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RESEARCH & DISCOVERIES Current Areas of Study SCANNING ELECTRON MICROSCOPY April 12, 2025 CELL CULTURE April 12, 2025 GENE SEQUENCING April 12, 2025

NEWS Stay Informed About Brain Hygiene Lab TOP DISCOVERIES OF THE DECADE April 12, 2025 EXCITING FINDINGS AT BRAIN HYGIENE LAB April 12, 2025 A REVOLUTIONARY SCIENTIFIC RESEARCH LABORATORY April 12, 2025

BRAIN HYGIENE LAB We aim to better understand the neurobiological underpinnings of how people successfully adapt to stress. By studying non-pharmacological ways to enhance brain health, we can evaluate topics as diverse as the effects of aerobic exercise on the brain to cognitive training programs designed to enhance interoceptive awareness and attention control. What if "difficult art" triggers the human imagination in really helpful ways? Art of Attention Nathaniel Thom About me I've always enjoyed the process of teaching and mentoring so I'm glad to have settled in at Wheaton College in the Biology Department. I teach a variety of courses including introductory biology, advanced cell and developmental biology, anatomy and physiology, and several neuroscience courses. Learn more about my approach to teaching and mentoring. In terms of research, I am interested in how to protect the brain from the deleterious effects of stress and in the evaluation of programs that aim to improve health through behavioral changes. My wife and I moved to Wheaton from San Diego, CA. While in SoCal, we lived in one of the most diverse neighborhoods in the city, with multi-million dollar homes less than six blocks away from extreme poverty. Through a variety of programs that I've labeled "The Pipeline" students can get involved in public health issues in SD. Read More

People Dedication. Expertise. Passion. Whether you are a fellow researcher, a prospective student, or simply interested in our work, we invite you to get to know our team, past and present. As a dedicated group of researchers, we share a common passion for advancing knowledge, exploring groundbreaking ideas, and collaborating on cutting-edge projects. 2025 Stone Preston Neuroscience ​ 2024 Madelyn Hartrim-Lowe Biology + Economics ​ 2024 Gloria Kim Computer Science + Psychology ​ 2023 Jessica Sun Biology ​ 2018 Paul Ha Biology After graduation, I hope to study in medical school to pursue a career in pediatrics. I am a member of Koinonia's praise band, and I also serve as a Sunday school teacher at a local church. 2018 Nathan Houlihan Biology Nathan has worked for Dr. Thom for 1 semester on several projects including the HRV aid worker project. He plans to attend medical school next year. 1 2 3 4 1 ... 1 2 3 4 ... 4 Local Students This is your Team section. It's a great place to introduce your team and talk about what makes it special, such as your culture and work philosophy. Don't be afraid to illustrate personality and character to help users connect with your team. Johann Van Nispen 2015 WashU Tory Leonard 2015 University of Iowa Stephanie Chen 2016 UCLA Kevin Chow 2016 UIC 1 2 1 ... 1 2 ... 2

< Back EEG Pipeline BESA (Updated 2023) 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

< Back HRV An active brain communicates with the body via the somatic and autonomic nervous systems. The somatic system manages voluntary muscle movement, while the autonomic system regulates internal organs during stress. It comprises the sympathetic and parasympathetic branches, and heart-rate variability measurement indicates their influence on the heart. The Life Events Checklist (LEC-5) is a standardized questionnaire that gathers information about traumatic life experiences. The LEC-5 provides an insight into one's life experiences and gives context to HRV and EEG data. LEC-5 The Pittsburgh Sleep Quality Index (PSQI) is used to determine the quality of your sleep in the last month. It is no secret that poor sleep is related to stress. The PSQI provides valuable information to our research team. PSQI The Patient Health Questionnaire-9 (PHQ-9) is used to determine the Major Depression Disorder (MDD) symptoms. Depression symptoms can help explain interoceptive data. PHQ-9 The Profile of Mood States (POMS) questionnaire takes record of the participant's mood over the past 7 days. It looks at mood states like anger, fear, depression, etc. POMS The Mini Screen is a survey that screens for psychiatric disorders. This includes eating disorders, depression, obsessive compulsive disorder, PTSD, and other. Mini Screen The Mini Screen Manual explains the results of the Mini Screen. Mini Screen The Generalized Anxiety Disorder-7 (GAD-7) questionnaire is used to determine the Anxiety symptoms. Anxiety is linked to other psychological disorders like PTSD. Anxiety symptoms can help explain interoceptive data. GAD-7 The Connor-Davidson Resilience Scale (CD-RISC) measures resilience. This short survey attempts to gauge how well one responds to stress and adversity. CD-RISC

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download

EEG Pipeline BESA - Updated 2023 1 Collect Data There are hundreds of sites with free, downloadable EEG data that can be imported into BESA. Next 2 Import Data After choosing a dataset, isolate the raw EEG data from the metadata and other contextual information about the experiment. Next 3 Re-reference Data You need to convert the data to an average reference. Next 4 Set Channel Locations Select the files that specifies the channel locations. Next 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. Next 6 Filtering We use filters to take out signals that are definitley not brain data. You need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz), as well as the 60 Hz line noise from the ambient electrical wires. Next 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. Next 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. Next 9 Baseline Removal The signal recorded just before the stimulus is considered the baseline. This is comprised of neural activity that is not in response to the stimulus as well as noise. This baseline is subtracted from the entire epoch, leaving the neural activity that occured in response to the stimulus. Next 10 Component Rejection Once you've run ICA on a dataset, it is possible to reject non-neural signal sources. This section explains how to do that and explains the Sasica plug in. Next 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. Next 12 Averaging Trials Separating the data for each stimulus type into its own file. Next 13 Check the Data Head Plots Plot the head maps for the time ranges of interest ​ Download Waveforms Plot the waveforms of the averaged data ​ Download Grand Averaging Averaging all of the data of one type together (such as all face data / scene data) Download 14 Analyze the Data Average Amplitudes Wavelet Analysis Fourier Transforms Phase Coherence Download Resources Filename Key Download Automation Scripts Download EEGLAB Scripts Download Other Download