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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. 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.
	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		-**Neuromarker: Generalized Biomarker Ontology**
	3	+----
4	4
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.
	5	+== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
6	6
7		-**Recommended Software**
	7	+=== **Project Overview** ===
8	8
9		-There is a suite of software that can help implement the workflow needed in Neurodiagnoses. Find a list of recommendations [[here>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/recommended_software]].
	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	10
11		-**Core Biomarker Categories**
	11	+== **Neuromarker: Generalized Biomarker Ontology** ==
12	12
13		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	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	14
	15	+=== **Core Biomarker Categories** ===
	16	+
	17	+The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
	18	+
15	15	|=**Category**|=**Description**
16	16	|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
17	17	|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
18		-|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
	22	+|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
19	19	|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
20	20	|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
21	21	|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
...	...	@@ -22,100 +22,123 @@
22	22	|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
23	23	|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
24	24
25		-**Integrating External Databases into Neurodiagnoses**
	29	+----
26	26
27		-To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
	31	+== **How to Use External Databases in Neurodiagnoses** ==
28	28
29		-1. (((
30		-**Register for Access**
	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.
31	31
32		-* Each external database requires individual registration and access approval.
33		-* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
34		-* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
35		-)))
36		-1. (((
37		-**Download & Prepare Data**
	35	+=== **Potential Data Sources** ===
38	38
39		-* Download datasets while adhering to database usage policies.
40		-* (((
41		-Ensure files meet Neurodiagnoses format requirements:
	37	+Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
42	42
	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	+
43	43	|=**Data Type**|=**Accepted Formats**
44	44	|**Tabular Data**|.csv, .tsv
45	45	|**Neuroimaging**|.nii, .dcm
46	46	|**Genomic Data**|.fasta, .vcf
47	47	|**Clinical Metadata**|.json, .xml
48		-)))
49		-* (((
50		-**Mandatory Fields for Integration**:
51	51
52		-* Subject ID: Unique patient identifier
53		-* Diagnosis: Standardized disease classification
54		-* Biomarkers: CSF, plasma, or imaging biomarkers
55		-* Genetic Data: Whole-genome or exome sequencing
56		-* Neuroimaging Metadata: MRI/PET acquisition parameters
57		-)))
58		-)))
59		-1. (((
60		-**Upload Data to Neurodiagnoses**
	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
61	61
62		-* (((
63		-**Option 1: Upload to EBRAINS Bucket**
	77	+----
64	64
65		-* Location: EBRAINS Neurodiagnoses Bucket
66		-* Ensure correct metadata tagging before submission.
67		-)))
68		-* (((
69		-**Option 2: Contribute via GitHub Repository**
	79	+== **3. Upload Data to Neurodiagnoses** ==
70	70
71		-* Location: GitHub Data Repository
72		-* Create a new folder under /data/ and include a dataset description.
73		-* For large datasets, contact project administrators before uploading.
74		-)))
75		-)))
76		-1. (((
77		-**Integrate Data into AI Models**
	81	+=== **Option 1: Upload to EBRAINS Bucket** ===
78	78
79		-* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
80		-* Standardize neuroimaging and biomarker formats using harmonization tools.
81		-* Utilize machine learning models to handle missing data and feature extraction.
82		-* Train AI models with newly integrated patient cohorts.
	83	+* Location: **EBRAINS Neurodiagnoses Bucket**
	84	+* Ensure **correct metadata tagging** before submission.
83	83
	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	+
84	84	**Reference**: See docs/data_processing.md for detailed instructions.
85		-)))
86	86
87		-**AI-Driven Biomarker Categorization**
	103	+----
88	88
89		-Neurodiagnoses employs advanced AI models for biomarker classification:
	105	+== **AI-Driven Biomarker Categorization** ==
90	90
	107	+Neurodiagnoses employs **AI models** for biomarker classification:
	108	+
91	91	|=**Model Type**|=**Application**
92	92	|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
93	93	|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
94	94	|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
95	95
96		-**Collaboration & Partnerships**
	114	+----
97	97
98		-Neurodiagnoses actively seeks partnerships with data providers to:
	116	+== [[image:workflow neurodiagnoses.png]] ==
99	99
100		-* Enable API-based data integration for real-time processing.
101		-* Co-develop harmonized AI-ready datasets with standardized annotations.
102		-* Secure funding opportunities through joint grant applications.
	118	+== **Collaboration & Partnerships** ==
103	103
	120	+=== **Partnering with Data Providers** ===
	121	+
	122	+Neurodiagnoses seeks partnerships with data repositories to:
	123	+
	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	+
104	104	**Interested in Partnering?**
105	105
106		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	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]]
107	107
108		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	133	+----
109	109
110		-**Final Notes**
	135	+== **Final Notes** ==
111	111
112		-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.
	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**.
113	113
114		-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.
	139	+**For additional technical documentation**:
115	115
116		-**For additional technical documentation and collaboration opportunities:**
	141	+* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	142	+* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
117	117
118		-* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
119		-* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	144	+**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
120	120
121		-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.
	146	+----
	147	+
	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.