• Propose a VAE for to predict WM structural anomalies in tractography.


Authors propose to use a convolutional VAE for anomaly detection between normal control and cognitively impaired subjects using tractography data.


They use a convolutional VAE as their DL model:

  • Their streamlines are resampled to 256.
  • They use batch normalization and average pooling layers.
  • Gradient clipping is used to prevent exploding gradients.


They investigate the effect of the latent space dimensionality in preserving the streamline space distance:

  • Streamline-wise distance: Minimum Direct Flip (MDF) distance in streamline space vs. Euclidean distance in latent space.
  • Centroid-wise distance: same as streamline-wise distance.
  • Bundle-wise distance: bundle-based Minimum Distance (BMD) distance in bundle-space vs. Wasserstein distance in latent space.

The Spearman and Pearson correlation, and the coefficient of determination values are computed to determine the optimal dimension.


  • ADNI dataset: 141 participants: 10 with Alzheimer’s Disease (AD), 22 mild cognitive impairment (MCI); 87 cognitively normal controls (CN).
  • Used multi-shell data.
  • Registered all subjects to MNI space.
  • 30 bundles were studied.
  • 10 CN subjects are used for training.


Mean absolute error (MAE) between the input and the reconstructed features.


Anomaly detection across bundles

Anomaly detection along bundles (tractometry)


Authors identified 6 bundles with statistically significant group differences and specific locations along the length of the tracts with anomalies after controlling for age and sex effect.