Compressed representation of brain genetic transcription.
James K RuffleHenry WatkinsRobert J GrayHarpreet HyareMichel Thiebaut de SchottenParashkev NachevPublished in: Human brain mapping (2024)
The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.
Keyphrases
- white matter
- gene expression
- resting state
- transcription factor
- electronic health record
- working memory
- functional connectivity
- big data
- dna methylation
- cerebral ischemia
- primary care
- multiple sclerosis
- magnetic resonance imaging
- quality improvement
- computed tomography
- magnetic resonance
- oxidative stress
- single cell
- genome wide
- positron emission tomography
- machine learning
- heat shock protein