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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**
++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**.
--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.
++=== **Workflow** ===
--**Recommended Software**
++1. (((
++**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
--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]].
++* Create a **GitHub repository** for AI scripts and models.
++* Use **GitHub Projects** to manage research milestones.
++)))
++1. (((
++**We Use EBRAINS for Data & Collaboration**
--**Core Biomarker Categories**
++* 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.
++)))
--Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
++----
--|=**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
++=== **1. Data Integration** ===
--**Integrating External Databases into Neurodiagnoses**
++== Overview ==
--To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
++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.
++
++== How It Works ==
++
++
 . (((
--**Register for Access**
++**Authentication & API 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.
++* Users must have an **EBRAINS account**.
++* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
  )))
 . (((
--**Download & Prepare Data**
++**Data Mapping & Harmonization:**
--* Download datasets while adhering to database usage policies.
--* (((
--Ensure files meet Neurodiagnoses format requirements:
--
--|=**Data Type**|=**Accepted Formats**
--|**Tabular Data**|.csv, .tsv
--|**Neuroimaging**|.nii, .dcm
--|**Genomic Data**|.fasta, .vcf
--|**Clinical Metadata**|.json, .xml
++* Retrieved data is **normalized** and converted to standard formats (.csv, .json).
++* Data from **multiple sources** is harmonized to ensure consistency for AI processing.
  )))
--* (((
--**Mandatory Fields for Integration**:
++1. (((
++**Security & Compliance:**
--* 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
++* 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)**.
  )))
--)))
--1. (((
--**Upload Data to Neurodiagnoses**
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++== Implementation Steps ==
--* Location: EBRAINS Neurodiagnoses Bucket
--* Ensure correct metadata tagging before submission.
++
++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**.
++
++For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
++
++----
++
++= Data Processing & Integration with Clinica.Run =
++
++
++== Overview ==
++
++
++Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
++
++== How It Works ==
++
++
++1. (((
++**Neuroimaging Preprocessing:**
++
++* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
++* Supports **longitudinal and cross-sectional analyses**.
  )))
--* (((
--**Option 2: Contribute via GitHub Repository**
++1. (((
++**Automated Biomarker Extraction:**
--* Location: GitHub Data Repository
--* Create a new folder under /data/ and include a dataset description.
--* For large datasets, contact project administrators before uploading.
++* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
++* Integration with machine learning models in Neurodiagnoses.
  )))
--)))
 . (((
--**Integrate Data into AI Models**
++**Data Security & Compliance:**
--* 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.
--
--**Reference**: See docs/data_processing.md for detailed instructions.
++* Clinica.Run operates in **compliance with GDPR and HIPAA**.
++* Neuroimaging data remains **within the original storage environment**.
  )))
--**AI-Driven Biomarker Categorization**
++== Implementation Steps ==
--Neurodiagnoses employs advanced AI models for biomarker classification:
--|=**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. 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.
--=== **Jupyter Integration with EBRAINS** ===
++For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
--=== **Overview** ===
++====   ====
--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**.
++==== **Data Sources** ====
--=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
++[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
--==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
++**Biomedical Ontologies & Databases:**
--* **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.
++* **Human Phenotype Ontology (HPO)** for symptom annotation.
++* **Gene Ontology (GO)** for molecular and cellular processes.
--==== **2. EEG and MEG Signal Processing** ====
++**Dimensionality Reduction and Interpretability:**
--* **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.
++* **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.
--==== **3. Machine Learning for Biomarker Discovery** ====
++**Neuroimaging & EEG/MEG Data:**
--* **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.
++* **MRI volumetric measures** for brain atrophy tracking.
++* **EEG functional connectivity patterns** (AI-Mind).
--==== **4. Computational Simulations & Virtual Brain Models** ====
++**Clinical & Biomarker Data:**
--* **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.
++* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
++* **Sleep monitoring and actigraphy data** (ADIS).
--==== **5. Interactive Data Visualization & Reporting** ====
++**Federated Learning Integration:**
--* **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.
++* **Secure multi-center data harmonization** (PROMINENT).
--=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
++----
--==== **1. Access EBRAINS Jupyter Environment** ====
++==== **Annotation System for Multi-Modal Data** ====
--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:
++To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
--{{{git clone https://github.com/neurodiagnoses
--cd neurodiagnoses
--pip install -r requirements.txt
--}}}
++* **Assign standardized metadata tags** to diagnostic features.
++* **Provide contextual explanations** for AI-based classifications.
++* **Track temporal disease progression annotations** to identify long-term trends.
--==== **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.
++=== **2. AI-Based Analysis** ===
--==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
++==== **Machine Learning & Deep Learning Models** ====
--* Use EBRAINS **GPU and HPC clusters** for deep learning training:
++**Risk Prediction Models:**
--{{{from neurodiagnoses.models import train_cnn_model
--train_cnn_model(data_path='data/mri/', model_type='ResNet50')
--}}}
--* Save trained models for deployment:
++* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
--{{{model.save('models/neurodiagnoses_cnn.h5')
--}}}
++**Biomarker Classification & Probabilistic Imputation:**
--For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
++* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
--**Collaboration & Partnerships**
++**Neuroimaging Feature Extraction:**
--Neurodiagnoses actively seeks partnerships with data providers to:
++* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
--* 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.
++==== **AI-Powered Annotation System** ====
--**Interested in Partnering?**
++* 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**).
--If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
++----
--**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
++=== **3. Diagnostic Framework & Clinical Decision Support** ===
--**Final Notes**
++==== **Tridimensional Diagnostic Axes** ====
--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.
++**Axis 1: Etiology (Pathogenic Mechanisms)**
--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.
++* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
++* **AI-assisted annotation** provides **causal interpretations** for clinical use.
--**For additional technical documentation and collaboration opportunities:**
++**Axis 2: Molecular Markers & Biomarkers**
--* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
--* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
++* **Integration of CSF, blood, and neuroimaging biomarkers**.
++* **Structured annotation** highlights **biological pathways linked to diagnosis**.
--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.
++**Axis 3: Neuroanatomoclinical Correlations**
++
++* **MRI and EEG data** provide anatomical and functional insights.
++* **AI-generated progression maps** annotate **brain structure-function relationships**.
++
++----
++
++=== **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**.
++
++----
++
++=== **5. Validation & Real-World Testing** ===
++
++==== **Prospective Clinical Study** ====
++
++* **Multi-center validation** of AI-based **annotations & risk stratifications**.
++* **Benchmarking against clinician-based diagnoses**.
++* **Real-world testing** of AI-powered **structured reporting**.
++
++==== **Quality Assurance & Explainability** ====
++
++* **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.

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