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--== **Overview** ==
++**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 develops a **tridimensional diagnostic framework** for **CNS diseases**, incorporating **AI-powered annotation tools** to improve **interpretability, standardization, and clinical utility.**
++**Neuromarker: Generalized Biomarker Ontology**
--This methodology integrates **multi-modal data**, including:
--**Genetic data** (whole-genome sequencing, polygenic risk scores).
--**Neuroimaging** (MRI, PET, EEG, MEG).
--**Neurophysiological data** (EEG-based biomarkers, sleep actigraphy).
--**CSF & Blood Biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
++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.
--By applying **machine learning models**, Neurodiagnoses generates **structured, explainable diagnostic outputs** to assist **clinical decision-making** and **biomarker-driven patient stratification.**
++**Recommended Software**
------
++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]].
--== **Data Integration & External Databases** ==
++**Core Biomarker Categories**
--=== **How to Use External Databases in Neurodiagnoses** ===
++Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
--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.
++|=**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
--**Potential Data Sources**
--**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**
--=== **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 approval.
--✔️ Follow the official **data access guidelines** of each provider.
--✔️ Ensure compliance with **ethical approvals** and **data-sharing agreements (DUAs).**
++1. (((
++**Register for Access**
--=== **Download & Prepare 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**
--Once access is granted, download datasets **following compliance guidelines** and **format requirements** for integration.
++* Download datasets while adhering to database usage policies.
++* (((
++Ensure files meet Neurodiagnoses format requirements:
--**Supported File Formats**
++|=**Data Type**|=**Accepted Formats**
++|**Tabular Data**|.csv, .tsv
++|**Neuroimaging**|.nii, .dcm
++|**Genomic Data**|.fasta, .vcf
++|**Clinical Metadata**|.json, .xml
++)))
++* (((
++**Mandatory Fields for Integration**:
--* **Tabular Data**: .csv, .tsv
--* **Neuroimaging Data**: .nii, .dcm
--* **Genomic Data**: .fasta, .vcf
--* **Clinical Metadata**: .json, .xml
++* 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**
--**Mandatory Fields for Integration**
++* (((
++**Option 1: Upload to EBRAINS Bucket**
--|=**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
++* Location: EBRAINS Neurodiagnoses Bucket
++* Ensure correct metadata tagging before submission.
++)))
++* (((
++**Option 2: Contribute via GitHub Repository**
--=== **Upload Data to Neurodiagnoses** ===
++* 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**
--**Option 1:** Upload to **EBRAINS Bucket** → [[Neurodiagnoses Data Storage>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Bucket]]
--**Option 2:** Contribute via **GitHub Repository** → [[GitHub Data Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/tree/main/data]]
++* 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.
--**For large datasets, please contact project administrators before uploading.**
++**Reference**: See docs/data_processing.md for detailed instructions.
++)))
--=== **Integrate Data into AI Models** ===
++**AI-Driven Biomarker Categorization**
--Use **Jupyter Notebooks** on EBRAINS for **data preprocessing.**
--Standardize data using **harmonization tools.**
--Train AI models with **newly integrated datasets.**
++Neurodiagnoses employs advanced AI models for biomarker classification:
--**Reference:** [[Data Processing Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/data_processing.md]]
++|=**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
------
++=== **Jupyter Integration with EBRAINS** ===
--== **AI-Powered Annotation & Machine Learning Models** ==
++=== **Overview** ===
--Neurodiagnoses applies **advanced machine learning models** to classify CNS diseases, extract features from **biomarkers and neuroimaging**, and provide **AI-powered annotation.**
++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**.
--=== **AI Model Categories** ===
++=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
--|=**Model Type**|=**Function**|=**Example Algorithms**
--|**Probabilistic Diagnosis**|Assigns probability scores to multiple CNS disorders.|Random Forest, XGBoost, Bayesian Networks
--|**Tridimensional Diagnosis**|Classifies disorders based on Etiology, Biomarkers, and Neuroanatomical Correlations.|CNNs, Transformers, Autoencoders
--|**Biomarker Prediction**|Predicts missing biomarker values using regression.|KNN Imputation, Bayesian Estimation
--|**Neuroimaging Feature Extraction**|Extracts patterns from MRI, PET, EEG.|CNNs, Graph Neural Networks
--|**Clinical Decision Support**|Generates AI-driven diagnostic reports.|SHAP Explainability Tools
++==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
--**Reference:** [[AI Model Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/models.md]]
++* **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.
