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T: Your poster title goes here

Author One¹, Author Two², Author Three¹ | corresponding.author@dartmouth.edu

¹ Dartmouth College | ² Collaborating Institution

I: Introduction and motivation [blue]

Start with the broad question your research addresses and narrow to your contribution.

🔬 Phenomenon
❓ Open question
💡 Your approach
Research paradigm: from observation to hypothesis-driven inquiry
  • H1: Feature X correlates with outcome Y
  • H2: Intervention Z modulates this relationship

M: Methods [violet]

Paradigm: $N=50$ participants, within-subject design, naturalistic video stimuli.

Design → Record → Process → Analyze
  • Preprocessing: fMRIPrep v20.2.1
  • Modeling: GLM with custom regressors
  • Inference: Non-parametric permutation tests
🐍 Python
🧠 Neuroimaging
📊 Visualization
Analysis toolkit spanning computation, brain imaging, and data visualization. Each stage of the pipeline leverages open-source libraries optimized for reproducibility and scalability across computing environments.

R: Results [green]

Significant interaction between Condition A and B ($p < 0.001$).

type: bar
labels: Condition A, Condition B, Control
data: 0.89, 0.72, 0.65
ylabel: Accuracy
caption: Accuracy by condition

type: bar
labels: Condition A, Condition B, Control
datasets:
  - label: Accuracy
    data: 0.89, 0.72, 0.65
  - label: F1 score
    data: 0.85, 0.68, 0.60
ylabel: Score
caption: Accuracy and F1 by condition

$$\hat{y} = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \epsilon, \quad R^2 = 0.73$$

Measure Group A Group B Group C p-value
Accuracy 0.89 0.72 0.65 < 0.01
RT (ms) 342 418 450 < 0.05
F1 Score 0.85 0.68 0.60 < 0.01
Performance metrics across experimental groups

Findings replicate across datasets and participant groups.

type: line
labels: Fold 1, Fold 2, Fold 3, Fold 4, Fold 5
datasets:
  - label: Dataset 1
    data: 0.85, 0.87, 0.83, 0.88, 0.86
  - label: Dataset 2
    data: 0.78, 0.81, 0.79, 0.82, 0.80
  - label: Dataset 3
    data: 0.71, 0.74, 0.70, 0.75, 0.73
xlabel: Cross-validation fold
ylabel: F1 score
legend: right
caption: Generalization across datasets

Cross-validation (k=5) and bootstrap resampling (1000 iterations) confirm stable effect size (Cohen's d = 0.8). Permutation testing corroborates significance (BF > 10). Sensitivity analyses varying regularization strength (λ = 0.01–10) show consistent results across parameter choices.

D: Discussion [teal]

  • Finding 1: Model outperforms baseline by 15%, supporting H1
  • Finding 2: Effect is robust across parameter variations
  • Finding 3: Results suggest a new cognitive control mechanism
type: radar
labels: Accuracy, Speed, Scalability, Robustness, Interpretability
datasets:
  - label: Current work
    data: 88, 72, 65, 80, 90
  - label: Next steps
    data: 93, 85, 80, 88, 92
caption: Current capabilities vs. planned improvements
  • Sample limited to college-age participants
  • Future: longitudinal designs, larger cohorts
  • Explore transfer learning to clinical populations
  • Extend to multimodal data (EEG + fMRI fusion)

E: References [orange]

  1. Author A et al. (2023). J. Neurosci.
  2. Author C et al. (2022). Nat. Hum. Behav.
  3. Author E et al. (2021). Psychol. Rev.
  4. Author G et al. (2020). PNAS.
  5. Author J et al. (2019). NeuroImage.

A: Acknowledgments [spring]

NSF EPSCoR #1632738 · NIH R01 MH112357 · NSF CAREER #1849109

📦 Data: github.com/ContextLab

💻 Code: github.com/ContextLab

🌐 PDF: context-lab.com/publications