Späth, H.: Cluster dissection and analysis: theory, FORTRAN programs, examples. (Translator: Johannes Goldschmidt.) Ellis Horwood Ltd Wiley, Chichester 1985. 226 pp. £25

Mucha, Hans‐Joachim

doi:10.1002/bimj.4710280207

Cited by 9 publications

(6 citation statements)

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“…Single-particle data from the CCSEM-EDX analysis were imported into MATLAB R2010a (MathWorks, Inc.) for analysis. Particles were analyzed through queries on particle composition (Fe > 2%) and clustering using the k-means algorithm in the MATLAB toolbox. , Clustering algorithms have been applied to CCSEM-EDX data for over 20 years, with the most commonly used algorithm being adaptive resonance theory 2a (ART2a). , In more recent years, the use of clustering for real-time single-particle mass spectrometry analysis has shown that k-means is as effective as ART2a and is considerably simpler and faster to run . Further discussion of the application of clustering and error reduction is given in the Supporting Information.…”

Section: Discussionmentioning

confidence: 99%

Single-Particle SEM-EDX Analysis of Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources and Distribution of Iron within Cleveland, Ohio

Ault

Peters

Sawvel

et al. 2012

Environ. Sci. Technol.

134

116

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The physicochemical properties of coarse-mode, iron-containing particles and their temporal and spatial distributions are poorly understood. Single-particle analysis combining X-ray elemental mapping and computer-controlled scanning electron microscopy (CCSEM-EDX) of passively collected particles was used to investigate the physicochemical properties of iron-containing particles in Cleveland, OH, in summer 2008 (Aug-Sept), summer 2009 (July-Aug), and winter 2010 (Feb-March). The most abundant classes of iron-containing particles were iron oxide fly ash, mineral dust, NaCl-containing agglomerates (likely from road salt), and Ca-S containing agglomerates (likely from slag, a byproduct of steel production, or gypsum in road salt). The mass concentrations of anthropogenic fly ash particles were highest in the Flats region (downtown) and decreased with distance away from this region. The concentrations of fly ash in the Flats region were consistent with interannual changes in steel production. These particles were observed to be highly spherical in the Flats region, but less so after transport away from downtown. This change in morphology may be attributed to atmospheric processing. Overall, this work demonstrates that the method of passive collection with single-particle analysis by electron microscopy is a powerful tool to study spatial and temporal gradients in components of coarse particles. These gradients may correlate with human health effects associated with exposure to coarse-mode particulate matter.

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Section: Discussionmentioning

confidence: 99%

Single-Particle SEM-EDX Analysis of Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources and Distribution of Iron within Cleveland, Ohio

Ault

Peters

Sawvel

et al. 2012

Environ. Sci. Technol.

134

116

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“…In this study, we used L1 distance with 100 times replications and random centroid position initialization. We adopted the 'kmeans' function incorporated in Matlab (Mucha, 1986).…”

Section: Kmeans Clusteringmentioning

confidence: 99%

Assessing HD-EEG functional connectivity states using a human brain computational model

Tabbal¹,

Kabbara²,

Yochum³

et al. 2022

J. Neural Eng.

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Objective. Electro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool for tracking fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of pipeline steps is challenging due to the lack of realistic ‘controlled’ data. Here, our aim is two-folded: 1) provide a quantitative assessment of the advantages and limitations of the analyzed techniques and 2) introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. Approach. We used a human brain computational model containing both physiologically based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging, to generate high-density EEG recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas to emulate a virtual picture-naming task. We identified fast time-varying network states and quantified the performance of the key steps involved in the pipeline: 1) inverse models to reconstruct cortical-level sources, 2) functional connectivity measures to compute statistical interdependency between regional signals, and 3) dimensionality reduction methods to derive dominant brain network states (BNS). Main Results. Using a systematic evaluation of the different decomposition techniques, results show significant variability among tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, and the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. Significance. We suggest using such brain models to go further in the evaluation of the different steps and parameters involved in the EEG/MEG source-space network analysis. This can reduce the empirical selection of inverse model, connectivity measure, and dimensionality reduction method as some of the methods can have a considerable impact on the results and interpretation.

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“…In this study, we used L1 distance with 100 times replications and random centroid positions initialization. We adopted the 'kmeans' function incorporated in Matlab (Mucha, 1986).…”

Section: Kmeans Clusteringmentioning

confidence: 99%

Assessing HD-EEG functional connectivity states using a human brain computational model

Tabbal

Kabbara²,

Yochum

et al. 2021

Preprint

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Electro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool to track fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of the various steps, from source localization and functional connectivity to clustering algorithms, is challenging, due to the lack of realistic controlled data. Here, we used a human brain computational model containing both physiologically-based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging -based structural connectivity, to generate realistic High Density-EEG (256 channels) recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas in order to emulate a virtual picture naming task. We identified the fast time-varying network states and quantified the performance of the key steps involved in the pipeline: (1) inverse models to reconstruct cortical-level sources, (2) functional connectivity measures to compute statistical interdependency between regional time series, and (3) dimensionality reduction methods to derive dominant brain network states (BNS). Using a systematic evaluation of the different independent/principal/non-negative decomposition techniques along with a clustering approach, results show significant variability among the tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, as well as the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. Our aim here is twofold: 1) to provide quantitative assessment on the advantages and the limitations of each of the analyzed techniques and 2) to introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. With such framework, other tasks can be generated and used for validation and other methodological points can be also addressed.

show abstract

Späth, H.: Cluster dissection and analysis: theory, FORTRAN programs, examples. (Translator: Johannes Goldschmidt.) Ellis Horwood Ltd Wiley, Chichester 1985. 226 pp. £25

Cited by 9 publications

References 0 publications

Single-Particle SEM-EDX Analysis of Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources and Distribution of Iron within Cleveland, Ohio

Single-Particle SEM-EDX Analysis of Iron-Containing Coarse Particulate Matter in an Urban Environment: Sources and Distribution of Iron within Cleveland, Ohio

Assessing HD-EEG functional connectivity states using a human brain computational model

Assessing HD-EEG functional connectivity states using a human brain computational model

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