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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	+**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
2	2
3		-**Neuromarker: Generalized Biomarker Ontology**
	3	+## **Project Overview**
	4	+Neurodiagnoses AI implements AI-driven diagnostic and prognostic models for central nervous system (CNS) disorders, adapting 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**.##
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.
	6	+## **How to Use External Databases in Neurodiagnoses**
	7	+To enhance diagnostic accuracy, 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.##
6	6
7		-**Core Biomarker Categories**
	9	+### **Potential Data Sources**
	10	+Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
8	8
9		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	12	+**Reference: List of Potential Databases**
	13	+- **ADNI**: Alzheimer's Disease data ([ADNI](https://adni.loni.usc.edu))
	14	+- **PPMI**: Parkinson’s Disease Imaging and biospecimens ([PPMI](https://www.ppmi-info.org))
	15	+- **GP2**: Whole-genome sequencing for PD ([GP2](https://gp2.org))
	16	+- **Enroll-HD**: Huntington’s Disease Clinical and genetic data ([Enroll-HD](https://www.enroll-hd.org))
	17	+- **GAAIN**: Multi-source Alzheimer’s data aggregation ([GAAIN](https://gaain.org))
	18	+- **UK Biobank**: Population-wide genetic, imaging, and health records ([UK Biobank](https://www.ukbiobank.ac.uk))
	19	+- **DPUK**: Dementia and Aging data ([DPUK](https://www.dementiasplatform.uk))
	20	+- **PRION Registry**: Prion Diseases clinical and genetic data ([PRION Registry](https://prionregistry.org))
	21	+- **DECIPHER**: Rare genetic disorder genomic variants ([DECIPHER](https://decipher.sanger.ac.uk))
10	10
11		-|=**Category**|=**Description**
12		-|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
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
15		-|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
16		-|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
17		-|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
18		-|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
19		-|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
	23	+### **1. Register for Access**
	24	+- Each external database requires **individual registration** and access approval.
	25	+- Ensure compliance with **ethical approvals** and **data usage agreements** before integrating datasets into Neurodiagnoses.
	26	+- Some repositories may require a **Data Usage Agreement (DUA)** for sensitive medical data.##
20	20
21		-**Integrating External Databases into Neurodiagnoses**
	28	+### **2. Download & Prepare Data**
	29	+- Download datasets while adhering to database usage policies.
	30	+- Ensure files meet **Neurodiagnoses format requirements**:
	31	+ - **Tabular Data**: `.csv`, `.tsv`
	32	+ - **Neuroimaging Data**: `.nii`, `.dcm`
	33	+ - **Genomic Data**: `.fasta`, `.vcf`
	34	+ - **Clinical Metadata**: `.json`, `.xml`##
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:
	36	+- **Mandatory Fields for Integration**:
	37	+ - **Subject ID**: Unique patient identifier
	38	+ - **Diagnosis**: Standardized disease classification
	39	+ - **Biomarkers**: CSF, plasma, or imaging biomarkers
	40	+ - **Genetic Data**: Whole-genome or exome sequencing
	41	+ - **Neuroimaging Metadata**: MRI/PET acquisition parameters
24	24
25		-1. (((
26		-**Register for Access**
	43	+### **3. Upload Data to Neurodiagnoses**
	44	+**Option 1: Upload to EBRAINS Bucket**
	45	+- Location: **EBRAINS Neurodiagnoses Bucket**
	46	+- Ensure correct **metadata tagging** before submission.##
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**
	48	+ **Option 2: Contribute via GitHub Repository**
	49	+- Location: **GitHub Data Repository**
	50	+- Create a new folder under `/data/` and include a **dataset description**.
	51	+- For large datasets, contact project administrators before uploading.
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
	53	+### **4. Integrate Data into AI Models**
	54	+- Open **Jupyter Notebooks** on EBRAINS to run **preprocessing scripts**.
	55	+- Standardize **neuroimaging and biomarker formats** using harmonization tools.
	56	+- Use **machine learning models** to handle missing data and feature extraction.
	57	+- Train AI models with **newly integrated patient cohorts**.##
38	38
39		-|=**Data Type**|=**Accepted Formats**
40		-|**Tabular Data**|.csv, .tsv
41		-|**Neuroimaging**|.nii, .dcm
42		-|**Genomic Data**|.fasta, .vcf
43		-|**Clinical Metadata**|.json, .xml
44		-)))
45		-* (((
46		-**Mandatory Fields for Integration**:
	59	+**Reference**: See `docs/data_processing.md` for detailed instructions.
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**
	61	+## **Collaboration & Partnerships**##
	62	+# **Partnering with Data Providers**
	63	+Neurodiagnoses seeks partnerships with data repositories to:
	64	+- Enable **API-based data integration** for real-time processing.
	65	+- Co-develop **harmonized AI-ready datasets** with standardized annotations.
	66	+- Secure **funding opportunities** through joint grant applications.
57	57
	68	+**Interested in Partnering?**
	69	+- If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	70	+- **Contact**: info@neurodiagnoses.com
	71	+
	72	+## **Final Notes**
	73	+Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute **new datasets and methodologies**.##
	74	+
	75	+For additional technical documentation:
	76	+- **GitHub Repository**: [Neurodiagnoses GitHub](https://github.com/neurodiagnoses)
	77	+- **EBRAINS Collaboration Page**: [EBRAINS Neurodiagnoses](https://ebrains.eu/collabs/neurodiagnoses)
	78	+
	79	+If you experience issues integrating data, **open a GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
	80	+
	81	+== **How to Use External Databases in Neurodiagnoses** ==
	82	+
	83	+To enhance the accuracy of our diagnostic models, Neurodiagnoses integrates data from multiple biomedical and neurological research databases. If you are a researcher, follow these steps to access, prepare, and integrate data into the Neurodiagnoses framework.
