Agentic Neuroimaging Data Lakes via MCP

A framework for intelligent navigation of petabyte-scale brain imaging data

"The purpose of computing is insight, not numbers."

Modern neuroimaging research generates data at unprecedented scale. The Human Connectome Project alone comprises over 20 terabytes of MRI data. Van Essen, D.C., et al. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62-79. Institutional data lakes now routinely hold tens of thousands of brain scans—far exceeding what any researcher can manually navigate.

The challenge is not storage, but access. How does one find the 47 subjects with treatment-resistant depression who also have high-resolution diffusion imaging? How does one orchestrate preprocessing pipelines across heterogeneous data, then synthesize results into publishable findings?

The answer lies in agentic systems: AI agents equipped with tools for data exploration, processing, and analysis. The Model Context Protocol (MCP) provides the connective tissue—a standardized interface through which language models invoke domain-specific capabilities.

System Architecture

The architecture comprises three layers, each with distinct responsibilities. This separation follows the principle of loose coupling: the data layer knows nothing of agents, the MCP layer is agnostic to specific LLMs, and agents are model-agnostic. Data flows upward from storage through protocol servers to reasoning agents; commands flow downward.

Data Lake Storage

MCP Server Protocol

AI Agent Reasoning

Figure 1. Three-layer architecture for agentic neuroimaging. Arrows indicate bidirectional communication; data and metadata flow up, tool invocations flow down.

The Data Lake

Raw neuroimaging data resides in cloud object storage—S3, GCS, or Azure Blob—organized according to the Brain Imaging Data Structure (BIDS). Gorgolewski, K.J., et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044. BIDS provides a predictable directory hierarchy and JSON metadata sidecars that enable automated discovery.

Volumetric data is stored in NIfTI format for interoperability, though the system also supports chunked Zarr arrays for efficient partial reads of large datasets. A metadata index—typically PostgreSQL with full-text search—enables sub-second queries across millions of records.

The MCP Server

The Model Context Protocol server exposes neuroimaging capabilities as discrete tools. MCP is an open protocol developed by Anthropic for connecting AI systems to external data sources and tools. See: modelcontextprotocol.io Each tool has a typed schema defining its inputs and outputs, enabling the agent to understand what operations are available and how to invoke them correctly.

Tools are composable. An agent might first query for subjects, then load their scans, then extract regional time series, then compute connectivity matrices—chaining tool calls in service of a higher-level research question.

The Agent Layer

A large language model serves as the reasoning engine. Given a natural language query ("Find subjects with schizophrenia and compare their default mode network connectivity to controls"), the agent decomposes the task, selects appropriate tools, interprets intermediate results, and iterates until the goal is achieved.

The agent's system prompt encodes domain knowledge: anatomical nomenclature, standard atlases, preprocessing best practices, and statistical conventions. This transforms a general-purpose LLM into a specialized neuroimaging assistant.

The Tool Suite

Tools are the vocabulary through which agents interact with data. A well-designed tool suite balances expressiveness with safety—powerful enough to support complex analyses, constrained enough to prevent catastrophic errors.

Tool	Purpose	Key Parameters
`query_subjects`	Search subjects by demographic and clinical criteria	diagnosis, age_range, sex, modality, site
`load_scan`	Retrieve imaging data with lazy loading	subject_id, session, modality, space
`run_fmriprep`	Execute standardized preprocessing pipeline	subject_id, output_space, ignore
`extract_roi`	Compute regional time series or volumes	atlas, regions, summary_method
`compute_connectivity`	Generate functional connectivity matrices	method, fisher_z, threshold
`statistical_test`	Run group comparisons with multiple testing correction	test_type, correction, covariates
`render_brain`	Generate publication-quality brain visualizations	view, colormap, threshold, surface
`export_results`	Package outputs with provenance metadata	format, include_qc, include_code

Each tool call is logged with full provenance: timestamp, parameters, software versions, and data checksums. This audit trail supports reproducibility requirements increasingly mandated by funding agencies and journals. See NIH's 2023 Data Management and Sharing Policy, which requires machine-readable provenance for federally-funded research data.

Implementation Guide

Building this system proceeds in six phases. Each phase produces working components that can be tested independently before integration.

Design the Data Lake Schema

Establish your storage hierarchy following BIDS conventions. Create a metadata catalog mapping subjects to their available scans, sessions, and derived products. Index this catalog in a database supporting complex queries.

Key decisions: Object storage provider, metadata database (PostgreSQL vs. Elasticsearch), data formats (NIfTI, Zarr, or both), backup and versioning strategy.

