Improving the analysis, storage and sharing of neuroimaging data using relational databases and distributed computing

ABrIL - Advanced Brain Imaging Lab : a cloud based computation environment for cooperative neuroimaging projects

Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.

Provision of an integrated data analysis platform for computational neuroscience experiments

Purpose

– The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

Design/methodology/approach

– The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

Findings

– The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

Originality/value

– The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

Interhemispheric functional connectivity following prenatal or perinatal brain injury predicts receptive language outcome

Early brain injury alters both structural and functional connectivity between the cerebral hemispheres. Despite increasing knowledge on the individual hemispheric contributions to recovery from such injury, we know very little about how their interactions affect this process. In the present study, we related interhemispheric structural and functional connectivity to receptive language outcome following early left hemisphere stroke. We used functional magnetic resonance imaging to study 14 people with neonatal brain injury, and 25 age-matched controls during passive story comprehension. With respect to structural connectivity, we found that increased volume of the corpus callosum predicted good receptive language outcome, but that this is not specific to people with injury. In contrast, we found that increased posterior superior temporal gyrus interhemispheric functional connectivity during story comprehension predicted better receptive language performance in people with early brain injury, but worse performance in typical controls. This suggests that interhemispheric functional connectivity is one potential compensatory mechanism following early injury. Further, this pattern of results suggests refinement of the prevailing notion that better language outcome following early left hemisphere injury relies on the contribution of the contralesional hemisphere (i.e., the "right-hemisphere-take-over" theory). This pattern of results was also regionally specific; connectivity of the angular gyrus predicted poorer performance in both groups, independent of brain injury. These results present a complex picture of recovery, and in some cases, such recovery relies on increased cooperation between the injured hemisphere and homologous regions in the contralesional hemisphere, but in other cases, the opposite appears to hold.

An Ontology-Based Collaborative Inter-Organisational Knowledge Management Network (CIK-NET)

Ontologies are widely used to represent knowledge explicitly but it is impractical to expect all individuals and organisations to agree on using one or a small subset of ontologies. The adoption of multiple ontologies causes ontology mismatches which make their inherent vocabularies and relationships become inconsistent, resulting in difficulty for one system to understand and reuse these ontologies. To achieve knowledge sharing and reuse, ontology mediation is required to reconcile mismatches between heterogeneous ontologies. In this paper, we investigate the application of ontology in knowledge management (KM). Many KM approaches have been developed with the purpose of managing organisational knowledge. However, these approaches only focus on managing intra-organisational knowledge, which is inadequate in current business environment because users are often required to access inter-organisational knowledge to complete their tasks. These approaches also fail to collaborate with each other as their designs are based on their own business and KM requirement in managing organisational knowledge. We argue that ontology and its mediation methods can be used to overcome limitation of non-collaborative problem in which individual organisation is unable to reuse inter-organisational knowledge. An ontology-based inter-organisational KM network is therefore proposed to allow organisations accessing and retrieving inter-organisational knowledge of common domain.

LORIS: a web-based data management system for multi-center studies

Longitudinal Online Research and Imaging System (LORIS) is a modular and extensible web-based data management system that integrates all aspects of a multi-center study: from heterogeneous data acquisition (imaging, clinical, behavior, and genetics) to storage, processing, and ultimately dissemination. It provides a secure, user-friendly, and streamlined platform to automate the flow of clinical trials and complex multi-center studies. A subject-centric internal organization allows researchers to capture and subsequently extract all information, longitudinal or cross-sectional, from any subset of the study cohort. Extensive error-checking and quality control procedures, security, data management, data querying, and administrative functions provide LORIS with a triple capability (1) continuous project coordination and monitoring of data acquisition (2) data storage/cleaning/querying, (3) interface with arbitrary external data processing “pipelines.” LORIS is a complete solution that has been thoroughly tested through a full 10 year life cycle of a multi-center longitudinal project¹ and is now supporting numerous international neurodevelopment and neurodegeneration research projects.

