Projects

HCP-Dummies: A Guideline to the Human Connectome Project

The Human Connectome Project (HCP) (humanconnectome.org) has tackled one of the great scientific challenges of the 21st century: mapping the human brain, aiming to connect its structure to function and behavior. There are currently thousands of articles published related to HCP however in this article we want to address the biggest scientific challenges in their simplest form. We will be using a literature survey and resources to learn more about the HCP project. We will also be using the online Imaging resources and performing connectome visualization based on the workbench tool designed by the Connectome project. Connectome Workbench is an open-source, freely available visualization and discovery tool used to map neuroimaging data, especially data generated by the Human Connectome Project. Workbench allows to explore data and activity on the surface, as well as in the volume of the brain.

CreativeInfluencers: A novel subset assessment Measuring the Creativity of Social Media Influencers

In the modern world, creativity can mean many different things. Assessments like the Creative Achievement Questionnaire (CAQ) and the Inventory of Creative Activities & Achievements (ICAA) determine exactly how creative an individual is by measuring achievement in various categories like music, art, and dance, among others. One category not present in either assessment is creative influencers: people who use social media to promote their creative skill and influence others to learn what they do. Today, influencers significantly impact social media consumers. Social media has influenced the very definition of creativity, and therefore it is vital to make out the difference between influencers and creative influencers.

Dissonance: Investigating Effects of the COVID-19 Pandemic on Adolescent Behavioral Plasticity

Cognitive dissonance theory states that when one’s attitude (internal values) conflicts with their behavior (external actions), a psychological and physical discomfort arises known as cognitive dissonance. To alleviate discomfort and justify this dissonance, cognitive rationalization occurs. Our study is the first of its kind and aims to understand the relationship between the COVID-19 pandemic and dissonant feelings high school-age teenagers have recently experienced, by studying how their thoughts about certain aspects of COVID-19 regulation influence how they think about others. We hypothesized that new and constantly changing legislation concerning vaccines, masks, public safety, and school regulations has led to students experiencing increased cognitive dissonance. The study outlines a novel qualitative measuring tool to model COVID-19-related cognitive dissonance in adolescents with a novel questionnaire with five primary batteries: General COVID-19, Vaccines, Masks, Government/Authority, and School. We have run analyses such as the ANOVA Test, Paired T Test, and Correlation Matrix to look for correlations between participant’s scores in different groups to see if behavior in one section of the pandemic can be used to predict behavior in the others. However, no such correlations were found between individual groups. Instead correlations between the masks and vaccines groups were correlated positively with overall dissonance score across all sections of the pandemic.

CovidVacMap: Modeling and Predicting Global COVID-19 Breakthroughs Using Network Analysis and Neural Networks

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has had a significant impact on the lives of many people, with numerous deaths and hospitalizations. In response, several vaccine manufacturers, such as Pfizer, Moderna, and Johnson & Johnson (J&J), have developed and tested COVID-19 vaccines. However, it is unlikely that society will achieve full immunity to COVID-19 due to the emergence of variants such as Omicron and Delta that may not be fully covered by current vaccines. The presence of multiple variants also means that current vaccines may not be as effective in preventing outbreaks. To help countries prepare for future outbreaks, the CovidVacMap project uses modeling to predict the risk of COVID-19 spread across the globe.

CovidSleep: Investigating the Relationship between COVID-19 Vaccination Status and Sleep Quality in Adults

COVID-19-related disturbances, such as the shelter-in-place order in 2020, have been linked with sleep disruption amongst the general population. Due to an increase of screen time and consumption of addictive substances correlated with the pandemic, sleep issues such as difficulty falling and/or staying asleep and imbalances in circadian rhythm have become increasingly common in adults. Such disturbances weaken the immune system, leading to an increased susceptibility to the COVID-19 virus, creating a further cycle of stress and loss of regular sleeping patterns. The purpose of the study is to examine the relationship between sleep quality and COVID-19 vaccination status by computing a sleep quality index score (SQI) for participants before and after vaccination. The null hypothesis stated there was no significant difference between sleep quality index scores before and after vaccination. The Chi Square analysis shows a significant effect of the vaccine, indicating that the null hypothesis can be rejected. The results of this study will introduce the possible psychological benefits, specifically related to sleep, of the COVID-19 vaccine to reduce biological and mental harm caused by the ongoing pandemic.

