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Summer Program in Computational Psychiatry Education

Welcome to the Summer Program in Computational Psychiatry Education (SPICE)! This GitHub repository contains a collection of course materials and resources designed for high school and undergraduate students interested in exploring the field of computational psychiatry and neuroscience.

SPICE is an educational initiative aimed at introducing high school and college students to the intersection of computer science and psychiatry. Students participate in an eight-week program that combines educational tutorials and hands-on research projects within laboratories at the Center for Computational Psychiatry in the Icahn School of Medicine at Mount Sinai.


Education Track

The Education Track consists of two parallel components in a two-week crash course format. Students will complete both to gain a preliminary understanding of computational psychiatry and neuroscience for application to their research projects. Please visit our Jupyter Book to navigate through the course materials: https://center-for-computational-psychiatry.github.io/course_spice/

(1) Fundamentals of Computational Psychiatry and Neuroscience

This course will cover the relationship between the brain, behavior, and cognition. We will begin with foundational neuroanatomy and physiology, building towards an understanding of complex cognitive processes. We will analyze how disruptions in neural processes can lead to neurological and psychiatric disorders. Research methods used in neuroscience will be covered, with a focus on computational psychiatry and how it complements and diverges from traditional approaches.

Computational Psychiatry and Neuroscience

Description

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Class 1 - Foundations of Neuroscience

Introduction to the brain and nervous system, essential for understanding subsequent topics.

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Class 2 - Brain Development Across the Lifespan

Examines how the brain changes over time, influencing its functions and potential disorders.

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Class 3 - From Classical to Computational Psychiatry

Overview of traditional psychiatry, its strengths and limitations, and the emergence of computational approaches.

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Class 4 - Research Methods in Computational Psychiatry and Neuroscience

Comprehensive look at key research methods and best practices in the field.

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Class 5 - Movement & Sensory Processing

Understanding brain functions related to motor and sensory systems, and their interaction with the body and environment.

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Class 6 - Memory & Learning

Exploration of the neurocognitive mechanisms of memory and learning, fundamental to cognitive functions.

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Class 7 - Decision-Making

Exploration of cognitive functions involved in decision-making, from simple choices to complex judgments.

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Class 8 - Metacognition

Examination of self-awareness processes involved in evaluating and adjusting one’s thinking.

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Class 9 - Journal Club: Ethics, Society and Responsible Science Communication

Exploration of the ethical, societal, and communication implications of neuroscientific and psychiatric research in a collaborative journal club format.

Available soon

(2) Fundamentals of Python Programming for Psychological and Neural Sciences

This component provides students with a hands-on introduction to Python programming for data analysis. Over the course of two weeks, students will use Google Colaboratory to learn the fundamentals of Python programming, basic data visualization techniques, and essential statistical analysis methods such as t-tests. The primary aim of this course is to develop practical coding skills and analytical tools to understand data, especially those within the fields of computational psychiatry and neuroscience.

The course begins with an introduction to Python and Jupyter Notebooks. Then, students will then dive into tutorials on Python basics (e.g., variables, data types, conditional statements, loops). As the course progresses, students will learn about data structures, reading and writing dataframes, and utilizing popular libraries such as Pandas, NumPy, SciPy, Matplotlib, and Seaborn for data manipulation and visualization. They will also gain proficiency in descriptive statistics, hypothesis testing, and analyzing relationships between variables. The course culminates with a project in which students will apply these skills to analyze data from their research project.

Please see below for the list of modules within this course.

Computational Science

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Module 00 - Introduction

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Module 01 - Jupyter Notebooks

Open In Colab

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Module 02 - Python Basics

Open In Colab

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Module 03 - Data Structures

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Module 04 - Data Processing

Open In Colab

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Module 05 - Data Visualization

Open In Colab

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Module 06 - Numerical Operations

Open In Colab

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Module 07 - Hypothesis Testing

Open In Colab

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Module 08 - Linear Modeling

Open In Colab

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Module 09 - Final Projects

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Research Track

This track matches students with a mentor for the summer based on research interests and availability. Students will work on a research project under the guidance of their mentor and present their findings at the end of the program. The primary aim of this course is to provide students with hands-on research experience in computational psychiatry and neuroscience. Please coordinate with your mentors to determine the scope and timeline of your project.


Contributing

We welcome contributions from the community. If you have additional suggestions or corrections, please submit a pull request. Together, we can create an educational resource for future participants. Please follow these steps to contribute:

  1. Fork the repository to your GitHub account.

  2. Make the desired changes or additions in your forked repository.

  3. Submit a pull request, detailing the changes you made and the reasons behind them.

Our team will review your contribution and merge if it aligns with the goals and scope of the repository.

If you have any questions, suggestions, or feedback regarding the Summer Program in Computational Psychiatry Education (SPICE) or this repository, please feel free to open an issue in the repository or contact us directly. We are here to help you and make your learning experience as enriching as possible.

License

Creative Commons License
Content in this book (i.e., any .md or .ipynb files in the content/ folder) is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.