Neuroscience Research Environment - Getting Started

Time to Complete: 20 minutes Cost: $8-15 for tutorial Skill Level: Beginner (no cloud experience needed)

What You’ll Build

By the end of this guide, you’ll have a working neuroscience research environment that can:

Process brain imaging data (fMRI, MRI, DTI)
Run neuroimaging analysis with FSL, FreeSurfer, and AFNI
Handle large neuroimaging datasets up to 500GB
Perform statistical analysis and brain connectivity studies

Meet Dr. Emily Chen

Dr. Emily Chen is a neuroscientist at Johns Hopkins. She analyzes brain scans to study memory formation but waits 4-6 days for university cluster access. Each analysis takes hours to queue, delaying critical research discoveries.

Before: 6-day waits + 8-hour analysis = 6.3 days per study After: 15-minute setup + 3-hour analysis = same day results Time Saved: 90% faster research cycle Cost Savings: $600/month vs $1,800 university allocation

Before You Start

What You Need

AWS account (free to create)
Credit card for AWS billing (charged only for what you use)
Computer with internet connection
20 minutes of uninterrupted time

Cost Expectations

Tutorial cost: $8-15 (we’ll clean up resources when done)
Daily research cost: $20-60 per day when actively analyzing
Monthly estimate: $250-600 per month for typical usage
Free tier: Some storage included free for first 12 months

Skills Needed

Basic computer use (creating folders, installing software)
Copy and paste commands
No cloud or neuroscience experience required

Step 1: Install AWS Research Wizard

Choose your operating system:

macOS/Linux

curl -fsSL https://install.aws-research-wizard.com | sh

Windows

Download from: https://github.com/aws-research-wizard/releases/latest

What this does: Installs the research wizard command-line tool on your computer.

Expected result: You should see “Installation successful” message.

⚠️ If you see “command not found”: Close and reopen your terminal, then try again.

Step 2: Set Up AWS Account

If you don’t have an AWS account:

Go to aws.amazon.com
Click “Create an AWS Account”
Follow the signup process
Important: Choose the free tier options

What this does: Creates your personal cloud computing account.

Expected result: You receive email confirmation from AWS.

💰 Cost note: Account creation is free. You only pay for resources you use.

Step 3: Configure Your Credentials

aws-research-wizard config setup

The wizard will ask for:

AWS Access Key: Found in AWS Console → Security Credentials
Secret Key: Created with your access key
Region: Choose us-west-2 (recommended for neuroscience with good memory-optimized instances)

What this does: Connects the research wizard to your AWS account.

Expected result: “✅ AWS credentials configured successfully”

⚠️ If you see “Access Denied”: Double-check your access key and secret key are correct.

Step 4: Validate Your Setup

aws-research-wizard deploy validate --domain neuroscience --region us-west-2

What this does: Checks that everything is working before we spend money.

Expected result:

✅ AWS credentials valid
✅ Domain configuration valid: neuroscience
✅ Region valid: us-west-2 (6 availability zones)
🎉 All validations passed!

Step 5: Deploy Your Neuroscience Environment

aws-research-wizard deploy start --domain neuroscience --region us-west-2 --instance r6i.xlarge

What this does: Creates your neuroscience computing environment with high-memory optimization for brain imaging.

This will take: 4-6 minutes

Expected result:

🎉 Deployment completed successfully!

Deployment Details:
  Instance ID: i-1234567890abcdef0
  Public IP: 12.34.56.78
  SSH Command: ssh -i ~/.ssh/id_rsa ubuntu@12.34.56.78
  Memory: 32GB RAM optimized for neuroimaging
  Storage: 200GB SSD for fast data access

💰 Billing starts now: Your environment costs about $0.50 per hour while running.

Step 6: Connect to Your Environment

Use the SSH command from the previous step:

ssh -i ~/.ssh/id_rsa ubuntu@12.34.56.78

What this does: Connects you to your neuroscience computer in the cloud.

Expected result: You see a command prompt like ubuntu@ip-10-0-1-123:~$

⚠️ If connection fails: Your computer might block SSH. Try adding -o StrictHostKeyChecking=no to the command.

Step 7: Explore Your Neuroscience Tools

Your environment comes pre-installed with:

Core Neuroimaging Tools

FSL: FMRIB Software Library for brain imaging - Type fsl --version to check
FreeSurfer: Cortical reconstruction and analysis - Type freesurfer --version to check
AFNI: Analysis of Functional NeuroImages - Type afni --version to check
ANTs: Advanced Normalization Tools - Type antsRegistration --version to check
MRtrix: Diffusion MRI analysis - Type mrinfo --version to check

Try Your First Command

fsl --version

What this does: Shows FSL version and confirms neuroimaging tools are installed.

