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--==== **Overview** ====
++== **Overview** ==
--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**.
++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.
--=== **Workflow** ===
--
--1. (((
--**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
--
--* Create a **GitHub repository** for AI scripts and models.
--* Use **GitHub Projects** to manage research milestones.
--)))
--1. (((
--**We Use EBRAINS for Data & Collaboration**
--
--* Store **biomarker and neuroimaging data** in **EBRAINS Buckets**.
--* Run **Jupyter Notebooks** in **EBRAINS Lab** to test AI models.
--* Use **EBRAINS Wiki** for structured documentation and research discussion.
--)))
--
  ----
--=== **1. Data Integration** ===
++== **How to Use External Databases in Neurodiagnoses** ==
--=== **EBRAINS Medical Informatics Platform (MIP)**. ===
++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.
--Neurodiagnoses integrates clinical data via the **EBRAINS Medical Informatics Platform (MIP)**. MIP federates decentralized clinical data, allowing Neurodiagnoses to securely access and process sensitive information for AI-based diagnostics.
++=== **Potential Data Sources** ===
--==== How It Works ====
++Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
++* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
--1. (((
--**Authentication & API Access:**
++=== **1. Register for Access** ===
--* Users must have an **EBRAINS account**.
--* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
--)))
--1. (((
--**Data Mapping & Harmonization:**
++Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
--* Retrieved data is **normalized** and converted to standard formats (.csv, .json).
--* Data from **multiple sources** is harmonized to ensure consistency for AI processing.
--)))
--1. (((
--**Security & Compliance:**
++* Ensure that you have completed all ethical approvals and data access agreements before integrating datasets into Neurodiagnoses.
++* Some repositories require a Data Usage Agreement (DUA) before downloading sensitive medical data.
--* All data access is **logged and monitored**.
--* Data remains on **MIP servers** using **federated learning techniques** when possible.
--* Access is granted only after signing a **Data Usage Agreement (DUA)**.
--)))
++=== **2. Download & Prepare Data** ===
--==== Implementation Steps ====
++Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
++==== **Supported File Formats** ====
--1. Clone the repository.
--1. Configure your **EBRAINS API credentials** in mip_integration.py.
--1. Run the script to **download and harmonize clinical data**.
--1. Process the data for **AI model training**.
++* Tabular Data: .csv, .tsv
++* Neuroimaging Data: .nii, .dcm
++* Genomic Data: .fasta, .vcf
++* Clinical Metadata: .json, .xml
--For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
++==== **Mandatory Fields for Integration** ====
------
++|=Field Name|=Description
++|Subject ID|Unique patient identifier
++|Diagnosis|Standardized disease classification
++|Biomarkers|CSF, plasma, or imaging biomarkers
++|Genetic Data|Whole-genome or exome sequencing
++|Neuroimaging Metadata|MRI/PET acquisition parameters
--=== Data Processing & Integration with Clinica.Run ===
++=== **3. Upload Data to Neurodiagnoses** ===
--Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
++Once preprocessed, data can be uploaded to EBRAINS or GitHub.
--==== How It Works ====
++* (((
++**Option 1: Upload to EBRAINS Bucket**
--
--1. (((
--**Neuroimaging Preprocessing:**
--
--* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
--* Supports **longitudinal and cross-sectional analyses**.
++* Location: [[EBRAINS Neurodiagnoses Bucket>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
++* Ensure correct metadata tagging before submission.
  )))
--1. (((
--**Automated Biomarker Extraction:**
++* (((
++**Option 2: Contribute via GitHub Repository**
--* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
--* Integration with machine learning models in Neurodiagnoses.
++* Location: [[GitHub Data Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/tree/main/data]]
++* Create a new folder under /data/ and include dataset description.
  )))
--1. (((
--**Data Security & Compliance:**
--* Clinica.Run operates in **compliance with GDPR and HIPAA**.
--* Neuroimaging data remains **within the original storage environment**.
--)))
++//Note: For large datasets, please contact the project administrators before uploading.//
--==== Implementation Steps ====
++=== **4. Integrate Data into AI Models** ===
++Once uploaded, datasets must be harmonized and formatted before AI model training.
--1. Install **Clinica.Run** dependencies.
--1. Configure your **Clinica.Run pipeline** in clinica_run_config.json.
--1. Run the pipeline for **preprocessing and biomarker extraction**.
--1. Use processed neuroimaging data for **AI-driven diagnostics** in Neurodiagnoses.
++==== **Steps for Data Integration** ====
--For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
++* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
++* Standardize neuroimaging and biomarker formats using harmonization tools.
++* Use machine learning models to handle missing data and feature extraction.
++* Train AI models with newly integrated patient cohorts.
++* 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]].
--====   ====
--
--==== **Data Sources** ====
--
--[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
--
--**Biomedical Ontologies & Databases:**
--
--* **Human Phenotype Ontology (HPO)** for symptom annotation.
--* **Gene Ontology (GO)** for molecular and cellular processes.
--
--**Dimensionality Reduction and Interpretability:**
--
--* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
--* **Leverage [[DEIBO>>https://github.com/Mellandd/DEIBO]] (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
--
--**Neuroimaging & EEG/MEG Data:**
--
--* **MRI volumetric measures** for brain atrophy tracking.
--* **EEG functional connectivity patterns** (AI-Mind).
--
--**Clinical & Biomarker Data:**
--
--* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
--* **Sleep monitoring and actigraphy data** (ADIS).
--
--**Federated Learning Integration:**
--
--* **Secure multi-center data harmonization** (PROMINENT).
