CiteULike: Author Ashburner

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns

Federico De Martino — 2008-08-20T16:34:52-00:00

NeuroImage, Vol. In Press, Corrected Proof

In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 × 2 × 2mm3) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

Barry Smith — 2007-11-08T02:21:16-00:00

Nat Biotech, Vol. 25, No. 11. (November 2007), pp. 1251-1255.

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

Chris Taylor — 2008-08-07T19:42:17-00:00

Nat Biotech, Vol. 26, No. 8. (2008), pp. 889-896.

Calling on a million minds for community annotation in WikiProteins

Barend Mons — 2008-05-28T10:54:00-00:00

Genome Biology, Vol. 9, No. 5. (2008)

WikiProteins enables Community Annotation in a Wiki-based system. Extracts of major data sources have been fused into an editable environment with a link out to the original sources. Data from Community edits create automatic copies of the original data. Semantic technology captures concepts co-occurring in one sentence and thus potential factual statements. In addition, indirect associations between concepts have been calculated. We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery.

Annotation of the Drosophila melanogaster euchromatic genome: a systematic review.

S Misra — 2008-08-12T12:49:03-00:00

Genome biology, Vol. 3, No. 12. (2002)

BACKGROUND: The recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences. RESULTS: Although the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes. CONCLUSIONS: Identification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations.

The genome sequence of Drosophila melanogaster.

MD Adams — 2005-05-09T16:56:36-00:00

Science, Vol. 287, No. 5461. (24 March 2000), pp. 2185-2195.

The fly Drosophila melanogaster is one of the most intensively studied organisms in biology and serves as a model system for the investigation of many developmental and cellular processes common to higher eukaryotes, including humans. We have determined the nucleotide sequence of nearly all of the approximately 120-megabase euchromatic portion of the Drosophila genome using a whole-genome shotgun sequencing strategy supported by extensive clone-based sequence and a high-quality bacterial artificial chromosome physical map. Efforts are under way to close the remaining gaps; however, the sequence is of sufficient accuracy and contiguity to be declared substantially complete and to support an initial analysis of genome structure and preliminary gene annotation and interpretation. The genome encodes approximately 13,600 genes, somewhat fewer than the smaller Caenorhabditis elegans genome, but with comparable functional diversity.

Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

M Ashburner — 2005-05-27T12:30:22-00:00

Nat Genet, Vol. 25, No. 1. (May 2000), pp. 25-29.

The Genome Sequence of Drosophila melanogaster

Mark Adams — 2005-06-14T01:43:05-00:00

Science, Vol. 287, No. 5461. (24 March 2000), pp. 2185-2195.

High-dimensional image registration using symmetric priors

J Ashburner — 2008-07-11T05:17:19-00:00

(1999)

This paper is about warping a brain image from one subject (the object image) so that it matches another (the template image). A high dimensional model is used, whereby a finite element approach is employed to estimate translations at the location of each voxel in the template image. Bayesian statistics are used to obtain a maximum a posteriori (MAP) estimate of the deformation field. The validity of any registration method is largely based upon the constraints, or in this instance, priors...

Evidence for Segregated and Integrative Connectivity Patterns in the Human Basal Ganglia

Bogdan Draganski — 2008-07-10T00:53:42-00:00

J. Neurosci., Vol. 28, No. 28. (9 July 2008), pp. 7143-7152.

Detailed knowledge of the anatomy and connectivity pattern of cortico-basal ganglia circuits is essential to an understanding of abnormal cortical function and pathophysiology associated with a wide range of neurological and neuropsychiatric diseases. We aim to study the spatial extent and topography of human basal ganglia connectivity in vivo. Additionally, we explore at an anatomical level the hypothesis of coexistent segregated and integrative cortico-basal ganglia loops. We use probabilistic tractography on magnetic resonance diffusion weighted imaging data to segment basal ganglia and thalamus in 30 healthy subjects based on their cortical and subcortical projections. We introduce a novel method to define voxel-based connectivity profiles that allow representation of projections from a source to more than one target region. Using this method, we localize specific relay nuclei within predefined functional circuits. We find strong correlation between tractography-based basal ganglia parcellation and anatomical data from previously reported invasive tracing studies in nonhuman primates. Additionally, we show in vivo the anatomical basis of segregated loops and the extent of their overlap in prefrontal, premotor, and motor networks. Our findings in healthy humans support the notion that probabilistic diffusion tractography can be used to parcellate subcortical gray matter structures on the basis of their connectivity patterns. The coexistence of clearly segregated and also overlapping connections from cortical sites to basal ganglia subregions is a neuroanatomical correlate of both parallel and integrative networks within them. We believe that this method can be used to examine pathophysiological concepts in a number of basal ganglia-related disorders. 10.1523/JNEUROSCI.1486-08.2008