CovidFatigue: Characterization and Severity Assessment
of COVID-19 After-Effects

This study is a survey of the long-term after effects of COVID-19. These effects are referred to as brain-fog and are characterized by fatigue and difficulty concentrating. To study the physiological and cognitive symptoms of brain-fog, we are designing a survey on Jotform that will ask people who have been infected with COVID-19 to answer a series of questions about their recovery from COVID-19 as well as to complete certain cognitive tasks. Data from this questionnaire will allow us to learn about the frequency of certain symptoms of brain-fog and will help us determine how long these symptoms typically last. Collecting this data also enables us to look for external factors that affect recovery, such as vaccination status and age. We have developed a scoring scale for this questionnaire, which we have tested with two sets of generated sample data. We are currently collecting and scoring data from pilot participants, implementing their feedback into all aspects of our data collection process.

fMRIusics: Exploring the Neural Correlates of Emotional Responses to music utilizing fMRIIprep, Docker, ICA, and CPAC

Functional magnetic resonance imaging, also known as fMRI, is a neuroimaging technique through which the functional connectivity between regions of interest can be detected after thorough preprocessing and processing. This study draws from the dataset of the paper “Dynamic intersubject neural synchronization reflects affective responses to sad music” (Sachs et al., 2020), which explores brain activity in 40 subjects listening to music that evokes specific emotional responses. The study has utilized fMRIprep and a Docker container to preprocess the data. fMRIprep was selected as it handles variations within scans smoothly while requiring minimal user input. Following fMRIPrep preprocessing, FSL ICA MELODIC was run to extract temporal and spatial components from the fMRI data. Currently, this project works to analyze the fMRIprep preprocessed outputs, through C-PAC, another neuroimaging tool, to create a processing pipeline for this study’s purposes. The resulting time-series outputs of C-PAC will be implemented in a machine learning algorithm for data analysis that will, hopefully, provide insight into how stimulus-driven changes in activity and connectivity in the brain correlate to emotional enjoyment and intensity.

NeuroTDA: Using Topological Data Analysis (TDA) and Mapper to Study the Homology of Neurodegenerative Diseases

Neurodegenerative diseases disrupt neuronal function, drastically impairing regular bodily activity and threatening the lives of patients. Along with environmental links, the presence of these diseases is often correlated with the expression of certain genes. For example, there is an association between the presence of the e4 form of the APOE gene and the risk of Alzheimer’s disease. While correlations for individual diseases have been pinpointed, further research is needed to identify shared mechanisms between the gene expressions across different diseases. Topological Data Analysis (TDA)—a technique to understand the underlying structure of a dataset—can be used to further analyze these similarities. TDA’s ability to cluster data while simultaneously connecting related data points and revealing the data’s global structure makes it a robust tool to uncover the aforementioned relationships across multiple neurodegenerative diseases. By identifying these clustered subgroups in gene expression, pharmaceutical drugs can be specialized and targeted for patients with neurodegenerative diseases.

StickTask: Utilizing novel task-based assessments to investigate Cognitive Biases, Creativity, and BFI Personality Types

Functional fixedness is a cognitive bias that limits one’s ability to think of novel and creative uses for an object, and it is known to limit one’s divergent thinking. This study models the relationship between functional fixedness, creativity, and personality types using novel task-based assessments inspired by Duncker’s candle problem, the Alternate Uses Test (AUT), the Remote Associates Test (RAT), the ICAA creative achievement assessment, and the BFI-10 personality assessment. As the current study is online via Zoom, an additional measure of rumination was incorporated to examine its potential impact on creativity. This project is currently in the process of collecting pilot data, ensuring that the study follows IRB protocol.

