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--**Neurodiagnoses AI** is an open-source, AI-driven framework designed to enhance the diagnosis and prognosis of central nervous system (CNS) disorders. It encompasses a broader spectrum of neurological conditions. The system integrates multimodal data sources—including EEG, neuroimaging, biomarkers, and genetics—and employs machine learning models to deliver explainable, real-time diagnostic insights. A key feature of this framework is the incorporation of the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker) **and** Disease Knowledge Transfer (DKT)**, which standardizes disease and biomarker classification across all CNS diseases, facilitating cross-disease AI training.
++**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
--**Neuromarker: Generalized Biomarker Ontology**
++## **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**.##
--Neuromarker extends the Common Alzheimer’s Disease Research Ontology (CADRO) into a comprehensive biomarker categorization framework applicable to all neurodegenerative diseases (NDDs). This ontology enables standardized classification, AI-based feature extraction, and seamless multimodal data integration.
++## **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.##
--**Recommended Software**
++### **Potential Data Sources**
++Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
--There is a suite of software that can help implement the workflow needed in Neurodiagnoses. Find a list of recommendations [[here>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/recommended_software]].
++**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))
--**Core Biomarker Categories**
++### **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.##
--Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
++### **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`##
--|=**Category**|=**Description**
--|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
--|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
--|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
--|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
--|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
--|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
--|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
--|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
++- **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
--**Integrating External Databases into Neurodiagnoses**
++### **3. Upload Data to Neurodiagnoses**
++**Option 1: Upload to EBRAINS Bucket**
++- Location: **EBRAINS Neurodiagnoses Bucket**
++- Ensure correct **metadata tagging** before submission.##
--To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
++ **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.
--1. (((
--**Register for Access**
++### **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**.##
--* 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.
--)))
--1. (((
--**Download & Prepare Data**
++**Reference**: See `docs/data_processing.md` for detailed instructions.
--* Download datasets while adhering to database usage policies.
--* (((
--Ensure files meet Neurodiagnoses format requirements:
++## **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.
--|=**Data Type**|=**Accepted Formats**
--|**Tabular Data**|.csv, .tsv
--|**Neuroimaging**|.nii, .dcm
--|**Genomic Data**|.fasta, .vcf
--|**Clinical Metadata**|.json, .xml
--)))
--* (((
--**Mandatory Fields for Integration**:
++**Interested in Partnering?**
++- If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
++- **Contact**: info@neurodiagnoses.com
--* 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. (((
--**Upload Data to Neurodiagnoses**
++## **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**.##
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++For additional technical documentation:
++- **GitHub Repository**: [Neurodiagnoses GitHub](https://github.com/neurodiagnoses)
++- **EBRAINS Collaboration Page**: [EBRAINS Neurodiagnoses](https://ebrains.eu/collabs/neurodiagnoses)
--* Location: EBRAINS Neurodiagnoses Bucket
--* Ensure correct metadata tagging before submission.
--)))
--* (((
--**Option 2: Contribute via GitHub Repository**
++If you experience issues integrating data, **open a GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
--* Location: GitHub Data Repository
--* Create a new folder under /data/ and include a dataset description.
--* For large datasets, contact project administrators before uploading.
--)))
--)))
--1. (((
--**Integrate Data into AI Models**
++== **How to Use External Databases in Neurodiagnoses** ==
--* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
--* Standardize neuroimaging and biomarker formats using harmonization tools.
--* Utilize machine learning models to handle missing data and feature extraction.
--* Train AI models with newly integrated patient cohorts.
++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.
--**Reference**: See docs/data_processing.md for detailed instructions.
--)))
++=== **Potential Data Sources** ===
--**AI-Driven Biomarker Categorization**
++Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
--Neurodiagnoses employs advanced AI models for biomarker classification:
++* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
--|=**Model Type**|=**Application**
--|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
--|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
--|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
++=== **1. Register for Access** ===
--=== **Jupyter Integration with EBRAINS** ===
++Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
--=== **Overview** ===
++* 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.
--Neurodiagnoses Open Source leverages **Jupyter Notebooks from EBRAINS** to facilitate neurodiagnostic research, biomarker analysis, and AI-driven data processing. This integration provides an interactive and reproducible environment for developing machine learning models, analyzing neuroimaging data, and exploring multimodal biomarkers. Jupyter integration in EBRAINS empowers **Neurodiagnoses Open Source** to: ✅ **Analyze MRI, EEG, and biomarker data efficiently**. ✅ **Train machine learning models with high-performance computing**. ✅ **Ensure transparency with interactive explainability tools**. ✅ **Enable collaborative neurodiagnostic research with reproducible notebooks**.
++=== **2. Download & Prepare Data** ===
--=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
++Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
--==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
++==== **Supported File Formats** ====
--* **Preprocessing Pipelines:**
--** Use **Nipype, NiLearn, ANTs, and FreeSurfer** for structural and functional MRI analysis.
--** Skull stripping, segmentation, and registration of MRI scans.
--** Entropy-based slice selection for training deep learning models.
--* **Deep Learning for Neuroimaging:**
--** Implement **CNN-based models (ResNet, VGG16, Autoencoders)** for biomarker extraction.
--** Feature-based classification of **Alzheimer’s, Parkinson’s, and MCI** from neuroimaging data.
