What is this about and what can I find here?

Overview

The Tridimensional Diagnostic Framework redefines how neurodegenerative diseases (NDDs) are classified by focusing on:

• Axis 1: Etiology (genetic/sporadic and environmental factors).
• Axis 2: Molecular Markers (biomarkers and proteinopathies).
• Axis 3: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).

This methodology enables:

• Greater precision in diagnosis.
• Integration of incomplete datasets using AI-driven probabilistic modeling.
• Stratification of patients for personalized treatment.

Diagnostic Axes

• Axis 1: Etiology

  • Description: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
  • Examples: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
  • Tests: Genetic testing, lifestyle and cardiovascular screening.

• Axis 2: Molecular Markers

  • Description: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
  • Examples: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
  • Tests: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).

• Axis 3: Neuroanatomoclinical

  • Description: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
  • Examples: Hippocampal atrophy correlating with memory deficits.
  • Tests: MRI volumetrics, FDG-PET, neuropsychological evaluations.

Applications

This system enhances:

• Research: By stratifying patients, reduces cohort heterogeneity in clinical trials.
• Clinical Practice: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.

Who has access?

We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!

How to Contribute:

• Access the `/docs` folder for guidelines.
• Use `/code` for the latest AI pipelines.
• Share feedback and ideas in the wiki discussion pages.

Key Objectives:

• Develop interpretable AI models for diagnosis and progression tracking. 
• Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
• Foster collaboration among neuroscientists, AI researchers, and clinicians.

Main contents:

• `/docs`: Documentation and contribution guidelines.
• `/code`: Machine learning pipelines and scripts.
• `/data`: Sample datasets for testing.
• `/outputs`: Generated models, visualizations, and reports.