Due Date: April 2 by 11:59 pm ET
Second Milestone Report Submission: Submit your second milestone report as a PDF on Gradescope, ensuring the person submitting tags all other team members. Include a link to your code in a GitHub repository.
The second milestone report evaluates the progress made since the first milestone report and focuses on applying computational models to derive meaningful interpretations from the connectome data. You are required to use either the LaTeX or the MS Word templates provided here. Write the report with Prof. Song as your intended audience. For example, avoid lengthy explanations of well-known concepts like logistic regression.
Format
The second milestone report should not exceed 3 pages, formatted according to the required template, excluding references. This 3-page limit includes all content, such as the main text, figures, and tables, to ensure consistent length across different teams. It should include:
- Title, Authors
- Introduction: Recap the project’s main goal and any refinements to research questions based on current findings.
- Code: Update the GitHub URL with newly implemented models and refined scripts.
- Dataset Description: Describe the dataset, including its source, structure, and preprocessing steps taken.
- Methodology: Explain the analytical methods applied, such as graph-theoretical models, machine learning approaches, or statistical techniques used to extract insights. Present results with supporting figures and/or tables.
- Build-Up Narrative: Construct a cohesive narrative that integrates all progress made so far, emphasizing how the analysis has evolved over time. Highlight the key findings derived from the current phase, how they refine or challenge earlier insights, and what new hypotheses or questions arise. Discuss the logical progression of the study, integrating methodological improvements, refined interpretations, and any shifts in focus based on the latest results.
- Interpretation and Discussion: Compare the findings with existing research to highlight alignments and discrepancies. Explain how these insights contribute to understanding brain connectivity and relate to prior hypotheses. Discuss whether the results support or challenge established models and propose directions for further investigation.
- Challenges and Solutions: Highlight any significant challenges encountered and strategies used to overcome them. These may include difficulties in data preprocessing, such as handling missing or inconsistent connectivity data, computational limitations when analyzing large-scale networks, and challenges in selecting appropriate modeling techniques. Address how these challenges were tackled, such as by implementing data imputation methods, optimizing algorithms for scalability, or leveraging domain knowledge to refine analytical approaches.
- Final Timeline and Remaining Work: Outline the remaining tasks needed to complete the final report and any adjustments to the initial project scope.
- Contributions: Provide a breakdown of team contributions for this phase.
Expected Outcomes
- Implemented computational models for connectome analysis
- Meaningful interpretations of results
- Cohesive narrative linking past and current findings
- Updated and refined code repository
- A clear plan for completing the final report
Grading: The milestone is intended as formative assessments to ensure steady progress. Feedback from the Instructor will help refine methods and interpretations, preparing for the final report submission. Following instructions, clearly articulating findings, and demonstrating analytical soundness will be key grading criteria. Please be sure to address any assumptions or challenges that could impact the completion of your project.
