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1		-== **Overview** ==
	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**.
2	2
3		-Neurodiagnoses develops a **tridimensional diagnostic framework** for **CNS diseases**, incorporating **AI-powered annotation tools** to improve **interpretability, standardization, and clinical utility.**
4		-
5		-This methodology integrates **multi-modal data**, including:
6		-**Genetic data** (whole-genome sequencing, polygenic risk scores).
7		-**Neuroimaging** (MRI, PET, EEG, MEG).
8		-**Neurophysiological data** (EEG-based biomarkers, sleep actigraphy).
9		-**CSF & Blood Biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
10		-
11		-By applying **machine learning models**, Neurodiagnoses generates **structured, explainable diagnostic outputs** to assist **clinical decision-making** and **biomarker-driven patient stratification.**
12		-
13	13	----
14	14
15		-== **Data Integration & External Databases** ==
	5	+== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
16	16
17		-=== **How to Use External Databases in Neurodiagnoses** ===
	7	+=== **Project Overview** ===
18	18
19		-Neurodiagnoses integrates data from multiple **biomedical and neurological research databases**. Researchers can follow these steps to **access, prepare, and integrate** data into the Neurodiagnoses framework.
	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**.
20	20
21		-**Potential Data Sources**
22		-**Reference:** [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
	11	+== **Neuromarker: Generalized Biomarker Ontology** ==
23	23
24		-=== **Register for Access** ===
	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**.
25	25
26		-Each **external database** requires **individual registration** and approval.
27		-✔️ Follow the official **data access guidelines** of each provider.
28		-✔️ Ensure compliance with **ethical approvals** and **data-sharing agreements (DUAs).**
	15	+=== **Core Biomarker Categories** ===
29	29
30		-=== **Download & Prepare Data** ===
	17	+The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
31	31
32		-Once access is granted, download datasets **following compliance guidelines** and **format requirements** for integration.
	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
33	33
34		-**Supported File Formats**
	29	+----
35	35
36		-* **Tabular Data**: .csv, .tsv
37		-* **Neuroimaging Data**: .nii, .dcm
38		-* **Genomic Data**: .fasta, .vcf
39		-* **Clinical Metadata**: .json, .xml
	31	+== **How to Use External Databases in Neurodiagnoses** ==
40	40
41		-**Mandatory Fields for Integration**
	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.
42	42
43		-|=**Field Name**|=**Description**
44		-|**Subject ID**|Unique patient identifier
45		-|**Diagnosis**|Standardized disease classification
46		-|**Biomarkers**|CSF, plasma, or imaging biomarkers
47		-|**Genetic Data**|Whole-genome or exome sequencing
48		-|**Neuroimaging Metadata**|MRI/PET acquisition parameters
	35	+=== **Potential Data Sources** ===
49	49
50		-=== **Upload Data to Neurodiagnoses** ===
	37	+Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
51	51
52		-**Option 1:** Upload to **EBRAINS Bucket** → [[Neurodiagnoses Data Storage>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
53		-**Option 2:** Contribute via **GitHub Repository** → [[GitHub Data Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/tree/main/data]]
	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/]]
54	54
55		-**For large datasets, please contact project administrators before uploading.**
	49	+----
56	56
57		-=== **Integrate Data into AI Models** ===
	51	+== **1. Register for Access** ==
58	58
59		-Use **Jupyter Notebooks** on EBRAINS for **data preprocessing.**
60		-Standardize data using **harmonization tools.**
61		-Train AI models with **newly integrated datasets.**
	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.
62	62
63		-**Reference:** [[Data Processing Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_processing.md]]
64		-
65	65	----
66	66
67		-== **AI-Powered Annotation & Machine Learning Models** ==
	59	+== **2. Download & Prepare Data** ==
68	68
69		-Neurodiagnoses applies **advanced machine learning models** to classify CNS diseases, extract features from **biomarkers and neuroimaging**, and provide **AI-powered annotation.**
	61	+* Download datasets while adhering to **database usage policies**.
	62	+* Ensure files meet **Neurodiagnoses format requirements**:
70	70
71		-=== **AI Model Categories** ===
	64	+|=**Data Type**|=**Accepted Formats**
	65	+|**Tabular Data**|.csv, .tsv
	66	+|**Neuroimaging**|.nii, .dcm
	67	+|**Genomic Data**|.fasta, .vcf
	68	+|**Clinical Metadata**|.json, .xml
72	72
73		-|=**Model Type**|=**Function**|=**Example Algorithms**
74		-|**Probabilistic Diagnosis**|Assigns probability scores to multiple CNS disorders.|Random Forest, XGBoost, Bayesian Networks
75		-|**Tridimensional Diagnosis**|Classifies disorders based on Etiology, Biomarkers, and Neuroanatomical Correlations.|CNNs, Transformers, Autoencoders
76		-|**Biomarker Prediction**|Predicts missing biomarker values using regression.|KNN Imputation, Bayesian Estimation
77		-|**Neuroimaging Feature Extraction**|Extracts patterns from MRI, PET, EEG.|CNNs, Graph Neural Networks
78		-|**Clinical Decision Support**|Generates AI-driven diagnostic reports.|SHAP Explainability Tools
	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
79	79
80		-**Reference:** [[AI Model Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/models.md]]
81		-
82	82	----
83	83
84		-== **Clinical Decision Support & Tridimensional Diagnostic Framework** ==
	79	+== **3. Upload Data to Neurodiagnoses** ==
85	85
86		-Neurodiagnoses generates **structured AI reports** for clinicians, combining:
	81	+=== **Option 1: Upload to EBRAINS Bucket** ===
87	87
88		-**Probabilistic Diagnosis:** AI-generated ranking of potential diagnoses.
