# Algorithms for cross-scale integration and analysis

> **NIH NIH P41** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $262,080

## Abstract

Abstract - The vast majority of information that neuroscience has obtained about the
microscopic structure of the human brain – the substrate for cognitive competencies and the
specific locations of neuropathological processes – has been obtained by the analysis of ex vivo
tissue. Historically this involves the decades (if not centuries) old procedure of cutting, staining,
mounting and imaging under a microscope. The last two decades have seen stunning advances
in imaging and analysis of the human brain. This include advances in microscopic (e.g. CLARITY1,
SWITCH), mesoscopic (e.g., polarized light imaging, PLI), optical coherence tomography (OCT),
RNA-seq and macroscopic imaging (e.g., MRI). While these techniques have generated huge
amounts of new information regarding the structural, molecular, connectomic, genetic and
transcriptional nature of the brain, they have thus far had little impact on in vivo analysis. In the
same way, while we have made great progress in our ability to localize important brain regions in
living subjects, these capabilities have had little impact in microscopy and neuropathology. In this
project we seek to use our mesoscopic imaging and analysis tools to remove these barriers and
facilitate the flow of information from microscopy to in vivo human studies, as well as in the reverse
direction. Examples of the impact of these new abilities would be: using resting-state fMRI
networks (rsFMRI) to guide the extraction of neuropathological blocks during autopsy to test
network-based theories of various neurodegenerative disease or using predicted vascular
distributions and densities to improve the laminar specificity of fMRI.

## Key facts

- **NIH application ID:** 10038179
- **Project number:** 1P41EB030006-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Bruce Fischl
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $262,080
- **Award type:** 1
- **Project period:** 2020-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10038179

## Citation

> US National Institutes of Health, RePORTER application 10038179, Algorithms for cross-scale integration and analysis (1P41EB030006-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10038179. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*