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1		-=== **Overview** ===
	1	+== **Overview** ==
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	+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.
4	4
5	5	----
6	6
7		-=== **1. Data Integration** ===
	7	+== **How to Use External Databases in Neurodiagnoses** ==
8	8
9		-==== **Data Sources** ====
	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.
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.
	11	+=== **Potential Data Sources** ===
18	18
	13	+Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
19	19
20		-==== **Data Preprocessing** ====
	15	+* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
21	21
22		-1. **Standardization**: Ensure all data sources are normalized to a common format.
23		-1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
24		-1. **Data Cleaning**: Handle missing values and remove duplicates.
	17	+=== **1. Register for Access** ===
25	25
26		-----
	19	+Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
27	27
28		-=== **2. AI-Based Analysis** ===
	21	+* Ensure that you have completed all ethical approvals and data access agreements before integrating datasets into Neurodiagnoses.
	22	+* Some repositories require a Data Usage Agreement (DUA) before downloading sensitive medical data.
29	29
30		-==== **Model Development** ====
	24	+=== **2. Download & Prepare Data** ===
31	31
32		-* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
33		-* **Classification Models**:
34		-** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
35		-** Purpose: Predict the likelihood of specific neurological conditions based on input data.
	26	+Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
36	36
37		-==== **Dimensionality Reduction and Interpretability** ====
	28	+==== **Supported File Formats** ====
38	38
39		-* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
40		-* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
	30	+* Tabular Data: .csv, .tsv
	31	+* Neuroimaging Data: .nii, .dcm
	32	+* Genomic Data: .fasta, .vcf
	33	+* Clinical Metadata: .json, .xml
41	41
42		-----
	35	+==== **Mandatory Fields for Integration** ====
43	43
44		-=== **3. Diagnostic Framework** ===
	37	+|=Field Name|=Description
	38	+|Subject ID|Unique patient identifier
	39	+|Diagnosis|Standardized disease classification
	40	+|Biomarkers|CSF, plasma, or imaging biomarkers
	41	+|Genetic Data|Whole-genome or exome sequencing
	42	+|Neuroimaging Metadata|MRI/PET acquisition parameters
45	45
46		-==== **Axes of Diagnosis** ====
	44	+=== **3. Upload Data to Neurodiagnoses** ===
47	47
48		-The framework organizes diagnostic data into three axes:
	46	+Once preprocessed, data can be uploaded to EBRAINS or GitHub.
49	49
50		-1. **Etiology**: Genetic and environmental risk factors.
51		-1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
52		-1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
	48	+* (((
	49	+**Option 1: Upload to EBRAINS Bucket**
53	53
54		-==== **Recommendation System** ====
	51	+* Location: [[EBRAINS Neurodiagnoses Bucket>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
	52	+* Ensure correct metadata tagging before submission.
	53	+)))
	54	+* (((
	55	+**Option 2: Contribute via GitHub Repository**
55	55
56		-* Suggests additional tests or biomarkers if gaps are detected in the data.
57		-* Prioritizes tests based on clinical impact and cost-effectiveness.
	57	+* Location: [[GitHub Data Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/tree/main/data]]
	58	+* Create a new folder under /data/ and include dataset description.
	59	+)))
58	58
59		-----
	61	+//Note: For large datasets, please contact the project administrators before uploading.//
60	60
61		-=== **4. Computational Workflow** ===
	63	+=== **4. Integrate Data into AI Models** ===
62	62
63		-1. **Data Loading**: Import data from storage (Drive or Bucket).
64		-1. **Feature Engineering**: Generate derived features from the raw data.
65		-1. **Model Training**:
66		-1*. Split data into training, validation, and test sets.
67		-1*. Train models with cross-validation to ensure robustness.
68		-1. **Evaluation**:
69		-1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
70		-1*. Compare against baseline models and domain benchmarks.
	65	+Once uploaded, datasets must be harmonized and formatted before AI model training.
71	71
	67	+==== **Steps for Data Integration** ====
	68	+
	69	+* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
	70	+* Standardize neuroimaging and biomarker formats using harmonization tools.
	71	+* Use machine learning models to handle missing data and feature extraction.
	72	+* Train AI models with newly integrated patient cohorts.
	73	+* 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]].
	74	+
72	72	----
73	73
74		-=== **5. Validation** ===
	77	+== **Database Sources Table** ==
75	75
76		-==== **Internal Validation** ====
	79	+=== **Where to Insert This** ===
77	77
78		-* Test the system using simulated datasets and known clinical cases.
79		-* Fine-tune models based on validation results.
	81	+* GitHub: [[docs/data_sources.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_sources.md]]
	82	+* EBRAINS Wiki: Collabs/neurodiagnoses/Data Sources
80	80
81		-==== **External Validation** ====
	84	+=== **Key Databases for Neurodiagnoses** ===
82	82
83		-* Collaborate with research institutions and hospitals to test the system in real-world settings.
84		-* Use anonymized patient data to ensure privacy compliance.
	86	+|=Database|=Focus Area|=Data Type|=Access Link
	87	+|ADNI|Alzheimer's Disease|MRI, PET, CSF, cognitive tests|ADNI
	88	+|PPMI|Parkinson’s Disease|Imaging, biospecimens|[[PPMI>>url:https://www.ppmi-info.org/]]
	89	+|GP2|Genetic Data for PD|Whole-genome sequencing|[[GP2>>url:https://gp2.org/]]
	90	+|Enroll-HD|Huntington’s Disease|Clinical, genetic, imaging|[[Enroll-HD>>url:https://enroll-hd.org/]]
	91	+|GAAIN|Alzheimer's & Cognitive Decline|Multi-source data aggregation|[[GAAIN>>url:https://www.gaain.org/]]
	92	+|UK Biobank|Population-wide studies|Genetic, imaging, health records|[[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
	93	+|DPUK|Dementia & Aging|Imaging, genetics, lifestyle factors|[[DPUK>>url:https://www.dementiasplatform.uk/]]
	94	+|PRION Registry|Prion Diseases|Clinical and genetic data|[[PRION Registry>>url:https://www.prionalliance.org/]]
	95	+|DECIPHER|Rare Genetic Disorders|Genomic variants|DECIPHER
85	85
	97	+If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
	98	+
86	86	----
87	87
88		-=== **6. Collaborative Development** ===
	101	+== **Collaboration & Partnerships** ==
89	89
90		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	103	+=== **Where to Insert This** ===
91	91
92		-* **Jupyter Notebooks**: For data analysis and pipeline development.
93		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
94		-* **Wiki Pages**: For documenting methods and results.
95		-* **Drive and Bucket**: For sharing code, data, and outputs.
96		-* **Related projects: **For instance: [[//Beyond the hype: AI in dementia – from early risk detection to disease treatment//>>https://www.lethe-project.eu/beyond-the-hype-ai-in-dementia-from-early-risk-detection-to-disease-treatment/]]
	105	+* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
	106	+* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
97	97
	108	+=== **Partnering with Data Providers** ===
	109	+
	110	+Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
	111	+
	112	+* Enable direct API-based data integration for real-time processing.
	113	+* Co-develop harmonized AI-ready datasets with standardized annotations.
	114	+* Secure funding opportunities through joint grant applications.
	115	+
	116	+=== **Interested in Partnering?** ===
	117	+
	118	+If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	119	+
	120	+* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	121	+
98	98	----
99	99
100		-=== **7. Tools and Technologies** ===
	124	+== **Final Notes** ==
101	101
102		-* **Programming Languages**: Python for AI and data processing.
103		-* **Frameworks**:
104		-** TensorFlow and PyTorch for machine learning.
105		-** Flask or FastAPI for backend services.
106		-* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
107		-* **EBRAINS Services**:
108		-** Collaboratory Lab for running Notebooks.
109		-** Buckets for storing large datasets.
	126	+Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
	127	+
	128	+For additional technical documentation:
	129	+
	130	+* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
	131	+* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
	132	+
	133	+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.