{
  "abstract": "Advances in high-throughput mesoscale microscopy and machine learning-based image analysis pipelines have made unbiased whole-brain imaging widely accessible. However, translating the resulting atlas-mapped datasets into biologically meaningful results remains a substantial barrier owing to their sheer magnitude and complex hierarchical organization. Consequently, reporting structure and analysis methods vary widely across studies, undermining rigor and reproducibility. To address this, we developed a user-friendly data reduction workflow, HERO (Hierarchy-aware Expression Region Organization), designed to perform hierarchy-aware selection, refinement, ranking, and visualization of whole-brain cell detection datasets. The workflow is customizable to specific needs, requires minimal coding experience, and outputs transparent, curated results. HERO functions as a seamless plug-in within larger-scale whole-brain cell detection analysis pipelines, providing efficient, unbiased region selection to streamline subsequent statistical analyses and comparative evaluations. Although HERO is developed with mouse cell-detection datasets, it can be applied to any atlas-mapped dataset that contains hierarchical information. Here, we applied the HERO pipeline to example cell detection datasets containing afferent cells from the bed nucleus of the stria terminalis (BST) and the insular cortex (insula). HERO provided top projecting regions consistent with historical tracing studies for the BST (e.g., amygdalar and hypothalamic regions) and the insula (e.g., somatosensory, autonomic, and interoceptive regions). Furthermore, the refinement step of HERO provides additional enriched regions originally obscured in the primary ranked outputs, such as memory- and emotion-associated structures. With HEROs dynamic processing, we were able to identify top-projecting regions consistent with the literature and, additionally, find regions often obscured in these large datasets. In sum, HERO offers a standardized analysis workflow to reduce whole-brain cell-detection datasets, transforming raw regional cell counts into curated results and advancing the effectiveness, interpretability, and accessibility of whole-brain imaging in neuroscience.",
  "abstract_shown": true,
  "affiliations": [
    "Wake Forest University School of Medicine"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "A. L. Shipman"
    },
    {
      "affs": [
        0
      ],
      "name": "S. W. Centanni"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ebr8kqpamkdbb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.02.736093",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.07.02.736093v2.full",
  "id": "ebr8kqpamkdbb",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.07.02.736093v2",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "neuroscience"
    ],
    [
      "openalex",
      "1304|Cell Image Analysis Techniques"
    ]
  ],
  "openalex": "W7167744682",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.07.02.736093v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ebr8kqpamkdbb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:28Z",
      "native_id": "10.64898/2026.07.02.736093",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.07.02.736093v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "HERO: A hierarchy-aware analysis pipeline for reducing and refining whole-brain atlas-mapped cellular datasets",
  "title_text": "HERO: A hierarchy-aware analysis pipeline for reducing and refining whole-brain atlas-mapped cellular datasets",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:28Z",
  "url": "https://research.draconitic.com/paper/ebr8kqpamkdbb",
  "venue": "bioRxiv",
  "versions": []
}