, the human fetal brain undergoes numerous changes that lead to substantial variation in the neonatal in terms of its morphology and tissue types. As fetal MRI is more and more widely used for studying the human brain development during this period, a spatiotemporal atlas becomes necessary for characterizing the dynamic structural changes. In this study, 34 postmortem human fetal brains with gestational ages ranging from 15 to 22 weeks were scanned using 7.0 T MR. We used automated morphometrics, tensor-based morphometry and surface modeling techniques to analyze the data. Spatiotemporal atlases of each week and the overall atlas covering the whole period with high resolution and contrast were created. These atlases were used for the analysis of age-specific shape changes during this period, including development of the cerebral wall, lateral ventricles, Sylvian fissure, and growth direction based on local surface measurements. Our findings indicate that growth of the subplate zone is especially striking and is the main cause for the lamination pattern changes. Changes in the cortex around Sylvian fissure demonstrate that cortical growth may be one of the mechanisms for gyration. Surface deformation mapping, revealed by local shape analysis, indicates that there is global anterior-posterior growth pattern, with frontal and temporal lobes developing relatively quickly during this period. Our results are valuable for understanding the normal brain development trajectories and anatomical characteristics. These week-by-week fetal brain atlases can be used as reference in in vivo studies, and may facilitate the quantification of fetal brain development across space and time.

FKBP5 and attention bias for threat: associations with hippocampal function and shape.

IMPORTANCE: The FKBP5 gene product regulates glucocorticoid receptor (GR) sensitivity and hypothalamic-pituitary-adrenal axis functioning and has been associated with many stress-related psychiatric disorders. The study of intermediate phenotypes, such as emotion-processing biases and their neural substrates, provides a way to clarify the mechanisms by which FKBP5 dysregulation mediates risk for psychiatric disorders. OBJECTIVE: To examine whether allelic variations for a putatively functional single-nucleotide polymorphism associated with FKBP5 gene regulation (rs1360780) would relate differentially to attention bias for threat. this was measured through behavioral response on a dot probe task and hippocampal activation during task performance. Morphologic substrates of differential hippocampal response were also measured. DESIGN: Cross-sectional study conducted from 2010 to 2012 examining associations between genotype, behavioral response, and neural response (using functional magnetic resonance imaging [fMRI]) on the dot probe; voxel-based morphometry and global and local shape analyses were used to measure structural differences in hippocampi between genotype groups. SETTING: Participants were recruited from primary care clinics of a publicly funded hospital in Atlanta, Georgia. PARTICIPANTS: An African American cohort of adults (N = 103) was separated into 2 groups by genotype: one genotype group included carriers of the rs1360780 T allele, which has been associated with increased risk for posttraumatic stress disorder and affective disorders; the other group did not carry this allele. Behavioral data included both sexes (N = 103); the MRI cohort (n = 36) included only women. MAIN OUTCOME MEASURES: Behavioral and fMRI (blood oxygen level-dependent) response, voxel-based morphometry, and shape analyses. RESULTS: Carriers of the rs1360780 T allele showed an attention bias toward threat compared with individuals without this allele (F1,90 = 5.19, P = .02). Carriers of this allele demonstrated corresponding increases in hippocampal activation and differences in morphology; global and local shape analyses revealed alterations in hippocampal shape for TT/TC compared with CC genotype groups. CONCLUSION: Genetic variants of FKBP5 may be associated with risk for stress-related psychiatric disorders via differential effects on hippocampal structure and function, resulting in altered attention response to perceived threat.

Next generation sequence analysis and computational genomics using graphical pipeline workflows.

Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at http://pipeline.loni.ucla.edu. These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders.

Applications of the pipeline environment for visual informatics and genomics computations.

