Frontiers in Neuroinformatics
Journal Impact: 1.31*
*This value is calculated using ResearchGate data and is based on average citation counts from work published in this journal. The data used in the calculation may not be exhaustive.
Journal impact history
|2016 Journal impact ||Available summer 2017 |
|2015 Journal impact ||1.31 |
|2014 Journal impact ||1.26 |
|2013 Journal impact ||1.57 |
|2012 Journal impact ||1.38 |
|2011 Journal impact ||3.34 |
|2010 Journal impact ||1.41 |
|2009 Journal impact ||0.75 |
Journal impact over time
|Cited half-life ||3.80 |
|Immediacy index ||0.71 |
|Eigenfactor ||0.00 |
|Article influence ||0.00 |
|ISSN ||1662-5196 |
|OCLC ||250621701 |
|Material type ||Document, Internet resource |
|Document type ||Internet Resource, Computer File, Journal / Magazine / Newspaper |
This journal may support self-archiving.Learn more
Publications in this journal
[Show abstract] [Hide abstract] ABSTRACT: After decades of independent morphological and functional brain research, a key point in neuroscience nowadays is to understand the combined relationships between the structure of the brain and its components and their dynamics on multiple scales, ranging from circuits of neurons at micro or mesoscale to brain regions at macroscale. With such a goal in mind, there is a vast amount of research focusing on modeling and simulating activity within neuronal structures, and these simulations generate large and complex datasets which have to be analyzed in order to gain the desired insight. In such context, this paper presents ViSimpl, which integrates a set of visualization and interaction tools that provide a semantic view of brain data with the aim of improving its analysis procedures. ViSimpl provides 3D particle-based rendering that allows visualizing simulation data with their associated spatial and temporal information, enhancing the knowledge extraction process. It also provides abstract representations of the time-varying magnitudes supporting different data aggregation and disaggregation operations and giving also focus and context clues. In addition, ViSimpl tools provide synchronized playback control of the simulation being analyzed. Finally, ViSimpl allows performing selection and filtering operations relying on an application called NeuroScheme. All these views are loosely coupled and can be used independently, but they can also work together as linked views, both in centralized and distributed computing environments, enhancing the data exploration and analysis procedures.
[Show description] [Hide description] DESCRIPTION: Symbolic dynamics (Rajagopalan et al. 2007) in the context of EEG are highly interwoven with the notion of Lehmann's microstates (Lehmann et al. 1987, 2010). These are detectable in the multichannel signal, from both event-related and spontaneous activity recordings, as recurrent quasi-stable scalp potential maps lasting from tens to hundreds of milliseconds. Discrete symbols can be assigned to these microstates, and since they are characterized by great consistency across subjects, this offers a very efficient data reduction method and further facilitates the derivation of brain dynamics descriptors (Ville et al. 2010; Dimitriadis et al., 2012). However, the whole approach relies on signal amplitude characteristics and, hence, ignores aspects of neural coordination that involves genuine interactions beyond coincidence in timing. Inspired by the simplicity and effectiveness of microstates, we have recently introduced an alternative representation that aims at analyzing functional-connectivity patterns (Dimitriadis et al. 2013, 2015). The relevant microstates, functional connectivity microstates (FCμstates), proved to be convenient descriptors for tracking inter-areal synchronization during cognitive ERP responses and revealed dynamical trends in a parsimonious fashion.