Gesture in the developing brain

Speakers convey meaning not only through words, but also through gestures. Although children are exposed to co-speech gestures from birth, we do not know how the developing brain comes to connect meaning conveyed in gesture with speech. We used functional magnetic resonance imaging (fMRI) to address this question and scanned 8- to 11-year-old children and adults listening to stories accompanied by hand movements, either meaningful co-speech gestures or meaningless self-adaptors. When listening to stories accompanied by both types of hand movement, both children and adults recruited inferior frontal, inferior parietal, and posterior temporal brain regions known to be involved in processing language not accompanied by hand movements. There were, however, age-related differences in activity in posterior superior temporal sulcus (STSp), inferior frontal gyrus, pars triangularis (IFGTr), and posterior middle temporal gyrus (MTGp) regions previously implicated in processing gesture. Both children and adults showed sensitivity to the meaning of hand movements in IFGTr and MTGp, but in different ways. Finally, we found that hand movement meaning modulates interactions between STSp and other posterior temporal and inferior parietal regions for adults, but not for children. These results shed light on the developing neural substrate for understanding meaning contributed by co-speech gesture.

The informatics core of the Alzheimer's Disease Neuroimaging Initiative

The Alzheimer's Diseases Neuroimaging Initiative project has brought together geographically distributed investigators, each collecting data on the progression of Alzheimer's disease. The quantity and diversity of the imaging, clinical, cognitive, biochemical, and genetic data acquired and generated throughout the study necessitated sophisticated informatics systems to organize, manage, and disseminate data and results. We describe, here, a successful and comprehensive system that provides powerful mechanisms for processing, integrating, and disseminating these data not only to support the research needs of the investigators who make up the Alzheimer's Diseases Neuroimaging Initiative cores, but also to provide widespread data access to the greater scientific community for the study of Alzheimer's Disease.

Neural development of networks for audiovisual speech comprehension

Everyday conversation is both an auditory and a visual phenomenon. While visual speech information enhances comprehension for the listener, evidence suggests that the ability to benefit from this information improves with development. A number of brain regions have been implicated in audiovisual speech comprehension, but the extent to which the neurobiological substrate in the child compares to the adult is unknown. In particular, developmental differences in the network for audiovisual speech comprehension could manifest through the incorporation of additional brain regions, or through different patterns of effective connectivity. In the present study we used functional magnetic resonance imaging and structural equation modeling (SEM) to characterize the developmental changes in network interactions for audiovisual speech comprehension. The brain response was recorded while children 8- to 11-years-old and adults passively listened to stories under audiovisual (AV) and auditory-only (A) conditions. Results showed that in children and adults, AV comprehension activated the same fronto-temporo-parietal network of regions known for their contribution to speech production and perception. However, the SEM network analysis revealed age-related differences in the functional interactions among these regions. In particular, the influence of the posterior inferior frontal gyrus/ventral premotor cortex on supramarginal gyrus differed across age groups during AV, but not A speech. This functional pathway might be important for relating motor and sensory information used by the listener to identify speech sounds. Further, its development might reflect changes in the mechanisms that relate visual speech information to articulatory speech representations through experience producing and perceiving speech.

Web-based multi-center data management system for clinical neuroscience research

Modern clinical research often involves multicenter studies, large and heterogeneous data flux, and intensive demands of collaboration, security and quality assurance. In the absence of commercial or academic management systems, we designed an open-source system to meet these requirements. Based on the Apache-PHP-MySQL platform on a Linux server, the system allows multiple users to access the database from any location on the internet using a web browser, and requires no specialized computer skills. Multi-level security system is implemented to safeguard the protected health information and allow partial or full access to the data by individual or class privilege. The system stores and manipulates various types of data including images, scanned documents, laboratory data and clinical ratings. Built-in functionality allows for various search, quality control, analytic data operations, visit scheduling and visit reminders. This approach offers a solution to a growing need for management of large multi-center clinical studies.