89		-**Tridimensional Classification:** Standardized diagnostic reports based on:
	83	+* Location: **EBRAINS Neurodiagnoses Bucket**
	84	+* Ensure **correct metadata tagging** before submission.
90	90
91		-1. **Axis 1:** **Etiology** → Genetic, Autoimmune, Prion, Toxic, Vascular.
92		-1. **Axis 2:** **Molecular Markers** → CSF, Neuroinflammation, EEG biomarkers.
93		-1. **Axis 3:** **Neuroanatomoclinical Correlations** → MRI atrophy, PET.
	86	+=== **Option 2: Contribute via GitHub Repository** ===
94	94
95		-**Reference:** [[Tridimensional Classification Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/classification.md]]
	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.
96	96
97	97	----
98	98
99		-== **Data Security, Compliance & Federated Learning** ==
	94	+== **4. Integrate Data into AI Models** ==
100	100
101		-✔ **Privacy-Preserving AI**: Implements **Federated Learning**, ensuring that patient data **never leaves** local institutions.
102		-✔ **Secure Data Access**: Data remains **stored in EBRAINS MIP servers** using **differential privacy techniques.**
103		-✔ **Ethical & GDPR Compliance**: Data-sharing agreements **must be signed** before use.
	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**.
104	104
105		-**Reference:** [[Data Protection & Federated Learning>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/security.md]]
	101	+**Reference**: See docs/data_processing.md for detailed instructions.
106	106
107	107	----
108	108
109		-== **Data Processing & Integration with Clinica.Run** ==
	105	+== **AI-Driven Biomarker Categorization** ==
110	110
111		-Neurodiagnoses now supports **Clinica.Run**, an **open-source neuroimaging platform** for **multimodal data processing.**
	107	+Neurodiagnoses employs **AI models** for biomarker classification:
112	112
113		-=== **How It Works** ===
	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
114	114
115		-✔ **Neuroimaging Preprocessing**: MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines.**
116		-✔ **Automated Biomarker Extraction**: Extracts volumetric, metabolic, and functional biomarkers.
117		-✔ **Data Security & Compliance**: Clinica.Run is **GDPR & HIPAA-compliant.**
	114	+----
118	118
119		-=== **Implementation Steps** ===
	116	+== **Collaboration & Partnerships** ==
120	120
121		-1. Install **Clinica.Run** dependencies.
122		-1. Configure **Clinica.Run pipeline** in clinica_run_config.json.
123		-1. Run **biomarker extraction pipelines** for AI-based diagnostics.
	118	+=== **Partnering with Data Providers** ===
124	124
125		-**Reference:** [[Clinica.Run Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/clinica_run.md]]
	120	+Neurodiagnoses seeks partnerships with data repositories to:
126	126
127		-----
	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.
128	128
129		-== **Collaborative Development & Research** ==
	126	+**Interested in Partnering?**
130	130
131		-**We Use GitHub to Develop AI Models & Store Research Data**
	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]]
132	132
133		-* **GitHub Repository:** AI model training scripts.
134		-* **GitHub Issues:** Tracks ongoing research questions.
135		-* **GitHub Wiki:** Project documentation & user guides.
	131	+----
136	136
137		-**We Use EBRAINS for Data & Collaboration**
	133	+== **Final Notes** ==
138	138
139		-* **EBRAINS Buckets:** Large-scale neuroimaging and biomarker storage.
140		-* **EBRAINS Jupyter Notebooks:** Cloud-based AI model execution.
141		-* **EBRAINS Wiki:** Research documentation and updates.
	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**.
142	142
143		-**Join the Project Forum:** [[GitHub Discussions>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
	137	+**For additional technical documentation**:
144	144
145		-----
	139	+* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	140	+* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
146	146
147		-**For Additional Documentation:**
	142	+**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
148	148
149		-* **GitHub Repository:** [[Neurodiagnoses AI Models>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
150		-* **EBRAINS Wiki:** [[Neurodiagnoses Research Collaboration>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
151		-
152	152	----
153	153
154		-**Neurodiagnoses is Open for Contributions – Join Us Today!**
	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.