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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. Building upon the Florey Dementia Index (FDI) methodology, it now 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)**, which standardizes biomarker classification across all neurodegenerative diseases, facilitating cross-disease AI training.
--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: Generalized Biomarker Ontology**
--=== **Workflow** ===
++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.
--1. (((
--**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
++**Core Biomarker Categories**
--* Create a **GitHub repository** for AI scripts and models.
--* Use **GitHub Projects** to manage research milestones.
--)))
--1. (((
--**We Use EBRAINS for Data & Collaboration**
++Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
--* 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.
--)))
++|=**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
------
++**Integrating External Databases into Neurodiagnoses**
--=== **1. Data Integration** ===
++To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
--== Overview ==
--
--
--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 ==
--
--
 . (((
--**Authentication & API Access:**
++**Register for Access**
--* Users must have an **EBRAINS account**.
--* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
++* 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.
  )))
 . (((
--**Data Mapping & Harmonization:**
++**Download & Prepare Data**
--* Retrieved data is **normalized** and converted to standard formats (.csv, .json).
--* Data from **multiple sources** is harmonized to ensure consistency for AI processing.
--)))
--1. (((
--**Security & Compliance:**
++* Download datasets while adhering to database usage policies.
++* (((
++Ensure files meet Neurodiagnoses format requirements:
--* 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)**.
++|=**Data Type**|=**Accepted Formats**
++|**Tabular Data**|.csv, .tsv
++|**Neuroimaging**|.nii, .dcm
++|**Genomic Data**|.fasta, .vcf
++|**Clinical Metadata**|.json, .xml
  )))
++* (((
++**Mandatory Fields for Integration**:
--== Implementation Steps ==
--
--
--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 ==
--
--
++* 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
++)))
++)))
 . (((
--**Neuroimaging Preprocessing:**
++**Upload Data to Neurodiagnoses**
--* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
--* Supports **longitudinal and cross-sectional analyses**.
++* (((
++**Option 1: Upload to EBRAINS Bucket**
++
++* Location: EBRAINS Neurodiagnoses Bucket
++* Ensure correct metadata tagging before submission.
  )))
--1. (((
--**Automated Biomarker Extraction:**
++* (((
++**Option 2: Contribute via GitHub Repository**
--* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
--* Integration with machine learning models in Neurodiagnoses.
++* Location: GitHub Data Repository
++* Create a new folder under /data/ and include a dataset description.
++* For large datasets, contact project administrators before uploading.
  )))
++)))
 . (((
--**Data Security & Compliance:**
++**Integrate Data into AI Models**
--* Clinica.Run operates in **compliance with GDPR and HIPAA**.
--* Neuroimaging data remains **within the original storage environment**.
++* 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.
  )))
--== Implementation Steps ==
++**AI-Driven Biomarker Categorization**
++Neurodiagnoses employs advanced AI models for biomarker classification:
--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.
++|=**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
--For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
++**Collaboration & Partnerships**
--====   ====
++Neurodiagnoses actively seeks partnerships with data providers to:
--==== **Data Sources** ====
++* 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.
--[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
++**Interested in Partnering?**
--**Biomedical Ontologies & Databases:**
++If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
--* **Human Phenotype Ontology (HPO)** for symptom annotation.
--* **Gene Ontology (GO)** for molecular and cellular processes.
++**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
--**Dimensionality Reduction and Interpretability:**
--
--* **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.
--
--**Neuroimaging & EEG/MEG Data:**
--
--* **MRI volumetric measures** for brain atrophy tracking.
--* **EEG functional connectivity patterns** (AI-Mind).
--
--**Clinical & Biomarker Data:**
--
--* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
--* **Sleep monitoring and actigraphy data** (ADIS).
--
--**Federated Learning Integration:**
--
--* **Secure multi-center data harmonization** (PROMINENT).
--
------
--
--==== **Annotation System for Multi-Modal Data** ====
--
--To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
--
--* **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. AI-Based Analysis** ===
--
--==== **Machine Learning & Deep Learning Models** ====
--
--**Risk Prediction Models:**
--
--* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
--
--**Biomarker Classification & Probabilistic Imputation:**
--
--* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
--
--**Neuroimaging Feature Extraction:**
--
--* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
--
--==== **AI-Powered Annotation System** ====
--
--* 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**).
--
------
--
--=== **3. Diagnostic Framework & Clinical Decision Support** ===
--
--==== **Tridimensional Diagnostic Axes** ====
--
--**Axis 1: Etiology (Pathogenic Mechanisms)**
--
--* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
--* **AI-assisted annotation** provides **causal interpretations** for clinical use.
--
--**Axis 2: Molecular Markers & Biomarkers**
--
--* **Integration of CSF, blood, and neuroimaging biomarkers**.
--* **Structured annotation** highlights **biological pathways linked to diagnosis**.
--
--**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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