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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.
++== **Overview** ==
--**Neuromarker: Generalized Biomarker Ontology**
++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.
--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.
++----
--**Recommended Software**
++== **How to Use External Databases in Neurodiagnoses** ==
--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]].
++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.
--**Core Biomarker Categories**
++=== **Potential Data Sources** ===
--Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
++Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
--|=**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
++* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
--**Integrating External Databases into Neurodiagnoses**
++=== **1. Register for Access** ===
--To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
++Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
--1. (((
--**Register for Access**
++* 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.
--* 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**
++=== **2. Download & Prepare Data** ===
--* Download datasets while adhering to database usage policies.
--* (((
--Ensure files meet Neurodiagnoses format requirements:
++Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
--|=**Data Type**|=**Accepted Formats**
--|**Tabular Data**|.csv, .tsv
--|**Neuroimaging**|.nii, .dcm
--|**Genomic Data**|.fasta, .vcf
--|**Clinical Metadata**|.json, .xml
--)))
--* (((
--**Mandatory Fields for Integration**:
++==== **Supported File Formats** ====
--* 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**
++* 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
++* 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
--* Create a new folder under /data/ and include a dataset description.
--* For large datasets, contact project administrators before uploading.
++* 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. (((
--**Integrate Data into AI Models**
--* 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.
++//Note: For large datasets, please contact the project administrators before uploading.//
--**Reference**: See docs/data_processing.md for detailed instructions.
--)))
++=== **4. Integrate Data into AI Models** ===
--**AI-Driven Biomarker Categorization**
++Once uploaded, datasets must be harmonized and formatted before AI model training.
--Neurodiagnoses employs advanced AI models for biomarker classification:
++==== **Steps for Data Integration** ====
--|=**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
++* 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]].
--=== **Jupyter Integration with EBRAINS** ===
++----
--=== **Overview** ===
++== **Database Sources Table** ==
--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**.
++=== **Where to Insert This** ===
--=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
++* 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
--==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
++=== **Key Databases for Neurodiagnoses** ===
--* **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.
++|=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. EEG and MEG Signal Processing** ====
++If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
--* **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.
++----
--==== **3. Machine Learning for Biomarker Discovery** ====
++== **Collaboration & Partnerships** ==
--* **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.
++=== **Where to Insert This** ===
--==== **4. Computational Simulations & Virtual Brain Models** ====
++* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
++* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
--* **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.
++=== **Partnering with Data Providers** ===
--==== **5. Interactive Data Visualization & Reporting** ====
++Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
--* **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.
--
--=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
--
--==== **1. Access EBRAINS Jupyter Environment** ====
--
--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:
--
--{{{git clone https://github.com/neurodiagnoses
--cd neurodiagnoses
--pip install -r requirements.txt
--}}}
--
--==== **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.
--
--==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
--
--* Use EBRAINS **GPU and HPC clusters** for deep learning training:
--
--{{{from neurodiagnoses.models import train_cnn_model
--train_cnn_model(data_path='data/mri/', model_type='ResNet50')
--}}}
--* Save trained models for deployment:
--
--{{{model.save('models/neurodiagnoses_cnn.h5')
--}}}
--
--For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
--
--**Collaboration & Partnerships**
--
--Neurodiagnoses actively seeks partnerships with data providers to:
--
--* Enable API-based data integration for real-time processing.
++* 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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