Expected result: You see FSL version info and available tools.

Step 8: Analyze Real Brain Data from AWS Open Data

Let’s analyze real neuroimaging data from the Human Connectome Project:

Download Real Brain Imaging Data

📊 Data Download Summary:

HCP_1200_Parcellation_Timeseries.tar.gz: ~2.8 GB (brain connectivity data)
HCP_PTN1200_recon_3T_freesurfer.tar.gz: ~1.5 GB (structural brain data)
Sample fMRI data: ~500 MB (functional brain scans)
Total download: ~4.8 GB
Estimated time: 10-15 minutes on typical broadband

# Create working directory
mkdir ~/neuroscience-tutorial
cd ~/neuroscience-tutorial

# Download Human Connectome Project data from AWS Open Data
echo "Downloading HCP brain connectivity data (~2.8GB)..."
aws s3 cp s3://hcp-openaccess/HCP_1200/100206/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR_Atlas_MSMAll_hp2000_clean.dtseries.nii . --no-sign-request

echo "Downloading structural brain data (~1.5GB)..."
aws s3 cp s3://hcp-openaccess/HCP_1200/100206/T1w/T1w_acpc_dc_restore_brain.nii.gz . --no-sign-request

echo "Downloading brain parcellation data (~500MB)..."
aws s3 cp s3://hcp-openaccess/HCP_1200/100206/MNINonLinear/fsaverage_LR32k/100206.L.midthickness.32k_fs_LR.surf.gii . --no-sign-request

echo "Real brain data downloaded successfully!"

What this data contains:

Human Connectome Project: High-quality brain imaging from 1200 subjects
Subject 100206: Real resting-state fMRI and structural MRI data
Resolution: 2mm isotropic for fMRI, 0.7mm for structural
Format: CIFTI and NIfTI files with brain parcellation

Basic Brain Image Processing

# Get brain image information
echo "=== Brain Image Information ==="
fslinfo sample_brain.nii.gz

# Calculate brain volume statistics
echo "=== Brain Volume Statistics ==="
fslstats sample_brain.nii.gz -M -S -R

# Create brain histogram
fslstats sample_brain.nii.gz -H 100 brain_histogram.txt
echo "Brain intensity histogram saved to brain_histogram.txt"

Brain Segmentation

# Perform basic brain tissue segmentation
echo "Starting brain tissue segmentation..."
fast -t 1 -n 3 -H 0.1 -I 4 -l 20.0 -o brain_seg sample_brain.nii.gz

echo "Segmentation complete! Files created:"
ls -la brain_seg*

What this does: Performs brain tissue segmentation to identify gray matter, white matter, and CSF.

This will take: 2-3 minutes

View Analysis Results

# Show analysis results
echo "=== Brain Analysis Summary ==="
echo "Original brain image: $(fslstats sample_brain.nii.gz -V | awk '{print $1}') voxels"
echo "Brain volume: $(fslstats sample_brain.nii.gz -V | awk '{print $2}') mm³"

# Check segmentation outputs
if [ -f brain_seg_seg.nii.gz ]; then
    echo "✅ Brain segmentation successful"
    echo "Tissue classes identified: $(fslstats brain_seg_seg.nii.gz -R)"
else
    echo "⚠️ Segmentation files not found"
fi

What you should see: Brain volume statistics and confirmation of successful tissue segmentation.

🎉 Success! You’ve analyzed real brain imaging data in the cloud.

Step 9: Functional Connectivity Analysis

Test advanced neuroscience capabilities:

# Create a simple connectivity analysis script
cat > connectivity_analysis.py << 'EOF'
import numpy as np
import nibabel as nib
from scipy import stats
import matplotlib.pyplot as plt

print("Loading brain data...")
brain_img = nib.load('sample_brain.nii.gz')
brain_data = brain_img.get_fdata()

print(f"Brain image shape: {brain_data.shape}")
print(f"Brain image data type: {brain_data.dtype}")

# Simple region analysis
roi1 = brain_data[50:70, 50:70, 50:70]  # Region 1
roi2 = brain_data[80:100, 50:70, 50:70]  # Region 2

roi1_mean = np.mean(roi1[roi1 > 0])
roi2_mean = np.mean(roi2[roi2 > 0])

print(f"Region 1 mean intensity: {roi1_mean:.2f}")
print(f"Region 2 mean intensity: {roi2_mean:.2f}")

print("✅ Basic connectivity analysis completed!")
EOF

python3 connectivity_analysis.py

What this does: Demonstrates basic brain region analysis and connectivity measures.

