1,780 research outputs found
Multivariate decoding of brain images using ordinal regression.
Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Suppose one is faced with the challenge of tissue segmentation in MR images,
without annotators at their center to provide labeled training data. One option
is to go to another medical center for a trained classifier. Sadly, tissue
classifiers do not generalize well across centers due to voxel intensity shifts
caused by center-specific acquisition protocols. However, certain aspects of
segmentations, such as spatial smoothness, remain relatively consistent and can
be learned separately. Here we present a smoothness prior that is fit to
segmentations produced at another medical center. This informative prior is
presented to an unsupervised Bayesian model. The model clusters the voxel
intensities, such that it produces segmentations that are similarly smooth to
those of the other medical center. In addition, the unsupervised Bayesian model
is extended to a semi-supervised variant, which needs no visual interpretation
of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International
Conference on Information Processing in Medical Imaging (2019
The gray matter volume of the amygdala is correlated with the perception of melodic intervals: a voxel-based morphometry study
Music is not simply a series of organized pitches, rhythms, and timbres, it is capable of evoking emotions. In the present study, voxel-based morphometry (VBM) was employed to explore the neural basis that may link music to emotion. To do this, we identified the neuroanatomical correlates of the ability to extract pitch interval size in a music segment (i.e., interval perception) in a large population of healthy young adults (N = 264). Behaviorally, we found that interval perception was correlated with daily emotional experiences, indicating the intrinsic link between music and emotion. Neurally, and as expected, we found that interval perception was positively correlated with the gray matter volume (GMV) of the bilateral temporal cortex. More important, a larger GMV of the bilateral amygdala was associated with better interval perception, suggesting that the amygdala, which is the neural substrate of emotional processing, is also involved in music processing. In sum, our study provides one of first neuroanatomical evidence on the association between the amygdala and music, which contributes to our understanding of exactly how music evokes emotional responses
Do proteinâprotein interaction databases identify moonlighting proteins?
One of the most striking results of the human (and mammalian)
genomes is the low number of protein-coding genes. To-date, the
main molecular mechanism to increase the number of different
protein isoforms and functions is alternative splicing. However, a
less-known way to increase the number of protein functions is the
existence of multifunctional, multitask, or ââmoonlightingââ,
proteins. By and large, moonlighting proteins are experimentally
disclosed by serendipity. Proteomics is becoming one of the very
active areas of biomedical research, which permits researchers to
identify previously unseen connections among proteins and pathways.
In principle, proteinâprotein interaction (PPI) databases
should contain information on moonlighting proteins and could
provide suggestions to further analysis in order to prove the
multifunctionality. As far as we know, nobody has verified
whether PPI databases actually disclose moonlighting proteins.
In the present work we check whether well-established moonlighting
proteins present in PPI databases connect with their
known partners and, therefore, a careful inspection of these
databases could help to suggest their different functions. The
results of our research suggest that PPI databases could be a
valuable tool to suggest multifunctionality
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
Validating module network learning algorithms using simulated data
In recent years, several authors have used probabilistic graphical models to
learn expression modules and their regulatory programs from gene expression
data. Here, we demonstrate the use of the synthetic data generator SynTReN for
the purpose of testing and comparing module network learning algorithms. We
introduce a software package for learning module networks, called LeMoNe, which
incorporates a novel strategy for learning regulatory programs. Novelties
include the use of a bottom-up Bayesian hierarchical clustering to construct
the regulatory programs, and the use of a conditional entropy measure to assign
regulators to the regulation program nodes. Using SynTReN data, we test the
performance of LeMoNe in a completely controlled situation and assess the
effect of the methodological changes we made with respect to an existing
software package, namely Genomica. Additionally, we assess the effect of
various parameters, such as the size of the data set and the amount of noise,
on the inference performance. Overall, application of Genomica and LeMoNe to
simulated data sets gave comparable results. However, LeMoNe offers some
advantages, one of them being that the learning process is considerably faster
for larger data sets. Additionally, we show that the location of the regulators
in the LeMoNe regulation programs and their conditional entropy may be used to
