Week 1: Introduction and Project Background

Hi everyone, welcome back to my senior project blog! Over the coming months, I’ll be building a rigorous scientific and technical foundation in EEG analysis, synaptic physiology, and biomarker discovery, working directly with clinical and preclinical datasets in collaboration with the Dr. Lee-Messer Lab. Being part of this lab has deepened my passion for applying computational tools and bioengineering to neurological disorders, a field I’m committed to contributing to in the future, whether through organizations or companies like Neuralink, Google DeepMind, or beyond.

Each post will walk through a distinct phase of my analysis, from background research and computational modeling to results interpretation and final outputs. My overarching goal is to work alongside my lab members to discover novel EEG biomarkers and develop a spike-detection framework for seizure prediction in both mice and humans with SYNGAP1-related epileptic encephalopathy, a genetic disorder caused by loss-of-function mutations in the SYNGAP1 gene that disrupt synaptic signaling, leading to severe epilepsy and cognitive impairment. But before I get started onto my project, I would like to go over some background information and terminology for this project.

First of all, an electroencephalogram, or more as known as EEG, is a non-invasive technique used to record the electrical activity of the brain. The brain is made up of billions of neurons that constantly communicate with each other by firing electrical signals. So when large groups of neurons fire together, they produce coordinated electrical patterns strong enough to be detected from the surface of the scalp through small sensors called electrodes as you may have seen with EEG headsets before. An EEG captures these patterns over time, producing a continuous waveform that reflects the brain’s activity in real time. These waveforms are organized into frequency bands, each associated with different brain states and functions. Delta waves (0.5 to 4 Hz) are linked to deep sleep, theta waves (4 to 8 Hz) to memory and drowsiness, alpha waves (8 to 13 Hz) to relaxed wakefulness, and beta and gamma waves to active thinking and sensory processing. In my research, analyzing the power within these bands, as well as how they interact with one another, is one of the core ways we look for differences between healthy and diseased brains in the search of novel EEG biomarkers predictive of SYNGAP1 disease progression. Currently, one of the main course texts for this research project, EEG for Beginners, is also focused on learning how to detect specific types of EEG spikes in recordings, extract and isolate specific frequency components to find meaningful signals, and preprocess and identify seizure-related EEG patterns, which I’m currently understanding as of now.

For example, our lab has already found that SYNGAP1 diseased mice show significantly elevated functional connectivity along the anterior-posterior axis of the brain, most strongly in the theta band. We measured this using a metric called the weighted phase lag index (wPLI), which quantifies the synchronization between two EEG signals to assess how different brain regions are communicating with one another. In the context of epilepsy, EEG is especially powerful because seizures and other abnormal events like spike-wave discharges leave distinct electrical spikes that can be identified visually or computationally. Beyond these classical frequency bands, we are also investigating activity at much higher frequencies, specifically high-frequency oscillations, or HFOs, which range from around 80 Hz up to 500 Hz and above. These oscillations are thought to represent activity originating from epileptogenic zones, the regions of the brain where seizures begin, and may serve as novel and sensitive EEG biomarkers for detecting and tracking epileptic activity. Together, this makes EEG one of the most important tools in both clinical neurology and neuroscience research.

Now, let me explain what SYNGAP1 is, it’s a gene that encodes a protein called synaptic protein critical for regulating communication between neurons at the synapse. The brain constantly adjusts how strong or weak individual synaptic connections are through a process called synaptic plasticity, which underlies learning, memory, and healthy cognitive function. So SYNGAP1 acts like this key brake on this process, and helps regulate the insertion and removal of AMPA receptors, which are these primary receptors responsible for fast excitatory transmission at synapses. So when SYNGAP1 is functioning normally, it keeps synaptic strengthening in check, ensuring that the brain maintains a healthy balance between excitation and inhibition, and if SYNGAP1 gene is missing, where only one functional copy of the gene is present rather than two, this regulation breaks down, making AMPA receptors to prematurely flood synapses earlier, and in greater numbers than they should. This makes these synaptic connections abnormally strong from early in development, and over time the brain loses its ability to adjust and strengthen those connections in response to new experiences. This saturation of synaptic strengthening mechanisms is one of the leading hypotheses for why SYNGAP1 mutations drive elevated seizure activity and significant cognitive impairment.

Based on this prior background research and knowledge, I’ve started developing the framework that I’ll apply to my project, which includes establishing a schedule for my lab visits and laying out the analytical skills I will be building over the coming months. The two major components I will be diving deeper into are automated HFO detection and the application of machine learning models to EEG data, both of which I will cover in much greater detail as the project progresses.

As of this week, I have successfully begun learning how to detect and analyze HFOs systematically using PyHFO, an open-source deep learning-based tool designed for automated HFO detection in EEG recordings. This has been a significant milestone, as it means I can now begin applying this tool directly to our lab’s dataset. Specifically, I am working to run PyHFO on EEG recordings from SYNGAP1 diseased mice provided by our collaborator Azin from the Knowles Lab, who has been collecting EEG data from these mice across different age points, specifically at P120 and P150, both before and after nortriptyline treatment, a drug that has shown promise in restoring normal synaptic signaling in SYNGAP1 models. Being able to systematically detect HFOs across these time points will allow us to start quantifying how pathological brain activity changes with disease progression and whether treatment has a measurable effect on these signals.

Now, this first week has been about setting the direction my research is heading, and as the project progresses, there will be increasingly more terminology, EEG signal processing techniques, and machine learning models that I’ll explain. Future posts will go much deeper into the practical side of things, including walkthroughs of how to use and apply tools like PyHFO and the results we are generating from our datasets. I’m looking forward to keeping you updated as the project develops over the next few months, and feel free to leave a comment below if there is anything you would like me to elaborate on or explain further!