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--**# Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions**
++**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.
--## **Project Overview**
--Neurodiagnoses AI implements AI-driven diagnostic and prognostic models for central nervous system (CNS) disorders, adapting the Florey Dementia Index (FDI) methodology to a broader set of neurological conditions. The approach integrates **multimodal data sources** (EEG, neuroimaging, biomarkers, and genetics) and employs **machine learning models** to provide **explainable, real-time diagnostic insights**.##
++**Neuromarker: Generalized Biomarker Ontology**
--## **How to Use External Databases in Neurodiagnoses**
--To enhance diagnostic accuracy, 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.##
++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.
--### **Potential Data Sources**
--Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases. ##
++**Recommended Software**
--**Reference: List of Potential Databases**
--- **ADNI**: Alzheimer's Disease data ([ADNI](https://adni.loni.usc.edu))
--- **PPMI**: Parkinson’s Disease Imaging and biospecimens ([PPMI](https://www.ppmi-info.org))
--- **GP2**: Whole-genome sequencing for PD ([GP2](https://gp2.org))
--- **Enroll-HD**: Huntington’s Disease Clinical and genetic data ([Enroll-HD](https://www.enroll-hd.org))
--- **GAAIN**: Multi-source Alzheimer’s data aggregation ([GAAIN](https://gaain.org))
--- **UK Biobank**: Population-wide genetic, imaging, and health records ([UK Biobank](https://www.ukbiobank.ac.uk))
--- **DPUK**: Dementia and Aging data ([DPUK](https://www.dementiasplatform.uk))
--- **PRION Registry**: Prion Diseases clinical and genetic data ([PRION Registry](https://prionregistry.org))
--- **DECIPHER**: Rare genetic disorder genomic variants ([DECIPHER](https://decipher.sanger.ac.uk))
++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]].
--### **1. Register for 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.##
++**Core Biomarker Categories**
--### **2. Download & Prepare Data**
--- Download datasets while adhering to database usage policies.
--- Ensure files meet **Neurodiagnoses format requirements**:
--  - **Tabular Data**: `.csv`, `.tsv`
--  - **Neuroimaging Data**: `.nii`, `.dcm`
--  - **Genomic Data**: `.fasta`, `.vcf`
--  - **Clinical Metadata**: `.json`, `.xml`##
++Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
--- **Mandatory Fields for 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
++|=**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
--### **3. Upload Data to Neurodiagnoses**
--**Option 1: Upload to EBRAINS Bucket**
--- Location: **EBRAINS Neurodiagnoses Bucket**
--- Ensure correct **metadata tagging** before submission.##
++**Integrating External Databases into Neurodiagnoses**
-- **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.
++To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
--### **4. Integrate Data into AI Models**
--- 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**.##
++1. (((
++**Register for Access**
--**Reference**: See `docs/data_processing.md` for detailed instructions.
++* 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**
--## **Collaboration & Partnerships**##
--# **Partnering with Data Providers**
--Neurodiagnoses seeks partnerships with data repositories to:
--- 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.
++* Download datasets while adhering to database usage policies.
++* (((
++Ensure files meet Neurodiagnoses format requirements:
--**Interested in Partnering?**
--- If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
--- **Contact**: info@neurodiagnoses.com
++|=**Data Type**|=**Accepted Formats**
++|**Tabular Data**|.csv, .tsv
++|**Neuroimaging**|.nii, .dcm
++|**Genomic Data**|.fasta, .vcf
++|**Clinical Metadata**|.json, .xml
++)))
++* (((
++**Mandatory Fields for Integration**:
--## **Final Notes**
--Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute **new datasets and methodologies**.##
++* 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**
--For additional technical documentation:
--- **GitHub Repository**: [Neurodiagnoses GitHub](https://github.com/neurodiagnoses)
--- **EBRAINS Collaboration Page**: [EBRAINS Neurodiagnoses](https://ebrains.eu/collabs/neurodiagnoses)
++* (((
++**Option 1: Upload to EBRAINS Bucket**
--If you experience issues integrating data, **open a GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
++* Location: EBRAINS Neurodiagnoses Bucket
++* Ensure correct metadata tagging before submission.
