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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 Questionnaires Surveys and questionnaires collect qualitative information that provides a detailed summary of your experiences. Researchers use these surveys to get to know you, to interpret your EEG and HRV data, and to develop a comprehensive, personalized Behavioral Health Report. 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

< Back fNIRS Brain cells require oxygen and fuel for metabolism, which is supplied by the blood. Near-infrared light passing through the scalp can measure oxygenation levels in the blood and its return to the scalp, offering spatially precise metrics for assessing brain activity and function. 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

< Back EEG Pipeline EEG stands for electroencephalography, which is a technique used to measure electrical activity in the brain. EEG is a non-invasive way to collect data from the brain. Follow this pipeline to collect and analyze EEG data. 1 Collect Data The recorded data will be saved as a .mff file. This will need to be moved to the appropriate folder in Box. 2 Import Data The data will need to be opened in BESA on one of the analysis computers. 3 Re-reference Data You need to convert the data to an average reference. 4 Set Channel Locations Select the files that specifies the channel locations. 5 Delete or Interpolate Bad Channels You may want to delete channels that are too noisy, such as the ones around the face. 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. 7 Run ICA ICA is used to separate out the data into its various signal sources, which are both neural and non-neural. 8 Epoching For each stimulus, we want to extract a segment of data just before and just after the stimulus. 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. 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. 11 Sorting by Stimulus Type Separating the data for each stimulus type into its own file. 12 Averaging Trials Separating the data for each stimulus type into its own file. 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 Heart Rate Variability HRV Functional near-infrared spectroscopy and imaging (fNIRS/fNIRI) is a tool that detects neuroactivation by measuring oxygenated and deoxygenated blood flow. This is accomplished through spectroscopically measuring absorbance of the chromophore hemoglobin in blood that flows to neurally activated regions. Figure 1 shows rat brain vasculature that is constantly modifying itself in response to nutrient and oxygen demands, pruning and sprouting new vessel branches within days. Heart Rate Variability What is Heart Rate Variability (HRV)? Heart rate variability (HRV) is the change in time from one R peak of a QRS cycle to the next... HRV Analysis Methods ECG analysis One method to obtain heart rate variability (HRV) data is through electrocardiogram (ECG) recordings. ECG recordings are... HRV Data Collection Steps ECG data collection using Physio16 Cords are in the brown box in the cabinet under the fridge. Take one blue and one white cord and plug... How to Export an ECG Channel to a .txt File Steps Open EEGLAB in MatLab (type “eeglab” in MatLab and hit enter) In EEGLAB, go to “file” → “import” → “using EEGLAB functions and... How to Filter Data and Correct Artifacts in AcqKnowledge Steps for an inverted QRS wave: Open AcqKnowledge Select “analyze only” and then click “ok” on the pop-up menu Change the file type to... How to Analyze HRV Data Using Kubios Importing Data Plug in the watch to the computer to have it automatically open up Polar Sync. When the sync is finished, the Polar...

< Back functional Near-Infrared Spectroscopy fNIRS Functional near-infrared spectroscopy and imaging (fNIRS/fNIRI) is a tool that detects neuroactivation by measuring oxygenated and deoxygenated blood flow. This is accomplished through spectroscopically measuring absorbance of the chromophore hemoglobin in blood that flows to neurally activated regions. Figure 1 shows rat brain vasculature that is constantly modifying itself in response to nutrient and oxygen demands, pruning and sprouting new vessel branches within days. functional Near-Infrared Spectroscopy About fNIRS Functional near-infrared spectroscopy and imaging (fNIRS/fNIRI) is a tool that detects neuroactivation by measuring... Data Collection Introduction In order to achieve precise and trustworthy data, proper setup and calibration of the NIRScout is key. The setup process... Data Analysis - Opening the File Overview ​The purpose of data analysis is twofold: Statistical Parametric Mapping (SPM) levels 1 and 2. Level 1 consists of... Data Analysis - Prepping the File To begin prepping the file for level 2, click on the “check” option in the “data processing” box (image: check raw data). This will open... Data Analysis - Preprocessing the File The purpose of the preprocessing stage is to remove artifacts or unwanted segments from the data, as well as apply a filter. To begin... Data Analysis - Level 1 Analyzing the Data There are two levels of analysis that are carried out when analyzing participant results, level 1 and level 2. The... Data Analysis - Level 2 Conducting Level 2 Analysis​ To begin level 2 data analysis, click on the “SPM level 2” option in the “Data Analysis” dox. This will open...

