Research:

My focus is in Cognitive
Neuroscience
research.

Research Interests

The brain provides an endless source of challenging philosophical, psychological, neuroscientific, and computational problems. During my time as an undergraduate at the University of Toronto majoring in Neuroscience, Cognitive Science, and minoring in Computer Science, I worked in the Duncan Memory Lab and became very interested in the role of the hippocampus in human cognition. Some of my recent work includes the release of an open-source research tool for generating virtual 3D environments for memory tasks. I am currently working on a novel computational neural network model of cholinergic activity in the hippocampus to help better understand the effects of acetylcholine on pattern completion and pattern separation tasks.

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Select Research

Computational Neural Network Model of Cholinergic Activity in the Hippocampus

Alex Gordienko

Abstract

An increasing body of research indicates that high cholinergic states in the hippocampus improve encoding ability while not affecting retrieval. While there exist previous computational models of cholinergic activity in the hippocampus (Hasselmo, 2006), they lack the breadth and ability of more recent hippocampal models which incorporate theta rhythms and error-driven learning, increasing model capacity and yielding more robust results (Ketz et al., 2013). Here, I instantiate a neural network model of cholinergic effect in the hippocampus, capable of executing pattern separation tasks under various levels of simulated cholinergic activity. Model testing revealed that high internal cholinergic states would improve performance on pattern separation tasks while low cholinergic states would impair performance - results that are in line with previous literature. This work provides a foundation from which to address further questions about cholinergic effects on the hippocampus through a modernized simulation.

OpenMaze: An Open-Source Toolbox for Creating Virtual Environment Experiments

Kyle Alsbury-Nealy, Hongyu Wang, Cody Howarth, Alex Gordienko, Margaret L. Schlichting, Katherine D. Duncan

Abstract

Incorporating 3D virtual environments into psychological experiments offers an innovative solution for balancing experimental control and ecological validity. Their flexible use, however, has been limited to those researchers with extensive coding experience because the field lacks accessible development tools. We created OpenMaze, an open-source toolbox for the Unity game engine, to overcome this barrier. OpenMaze offers researchers the ability to conduct a wide range of 3D spatial navigation experiment paradigms in fully customized 3D environments. Crucially, because all experiment configurations are defined in user-friendly JavaScript Object Notation (JSON) files, our toolbox allows even those with no prior coding experience to build bespoke tasks. OpenMaze is also compatible with a variety of input devices and operating systems, broadening its possible applications. To demonstrate its advantages, we review and contrast other available software options before guiding the reader through building an experiment in OpenMaze.

The Effect of Neurodegenerative Disease on the Economy of Words

Alex Gordienko

Abstract

Though it is known that neurodegenerative disease affects word use and that depression impacts the number of first-person pronouns used in written text, it is yet to be seen whether or not individuals with depression have significant changes in word frequency and use as they age. This study makes use of various word frequency metrics to investigate whether or not the progression of neurodegenerative disease affects word frequency. The primary hypothesis is that that neurodegenerative disease affects original prose writing and causes texts to divert away from standard word frequency distributions as the authors age by either significantly increasing or decreasing frequency of word usage. The secondary hypothesis is that authors with depression use the word “I” more often in writing than non-depressed authors. Results demonstrated that there was no significant difference between test and control groups both in word frequency distribution and in first-person pronoun use. Future studies should aim to obtain a more robust and varied data set and construct a predictive algorithm for determining the probability of an author's depression based on their produced literature throughout their lifetime.

Predicting Patient Responsiveness to rTMS Depression Treatment by Leveraging EEG Data.

Alex Gordienko

Abstract

Though it is known that repetitive transcranial magnetic stimulation (rTMS) works for some patients with treatment resistant depression (TRD), a significant number of TRD patients do not respond to rTMS. Since it is resource-intensive for patients and clinics to conduct rTMS treatments, a predictive model based on accessible and non-invasively collected data for determining which patients will or will not react to rTMS would prove very useful. While predictors do exist, the data needed for them are often challenging to acquire or the models are not very accurate. This review looks at a recent study that uses EEG results to predict TRD patient responsiveness. It was found that responders have a consistently higher level of fronto-midline theta power than non-responders and that gamma-connectivity increases in responders while staying the same in non-responders as treatment continues. Using 30 features from EEG data across 39 participants, a predictive model was created and demonstrated a 91% accuracy for determining if a given patient will respond to rTMS treatment or not.

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© Alex Gordienko 2021