BACKGROUND: Contemporary informatics and genomics research require efficient, flexible and robust management of large heterogeneous data, advanced computational tools, powerful visualization, reliable hardware infrastructure, interoperability of computational resources, and detailed data and analysis-protocol provenance. The Pipeline is a client-server distributed computational environment that facilitates the visual graphical construction, execution, monitoring, validation and dissemination of advanced data analysis protocols. RESULTS: This paper reports on the applications of the LONI Pipeline environment to address two informatics challenges - graphical management of diverse genomics tools, and the interoperability of informatics software. Specifically, this manuscript presents the concrete details of deploying general informatics suites and individual software tools to new hardware infrastructures, the design, validation and execution of new visual analysis protocols via the Pipeline graphical interface, and integration of diverse informatics tools via the Pipeline eXtensible Markup Language syntax. We demonstrate each of these processes using several established informatics packages (e.g., miBLAST, EMBOSS, mrFAST, GWASS, MAQ, SAMtools, Bowtie) for basic local sequence alignment and search, molecular biology data analysis, and genome-wide association studies. These examples demonstrate the power of the Pipeline graphical workflow environment to enable integration of bioinformatics resources which provide a well-defined syntax for dynamic specification of the input/output parameters and the run-time execution controls. CONCLUSIONS: The LONI Pipeline environment http://pipeline.loni.ucla.edu provides a flexible graphical infrastructure for efficient biomedical computing and distributed informatics research. The interactive Pipeline resource manager enables the utilization and interoperability of diverse types of informatics resources. The Pipeline client-server model provides computational power to a broad spectrum of informatics investigators--experienced developers and novice users, user with or without access to advanced computational-resources (e.g., Grid, data), as well as basic and translational scientists. The open development, validation and dissemination of computational networks (pipeline workflows) facilitates the sharing of knowledge, tools, protocols and best practices, and enables the unbiased validation and replication of scientific findings by the entire community.

The link between callosal thickness and intelligence in healthy children and adolescents.

The link between brain structure and intelligence is a well-investigated topic, but existing analyses have mainly focused on adult samples. Studies in healthy children and adolescents are rare, and normative data specifically addressing the association between corpus callosum morphology and intellectual abilities are quite limited. To advance this field of research, we mapped the correlations between standardized intelligence measures and callosal thickness based on high-resolution magnetic resonance imaging (MRI) data. Our large and well-matched sample included 200 normally developing subjects (100 males, 100 females) ranging from 6 to 17 years of age. Although the strongest correlations were negative and confined to the splenium, the strength and the direction of intelligence-callosal thickness associations varied considerably. While significant correlations in females were mainly positive, significant correlations in males were exclusively negative. However, only the negative correlations in the overall sample (i.e., males and females combined) remained significant when controlling for multiple comparisons. The observed negative correlations between callosal thickness and intelligence in children and adolescents contrast with the positive correlations typically reported in adult samples. However, negative correlations are in line with reports from other pediatric studies relating cognitive measures to other brain attributes such as cortical thickness, gray matter volume, and gray matter density. Altogether, these findings suggest that relationships between callosal morphology and cognition are highly dynamic during brain maturation. Sex effects on links between callosal thickness and intelligence during childhood and adolescence are present but appear rather weak in general.

Cortical sulcal atlas construction using a diffeomorphic mapping approach.

We present a geometric approach for constructing shape atlases of sulcal curves on the human cortex. Sulci and gyri are represented as continuous open curves in R3, and their shapes are studied as elements of an infinite-dimensional sphere. This shape manifold has some nice properties--it is equipped with a Riemannian L2 metric on the tangent space and facilitates computational analyses and correspondences between sulcal shapes. Sulcal mapping is achieved by computing geodesics in the quotient space of shapes modulo rigid rotations and reparameterizations. The resulting sulcal shape atlas is shown to preserve important local geometry inherently present in the sample population. This is demonstrated in our experimental results for deep brain sulci, where we integrate the elastic shape model into surface registration framework for a population of 69 healthy young adult subjects.

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline.

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges--management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu.