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1		-=== **Overview** ===
	1	+**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
2	2
3		-This section describes the step-by-step process used in the **Neurodiagnoses** project to develop a novel diagnostic framework for neurological diseases. The methodology integrates artificial intelligence (AI), biomedical ontologies, and computational neuroscience to create a structured, interpretable, and scalable diagnostic system.
	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
7		-=== **1. Data Integration** ===
	9	+### **Potential Data Sources**
	10	+Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
8	8
9		-==== **Data Sources** ====
	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		-* **Biomedical Ontologies**:
12		-** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
13		-** Gene Ontology (GO) for molecular and cellular processes.
14		-* **Neuroimaging Datasets**:
15		-** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
16		-* **Clinical and Biomarker Data**:
17		-** Anonymized clinical reports, molecular biomarkers, and test results.
	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.##
18	18
19		-==== **Data Preprocessing** ====
	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`##
20	20
21		-1. **Standardization**: Ensure all data sources are normalized to a common format.
22		-1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
23		-1. **Data Cleaning**: Handle missing values and remove duplicates.
	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		-----
	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.##
26	26
27		-=== **2. AI-Based Analysis** ===
	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.
28	28
29		-==== **Model Development** ====
	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**.##
30	30
31		-* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
32		-* **Classification Models**:
33		-** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
34		-** Purpose: Predict the likelihood of specific neurological conditions based on input data.
	59	+**Reference**: See `docs/data_processing.md` for detailed instructions.
35	35
36		-==== **Dimensionality Reduction and Interpretability** ====
	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.
37	37
38		-* Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
39		-* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
	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
40	40
41		-----
	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**.##
42	42
43		-=== **3. Diagnostic Framework** ===
	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)
44	44
45		-==== **Axes of Diagnosis** ====
	79	+If you experience issues integrating data, **open a GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
46	46
47		-The framework organizes diagnostic data into three axes:
	81	+== **How to Use External Databases in Neurodiagnoses** ==
48	48
49		-1. **Etiology**: Genetic and environmental risk factors.
50		-1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
51		-1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
	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.
52	52
53		-==== **Recommendation System** ====
	85	+=== **Potential Data Sources** ===
54	54
55		-* Suggests additional tests or biomarkers if gaps are detected in the data.
56		-* Prioritizes tests based on clinical impact and cost-effectiveness.
	87	+Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
57	57
58		-----
	89	+* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
59	59
60		-=== **4. Computational Workflow** ===
	91	+=== **1. Register for Access** ===
61	61
62		-1. **Data Loading**: Import data from storage (Drive or Bucket).
63		-1. **Feature Engineering**: Generate derived features from the raw data.
64		-1. **Model Training**:
65		-1*. Split data into training, validation, and test sets.
66		-1*. Train models with cross-validation to ensure robustness.
67		-1. **Evaluation**:
68		-1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
69		-1*. Compare against baseline models and domain benchmarks.
	93	+Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
70	70
	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	+
	122	+* (((
	123	+**Option 1: Upload to EBRAINS Bucket**
	124	+
	125	+* Location: [[EBRAINS Neurodiagnoses Bucket>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
	126	+* Ensure correct metadata tagging before submission.
	127	+)))
	128	+* (((
	129	+**Option 2: Contribute via GitHub Repository**
	130	+
	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.
	133	+)))
	134	+
	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	+
	143	+* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
	144	+* Standardize neuroimaging and biomarker formats using harmonization tools.
	145	+* Use machine learning models to handle missing data and feature extraction.
	146	+* 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]].
	148	+
71	71	----
72	72
73		-=== **5. Validation** ===
	151	+== **Database Sources Table** ==
74	74
75		-==== **Internal Validation** ====
	153	+=== **Where to Insert This** ===
76	76
77		-* Test the system using simulated datasets and known clinical cases.
78		-* Fine-tune models based on validation results.
	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
79	79
80		-==== **External Validation** ====
	158	+=== **Key Databases for Neurodiagnoses** ===
81	81
82		-* Collaborate with research institutions and hospitals to test the system in real-world settings.
83		-* Use anonymized patient data to ensure privacy compliance.
	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
84	84
	171	+If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
	172	+
85	85	----
86	86
87		-=== **6. Collaborative Development** ===
	175	+== **Collaboration & Partnerships** ==
88	88
89		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	177	+=== **Where to Insert This** ===
90	90
91		-* **Jupyter Notebooks**: For data analysis and pipeline development.
92		-* **Wiki Pages**: For documenting methods and results.
93		-* **Drive and Bucket**: For sharing code, data, and outputs.
	179	+* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
	180	+* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
94	94
	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.
	187	+* Co-develop harmonized AI-ready datasets with standardized annotations.
	188	+* Secure funding opportunities through joint grant applications.
	189	+
	190	+=== **Interested in Partnering?** ===
	191	+
	192	+If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	193	+
	194	+* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	195	+
95	95	----
96	96
97		-=== **7. Tools and Technologies** ===
	198	+== **Final Notes** ==
98	98
99		-* **Programming Languages**: Python for AI and data processing.
100		-* **Frameworks**:
101		-** TensorFlow and PyTorch for machine learning.
102		-** Flask or FastAPI for backend services.
103		-* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
104		-* **EBRAINS Services**:
105		-** Collaboratory Lab for running Notebooks.
106		-** Buckets for storing large datasets.
	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.