Implement the MCP Server

Using the MCP Python SDK, create a server exposing your tool suite. Each tool function accepts typed parameters and returns structured results. Implement proper error handling—agents must receive informative messages when operations fail.

from mcp.server import Server

server = Server("neuro-mcp")

@server.tool()
async def query_subjects(
    diagnosis: str | None = None,
    age_range: tuple[int, int] | None = None,
    modality: str = "bold"
) -> list[dict]:
    """Query subjects matching specified criteria."""
    return await catalog.search(
        diagnosis=diagnosis,
        age_range=age_range,
        modality=modality
    )

Wrap Processing Pipelines

Containerize standard pipelines (fMRIPrep, FreeSurfer, MRIQC) using Singularity for HPC compatibility. Create a job queue system—Celery with Redis, or SLURM for cluster environments—that accepts processing requests and reports progress.

Considerations: Resource allocation (CPU, memory, GPU), job priority, failure recovery, and output validation.

Configure the Agent

Craft a system prompt embedding neuroimaging domain knowledge. Include anatomical terminology, standard atlas references (AAL, Schaefer, Harvard-Oxford), and guidelines for statistical analysis. Connect the agent to your MCP server.

# System prompt excerpt
"""
You are a neuroimaging research assistant with access
to a data lake containing 50,000 brain scans.

When analyzing functional connectivity:
- Use the Schaefer 400-parcel atlas by default
- Apply Fisher z-transformation before group comparisons
- Report effect sizes alongside p-values
- Always verify data quality before analysis
"""

Implement Governance

Add role-based access control restricting which users can access which datasets and tools. Implement audit logging for all data access. For datasets containing protected health information, ensure HIPAA-compliant de-identification and access controls.

Requirements: Authentication (OAuth/SAML), authorization rules, PHI detection and scrubbing, comprehensive audit logs.

Deploy and Scale

Deploy MCP servers as containerized services with horizontal autoscaling. Configure caching layers—Redis for metadata, local NVMe for hot data. Establish monitoring and alerting for system health and cost management.

Infrastructure: Kubernetes for orchestration, Prometheus for metrics, cloud spot instances for burst compute capacity.

Example Interaction

To illustrate the system in action, consider a researcher investigating altered brain connectivity in schizophrenia. The following transcript shows how an agent navigates the data lake to answer their query.

User Find subjects with schizophrenia who have resting-state fMRI data, and compare their default mode network connectivity to healthy controls.

Agent I'll query the data lake for subjects matching these criteria, then perform the connectivity analysis. Let me start by identifying available subjects. query_subjects(diagnosis="schizophrenia", modality="rest")

Result Found 847 subjects with schizophrenia diagnosis and resting-state fMRI. Age range: 18–65 years. Sites: 12. Mean scan duration: 8.2 minutes.

Agent Now I'll query for matched healthy controls. query_subjects(diagnosis="healthy_control", modality="rest", match_to="schizophrenia_cohort")

Result Found 1,203 age- and sex-matched healthy controls from the same sites.

Agent I'll extract default mode network time series using the Schaefer atlas, compute within-network connectivity for each subject, then run a group comparison. extract_roi(subjects=[...], atlas="Schaefer400", networks=["DMN"]) compute_connectivity(method="correlation", fisher_z=True) statistical_test(test="t_test", correction="fdr", covariates=["age", "sex", "site"])

Result Analysis complete. Schizophrenia group shows significantly reduced within-DMN connectivity (t = −4.32, p < 0.001, FDR-corrected). Effect size: Cohen's d=−0.38.

Agent The analysis confirms reduced default mode network connectivity in schizophrenia, consistent with prior literature on functional dysconnectivity in this population. The effect size (d = −0.38) indicates a small-to-medium effect. Shall I generate a visualization or export the full results?

The entire interaction—from natural language query to statistical result—required no manual data wrangling, no custom scripts, no navigation of directory structures. The agent composed tool calls, interpreted intermediate results, and synthesized a coherent answer.

Considerations

Data Volume

The scale of neuroimaging data lakes varies considerably across institutions. The following represents typical ranges:

Small lab

1–5 TB

Department

10–50 TB

Medical center

100–500 TB

Consortium

1–10 PB

Latency and Caching

Agent interactions should feel conversational, with responses in seconds rather than minutes. This requires aggressive caching: metadata queries must hit the index, not scan storage; frequently-accessed derived products should reside on fast local storage; and long-running pipelines should provide progress updates rather than blocking.

Reproducibility

Every tool invocation should be logged with sufficient detail to reproduce the result. This includes not only parameters but also software versions, random seeds, and data checksums. The PROV-O ontology provides a standard vocabulary for such provenance graphs. Lebo, T., et al. (2013). PROV-O: The PROV Ontology. W3C Recommendation.

The convergence of large language models, standardized protocols, and cloud infrastructure creates new possibilities for scientific data access. The framework described here represents one instantiation—tailored to neuroimaging, but adaptable to genomics, astronomy, climate science, or any domain with large, structured data repositories.

The goal is not to replace human expertise, but to amplify it: freeing researchers from mechanical data wrangling so they can focus on the questions that matter.