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1		-== **Overview** ==
	1	+=== **Overview** ===
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	+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.
4	4
5	5	----
6	6
7		-== **How to Use External Databases in Neurodiagnoses** ==
	7	+=== **1. Data Integration** ===
8	8
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.
	9	+==== **Data Sources** ====
10	10
11		-=== **Potential Data Sources** ===
	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.
12	12
13		-Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
	19	+==== **Data Preprocessing** ====
14	14
15		-* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
	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.
16	16
17		-=== **1. Register for Access** ===
	25	+----
18	18
19		-Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
	27	+=== **2. AI-Based Analysis** ===
20	20
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	+==== **Model Development** ====
23	23
24		-=== **2. Download & Prepare Data** ===
	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.
25	25
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	+==== **Dimensionality Reduction and Interpretability** ====
27	27
28		-==== **Supported File Formats** ====
	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).
29	29
30		-* Tabular Data: .csv, .tsv
31		-* Neuroimaging Data: .nii, .dcm
32		-* Genomic Data: .fasta, .vcf
33		-* Clinical Metadata: .json, .xml
	41	+----
34	34
35		-==== **Mandatory Fields for Integration** ====
	43	+=== **3. Diagnostic Framework** ===
36	36
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	+==== **Axes of Diagnosis** ====
43	43
44		-=== **3. Upload Data to Neurodiagnoses** ===
	47	+The framework organizes diagnostic data into three axes:
45	45
46		-Once preprocessed, data can be uploaded to EBRAINS or GitHub.
	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).
47	47
48		-* (((
49		-**Option 1: Upload to EBRAINS Bucket**
	53	+==== **Recommendation System** ====
50	50
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	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	56	+* Prioritizes tests based on clinical impact and cost-effectiveness.
56	56
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	+----
60	60
61		-//Note: For large datasets, please contact the project administrators before uploading.//
	60	+=== **4. Computational Workflow** ===
62	62
63		-=== **4. Integrate Data into AI Models** ===
	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.
64	64
65		-Once uploaded, datasets must be harmonized and formatted before AI model training.
66		-
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		-
75	75	----
76	76
77		-== **Database Sources Table** ==
	73	+=== **5. Validation** ===
78	78
79		-=== **Where to Insert This** ===
	75	+==== **Internal Validation** ====
80	80
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
	77	+* Test the system using simulated datasets and known clinical cases.
	78	+* Fine-tune models based on validation results.
83	83
84		-=== **Key Databases for Neurodiagnoses** ===
	80	+==== **External Validation** ====
85	85
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
	82	+* Collaborate with research institutions and hospitals to test the system in real-world settings.
	83	+* Use anonymized patient data to ensure privacy compliance.
96	96
97		-If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
98		-
99	99	----
100	100
101		-== **Collaboration & Partnerships** ==
	87	+=== **6. Collaborative Development** ===
102	102
103		-=== **Where to Insert This** ===
	89	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
104	104
105		-* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
106		-* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
	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.
107	107
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		-
122	122	----
123	123
124		-== **Final Notes** ==
	97	+=== **7. Tools and Technologies** ===
125	125
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.
	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.