ADHD_AI Assist: Exploring the Feasibility of Biofeedback Mechanisms for Managing Symptoms of Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity that can significantly impair academic, social, and occupational functioning. Traditional interventions for ADHD often rely on student-teacher-parent interactions to manage symptoms, but the implementation and effectiveness of these interventions can vary greatly. In recent years, there has been increasing interest in exploring the use biofeedback mechanisms with Natural Language Processing (NLP)-based interventions for managing symptoms of ADHD. This presentation aims to explore the efficacy and feasibility of biofeedback mechanisms for monitoring and managing symptoms of ADHD through the use of wearable sensors, with a focus on the importance of student-teacher-parent interactions in promoting positive outcomes. By examining the current research and identifying potential areas for future investigation, this presentation seeks to provide a comprehensive overview of the potential benefits and limitations of biofeedback mechanisms for managing symptoms of ADHD, and to highlight the importance of collaboration between students, teachers, and parents in supporting individuals with ADHD in order to improve their learning experience.

DNBWM-CT: Using Multidimensionality and Engagement through the Dopaminergic System to increase Working Memory

N-back tasks are a form of cognitive training requiring patients to remember and recall information previously shown to them. In previous studies, cognitive patients completed N-back tasks while undergoing fMRI, and areas associated with working memory, such as the prefrontal cortex, fronto parietal network, and salience network, activated during this task. Working memory involves the use of attention to manipulate and store short term memory. There has been a scientifically proven correlation between N-back training and increase of working memory. Furthermore, the dopaminergic system, located in the midbrain, consist of the Mesolimbic, Mesocortical, Nigrostriatal, and Tuberoinfundibular pathways. Within this system contains dopamine, a neurotransmitter and hormone produced during blissful and pleasantful experiences. There is a scientific correlation between increase of dopamine and increase of productivity during cognitive tasks. Nevertheless, cognitive research patients often complain that cognitive tasks are boring and mundane. In this study, we aim to measure the effect of multidimensionality and gamification on cognitive research tasks.

SpEEGd-BCI: Using EEG Data to Develop a BCI for Detecting Inner Speech

Brain-Computer Interfaces (BCI) detect brain signals and translate them into commands which are carried out by other devices. For people affected by neuromuscular disorders, BCIs can greatly improve their quality of life by restoring lost function. These neuromuscular disorders often impede an individual’s ability to communicate, thus presenting a need for BCIs that can interpret inner speech. Electroencephalography (EEG) is a standard noninvasive neuroimaging technique measuring electrophysiological responses in the brain produced by synced neurons. Recent improvements in machine learning have led to advances in detecting brain patterns present in EEG data, allowing more promising and reliable BCIs. In this project, we utilize a dataset consisting of EEG data of inner speech commands from 10 subjects. Through analysis of the data using MATLAB and the application of machine learning, we aim to develop a model that can accurately interpret inner speech.

SynapticYoga: Using Electroencephalography (EEG) and data preprocessing to study the effects of meditation practices on brain activity

Analysis on the effects of mediation through electroencephalography (EEG) has been done for decades but its impacts are still uncertain. This is due to how various meditation practices affect brain activity differently, shown evident in our dataset. The dataset included data from four blocks of meditation: two thinking blocks, one breathing block, and one tradition-specific-meditation block. After sorting through 98 subjects, and running it through our software titled EEGLab, the data had to be pre-processed to make accurate conclusions. With continuing data analysis through EEGLab, we hope to find the direct effects of meditation and how it may benefit cognition, perception, and emotional processing.

CovidEat: Dietary patterns during COVID-19: Analyzing changes in consumption habits in adults before, during, and after COVID-19 lockdown

The COVID-19 pandemic and the resulting quarantine have caused drastic changes in an individual’s lifestyle. Dietary lifestyle, relating to an individual’s relationship with both food and exercise, is a major part of this change. We hypothesize that the pandemic caused participants to change their consumption patterns drastically, and that these changes have remained in place even after the lockdown period ended. Our questionnaire, which borrows ideas from four standardized eating assessments (SCOFF, EAT-26, CET, and EDE-Q) will aim to test this in adult participants. Our outreach strategies include reaching out to friends and family, and emailing professors at universities across the country. After completing data collection, we will conduct data analysis using R and various statistical softwares. The results of this study will provide a better understanding of one of the several changes that the pandemic has brought upon us.

