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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.**
	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		-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		-
13	13	----
14	14
15		-== **Data Integration & External Databases** ==
	7	+=== **1. Data Integration** ===
16	16
17		-=== **How to Use External Databases in Neurodiagnoses** ===
	9	+==== **Data Sources** ====
18	18
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.
	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.
20	20
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]]
23	23
24		-=== **Register for Access** ===
	20	+==== **Data Preprocessing** ====
25	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).**
	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.
29	29
30		-=== **Download & Prepare Data** ===
31		-
32		-Once access is granted, download datasets **following compliance guidelines** and **format requirements** for integration.
33		-
34		-**Supported File Formats**
35		-
36		-* **Tabular Data**: .csv, .tsv
37		-* **Neuroimaging Data**: .nii, .dcm
38		-* **Genomic Data**: .fasta, .vcf
39		-* **Clinical Metadata**: .json, .xml
40		-
41		-**Mandatory Fields for Integration**
42		-
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		-
65	65	----
66	66
67		-== **AI-Powered Annotation & Machine Learning Models** ==
	28	+=== **2. AI-Based Analysis** ===
68	68
69		-Neurodiagnoses applies **advanced machine learning models** to classify CNS diseases, extract features from **biomarkers and neuroimaging**, and provide **AI-powered annotation.**
	30	+==== **Model Development** ====
70	70
71		-=== **AI Model Categories** ===
	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.
72	72
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
	37	+==== **Dimensionality Reduction and Interpretability** ====
79	79
80		-**Reference:** [[AI Model Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/models.md]]
	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).
81	81
82	82	----
83	83
84		-== **Clinical Decision Support & Tridimensional Diagnostic Framework** ==
	44	+=== **3. Diagnostic Framework** ===
85	85
86		-Neurodiagnoses generates **structured AI reports** for clinicians, combining:
	46	+==== **Axes of Diagnosis** ====
87	87
88		-**Probabilistic Diagnosis:** AI-generated ranking of potential diagnoses.
89		-**Tridimensional Classification:** Standardized diagnostic reports based on:
	48	+The framework organizes diagnostic data into three axes:
90	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.
	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).
94	94
95		-**Reference:** [[Tridimensional Classification Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/classification.md]]
	54	+==== **Recommendation System** ====
96	96
	56	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	57	+* Prioritizes tests based on clinical impact and cost-effectiveness.
	58	+
97	97	----
98	98
99		-== **Data Security, Compliance & Federated Learning** ==
	61	+=== **4. Computational Workflow** ===
100	100
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.
	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.
104	104
105		-**Reference:** [[Data Protection & Federated Learning>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/security.md]]
106		-
107	107	----
108	108
109		-== **Data Processing & Integration with Clinica.Run** ==
	74	+=== **5. Validation** ===
110	110
111		-Neurodiagnoses now supports **Clinica.Run**, an **open-source neuroimaging platform** for **multimodal data processing.**
	76	+==== **Internal Validation** ====
112	112
113		-=== **How It Works** ===
	78	+* Test the system using simulated datasets and known clinical cases.
	79	+* Fine-tune models based on validation results.
114	114
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	+==== **External Validation** ====
118	118
119		-=== **Implementation Steps** ===
	83	+* Collaborate with research institutions and hospitals to test the system in real-world settings.
	84	+* Use anonymized patient data to ensure privacy compliance.
120	120
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		-
127	127	----
128	128
129		-== **Collaborative Development & Research** ==
	88	+=== **6. Collaborative Development** ===
130	130
131		-**We Use GitHub to Develop AI Models & Store Research Data**
	90	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
132	132
133		-* **GitHub Repository:** AI model training scripts.
134		-* **GitHub Issues:** Tracks ongoing research questions.
135		-* **GitHub Wiki:** Project documentation & user guides.
	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	+* **Collaboration with 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/]]
136	136
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		-
145	145	----
146	146
147		-**For Additional Documentation:**
	100	+=== **7. Tools and Technologies** ===
148	148
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!**
	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.