Variational free energy and the Laplace approximation

Karl Friston — 2008-07-07T12:02:12-00:00

NeuroImage, Vol. 34, No. 1. (1 January 2007), pp. 220-234.

This note derives the variational free energy under the Laplace approximation, with a focus on accounting for additional model complexity induced by increasing the number of model parameters. This is relevant when using the free energy as an approximation to the log-evidence in Bayesian model averaging and selection. By setting restricted maximum likelihood (ReML) in the larger context of variational learning and expectation maximisation (EM), we show how the ReML objective function can be adjusted to provide an approximation to the log-evidence for a particular model. This means ReML can be used for model selection, specifically to select or compare models with different covariance components. This is useful in the context of hierarchical models because it enables a principled selection of priors that, under simple hyperpriors, can be used for automatic model selection and relevance determination (ARD). Deriving the ReML objective function, from basic variational principles, discloses the simple relationships among Variational Bayes, EM and ReML. Furthermore, we show that EM is formally identical to a full variational treatment when the precisions are linear in the hyperparameters. Finally, we also consider, briefly, dynamic models and how these inform the regularisation of free energy ascent schemes, like EM and ReML.

The minimum information about a genome sequence (MIGS) specification

Dawn Field — 2008-05-08T23:35:18-00:00

Nat Biotech, Vol. 26, No. 5. (May 2008), pp. 541-547.

Comparative genomics of the eukaryotes.

GM Rubin — 2005-04-18T15:09:18-00:00

Science, Vol. 287, No. 5461. (24 March 2000), pp. 2204-2215.

A comparative analysis of the genomes of Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae-and the proteins they are predicted to encode-was undertaken in the context of cellular, developmental, and evolutionary processes. The nonredundant protein sets of flies and worms are similar in size and are only twice that of yeast, but different gene families are expanded in each genome, and the multidomain proteins and signaling pathways of the fly and worm are far more complex than those of yeast. The fly has orthologs to 177 of the 289 human disease genes examined and provides the foundation for rapid analysis of some of the basic processes involved in human disease.

Learning arbitrary visuomotor associations: temporal dynamic of brain activity.

I Toni — 2008-06-19T08:46:55-00:00

NeuroImage, Vol. 14, No. 5. (November 2001), pp. 1048-1057.

Primates can give behavioral responses on the basis of arbitrary, context-dependent rules. When sensory instructions and behavioral responses are associated by arbitrary rules, these rules need to be learned. This study investigates the temporal dynamics of functional segregation at the basis of visuomotor associative learning in humans, isolating specific learning-related changes in neurovascular activity across the whole brain. We have used fMRI to measure human brain activity during performance of two tasks requiring the association of visual patterns with motor responses. Both tasks were learned by trial and error, either before (visuomotor control) or during (visuomotor learning) the scanning session. Epochs of tasks performance ( approximately 30 s) were alternated with a baseline period over the whole scanning session ( approximately 50 min). We have assessed both linear and nonlinear modulations in the differential signal between tasks, independently from overall task differences. The performance indices of the visuomotor learning task smoothly converged onto the values of a steady-state control condition, according to nonlinear timecourses. Specific visuomotor learning-related activity has been found over a distributed cortical network, centred on a temporo-prefrontal circuit. These cortical time-modulated activities were supported early in learning by the hippocampal/parahippocampal complex, and late in learning by the basal ganglia system. These findings suggest the inferior temporal and the ventral prefrontal cortex are critical neural nodes for integrating perceptual information with executive processes.

The Sequence Ontology: a tool for the unification of genome annotations.