Co-speech gestures influence neural activity in brain regions associated with processing semantic information

Everyday communication is accompanied by visual information from several sources, including co-speech gestures, which provide semantic information listeners use to help disambiguate the speaker's message. Using fMRI, we examined how gestures influence neural activity in brain regions associated with processing semantic information. The BOLD response was recorded while participants listened to stories under three audiovisual conditions and one auditory-only (speech alone) condition. In the first audiovisual condition, the storyteller produced gestures that naturally accompany speech. In the second, the storyteller made semantically unrelated hand movements. In the third, the storyteller kept her hands still. In addition to inferior parietal and posterior superior and middle temporal regions, bilateral posterior superior temporal sulcus and left anterior inferior frontal gyrus responded more strongly to speech when it was further accompanied by gesture, regardless of the semantic relation to speech. However, the right inferior frontal gyrus was sensitive to the semantic import of the hand movements, demonstrating more activity when hand movements were semantically unrelated to the accompanying speech. These findings show that perceiving hand movements during speech modulates the distributed pattern of neural activation involved in both biological motion perception and discourse comprehension, suggesting listeners attempt to find meaning, not only in the words speakers produce, but also in the hand movements that accompany speech.

Grid infrastructures for computational neuroscience: the neuGRID example

Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid- and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer’s and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer’s disease imaging community, which will allow the collection and archiving of large amounts of imaging data coupled with Grid-based algorithms and sufficiently powered computational resources. The major benefit will be the faster discovery of new disease markers that will be valuable for earlier diagnosis and development of innovative drugs. The initial setup of neuGRID will feature three nodes equipped with supercomputer capabilities and resources of more than 300 processor cores, 300 GB of RAM memory and approximately 20 TB of physical space. The scope of this article is highlights the new perspectives and potential for the study of the neurodegenerative disorders using the emerging Grid technology.

Parallel workflows for data-driven structural equation modeling in functional neuroimaging

We present a computational framework suitable for a data-driven approach to structural equation modeling (SEM) and describe several workflows for modeling functional magnetic resonance imaging (fMRI) data within this framework. The Computational Neuroscience Applications Research Infrastructure (CNARI) employs a high-level scripting language called Swift, which is capable of spawning hundreds of thousands of simultaneous R processes (R Development Core Team, 2008), consisting of self-contained SEMs, on a high performance computing system (HPC). These self-contained R processing jobs are data objects generated by OpenMx, a plug-in for R, which can generate a single model object containing the matrices and algebraic information necessary to estimate parameters of the model. With such an infrastructure in place a structural modeler may begin to investigate exhaustive searches of the model space. Specific applications of the infrastructure, statistics related to model fit, and limitations are discussed in relation to exhaustive SEM. In particular, we discuss how workflow management techniques can help to solve large computational problems in neuroimaging.

Is it time to re-prioritize neuroimaging databases and digital repositories?

The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

Integration of Neuroimaging and Microarray Datasets through Mapping and Model-Theoretic Semantic Decomposition of Unstructured Phenotypes

An approach towards heterogeneous neuroscience dataset integration is proposed that uses Natural Language Processing (NLP) and a knowledge-based phenotype organizer system (PhenOS) to link ontology-anchored terms to underlying data from each database, and then maps these terms based on a computable model of disease (SNOMED CT(R)). The approach was implemented using sample datasets from fMRIDC, GEO, The Whole Brain Atlas and Neuronames and allowed for complex queries such as "List all disorders with a finding site of brain region X, and then find the semantically related references in all participating databases based on the ontological model of the disease or its anatomical and morphological attributes". Precision of the NLP-derived coding of the unstructured phenotypes in each dataset was 88% (n=50), and precision of the semantic mapping between these terms across datasets was 98% (n=100). To our knowledge, this is the first example of the use of both semantic decomposition of disease relationships and hierarchical information found in ontologies to integrate heterogeneous phenotypes across clinical and molecular datasets.

