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1		-== **Overview** ==
	1	+**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
2	2
3		-Neurodiagnoses develops a tridimensional diagnostic framework for CNS diseases, incorporating AI-powered annotation tools to improve interpretability, standardization, and clinical utility. The methodology integrates multi-modal data, including genetic, neuroimaging, neurophysiological, and biomarker datasets, and applies machine learning models to generate structured, explainable diagnostic outputs.
	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		-----
	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
	9	+### **Potential Data Sources**
	10	+Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
	11	+
	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))
	22	+
	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.##
	27	+
	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`##
	35	+
	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
	42	+
	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.##
	47	+
	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.
	52	+
	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**.##
	58	+
	59	+**Reference**: See `docs/data_processing.md` for detailed instructions.
	60	+
	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.
	67	+
	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	+
7	7	== **How to Use External Databases in Neurodiagnoses** ==
8	8
9	9	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.