Methodology
Neurodiagnoses AI is an open-source, AI-driven framework designed to enhance the diagnosis and prognosis of central nervous system (CNS) disorders. It encompasses a broader spectrum of neurological conditions. The system integrates multimodal data sources—including EEG, neuroimaging, biomarkers, and genetics—and employs machine learning models to deliver explainable, real-time diagnostic insights. A key feature of this framework is the incorporation of the Generalized Neuro Biomarker Ontology Categorization (Neuromarker) and Disease Knowledge Transfer (DKT), which standardizes disease and biomarker classification across all CNS diseases, facilitating cross-disease AI training.
Neuromarker: Generalized Biomarker Ontology
Neuromarker extends the Common Alzheimer’s Disease Research Ontology (CADRO) into a comprehensive biomarker categorization framework applicable to all neurodegenerative diseases (NDDs). This ontology enables standardized classification, AI-based feature extraction, and seamless multimodal data integration.
Recommended Software
There is a suite of software that can help implement the workflow needed in Neurodiagnoses. Find a list of recommendations here.
Core Biomarker Categories
Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
| Category | Description |
|---|---|
| Molecular Biomarkers | Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic) |
| Neuroimaging Biomarkers | Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein) |
| Fluid Biomarkers | CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides |
| Neurophysiological Biomarkers | EEG, MEG, evoked potentials (ERP), sleep-related markers |
| Digital Biomarkers | Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers |
| Clinical Phenotypic Markers | Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS) |
| Genetic Biomarkers | Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores |
| Environmental & Lifestyle Factors | Toxins, infections, diet, microbiome, comorbidities |
Integrating External Databases into Neurodiagnoses
To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
Register for Access
- Each external database requires individual registration and access approval.
- Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
- Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
Download & Prepare Data
- Download datasets while adhering to database usage policies.
Ensure files meet Neurodiagnoses format requirements:
Data Type Accepted Formats Tabular Data .csv, .tsv Neuroimaging .nii, .dcm Genomic Data .fasta, .vcf Clinical Metadata .json, .xml Mandatory Fields for Integration:
- Subject ID: Unique patient identifier
- Diagnosis: Standardized disease classification
- Biomarkers: CSF, plasma, or imaging biomarkers
- Genetic Data: Whole-genome or exome sequencing
- Neuroimaging Metadata: MRI/PET acquisition parameters
Upload Data to Neurodiagnoses
Option 1: Upload to EBRAINS Bucket
- Location: EBRAINS Neurodiagnoses Bucket
- Ensure correct metadata tagging before submission.
Option 2: Contribute via GitHub Repository
- Location: GitHub Data Repository
- Create a new folder under /data/ and include a dataset description.
- For large datasets, contact project administrators before uploading.
Integrate Data into AI Models
- Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
- Standardize neuroimaging and biomarker formats using harmonization tools.
- Utilize machine learning models to handle missing data and feature extraction.
- Train AI models with newly integrated patient cohorts.
Reference: See docs/data_processing.md for detailed instructions.
AI-Driven Biomarker Categorization
Neurodiagnoses employs advanced AI models for biomarker classification:
| Model Type | Application |
|---|---|
| Graph Neural Networks (GNNs) | Identify shared biomarker pathways across diseases |
| Contrastive Learning | Distinguish overlapping vs. unique biomarkers |
| Multimodal Transformer Models | Integrate imaging, omics, and clinical data |
Collaboration & Partnerships
Neurodiagnoses actively seeks partnerships with data providers to:
- Enable API-based data integration for real-time processing.
- Co-develop harmonized AI-ready datasets with standardized annotations.
- Secure funding opportunities through joint grant applications.
Interested in Partnering?
If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
Contact: info@neurodiagnoses.com
Final Notes
Neurodiagnoses AI is committed to advancing the integration of artificial intelligence in neurodiagnostic processes. By continuously expanding our data ecosystem and incorporating standardized biomarker classifications through the Neuromarker ontology, we aim to enhance cross-disease AI training and improve diagnostic accuracy across neurodegenerative disorders.
We encourage researchers and institutions to contribute new datasets and methodologies to further enrich this collaborative platform. Your participation is vital in driving innovation and fostering a deeper understanding of complex neurological conditions.
For additional technical documentation and collaboration opportunities:
- GitHub Repository: Neurodiagnoses GitHub
- EBRAINS Collaboration Page: EBRAINS Neurodiagnoses
If you encounter any issues during data integration or have suggestions for improvement, please open a GitHub Issue or consult the EBRAINS Neurodiagnoses Forum. Together, we can advance the field of neurodiagnostics and contribute to better patient outcomes.