++)))
++* (((
++**Option 2: Contribute via GitHub Repository**
--== **How to Use External Databases 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.
++)))
++)))
++1. (((
++**Integrate Data into AI Models**
--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.
++* 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.
--=== **Potential Data Sources** ===
++**Reference**: See docs/data_processing.md for detailed instructions.
++)))
--Neurodiagnoses maintains an updated list of potential biomedical databases relevant to neurodegenerative diseases.
++**AI-Driven Biomarker Categorization**
--* Reference: [[List of Potential Databases>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
++Neurodiagnoses employs advanced AI models for biomarker classification:
--=== **1. Register for Access** ===
++|=**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
--Each external database requires individual registration and access approval. Follow the official guidelines of each database provider.
++=== **Jupyter Integration with EBRAINS** ===
--* 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.
++=== **Overview** ===
--=== **2. Download & Prepare Data** ===
++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**.
--Once access is granted, download datasets while complying with data usage policies. Ensure that the files meet Neurodiagnoses’ format requirements for smooth integration.
++=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
--==== **Supported File Formats** ====
++==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
--* Tabular Data: .csv, .tsv
--* Neuroimaging Data: .nii, .dcm
--* Genomic Data: .fasta, .vcf
--* Clinical Metadata: .json, .xml
++* **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.
--==== **Mandatory Fields for Integration** ====
++==== **2. EEG and MEG Signal Processing** ====
--|=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
++* **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. Upload Data to Neurodiagnoses** ===
++==== **3. Machine Learning for Biomarker Discovery** ====
--Once preprocessed, data can be uploaded to EBRAINS or GitHub.
++* **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.
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++==== **4. Computational Simulations & Virtual Brain Models** ====
--* 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**
++* **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.
--* 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.
--)))
++==== **5. Interactive Data Visualization & Reporting** ====
--//Note: For large datasets, please contact the project administrators before uploading.//
++* **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.
--=== **4. Integrate Data into AI Models** ===
++=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
--Once uploaded, datasets must be harmonized and formatted before AI model training.
++==== **1. Access EBRAINS Jupyter Environment** ====
--==== **Steps for Data Integration** ====
++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:
--* 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]].
++{{{git clone https://github.com/neurodiagnoses
++cd neurodiagnoses
++pip install -r requirements.txt
++}}}
------
++==== **2. Run Prebuilt Neurodiagnoses Notebooks** ====
--== **Database Sources Table** ==
++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.
--=== **Where to Insert This** ===
++==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
--* 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
++* Use EBRAINS **GPU and HPC clusters** for deep learning training:
--=== **Key Databases for Neurodiagnoses** ===
++{{{from neurodiagnoses.models import train_cnn_model
++train_cnn_model(data_path='data/mri/', model_type='ResNet50')
++}}}
++* Save trained models for deployment:
--|=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
++{{{model.save('models/neurodiagnoses_cnn.h5')
++}}}
--If you know a relevant dataset, submit a proposal in [[GitHub Issues>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/issues]].
++For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
------
++**Collaboration & Partnerships**
--== **Collaboration & Partnerships** ==
++Neurodiagnoses actively seeks partnerships with data providers to:
--=== **Where to Insert This** ===
--
--* GitHub: [[docs/collaboration.md>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/docs/collaboration.md]]
--* EBRAINS Wiki: Collabs/neurodiagnoses/Collaborations
--
--=== **Partnering with Data Providers** ===
--
--Beyond using existing datasets, Neurodiagnoses seeks partnerships with data repositories to:
--
--* Enable direct API-based data integration for real-time processing.
++* 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?** ===
++**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**
--== **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.
--Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
++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:
++**For additional technical documentation and collaboration opportunities:**
--* [[GitHub Repository>>url:https://github.com/Fundacion-de-Neurociencias/neurodiagnoses]]
--* [[EBRAINS Collaboration Page>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/]]
++* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
++* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
--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.
++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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