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Peer-reviewedCC BY 4.0Neuroinformatics

DPA: Deep Parcellation Analysis for Human Trait Prediction

  • Xiaohe Chen1
  • Zeliang Sun2
  • Chao Huang2
  • Rongjie Liu3
  1. 1Department of Statistics, Florida State University, 117 N Woodward Ave, Tallahassee, FL 32304 USA
  2. 2Department of Epidemiology & Biostatistics, University of Georgia, 101 Buck Rd, Athens, GA 30602 USA
  3. 3Department of Statistics, University of Georgia, 310 Herty Dr, Athens, GA 30602 USA
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Abstract

Mapping individual differences in white matter organization is a central goal of human brain mapping. Atlas-based structural connectome analyses are widely used but are sensitive to anatomical assumptions and may miss fine-grained connectivity patterns. Data-driven fiber clustering mitigates some limitations but typically relies on streamline endpoints, overlooking rich geometric information along full trajectories. In this paper, we introduce Deep Parcellation Analysis (DPA), a geometry-aware framework for white matter parcellation and trait association. DPA samples multiscale geometric features along entire fiber streamlines and encodes them with a Multiscale Fiber AutoEncoder (MFAE) to produce compact latent representations. These enable clustering that preserves intrinsic fiber geometry independently of anatomical atlases, supporting interpretable mapping of connectivity patterns linked to individual differences. Applied to diffusion magnetic resonance imaging data from the Human Connectome Project Young Adult (HCP-YA) cohort, DPA captures white matter substrates underlying language-related cognitive traits. It achieves comparable or superior predictive performance relative to atlas-based and principal parcellation approaches while revealing biologically plausible fiber pathways. Critically, DPA identifies trait-relevant connections missed by endpoint-based methods, emphasizing the importance of full streamline geometry. Overall, DPA demonstrates that deep, geometry-informed parcellation provides a powerful, interpretable approach for linking white matter organization to behavior, advancing structure–function studies in human brain mapping.

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Cite

BibTeXRIS
BibTeX
@article{chen2026deep,
  title = {{DPA: Deep Parcellation Analysis for Human Trait Prediction}},
  author = {Xiaohe Chen and Zeliang Sun and Chao Huang and Rongjie Liu},
  journal = {Neuroinformatics},
  year = {2026},
  month = nov,
  doi = {10.1007/s12021-026-09823-z},
  url = {https://doi.org/10.1007/s12021-026-09823-z},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - DPA: Deep Parcellation Analysis for Human Trait Prediction
AU  - Xiaohe Chen
AU  - Zeliang Sun
AU  - Chao Huang
AU  - Rongjie Liu
PY  - 2026
DA  - 2026/11/03
JO  - Neuroinformatics
DO  - 10.1007/s12021-026-09823-z
UR  - https://doi.org/10.1007/s12021-026-09823-z
AB  - Mapping individual differences in white matter organization is a central goal of human brain mapping. Atlas-based structural connectome analyses are widely used but are sensitive to anatomical assumptions and may miss fine-grained connectivity patterns. Data-driven fiber clustering mitigates some limitations but typically relies on streamline endpoints, overlooking rich geometric information along full trajectories. In this paper, we introduce Deep Parcellation Analysis (DPA), a geometry-aware framework for white matter parcellation and trait association. DPA samples multiscale geometric features along entire fiber streamlines and encodes them with a Multiscale Fiber AutoEncoder (MFAE) to produce compact latent representations. These enable clustering that preserves intrinsic fiber geometry independently of anatomical atlases, supporting interpretable mapping of connectivity patterns linked to individual differences. Applied to diffusion magnetic resonance imaging data from the Human Connectome Project Young Adult (HCP-YA) cohort, DPA captures white matter substrates underlying language-related cognitive traits. It achieves comparable or superior predictive performance relative to atlas-based and principal parcellation approaches while revealing biologically plausible fiber pathways. Critically, DPA identifies trait-relevant connections missed by endpoint-based methods, emphasizing the importance of full streamline geometry. Overall, DPA demonstrates that deep, geometry-informed parcellation provides a powerful, interpretable approach for linking white matter organization to behavior, advancing structure–function studies in human brain mapping.
SN  - 1539-2791
SN  - 1559-0089
ER  -