Applications & workflows
Imaging Solutions & Use Cases
Educational guides for common preclinical neuroimaging questions — neurovascular and neuroimmune interfaces, neurodegenerative disease models, brain atlas registration, multi-modal integration, and institutional deployment. Each answer links to peer-reviewed publications and protocol guides on this site. Use search to filter by topic (e.g. neurovascular, Alzheimer's, Parkinson's) or modality (e.g. micro-computed tomography, light-sheet microscopy).
Scientific workflows
Educational guides to established and in situ workflows — neurovascular imaging, meningeal neuroimmunology, atlas registration, and quantitative cohort comparison. Each page links to peer-reviewed publications.
Neurovascular imaging · Preclinical models
What tools are available to study the brain and blood vessels in preclinical models?
Most preclinical neurovascular studies use histology, immunohistochemistry, whole-slide imaging, light-sheet microscopy, or confocal microscopy — each with separate analysis tools and usually requiring brain extraction from the skull. That dissection disrupts meningeal vessels and connections between brain vessels and the skull — structures now recognized as critical to neurovascular and neuroimmune biology — and can alter brain and vessel morphology, potentially distorting findings. The in situ micro-computed tomography (micro-CT) workflow published in Cell Reports Methods (2021) and STAR Protocols (2023) maps brain–vessel–skull interfaces in the same intact specimen. The NeuroSimplicity Anatomic Imaging Module works seamlessly with this workflow — and is the only analysis tool available that does. With the full Imaging Suite (Anatomic Imaging, Molecular Imaging, Digital Pathology, and Spatial Omics Modules), labs register light-sheet, histology, confocal, and other modalities from the same cohort into one sample space.
Read the full answerNeuroimmunology · Meningeal immunity
How do researchers study meningeal immunity and neuroimmune interfaces in preclinical models?
Neuroimmunology labs have long relied on dural whole mounts with fluorescent staining, light and confocal microscopy, and intravital two-photon imaging — approaches that established much of what we know about meningeal immunity. Dural whole mounts require separating the dura from the brain and skull; intravital two-photon enables live imaging but typically involves localized skull thinning with limited imaging depth — each method suits different experimental questions. The in situ neurovascular workflow published in Cell Reports Methods (2021) and STAR Protocols (2023) maps meningeal structures in the intact cranium. NIH NINDS collaborators used this workflow in Nature (2024, 2026) to map vascular connectomics and meningeal lymphatics. With the full Imaging Suite, labs can additionally register histology, whole-slide imaging, and light-sheet data from the same cohort into one sample space.
Read the full answerAtlas registration · Sample space
How do preclinical neuroscience labs compare their imaging data to reference atlases?
Most labs warp the specimen into atlas standard space using open-source toolkits (BrainGlobe, ANTs, custom Python) or commercial atlas modules — which can distort anatomy. The NeuroSimplicity Imaging Suite registers reference atlases to your sample in native sample space (e.g., Allen Atlas CCFv3, Blue Brain Atlas, Z-Fish Atlas, NeuroSimplicity Cranial and Vessel Atlases) so regional labels and quantitative metrics reflect true geometry. When you add multiple samples of the same modality, the platform automatically generates population atlases for cohort comparison — against reference atlases where available and between study groups.
Read the full answerCohort comparison · Sample space
How do preclinical labs run automated quantitative side-by-side cohort comparison?
Most labs first align specimens by eye or with manual or semi-automated one-off registration pipelines, then export regional metrics from ImageJ, Amira, Imaris, or lab-specific scripts into spreadsheets for group tests. Side-by-side review is hard to standardize, and quantitative group differences are difficult to audit across operators or sites. The NeuroSimplicity Imaging Suite keeps every specimen in a shared native sample-space framework, generates population atlases automatically when multiple samples of the same modality are loaded, and supports automated quantitative comparison between study groups — with aligned side-by-side views so group differences are visible and reproducible.
Read the full answerDisease models & drug discovery
How preclinical labs quantitatively study neurodegenerative disease models: regional brain volume at cohort scale, atlas comparison across treatment arms, and spatial biology mapped to anatomic reference imaging for therapeutic development.
Modalities & integration
How labs process each imaging modality — micro-computed tomography (micro-CT), light-sheet, whole-slide imaging, and spatial omics — then how those datasets integrate from the same specimen in native sample space.
Micro-CT · Anatomic imaging
How do preclinical labs analyze, visualize, and process micro-computed tomography (micro-CT) data?
Most labs open micro-CT volumes in ImageJ/Fiji, Amira, Dragonfly, or scanner vendor software, then move segmentation, atlas alignment, and quantification to separate tools or custom scripts. Iterative scan series and terabyte-scale cohorts are difficult to share across the lab, compare reproducibly, or connect to light-sheet, histology, or whole-slide imaging from the same specimen. The NeuroSimplicity Anatomic Imaging Module processes micro-CT data on institutional infrastructure with automated feature extraction, 3D visualization, quantitative analysis, and cohort comparison through deterministic, auditable pipelines, plus iterative scan and atlas registration to the sample.
Read the full answerLight-sheet microscopy · Molecular imaging
How do preclinical labs analyze, visualize, and process light-sheet microscopy data?
Most labs analyze light-sheet data in Fiji/ImageJ, Imaris, Aivia, MBF Bioscience tools, or instrument vendor software — platforms that offer rendering plus semi-automated or manual segmentation, atlas alignment into standard atlas space, and quantification, often through companion products or AI plugins. Those workflows suit many studies but can introduce operator-dependent variability, limit automation across large cohorts, and make terabyte-scale batch processing and audit-ready reproducibility difficult. The NeuroSimplicity Molecular Imaging Module enables automated exploration, rendering, segmentation, and cohort analysis through fully automated, deterministic, and auditable pipelines on institutional infrastructure.