--
  ----
--==== **Annotation System for Multi-Modal Data** ====
++== **Database Sources Table** ==
--To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
++=== **Where to Insert This** ===
--* **Assign standardized metadata tags** to diagnostic features.
--* **Provide contextual explanations** for AI-based classifications.
--* **Track temporal disease progression annotations** to identify long-term trends.
++* GitHub: [[docs/data_sources.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_sources.md]]
++* EBRAINS Wiki: Collabs/neurodiagnoses/Data Sources
------
++=== **Key Databases for Neurodiagnoses** ===
--=== **2. AI-Based Analysis** ===
++|=Database|=Focus Area|=Data Type|=Access Link
++|ADNI|Alzheimer's Disease|MRI, PET, CSF, cognitive tests|ADNI
++|PPMI|Parkinson’s Disease|Imaging, biospecimens|[[PPMI>>url:https://www.ppmi-info.org/]]
++|GP2|Genetic Data for PD|Whole-genome sequencing|[[GP2>>url:https://gp2.org/]]
++|Enroll-HD|Huntington’s Disease|Clinical, genetic, imaging|[[Enroll-HD>>url:https://enroll-hd.org/]]
++|GAAIN|Alzheimer's & Cognitive Decline|Multi-source data aggregation|[[GAAIN>>url:https://www.gaain.org/]]
++|UK Biobank|Population-wide studies|Genetic, imaging, health records|[[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
++|DPUK|Dementia & Aging|Imaging, genetics, lifestyle factors|[[DPUK>>url:https://www.dementiasplatform.uk/]]
++|PRION Registry|Prion Diseases|Clinical and genetic data|[[PRION Registry>>url:https://www.prionalliance.org/]]
++|DECIPHER|Rare Genetic Disorders|Genomic variants|DECIPHER
--==== **Machine Learning & Deep Learning Models** ====
++If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
--**Risk Prediction Models:**
--
--* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
--
--**Biomarker Classification & Probabilistic Imputation:**
--
--* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
--
--**Neuroimaging Feature Extraction:**
--
--* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
--
--==== **AI-Powered Annotation System** ====
--
--* Uses **SHAP-based interpretability tools** to explain model decisions.
--* Generates **automated clinical annotations** in structured reports.
--* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
--
  ----
--=== **3. Diagnostic Framework & Clinical Decision Support** ===
++== **Collaboration & Partnerships** ==
--==== **Tridimensional Diagnostic Axes** ====
++=== **Where to Insert This** ===
--**Axis 1: Etiology (Pathogenic Mechanisms)**
++* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
++* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
--* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
--* **AI-assisted annotation** provides **causal interpretations** for clinical use.
++=== **Partnering with Data Providers** ===
--**Axis 2: Molecular Markers & Biomarkers**
++Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
--* **Integration of CSF, blood, and neuroimaging biomarkers**.
--* **Structured annotation** highlights **biological pathways linked to diagnosis**.
++* Enable direct API-based data integration for real-time processing.
++* Co-develop harmonized AI-ready datasets with standardized annotations.
++* Secure funding opportunities through joint grant applications.
--**Axis 3: Neuroanatomoclinical Correlations**
++=== **Interested in Partnering?** ===
--* **MRI and EEG data** provide anatomical and functional insights.
--* **AI-generated progression maps** annotate **brain structure-function relationships**.
++If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
------
++* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
--=== **4. Computational Workflow & Annotation Pipelines** ===
--
--==== **Data Processing Steps** ====
--
--**Data Ingestion:**
--
--* **Harmonized datasets** stored in **EBRAINS Bucket**.
--* **Preprocessing pipelines** clean and standardize data.
--
--**Feature Engineering:**
--
--* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
--
--**AI-Generated Annotations:**
--
--* **Automated tagging** of diagnostic features in **structured reports**.
--* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
--
--**Clinical Decision Support Integration:**
--
--* **AI-annotated findings** fed into **interactive dashboards**.
--* **Clinicians can adjust, validate, and modify annotations**.
--
  ----
--=== **5. Validation & Real-World Testing** ===
++== **Final Notes** ==
--==== **Prospective Clinical Study** ====
++Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
--* **Multi-center validation** of AI-based **annotations & risk stratifications**.
--* **Benchmarking against clinician-based diagnoses**.
--* **Real-world testing** of AI-powered **structured reporting**.
++For additional technical documentation:
--==== **Quality Assurance & Explainability** ====
++* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
++* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
--* **Annotations linked to structured knowledge graphs** for improved transparency.
--* **Interactive annotation editor** allows clinicians to validate AI outputs.
--
------
--
--=== **6. Collaborative Development** ===
--
--The project is **open to contributions** from **researchers, clinicians, and developers**.
--
--**Key tools include:**
--
--* **Jupyter Notebooks**: For data analysis and pipeline development.
--** Example: **probabilistic imputation**
--* **Wiki Pages**: For documenting methods and results.
--* **Drive and Bucket**: For sharing code, data, and outputs.
--* **Collaboration with related projects**:
--** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
--
------
--
--=== **7. Tools and Technologies** ===
--
--==== **Programming Languages:** ====
--
--* **Python** for AI and data processing.
--
--==== **Frameworks:** ====
--
--* **TensorFlow** and **PyTorch** for machine learning.
--* **Flask** or **FastAPI** for backend services.
--
--==== **Visualization:** ====
--
--* **Plotly** and **Matplotlib** for interactive and static visualizations.
--
--==== **EBRAINS Services:** ====
--
--* **Collaboratory Lab** for running Notebooks.
--* **Buckets** for storing large datasets.
--
------
--
--=== **Why This Matters** ===
--
--* The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
--* It enables real-time tracking of disease progression across the three diagnostic axes.
--* It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.
++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.