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--**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
++==== **Overview** ====
--## **Project Overview**
--Neurodiagnoses AI implements AI-driven diagnostic and prognostic models for central nervous system (CNS) disorders, adapting the Florey Dementia Index (FDI) methodology to a broader set of neurological conditions. The approach integrates **multimodal data sources** (EEG, neuroimaging, biomarkers, and genetics) and employs **machine learning models** to provide **explainable, real-time diagnostic insights**.##
++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**.
--## **How to Use External Databases in Neurodiagnoses**
--To enhance diagnostic accuracy, 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.##
++=== **Workflow** ===
--### **Potential Data Sources**
--Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
++1. (((
++**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
--**Reference: List of Potential Databases**
--- **ADNI**: Alzheimer's Disease data ([ADNI](https://adni.loni.usc.edu))
--- **PPMI**: Parkinson’s Disease Imaging and biospecimens ([PPMI](https://www.ppmi-info.org))
--- **GP2**: Whole-genome sequencing for PD ([GP2](https://gp2.org))
--- **Enroll-HD**: Huntington’s Disease Clinical and genetic data ([Enroll-HD](https://www.enroll-hd.org))
--- **GAAIN**: Multi-source Alzheimer’s data aggregation ([GAAIN](https://gaain.org))
--- **UK Biobank**: Population-wide genetic, imaging, and health records ([UK Biobank](https://www.ukbiobank.ac.uk))
--- **DPUK**: Dementia and Aging data ([DPUK](https://www.dementiasplatform.uk))
--- **PRION Registry**: Prion Diseases clinical and genetic data ([PRION Registry](https://prionregistry.org))
--- **DECIPHER**: Rare genetic disorder genomic variants ([DECIPHER](https://decipher.sanger.ac.uk))
++* Create a **GitHub repository** for AI scripts and models.
++* Use **GitHub Projects** to manage research milestones.
++)))
++1. (((
++**We Use EBRAINS for Data & Collaboration**
--### **1. Register for Access**
--- Each external database requires **individual registration** and access approval.
--- Ensure compliance with **ethical approvals** and **data usage agreements** before integrating datasets into Neurodiagnoses.
--- Some repositories may require a **Data Usage Agreement (DUA)** for sensitive medical data.##
++* 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.
++)))
--### **2. Download & Prepare Data**
--- Download datasets while adhering to database usage policies.
--- Ensure files meet **Neurodiagnoses format requirements**:
--  - **Tabular Data**: `.csv`, `.tsv`
--  - **Neuroimaging Data**: `.nii`, `.dcm`
--  - **Genomic Data**: `.fasta`, `.vcf`
--  - **Clinical Metadata**: `.json`, `.xml`##
++----
--- **Mandatory Fields for Integration**:
--  - **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
++=== **1. Data Integration** ===
--### **3. Upload Data to Neurodiagnoses**
--**Option 1: Upload to EBRAINS Bucket**
--- Location: **EBRAINS Neurodiagnoses Bucket**
--- Ensure correct **metadata tagging** before submission.##
++=== **EBRAINS Medical Informatics Platform (MIP)**. ===
-- **Option 2: Contribute via GitHub Repository**
--- Location: **GitHub Data Repository**
--- Create a new folder under `/data/` and include a **dataset description**.
--- For large datasets, contact project administrators before uploading.
++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.
--### **4. Integrate Data into AI Models**
--- 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**.##
++==== How It Works ====
--**Reference**: See `docs/data_processing.md` for detailed instructions.
--## **Collaboration & Partnerships**##
--# **Partnering with Data Providers**
--Neurodiagnoses seeks partnerships with data repositories to:
--- Enable **API-based data integration** for real-time processing.
--- Co-develop **harmonized AI-ready datasets** with standardized annotations.
--- Secure **funding opportunities** through joint grant applications.
++1. (((
++**Authentication & API Access:**
--**Interested in Partnering?**
--- If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
--- **Contact**: info@neurodiagnoses.com
++* 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:**
--## **Final Notes**
--Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute **new datasets and methodologies**.##
++* 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:**
--For additional technical documentation:
--- **GitHub Repository**: [Neurodiagnoses GitHub](https://github.com/neurodiagnoses)
--- **EBRAINS Collaboration Page**: [EBRAINS Neurodiagnoses](https://ebrains.eu/collabs/neurodiagnoses)
++* 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)**.