++* Tabular Data: .csv, .tsv
++* Neuroimaging Data: .nii, .dcm
++* Genomic Data: .fasta, .vcf
++* Clinical Metadata: .json, .xml
--==== **2. EEG and MEG Signal Processing** ====
++==== **Mandatory Fields for Integration** ====
--* **Data Preprocessing & Artifact Removal:**
--** Use **MNE-Python** for filtering, ICA-based artifact rejection, and time-series normalization.
--** Extract frequency and time-domain features from EEG/MEG signals.
--* **Feature Engineering & Connectivity Analysis:**
--** Functional connectivity analysis using **coherence and phase synchronization metrics**.
--** Graph-theory-based EEG biomarkers for neurodegenerative disease classification.
--* **Deep Learning for EEG Analysis:**
--** Train LSTMs and CNNs for automatic EEG-based classification of MCI and cognitive decline.
++|=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. Machine Learning for Biomarker Discovery** ====
++=== **3. Upload Data to Neurodiagnoses** ===
--* **SHAP-based Explainability for Biomarkers:**
--** Use **Random Forest + SHAP** to rank the most predictive CSF, blood, and imaging biomarkers.
--** Generate SHAP summary plots to interpret the impact of individual biomarkers.
--* **Multi-Modal Feature Selection:**
--** Implement **Anchor-Graph Feature Selection** to combine MRI, EEG, and CSF data.
--** PCA and autoencoders for dimensionality reduction and feature extraction.
--* **Automated Risk Prediction Models:**
--** Train ensemble models combining **deep learning and classical ML algorithms**.
--** Apply **subject-level cross-validation** to prevent data leakage and ensure reproducibility.
++Once preprocessed, data can be uploaded to EBRAINS or GitHub.
--==== **4. Computational Simulations & Virtual Brain Models** ====
++* (((
++**Option 1: Upload to EBRAINS Bucket**
--* **Integration with The Virtual Brain (TVB):**
--** Simulate large-scale brain networks based on individual neuroimaging data.
--** Model the effect of neurodegenerative progression on brain activity.
--* **Cortical and Subcortical Connectivity Analysis:**
--** Generate connectivity matrices using diffusion MRI and functional MRI correlations.
--** Validate computational simulations with real patient data from EBRAINS datasets.
++* 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**
--==== **5. Interactive Data Visualization & Reporting** ====
++* 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.
++)))
--* **Dynamic Plots & Dashboards:**
--** Use **Matplotlib, Seaborn, Plotly** for interactive visualizations of biomarkers.
--** Implement real-time MRI slice rendering and EEG signal visualization.
--* **Automated Report Generation:**
--** Generate **Jupyter-based PDF reports** summarizing key findings.
--** Export analysis results in JSON, CSV, and interactive web dashboards.
++//Note: For large datasets, please contact the project administrators before uploading.//
--=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
++=== **4. Integrate Data into AI Models** ===
--==== **1. Access EBRAINS Jupyter Environment** ====
++Once uploaded, datasets must be harmonized and formatted before AI model training.
--1. Create an **EBRAINS account** at [[EBRAINS.eu>>url:https://ebrains.eu/]].
--1. Navigate to the **Collaboratory** and open the Jupyter Lab interface.
--1. Clone the Neurodiagnoses repository:
++==== **Steps for Data Integration** ====
--{{{git clone https://github.com/neurodiagnoses
--cd neurodiagnoses
--pip install -r requirements.txt
--}}}
++* 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]].
--==== **2. Run Prebuilt Neurodiagnoses Notebooks** ====
++----
--1. Open the **notebooks/** directory inside Jupyter.
--1. Run any of the available notebooks:
--1*. mri_biomarker_analysis.ipynb → Extracts MRI-based biomarkers.
--1*. eeg_preprocessing.ipynb → Cleans and processes EEG signals.
--1*. shap_biomarker_explainability.ipynb → Visualizes biomarker importance.
--1*. disease_risk_prediction.ipynb → Runs ML models for disease classification.
++== **Database Sources Table** ==
--==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
++=== **Where to Insert This** ===
--* Use EBRAINS **GPU and HPC clusters** for deep learning training:
++* 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
--{{{from neurodiagnoses.models import train_cnn_model
--train_cnn_model(data_path='data/mri/', model_type='ResNet50')
--}}}
--* Save trained models for deployment:
++=== **Key Databases for Neurodiagnoses** ===
--{{{model.save('models/neurodiagnoses_cnn.h5')
--}}}
++|=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
--For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
++If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
--**Collaboration & Partnerships**
++----
--Neurodiagnoses actively seeks partnerships with data providers to:
++== **Collaboration & Partnerships** ==
--* Enable API-based data integration for real-time processing.
++=== **Where to Insert This** ===
++
++* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
++* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
++
++=== **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?**
++=== **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]]
++* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
--**Final Notes**
++----
--Neurodiagnoses AI is committed to advancing the integration of artificial intelligence in neurodiagnostic processes. By continuously expanding our data ecosystem and incorporating standardized biomarker classifications through the Neuromarker ontology, we aim to enhance cross-disease AI training and improve diagnostic accuracy across neurodegenerative disorders.
++== **Final Notes** ==
--We encourage researchers and institutions to contribute new datasets and methodologies to further enrich this collaborative platform. Your participation is vital in driving innovation and fostering a deeper understanding of complex neurological conditions.
++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 and collaboration opportunities:**
++For additional technical documentation:
--* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
--* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
++* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
++* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
--If you encounter any issues during data integration or have suggestions for improvement, please open a GitHub Issue or consult the EBRAINS Neurodiagnoses Forum. Together, we can advance the field of neurodiagnostics and contribute to better patient outcomes.
++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.

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