We increase the descriptive power of the FCμstates approach by integrating a non-negative matrix factorization (NNMF) (Lee and Sung, 1999) algorithmic step. Here -for the first time- NNMF is applied to functional connectivity patterns, which are formed by inherently positively-valued measurements. The introduced approach starts by deriving time-indexed functional connectivity profiles based on pairwise, quasi-instantaneous, estimates of inter-areal phase synchronization. Time series of functional connectivity patterns are then formed (Lachaux et al. 1999). As a crucial step towards understanding the connectivity dynamics, non-negative matrix factorization (NNMF) is employed for data learning purposes. The extracted basis vectors are used for the re-parameterization of the functional connectivity within a ‘‘reduced’’ space. They correspond to the most essential ingredients for a parsimonious, part-based, representation of the original connectivity patterns. Within this low-dimensional, re-parameterized space, symbolization (by means of vector-quantization) (Martinetz et al. 1993) is performed and associated with the semantics of the brain's network organization. This steps transforms the sequence of connectivity patterns into a time series of symbols. Each symbol represents a homogeneous class of patterns, or equivalently, a connectivity microstate (FCμstate). The study of emerging symbolic dynamics can mediate the understanding of network-organization dynamics, since descriptors like Markov-Chains can now be applied in order to model complex neurodynamics phenomena. The whole procedure can be applied in different ways depending on the scope of each study. For instance, a subject-specific ‘alphabet’ of prototypical connectivity patterns can be designed and used to compare different recording conditions based on the recurrence of FCμstates. Alternatively, by adopting a collective design strategy (across a large population of subjects), commonalities in a population can be deduced that will reflect universal functional connectivity (re)organization trends.
The proposed methodology was applied to multichannel EEG signals, to investigate how music influences the spatiotemporal network profile of ongoing brain activity. Brain activity at rest and during music listening was recorded from 14 volunteers. The favorite song of each participant and a 'neutral' piece of music, common among the participants, were employed to bring a subject's brain in two distinct states. Our design aimed at detecting music induced changes (by comparing resting state vs. music listening), providing indications about subject's engagement to music (by contrasting 'neutral' vs. 'favorite' music) and facilitating the detection of commonalities across subjects (by delivering a common music track). The included results point to the importance of particular brain rhythms in shaping connectivity during listening to music, the realization of distinctive functional-connectivity microstates (organization patterns) and the emergence of music-related metastability phenomena (Tognoli and Kelso, 2014). Based on class separability and statistical analysis of the symbolic time series, we identified two microstates the presence/absence of which signals the states of resting/musing-listening. Using the two mined prototypical connectivity patterns, we attempted a network characterization (Sporns, 2011) of the most distinctive FCμstates. The most important difference between these two microstates was detected in the spatial layout of the connectivity strength.
Rajagopalan, V., Ray, A., Samsi, R., & Mayer, J. (2007). Pattern identification in dynamical systems via symbolic time series analysis. Pattern Recognition,40(11), 2897-2907.
Lehmann, D., Ozaki, H., & Pal, I. (1987). EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalography And Clinical Neurophysiology, 67(3), 271-288.
Lehmann, D., Pascual-Marqui, R., Strik, W., & Koenig, T. (2010). Core networks for visual-concrete and abstract thought content: A brain electric microstate analysis. Neuroimage, 49(1), 1073-1079.
Van de Ville, D., Britz, J., and Michel, C. M. Eeg microstate sequences in healthy humans at rest reveal scale-free dynamics. Proceedings of the National Academy of Sciences 107, 42 (2010), 18179–18184.
Dimitriadis SI, Laskaris NA, Tsirka V, Erimaki S, Vourkas M, Micheloyannis S, Fotopoulos S.A novel symbolization scheme for multichannel recordings with emphasis on phase information and its application to differentiate EEG activity from different mental tasks. Cognitive Neurodynamics, Volume 6, Number 1, 8 February 2012, pp.107-113.
Dimitriadis SI, Laskaris NA, Tzelepi A. On the quantization of time-varying phase synchrony patterns into distinct Functional Connectivity Microstates (FCμstates) in a multi-trial visual ERP paradigm, Volume 26, Issue 3, July 2013, pp 397-409
Dimitriadis SI, Laskaris NA, Micheloyannis S.Transition Dynamics of EEG Network Microstates unmask developmental and task differences during mental arithmetic and resting wakefulness. Cognitive Neurodynamics August 2015, Volume 9, Issue 4, pp 371-387
Lee, D. D., and Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 6755 (October 1999), 788–791.
Lachaux, J., Rodriguez, E., Martinerie, J., Varela, F., & others,. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8(4), 194-208.
Martinetz, T. M., Berkovich, S. G., & Schulten, K. J. (1993). Neural-gas' network for vector quantization and its application to time-series prediction. Neural Networks, IEEE Transactions on, 4(4), 558-569.
