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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 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.
--## **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.##
++----
--### **Potential Data Sources**
--Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
++=== **1. Data Integration** ===
--**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))
++==== **Data Sources** ====
--### **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.##
++* **Biomedical Ontologies**:
++** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
++** Gene Ontology (GO) for molecular and cellular processes.
++* **Neuroimaging Datasets**:
++** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
++* **Clinical and Biomarker Data**:
++** Anonymized clinical reports, molecular biomarkers, and test results.
--### **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
++==== **Data Preprocessing** ====
--### **3. Upload Data to Neurodiagnoses**
--**Option 1: Upload to EBRAINS Bucket**
--- Location: **EBRAINS Neurodiagnoses Bucket**
--- Ensure correct **metadata tagging** before submission.##
++1. **Standardization**: Ensure all data sources are normalized to a common format.
++1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
++1. **Data Cleaning**: Handle missing values and remove duplicates.
-- **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.
++----
--### **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**.##
++=== **2. AI-Based Analysis** ===
--**Reference**: See `docs/data_processing.md` for detailed instructions.
++==== **Model Development** ====
--## **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.
++* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
++* **Classification Models**:
++** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
++** Purpose: Predict the likelihood of specific neurological conditions based on input data.
--**Interested in Partnering?**
--- If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
--- **Contact**: info@neurodiagnoses.com
++==== **Dimensionality Reduction and Interpretability** ====
--## **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**.##
++* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
++* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
--For additional technical documentation:
--- **GitHub Repository**: [Neurodiagnoses GitHub](https://github.com/neurodiagnoses)
--- **EBRAINS Collaboration Page**: [EBRAINS Neurodiagnoses](https://ebrains.eu/collabs/neurodiagnoses)
++----
--If you experience issues integrating data, **open a GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
++=== **3. Diagnostic Framework** ===
--== **How to Use External Databases in Neurodiagnoses** ==
++==== **Axes of Diagnosis** ====
--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.
++The framework organizes diagnostic data into three axes:
--=== **Potential Data Sources** ===
++1. **Etiology**: Genetic and environmental risk factors.
++1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
++1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
--Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
++==== **Recommendation System** ====
--* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
++* Suggests additional tests or biomarkers if gaps are detected in the data.
++* Prioritizes tests based on clinical impact and cost-effectiveness.
--=== **1. Register for Access** ===
++----
--Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
++=== **4. Computational Workflow** ===
--* 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.
++1. **Data Loading**: Import data from storage (Drive or Bucket).
++1. **Feature Engineering**: Generate derived features from the raw data.
++1. **Model Training**:
++1*. Split data into training, validation, and test sets.
++1*. Train models with cross-validation to ensure robustness.
++1. **Evaluation**:
++1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
++1*. Compare against baseline models and domain benchmarks.
--=== **2. Download & Prepare Data** ===
--
--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** ====
--
--* Tabular Data: .csv, .tsv
--* Neuroimaging Data: .nii, .dcm
--* Genomic Data: .fasta, .vcf
--* Clinical Metadata: .json, .xml
--
--==== **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
--
--=== **3. Upload Data to Neurodiagnoses** ===
--
--Once preprocessed, data can be uploaded to EBRAINS or GitHub.
--
--* (((
--**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**
--
--* 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.
--)))
--
--//Note: For large datasets, please contact the project administrators before uploading.//
--
--=== **4. Integrate Data into AI Models** ===
--
--Once uploaded, datasets must be harmonized and formatted before AI model training.
--
--==== **Steps for Data Integration** ====
--
--* 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]].
--
  ----
--== **Database Sources Table** ==
++=== **5. Validation** ===
--=== **Where to Insert This** ===
++==== **Internal Validation** ====
--* 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
++* Test the system using simulated datasets and known clinical cases.
++* Fine-tune models based on validation results.
--=== **Key Databases for Neurodiagnoses** ===
++==== **External Validation** ====
--|=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
++* Collaborate with research institutions and hospitals to test the system in real-world settings.
++* Use anonymized patient data to ensure privacy compliance.
--If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
--
  ----
--== **Collaboration & Partnerships** ==
++=== **6. Collaborative Development** ===
--=== **Where to Insert This** ===
++The project is open to contributions from researchers, clinicians, and developers. Key tools include:
--* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
--* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
++* **Jupyter Notebooks**: For data analysis and pipeline development.
++** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
++* **Wiki Pages**: For documenting methods and results.
++* **Drive and Bucket**: For sharing code, data, and outputs.
++* **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/]]
--=== **Partnering with Data Providers** ===
--
--Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
--
--* 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.
--
--=== **Interested in Partnering?** ===
--
--If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
--
--* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
--
  ----
--== **Final Notes** ==
++=== **7. Tools and Technologies** ===
--Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
--
--For additional technical documentation:
--
--* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
--* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
--
--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.
++* **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.