Multifractal analysis of information processing in hippocampal neural ensembles during working memory under δ9-tetrahydrocannabinol administration

Dustin Fetterhoff, Ioan Opris, Sean L. Simpson, Sam A. Deadwyler, Robert E. Hampson, Robert A. Kraft

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Background: Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. New method: Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. Results: Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. Comparison with existing methods: WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. Conclusion: z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces.

Original languageEnglish (US)
Pages (from-to)136-153
Number of pages18
JournalJournal of Neuroscience Methods
StatePublished - Apr 15 2015
Externally publishedYes


  • Cannabinoid
  • Cognition
  • Delayed non-match to sample
  • Electrophysiology
  • Wavelet leaders
  • Working memory

ASJC Scopus subject areas

  • Neuroscience(all)


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