Dustin Fetterhoff1, Ioan Opris2, Sean L Simpson3, Sam A Deadwyler2, Robert E Hampson2, Robert A Kraft4. 1. Neuroscience Program, Wake Forest School of Medicine, Winston-Salem, NC, USA; Dept. of Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, USA. Electronic address: dfetterh@wakehealth.edu. 2. Dept. of Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, USA. 3. Dept. of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA. 4. Dept. of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC, USA.
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.
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.
Authors: Robert E Hampson; Dong Song; Ioan Opris; Lucas M Santos; Dae C Shin; Greg A Gerhardt; Vasilis Z Marmarelis; Theodore W Berger; Sam A Deadwyler Journal: J Neural Eng Date: 2013-11-12 Impact factor: 5.379
Authors: Stephen R Meier; Jarrett L Lancaster; Dustin Fetterhoff; Robert A Kraft; Robert E Hampson; Joseph M Starobin Journal: J Comput Neurosci Date: 2016-12-01 Impact factor: 1.621
Authors: Roman A Sandler; Dustin Fetterhoff; Robert E Hampson; Sam A Deadwyler; Vasilis Z Marmarelis Journal: PLoS Comput Biol Date: 2017-07-07 Impact factor: 4.475
Authors: Dustin Fetterhoff; Robert A Kraft; Roman A Sandler; Ioan Opris; Cheryl A Sexton; Vasilis Z Marmarelis; Robert E Hampson; Sam A Deadwyler Journal: Front Syst Neurosci Date: 2015-09-17