Tognoli E, Kelso JAS.The metastable brain. Neuron Volume 81, Issue 1, 8 January 2014, Pages 35–48
Sporns, O. Networks of the Brain. MIT press, 2011
[Show abstract] [Hide abstract] ABSTRACT: pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
[Show abstract] [Hide abstract] ABSTRACT: Brain magnetic resonance imaging provides detailed information which can be used to detect and segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1w and fluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small focal abnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1w image is first used to classify the three main tissues of the brain, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF), while the FLAIR image is then used to detect focal WML as outliers of its GM intensity distribution. A set of post-processing steps based on lesion size, tissue neighborhood, and location are used to refine the lesion candidates. The proposal is evaluated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the approach to automatically detect and segment lupus lesions. Besides, our approach is publicly available as a SPM8/12 toolbox extension with a simple parameter configuration.
Available from: escholarship.umassmed.edu
[Show abstract] [Hide abstract] ABSTRACT: Data sharing and reuse, while widely accepted as good ideas, have been slow to catch on in any concrete and consistent way. One major hurdle within the scientific community has been the lack of widely accepted standards for citing that data, making it difficult to track usage and measure impact. Within the neuroimaging community, there is a need for a way to not only clearly identify and cite datasets, but also to derive new aggregate sets from multiple sources while clearly maintaining lines of attribution. This work presents a functional prototype of a system to integrate Digital Object Identifiers (DOI) and a standardized metadata schema into a XNAT-based repository workflow, allowing for identification of data at both the project and image level. These item and source level identifiers allow any newly defined combination of images, from any number of projects, to be tagged with a new group-level DOI that automatically inherits the individual attributes and provenance information of its constituent parts. This system enables the tracking of data reuse down to the level of individual images. The implementation of this type of data identification system would impact researchers and data creators, data hosting facilities, and data publishers, but the benefit of having widely accepted standards for data identification and attribution would go far toward making data citation practical and advantageous.
[Show abstract] [Hide abstract] ABSTRACT: The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%.
[Show abstract] [Hide abstract] ABSTRACT: Brain research typically requires large amounts of data from different sources, and often of different nature. The use of different software tools adapted to the nature of each data source can make research work cumbersome and time consuming. It follows that data is not often used to its fullest potential thus limiting exploratory analysis. This paper presents an ancillary software tool called BRAVIZ that integrates interactive visualization with real-time statistical analyses, facilitating access to multi-facetted neuroscience data and automating many cumbersome and error-prone tasks required to explore such data. Rather than relying on abstract numerical indicators, BRAVIZ emphasizes brain images as the main object of the analysis process of individuals or groups. BRAVIZ facilitates exploration of trends or relationships to gain an integrated view of the phenomena studied, thus motivating discovery of new hypotheses. A case study is presented that incorporates brain structure and function outcomes together with different types of clinical data.
[Show abstract] [Hide abstract] ABSTRACT: Segmenting axon and myelin from microscopic images is relevant for studying the peripheral and central nervous system and for validating new MRI techniques that aim at quantifying tissue microstructure. While several software packages have been proposed, their interface is sometimes limited and/or they are designed to work with a specific modality (e.g., scanning electron microscopy (SEM) only). Here we introduce AxonSeg, which allows to perform automatic axon and myelin segmentation on histology images, and to extract relevant morphometric information, such as axon diameter distribution, axon density and the myelin g-ratio. AxonSeg includes a simple and intuitive MATLAB-based graphical user interface (GUI) and can easily be adapted to a variety of imaging modalities. The main steps of AxonSeg consist of: (i) image pre-processing; (ii) pre-segmentation of axons over a cropped image and discriminant analysis (DA) to select the best parameters based on axon shape and intensity information; (iii) automatic axon and myelin segmentation over the full image; and (iv) atlas-based statistics to extract morphometric information. Segmentation results from standard optical microscopy (OM), SEM and coherent anti-Stokes Raman scattering (CARS) microscopy are presented, along with validation against manual segmentations. Being fully-automatic after a quick manual intervention on a cropped image, we believe AxonSeg will be useful to researchers interested in large throughput histology. AxonSeg is open source and freely available at: https://github.com/neuropoly/axonseg.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.