Analyzing Gut Motility: A Step-by-Step Guide

1. Image Acquisition and Preprocessing

• Acquire high-resolution time-lapse images of the hindgut.
• Crop raw time-lapses to zoom in on the hindgut region (using ImageJ).
• Remove transition frames (e.g., due to denervation or drug application).
• Downsample from 30 to 3 fps to match the long timescales of gut movement.
• Temporally smooth movies using Simoncelli filter (5-point weights).
• Avoid spatial smoothing to retain features critical for flow analysis.

2. Flow Field Computation

• Hindgut Segmentation: Use MATLAB's strel and imerode to segment gut outlines frame-by-frame.
• Optical Flow Analysis: Apply the Lucas-Kanade method using a Gaussian-weighted neighborhood (σ = 2 px).
  • Impose a reliability threshold (0.01) to remove unreliable vectors.
  • Set flow vectors below the threshold to zero.
• Dorsal-Ventral Motility Map: Average lateral flow components across the dorsal-ventral axis to generate motility maps.
• Flow Smoothing for Visualization: Apply Gaussian smoothing to visualize motion direction and reduce localized noise.

3. Frequency-Domain Analysis

• Apply FFT along the time axis to compute 2D power spectral density (PSD).
  • Extract dominant motion frequencies and harmonics.
  • Nyquist frequency is 1.5 Hz (sampling at 3 Hz).
• Calculate power as the sum of squared FFT amplitudes normalized by signal length.
• Also compute instantaneous power using FFT along the spatial axis.

4. Rhythmic Power Quantification

• Define Relative Rhythmic Power as the power in the peak frequency band (±0.04 Hz) divided by total power.
• Used to quantify signal coordination:
  • White noise → flat PSD → low rhythmic power.
  • Single-frequency signal → narrow PSD peak → high rhythmic power (~1).
• Metric is robust to small changes in frequency window width.

5. Lateral Wave Detection

• Apply speed threshold (top 30% globally) to highlight wave regions.
• Use MATLAB's bwareaopen and bwlabeln to find connected wave regions (area > 100 px).
• Extract waves that span >1/5 of gut length.
• Use bwskel to trace wave skeletons and compute speed via slope.
• Label wave direction:
  • AP (anterior → posterior): positive speed
  • PA (posterior → anterior): negative speed
  • Mixed: zero speed

6. Statistical Time-Window Selection

• For each phase:
  • Use last 15 minutes of Phases 1 & 2
  • Use first 15 minutes of Phase 3
• Rationale:
  • Capture short-term changes following 5-HT application.
  • Supported by statistical testing between 15-min windows.

7. Data Analysis and Visualization

• Statistical Analysis:
  • Compare motility features across conditions (e.g., saline vs. N7 cut vs. serotonin).
  • Use non-parametric tests (e.g., Wilcoxon signed-rank).
• Visualization:
  • Motility kymographs, PSD plots, and boxplots.
  • Color-coded vector fields for flow direction and magnitude.

Key Tools and Techniques

• Image Processing: Cropping, thresholding, morphological operations.
• Flow Estimation: Lucas-Kanade method with Gaussian weighting.
• Signal Processing: FFT-based PSD, rhythmic power analysis.
• Wave Extraction: Connected component analysis and wave skeleton tracing.
• Statistical Analysis: MATLAB functions (signrank, boxplot) for paired comparison.
• Software: MATLAB, ImageJ.