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 journalArticle

7 Citations (Scopus)

Abstract

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
Volume244
DOIs
StatePublished - Apr 5 2014
Externally publishedYes

Fingerprint

Dronabinol
Automatic Data Processing
Short-Term Memory
Neurons
Brain-Computer Interfaces
Cannabinoid Receptors
Nonlinear Dynamics

Keywords

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

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Multifractal analysis of information processing in hippocampal neural ensembles during working memory under δ9-tetrahydrocannabinol administration. / Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L.; Deadwyler, Sam A.; Hampson, Robert E.; Kraft, Robert A.

In: Journal of Neuroscience Methods, Vol. 244, 05.04.2014, p. 136-153.

Research output: Contribution to journalArticle

Fetterhoff, Dustin ; Opris, Ioan ; Simpson, Sean L. ; Deadwyler, Sam A. ; Hampson, Robert E. ; Kraft, Robert A. / Multifractal analysis of information processing in hippocampal neural ensembles during working memory under δ9-tetrahydrocannabinol administration. In: Journal of Neuroscience Methods. 2014 ; Vol. 244. pp. 136-153.
@article{fe98f4185cd948499e894196ecfa6c5c,
title = "Multifractal analysis of information processing in hippocampal neural ensembles during working memory under δ9-tetrahydrocannabinol administration",
abstract = "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.",
keywords = "Cannabinoid, Cognition, Delayed non-match to sample, Electrophysiology, Wavelet leaders, Working memory",
author = "Dustin Fetterhoff and Ioan Opris and Simpson, {Sean L.} and Deadwyler, {Sam A.} and Hampson, {Robert E.} and Kraft, {Robert A.}",
year = "2014",
month = "4",
day = "5",
doi = "10.1016/j.jneumeth.2014.07.013",
language = "English (US)",
volume = "244",
pages = "136--153",
journal = "Journal of Neuroscience Methods",
issn = "0165-0270",
publisher = "Elsevier",

}

TY - JOUR

T1 - Multifractal analysis of information processing in hippocampal neural ensembles during working memory under δ9-tetrahydrocannabinol administration

AU - Fetterhoff, Dustin

AU - Opris, Ioan

AU - Simpson, Sean L.

AU - Deadwyler, Sam A.

AU - Hampson, Robert E.

AU - Kraft, Robert A.

PY - 2014/4/5

Y1 - 2014/4/5

N2 - 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.

AB - 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.

KW - Cannabinoid

KW - Cognition

KW - Delayed non-match to sample

KW - Electrophysiology

KW - Wavelet leaders

KW - Working memory

UR - http://www.scopus.com/inward/record.url?scp=84939945698&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84939945698&partnerID=8YFLogxK

U2 - 10.1016/j.jneumeth.2014.07.013

DO - 10.1016/j.jneumeth.2014.07.013

M3 - Article

C2 - 25086297

AN - SCOPUS:84939945698

VL - 244

SP - 136

EP - 153

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

ER -