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Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences.

Fetterhoff D, Kraft RA, Sandler RA, Opris I, Sexton CA, Marmarelis VZ, Hampson RE, Deadwyler SA - Front Syst Neurosci (2015)

Bottom Line: Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses.These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality.Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.

View Article: PubMed Central - PubMed

Affiliation: Neuroscience Program, Wake Forest School of Medicine Winston-Salem, NC, USA ; Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA.

ABSTRACT
Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.

No MeSH data available.


Related in: MedlinePlus

Distinction between recording phases and drug conditions using frequency spectra and spike train variability measures. Each bar was obtained by averaging values from individual spike trains within specified recording phase and drug treatment combinations (n = 771–1004 neurons per group). Vehicle (blue) or THC (green) was given after pre-task recording, so measures obtained during the pre-task recording should be the same for drug conditions. Statistical significance is designated by * indicating p < 0.0083 (Bonferoni correction). Error bars represent S.E.M. (A) Repeated measures ANOVA using coefficient of variation (CV) as the dependent variable yielded a significant main effect of recording phase but no significant interaction between drug condition and recording phase. CV was greater during pre-task recordings compared to recordings during the DNMS task. (B) A significant interaction between recording phase and drug condition revealed that ISI standard deviation is greater during the task compared to pre- and post-task recordings. (C) A significant interaction between recording phase and drug condition revealed that ISIs recorded under vehicle administration were larger during the task vs. pre- and post-task conditions. THC increased mean ISI only during the post-task recording but had no effect during the task. (D) A significant main effect of recording phase was found when assessing delta power: Delta power was larger during task and post-task vs. pre-task sessions. (E) Theta power of hippocampal neurons was higher during the task-independent (pre- and post-task) resting phases compared to during DNMS task performance.
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Figure 8: Distinction between recording phases and drug conditions using frequency spectra and spike train variability measures. Each bar was obtained by averaging values from individual spike trains within specified recording phase and drug treatment combinations (n = 771–1004 neurons per group). Vehicle (blue) or THC (green) was given after pre-task recording, so measures obtained during the pre-task recording should be the same for drug conditions. Statistical significance is designated by * indicating p < 0.0083 (Bonferoni correction). Error bars represent S.E.M. (A) Repeated measures ANOVA using coefficient of variation (CV) as the dependent variable yielded a significant main effect of recording phase but no significant interaction between drug condition and recording phase. CV was greater during pre-task recordings compared to recordings during the DNMS task. (B) A significant interaction between recording phase and drug condition revealed that ISI standard deviation is greater during the task compared to pre- and post-task recordings. (C) A significant interaction between recording phase and drug condition revealed that ISIs recorded under vehicle administration were larger during the task vs. pre- and post-task conditions. THC increased mean ISI only during the post-task recording but had no effect during the task. (D) A significant main effect of recording phase was found when assessing delta power: Delta power was larger during task and post-task vs. pre-task sessions. (E) Theta power of hippocampal neurons was higher during the task-independent (pre- and post-task) resting phases compared to during DNMS task performance.

Mentions: The singularity and frequency spectra for two different example neurons are shown in Figure 4 while the population singularity spectra are shown in Figure 5. Repeated measures ANOVA results from population analyses are briefly mentioned here and presented fully in the subsequent sections. The first example permits comparision of all three recording phases taken from vehicle treatment conditions (Figures 4A,B, 5B). One example neuron exhibits increased multifractal complexity during the DNMS compared to either resting state recordings (Figure 4A). The frequency spectra for this same neuron exhibits both delta and theta power in all recording phases (Figure 4B). This neuron illustrates the same effect found in the population (Figure 5B): multifractal complexity (width) increases from post-task to pre-task to task (Figure 7F) and LRTCs (Hurst exponent) are larger during the resting states (pre- and post-task) compared to the task (Figure 6F). Although the singularity spectra are discernable across task phases for this neuron, the frequency spectra were not (Figure 4B). However, the population analyses revealed increased theta power during vehicle resting state recordings (pre- and post-task) compared to vehicle task recordings (Figure 8E).


Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences.

Fetterhoff D, Kraft RA, Sandler RA, Opris I, Sexton CA, Marmarelis VZ, Hampson RE, Deadwyler SA - Front Syst Neurosci (2015)

Distinction between recording phases and drug conditions using frequency spectra and spike train variability measures. Each bar was obtained by averaging values from individual spike trains within specified recording phase and drug treatment combinations (n = 771–1004 neurons per group). Vehicle (blue) or THC (green) was given after pre-task recording, so measures obtained during the pre-task recording should be the same for drug conditions. Statistical significance is designated by * indicating p < 0.0083 (Bonferoni correction). Error bars represent S.E.M. (A) Repeated measures ANOVA using coefficient of variation (CV) as the dependent variable yielded a significant main effect of recording phase but no significant interaction between drug condition and recording phase. CV was greater during pre-task recordings compared to recordings during the DNMS task. (B) A significant interaction between recording phase and drug condition revealed that ISI standard deviation is greater during the task compared to pre- and post-task recordings. (C) A significant interaction between recording phase and drug condition revealed that ISIs recorded under vehicle administration were larger during the task vs. pre- and post-task conditions. THC increased mean ISI only during the post-task recording but had no effect during the task. (D) A significant main effect of recording phase was found when assessing delta power: Delta power was larger during task and post-task vs. pre-task sessions. (E) Theta power of hippocampal neurons was higher during the task-independent (pre- and post-task) resting phases compared to during DNMS task performance.
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Figure 8: Distinction between recording phases and drug conditions using frequency spectra and spike train variability measures. Each bar was obtained by averaging values from individual spike trains within specified recording phase and drug treatment combinations (n = 771–1004 neurons per group). Vehicle (blue) or THC (green) was given after pre-task recording, so measures obtained during the pre-task recording should be the same for drug conditions. Statistical significance is designated by * indicating p < 0.0083 (Bonferoni correction). Error bars represent S.E.M. (A) Repeated measures ANOVA using coefficient of variation (CV) as the dependent variable yielded a significant main effect of recording phase but no significant interaction between drug condition and recording phase. CV was greater during pre-task recordings compared to recordings during the DNMS task. (B) A significant interaction between recording phase and drug condition revealed that ISI standard deviation is greater during the task compared to pre- and post-task recordings. (C) A significant interaction between recording phase and drug condition revealed that ISIs recorded under vehicle administration were larger during the task vs. pre- and post-task conditions. THC increased mean ISI only during the post-task recording but had no effect during the task. (D) A significant main effect of recording phase was found when assessing delta power: Delta power was larger during task and post-task vs. pre-task sessions. (E) Theta power of hippocampal neurons was higher during the task-independent (pre- and post-task) resting phases compared to during DNMS task performance.
Mentions: The singularity and frequency spectra for two different example neurons are shown in Figure 4 while the population singularity spectra are shown in Figure 5. Repeated measures ANOVA results from population analyses are briefly mentioned here and presented fully in the subsequent sections. The first example permits comparision of all three recording phases taken from vehicle treatment conditions (Figures 4A,B, 5B). One example neuron exhibits increased multifractal complexity during the DNMS compared to either resting state recordings (Figure 4A). The frequency spectra for this same neuron exhibits both delta and theta power in all recording phases (Figure 4B). This neuron illustrates the same effect found in the population (Figure 5B): multifractal complexity (width) increases from post-task to pre-task to task (Figure 7F) and LRTCs (Hurst exponent) are larger during the resting states (pre- and post-task) compared to the task (Figure 6F). Although the singularity spectra are discernable across task phases for this neuron, the frequency spectra were not (Figure 4B). However, the population analyses revealed increased theta power during vehicle resting state recordings (pre- and post-task) compared to vehicle task recordings (Figure 8E).

Bottom Line: Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses.These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality.Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.

View Article: PubMed Central - PubMed

Affiliation: Neuroscience Program, Wake Forest School of Medicine Winston-Salem, NC, USA ; Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA.

ABSTRACT
Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.

No MeSH data available.


Related in: MedlinePlus