Wiki source code of Methodology

Version 21.1 by manuelmenendez on 2025/02/14 22:09
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1 Here is the updated **Methodology** section for the EBRAINS Wiki, incorporating the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)** for **biomarker classification across all neurodegenerative diseases**.
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5 == **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
6
7 === **Project Overview** ===
8
9 Neurodiagnoses AI implements **AI-driven diagnostic and prognostic models** for central nervous system (CNS) disorders, expanding the **Florey Dementia Index (FDI) methodology** to a broader set of neurological conditions. The approach integrates **multimodal data sources** (EEG, neuroimaging, biomarkers, and genetics) and employs machine learning models to provide **explainable, real-time diagnostic insights**. This framework now incorporates **Neuromarker**, a **generalized biomarker ontology** that categorizes biomarkers across neurodegenerative diseases, enabling **standardized, cross-disease AI training**.
10
11 == **Neuromarker: Generalized Biomarker Ontology** ==
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13 Neuromarker extends the **Common Alzheimer’s Disease Research Ontology (CADRO)** into a **cross-disease biomarker categorization framework** applicable to all neurodegenerative diseases (NDDs). It allows for **standardized classification, AI-based feature extraction, and multimodal integration**.
14
15 === **Core Biomarker Categories** ===
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17 The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
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19 |=**Category**|=**Description**
20 |**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
21 |**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
22 |**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
23 |**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
24 |**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
25 |**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
26 |**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
27 |**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
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30
31 == **How to Use External Databases in Neurodiagnoses** ==
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33 To enhance diagnostic accuracy, Neurodiagnoses AI integrates data from **multiple biomedical and neurological research databases**. Researchers can follow these steps to access, prepare, and integrate data into the Neurodiagnoses framework.
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35 === **Potential Data Sources** ===
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37 Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
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39 * **ADNI**: Alzheimer's Disease Imaging & Biomarkers → [[ADNI>>url:https://adni.loni.usc.edu/]]
40 * **PPMI**: Parkinson’s Disease Imaging & Biospecimens → [[PPMI>>url:https://www.ppmi-info.org/]]
41 * **GP2**: Whole-Genome Sequencing for PD → [[GP2>>url:https://gp2.org/]]
42 * **Enroll-HD**: Huntington’s Disease Clinical & Genetic Data → [[Enroll-HD>>url:https://www.enroll-hd.org/]]
43 * **GAAIN**: Multi-Source Alzheimer’s Data Aggregation → [[GAAIN>>url:https://gaain.org/]]
44 * **UK Biobank**: Population-Wide Genetic, Imaging & Health Records → [[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
45 * **DPUK**: Dementia & Aging Data → [[DPUK>>url:https://www.dementiasplatform.uk/]]
46 * **PRION Registry**: Prion Diseases Clinical & Genetic Data → [[PRION Registry>>url:https://prionregistry.org/]]
47 * **DECIPHER**: Rare Genetic Disorder Genomic Variants → [[DECIPHER>>url:https://decipher.sanger.ac.uk/]]
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50
51 == **1. Register for Access** ==
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53 * Each external database requires **individual registration and access approval**.
54 * Ensure compliance with **ethical approvals and data usage agreements** before integrating datasets into Neurodiagnoses.
55 * Some repositories may require a **Data Usage Agreement (DUA)** for sensitive medical data.
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58
59 == **2. Download & Prepare Data** ==
60
61 * Download datasets while adhering to **database usage policies**.
62 * Ensure files meet **Neurodiagnoses format requirements**:
63
64 |=**Data Type**|=**Accepted Formats**
65 |**Tabular Data**|.csv, .tsv
66 |**Neuroimaging**|.nii, .dcm
67 |**Genomic Data**|.fasta, .vcf
68 |**Clinical Metadata**|.json, .xml
69
70 * **Mandatory Fields for Integration**:
71 ** **Subject ID**: Unique patient identifier
72 ** **Diagnosis**: Standardized disease classification
73 ** **Biomarkers**: CSF, plasma, or imaging biomarkers
74 ** **Genetic Data**: Whole-genome or exome sequencing
75 ** **Neuroimaging Metadata**: MRI/PET acquisition parameters
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78
79 == **3. Upload Data to Neurodiagnoses** ==
80
81 === **Option 1: Upload to EBRAINS Bucket** ===
82
83 * Location: **EBRAINS Neurodiagnoses Bucket**
84 * Ensure **correct metadata tagging** before submission.
85
86 === **Option 2: Contribute via GitHub Repository** ===
87
88 * Location: **GitHub Data Repository**
89 * Create a **new folder under /data/** and include a **dataset description**.
90 * **For large datasets**, contact project administrators before uploading.
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93
94 == **4. Integrate Data into AI Models** ==
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96 * Open **Jupyter Notebooks** on EBRAINS to run **preprocessing scripts**.
97 * **Standardize neuroimaging and biomarker formats** using harmonization tools.
98 * Use **machine learning models** to handle **missing data** and **feature extraction**.
99 * Train AI models with **newly integrated patient cohorts**.
100
101 **Reference**: See docs/data_processing.md for detailed instructions.
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104
105 == **AI-Driven Biomarker Categorization** ==
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107 Neurodiagnoses employs **AI models** for biomarker classification:
108
109 |=**Model Type**|=**Application**
110 |**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
111 |**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
112 |**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
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115
116 == [[image:workflow neurodiagnoses.png]] ==
117
118 == **Collaboration & Partnerships** ==
119
120 === **Partnering with Data Providers** ===
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122 Neurodiagnoses seeks partnerships with data repositories to:
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124 * Enable **API-based data integration** for real-time processing.
125 * Co-develop **harmonized AI-ready datasets** with standardized annotations.
126 * Secure **funding opportunities** through joint grant applications.
127
128 **Interested in Partnering?**
129
130 * If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
131 * **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
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134
135 == **Final Notes** ==
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137 Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
138
139 **For additional technical documentation**:
140
141 * **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
142 * **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
143
144 **If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
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148 This **updated methodology** now incorporates [[https:~~/~~/github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/biomarker_ontology>>https://Neuromarker]] for standardized biomarker classification, enabling **cross-disease AI training** across neurodegenerative disorders.