K Eilbeck — 2006-05-15T20:11:27-00:00

Genome Biol, Vol. 6, No. 5. (2005)

The Sequence Ontology (SO) is a structured controlled vocabulary for the parts of a genomic annotation. SO provides a common set of terms and definitions that will facilitate the exchange, analysis and management of genomic data. Because SO treats part-whole relationships rigorously, data described with it can become substrates for automated reasoning, and instances of sequence features described by the SO can be subjected to a group of logical operations termed extensional mereology operators.

ChEBI: a database and ontology for chemical entities of biological interest.

Kirill Degtyarenko — 2007-10-26T16:38:50-00:00

Nucleic Acids Res (11 October 2007)

Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chemical compounds. The molecular entities in question are either natural products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/

On ontologies for biologists: the Gene Ontology--untangling the web.

M Ashburner — 2005-02-22T16:33:28-00:00

Novartis Found Symp, Vol. 247 (2002)

The mantra of the 'post-genomic' era is 'gene function'. Yet surprisingly little attention has been given to how functional and other information concerning genes is to be captured, made accessible to biologists or structured in a computable form. The aim of the Gene Ontology (GO) Consortium is to provide a framework for both the description and the organisation of such information. The GO Consortium is presently concerned with three structured controlled vocabularies which can be used to describe three discrete biological domains, building structured vocabularies which can be used to describe the molecular function, biological roles and cellular locations of gene products.

Automatic classification of MR scans in Alzheimer's disease

Stefan Kloppel — 2008-03-31T00:10:05-00:00

Brain, Vol. 131, No. 3. (1 March 2008), pp. 681-689.

To be diagnostically useful, structural MRI must reliably distinguish Alzheimer's disease (AD) from normal aging in individual scans. Recent advances in statistical learning theory have led to the application of support vector machines to MRI for detection of a variety of disease states. The aims of this study were to assess how successfully support vector machines assigned individual diagnoses and to determine whether data-sets combined from multiple scanners and different centres could be used to obtain effective classification of scans. We used linear support vector machines to classify the grey matter segment of T1-weighted MR scans from pathologically proven AD patients and cognitively normal elderly individuals obtained from two centres with different scanning equipment. Because the clinical diagnosis of mild AD is difficult we also tested the ability of support vector machines to differentiate control scans from patients without post-mortem confirmation. Finally we sought to use these methods to differentiate scans between patients suffering from AD from those with frontotemporal lobar degeneration. Up to 96% of pathologically verified AD patients were correctly classified using whole brain images. Data from different centres were successfully combined achieving comparable results from the separate analyses. Importantly, data from one centre could be used to train a support vector machine to accurately differentiate AD and normal ageing scans obtained from another centre with different subjects and different scanner equipment. Patients with mild, clinically probable AD and age/sex matched controls were correctly separated in 89% of cases which is compatible with published diagnosis rates in the best clinical centres. This method correctly assigned 89% of patients with post-mortem confirmed diagnosis of either AD or frontotemporal lobar degeneration to their respective group. Our study leads to three conclusions: Firstly, support vector machines successfully separate patients with AD from healthy aging subjects. Secondly, they perform well in the differential diagnosis of two different forms of dementia. Thirdly, the method is robust and can be generalized across different centres. This suggests an important role for computer based diagnostic image analysis for clinical practice. 10.1093/brain/awm319

A methodology to migrate the gene ontology to a description logic environment using DAML+OIL.

CJ Wroe — 2008-04-26T18:25:12-00:00

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2003), pp. 624-635.

The Gene Ontology Next Generation Project (GONG) is developing a staged methodology to evolve the current representation of the Gene Ontology into DAML+OIL in order to take advantage of the richer formal expressiveness and the reasoning capabilities of the underlying description logic. Each stage provides a step level increase in formal explicit semantic content with a view to supporting validation, extension and multiple classification of the Gene Ontology. The paper introduces DAML+OIL and demonstrates the activity within each stage of the methodology and the functionality gained.

A short study on the success of the Gene Ontology

Michael Bada — 2006-12-29T18:39:34-00:00

Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 1, No. 2. (February 2004), pp. 235-240.

While most ontologies have been used only by the groups who created them and for their initially defined purposes, the Gene Ontology (GO), an evolving structured controlled vocabulary of nearly 16,000 terms in the domain of biological functionality, has been widely used for annotation of biological-database entries and in biomedical research. As a set of learned lessons offered to other ontology developers, we list and briefly discuss the characteristics of GO that we believe are most responsible for its success: community involvement; clear goals; limited scope; simple, intuitive structure; continuous evolution; active curation; and early use.

