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Dendritic Spine Shape Analysis: A Clustering Perspective

  • Muhammad Usman Ghani
  • Ertunç Erdil
  • Sümeyra Demir Kanık
  • Ali Özgür Argunşah
  • Anna Felicity Hobbiss
  • Inbal Israely
  • Devrim Ünay
  • Tolga Taşdizen
  • Müjdat Çetin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9913)

Abstract

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.

Keywords

Dendritic spines Shape analysis Clustering x-means Microscopy Neuroimaging 

Notes

Acknowledgement

This work has been supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 113E603, and by TUBITAK-2218 Fellowship for Postdoctoral Researchers.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Muhammad Usman Ghani
    • 1
  • Ertunç Erdil
    • 1
  • Sümeyra Demir Kanık
    • 1
  • Ali Özgür Argunşah
    • 2
  • Anna Felicity Hobbiss
    • 2
  • Inbal Israely
    • 2
  • Devrim Ünay
    • 3
  • Tolga Taşdizen
    • 4
  • Müjdat Çetin
    • 1
  1. 1.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey
  2. 2.Champalimaud Neuroscience Programme, Champalimaud Centre for the UnknownLisbonPortugal
  3. 3.Faculty of Engineering and Computer SciencesIzmir University of EconomicsIzmirTurkey
  4. 4.Electrical and Computer Engineering DepartmentUniversity of UtahSalt Lake CityUSA

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