Derivative temporal clustering analysis: detecting prolonged neuronal activity

Xia Zhao, Geng Li, David C. Glahn, Peter T. Fox, Jia Hong Gao

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Temporal clustering analysis (TCA) and independent component analysis (ICA) are promising data-driven techniques in functional magnetic resonance imaging (fMRI) experiments to obtain brain activation maps in conditions with unknown temporal information regarding the neuronal activity. Although comparable to ICA in detecting transient neuronal activities, TCA fails to detect prolonged plateau brain activations. To eliminate this pitfall, a novel derivative TCA (DTCA) method was introduced and its algorithms with different subtraction intervals were tested on simulated data with a pattern of prolonged plateau brain activation. It was found that the best performance of DTCA method in generating functional maps could be obtained if the subtraction interval is equal to or larger than the length of the rising time of the fMRI response. The DTCA method and its theoretical predication were further investigated and validated using in vivo fMRI data sets. By removing the limitations in the previous TCA, DTCA has shown its powerful capability in detecting prolonged plateau neuronal activities.

Original languageEnglish (US)
Pages (from-to)183-187
Number of pages5
JournalMagnetic Resonance Imaging
Issue number2
StatePublished - Feb 1 2007


  • Data processing method
  • MRI
  • Paradigm independent
  • Plateau brain activation
  • fMRI

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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