Sam Hobel

Irritable Bowel Syndrome in female patients is associated with alterations in structural brain networks

Summary Compared to healthy females, females with chronic visceral pain show alterations in regional gray matter volume and network properties of large‐scale structural brain networks. ABSTRACT 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.

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.

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.

Coiling and maturation of a high-performance fibre in hagfish slime gland thread cells

The defensive slime of hagfishes contains thousands of intermediate filament protein threads that are manufactured within specialized gland thread cells. The material properties of these threads rival those of spider dragline silks, which makes them an ideal model for biomimetic efforts to produce sustainable protein materials, yet how the thread is produced and organized within the cell is not well understood. Here we show how changes in nuclear morphology, size and position can explain the three-dimensional pattern of thread coiling in gland thread cells, and how the ultrastructure of the thread changes as very young thread cells develop into large cells with fully mature coiled threads. Our model provides an explanation for the complex process of thread assembly and organization that has fascinated and perplexed biologists for over a century, and provides valuable insights for the quest to manufacture high-performance biomimetic protein materials.

High-throughput neuroimaging-genetics computational infrastructure.

Many contemporary neuroscientific investigations face significant challenges in terms of data management, computational processing, data mining, and results interpretation. These four pillars define the core infrastructure necessary to plan, organize, orchestrate, validate, and disseminate novel scientific methods, computational resources, and translational healthcare findings. Data management includes protocols for data acquisition, archival, query, transfer, retrieval, and aggregation. Computational processing involves the necessary software, hardware, and networking infrastructure required to handle large amounts of heterogeneous neuroimaging, genetics, clinical, and phenotypic data and meta-data. Data mining refers to the process of automatically extracting data features, characteristics and associations, which are not readily visible by human exploration of the raw dataset. Result interpretation includes scientific visualization, community validation of findings and reproducible findings. In this manuscript we describe the novel high-throughput neuroimaging-genetics computational infrastructure available at the Institute for Neuroimaging and Informatics (INI) and the Laboratory of Neuro Imaging (LONI) at University of Southern California (USC). INI and LONI include ultra-high-field and standard-field MRI brain scanners along with an imaging-genetics database for storing the complete provenance of the raw and derived data and meta-data. In addition, the institute provides a large number of software tools for image and shape analysis, mathematical modeling, genomic sequence processing, and scientific visualization. A unique feature of this architecture is the Pipeline environment, which integrates the data management, processing, transfer, and visualization. Through its client-server architecture, the Pipeline environment provides a graphical user interface for designing, executing, monitoring validating, and disseminating of complex protocols that utilize diverse suites of software tools and web-services. These pipeline workflows are represented as portable XML objects which transfer the execution instructions and user specifications from the client user machine to remote pipeline servers for distributed computing. Using Alzheimers and Parkinsons data, we provide several examples of translational applications using this infrastructure1.

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.

Structural Neuroimaging Genetics Interactions in Alzheimer’s Disease

This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimers Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimers 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.

This study investigates 36 subjects aged 55‐65 from the Alzheimers Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early‐onset (EO) Alzheimers Disease (EO‐AD) using neuroimaging biomarkers.

Su1963 Cortical Thinning in Female Patients With Irritable Bowel Syndrome

Background. Converging evidence suggests that increased attention to afferent signals from the gut plays an important role in the increased perceptual response to visceral stimuli reported in irritable bowel syndrome (IBS). We hypothesize that prefrontal circuitry controlling attention to threat-related stimuli is less efficient in IBS patients compared to healthy controls (HCs). Aims. To quantify and compare the efficiency of cognitive control regions underlying selective attention to threat-related stimuli between IBS patients and HCs, using a GI symptom unrelated paradigm. Methods. We measured brain response (Siemens 3 Tesla Trio MRI scanner) in 32 females (16 IBS patients, 16 HCs) during administration of the house face matching task, a cognitive task developed and validated to test selective attention to threatening stimuli as well as index processing of task-irrelevant threat stimuli. Subjects were presented with pictures of pairs of houses and faces (fearful or neutral) arranged in a vertical or horizontal orientation around a central fixation cross. During the task subjects were asked to match either houses or faces.. SPM8 was employed to preprocess and analyze the imaging data using the general linear model and a region of interest analysis examining cognitive control and emotional arousal regions. Results were considered significant at p<.05, corrected using family wise error. Results. No significant group differences were observed for reaction time or accuracy on the house face matching task. However, when examining brain responses to viewing attended fearful versus attended neutral faces (AF-AN), IBS patients exhibited significantly greater brain activation compared with HCs in prefrontal control regions [dorso and ventrolateral prefrontal cortex (PFC), medial PFC] as well as in dorsal anterior insula (INS) and left anterior mid-cingulate cortex (MCC). Within group analysis indicated activations in these regions for IBS, but not for HCs, the latter showing deactivation of the ventrolateral PFC, left anterior INS and hippocampus. HCs also showed significant activation in the L middle and posterior INS. Conclusions. In response to an IBS symptom unrelated paradigm (attended fearful versus neutral faces), IBS show greater activity in prefrontal control regions [lPFC, medial PFC) as well as in dorsal anterior insula and left anterior mid-cingulate cortex. Results suggest greater recruitment of cognitive control mechanisms during presentation of emotionally salient or threat-related stimuli in IBS patients. Grants: K08 DK071626, R03 DK084169 (JSL) ,K23DK073451, R01 AT007137KT),T32 DK007180(CH),P50 DK064539, R24 AT002681, R01 DK048351(EAM)

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 Alzheimers 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 (Alzheimers 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.