Methodology

Version 7.1 by manuelmenendez on 2025/02/01 14:11

Overview

This project develops a tridimensional diagnostic framework for CNS diseases, incorporating AI-powered annotation tools to improve interpretability, standardization, and clinical utility. The methodology integrates multi-modal data, including genetic, neuroimaging, neurophysiological, and biomarker datasets, and applies machine learning models to generate structured, explainable diagnostic outputs.

Workflow

  1. We Use GitHub for AI Development

    • Create a GitHub repository for AI scripts and models.
    • Use GitHub Projects to manage research milestones.

  2. We Use EBRAINS for Data & Collaboration

    • Store biomarker and neuroimaging data in EBRAINS Buckets.
    • Run Jupyter Notebooks in EBRAINS Lab to test AI models.
    • Use EBRAINS Wiki for structured documentation and research discussion.

1. Data Integration

Data Sources

Biomedical Ontologies & Databases:

  • Human Phenotype Ontology (HPO) for symptom annotation.
  • Gene Ontology (GO) for molecular and cellular processes.

Dimensionality Reduction and Interpretability:

  • Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
  • Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.

Neuroimaging & EEG/MEG Data:

  • MRI volumetric measures for brain atrophy tracking.
  • EEG functional connectivity patterns (AI-Mind).

Clinical & Biomarker Data:

  • CSF biomarkers (Amyloid-beta, Tau, Neurofilament Light).
  • Sleep monitoring and actigraphy data (ADIS).

Federated Learning Integration:

  • Secure multi-center data harmonization (PROMINENT).

Annotation System for Multi-Modal Data

To ensure structured integration of diverse datasets, Neurodiagnoses will implement an AI-driven annotation system, which will:

  • Assign standardized metadata tags to diagnostic features.
  • Provide contextual explanations for AI-based classifications.
  • Track temporal disease progression annotations to identify long-term trends.

2. AI-Based Analysis

Machine Learning & Deep Learning Models

Risk Prediction Models:

  • LETHE’s cognitive risk prediction model integrated into the annotation framework.

Biomarker Classification & Probabilistic Imputation:

  • KNN Imputer and Bayesian models used for handling missing biomarker data.

Neuroimaging Feature Extraction:

  • MRI & EEG data annotated with neuroanatomical feature labels.

AI-Powered Annotation System

  • Uses SHAP-based interpretability tools to explain model decisions.
  • Generates automated clinical annotations in structured reports.
  • Links findings to standardized medical ontologies (e.g., SNOMED, HPO).

3. Diagnostic Framework & Clinical Decision Support

Tridimensional Diagnostic Axes

Axis 1: Etiology (Pathogenic Mechanisms)

  • Classification based on genetic markers, cellular pathways, and environmental risk factors.
  • AI-assisted annotation provides causal interpretations for clinical use.

Axis 2: Molecular Markers & Biomarkers

  • Integration of CSF, blood, and neuroimaging biomarkers.
  • Structured annotation highlights biological pathways linked to diagnosis.

Axis 3: Neuroanatomoclinical Correlations

  • MRI and EEG data provide anatomical and functional insights.
  • AI-generated progression maps annotate brain structure-function relationships.

4. Computational Workflow & Annotation Pipelines

Data Processing Steps

Data Ingestion:

  • Harmonized datasets stored in EBRAINS Bucket.
  • Preprocessing pipelines clean and standardize data.

Feature Engineering:

  • AI models extract clinically relevant patterns from EEG, MRI, and biomarkers.

AI-Generated Annotations:

  • Automated tagging of diagnostic features in structured reports.
  • Explainability modules (SHAP, LIME) ensure transparency in predictions.

Clinical Decision Support Integration:

  • AI-annotated findings fed into interactive dashboards.
  • Clinicians can adjust, validate, and modify annotations.

5. Validation & Real-World Testing

Prospective Clinical Study

  • Multi-center validation of AI-based annotations & risk stratifications.
  • Benchmarking against clinician-based diagnoses.
  • Real-world testing of AI-powered structured reporting.

Quality Assurance & Explainability

  • Annotations linked to structured knowledge graphs for improved transparency.
  • Interactive annotation editor allows clinicians to validate AI outputs.

6. Collaborative Development

The project is open to contributions from researchers, clinicians, and developers.

Key tools include:

  • Jupyter Notebooks: For data analysis and pipeline development.
    • Example: probabilistic imputation
  • Wiki Pages: For documenting methods and results.
  • Drive and Bucket: For sharing code, data, and outputs.
  • Collaboration with related projects:
    • Example: Beyond the hype: AI in dementia – from early risk detection to disease treatment

7. Tools and Technologies

Programming Languages:

  • Python for AI and data processing.

Frameworks:

  • TensorFlow and PyTorch for machine learning.
  • Flask or FastAPI for backend services.

Visualization:

  • Plotly and Matplotlib for interactive and static visualizations.

EBRAINS Services:

  • Collaboratory Lab for running Notebooks.
  • Buckets for storing large datasets.

Why This Matters

  • The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
  • It enables real-time tracking of disease progression across the three diagnostic axes.
  • It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.