Author ORCID Identifier
0000-0002-7791-9483
Date of Graduation
5-2026
Document Type
Dissertation (PhD)
Program Affiliation
Quantitative Sciences
Degree Name
Doctor of Philosophy (PhD)
Advisor/Committee Chair
Yuri Dabaghian
Committee Member
Valentin Dragoi
Committee Member
Ruth Heidelberger
Committee Member
Daoyun Ji
Committee Member
Harel Shouval
Committee Member
Edgar Terry Walters
Abstract
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 complex neuronal 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 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 series of sharp wave ripples or spindles, the structure of spike trains, etc.
However, recent mathematical developments provide ideas about how circuit dynamics may be described. 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. For example, 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.
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. These dependencies are altered in mouse models of tauopathy, which indicates circuit-level pathology compromises information exchange within the AD brain.
Recommended Citation
Hoffman, Clarissa M., "Pattern Dynamics and Stochasticity of Brain Rhythms and Spike Trains in a Tauopathy Mouse Model of Alzheimer’s Disease" (2026). Dissertations & Theses (Open Access). 1528.
https://digitalcommons.library.tmc.edu/utgsbs_dissertations/1528
Keywords
Neuroscience, Tauopathy, Stochasticity, Networks, Brain Waves, Dynamics, Patterns
Included in
Bioinformatics Commons, Dynamic Systems Commons, Longitudinal Data Analysis and Time Series Commons, Nervous System Diseases Commons, Neurology Commons, Statistical Models Commons