No abstract available

Alterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks.

Though it is widely appreciated that complex structural, functional and morphological relationships exist between distinct areas of the human cerebral cortex, the extent to which such relationships coincide remains insufficiently appreciated. Here we determine the extent to which correlations between brain regions are modulated by either structural, connectomic or network-theoretic properties using a structural neuroimaging data set of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) volumes acquired from N=110 healthy human adults. To identify the linear relationships between all available pairs of regions, we use canonical correlation analysis to test whether a statistically significant correlation exists between each pair of cortical parcels as quantified via structural, connectomic or network-theoretic measures. In addition to this, we investigate (1) how each group of canonical variables (whether structural, connectomic or network-theoretic) contributes to the overall correlation and, additionally, (2) whether each individual variable makes a significant contribution to the test of the omnibus null hypothesis according to which no correlation between regions exists across subjects. We find that, although region-to-region correlations are extensively modulated by structural and connectomic measures, there are appreciable differences in how these two groups of measures drive inter-regional correlation patterns. Additionally, our results indicate that the network-theoretic properties of the cortex are strong modulators of region-to-region covariance. Our findings are useful for understanding the structural and connectomic relationship between various parts of the brain, and can inform theoretical and computational models of cortical information processing.

Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy.

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.

Autism spectrum disorder (ASD) encompasses a set of neurodevelopmental conditions whose striking sex-related disparity (with an estimated male-to-female ratio of 4:1) remains unknown. Here we use magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) to identify the brain structure correlates of the sex-by-ASD diagnosis interaction in a carefully selected cohort of 110 ASD patients (55 females) and 83 typically-developing (TD) subjects (40 females). The interaction was found to be predicated primarily upon white matter connectivity density innervating, bilaterally, the lateral aspect of the temporal lobe, the temporo-parieto-occipital junction and the medial parietal lobe. By contrast, regional gray matter (GM) thickness and volume are not found to modulate this interaction significantly. When interpreted in the context of previous studies, our findings add considerable weight to three long-standing hypotheses according to which the sex disparity of ASD incidence is (A) due to WM connectivity rather than to GM differences, (B) modulated to a large extent by temporoparietal connectivity, and (C) accompanied by brain function differences driven by these effects. Our results contribute substantially to the task of unraveling the biological mechanisms giving rise to the sex disparity in ASD incidence, whose clinical implications are significant.

The neural processes that support inhibitory control in the face of stimuli with a history of reward association are not yet well understood. Yet, the ability to flexibly adapt behavior to changing reward-contingency contexts is important for daily functioning and warrants further investigation. This study aimed to characterize neural and behavioral impacts of stimuli with a history of conditioned reward association on motor inhibitory control in healthy young adults by investigating group-level effects as well as individual variation in the ability to inhibit responses to stimuli with a reward history. Participants (N = 41) first completed a reward conditioning phase, during which responses to rewarded stimuli were associated with money and responses to unrewarded stimuli were not. Rewarded and unrewarded stimuli from training were carried forward as No-Go targets in a subsequent go/no-go task to test the effect of reward history on inhibitory control. Participants underwent functional brain imaging during the go/no-go portion of the task. On average, a history of reward conditioning disrupted inhibitory control. Compared to inhibition of responses to stimuli with no reward history, trials that required inhibition of responses to previously rewarded stimuli were associated with greater activity in frontal and striatal regions, including the inferior frontal gyrus, insula, striatum, and thalamus. Activity in the insula and thalamus during false alarms and in the ventromedial prefrontal cortex during correctly withheld trials predicted behavioral performance on the task. Overall, these results suggest that reward history serves to disrupt inhibitory control and provide evidence for diverging roles of the insula and ventromedial prefrontal cortex while inhibiting responses to stimuli with a reward history.