Principles of Genome Evolution in the Drosophila melanogaster Species Group

José Ranz — 2007-06-14T01:02:58-00:00

PLoS Biology, Vol. 5, No. 6. (1 June 2007), e152.

That closely related species often differ by chromosomal inversions was discovered by Sturtevant and Plunkett in 1926. Our knowledge of how these inversions originate is still very limited, although a prevailing view is that they are facilitated by ectopic recombination events between inverted repetitive sequences. The availability of genome sequences of related species now allows us to study in detail the mechanisms that generate interspecific inversions. We have analyzed the breakpoint regions of the 29 inversions that differentiate the chromosomes of Drosophila melanogaster and two closely related species, D. simulans and D. yakuba, and reconstructed the molecular events that underlie their origin. Experimental and computational analysis revealed that the breakpoint regions of 59% of the inversions (17/29) are associated with inverted duplications of genes or other nonrepetitive sequences. In only two cases do we find evidence for inverted repetitive sequences in inversion breakpoints. We propose that the presence of inverted duplications associated with inversion breakpoint regions is the result of staggered breaks, either isochromatid or chromatid, and that this, rather than ectopic exchange between inverted repetitive sequences, is the prevalent mechanism for the generation of inversions in the melanogaster species group. Outgroup analysis also revealed evidence for widespread breakpoint recycling. Lastly, we have found that expression domains in D. melanogaster may be disrupted in D. yakuba, bringing into question their potential adaptive significance.

National Center for Biomedical Ontology: advancing biomedicine through structured organization of scientific knowledge.

DL Rubin — 2006-09-18T17:20:33-00:00

OMICS, Vol. 10, No. 2. (2006), pp. 185-198.

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease.

Bayesian decoding of brain images

Karl Friston — 2007-11-21T09:36:40-00:00

NeuroImage, Vol. 39, No. 1. (1 January 2008), pp. 181-205.

This paper introduces a multivariate Bayesian (MVB) scheme to decode or recognise brain states from neuroimages. It resolves the ill-posed many-to-one mapping, from voxel values or data features to a target variable, using a parametric empirical or hierarchical Bayesian model. This model is inverted using standard variational techniques, in this case expectation maximisation, to furnish the model evidence and the conditional density of the model's parameters. This allows one to compare different models or hypotheses about the mapping from functional or structural anatomy to perceptual and behavioural consequences (or their deficits). We frame this approach in terms of decoding measured brain states to predict or classify outcomes using the rhetoric established in pattern classification of neuroimaging data. However, the aim of MVB is not to predict (because the outcomes are known) but to enable inference on different models of structure-function mappings; such as distributed and sparse representations. This allows one to optimise the model itself and produce predictions that outperform standard pattern classification approaches, like support vector machines. Technically, the model inversion and inference uses the same empirical Bayesian procedures developed for ill-posed inverse problems (e.g., source reconstruction in EEG). However, the MVB scheme used here extends this approach to include a greedy search for sparse solutions. It reduces the problem to the same form used in Gaussian process modelling, which affords a generic and efficient scheme for model optimisation and evaluating model evidence. We illustrate MVB using simulated and real data, with a special focus on model comparison; where models can differ in the form of the mapping (i.e., neuronal representation) within one region, or in the (combination of) regions per se.

Combined evidence annotation of transposable elements in genome sequences.

H Quesneville — 2005-09-27T23:45:05-00:00

PLoS Comput Biol, Vol. 1, No. 2. (July 2005)