CreativityGPT: Using Language Models to Bolster Human Creativity

Within society and popular culture, AI has been generally regarded as less creative than humans; however, our study challenges this belief, aiming to assess the creative abilities of AI and compare them to humans, specifically focusing on AI’s capacity to personify distinct individuals. To do so, we have employed a range of creativity tests like the Alternate Uses Task (AUT), Remote Associates Task (RAT), and Torrance Tests of Creative Thinking (TTCT) to cover various creativity aspects. Currently, we have completed the initial phase by subjecting an AI language model to these tests, from which we have found that AI does demonstrate a certain extent of creativity. Continuing our study, we aim to administer the same tests to human participants in order to gather comparative data. Ultimately, we will create an API that harnesses language model creativity to enhance human creative endeavors.

PsychGPT: Developing an accessible, online source of comprehensive mental health services

Veterans, particularly those dealing with conditions like bipolar disorder, often face challenges in accessing effective mental health care. Oftentimes, they face barriers such as limited access to resources, the stigma around mental health, and the financial and time constraints of talking to medical professionals. Our study introduces an innovative solution through an interactive online platform, PsychGPT, designed to meet veterans' unique needs and provide rapid, effective therapy. PsychGPT utilizes advanced data-tracking and a personalized chatbot to offer emotion tracking and analysis over extended periods, predicting mood patterns precisely. The platform will guide users through an emotions wheel, categorizing comfortable and uncomfortable feelings to help users better understand their emotions. This information is integrated into user statistics and API communications, delivering a tailored experience. The dynamic mental health API chatbot provides real-time support and data-driven insights, enhancing the therapeutic experience.

PersonalityGPT: Closing Communication Gaps in Texting using Artificial Intelligence

In the realm of AI-driven communication, PersonalityGPT is a groundbreaking solution that leveraging the influential Big Five personality traits to bridge communication gaps across various personality types. This innovative model integrates with users’ emotional states, aided by sentient AI components, enhancing empathy and understanding. By seamlessly adapting its responses to different personality types, the model provides more authentic and meaningful conversations. Furthermore, the integration of sentient AI components amplifies its empathetic capabilities, enabling it to evaluate and respond to subtle emotional states, thereby fostering a deeper understanding of users’ mental health, a field that is becoming ever more important.

DissonanceTech: Analysis of Technology-Induced Cognitive Dissonance

Cognitive dissonance theory states that psychological discomfort, also known as cognitive dissonance, is caused by inconsistency between one’s external actions (behavior) and their internal values (attitude). This can lead to a decreasing flexibility in behavior, as one must change their values and beliefs in order to rationalize their actions to fit their existing values. The purpose of this study is to better understand the behavioral plasticity that both adolescents and adults may feel in relation to technology adoption, a phenomenon which describes the process of integrating growing technology into society. The study is focused on measuring the effects of technology such as ChatGPT, autonomous vehicles, artificial intelligence, social media (Tiktok, Instagram, Facebook), smart home devices, etc, on the degree of cognitive dissonance one may feel. This project is currently in the process of collecting and scoring data from pilot participants.

A Systematic Approach Towards Understanding the Dynamics of Semantic Learning and Memory

This project aims to deepen our understanding of semantic memory by employing diverse assessment methodologies to explore how demographic factors such as age, education level, race, and bilingualism affect semantic memory encoding, storage, and retrieval processes. We administered a questionnaire with various categories of questions including factual knowledge, associative reasoning, phonemic fluency, memory recall, and abstract figure identification, to capture a comprehensive view of semantic memory function. By utilizing these varied approaches, we aim to better understand how semantic memory operates across different groups.

Investigating How the Integration of AI Systems Into Daily Life, Especially For Children, Might Impact Cognitive Development, Learning, and Social Interaction

The Large Language Models and Cognitive Development (LLMCD) project explores how AI, particularly large language models, affects children’s cognitive development, learning, and social skills. With AI becoming increasingly popular in classrooms and homes, LLMCD examines how these technologies impact cognitive skills such as attention, problem-solving, adaptability, and emotional intelligence. With the use of a questionnaire and a statistical analysis model, LLMCD seeks to develop an understanding of how responsible AI practices support healthy growth, providing guidelines for creating AI applications that nurture children’s learning and social growth.

