Changes for page Methodology

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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.
++Here is the updated **Methodology** section for the EBRAINS Wiki, incorporating the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)** for **biomarker classification across all neurodegenerative diseases**.
--**Neuromarker: Generalized Biomarker Ontology**
++----
--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.
++== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
--**Recommended Software**
++=== **Project Overview** ===
--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]].
++Neurodiagnoses AI implements **AI-driven diagnostic and prognostic models** for central nervous system (CNS) disorders, expanding 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**. This framework now incorporates **Neuromarker**, a **generalized biomarker ontology** that categorizes biomarkers across neurodegenerative diseases, enabling **standardized, cross-disease AI training**.
--**Core Biomarker Categories**
++== **Neuromarker: Generalized Biomarker Ontology** ==
--Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
++Neuromarker extends the **Common Alzheimer’s Disease Research Ontology (CADRO)** into a **cross-disease biomarker categorization framework** applicable to all neurodegenerative diseases (NDDs). It allows for **standardized classification, AI-based feature extraction, and multimodal integration**.
++=== **Core Biomarker Categories** ===
++
++The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
++
  |=**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
++|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
  |**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)
@@ -22,195 +22,121 @@
  |**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**
++----
--To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
++== **How to Use External Databases in Neurodiagnoses** ==
--1. (((
--**Register for Access**
++To enhance diagnostic accuracy, Neurodiagnoses AI integrates data from **multiple biomedical and neurological research databases**. Researchers can follow these steps to access, prepare, and integrate data into the Neurodiagnoses framework.
--* 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**
++=== **Potential Data Sources** ===
--* Download datasets while adhering to database usage policies.
--* (((
--Ensure files meet Neurodiagnoses format requirements:
++Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
--|=**Data Type**|=**Accepted Formats**
--|**Tabular Data**|.csv, .tsv
--|**Neuroimaging**|.nii, .dcm
--|**Genomic Data**|.fasta, .vcf
--|**Clinical Metadata**|.json, .xml
--)))
--* (((
--**Mandatory Fields for Integration**:
++* **ADNI**: Alzheimer's Disease Imaging & Biomarkers → [[ADNI>>url:https://adni.loni.usc.edu/]]
++* **PPMI**: Parkinson’s Disease Imaging & Biospecimens → [[PPMI>>url:https://www.ppmi-info.org/]]
++* **GP2**: Whole-Genome Sequencing for PD → [[GP2>>url:https://gp2.org/]]
++* **Enroll-HD**: Huntington’s Disease Clinical & Genetic Data → [[Enroll-HD>>url:https://www.enroll-hd.org/]]
++* **GAAIN**: Multi-Source Alzheimer’s Data Aggregation → [[GAAIN>>url:https://gaain.org/]]
++* **UK Biobank**: Population-Wide Genetic, Imaging & Health Records → [[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
++* **DPUK**: Dementia & Aging Data → [[DPUK>>url:https://www.dementiasplatform.uk/]]
++* **PRION Registry**: Prion Diseases Clinical & Genetic Data → [[PRION Registry>>url:https://prionregistry.org/]]
++* **DECIPHER**: Rare Genetic Disorder Genomic Variants → [[DECIPHER>>url:https://decipher.sanger.ac.uk/]]
--* 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**
++----
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++== **1. Register for Access** ==
--* Location: EBRAINS Neurodiagnoses Bucket
--* Ensure correct metadata tagging before submission.
--)))
--* (((
--**Option 2: Contribute via GitHub Repository**
++* 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.
--* 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**
++----
--* 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.
++== **2. Download & Prepare Data** ==
--**Reference**: See docs/data_processing.md for detailed instructions.
--)))
++* Download datasets while adhering to **database usage policies**.
++* Ensure files meet **Neurodiagnoses format requirements**:
--**AI-Driven Biomarker Categorization**
++|=**Data Type**|=**Accepted Formats**
++|**Tabular Data**|.csv, .tsv
++|**Neuroimaging**|.nii, .dcm
++|**Genomic Data**|.fasta, .vcf
++|**Clinical Metadata**|.json, .xml
--Neurodiagnoses employs advanced AI models for biomarker classification:
++* **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
--|=**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** ===
++== **3. Upload Data to Neurodiagnoses** ==
--=== **Overview** ===
++=== **Option 1: Upload to EBRAINS Bucket** ===
--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**.
++* Location: **EBRAINS Neurodiagnoses Bucket**
++* Ensure **correct metadata tagging** before submission.
--=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
++=== **Option 2: Contribute via GitHub Repository** ===
--==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
++* Location: **GitHub Data Repository**
++* Create a **new folder under /data/** and include a **dataset description**.
++* **For large datasets**, contact project administrators before uploading.
--* **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** ====
++== **4. Integrate Data into AI Models** ==
--* **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.
++* 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**.
--==== **3. Machine Learning for Biomarker Discovery** ====
++**Reference**: See docs/data_processing.md for detailed instructions.
--* **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.
++----
--==== **4. Computational Simulations & Virtual Brain Models** ====
++== **AI-Driven Biomarker Categorization** ==
--* **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.
++Neurodiagnoses employs **AI models** for biomarker classification:
--==== **5. Interactive Data Visualization & Reporting** ====
++|=**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
--* **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.
++----
--=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
++== **Collaboration & Partnerships** ==
--==== **1. Access EBRAINS Jupyter Environment** ====
++=== **Partnering with Data Providers** ===
--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:
++Neurodiagnoses seeks partnerships with data repositories to:
--{{{git clone https://github.com/neurodiagnoses
--cd neurodiagnoses
--pip install -r requirements.txt
--}}}
++* 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.
--==== **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.
--
--==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
--
--* Use EBRAINS **GPU and HPC clusters** for deep learning training:
--
--{{{from neurodiagnoses.models import train_cnn_model
--train_cnn_model(data_path='data/mri/', model_type='ResNet50')
--}}}
--* Save trained models for deployment:
--
--{{{model.save('models/neurodiagnoses_cnn.h5')
--}}}
--
--For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
--
--**Collaboration & Partnerships**
--
--Neurodiagnoses actively seeks partnerships with data providers 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.
--
  **Interested in Partnering?**
--If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
++* 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**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
++* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/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** 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.
++----
++
++This **updated methodology** now incorporates [[https:~~/~~/github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/biomarker_ontology>>https://Neuromarker]] for standardized biomarker classification, enabling **cross-disease AI training** across neurodegenerative disorders.

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