++)))
--If you experience issues integrating data, **open a GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
++==== Implementation Steps ====
--== **How to Use External Databases in Neurodiagnoses** ==
--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.
++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**.
--=== **Potential Data Sources** ===
++For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
--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]]
++=== Data Processing & Integration with Clinica.Run ===
--=== **1. Register for Access** ===
++Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
--Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
++==== How It Works ====
--* 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.
--=== **2. Download & Prepare Data** ===
++1. (((
++**Neuroimaging Preprocessing:**
--Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
++* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
++* Supports **longitudinal and cross-sectional analyses**.
++)))
++1. (((
++**Automated Biomarker Extraction:**
--==== **Supported File Formats** ====
++* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
++* Integration with machine learning models in Neurodiagnoses.
++)))
++1. (((
++**Data Security & Compliance:**
--* Tabular Data: .csv, .tsv
--* Neuroimaging Data: .nii, .dcm
--* Genomic Data: .fasta, .vcf
--* Clinical Metadata: .json, .xml
++* Clinica.Run operates in **compliance with GDPR and HIPAA**.
++* Neuroimaging data remains **within the original storage environment**.
++)))
--==== **Mandatory Fields for Integration** ====
++==== Implementation Steps ====
--|=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
--=== **3. Upload Data to Neurodiagnoses** ===
++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.
--Once preprocessed, data can be uploaded to EBRAINS or GitHub.
++For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++====   ====
--* Location: [[EBRAINS Neurodiagnoses Bucket>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
--* Ensure correct metadata tagging before submission.
--)))
--* (((
--**Option 2: Contribute via GitHub Repository**
++==== **Data Sources** ====
--* 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.
--)))
++[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
--//Note: For large datasets, please contact the project administrators before uploading.//
++**Biomedical Ontologies & Databases:**
--=== **4. Integrate Data into AI Models** ===
++* **Human Phenotype Ontology (HPO)** for symptom annotation.
++* **Gene Ontology (GO)** for molecular and cellular processes.
--Once uploaded, datasets must be harmonized and formatted before AI model training.
++**Dimensionality Reduction and Interpretability:**
--==== **Steps for Data Integration** ====
++* **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.
--* 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]].
++**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).
++
  ----
--== **Database Sources Table** ==
++==== **Annotation System for Multi-Modal Data** ====
--=== **Where to Insert This** ===
++To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
--* 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
++* **Assign standardized metadata tags** to diagnostic features.
++* **Provide contextual explanations** for AI-based classifications.
++* **Track temporal disease progression annotations** to identify long-term trends.
--=== **Key Databases for Neurodiagnoses** ===
++----
--|=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
++=== **2. AI-Based Analysis** ===
--If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
++==== **Machine Learning & Deep Learning Models** ====
++**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**).
++
  ----
--== **Collaboration & Partnerships** ==
++=== **3. Diagnostic Framework & Clinical Decision Support** ===
--=== **Where to Insert This** ===
++==== **Tridimensional Diagnostic Axes** ====
--* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
--* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
++**Axis 1: Etiology (Pathogenic Mechanisms)**
--=== **Partnering with Data Providers** ===
++* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
++* **AI-assisted annotation** provides **causal interpretations** for clinical use.
--Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
++**Axis 2: Molecular Markers & Biomarkers**
--* 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.
++* **Integration of CSF, blood, and neuroimaging biomarkers**.
++* **Structured annotation** highlights **biological pathways linked to diagnosis**.
--=== **Interested in Partnering?** ===
++**Axis 3: Neuroanatomoclinical Correlations**
--If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
++* **MRI and EEG data** provide anatomical and functional insights.
++* **AI-generated progression maps** annotate **brain structure-function relationships**.
--* 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**.
++
  ----
--== **Final Notes** ==
++=== **5. Validation & Real-World Testing** ===
--Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
++==== **Prospective Clinical Study** ====
--For additional technical documentation:
++* **Multi-center validation** of AI-based **annotations & risk stratifications**.
++* **Benchmarking against clinician-based diagnoses**.
++* **Real-world testing** of AI-powered **structured reporting**.
--* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
--* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
++==== **Quality Assurance & Explainability** ====
--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.
++* **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.