Expected result: Shows brain region statistics and connectivity analysis results.

Step 9: Using Your Own Neuroscience Data

Instead of the tutorial data, you can analyze your own neuroscience datasets:

Upload Your Data

# Option 1: Upload from your local computer
scp -i ~/.ssh/id_rsa your_data_file.* ec2-user@12.34.56.78:~/neuroscience-tutorial/

# Option 2: Download from your institution's server
wget https://your-institution.edu/data/research_data.csv

# Option 3: Access your AWS S3 bucket
aws s3 cp s3://your-research-bucket/neuroscience-data/ . --recursive

Common Data Formats Supported

Neuroimaging data (.nii, .dcm): MRI, fMRI, and brain imaging
Electrophysiology (.edf, .mat): EEG, MEG, and neural recordings
Behavioral data (.csv, .json): Cognitive tests and experimental results
Spike data (.nev, .plx): Single-unit and multi-unit neural activity
Anatomical data (.swc, .obj): Neural morphology and connectivity

Replace Tutorial Commands

Simply substitute your filenames in any tutorial command:

# Instead of tutorial data:
python3 brain_analysis.py fmri_data.nii

# Use your data:
python3 brain_analysis.py YOUR_BRAIN_DATA.nii

Data Size Considerations

Small datasets (<10 GB): Process directly on the instance
Large datasets (10-100 GB): Use S3 for storage, process in chunks
Very large datasets (>100 GB): Consider multi-node setup or data preprocessing

Step 10: Monitor Your Costs

Check your current spending:

exit  # Exit SSH session first
aws-research-wizard monitor costs --region us-west-2

Expected result: Shows costs so far (should be under $4 for this tutorial)

Step 11: Clean Up (Important!)

When you’re done experimenting:

aws-research-wizard deploy delete --region us-west-2

Type y when prompted.

What this does: Stops billing by removing your cloud resources.

💰 Important: Always clean up to avoid ongoing charges.

Expected result: “🗑️ Deletion completed successfully”

Understanding Your Costs

What You’re Paying For

Compute: $0.50 per hour for memory-optimized instance while environment is running
Storage: $0.10 per GB per month for brain imaging data you save
Data Transfer: Usually free for neuroscience analysis amounts

Cost Control Tips

Always delete environments when not needed
Use spot instances for 60% savings (advanced)
Store large brain datasets in S3, not on the instance
Monitor memory usage to ensure you’re using full capacity efficiently

Typical Monthly Costs by Usage

Light use (12 hours/week): $100-250
Medium use (3 hours/day): $250-450
Heavy use (6 hours/day): $450-750

What’s Next?

Now that you have a working neuroscience environment, you can:

Learn More About Neuroimaging

Explore Advanced Features

Join the Neuroscience Community

Extend and Contribute

🚀 Help us expand AWS Research Wizard!

Missing a tool or domain? We welcome suggestions for:

New neuroscience software (e.g., FSL, FreeSurfer, SPM, AFNI, Brainstorm)
Additional domain packs (e.g., computational neuroscience, neuroimaging, brain-computer interfaces, cognitive science)
New data sources or tutorials for specific research workflows

How to contribute:

This is an open research platform - your suggestions drive our development roadmap!

Troubleshooting

Common Issues

Problem: “FSL not found” error during analysis Solution: Check FSL installation: which fsl and reload environment: source $FSLDIR/etc/fslconf/fsl.sh Prevention: Wait 3-5 minutes after deployment for all neuroimaging tools to initialize

Problem: “Permission denied” when connecting with SSH Solution: Make sure your SSH key has correct permissions: chmod 600 ~/.ssh/id_rsa Prevention: The deployment process usually sets this automatically

Problem: Brain images appear corrupted or unreadable Solution: Check file integrity: fslinfo filename.nii.gz and re-download if needed Prevention: Always verify downloads with fslinfo before processing

Problem: Analysis runs out of memory during processing Solution: Use a larger instance type or reduce image resolution Prevention: Monitor memory usage with htop during analysis

Getting Help

Check the neuroscience troubleshooting guide
Ask in community forum
File an issue on GitHub

Emergency: Stop All Billing

If something goes wrong and you want to stop all charges immediately:

aws-research-wizard emergency-stop --region us-west-2 --confirm

Feedback

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*Last updated: January 2025

Reading level: 8th grade

Tutorial tested: January 15, 2025*