Deciphering the Intricate Network of the Human Connectome

The Human Connectome Project (HCP) (humanconnectome.org) has tackled one of the great scientific challenges of the 21st century: comprehensively mapping the intricate neural connections within the human brain, utilizing extensive datasets to explore both functional and structural aspects of brain connectivity. While using advanced tools such as the Connectome Workbench software, we analyze neuroimaging data to visualize connectivity patterns, brain structure and critical roles of specific regions in various cognitive functions. This research underlines the brain’s intricate composition and elucidates fundamental mechanisms underlying human cognition and behavior.

PsyColor Therapy: Investigating the Effect of Color Therapy on the Psychology of Stressed Minds

Color Therapy is the study of how certain colors impact humans and their behavior. It explores the effect of different factors, such as culture, age, socioeconomic status on human reaction to different colors. Previous studies have explored the applications of color psychology in the realm of diseases and other physical ailments (stroke, fatigue), as well as the effect it may have on human emotion. This has also led to many other uses in the field of marketing. In this study, we aim to further investigate the effects of color psychology on physical and mental health along with the impact of demographics on personality and favorite color.

Molecular TDA: Building Models to Personalize Drugs

Molecular TDA is an intersection between computational chemistry, mathematics, and AI. The goal of this project is to further the drug industry, personalize drugs, and decrease the risk of side effects. In this project, we will be building a model to evaluate a dataset of diseases and their related drugs. Topological Data Analysis (TDA), a technique to understand the underlying structure of a dataset, will be utilized to help build the model. We aim to find similarities between different drug expressions to help the drug development industry. By identifying similarities, drugs can be better developed and specialized. (edited)

Design Thinking and its application to healthcare innovation: an AI-enhanced framework for drug discovery

This project aims to implement Design Thinking in drug discovery to create a more effective approach for pharmaceutical trials and research. By emphasizing empathy and iterative testing, it enhances treatment personalization and effectiveness. Integrating Artificial Intelligence (AI) will refine Design Thinking models for healthcare and analyze AI decision-making in drug development. The project will utilize qualitative methods to gather insights and quantitative techniques like QSAR modeling to optimize drug candidates’ efficacy and safety. Ultimately, it seeks to improve clinical trial efficiency, strengthen stakeholder communication, and reduce costs in the drug discovery process.

All about Brain-Computer Interfaces Comprehensive Review of BCIs

This project is a comprehensive review of Brain-Computer Interfaces, including their applications, challenges, and future directions. It explores how BCIs translate brain activity into commands for external devices. As part of the project, we are analyzing raw EEG data using MATLAB to examine neural activity patterns. This data analysis will contribute to an overall review of BCIs. The review also addresses challenges such as improving BCI implantation and potential risks, as well as future directions like the integration of AI into BCI systems. (edited)

An Investigation on the Effects of Chronic Brain Fatigue in Silicon Valley

Silicon Valley is a place known around the world for being a birthplace of innovation and technology, boasting many forward-thinking companies in its area. One thing these companies all need are skilled employees, and these employees can be overworked in the high-pressure environments that are created at these companies. Overworking employees leads to burnout, and brain fatigue, and our study aims to investigate that. By using a questionnaire on the platform Jotform, we can assess the mental cognitive effects of chronic mental fatigue and high cognitive load in professionals working in high-pressure environments in Silicon Valley. This project is currently in the process of making its questionnaire.

ADHD: Revolutionizing ADHD Diagnosis: Integrating Gamification and Dynamic Questionnaires for Enhanced Assessment

This project aims to develop and test a dynamic ADHD assessment tool that integrates gamification with questionnaires to capture and diagnose the large spectra of ADHD symptoms; this is also done in a more engaging manner. Traditional assessment methods often are too long and insipid for an ADHD patient to take, which prompted us to aim to create a more innovative approach. This platform will include a dashboard in which a patient and their respective professional will be able to view data related to diagnosis. This tool focuses on its effectiveness in diagnosing ADHD across a large diverse group, and potentially transform ADHD diagnosis.