Transposable elements (TEs) are mobile, repetitive sequences that make up significant fractions of metazoan genomes. Despite their near ubiquity and importance in genome and chromosome biology, most efforts to annotate TEs in genome sequences rely on the results of a single computational program, RepeatMasker. In contrast, recent advances in gene annotation indicate that high-quality gene models can be produced from combining multiple independent sources of computational evidence. To elevate the quality of TE annotations to a level comparable to that of gene models, we have developed a combined evidence-model TE annotation pipeline, analogous to systems used for gene annotation, by integrating results from multiple homology-based and de novo TE identification methods. As proof of principle, we have annotated "TE models" in Drosophila melanogaster Release 4 genomic sequences using the combined computational evidence derived from RepeatMasker, BLASTER, TBLASTX, all-by-all BLASTN, RECON, TE-HMM and the previous Release 3.1 annotation. Our system is designed for use with the Apollo genome annotation tool, allowing automatic results to be curated manually to produce reliable annotations. The euchromatic TE fraction of D. melanogaster is now estimated at 5.3% (cf. 3.86% in Release 3.1), and we found a substantially higher number of TEs (n = 6,013) than previously identified (n = 1,572). Most of the new TEs derive from small fragments of a few hundred nucleotides long and highly abundant families not previously annotated (e.g., INE-1). We also estimated that 518 TE copies (8.6%) are inserted into at least one other TE, forming a nest of elements. The pipeline allows rapid and thorough annotation of even the most complex TE models, including highly deleted and/or nested elements such as those often found in heterochromatic sequences. Our pipeline can be easily adapted to other genome sequences, such as those of the D. melanogaster heterochromatin or other species in the genus Drosophila.

Classical and Bayesian inference in neuroimaging: theory.

KJ Friston — 2007-02-01T17:34:11-00:00

Neuroimage, Vol. 16, No. 2. (June 2002), pp. 465-483.

This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian light. It emphasizes the common ground shared by classical and Bayesian methods to show that conventional analyses of neuroimaging data can be usefully extended within an empirical Bayesian framework. In particular we formulate the procedures used in conventional data analysis in terms of hierarchical linear models and establish a connection between classical inference and parametric empirical Bayes (PEB) through covariance component estimation. This estimation is based on an expectation maximization or EM algorithm. The key point is that hierarchical models not only provide for appropriate inference at the highest level but that one can revisit lower levels suitably equipped to make Bayesian inferences. Bayesian inferences eschew many of the difficulties encountered with classical inference and characterize brain responses in a way that is more directly predicated on what one is interested in. The motivation for Bayesian approaches is reviewed and the theoretical background is presented in a way that relates to conventional methods, in particular restricted maximum likelihood (ReML). This paper is a technical and theoretical prelude to subsequent papers that deal with applications of the theory to a range of important issues in neuroimaging. These issues include; (i) Estimating nonsphericity or variance components in fMRI time-series that can arise from serial correlations within subject, or are induced by multisubject (i.e., hierarchical) studies. (ii) Spatiotemporal Bayesian models for imaging data, in which voxels-specific effects are constrained by responses in other voxels. (iii) Bayesian estimation of nonlinear models of hemodynamic responses and (iv) principled ways of mixing structural and functional priors in EEG source reconstruction. Although diverse, all these estimation problems are accommodated by the PEB framework described in this paper.

Voxel-based morphometry--the methods.

J Ashburner — 2005-12-02T15:21:07-00:00

Neuroimage, Vol. 11, No. 6 Pt 1. (June 2000), pp. 805-821.

At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.

Classical and Bayesian inference in neuroimaging: applications.

KJ Friston — 2007-05-23T13:38:16-00:00

Neuroimage, Vol. 16, No. 2. (June 2002), pp. 484-512.

In Friston et al. ((2002) Neuroimage 16: 465-483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To illustrate the use of the expectation-maximization (EM) algorithm in covariance component estimation we focus first on two important problems in fMRI: nonsphericity induced by (i) serial or temporal correlations among errors and (ii) variance components caused by the hierarchical nature of multisubject studies. In hierarchical observation models, variance components at higher levels can be used as constraints on the parameter estimates of lower levels. This enables the use of parametric empirical Bayesian (PEB) estimators, as distinct from classical maximum likelihood (ML) estimates. We develop this distinction to address: (i) The difference between response estimates based on ML and the conditional means from a Bayesian approach and the implications for estimates of intersubject variability. (ii) The relationship between fixed- and random-effect analyses. (iii) The specificity and sensitivity of Bayesian inference and, finally, (iv) the relative importance of the number of scans and subjects. The forgoing is concerned with within- and between-subject variability in multisubject hierarchical fMRI studies. In the second half of this paper we turn to Bayesian inference at the first (within-voxel) level, using PET data to show how priors can be derived from the (between-voxel) distribution of activations over the brain. This application uses exactly the same ideas and formalism but, in this instance, the second level is provided by observations over voxels as opposed to subjects. The ensuing posterior probability maps (PPMs) have enhanced anatomical precision and greater face validity, in relation to underlying anatomy. Furthermore, in comparison to conventional SPMs they are not confounded by the multiple comparison problem that, in a classical context, dictates high thresholds and low sensitivity. We conclude with some general comments on Bayesian approaches to image analysis and on some unresolved issues.

