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1		-**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
	1	+== **Overview** ==
2	2
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**.##
	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.
5	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.##
	5	+----
8	8
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		-
81	81	== **How to Use External Databases in Neurodiagnoses** ==
82	82
83	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.