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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**.
	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.
2	2
3		-----
	3	+**Neuromarker: Generalized Biomarker Ontology**
4	4
5		-== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
	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.
6	6
7		-=== **Project Overview** ===
	7	+**Core Biomarker Categories**
8	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**.
	9	+Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
10	10
11		-== **Neuromarker: Generalized Biomarker Ontology** ==
12		-
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** ===
16		-
17		-The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
18		-
19	19	|=**Category**|=**Description**
20	20	|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
21	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
	14	+|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
23	23	|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
24	24	|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
25	25	|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
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26	26	|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
27	27	|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
28	28
29		-----
	21	+**Integrating External Databases into Neurodiagnoses**
30	30
31		-== **How to Use External Databases in Neurodiagnoses** ==
	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:
32	32
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.
	25	+1. (((
	26	+**Register for Access**
34	34
35		-=== **Potential Data Sources** ===
	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.
	31	+)))
	32	+1. (((
	33	+**Download & Prepare Data**
36	36
37		-Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
	35	+* Download datasets while adhering to database usage policies.
	36	+* (((
	37	+Ensure files meet Neurodiagnoses format requirements:
38	38
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/]]
48		-
49		-----
50		-
51		-== **1. Register for Access** ==
52		-
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.
56		-
57		-----
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	64	|=**Data Type**|=**Accepted Formats**
65	65	|**Tabular Data**|.csv, .tsv
66	66	|**Neuroimaging**|.nii, .dcm
67	67	|**Genomic Data**|.fasta, .vcf
68	68	|**Clinical Metadata**|.json, .xml
	44	+)))
	45	+* (((
	46	+**Mandatory Fields for Integration**:
69	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
	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
	53	+)))
	54	+)))
	55	+1. (((
	56	+**Upload Data to Neurodiagnoses**
76	76
77		-----
	58	+* (((
	59	+**Option 1: Upload to EBRAINS Bucket**
78	78
79		-== **3. Upload Data to Neurodiagnoses** ==
	61	+* Location: EBRAINS Neurodiagnoses Bucket
	62	+* Ensure correct metadata tagging before submission.
	63	+)))
	64	+* (((
	65	+**Option 2: Contribute via GitHub Repository**
80	80
81		-=== **Option 1: Upload to EBRAINS Bucket** ===
	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.
	70	+)))
	71	+)))
	72	+1. (((
	73	+**Integrate Data into AI Models**
82	82
83		-* Location: **EBRAINS Neurodiagnoses Bucket**
84		-* Ensure **correct metadata tagging** before submission.
	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.
85	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.
91		-
92		-----
93		-
94		-== **4. Integrate Data into AI Models** ==
95		-
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	101	**Reference**: See docs/data_processing.md for detailed instructions.
	81	+)))
102	102
103		-----
	83	+**AI-Driven Biomarker Categorization**
104	104
105		-== **AI-Driven Biomarker Categorization** ==
	85	+Neurodiagnoses employs advanced AI models for biomarker classification:
106	106
107		-Neurodiagnoses employs **AI models** for biomarker classification:
108		-
109	109	|=**Model Type**|=**Application**
110	110	|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
111	111	|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
112	112	|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
113	113
114		-----
	92	+**Collaboration & Partnerships**
115	115
116		-== **Collaboration & Partnerships** ==
	94	+Neurodiagnoses actively seeks partnerships with data providers to:
117	117
118		-=== **Partnering with Data Providers** ===
	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.
119	119
120		-Neurodiagnoses seeks partnerships with data repositories to:
121		-
122		-* Enable **API-based data integration** for real-time processing.
123		-* Co-develop **harmonized AI-ready datasets** with standardized annotations.
124		-* Secure **funding opportunities** through joint grant applications.
125		-
126	126	**Interested in Partnering?**
127	127
128		-* If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
129		-* **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	102	+If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
130	130
131		-----
	104	+**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
132	132
133		-== **Final Notes** ==
134		-
135		-Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
136		-
137		-**For additional technical documentation**:
138		-
139		-* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
140		-* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
141		-
142		-**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
143		-
144		-----
145		-
146		-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.
	106	+

workflow neurodiagnoses.png

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	1	+XWiki.manuelmenendez

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