Modeling Geometric Deformations in EPI Time Series

Jesper Andersson — 2008-03-09T00:52:41-00:00

NeuroImage, Vol. 13, No. 5. (May 2001), pp. 903-919.

Even after realignment there is residual movement-related variance present in fMRI time-series, causing loss of sensitivity and, potentially, also specificity. One cause is the differential deformation of the sampling matrix, by field inhomogeneities, at different object positions, i.e., a movement-by-inhomogeneity interaction. This has been addressed previously by using empirical field measurements. In the present paper we suggest a forward model of how data is affected by an inhomogeneous field at different object positions. From this model we derive a method to solve the inverse problem of estimating the field inhomogeneities and their derivatives with respect to object position, directly from the EPI data and estimated realignment parameters. The field is modeled as a linear combination of cosine basis fields, which facilitates a fast way of implementing the necessary matrix operations. Simulations suggest that the solution is tractable and that the fields are estimable given the deformed images and knowledge of the relative positions at which they have been acquired. An experiment on a subject performing voluntary movements in the scanner yielded plausible estimates of the deformation fields and their application to "unwarp" the time series significantly reduced movement-related variance.

Unified segmentation

John Ashburner — 2005-10-31T13:06:43-00:00

NeuroImage, Vol. 26, No. 3. (1 July 2005), pp. 839-851.

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

The Gene Ontology (GO) database and informatics resource.

MA Harris — 2005-07-14T22:06:49-00:00

Nucleic Acids Res, Vol. 32, No. Database issue. (1 January 2004)

The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

fMRI Activity Patterns in Human LOC Carry Information about Object Exemplars within Category.

E Eger — 2008-02-19T21:11:35-00:00

J Cogn Neurosci, Vol. 20, No. 2. (February 2008), pp. 356-370.

Abstract The lateral occipital complex (LOC) is a set of areas in the human occipito-temporal cortex responding to objects as opposed to low-level control stimuli. Conventional functional magnetic resonance imaging (fMRI) analysis methods based on regional averages could not detect signals discriminative of different types of objects in this region. Here, we examined fMRI signals using multivariate pattern recognition (support vector classification) to systematically explore the nature of object-related information available in fine-grained activity patterns in the LOC. Distributed fMRI signals from the LOC allowed for above-chance discrimination not only of the category but also of within-category exemplars of everyday man-made objects, and such exemplar-specific information generalized across changes in stimulus size and viewpoint, particularly in posterior subregions. Object identity could also be predicted from responses of the early visual cortex, even significantly across the changes in size and viewpoint used here. However, a dissociation was observed between these two regions of interest in the degree of discrimination for objects relative to size: In the early visual cortex, two different sizes of the same object were even better discriminated than two different objects (in accordance with measures of pixelwise stimulus similarity), whereas the opposite was true in the LOC. These findings provide the first evidence that direct evoked fMRI activity patterns in the LOC can be different for individual object exemplars (within a single category). We propose that pattern recognition methods as used here may provide an alternative approach to study mechanisms of neuronal representation based on aspects of the fMRI response independent of those assessed in adaptation paradigms.

EGASP: the human ENCODE Genome Annotation Assessment Project.

