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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.**
4	4
	5	+This methodology integrates **multi-modal data**, including:
	6	+**Genetic data** (whole-genome sequencing, polygenic risk scores).
	7	+**Neuroimaging** (MRI, PET, EEG, MEG).
	8	+**Neurophysiological data** (EEG-based biomarkers, sleep actigraphy).
	9	+**CSF & Blood Biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	10	+
	11	+By applying **machine learning models**, Neurodiagnoses generates **structured, explainable diagnostic outputs** to assist **clinical decision-making** and **biomarker-driven patient stratification.**
	12	+
5	5	----
6	6
7		-=== **1. Data Integration** ===
	15	+== **Data Integration & External Databases** ==
8	8
9		-==== **Data Sources** ====
	17	+=== **How to Use External Databases in Neurodiagnoses** ===
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.
	19	+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.
18	18
19		-==== **Data Preprocessing** ====
	21	+**Potential Data Sources**
	22	+**Reference:** [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
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.
	24	+=== **Register for Access** ===
24	24
25		-----
	26	+Each **external database** requires **individual registration** and approval.
	27	+✔️ Follow the official **data access guidelines** of each provider.
	28	+✔️ Ensure compliance with **ethical approvals** and **data-sharing agreements (DUAs).**
26	26
27		-=== **2. AI-Based Analysis** ===
	30	+=== **Download & Prepare Data** ===
28	28
29		-==== **Model Development** ====
	32	+Once access is granted, download datasets **following compliance guidelines** and **format requirements** for integration.
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.
	34	+**Supported File Formats**
35	35
36		-==== **Dimensionality Reduction and Interpretability** ====
	36	+* **Tabular Data**: .csv, .tsv
	37	+* **Neuroimaging Data**: .nii, .dcm
	38	+* **Genomic Data**: .fasta, .vcf
	39	+* **Clinical Metadata**: .json, .xml
37	37
38		-* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (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).
	41	+**Mandatory Fields for Integration**
40	40
	43	+|=**Field Name**|=**Description**
	44	+|**Subject ID**|Unique patient identifier
	45	+|**Diagnosis**|Standardized disease classification
	46	+|**Biomarkers**|CSF, plasma, or imaging biomarkers
	47	+|**Genetic Data**|Whole-genome or exome sequencing
	48	+|**Neuroimaging Metadata**|MRI/PET acquisition parameters
	49	+
	50	+=== **Upload Data to Neurodiagnoses** ===
	51	+
	52	+**Option 1:** Upload to **EBRAINS Bucket** → [[Neurodiagnoses Data Storage>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
	53	+**Option 2:** Contribute via **GitHub Repository** → [[GitHub Data Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/tree/main/data]]
	54	+
	55	+**For large datasets, please contact project administrators before uploading.**
	56	+
	57	+=== **Integrate Data into AI Models** ===
	58	+
	59	+Use **Jupyter Notebooks** on EBRAINS for **data preprocessing.**
	60	+Standardize data using **harmonization tools.**
	61	+Train AI models with **newly integrated datasets.**
	62	+
	63	+**Reference:** [[Data Processing Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_processing.md]]
	64	+
41	41	----
42	42
43		-=== **3. Diagnostic Framework** ===
	67	+== **AI-Powered Annotation & Machine Learning Models** ==
44	44
45		-==== **Axes of Diagnosis** ====
	69	+Neurodiagnoses applies **advanced machine learning models** to classify CNS diseases, extract features from **biomarkers and neuroimaging**, and provide **AI-powered annotation.**
46	46
47		-The framework organizes diagnostic data into three axes:
	71	+=== **AI Model Categories** ===
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).
	73	+|=**Model Type**|=**Function**|=**Example Algorithms**
	74	+|**Probabilistic Diagnosis**|Assigns probability scores to multiple CNS disorders.|Random Forest, XGBoost, Bayesian Networks
	75	+|**Tridimensional Diagnosis**|Classifies disorders based on Etiology, Biomarkers, and Neuroanatomical Correlations.|CNNs, Transformers, Autoencoders
	76	+|**Biomarker Prediction**|Predicts missing biomarker values using regression.|KNN Imputation, Bayesian Estimation
	77	+|**Neuroimaging Feature Extraction**|Extracts patterns from MRI, PET, EEG.|CNNs, Graph Neural Networks
	78	+|**Clinical Decision Support**|Generates AI-driven diagnostic reports.|SHAP Explainability Tools
52	52
53		-==== **Recommendation System** ====
	80	+**Reference:** [[AI Model Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/models.md]]
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.