Interested? There are multiple ways to get involved with my research on resilience: as a student, as a donor, or by simply passing my information along to other interested parties. ​ It is difficult to find funding for the work that I do with aid workers; therefore, I rely heavily on private donations to fund this part my research. Your donations help me do things like buy supplies and pay research assistants, upgrade equipment, and help pay for resilience-based training programs. Let's Talk 501 College Avenue Biology Department Wheaton College Wheaton, IL 60187 630-752-7112 nate.thom@wheaton.edu First Name Last Name Email Message Send Thanks for submitting!

KEY PUBLICATIONS READ IT Enhancing Resilience Military deployment can have profound effects on physical and mental health. Few studies have examined whether interventions prior to deployment can improve mechanisms underlying resilience. Mindfulness-based techniques have been shown to aid recovery from stress and may affect brain-behavior relationships prior to deployment. The authors examined the effect of mindfulness training on resilience mechanisms in active-duty Marines preparing for deployment. READ IT Navy SEALs during interoceptive stress How do you feel? In order to answer this question, your interoceptive system integrates sensory information regarding the homeostatic state of your body and feeds it forward to other higher-order brain regions to form a snapshot of how you feel. We've shown that elite perfomers, like SEALs and adventure racers process this type of interoceptive information differently, and that this is what allow them to perform so well under extreme stress. READ IT Elite Adventure Racers: Detecting Emotion in Others The ability to efficiently process salient information and integrate it with the current moment is invaluable to individuals that stive toward ultimate performance in extreme environments. In this article, we explore how elite adventure racers process emotional information differently than normal individuals and how those neural differences allow them to perform when it matters the most. Dr. Thom won SAIC's "Best Publication" Award for this work. He received the award from retired Chief of Staff and 4-star General, John Jumper. READ IT Manipulating Emotions: Faces or Scenes? Because of the important role that emotions play in our daily lives, scientists spend a lot of time evaluating the way we process emotional information. For the most part, pictures of emotional faces or emotionally evocative scenes are used to manipulate emotional systems in the brain. In this manuscript, we explore how these two methods compare on a neurobiological level. For a complete list of publications, see my CV.

< Back 7 Steps of Meta-Analysis This page contains instructions on how to conduct a meta-analysis from its beginnings through its completion. For explanations of each step in greater detail, click the corresponding link. Step 1: Understanding Meta-Analysis Meta-analysis is a powerful tool used to QUANTITATIVELY review the existing research in a particular field. Step 2: Establishing a Question The question will determine not only the search terms you use to collect studies, but also the inclusion criteria and the moderators. Step 3: Collecting Articles Before you can begin collecting articles, you must create search terms (that can be put into different databases) in order to retrieve... Step 5: Code Articles Once you have developed a coding framework, you can begin the actual data collection process. By the end of this step you will know more... Step 4: Developing a Coding Framework This step is EXTREMELY important when conducting a meta-analysis. The coding framework determines what data you will and will not collect... Step 6: Analyze The analysis process may seem complicated at first, but do not despair! With practice you will soon become a pro at it. Before beginning... Step 7: Drawing Conclusions Once you have completed the analysis of your data, you can begin drawing conclusions and summarizing your findings in a presentable (and... Meta Analysis in R Setting up R Preliminary Meta Meta Regression Data/Output Filtering Moderator Analysis Aggregating Data Plotting R Meta Poster