The claustrum seems to have been waiting for the science of connectomics. Due to its tiny size, the structure has remained remarkably difficult to study until modern technological and mathematical advancements like graph theory, connectomics, diffusion tensor imaging, HARDI, and excitotoxic lesioning. That does not mean, however, that early methods allowed researchers to assess micro-connectomics. In fact, the claustrum is such an enigma that the only things known for certain about it are its histology, and that it is extraordinarily well connected. In this literature review, we provide background details on the claustrum and the history of its study in the human and in other animal species. By providing an explanation of the neuroimaging and histology methods have been undertaken to study the claustrum thus far-and the conclusions these studies have drawn-we illustrate this example of how the shift from micro-connectomics to macro-connectomics advances the field of neuroscience and improves our capacity to understand the brain.

The LONI Pipeline is a graphical environment for construction, validation and execution of advanced neuroimaging data analysis protocols (Rex et al., 2003). It enables automated data format conversion, allows Grid utilization, facilitates data provenance, and provides a significant library of computational tools. There are two main advantages of the LONI Pipeline over other graphical analysis workflow architectures. It is built as a distributed Grid computing environment and permits efficient tool integration, protocol validation and broad resource distribution. To integrate existing data and computational tools within the LONI Pipeline environment, no modification of the resources themselves is required. The LONI Pipeline provides several types of process submissions based on the underlying server hardware infrastructure. Only workflow instructions and references to data, executable scripts and binary instructions are stored within the LONI Pipeline environment. This makes it portable, computationally efficient, distributed and independent of the individual binary processes involved in pipeline data-analysis workflows. We have expanded the LONI Pipeline (V.4.2) to include server-to-server (peer-to-peer) communication and a 3-tier failover infrastructure (Grid hardware, Sun Grid Engine/Distributed Resource Management Application API middleware, and the Pipeline server). Additionally, the LONI Pipeline provides three layers of background-server executions for all users/sites/systems. These new LONI Pipeline features facilitate resource-interoperability, decentralized computing, construction and validation of efficient and robust neuroimaging data-analysis workflows. Using brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), we demonstrate integration of disparate resources, graphical construction of complex neuroimaging analysis protocols and distributed parallel computing. The LONI Pipeline, its features, specifications, documentation and usage are available online (http://Pipeline.loni.ucla.edu).

Brain connectivity loss due to traumatic brain injury, stroke or multiple sclerosis can have serious consequences on life quality and a measurable impact upon neural and cognitive function. Though brain network properties are known to be affected disproportionately by injuries to certain gray matter regions, the manner in which white matter (WM) insults affect such properties remains poorly understood. Here, network-theoretic analysis allows us to identify the existence of a macroscopic neural connectivity core in the adult human brain which is particularly sensitive to network lesioning. The systematic lesion analysis of brain connectivity matrices from diffusion neuroimaging over a large sample (N = 110) reveals that the global vulnerability of brain networks can be predicated upon the extent to which injuries disrupt this connectivity core, which is found to be quite distinct from the set of connections between rich club nodes in the brain. Thus, in addition to connectivity within the rich club, the brain as a network also contains a distinct core scaffold of network edges consisting of WM connections whose damage dramatically lowers the integrative properties of brain networks. This pattern of core WM fasciculi whose injury results in major alterations to overall network integrity presents new avenues for clinical outcome prediction following brain injury by relating lesion locations to connectivity core disruption and implications for recovery. The findings of this study contribute substantially to current understanding of the human WM connectome, its sensitivity to injury, and clarify a long-standing debate regarding the relative prominence of gray vs. WM regions in the context of brain structure and connectomic architecture.

The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing a magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnectomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.

Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder.

During adolescence, rapid development and reorganization of the dopaminergic system supports increasingly sophisticated reward learning and the ability to exert behavioral control. Disruptions in the ability to exert control over previously rewarded behavior may underlie some forms of adolescent psychopathology. Specifically, symptoms of externalizing psychopathology may be associated with difficulties in flexibly adapting behavior in the context of reward. However, the direct interaction of cognitive control and reward learning in adolescent psychopathology symptoms has not yet been investigated. The present study used a Research Domain Criteria framework to investigate whether behavioral and neuronal indices of inhibition to previously rewarded stimuli underlie individual differences in externalizing symptoms in N = 61 typically developing adolescents. Using a task that integrates the Monetary Incentive Delay and Go-No-Go paradigms, we observed a positive association between externalizing symptoms and activation of the left middle frontal gyrus during response inhibition to cues with a history of reward. These associations were robust to controls for internalizing symptoms and neural recruitment during inhibition of cues with no reward history. Our findings suggest that inhibitory control over stimuli with a history of reward may be a useful marker for future inquiry into the development of externalizing psychopathology in adolescence.