	84	+
	85	+=== **Potential Data Sources** ===
	86	+
	87	+Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
	88	+
	89	+* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
	90	+
	91	+=== **1. Register for Access** ===
	92	+
	93	+Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
	94	+
	95	+* Ensure that you have completed all ethical approvals and data access agreements before integrating datasets into Neurodiagnoses.
	96	+* Some repositories require a Data Usage Agreement (DUA) before downloading sensitive medical data.
	97	+
	98	+=== **2. Download & Prepare Data** ===
	99	+
	100	+Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
	101	+
	102	+==== **Supported File Formats** ====
	103	+
	104	+* Tabular Data: .csv, .tsv
	105	+* Neuroimaging Data: .nii, .dcm
	106	+* Genomic Data: .fasta, .vcf
	107	+* Clinical Metadata: .json, .xml
	108	+
	109	+==== **Mandatory Fields for Integration** ====
	110	+
	111	+|=Field Name|=Description
	112	+|Subject ID|Unique patient identifier
	113	+|Diagnosis|Standardized disease classification
	114	+|Biomarkers|CSF, plasma, or imaging biomarkers
	115	+|Genetic Data|Whole-genome or exome sequencing
	116	+|Neuroimaging Metadata|MRI/PET acquisition parameters
	117	+
	118	+=== **3. Upload Data to Neurodiagnoses** ===
	119	+
	120	+Once preprocessed, data can be uploaded to EBRAINS or GitHub.
	121	+
58	58	* (((
59	59	**Option 1: Upload to EBRAINS Bucket**
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
	125	+* Location: [[EBRAINS Neurodiagnoses Bucket>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
62	62	* Ensure correct metadata tagging before submission.
63	63	)))
64	64	* (((
65	65	**Option 2: Contribute via GitHub Repository**
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.
	131	+* Location: [[GitHub Data Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/tree/main/data]]
	132	+* Create a new folder under /data/ and include dataset description.
70	70	)))
71		-)))
72		-1. (((
73		-**Integrate Data into AI Models**
74	74
	135	+//Note: For large datasets, please contact the project administrators before uploading.//
	136	+
	137	+=== **4. Integrate Data into AI Models** ===
	138	+
	139	+Once uploaded, datasets must be harmonized and formatted before AI model training.
	140	+
	141	+==== **Steps for Data Integration** ====
	142	+
75	75	* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
76	76	* Standardize neuroimaging and biomarker formats using harmonization tools.
77		-* Utilize machine learning models to handle missing data and feature extraction.
	145	+* Use machine learning models to handle missing data and feature extraction.
78	78	* Train AI models with newly integrated patient cohorts.
	147	+* Reference: [[Detailed instructions can be found in docs/data_processing.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_processing.md]].
79	79
80		-**Reference**: See docs/data_processing.md for detailed instructions.
81		-)))
	149	+----
82	82
83		-**AI-Driven Biomarker Categorization**
	151	+== **Database Sources Table** ==
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
	153	+=== **Where to Insert This** ===
86	86
87		-|=**Model Type**|=**Application**
88		-|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
89		-|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
90		-|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
	155	+* GitHub: [[docs/data_sources.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_sources.md]]
	156	+* EBRAINS Wiki: Collabs/neurodiagnoses/Data Sources
91	91
92		-**Collaboration & Partnerships**
	158	+=== **Key Databases for Neurodiagnoses** ===
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	160	+|=Database|=Focus Area|=Data Type|=Access Link
	161	+|ADNI|Alzheimer's Disease|MRI, PET, CSF, cognitive tests|ADNI
	162	+|PPMI|Parkinson’s Disease|Imaging, biospecimens|[[PPMI>>url:https://www.ppmi-info.org/]]
	163	+|GP2|Genetic Data for PD|Whole-genome sequencing|[[GP2>>url:https://gp2.org/]]
	164	+|Enroll-HD|Huntington’s Disease|Clinical, genetic, imaging|[[Enroll-HD>>url:https://enroll-hd.org/]]
	165	+|GAAIN|Alzheimer's & Cognitive Decline|Multi-source data aggregation|[[GAAIN>>url:https://www.gaain.org/]]
	166	+|UK Biobank|Population-wide studies|Genetic, imaging, health records|[[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
	167	+|DPUK|Dementia & Aging|Imaging, genetics, lifestyle factors|[[DPUK>>url:https://www.dementiasplatform.uk/]]
	168	+|PRION Registry|Prion Diseases|Clinical and genetic data|[[PRION Registry>>url:https://www.prionalliance.org/]]
	169	+|DECIPHER|Rare Genetic Disorders|Genomic variants|DECIPHER
95	95
96		-* Enable API-based data integration for real-time processing.
	171	+If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
	172	+
	173	+----
	174	+
	175	+== **Collaboration & Partnerships** ==
	176	+
	177	+=== **Where to Insert This** ===
	178	+
	179	+* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
	180	+* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
	181	+
	182	+=== **Partnering with Data Providers** ===
	183	+
	184	+Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
	185	+
	186	+* Enable direct API-based data integration for real-time processing.
97	97	* Co-develop harmonized AI-ready datasets with standardized annotations.
98	98	* Secure funding opportunities through joint grant applications.
99	99
100		-**Interested in Partnering?**
	190	+=== **Interested in Partnering?** ===
101	101
102	102	If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	194	+* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
105	105
106		-
	196	+----
	197	+
	198	+== **Final Notes** ==
	199	+
	200	+Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
	201	+
	202	+For additional technical documentation:
	203	+
	204	+* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
	205	+* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
	206	+
	207	+If you experience issues integrating data, open a [[GitHub Issue>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]] or consult the EBRAINS Neurodiagnoses Forum.

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