Gestures orchestrate brain networks for language understanding

Although the linguistic structure of speech provides valuable communicative information, nonverbal behaviors can offer additional, often disambiguating cues. In particular, being able to see the face and hand movements of a speaker facilitates language comprehension [1]. But how does the brain derive meaningful information from these movements? Mouth movements provide information about phonological aspects of speech [2-3]. In contrast, cospeech gestures display semantic information relevant to the intended message [4-6]. We show that when language comprehension is accompanied by observable face movements, there is strong functional connectivity between areas of cortex involved in motor planning and production and posterior areas thought to mediate phonological aspects of speech perception. In contrast, language comprehension accompanied by cospeech gestures is associated with tuning of and strong functional connectivity between motor planning and production areas and anterior areas thought to mediate semantic aspects of language comprehension. These areas are not tuned to hand and arm movements that are not meaningful. Results suggest that when gestures accompany speech, the motor system works with language comprehension areas to determine the meaning of those gestures. Results also suggest that the cortical networks underlying language comprehension, rather than being fixed, are dynamically organized by the type of contextual information available to listeners during face-to-face communication.

Brain dysfunction behind functional symptoms: neuroimaging and somatoform, conversive, and dissociative disorders

PURPOSE OF REVIEW

Neuroimaging research in psychiatry has been increasing exponentially in recent years, yet many psychiatrists are relatively unfamiliar with this field. This article summarizes the findings of the most relevant research articles on the neuroimaging of somatoform, conversive, and dissociative disorders published from January 2007 through June 2008.

RECENT FINDINGS

Neuroimaging findings summarized here include alterations of stress regulation and coping in somatoform pain disorders, the importance of catastrophizing in somatization disorder, and the relevance of a history of physical/sexual abuse in irritable bowel syndrome. Regarding fibromyalgia, three of the most significant advances have been the impossibility of differentiating primary and concomitant fibromyalgia in the presence of quiescent underlying disease, the role of hippocampal dysfunction, and the possibility that fibromyalgia may be characterized as an aging process. In dissociative disorders, the high levels of elaborative memory encoding and the reduced size of the parietal lobe are highlighted.

SUMMARY

The most promising clinical consequence of these studies, in addition to improving knowledge about the etiology of these illnesses, is the possibility of using neuroimaging findings to identify subgroups of patients, which could allow treatments to be tailored.

Towards an ontology for sharing medical images and regions of interest in neuroimaging

The goal of the NeuroBase project is to facilitate collaborative research in neuroimaging through a federated system based on semantic web technologies. The cornerstone and focus of this paper is the design of a common semantic model providing a unified view on all data and tools to be shared. For this purpose, we built a multi-layered and multi-components formal ontology. This paper presents two major contributions. The first is related to the general methodology we propose for building an application ontology based on consistent conceptualization choices provided by the DOLCE foundational ontology and core ontologies of domains that we reuse; the second concerns the domain ontology we designed for neuroimaging, which encompasses both the objective nature of image data and the subjective nature of image content, through annotations based on regions of interest made by agents (humans or computer programs). We report on realistic domain use-case queries referring to our application ontology.

Speech-associated gestures, Broca's area, and the human mirror system

Speech-associated gestures are hand and arm movements that not only convey semantic information to listeners but are themselves actions. Broca's area has been assumed to play an important role both in semantic retrieval or selection (as part of a language comprehension system) and in action recognition (as part of a "mirror" or "observation-execution matching" system). We asked whether the role that Broca's area plays in processing speech-associated gestures is consistent with the semantic retrieval/selection account (predicting relatively weak interactions between Broca's area and other cortical areas because the meaningful information that speech-associated gestures convey reduces semantic ambiguity and thus reduces the need for semantic retrieval/selection) or the action recognition account (predicting strong interactions between Broca's area and other cortical areas because speech-associated gestures are goal-direct actions that are "mirrored"). We compared the functional connectivity of Broca's area with other cortical areas when participants listened to stories while watching meaningful speech-associated gestures, speech-irrelevant self-grooming hand movements, or no hand movements. A network analysis of neuroimaging data showed that interactions involving Broca's area and other cortical areas were weakest when spoken language was accompanied by meaningful speech-associated gestures, and strongest when spoken language was accompanied by self-grooming hand movements or by no hand movements at all. Results are discussed with respect to the role that the human mirror system plays in processing speech-associated movements.