Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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The Scalable Brain Atlas: instant web-based access to public brain atlases and related content

The Scalable Brain Atlas (SBA) is a collection of web services that provide unified access to a large collection of brain atlas templates for different species. Its main component is an atlas viewer that displays brain atlas data as a stack of slices in which stereotaxic coordinates and brain regions can be selected. These are subsequently used to launch web queries to resources that require coordinates or region names as input. It supports plugins which run inside the viewer and respond when a new slice, coordinate or region is selected. It contains 20 atlas templates in six species, and plugins to compute coordinate transformations, display anatomical connectivity and fiducial points, and retrieve properties, descriptions, definitions and 3d reconstructions of brain regions. The ambition of SBA is to provide a unified representation of all publicly available brain atlases directly in the web browser, while remaining a responsive and light weight resource that specializes in atlas comparisons, searches, coordinate transformations and interactive displays.Comment: Rolf K\"otter sadly passed away on June 9th, 2010. He co-initiated this project and played a crucial role in the design and quality assurance of the Scalable Brain Atla

Automated Recognition of Brain Region Mentions in Neuroscience Literature

The ability to computationally extract mentions of neuroanatomical regions from the literature would assist linking to other entities within and outside of an article. Examples include extracting reports of connectivity or region-specific gene expression. To facilitate text mining of neuroscience literature we have created a corpus of manually annotated brain region mentions. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. The corpus is freely available at http://www.chibi.ubc.ca/WhiteText/

Collaborative development of the Arrowsmith two node search interface designed for laboratory investigators.

Arrowsmith is a unique computer-assisted strategy designed to assist investigators in detecting biologically-relevant connections between two disparate sets of articles in Medline. This paper describes how an inter-institutional consortium of neuroscientists used the UIC Arrowsmith web interface http://arrowsmith.psych.uic.edu in their daily work and guided the development, refinement and expansion of the system into a suite of tools intended for use by the wider scientific community

The Neuroterrain 3D Mouse Brain Atlas

A significant objective of neuroinformatics is the construction of tools to readily access, search, and analyze anatomical imagery. This goal can be subdivided into development of the necessary databases and of the computer vision tools for image analysis. When considering mesoscale images, the latter tools can be further divided into registration algorithms and anatomical models. The models are atlases that contain both bitmap images and templates of anatomical boundaries. We report here on construction of such a model for the C57BL/6J mouse. The intended purpose of this atlas is to aid in automated delineation of the Mouse Brain Library, a database of brain histological images of importance to neurogenetic research

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®). 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

The basal ganglia and thalamus of the long-tailed macaque in stereotaxic coordinates. A template atlas based on coronal, sagittal and horizontal brain sections

A stereotaxic brain atlas of the basal ganglia and thalamus of Macaca fascicularis presented here is designed with a surgical perspective. In this regard, all coordinates have been referenced to a line linking the anterior and posterior commissures (ac–pc line) and considering the center of the ac at the midline as the origin of the bicommissural space. The atlas comprises of 43 different plates (19 coronal levels, 10 sagittal levels and 14 horizontal levels). In addition to ‘classical’ cyto- and chemoarchitectural techniques such as the Nissl method and the acetylcholinesterase stain, several immunohistochemical stains have been performed in adjacent sections, including the detection of tyrosine hydroxylase, enkephalin, neurofilaments, parvalbumin and calbindin. In comparison to other existing stereotaxic atlases for M. fasicularis, this atlas has two main advantages: firstly, brain cartography is based on a wide variety of cyto- and chemoarchitectural stains carried out on adjacent sections, therefore enabling accurate segmentation. Secondly and most importantly, sagittal and horizontal planes are included. Sagittal planes are very useful for calculating oblique trajectories, whereas, clinical researchers engaged in neuroimaging studies will be more familiar with horizontal sections, as they use horizontal (also called “axial”) brain images in their daily routine of their clinical practices

Large-scale extraction of brain connectivity from the neuroscientific literature

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. Results: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against invivo connectivity data from ABA with an estimated precision of 78%. The resulting database contains over 4 million brain region mentions and over 100 000 (ABA) and 122 000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists. Availability and implementation: The resulting models are publicly available at github.com/BlueBrain/bluima. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Grid computing application for brain magnetic resonance image processing

This work emphasizes the use of grid computing and web technology for automatic postprocessing of brain magnetic resonance images (MRI) in the context of neuropsychiatric (Alzheimer’s disease) research. Post-acquisition image processing is achieved through the interconnection of several individual processes into pipelines. Each process has input and output data ports, options and execution parameters, and performs single tasks such as: a) extracting individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear registration), c) performing image statistical analyses, and d) producing the necessary quality control images and/or files for user review. The pipelines are built to perform specific sequences of tasks on the alphanumeric data and MRIs contained in our database. The web application is coded in PHP and allows the creation of scripts to create, store and execute pipelines and their instances either on our local cluster or on high-performance computing platforms. To run an instance on an external cluster, the web application opens a communication tunnel through which it copies the necessary files, submits the execution commands and collects the results. We present result on system tests for the processing of a set of 821 brain MRIs from the Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed of 10 processes. Our results show successful execution on both local and external clusters, and a 4- fold increase in performance if using the external cluster. However, the latter’s performance does not scale linearly as queue waiting times and execution overhead increase with the number of tasks to be executed