Wiki source code of Methodology

Version 23.1 by manuelmenendez on 2025/02/15 12:55
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1 **Neurodiagnoses AI** is an open-source, AI-driven framework designed to enhance the diagnosis and prognosis of central nervous system (CNS) disorders. Building upon the Florey Dementia Index (FDI) methodology, it now 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)**, which standardizes biomarker classification across all neurodegenerative diseases, facilitating cross-disease AI training.
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3 **Neuromarker: Generalized Biomarker Ontology**
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5 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.
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7 **Core Biomarker Categories**
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9 Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
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11 |=**Category**|=**Description**
12 |**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
13 |**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
14 |**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
15 |**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
16 |**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
17 |**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
18 |**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
19 |**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
20
21 **Integrating External Databases into Neurodiagnoses**
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23 To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
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25 1. (((
26 **Register for Access**
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28 * Each external database requires individual registration and access approval.
29 * Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
30 * Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
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32 1. (((
33 **Download & Prepare Data**
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35 * Download datasets while adhering to database usage policies.
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37 Ensure files meet Neurodiagnoses format requirements:
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39 |=**Data Type**|=**Accepted Formats**
40 |**Tabular Data**|.csv, .tsv
41 |**Neuroimaging**|.nii, .dcm
42 |**Genomic Data**|.fasta, .vcf
43 |**Clinical Metadata**|.json, .xml
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45 * (((
46 **Mandatory Fields for Integration**:
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48 * Subject ID: Unique patient identifier
49 * Diagnosis: Standardized disease classification
50 * Biomarkers: CSF, plasma, or imaging biomarkers
51 * Genetic Data: Whole-genome or exome sequencing
52 * Neuroimaging Metadata: MRI/PET acquisition parameters
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55 1. (((
56 **Upload Data to Neurodiagnoses**
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58 * (((
59 **Option 1: Upload to EBRAINS Bucket**
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61 * Location: EBRAINS Neurodiagnoses Bucket
62 * Ensure correct metadata tagging before submission.
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64 * (((
65 **Option 2: Contribute via GitHub Repository**
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67 * Location: GitHub Data Repository
68 * Create a new folder under /data/ and include a dataset description.
69 * For large datasets, contact project administrators before uploading.
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72 1. (((
73 **Integrate Data into AI Models**
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75 * Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
76 * Standardize neuroimaging and biomarker formats using harmonization tools.
77 * Utilize machine learning models to handle missing data and feature extraction.
78 * Train AI models with newly integrated patient cohorts.
79
80 **Reference**: See docs/data_processing.md for detailed instructions.
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82
83 **AI-Driven Biomarker Categorization**
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85 Neurodiagnoses employs advanced AI models for biomarker classification:
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87 |=**Model Type**|=**Application**
88 |**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
89 |**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
90 |**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
91
92 **Collaboration & Partnerships**
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94 Neurodiagnoses actively seeks partnerships with data providers to:
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96 * Enable API-based data integration for real-time processing.
97 * Co-develop harmonized AI-ready datasets with standardized annotations.
98 * Secure funding opportunities through joint grant applications.
99
100 **Interested in Partnering?**
101
102 If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
103
104 **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
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