Pattern Dynamics and Stochasticity of Brain Rhythms and Spike Trains in a Tauopathy Mouse Model of Alzheimer’s Disease

Advisor: Yuri Dabaghian, PhD

Systems neuroscience posits that every aspect of perceived physical reality, every aspect of animal and human behavior, and every cognitive phenomenon emerges from patterns of neuronal activity. While most researchers embrace this idea, there are major difficulties in describing and analyzing these dynamics—spike flows produced by cells ensembles, synchronized extracellular field oscillations, and other patterns—which limits our understanding of how the activity of individual neurons and the whole-animal cognition and behavior might be connected. In particular, we lack the approaches and even the semantics for describing brain activity at intermediate spatiotemporal scales—the means for connecting the individual cell outputs and the integrated results of their activity. Current data analysis techniques focus either on instantaneous parameters, agnostic of protracted behaviors, or time-averaged characteristics, which highlight mesial trends. What remains unexplored, is the overall structure of neural dynamics, e.g., the temporal arrangement of peaks and troughs within brain waves, and the sequential organization of wave features, such as sharp wave ripples or spindles, the structure of spike trains, etc.

However, recent mathematical developments provide ideas about how neural dynamics may be described at circuit level. Over the last few years, I dedicated my research to developing methodologies for capturing these dynamics at a 'temporal mesoscale,' describing waveforms and spike patterns as single entities, without defeaturing, and putting each pattern, as a whole, into a statistical perspective. The resulting approach allows studying the structures of spike trains produced by the hippocampal place cells and interneurons, and patterns of theta (θ), gamma (γ), and ripple waves recorded in mice hippocampi. This has many practical implications. A host of studies are dedicated to cognitive impairments induced by aging and age-associated disorders, such as Alzheimer’s Disease (AD), psychoactive drugs, environmental toxins, and so forth. In fact, several studies established that these alterations correlate with changes in neuronal firing patterns, e.g., decreased spatial specificity of spiking, altered structure of oscillating extracellular fields.  

We identified speed-modulated changes of the wave’s cadence, an antiphase relationship between the orderliness of brain rhythms and the animal’s acceleration, the spatial selectiveness of waveforms and spiking patterns, and complex dependencies of wave and spike patterning on physiological states. In contrast, mouse models of tauopathy in AD exhibit a number of alterations in LFP rhythmicity, indicating circuit-level pathology. Namely, the coupling between wave dynamics and speed/acceleration is weak and spatial selectivity is lost, suggesting that information exchange within the AD brain is compromised.

Curiously, this approach allows attributing precise meaning to commonly used intuitive notions such as a brain wave’s 'regularity,' 'typicality,' or 'orderliness,' and affords distinguishing statistically mundane wave patterns from atypical ones as well as capturing transitions between them. Applying these analyses to local field potentials recorded in mice hippocampi and correlating the pattern dynamics with changes in the animals’ motor activity or other behavioral parameters, produces a fresh outlook on and provides a deeper understanding of hippocampal circuit dynamics and functionality.

Advisory Committee:
Yuri Dabaghian, PhD, Chair
Valentin Dragoi, PhD
Ruth Heidelberger, MD, PhD
Daoyun Ji, PhD
Harel Shouval, PhD
Edgar Walters, PhD

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