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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 project 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		-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).
	5	+----
10	10
11		-By applying **machine learning models**, Neurodiagnoses generates **structured, explainable diagnostic outputs** to assist **clinical decision-making** and **biomarker-driven patient stratification.**
	7	+=== **1. Data Integration** ===
12	12
13		-----
	9	+==== **Data Sources** ====
14	14
15		-== **Data Integration & External Databases** ==
	11	+**Biomedical Ontologies & Databases:**
16	16
17		-=== **How to Use External Databases in Neurodiagnoses** ===
	13	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	14	+* **Gene Ontology (GO)** for molecular and cellular processes.
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.
	16	+**Dimensionality Reduction and Interpretability:**
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]]
	18	+* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
	19	+* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
23	23
24		-=== **Register for Access** ===
	21	+**Neuroimaging & EEG/MEG Data:**
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).**
	23	+* **MRI volumetric measures** for brain atrophy tracking.
	24	+* **EEG functional connectivity patterns** (AI-Mind).
29	29
30		-=== **Download & Prepare Data** ===
	26	+**Clinical & Biomarker Data:**
31	31
32		-Once access is granted, download datasets **following compliance guidelines** and **format requirements** for integration.
	28	+* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	29	+* **Sleep monitoring and actigraphy data** (ADIS).
33	33
34		-**Supported File Formats**
	31	+**Federated Learning Integration:**
35	35
36		-* **Tabular Data**: .csv, .tsv
37		-* **Neuroimaging Data**: .nii, .dcm
38		-* **Genomic Data**: .fasta, .vcf
39		-* **Clinical Metadata**: .json, .xml
	33	+* **Secure multi-center data harmonization** (PROMINENT).
40	40
41		-**Mandatory Fields for Integration**
	35	+----
42	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
	37	+==== **Annotation System for Multi-Modal Data** ====
49	49
50		-=== **Upload Data to Neurodiagnoses** ===
	39	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
51	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]]
	41	+* **Assign standardized metadata tags** to diagnostic features.
	42	+* **Provide contextual explanations** for AI-based classifications.
	43	+* **Track temporal disease progression annotations** to identify long-term trends.
54	54
55		-**For large datasets, please contact project administrators before uploading.**
	45	+----
56	56
57		-=== **Integrate Data into AI Models** ===
	47	+=== **2. AI-Based Analysis** ===
58	58
59		-Use **Jupyter Notebooks** on EBRAINS for **data preprocessing.**
60		-Standardize data using **harmonization tools.**
61		-Train AI models with **newly integrated datasets.**
	49	+==== **Machine Learning & Deep Learning Models** ====
62	62
63		-**Reference:** [[Data Processing Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_processing.md]]
	51	+**Risk Prediction Models:**
64	64
65		-----
	53	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
66	66
67		-== **AI-Powered Annotation & Machine Learning Models** ==
	55	+**Biomarker Classification & Probabilistic Imputation:**
68	68
69		-Neurodiagnoses applies **advanced machine learning models** to classify CNS diseases, extract features from **biomarkers and neuroimaging**, and provide **AI-powered annotation.**
	57	+* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
70	70
71		-=== **AI Model Categories** ===
	59	+**Neuroimaging Feature Extraction:**
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
	61	+* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
79	79
80		-**Reference:** [[AI Model Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/models.md]]
	63	+==== **AI-Powered Annotation System** ====
81	81
	65	+* Uses **SHAP-based interpretability tools** to explain model decisions.
	66	+* Generates **automated clinical annotations** in structured reports.
	67	+* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
	68	+
82	82	----
83	83
84		-== **Clinical Decision Support & Tridimensional Diagnostic Framework** ==
	71	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
85	85
86		-Neurodiagnoses generates **structured AI reports** for clinicians, combining:
	73	+==== **Tridimensional Diagnostic Axes** ====
87	87
88		-**Probabilistic Diagnosis:** AI-generated ranking of potential diagnoses.
89		-**Tridimensional Classification:** Standardized diagnostic reports based on:
	75	+**Axis 1: Etiology (Pathogenic Mechanisms)**
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.
