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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.
++----
--**Recommended Software**
++=== **1. Data Integration** ===
--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 Sources** ====
--**Core Biomarker Categories**
++**Biomedical Ontologies & Databases:**
--Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
++* **Human Phenotype Ontology (HPO)** for symptom annotation.
++* **Gene Ontology (GO)** for molecular and cellular processes.
--|=**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
++**Dimensionality Reduction and Interpretability:**
--**Integrating External Databases into Neurodiagnoses**
++* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
++* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
--To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
++**Neuroimaging & EEG/MEG Data:**
--1. (((
--**Register for Access**
++* **MRI volumetric measures** for brain atrophy tracking.
++* **EEG functional connectivity patterns** (AI-Mind).
--* 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**
++**Clinical & Biomarker Data:**
--* Download datasets while adhering to database usage policies.
--* (((
--Ensure files meet Neurodiagnoses format requirements:
++* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
++* **Sleep monitoring and actigraphy data** (ADIS).
--|=**Data Type**|=**Accepted Formats**
--|**Tabular Data**|.csv, .tsv
--|**Neuroimaging**|.nii, .dcm
--|**Genomic Data**|.fasta, .vcf
--|**Clinical Metadata**|.json, .xml
--)))
--* (((
--**Mandatory Fields for Integration**:
++**Federated Learning 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
--)))
--)))
--1. (((
--**Upload Data to Neurodiagnoses**
++* **Secure multi-center data harmonization** (PROMINENT).
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++----
--* Location: EBRAINS Neurodiagnoses Bucket
--* Ensure correct metadata tagging before submission.
--)))
--* (((
--**Option 2: Contribute via GitHub Repository**
++==== **Annotation System for Multi-Modal Data** ====
--* 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**
++To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
--* 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.
++* **Assign standardized metadata tags** to diagnostic features.
++* **Provide contextual explanations** for AI-based classifications.
++* **Track temporal disease progression annotations** to identify long-term trends.
--**Reference**: See docs/data_processing.md for detailed instructions.
--)))
++----
--**AI-Driven Biomarker Categorization**
++=== **2. AI-Based Analysis** ===
--Neurodiagnoses employs advanced AI models for biomarker classification:
++==== **Machine Learning & Deep Learning Models** ====
--|=**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
++**Risk Prediction Models:**
--=== **Jupyter Integration with EBRAINS** ===
++* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
--=== **Overview** ===
++**Biomarker Classification & Probabilistic Imputation:**
--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**.
++* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
--=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
++**Neuroimaging Feature Extraction:**
--==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
++* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
--* **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.
++==== **AI-Powered Annotation System** ====
--==== **2. EEG and MEG Signal Processing** ====
++* 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**).
--* **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** ====
++=== **3. Diagnostic Framework & Clinical Decision Support** ===
--* **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.
++==== **Tridimensional Diagnostic Axes** ====
--==== **4. Computational Simulations & Virtual Brain Models** ====
++**Axis 1: Etiology (Pathogenic Mechanisms)**
--* **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.
++* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
++* **AI-assisted annotation** provides **causal interpretations** for clinical use.
--==== **5. Interactive Data Visualization & Reporting** ====
++**Axis 2: Molecular Markers & Biomarkers**
--* **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.
++* **Integration of CSF, blood, and neuroimaging biomarkers**.
++* **Structured annotation** highlights **biological pathways linked to diagnosis**.
--=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
++**Axis 3: Neuroanatomoclinical Correlations**
--==== **1. Access EBRAINS Jupyter Environment** ====
++* **MRI and EEG data** provide anatomical and functional insights.
++* **AI-generated progression maps** annotate **brain structure-function relationships**.
--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
--}}}
++=== **4. Computational Workflow & Annotation Pipelines** ===
--==== **2. Run Prebuilt Neurodiagnoses Notebooks** ====
++==== **Data Processing Steps** ====
--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.
++**Data Ingestion:**
--==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
++* **Harmonized datasets** stored in **EBRAINS Bucket**.
++* **Preprocessing pipelines** clean and standardize data.
--* Use EBRAINS **GPU and HPC clusters** for deep learning training:
++**Feature Engineering:**
--{{{from neurodiagnoses.models import train_cnn_model
--train_cnn_model(data_path='data/mri/', model_type='ResNet50')
--}}}
--* Save trained models for deployment:
++* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
--{{{model.save('models/neurodiagnoses_cnn.h5')
--}}}
++**AI-Generated Annotations:**
--For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
++* **Automated tagging** of diagnostic features in **structured reports**.
++* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
--**Collaboration & Partnerships**
++**Clinical Decision Support Integration:**
--Neurodiagnoses actively seeks partnerships with data providers to:
++* **AI-annotated findings** fed into **interactive dashboards**.
++* **Clinicians can adjust, validate, and modify annotations**.
--* 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.
++----
--**Interested in Partnering?**
++=== **5. Validation & Real-World Testing** ===
--If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
++==== **Prospective Clinical Study** ====
--**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
++* **Multi-center validation** of AI-based **annotations & risk stratifications**.
++* **Benchmarking against clinician-based diagnoses**.
++* **Real-world testing** of AI-powered **structured reporting**.
--**Final Notes**
++==== **Quality Assurance & Explainability** ====
--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.
++* **Annotations linked to structured knowledge graphs** for improved transparency.
++* **Interactive annotation editor** allows clinicians to validate AI outputs.
--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:**
++=== **6. Collaborative Development** ===
--* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
--* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
++The project is **open to contributions** from **researchers, clinicians, and developers**.
--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.
++**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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