How do researchers study the brain in models of Alzheimer's disease, Parkinson's disease, or dementia?
Preclinical drug development in neurodegeneration depends on quantitative readouts across treatment arms and genotypes. Labs run transgenic, pharmacological, and aging models of Alzheimer's disease, Parkinson's disease, and dementia.
Researchers need reproducible regional brain volumes and atlas-grounded comparison between vehicle, drug-treated, and control groups. And, increasingly, studies also need molecular maps that connect pathology to anatomic structure.
Manual tracing pipelines that differ per lab rarely meet that bar when a full cohort must reproduce across operators, sites, or CRO partners.
What researchers typically do
Preclinical models often include APP/PSEN transgenic mice for Alzheimer's disease, alpha-synuclein or MPTP paradigms for Parkinson's disease, and vascular or aging models related to dementia. From those studies, labs collect ex vivo micro-CT, in vivo or ex vivo MRI, serial hematoxylin and eosin (H&E) and immunohistochemistry (IHC), cleared-tissue light-sheet, and spatial transcriptomics or proteomics from serial sections.
Analysis usually splits across separate tools. Regional volumes may be traced manually or with lab-specific scripts that are hard to audit. Atlas alignment may warp specimens into standard space and obscure true geometry. Spatial omics may stay in instrument software, disconnected from the anatomic volumes that define treatment effect size.
Multi-site preclinical studies, core facility cohort processing, and CRO packages need automated regional quantification that reproduces across operators. That need is especially acute when comparing vehicle, low-dose, and high-dose arms, or mutant versus wild-type littermates over time.
Quantitative regional analysis and spatial biology in disease model cohorts
With the NeuroSimplicity Imaging Suite, you can register Allen Atlas CCFv3 and other references to each specimen in native sample space, run automated regional brain volume extraction, and compare treatment and control groups through population atlases. Deterministic pipelines are built for preclinical drug trial cohorts. You can process micro-CT and MRI with the Anatomic Imaging Module, cleared-tissue light-sheet with the Molecular Imaging Module, and spatial omics from serial slides with the Spatial Omics Module. When you integrate those modules, you can map transcriptomic, proteomic, and fluorescence readouts to anatomic reference volumes from the same study, probing how therapeutic intervention, genotype, or lesion paradigm alters regional structure and spatial biology together. No other platform combines automated cohort regional volumetry with native sample-space multi-modal registration for these disease model workflows.
What the platform enables
Automated regional brain volume extraction with atlas registration to the sample (e.g., Allen Atlas CCFv3)
Population atlas generation and quantitative comparison across treatment, genotype, and dosing cohorts
Deterministic batch pipelines for preclinical drug trial readouts across sites and operators
Spatial transcriptomics and proteomics mapped to micro-CT or MRI anatomic reference via Spatial Omics Module integration
Cleared-tissue light-sheet fluorescence registered to true anatomic space for in situ molecular and systems biology readouts (no other platform places cleared-tissue insights in native anatomic reference)
On-premises private cloud for regulated and multi-site preclinical programs
More detail
Atlas registration to the sample in native sample space is validated in Cell Reports Methods cranial micro-CT workflows. The same framework extends to MRI and ex vivo brain volumes used in neurodegeneration studies. Population atlas generation for cohort comparison is available on the platform when multiple specimens of the same modality are loaded.
For per-modality processing detail, see the micro-CT, preclinical MRI, spatial omics, and multi-modal integration guides linked in Next steps.
For capability-by-capability comparison against point tools used in disease model studies, see the competitive matrix.
Peer-reviewed validation
This workflow is documented in Cell Reports Methods and STAR Protocols, and applied in Nature discovery studies by NIH NINDS collaborators. Full citations, figures, and paper links are on our publications page.