1,720,998 research outputs found
Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization
No full textMagnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the "hub" of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed "strong-weak" pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression
Data integration in genomics and systems biology
No full textMulti-view learning is the branch of machine learning that deals with multi modal data, i.e. with patterns represented by different sets of features. The fast spread of this learning technique is motivated by the continuing increase of real applications based on multi-view data. For example, in bioinformatics multiple experiments can be available (mRNA, miRNA and protein expression, genome wide association studies (GWAS) and others) for a set of samples. In bioinformatics multi-view approaches are useful since heterogeneous genome-wide data sources capture information on different aspects of complex biological systems. Each view provides a distinct facet of the same domain, encoding different biologically-relevant patterns. The integration of such views can provide a richer model of the underlying system than those produced by a single view alone. This paper provides a review of the literature with respect to bioinformatics, with the purpose to understand the principles and operation modes of the existing methods and their possible applications. In order to organize the proposed methods in literature and to find similarities between them, these approaches are organized according to three categories: the type of data used in the papers, the statistical problem and the stage of integration
SASCr2: Enhanced hardware string alignment coprocessor for stereo correspondence
No full textIn this paper new and significant improvements for a recently proposed hardware co-processor for Stereo Matching are introduced. Main focus is on small memory requirements while preserving the needed accuracy. Starting from a pair of stereo images, the co-processor computes the “disparity map” used to define corresponding points on the two images. The approach follows the well-known scheme for string alignment by Needleman & Wunsch, commonly used in bioinformatics. The architecture, highly modular, was designed by using Bluespec SystemVerilog development tool and is described in detail. Synthesis results for several FPGA platforms are shown. The actual circuit is an updated version of SASC design. Performance is benchmarked against the former implementation as well as against two reference software versions
SASC: A hardware string alignment coprocessor for stereo correspondence2012 IEEE International Conference on Electronics Design, Systems and Applications (ICEDSA)
No full textIn this paper a design scheme is proposed for a hardware co-processor that, starting from a pair of stereo images, computes the “disparity map” between them used to define corresponding points on the two images. The followed approach, based on Dynamic Programming, is that proposed in a recent paper and exploits the well known Needleman & Wunsch's string-alignment algorithm used in bioinformatics. The architecture, highly modular, was designed using Bluespec System Verilog development tool and is described in detail. Synthesis results are shown for several FPGA platforms and demonstrates that the processor can result sufficiently small to be embedded in a totally hardware stereo images processing chain. Performance obtained and reported at the end of the paper show that the processor can run fast enough to be employed in real time instances
Stochastic Rank Aggregation for the Identification of Functional Neuromarkers
No full textThe main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N > 100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and stochastic rank aggregation to identify parcels that exhibit a coherent behaviour in groups of subjects affected by the same disorder and apply it to default-mode network independent component maps from resting-state fMRI data sets. Brain voxels are partitioned into parcels through k-means clustering, then solutions are enhanced by means of consensus techniques. For each subject, clusters are ranked according to their median value and a stochastic rank aggregation method, TopKLists, is applied to combine the individual rankings within each class of subjects. For comparison, the same approach was tested on an anatomical parcellation. We found parcels for which the rankings were different among control subjects and subjects affected by Parkinson's disease and amyotrophic lateral sclerosis and found evidence in literature for the relevance of top ranked regions in default-mode brain activity. The proposed framework represents a valid method for the identification of functional neuromarkers from resting-state fMRI data, and it might therefore constitute a step forward in the development of fully automated data-driven techniques to support early diagnoses of neurodegenerative diseases
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Multi-View Ensemble Classification of Brain Connectivity Images for Neurodegeneration Type Discrimination
No full textBrain connectivity analyses using voxels as features are not robust enough for single-patient classification because of the inter-subject anatomical and functional variability. To construct more robust features, voxels can be aggregated into clusters that are maximally coherent across subjects. Moreover, combining multi-modal neuroimaging and multi-view data integration techniques allows generating multiple independent connectivity features for the same patient. Structural and functional connectivity features were extracted from multi-modal MRI images with a clustering technique, and used for the multi-view classification of different phenotypes of neurodegeneration by an ensemble learning method (random forest). Two different multi-view models (intermediate and late data integration) were trained on, and tested for the classification of, individual whole-brain default-mode network (DMN) and fractional anisotropy (FA) maps, from 41 amyotrophic lateral sclerosis (ALS) patients, 37 Parkinson’s disease (PD) patients and 43 healthy control (HC) subjects. Both multi-view data models exhibited ensemble classification accuracies significantly above chance. In ALS patients, multi-view models exhibited the best performances (intermediate: 82.9%, late: 80.5% correct classification) and were more discriminative than each single-view model. In PD patients and controls, multi-view models’ performances were lower (PD: 59.5%, 62.2%; HC: 56.8%, 59.1%) but higher than at least one single-view model. Training the models only on patients, produced more than 85% patients correctly discriminated as ALS or PD type and maximal performances for multi-view models. These results highlight the potentials of mining complementary information from the integration of multiple data views in the classification of connectivity patterns from multi-modal brain images in the study of neurodegenerative diseases
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