	77	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	78	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
94	94
95		-**Reference:** [[Tridimensional Classification Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/classification.md]]
	80	+**Axis 2: Molecular Markers & Biomarkers**
96	96
	82	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	83	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	84	+
	85	+**Axis 3: Neuroanatomoclinical Correlations**
	86	+
	87	+* **MRI and EEG data** provide anatomical and functional insights.
	88	+* **AI-generated progression maps** annotate **brain structure-function relationships**.
	89	+
97	97	----
98	98
99		-== **Data Security, Compliance & Federated Learning** ==
	92	+=== **4. Computational Workflow & Annotation Pipelines** ===
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.
	94	+==== **Data Processing Steps** ====
104	104
105		-**Reference:** [[Data Protection & Federated Learning>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/security.md]]
	96	+**Data Ingestion:**
106	106
	98	+* **Harmonized datasets** stored in **EBRAINS Bucket**.
	99	+* **Preprocessing pipelines** clean and standardize data.
	100	+
	101	+**Feature Engineering:**
	102	+
	103	+* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	104	+
	105	+**AI-Generated Annotations:**
	106	+
	107	+* **Automated tagging** of diagnostic features in **structured reports**.
	108	+* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
	109	+
	110	+**Clinical Decision Support Integration:**
	111	+
	112	+* **AI-annotated findings** fed into **interactive dashboards**.
	113	+* **Clinicians can adjust, validate, and modify annotations**.
	114	+
107	107	----
108	108
109		-== **Data Processing & Integration with Clinica.Run** ==
	117	+=== **5. Validation & Real-World Testing** ===
110	110
111		-Neurodiagnoses now supports **Clinica.Run**, an **open-source neuroimaging platform** for **multimodal data processing.**
	119	+==== **Prospective Clinical Study** ====
112	112
113		-=== **How It Works** ===
	121	+* **Multi-center validation** of AI-based **annotations & risk stratifications**.
	122	+* **Benchmarking against clinician-based diagnoses**.
	123	+* **Real-world testing** of AI-powered **structured reporting**.
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.**
	125	+==== **Quality Assurance & Explainability** ====
118	118
119		-=== **Implementation Steps** ===
	127	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	128	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
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.
	130	+----
124	124
125		-**Reference:** [[Clinica.Run Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/clinica_run.md]]
	132	+=== **6. Collaborative Development** ===
126	126
	134	+The project is **open to contributions** from **researchers, clinicians, and developers**.
	135	+
	136	+**Key tools include:**
	137	+
	138	+* **Jupyter Notebooks**: For data analysis and pipeline development.
	139	+** Example: **probabilistic imputation**
	140	+* **Wiki Pages**: For documenting methods and results.
	141	+* **Drive and Bucket**: For sharing code, data, and outputs.
	142	+* **Collaboration with related projects**:
	143	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
	144	+
127	127	----
128	128
129		-== **Collaborative Development & Research** ==
	147	+=== **7. Tools and Technologies** ===
130	130
131		-**We Use GitHub to Develop AI Models & Store Research Data**
	149	+==== **Programming Languages:** ====
132	132
133		-* **GitHub Repository:** AI model training scripts.
134		-* **GitHub Issues:** Tracks ongoing research questions.
135		-* **GitHub Wiki:** Project documentation & user guides.
	151	+* **Python** for AI and data processing.
136	136
137		-**We Use EBRAINS for Data & Collaboration**
	153	+==== **Frameworks:** ====
138	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.
	155	+* **TensorFlow** and **PyTorch** for machine learning.
	156	+* **Flask** or **FastAPI** for backend services.
142	142
143		-**Join the Project Forum:** [[GitHub Discussions>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
	158	+==== **Visualization:** ====
144	144
145		-----
	160	+* **Plotly** and **Matplotlib** for interactive and static visualizations.
146	146
147		-**For Additional Documentation:**
	162	+==== **EBRAINS Services:** ====
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/]]
	164	+* **Collaboratory Lab** for running Notebooks.
	165	+* **Buckets** for storing large datasets.
151	151
152	152	----
153	153
154		-**Neurodiagnoses is Open for Contributions – Join Us Today!**
	169	+=== **Why This Matters** ===
	170	+
	171	+* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
	172	+* **It enables real-time tracking of disease progression across the three diagnostic axes.**
	173	+* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**