prioritize regulators for functional validation, and that the combination of
the bottom-up clustering strategy with the conditional entropy-based assignment
of regulators improves the handling of missing or hidden regulators.Comment: 13 pages, 6 figures + 2 pages, 2 figures supplementary informatio
ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and kernel ridge regression
We applied several regression and deep learning methods to predict fluid
intelligence scores from T1-weighted MRI scans as part of the ABCD
Neurocognitive Prediction Challenge (ABCD-NP-Challenge) 2019. We used voxel
intensities and probabilistic tissue-type labels derived from these as features
to train the models. The best predictive performance (lowest mean-squared
error) came from Kernel Ridge Regression (KRR; ), which produced a
mean-squared error of 69.7204 on the validation set and 92.1298 on the test
set. This placed our group in the fifth position on the validation leader board
and first place on the final (test) leader board.Comment: Winning entry in the ABCD Neurocognitive Prediction Challenge at
MICCAI 2019. 7 pages plus references, 3 figures, 1 tabl
Genome Wide Transcriptome Analysis of Dendritic Cells Identifies Genes with Altered Expression in Psoriasis
Activation of dendritic cells by different pathogens induces the secretion of proinflammatory mediators resulting in
local inflammation. Importantly, innate immunity must be properly controlled, as its continuous activation leads to the
development of chronic inflammatory diseases such as psoriasis. Lipopolysaccharide (LPS) or peptidoglycan (PGN)
induced tolerance, a phenomenon of transient unresponsiveness of cells to repeated or prolonged stimulation,
proved valuable model for the study of chronic inflammation. Thus, the aim of this study was the identification of the
transcriptional diversity of primary human immature dendritic cells (iDCs) upon PGN induced tolerance. Using SAGESeq
approach, a tag-based transcriptome sequencing method, we investigated gene expression changes of primary
human iDCs upon stimulation or restimulation with Staphylococcus aureus derived PGN, a widely used TLR2 ligand.
Based on the expression pattern of the altered genes, we identified non-tolerizeable and tolerizeable genes. Gene
Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (Kegg) analysis showed marked enrichment of
immune-, cell cycle- and apoptosis related genes. In parallel to the marked induction of proinflammatory mediators,
negative feedback regulators of innate immunity, such as TNFAIP3, TNFAIP8, Tyro3 and Mer are markedly
downregulated in tolerant cells. We also demonstrate, that the expression pattern of TNFAIP3 and TNFAIP8 is
altered in both lesional, and non-lesional skin of psoriatic patients. Finally, we show that pretreatment of immature
dendritic cells with anti-TNF-α inhibits the expression of IL-6 and CCL1 in tolerant iDCs and partially releases the
suppression of TNFAIP8. Our findings suggest that after PGN stimulation/restimulation the host cell utilizes different
mechanisms in order to maintain critical balance between inflammation and tolerance. Importantly, the transcriptome
sequencing of stimulated/restimulated iDCs identified numerous genes with altered expression to date not associated
with role in chronic inflammation, underlying the relevance of our in vitro model for further characterization of IFNprimed
iDCs
Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis
Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS
Systems developmental biology: the use of ontologies in annotating models and in identifying gene function within and across species
Systems developmental biology is an approach to the study of embryogenesis that attempts to analyze complex developmental processes through integrating the roles of their molecular, cellular, and tissue participants within a computational framework. This article discusses ways of annotating these participants using standard terms and IDs now available in public ontologies (these are areas of hierarchical knowledge formalized to be computationally accessible) for tissues, cells, and processes. Such annotations bring two types of benefit. The first comes from using standard terms: This allows linkage to other resources that use them (e.g., GXD, the gene-expression [G-E] database for mouse development). The second comes from the annotation procedure itself: This can lead to the identification of common processes that are used in very different and apparently unrelated events, even in other organisms. One implication of this is the potential for identifying the genes underpinning common developmental processes in different tissues through Boolean analysis of their G-E profiles. While it is easiest to do this for single organisms, the approach is extendable to analyzing similar processes in different organisms. Although the full computational infrastructure for such an analysis has yet to be put in place, two examples are briefly considered as illustration. First, the early development of the mouse urogenital system shows how a line of development can be graphically formalized using ontologies. Second, Boolean analysis of the G-E profiles of the mesenchyme-to-epithelium transitions that take place during mouse development suggest Lhx1, Foxc1, and Meox1 as candidate transcription factors for mediating this process
- âŠ