Dataset Information

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

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.

SUBMITTER: Fetterhoff D

PROVIDER: S-EPMC4585000 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Fetterhoff Dustin D Kraft Robert A RA Sandler Roman A RA Opris Ioan I Sexton Cheryl A CA Marmarelis Vasilis Z VZ Hampson Robert E RE Deadwyler Sam A SA

Frontiers in systems neuroscience 20150917

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 follo ...[more]

PMID: 26441562

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Project description:The generation of neural action potentials (spikes) is random but nevertheless may result in a rich statistical structure of the spike sequence. In particular, contrary to the popular renewal assumption of theoreticians, the intervals between adjacent spikes are often correlated. Experimentally, different patterns of interspike-interval correlations have been observed and computational studies have identified spike-frequency adaptation and correlated noise as the two main mechanisms that can lead to such correlations. Analytical studies have focused on the single cases of either correlated (colored) noise or adaptation currents in combination with uncorrelated (white) noise. For low-pass filtered noise or adaptation, the serial correlation coefficient can be approximated as a single geometric sequence of the lag between the intervals, providing an explanation for some of the experimentally observed patterns. Here we address the problem of interval correlations for a widely used class of models, multidimensional integrate-and-fire neurons subject to a combination of colored and white noise sources and a spike-triggered adaptation current. Assuming weak noise, we derive a simple formula for the serial correlation coefficient, a sum of two geometric sequences, which accounts for a large class of correlation patterns. The theory is confirmed by means of numerical simulations in a number of special cases including the leaky, quadratic, and generalized integrate-and-fire models with colored noise and spike-frequency adaptation. Furthermore we study the case in which the adaptation current and the colored noise share the same time scale, corresponding to a slow stochastic population of adaptation channels; we demonstrate that our theory can account for a nonmonotonic dependence of the correlation coefficient on the channel's time scale. Another application of the theory is a neuron driven by network-noise-like fluctuations (green noise). We also discuss the range of validity of our weak-noise theory and show that by changing the relative strength of white and colored noise sources, we can change the sign of the correlation coefficient. Finally, we apply our theory to a conductance-based model which demonstrates its broad applicability.

Project description:The diverse prokaryotic communities associated with reef-building corals may provide important ecological advantages to their threatened hosts. The consistency of relationships between corals and specific prokaryotes, however, is debated, and the locations where microbially mediated processes occur in the host are not resolved. Here, we examined how the prokaryotic associates of five common Caribbean corals with different evolutionary and ecological traits differ across mucus and tissue habitats. We used physical and chemical separation of coral mucus and tissue and sequencing of partial small-subunit rRNA genes of bacteria and archaea from these samples to demonstrate that coral tissue and mucus harbor unique reservoirs of prokaryotes, with 23 to 49% and 31 to 56% of sequences exclusive to the tissue and mucus habitats, respectively. Across all coral species, we found that 46 tissue- and 22 mucus-specific microbial members consistently associated with the different habitats. Sequences classifying as "Candidatus Amoebophilus," Bacteroidetes-affiliated intracellular symbionts of amoebae, emerged as previously unrecognized tissue associates of three coral species. This study demonstrates how coral habitat differentiation enables highly resolved examination of ecological interactions between corals and their associated microorganisms and identifies previously unrecognized tissue and mucus associates of Caribbean corals for future targeted study. IMPORTANCE This study demonstrates that coral tissue or mucus habitats structure the microbiome of corals and that separation of these habitats facilitates identification of consistent microbial associates. Using this approach, we demonstrated that sequences related to "Candidatus Amoebophilus," recognized intracellular symbionts of amoebae, were highly associated with the tissues of Caribbean corals and possibly endosymbionts of a protistan host within corals, adding a further degree of intricacy to coral holobiont symbioses. Examining specific habitats within complex hosts such as corals is useful for targeting important microbial associations that may otherwise be masked by the sheer microbial diversity associated with all host habitats.

Dataset Information

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

Publications

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

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