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Neurodiagnoses

Version 17.1 by manuelmenendez on 2025/01/28 00:00

A new tridimensional diagnostic framework for neurodegenerative diseases

This project is focused on developing a novel nosological and diagnostic framework for neurological diseases by using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies.
We aim to create a structured, interpretable, and scalable diagnostic tool.

What is this about and what can I find here?

Overview

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

  • Axis 1: Etiology (genetic or other causes of diseases).
  • Axis 2: Molecular Markers (biomarkers).
  • Axis 3: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
tridimensional view of neurodegenerative diseases

Neurodegenerative diseases can be studied and classified in a tridimensional scheme with three axes: anatomic–clinical, molecular, and etiologic. CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography.

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.
Contents

Main contents

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