Sample interview questions: How do you approach analyzing functional connectivity data to understand brain networks?
Sample answer:
Approaching Functional Connectivity Data Analysis for Brain Network Understanding
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Data Preprocessing:
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Correct motion artifacts, registration, smoothing, and normalization.
- Removal of physiological noise (e.g., heart rate, respiration).
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Filtering to isolate specific frequency bands of interest.
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Connectivity Metrics:
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Correlation analysis: Pearson’s correlation coefficient or related measures to assess pairwise relationships between brain regions.
- Graph theory: Create brain networks and analyze their structural and functional properties, such as clustering, modularity, and path length.
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Multivariate pattern analysis: Techniques like principal component analysis or independent component analysis to identify patterns and relationships among multiple brain regions.
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Clustering and Group Comparisons:
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Apply hierarchical clustering or community detection algorithms to identify functional modules or subnetworks within the brain.
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Compare connectivity patterns between different groups (e.g., patients vs. controls) to identify group differences or network alterations.
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Network Inference:
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Use structural equation modeling or Granger causality analysis to determine the directionality a… Read full answer
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