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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.
	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		-**Core Biomarker Categories**
	7	+=== **Project Overview** ===
8	8
9		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	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	+== **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	+
11	11	|=**Category**|=**Description**
12	12	|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
13	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
	22	+|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
15	15	|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
16	16	|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
17	17	|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
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18	18	|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
19	19	|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
20	20
21		-**Integrating External Databases into Neurodiagnoses**
	29	+----
22	22
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:
	31	+== **How to Use External Databases in Neurodiagnoses** ==
24	24
25		-1. (((
26		-**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.
27	27
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**
	35	+=== **Potential Data Sources** ===
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
	37	+Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
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	+
39	39	|=**Data Type**|=**Accepted Formats**
40	40	|**Tabular Data**|.csv, .tsv
41	41	|**Neuroimaging**|.nii, .dcm
42	42	|**Genomic Data**|.fasta, .vcf
43	43	|**Clinical Metadata**|.json, .xml
44		-)))
45		-* (((
46		-**Mandatory Fields for Integration**:
47	47
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**
	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
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	77	+----
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
63		-)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	79	+== **3. Upload Data to Neurodiagnoses** ==
66	66
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**
	81	+=== **Option 1: Upload to EBRAINS Bucket** ===
74	74
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.
	83	+* Location: **EBRAINS Neurodiagnoses Bucket**
	84	+* Ensure **correct metadata tagging** before submission.
79	79
	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	+
80	80	**Reference**: See docs/data_processing.md for detailed instructions.
81		-)))
82	82
83		-**AI-Driven Biomarker Categorization**
	103	+----
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
	105	+== **AI-Driven Biomarker Categorization** ==
86	86
	107	+Neurodiagnoses employs **AI models** for biomarker classification:
	108	+
87	87	|=**Model Type**|=**Application**
88	88	|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
89	89	|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
90	90	|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
91	91
92		-**Collaboration & Partnerships**
	114	+----
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	116	+== **Collaboration & Partnerships** ==
95	95
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.
	118	+=== **Partnering with Data Providers** ===
99	99
	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	+
100	100	**Interested in Partnering?**
101	101
102		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	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]]
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	131	+----
105	105
106		-
	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.

workflow neurodiagnoses.png

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