This document outlines the full workflow for Neurodiagnoses—from data acquisition and AI model training to clinical validation, ethical compliance, cloud deployment, and future expansion into CNS Digital Twins.

1. Data Management & Integration

• Data Acquisition & Storage:

  • Download raw data from external sources (e.g., ADNI, GP2, PPMI, Enroll-HD, UK Biobank, etc.).
  • Upload and organize datasets in EBRAINS Buckets and in the /datasets/ directory on GitHub.

• Data Conversion & Format:

  • Convert all datasets to standardized formats (.csv, .json, .h5) to facilitate AI processing.

• Data Harmonization:

  • Implement automated data ingestion scripts to streamline updates from new sources.
  • Set up data harmonization methods to ensure consistency across different sources (e.g., genetics, neuroimaging, biomarkers, digital health).

• Federated Learning:

  • Enable federated learning techniques to train AI models on multi-center data without sharing raw patient data (ensuring GDPR compliance).

2. AI-Based Risk Prediction & Diagnosis

• Predictive Modeling:

  • Implement machine learning models (e.g., Random Forest, Neural Networks) for dementia risk stratification.
  • Develop probabilistic models (e.g., KNN Imputer, Bayesian approaches) to handle missing data.

• Training with Multi-Modal Data:

  • Train AI models using data from biomarkers, EEG, MRI, and lifestyle factors.
  • Store pre-trained models in the /models/ directory for future use.

• Diagnostic Annotation System:

  • Implement real-time AI-based diagnostic annotation that produces two types of reports for each case:
    • Probabilistic Diagnosis: Traditional diagnosis with associated probability percentages.
    • Tridimensional Diagnosis: A structured classification based on three axes—etiology, molecular markers, and neuroanatomoclinical correlations.
  • Integrate Explainable AI techniques (e.g., SHAP) to ensure transparency in predictions.
  • Explore advanced deep learning methods for pattern recognition in neuroimaging data.
  • Investigate the use of Large Language Models (LLMs) for summarizing and generating medical reports.

3. EEG, Neuroimaging & Sleep Analysis

• EEG/MEG Analysis:

  • Process EEG/MEG data using tools like MNE-Python.
  • Apply spectral analysis and connectivity metrics to derive EEG biomarkers for dementia detection.

• Sleep Monitoring:

  • Integrate sleep data from wearables (smartwatches, headbands) as early biomarkers.

• Neuroimaging Analysis:

  • Utilize MRI volumetric analysis to assess brain atrophy in high-risk patients.
  • Implement functional MRI (fMRI) analysis to correlate neuroanatomical changes with cognitive function.

4. Clinical Validation & Pilot Testing

• Pilot Study Design:

  • Design a multicenter pilot study to validate AI-generated diagnostic scores.
  • Recruit a clinical validation cohort from European research hospitals.

• Performance Evaluation:

  • Compare AI-based diagnoses with traditional clinician diagnoses.
  • Develop and track validation metrics (e.g., AUROC, precision-recall, false positive rates).

• Feedback and Refinement:

  • Implement clinician feedback loops to refine the AI model based on real-world usage.
  • Publish validation results in peer-reviewed journals to enhance credibility.

5. Ethical, Regulatory & GDPR Compliance

• Regulatory Compliance:

  • Ensure all AI models comply with relevant regulations (e.g., EU AI Act, GDPR).

• Privacy Preservation:

  • Implement privacy-preserving techniques (Federated Learning, Differential Privacy) to protect patient data.
  • Develop data anonymization pipelines prior to AI processing.

• Consent & Data Governance:

  • Establish consent management systems for patient data contributions.
  • Ensure interoperability with hospital Electronic Health Record (EHR) systems.

6. EBRAINS Deployment & Cloud Infrastructure

• Cloud Deployment:
  • Deploy AI models on the EBRAINS Cloud for real-time inference.
• Collaborative Development:
  • Set up Jupyter Notebooks in EBRAINS Lab for collaborative development and testing.
  • Automate model training pipelines using GitHub Actions or EBRAINS HPC resources.
• Optimization:
  • Optimize computational efficiency to enable real-time processing of clinical data.

7. Interactive Web Application for Clinicians & Researchers

• Web App Development:
  • Develop an interactive web-based diagnostic tool using frameworks such as Flask, FastAPI, or Streamlit.
  • Allow clinicians to input biomarker data and receive real-time AI predictions.
• Report Generation:
  • Enable the generation of PDF reports for clinical decision support.
• Custom Dashboards:
  • Integrate dashboards that display risk stratification results.
• Deployment:
  • Deploy the web app on neurodiagnoses.com using hosting services like Netlify, Vercel, or AWS.

8. Cross-Project Collaborations

• External Partnerships:

  • Collaborate with projects such as AI-Mind for EEG-based predictive modeling.
  • Work with LETHE for lifestyle-based cognitive decline risk scoring.
  • Leverage PROMINENT’s multi-modal AI pipeline to refine dementia subtype classification.
  • Expand partnerships with clinical institutions to enhance dataset diversity.

• Open-Source Community:

  • Encourage contributions via GitHub (code improvements, new features) and EBRAINS discussion pages (research and validation).

9. Long-Term Expansion & Future Goals

• Disease Progression Modeling:
  • Explore AI-powered models for tracking neurodegeneration over time.
• CNS Digital Twins:
  • Develop CNS Digital Twins by integrating multi-omics data, neuroimaging, and digital health records to create personalized simulations of disease progression.
• Continuous Monitoring:
  • Investigate the integration of wearable health tracking devices for ongoing cognitive assessment.
• Open-Access API:
  • Create an API to allow global research collaborations with access to AI diagnostic tools.
• Sustainability & Updates:
  • Regularly update the system with new data and algorithm improvements.
  • Establish long-term funding and partnership strategies to ensure sustainability.

Key Resources

• GitHub Repository: Neurodiagnoses on GitHub
• EBRAINS Collaboratory: Neurodiagnoses on EBRAINS