A fundamental tenet in the field of developmental neuroscience is that brain maturation generally proceeds from posterior/inferior to anterior/superior. This pattern is thought to underlie the similar timing of cognitive development in related domains, with the dorsal frontal cortices-important for decision making and cognitive control-the last to fully mature. While this caudal to rostral wave of structural development was first qualitatively described for white matter in classical postmortem studies, and has been discussed frequently in the developmental neuroimaging literature and in the popular press, it has never been formally demonstrated continuously and quantitatively across the whole brain with magnetic resonance imaging (MRI). Here we use diffusion imaging to map developmental changes in the white matter in 32 typically-developing individuals age 5-28 years. We then employ a novel meta-statistic that is sensitive to the timing of this developmental trajectory, and use this integrated strategy to both confirm these long-postulated broad regional gradients in the timing of white matter maturation in vivo, and demonstrate a surprisingly smooth transition in the timing of white matter maturational peaks along a caudal-rostral arc in this cross-sectional sample. These results provide further support for the notion of continued plasticity in these regions well into adulthood, and may provide a new approach for the investigation of neurodevelopmental disorders that could alter the timing of this typical developmental sequence.

Brain connectomics research has rapidly expanded using functional MRI (fMRI) and diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a connectivity matrix (CM). A CM stores the connection strength between any two regions ("nodes") in a brain network. This format is useful for several reasons: (1) it is highly distilled, with minimal data size and complexity, (2) graph theory can be applied to characterize the network's topology, and (3) it retains sufficient information to capture individual differences such as age, gender, intelligence quotient (IQ), or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI) and disorders (Alzheimer's, autism, Attention Deficit Hyperactive Disorder). To demonstrate the site's functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects' (ages 26-45) resting state fMRI (rs-fMRI) and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta-analyses comparing networks across imaging modality, age group, and disease state.

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 Alzheimer's and Parkinson's data, we provide several examples of translational applications using this infrastructure.

Provenance, the description of the history of a set of data, has grown more important with the proliferation of research consortia-related efforts in neuroimaging. Knowledge about the origin and history of an image is crucial for establishing data and results quality; detailed information about how it was processed, including the specific software routines and operating systems that were used, is necessary for proper interpretation, high fidelity replication and re-use. We have drafted a mechanism for describing provenance in a simple and easy to use environment, alleviating the burden of documentation from the user while still providing a rich description of an image's provenance. This combination of ease of use and highly descriptive metadata should greatly facilitate the collection of provenance and subsequent sharing of data.

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.

Autism spectrum disorder (ASD) is more prevalent in males than in females, but the neurobiological mechanisms that give rise to this sex-bias are poorly understood. The female protective hypothesis suggests that the manifestation of ASD in females requires higher cumulative genetic and environmental risk relative to males. Here, we test this hypothesis by assessing the additive impact of several ASD-associated OXTR variants on reward network resting-state functional connectivity in males and females with and without ASD, and explore how genotype, sex, and diagnosis relate to heterogeneity in neuroendophenotypes. Females with ASD who carried a greater number of ASD-associated risk alleles in the OXTR gene showed greater functional connectivity between the nucleus accumbens (NAcc; hub of the reward network) and subcortical brain areas important for motor learning. Relative to males with ASD, females with ASD and higher OXTR risk-allele-dosage showed increased connectivity between the NAcc, subcortical regions, and prefrontal brain areas involved in mentalizing. This increased connectivity between NAcc and prefrontal cortex mirrored the relationship between genetic risk and brain connectivity observed in neurotypical males showing that, under increased OXTR genetic risk load, females with ASD and neurotypical males displayed increased connectivity between reward-related brain regions and prefrontal cortex. These results indicate that females with ASD differentially modulate the effects of increased genetic risk on brain connectivity relative to males with ASD, providing new insights into the neurobiological mechanisms through which the female protective effect may manifest.

A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.