Skip to main content
Journal cover image

Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.

Publication ,  Journal Article
Lu, Y; Carin, L; Coifman, R; Shain, W; Roysam, B
Published in: Neuroinformatics
January 2015

This paper presents a robust unsupervised harmonic co-clustering method for profiling arbor morphologies for ensembles of reconstructed brain cells (e.g., neurons, microglia) based on quantitative measurements of the cellular arbors. Specifically, this method can identify groups and sub-groups of cells with similar arbor morphologies, and simultaneously identify the hierarchical grouping patterns among the quantitative arbor measurements. The robustness of the proposed algorithm derives from use of the diffusion distance measure for comparing multivariate data points, harmonic analysis theory, and a Haar-like wavelet basis for multivariate data smoothing. This algorithm is designed to be practically usable, and is embedded into the actively linked three-dimensional (3-D) visualization and analytics system in the free and open source FARSIGHT image analysis toolkit for interactive exploratory population-scale neuroanatomic studies. Studies on synthetic datasets demonstrate its superiority in clustering data matrices compared to recent hierarchical clustering algorithms. Studies on heterogeneous ensembles of real neuronal 3-D reconstructions drawn from the NeuroMorpho database show that the proposed method identifies meaningful grouping patterns among neurons based on arbor morphology, and revealing the underlying morphological differences.

Duke Scholars

Published In

Neuroinformatics

DOI

EISSN

1559-0089

ISSN

1539-2791

Publication Date

January 2015

Volume

13

Issue

1

Start / End Page

47 / 63

Related Subject Headings

  • Pattern Recognition, Automated
  • Neurology & Neurosurgery
  • Image Processing, Computer-Assisted
  • Humans
  • Brain Mapping
  • Brain
  • Animals
  • Algorithms
  • 5202 Biological psychology
  • 3209 Neurosciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lu, Y., Carin, L., Coifman, R., Shain, W., & Roysam, B. (2015). Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure. Neuroinformatics, 13(1), 47–63. https://doi.org/10.1007/s12021-014-9237-2
Lu, Yanbin, Lawrence Carin, Ronald Coifman, William Shain, and Badrinath Roysam. “Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.Neuroinformatics 13, no. 1 (January 2015): 47–63. https://doi.org/10.1007/s12021-014-9237-2.
Lu Y, Carin L, Coifman R, Shain W, Roysam B. Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure. Neuroinformatics. 2015 Jan;13(1):47–63.
Lu, Yanbin, et al. “Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure.Neuroinformatics, vol. 13, no. 1, Jan. 2015, pp. 47–63. Epmc, doi:10.1007/s12021-014-9237-2.
Lu Y, Carin L, Coifman R, Shain W, Roysam B. Quantitative arbor analytics: unsupervised harmonic co-clustering of populations of brain cell arbors based on L-measure. Neuroinformatics. 2015 Jan;13(1):47–63.
Journal cover image

Published In

Neuroinformatics

DOI

EISSN

1559-0089

ISSN

1539-2791

Publication Date

January 2015

Volume

13

Issue

1

Start / End Page

47 / 63

Related Subject Headings

  • Pattern Recognition, Automated
  • Neurology & Neurosurgery
  • Image Processing, Computer-Assisted
  • Humans
  • Brain Mapping
  • Brain
  • Animals
  • Algorithms
  • 5202 Biological psychology
  • 3209 Neurosciences