The early years I grew up a block from where this picture was taken, in Stevens Point, Wisconsin, a small river-city in central Wisconsin. It was a great place to grow up. I spent much of my time playing sports of all kinds (but especially soccer and hockey) and hunting, fishing, and camping with my dad. Education B.S. University of Wisconsin, Madison M.S. & Ph.D University of Georgia Post-doctoral Fellowship Naval Health Research Center I began my formal training as a scientist at the University of Wisconsin-Madison. By my sophomore year I worked as a research assistant in Dr. Paul Sondel's lab in the Comprehensive Cancer Center. I primarily supported the work of Alexander L. Rakhmilevich, MD, PhD, who was focused on gene-based vaccinations for melanoma. I am forever indebted to the scientists in the Sondel lab for fueling my curiosity and developing me as a young scientist. The main focus of the lab was on cancer treatment, and I always wanted to know how to prevent cancer. This way of thinking, combined with my love of triathlon, eventually led me to pursue graduate training in exercise neuroscience to study the relationship between the brain, stress, and the immune system. After a year and a half off after undergrad, I headed south to the University of Georgia to study with Dr. Rod Dishman in the Exercise Science Department. Dr. Dishman had done some work in animals on the effects of exercise on the immune system so I was excited to continue my studies of the immune system. Unfortunately, we could not secure funding to continue this line of research, so my attention shifted toward how exercise might prevent psychopathology. I studied the effects of chronic aerobic exercise on brain function and behavior for my master's thesis in Dr. Phil Holmes' lab and then began a collaboration with Dr. Brett Clementz's Cognitive Psychology lab during my doctorate. I was fortunate to win a Neuroimaging Fellowship in the Bioimaging Research Center at UGA to support my studies of how exercise alters emotion processing in the brain. In 2009, with my wife pregnant, we moved to sunny Southern California so I could start a post-doc in the Warfighter Performance Department at the Naval Health Research Center. Over the next 4 years I would have the honor of serving those who serve our country by studying ways to enhance resilience. Download CV Recognition Here is a sample of some of the awards I've won doing what I love to do: study ways to enhance brain health. In the News During my doctoral training, I investigated the effects of exercise on the brain. For my dissertation I evaluated the effects of an acute bout of moderate intensity aerobic exercise on feelings of anger. The initial results suggest that exercise prevented the onset of an angry mood, demonstrating that exercise is indeed medicine! This work was featured in the New York Times and WebMD . During my post-doc, we executed the Marine Corps first randomized controlled trial of a mental skills training program designed to enhance resilience. This work was featured in multiple news outlets including USA Today and Time Magazine . Neuroimaging Fellowship From 2006-2008, I was selected as a Paul D. Coverdell Neuroimaging Program Fellow at the Bioimaging Research Center (BIRC ) housed at the University of Georgia's Biomedical and Health Sciences Institute . During my time at BIRC, I was part of a large group that used EEG, MEG, and fMRI to study the brain. SAIC: Best Overall Publication In 2012, while working at the Naval Health Research Center's (NHRC ) Warfighter Performance Department in San Diego, I was fortunate enough to collaborate with the Optibrain Group, a consortium of scientists from UCSD, Naval Special Warfare , the Olympic Training Center , and NHRC . As part of this work, I helped analyze and publish several manuscripts related to how elite adventure racers process interoceptive information. One of those manuscripts was published in Social, Cognitive, and Affective Neuroscience (impact factor = 7), and was awarded SAIC's "Best publication" award. The highlight of the evening was receiving the award from retired 4-star General and former Air Force Chief of Staff, John Jumper .

functional Near-Infrared Spectroscopy fNIRS Functional near-infrared spectroscopy and imaging (fNIRS/fNIRI) is a tool that detects neuroactivation by measuring oxygenated and deoxygenated blood flow. This is accomplished through spectroscopically measuring absorbance of the chromophore hemoglobin in blood that flows to neurally activated regions. Figure 1 shows rat brain vasculature that is constantly modifying itself in response to nutrient and oxygen demands, pruning and sprouting new vessel branches within days. functional Near-Infrared Spectroscopy About fNIRS Functional near-infrared spectroscopy and imaging (fNIRS/fNIRI) is a tool that detects neuroactivation by measuring... Data Collection Introduction In order to achieve precise and trustworthy data, proper setup and calibration of the NIRScout is key. The setup process... Data Analysis - Opening the File Overview ​The purpose of data analysis is twofold: Statistical Parametric Mapping (SPM) levels 1 and 2. Level 1 consists of... Data Analysis - Prepping the File To begin prepping the file for level 2, click on the “check” option in the “data processing” box (image: check raw data). This will open... Data Analysis - Preprocessing the File The purpose of the preprocessing stage is to remove artifacts or unwanted segments from the data, as well as apply a filter. To begin... Data Analysis - Level 1 Analyzing the Data There are two levels of analysis that are carried out when analyzing participant results, level 1 and level 2. The... Data Analysis - Level 2 Conducting Level 2 Analysis​ To begin level 2 data analysis, click on the “SPM level 2” option in the “Data Analysis” dox. This will open...