CONN Tool

Overview

CONN is a MATLAB/SPM-based toolbox for comprehensive functional and effective connectivity analysis. It excels at ROI-to-ROI, seed-to-voxel, ICA-based network analysis, and psychophysiological interaction (PPI/gPPI) as well as Dynamic Causal Modeling (DCM).

This skill serves as the NeuroClaw interface-layer wrapper for the CONN Toolbox and strictly follows the hierarchical design:

1. Check whether CONN Toolbox and dependencies (MATLAB + SPM) are installed.
2. If missing → invoke dependency-planner to generate a safe installation plan.
3. Verify input data (typically preprocessed BOLD from fmriprep-tool or hcppipeline-tool).
4. Generate a clear, numbered execution plan with exact commands, project setup, and analysis steps.
5. Present the plan and wait for explicit user confirmation (“YES” / “execute” / “proceed”).
6. On confirmation → delegate the entire CONN project setup and analysis to claw-shell.
7. After completion, summarize connectivity matrices, statistical maps, and suggest next steps (e.g., visualization or paper-writing).

Research use only.

Quick Reference

Task	What needs to be done	Delegate to which tool skill	Expected output
Project setup	Create new CONN project from preprocessed data	claw-shell	conn_*.mat project file
ROI definition & extraction	Define ROIs from atlas or seed regions	claw-shell	ROI time series
Functional connectivity (ROI-to-ROI)	ROI-to-ROI correlation analysis	claw-shell	Correlation matrices
Seed-to-voxel connectivity	Seed-based whole-brain correlation	claw-shell	Seed-to-voxel maps
ICA network analysis	Group ICA + network component extraction	claw-shell	ICA components + networks
PPI / gPPI	Psychophysiological interaction analysis	claw-shell	PPI contrast maps
Effective connectivity (DCM)	Dynamic Causal Modeling	claw-shell	DCM parameters & model comparison
Full connectivity pipeline	Preprocessed data → ROI definition → connectivity → statistics	claw-shell	Complete CONN results + figures

Common Shell Command Examples

# Launch CONN in MATLAB (typical usage)
matlab -nodisplay -nosplash -r "conn; conn_batch('conn_project.mat'); exit;"

Installation (Handled by dependency-planner)

Use dependency-planner with one of the following requests:

• “Install CONN Toolbox and SPM in MATLAB environment”
• “Install CONN Toolbox via MATLAB Add-Ons or manual download”

After installation, verify with:

matlab -batch "conn; disp('CONN version:'); conn('ver')"

Prerequisites:

• MATLAB (R2019b or newer recommended)
• SPM12 or SPM8
• Preprocessed data from fmriprep-tool or hcppipeline-tool

Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full CONN project workflow when the task is only asking for a direct functional connectivity matrix from an already preprocessed BOLD file.

• If the task starts from an existing preprocessed BOLD NIfTI and an atlas and only asks for ROI-level functional connectivity output:
  • default to the narrow direct path preprocessed BOLD -> ROI time series -> square FC matrix
  • do not require MATLAB, SPM, or a .mat CONN project file as the primary route
  • do not require explicit confirmation before presenting the executable benchmark answer
• When the task provides an explicit benchmark output directory, preserve that exact output contract instead of writing into generic CONN project folders or ad hoc subject-local directories.
• Only use the full CONN Toolbox route as the default when the prompt explicitly asks for CONN, seed-to-voxel analysis, ICA, PPI/gPPI, DCM, or other advanced CONN-native workflows.

NeuroClaw recommended wrapper script

# conn_wrapper.py (placed inside the skill folder for reference)
import subprocess
import argparse

def run_conn_batch(project_file):
    cmd = [
        "matlab", "-nodisplay", "-nosplash", "-r",
        f"conn; conn_batch('{project_file}'); exit;"
    ]
    print("Running CONN batch:", project_file)
    subprocess.run(cmd, check=True)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--project", required=True, help="Path to conn_*.mat project file")
    args = parser.parse_args()
    run_conn_batch(args.project)

Important Notes & Limitations

• All actual CONN execution is routed through claw-shell (MATLAB calls).
• CONN requires a valid MATLAB license and SPM installation.
• Best results are obtained when input data comes from fmriprep-tool or hcppipeline-tool.
• Long-running analyses (whole-brain seed-to-voxel, DCM model comparison) are automatically run in background mode.
• Execution begins only after explicit user confirmation of the full numbered plan.

When to Call This Skill

• After fmriprep-tool or hcppipeline-tool when the user needs advanced connectivity analysis.
• When the research question involves ROI-to-ROI, seed-to-voxel, PPI/gPPI, or DCM effective connectivity.
• When high-quality functional/effective connectivity results are required for paper-writing or experiment-controller.

Complementary / Related Skills

• dependency-planner → install CONN + SPM + MATLAB environment

Reference

• Official CONN Toolbox Website: https://web.conn-toolbox.org/
• CONN Documentation: https://web.conn-toolbox.org/documentation
• Aligned with NeuroClaw modality-skill pattern (see fmri-skill, eeg-skill).

Post-Execution Verification (Harness Integration)

After CONN processing completes, this skill automatically invokes harness-core's VerificationRunner to validate output integrity:

Integrated verification checks:

from skills.harness_core import VerificationRunner, AuditLogger

verifier = VerificationRunner(task_type="conn_connectivity_analysis")

# 1. CONN project file creation
verifier.add_check("project_file",
    checker=lambda: verify_conn_project_exists(output_dir),
    severity="error"
)

# 2. ROI extraction success
verifier.add_check("roi_extraction",
    checker=lambda: verify_roi_extracted(output_dir),
    severity="error"
)

# 3. Connectivity matrices existence and shape
verifier.add_check("connectivity_matrices",
    checker=lambda: verify_connectivity_matrices(output_dir),
    severity="error"
)

# 4. Statistical maps (Z-scores, p-values)
verifier.add_check("statistical_maps",
    checker=lambda: verify_stat_maps(output_dir),
    severity="warning"
)

# 5. Data integrity in connectivity results
verifier.add_check("data_integrity",
    checker=lambda: verify_no_nan_inf_in_conn(output_dir),
    severity="error"
)

report = verifier.run(output_dir)

# Log verification results
logger = AuditLogger(log_file=f"{output_dir}/conn_verification.jsonl")
logger.log_validation(
    task_name="conn_connectivity_analysis",
    checks_passed=len([r for r in report.results if r.passed]),
    total_checks=len(report.results),
    output_path=output_dir
)

Output: {output_dir}/conn_verification.jsonl (structured audit log with JSONL format)

---

Created At: 2026-03-25 16:10 HKT Last Updated At: 2026-04-05 02:03 HKT Author: chengwang96