------
++==== **2. EEG and MEG Signal Processing** ====
--== **Clinical Decision Support & Tridimensional Diagnostic Framework** ==
++* **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.
--Neurodiagnoses generates **structured AI reports** for clinicians, combining:
++==== **3. Machine Learning for Biomarker Discovery** ====
--**Probabilistic Diagnosis:** AI-generated ranking of potential diagnoses.
--**Tridimensional Classification:** Standardized diagnostic reports based on:
++* **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.
--1. **Axis 1:** **Etiology** → Genetic, Autoimmune, Prion, Toxic, Vascular.
--1. **Axis 2:** **Molecular Markers** → CSF, Neuroinflammation, EEG biomarkers.
--1. **Axis 3:** **Neuroanatomoclinical Correlations** → MRI atrophy, PET.
++==== **4. Computational Simulations & Virtual Brain Models** ====
--**Reference:** [[Tridimensional Classification Guide>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/classification.md]]
++* **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.
------
++==== **5. Interactive Data Visualization & Reporting** ====
--== **Data Security, Compliance & Federated Learning** ==
++* **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.
--✔ **Privacy-Preserving AI**: Implements **Federated Learning**, ensuring that patient data **never leaves** local institutions.
--✔ **Secure Data Access**: Data remains **stored in EBRAINS MIP servers** using **differential privacy techniques.**
--✔ **Ethical & GDPR Compliance**: Data-sharing agreements **must be signed** before use.
++=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
--**Reference:** [[Data Protection & Federated Learning>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/security.md]]
++==== **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:
--== **Data Processing & Integration with Clinica.Run** ==
++{{{git clone https://github.com/neurodiagnoses
++cd neurodiagnoses
++pip install -r requirements.txt
++}}}
--Neurodiagnoses now supports **Clinica.Run**, an **open-source neuroimaging platform** for **multimodal data processing.**
++==== **2. Run Prebuilt Neurodiagnoses Notebooks** ====
--=== **How It Works** ===
++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.
--✔ **Neuroimaging Preprocessing**: MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines.**
--✔ **Automated Biomarker Extraction**: Extracts volumetric, metabolic, and functional biomarkers.
--✔ **Data Security & Compliance**: Clinica.Run is **GDPR & HIPAA-compliant.**
++==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
--=== **Implementation Steps** ===
++* Use EBRAINS **GPU and HPC clusters** for deep learning training:
--1. Install **Clinica.Run** dependencies.
--1. Configure **Clinica.Run pipeline** in clinica_run_config.json.
--1. Run **biomarker extraction pipelines** for AI-based diagnostics.
++{{{from neurodiagnoses.models import train_cnn_model
++train_cnn_model(data_path='data/mri/', model_type='ResNet50')
++}}}
++* Save trained models for deployment:
--**Reference:** [[Clinica.Run Documentation>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/clinica_run.md]]
++{{{model.save('models/neurodiagnoses_cnn.h5')
++}}}
------
++For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
--== **Collaborative Development & Research** ==
++**Collaboration & Partnerships**
--**We Use GitHub to Develop AI Models & Store Research Data**
++Neurodiagnoses actively seeks partnerships with data providers to:
--* **GitHub Repository:** AI model training scripts.
--* **GitHub Issues:** Tracks ongoing research questions.
--* **GitHub Wiki:** Project documentation & user guides.
++* 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.
--**We Use EBRAINS for Data & Collaboration**
++**Interested in Partnering?**
--* **EBRAINS Buckets:** Large-scale neuroimaging and biomarker storage.
--* **EBRAINS Jupyter Notebooks:** Cloud-based AI model execution.
--* **EBRAINS Wiki:** Research documentation and updates.
++If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
--**Join the Project Forum:** [[GitHub Discussions>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
++**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
------
++**Final Notes**
--**For Additional Documentation:**
++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.
--* **GitHub Repository:** [[Neurodiagnoses AI Models>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
--* **EBRAINS Wiki:** [[Neurodiagnoses Research Collaboration>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
++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.
------
++**For additional technical documentation and collaboration opportunities:**
--**Neurodiagnoses is Open for Contributions – Join Us Today!**
++* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
++* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/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.

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