	82	+----
57	57
	84	+== **Clinical Decision Support & Tridimensional Diagnostic Framework** ==
	85	+
	86	+Neurodiagnoses generates **structured AI reports** for clinicians, combining:
	87	+
	88	+**Probabilistic Diagnosis:** AI-generated ranking of potential diagnoses.
	89	+**Tridimensional Classification:** Standardized diagnostic reports based on:
	90	+
	91	+1. **Axis 1:** **Etiology** → Genetic, Autoimmune, Prion, Toxic, Vascular.
	92	+1. **Axis 2:** **Molecular Markers** → CSF, Neuroinflammation, EEG biomarkers.
	93	+1. **Axis 3:** **Neuroanatomoclinical Correlations** → MRI atrophy, PET.
	94	+
	95	+**Reference:** [[Tridimensional Classification Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/classification.md]]
	96	+
58	58	----
59	59
60		-=== **4. Computational Workflow** ===
	99	+== **Data Security, Compliance & Federated Learning** ==
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.
	101	+✔ **Privacy-Preserving AI**: Implements **Federated Learning**, ensuring that patient data **never leaves** local institutions.
	102	+✔ **Secure Data Access**: Data remains **stored in EBRAINS MIP servers** using **differential privacy techniques.**
	103	+✔ **Ethical & GDPR Compliance**: Data-sharing agreements **must be signed** before use.
70	70
	105	+**Reference:** [[Data Protection & Federated Learning>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/security.md]]
	106	+
71	71	----
72	72
73		-=== **5. Validation** ===
	109	+== **Data Processing & Integration with Clinica.Run** ==
74	74
75		-==== **Internal Validation** ====
	111	+Neurodiagnoses now supports **Clinica.Run**, an **open-source neuroimaging platform** for **multimodal data processing.**
76	76
77		-* Test the system using simulated datasets and known clinical cases.
78		-* Fine-tune models based on validation results.
	113	+=== **How It Works** ===
79	79
80		-==== **External Validation** ====
	115	+✔ **Neuroimaging Preprocessing**: MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines.**
	116	+✔ **Automated Biomarker Extraction**: Extracts volumetric, metabolic, and functional biomarkers.
	117	+✔ **Data Security & Compliance**: Clinica.Run is **GDPR & HIPAA-compliant.**
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.
	119	+=== **Implementation Steps** ===
84	84
	121	+1. Install **Clinica.Run** dependencies.
	122	+1. Configure **Clinica.Run pipeline** in clinica_run_config.json.
	123	+1. Run **biomarker extraction pipelines** for AI-based diagnostics.
	124	+
	125	+**Reference:** [[Clinica.Run Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/clinica_run.md]]
	126	+
85	85	----
86	86
87		-=== **6. Collaborative Development** ===
	129	+== **Collaborative Development & Research** ==
88	88
89		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	131	+**We Use GitHub to Develop AI Models & Store Research Data**
90	90
91		-* **Jupyter Notebooks**: For data analysis and pipeline development.
92		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
93		-* **Wiki Pages**: For documenting methods and results.
94		-* **Drive and Bucket**: For sharing code, data, and outputs.
	133	+* **GitHub Repository:** AI model training scripts.
	134	+* **GitHub Issues:** Tracks ongoing research questions.
	135	+* **GitHub Wiki:** Project documentation & user guides.
95	95
	137	+**We Use EBRAINS for Data & Collaboration**
	138	+
	139	+* **EBRAINS Buckets:** Large-scale neuroimaging and biomarker storage.
	140	+* **EBRAINS Jupyter Notebooks:** Cloud-based AI model execution.
	141	+* **EBRAINS Wiki:** Research documentation and updates.
	142	+
	143	+**Join the Project Forum:** [[GitHub Discussions>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
	144	+
96	96	----
97	97
98		-=== **7. Tools and Technologies** ===
	147	+**For Additional Documentation:**
99	99
100		-* **Programming Languages**: Python for AI and data processing.
101		-* **Frameworks**:
102		-** TensorFlow and PyTorch for machine learning.
103		-** Flask or FastAPI for backend services.
104		-* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
105		-* **EBRAINS Services**:
106		-** Collaboratory Lab for running Notebooks.
107		-** Buckets for storing large datasets.
	149	+* **GitHub Repository:** [[Neurodiagnoses AI Models>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
	150	+* **EBRAINS Wiki:** [[Neurodiagnoses Research Collaboration>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
	151	+
	152	+----
	153	+
	154	+**Neurodiagnoses is Open for Contributions – Join Us Today!**