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.
Highlights
By analyzing the mono- and multifractal properties of neural temporal dynamics, we may generate new insights concerning how the brain functions with implications for detection of cognitive, physiological, and pathological states
To investigate dynamical interspike interval (ISI) patterns associated with different hippocampal microcircuit processing states, a comparision was made between those generated during the delayed nonmatch-to-sample (DNMS) task (Deadwyler et al, 1996; Hampson et al, 1999) and those occuring during a resting state after either vehicle or THC administration (Hampson and Deadwyler, 2000)
Our first hypothesis was verified by the finding that long range temporal correlations (LRTCs), indicated by the Hurst exponent, were decreased during task performance compared to the resting state (Figures 6A–C,F)
Summary
By analyzing the mono- and multifractal properties of neural temporal dynamics, we may generate new insights concerning how the brain functions with implications for detection of cognitive, physiological, and pathological states. Such analyses have been used successfully to detect pathological conditions such as heart disease (Ivanov et al, 1999), Alzheimer’s disease (Lahmiri and Boukadoum, 2013), Parkinson’s disease (Zheng et al, 2005), and epilepsy (Serletis et al, 2012; Dutta et al, 2014). Interactions across brain regions, detected as multifractal complexity, regularly fluctuate between task and rest conditions in regions associated with the task (Ciuciu et al, 2012). In order to assess cognitive state detection abilities, a paradigm was implemented to examine how multifractal complexity is reflected by active (i.e., task-related) hippocampal microcircuit processing
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