R Guigó — 2006-11-08T10:04:37-00:00

Genome Biol, Vol. 7 Suppl 1 (2006)

BACKGROUND: We present the results of EGASP, a community experiment to assess the state-of-the-art in genome annotation within the ENCODE regions, which span 1% of the human genome sequence. The experiment had two major goals: the assessment of the accuracy of computational methods to predict protein coding genes; and the overall assessment of the completeness of the current human genome annotations as represented in the ENCODE regions. For the computational prediction assessment, eighteen groups contributed gene predictions. We evaluated these submissions against each other based on a 'reference set' of annotations generated as part of the GENCODE project. These annotations were not available to the prediction groups prior to the submission deadline, so that their predictions were blind and an external advisory committee could perform a fair assessment. RESULTS: The best methods had at least one gene transcript correctly predicted for close to 70% of the annotated genes. Nevertheless, the multiple transcript accuracy, taking into account alternative splicing, reached only approximately 40% to 50% accuracy. At the coding nucleotide level, the best programs reached an accuracy of 90% in both sensitivity and specificity. Programs relying on mRNA and protein sequences were the most accurate in reproducing the manually curated annotations. Experimental validation shows that only a very small percentage (3.2%) of the selected 221 computationally predicted exons outside of the existing annotation could be verified. CONCLUSION: This is the first such experiment in human DNA, and we have followed the standards established in a similar experiment, GASP1, in Drosophila melanogaster. We believe the results presented here contribute to the value of ongoing large-scale annotation projects and should guide further experimental methods when being scaled up to the entire human genome sequence.

The product of the Drosophila gene vasa is very similar to eukaryotic initiation factor-4A

Paul Lasko — 2008-02-17T01:19:26-00:00

Nature, Vol. 335, No. 6191. (13 October 1988), pp. 611-617.

The p65 (RelA) subunit of NF-kappaB interacts with the histone deacetylase (HDAC) corepressors HDAC1 and HDAC2 to negatively regulate gene expression.

BP Ashburner — 2008-02-01T03:27:42-00:00

Mol Cell Biol, Vol. 21, No. 20. (October 2001), pp. 7065-7077.

Regulation of NF-kappaB transactivation function is controlled at several levels, including interactions with coactivator proteins. Here we show that the transactivation function of NF-kappaB is also regulated through interaction of the p65 (RelA) subunit with histone deacetylase (HDAC) corepressor proteins. Our results show that inhibition of HDAC activity with trichostatin A (TSA) results in an increase in both basal and induced expression of an integrated NF-kappaB-dependent reporter gene. Chromatin immunoprecipitation (ChIP) assays show that TSA treatment causes hyperacetylation of the wild-type integrated NF-kappaB-dependent reporter but not of a mutant version in which the NF-kappaB binding sites were mutated. Expression of HDAC1 and HDAC2 repressed tumor necrosis factor (TNF)-induced NF-kappaB-dependent gene expression. Consistent with this, we show that HDAC1 and HDAC2 target NF-kappaB through a direct association of HDAC1 with the Rel homology domain of p65. HDAC2 does not interact with NF-kappaB directly but can regulate NF-kappaB activity through its association with HDAC1. Finally, we show that inhibition of HDAC activity with TSA causes an increase in both basal and TNF-induced expression of the NF-kappaB-regulated interleukin-8 (IL-8) gene. Similar to the wild-type integrated NF-kappaB-dependent reporter, ChIP assays showed that TSA treatment resulted in hyperacetylation of the IL-8 promoter. These data indicate that the transactivation function of NF-kappaB is regulated in part through its association with HDAC corepressor proteins. Moreover, it suggests that the association of NF-kappaB with the HDAC1 and HDAC2 corepressor proteins functions to repress expression of NF-kappaB-regulated genes as well as to control the induced level of expression of these genes.

Spatial registration and normalization of images

Karl — 2006-11-28T16:56:36-00:00

Human Brain Mapping, Vol. 3, No. 3. (1995), pp. 165-189.

This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley-Liss, Inc.

Nonlinear spatial normalization using basis functions

John Ashburner — 2007-10-05T11:25:57-00:00

Human Brain Mapping, Vol. 7, No. 4. (1999), pp. 254-266.

We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be fitted, the nonlinear warps are described by a linear combination of low spatial frequency basis functions. The objective is to determine the optimum coefficients for each of the bases by minimizing the sum of squared differences between the image and template, while simultaneously maximizing the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data, and also corrects for the correlations between neighboring voxels. This makes the same approach suitable for the spatial normalization of both high-quality magnetic resonance images, and low-resolution noisy positron emission tomography images. A fast algorithm has been developed that utilizes Taylor's theorem and the separable nature of the basis functions, meaning that most of the nonlinear spatial variability between images can be automatically corrected within a few minutes. Hum. Brain Mapping 7:254-266, 1999. © 1999 Wiley-Liss, Inc.