Read the full answerWhole-slide imaging · Digital pathology
How do preclinical labs reconstruct serial whole-slide imaging into 3D volumes?
Serial histology and whole-slide imaging (WSI) are usually reviewed slide-by-slide in QuPath, HALO, Aperio viewers, or ImageJ. Those tools are strong for 2D pathology but rarely reconstruct coherent 3D volumes without custom per-lab pipelines. Three-dimensional reconstruction reveals what isolated slides cannot: continuous vessel courses, immune microenvironments, and patterns of tumor infiltration across the specimen. The NeuroSimplicity Digital Pathology Module reconstructs H&E, IHC, and other stains from serial sections into 3D sample-space volumes on institutional infrastructure, with deterministic registration across the stack and quantitative cohort analysis.
Read the full answerSerial slides · Spatial omics
How do labs reconstruct and interact with spatial omics data, especially from serial slides?
Spatial omics instruments and slide-based assays (MERFISH, Visium, spatial proteomics, serial-section spatial transcriptomics) typically produce two-dimensional spot maps or per-slide coordinate files analyzed in vendor or open-source tools. Building a three-dimensional spatial volume you can explore interactively, especially when data span serial slides, often requires custom reconstruction scripts disconnected from anatomic references. The NeuroSimplicity Spatial Omics Module reconstructs spatial genomics, transcriptomics, and proteomics in 3D and supports interactive exploration on institutional infrastructure.
Read the full answerMRI · Anatomic imaging
How do preclinical labs analyze and process MRI data?
Preclinical MRI is often analyzed in scanner vendor software, ImageJ, ITK-SNAP, or study-specific pipelines, separate from ex vivo micro-CT, histology, or whole-slide imaging collected in the same project. Using MRI as the anatomic reference for light-sheet, reconstructed histology, or spatial omics is scientifically valuable but hard to do reproducibly across operators and longitudinal time points. The NeuroSimplicity Anatomic Imaging Module supports MRI processing, segmentation, and atlas registration to the sample in native sample space, alongside micro-CT within the same module.
Read the full answerMulti-modal integration · Sample space
How do I integrate micro-CT, light-sheet, whole-slide imaging, and spatial omics from the same specimen?
Correlative studies typically process micro-CT or MRI in Amira, Dragonfly, or scanner vendor software; whole-slide imaging in QuPath or HALO; spatial omics in instrument or open-source viewers; and light-sheet in Fiji/ImageJ, Imaris, or Aivia, generalist fluorescence platforms that many labs adapt for cleared-tissue volumes. Each modality lands in its own coordinate system. Registering light-sheet fluorescence, reconstructed histology, and spatial omics to a shared anatomic reference (usually micro-CT or MRI from the same specimen) is scientifically necessary but hard to do reproducibly with manual landmarks, ad hoc Python, or chained handoffs between siloed tools. The NeuroSimplicity Imaging Suite processes and registers all modalities in one native sample-space framework with deterministic cross-modal registration on institutional infrastructure.
Read the full answerUnified platform · Module architecture
Is there one platform that handles micro-CT, light-sheet, whole-slide imaging, spatial omics, and MRI together?
Most labs assemble point tools per modality or reuse generalist 3D platforms such as Fiji/ImageJ, Imaris, Amira, and Aivia with different plugins for micro-CT, light-sheet, whole-slide imaging, and spatial omics. Those workflows suit many individual datasets, but terabyte volumes, cohort reproducibility, and cross-modal registration are hard to sustain across separate desktop applications and custom scripts. The NeuroSimplicity Imaging Suite is structured as four modules on one on-premises platform: the first module is included in the base license, each additional module is an add-on, and when all are licensed together they share one native sample-space framework.
Read the full answerInstitutional deployment
On-premises operation, reproducibility, and core-facility throughput for shared preclinical imaging resources.
Deployment · Compliance
Which neuroimaging platforms run fully on-premises or air-gapped?
Most labs still analyze preclinical imaging on desktop workstation tools with single-user licenses: Imaris, Fiji, Amira, QuPath, or vendor software shared across investigators. Queues build when one seat serves multiple labs, terabyte datasets crash or stall those workstations, and volumes are hard to move back to the bench for PI review or collaboration with groups that cannot open the same formats. Cloud and hybrid tools are another option, but many institutions cannot upload raw preclinical data because of sovereignty, IRB, or air-gapped network policies. The NeuroSimplicity Imaging Suite runs as an on-premises private cloud on institutional infrastructure with concurrent licenses and terabyte batch pipelines, and can run fully offline after install when required.
Read the full answerCore facilities · Scale
What do imaging core facilities need for terabyte-scale multi-modal throughput?
Imaging cores serve multiple PIs across micro-CT, light-sheet, whole-slide imaging, and spatial omics, but most still provide access through shared desktop workstations with single-user licenses and operator-dependent pipelines. Work backs up, terabyte cohorts stall, results vary by operator, and labs often cannot bring data back to their own bench to analyze, build figures, or share with their group. The NeuroSimplicity Imaging Suite supports centralized core infrastructure with concurrent licenses, terabyte batch pipelines, deterministic processing, and per-project sublicensing so PI labs can open and use their data.
Read the full answerProcurement & designation
NIH sole-source context and procurement documentation paths, with links to peer-reviewed research.
Platform comparison
Evaluate when unified multi-modal registration and institutional batch pipelines replace chained point-tool workflows. A starting point for procurement and grant reviewer questions.