Information access. Building a "GenBank" of the published literature.

RJ Roberts — 2008-01-21T13:32:31-00:00

Science, Vol. 291, No. 5512. (23 March 2001), pp. 2318-2319.

Systematic determination of patterns of gene expression during Drosophila embryogenesis.

P Tomancak — 2005-03-29T22:15:45-00:00

Genome Biol, Vol. 3, No. 12. (2002)

BACKGROUND: Cell-fate specification and tissue differentiation during development are largely achieved by the regulation of gene transcription. RESULTS: As a first step to creating a comprehensive atlas of gene-expression patterns during Drosophila embryogenesis, we examined 2,179 genes by in situ hybridization to fixed Drosophila embryos. Of the genes assayed, 63.7% displayed dynamic expression patterns that were documented with 25,690 digital photomicrographs of individual embryos. The photomicrographs were annotated using controlled vocabularies for anatomical structures that are organized into a developmental hierarchy. We also generated a detailed time course of gene expression during embryogenesis using microarrays to provide an independent corroboration of the in situ hybridization results. All image, annotation and microarray data are stored in publicly available database. We found that the RNA transcripts of about 1% of genes show clear subcellular localization. Nearly all the annotated expression patterns are distinct. We present an approach for organizing the data by hierarchical clustering of annotation terms that allows us to group tissues that express similar sets of genes as well as genes displaying similar expression patterns. CONCLUSIONS: Analyzing gene-expression patterns by in situ hybridization to whole-mount embryos provides an extremely rich dataset that can be used to identify genes involved in developmental processes that have been missed by traditional genetic analysis. Systematic analysis of rigorously annotated patterns of gene expression will complement and extend the types of analyses carried out using expression microarrays.

FlyMine: an integrated database for Drosophila and Anopheles genomics

Rachel Lyne — 2007-07-07T08:30:31-00:00

Genome Biology, Vol. 8 (05 July 2007), R129.

Phenotype ontologies: the bridge between genomics and evolution

Paula Mabee — 2007-08-18T07:56:25-00:00

Trends in Ecology & Evolution, Vol. 22, No. 7. (July 2007), pp. 345-350.

Understanding the developmental and genetic underpinnings of particular evolutionary changes has been hindered by inadequate databases of evolutionary anatomy and by the lack of a computational approach to identify underlying candidate genes and regulators. By contrast, model organism studies have been enhanced by ontologies shared among genomic databases. Here, we suggest that evolutionary and genomics databases can be developed to exchange and use information through shared phenotype and anatomy ontologies. This would facilitate computing on evolutionary questions pertaining to the genetic basis of evolutionary change, the genetic and developmental bases of correlated characters and independent evolution, biomedical parallels to evolutionary change, and the ecological and paleontological correlates of particular types of change in genes, gene networks and developmental pathways.

Interpreting scan data acquired from multiple scanners: A study with Alzheimer's disease

Cynthia Stonnington — 2008-01-14T10:46:58-00:00

NeuroImage, Vol. 39, No. 3. (1 February 2008), pp. 1180-1185.

Large, multi-site studies utilizing MRI-derived measures from multiple scanners present an opportunity to advance research by pooling data. On the other hand, it remains unclear whether or not the potential confound introduced by different scanners and upgrades will devalue the integrity of any results. Although there are studies of scanner differences for the purpose of calibration and quality control, the current literature is devoid of studies that describe the analysis of multi-scanner data with regard to the interaction of scanner(s) with effects of interest. We investigated a data-set of 136 subjects, 62 patients with mild to moderate Alzheimer's disease and 74 cognitively normal elderly controls, with MRI scans from one center that were acquired over 10 years with 6 different scanners and multiple upgrades over time. We used a whole-brain voxel-wise analysis to evaluate the effect of scanner, effect of disease, and the interaction of scanner and disease for the 6 different scanners. The effect of disease in patients showed the expected significant reduction of grey matter in the medial temporal lobe. Scanner differences were substantially less than the group differences and only significant in the thalamus. There was no significant interaction of scanner with disease group. We describe the rationale for concluding that our results were not confounded by scanner differences. Similar analyses in other multi-scanner data-sets could be used to justify the pooling of data when needed, such as in studies of rare disorders or in multi-center designs.