Wiki source code of Methodology

Version 21.1 by manuelmenendez on 2025/02/14 22:09

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20.1	1	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.
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20.1	5	== Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions ==
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20.1	7	=== Project Overview ===
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20.1	9	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.
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20.1	11	== Neuromarker: Generalized Biomarker Ontology ==
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20.1	13	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.
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20.1	15	=== Core Biomarker Categories ===
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20.1	17	The following ontology is used within Neurodiagnoses AI for biomarker categorization:
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20.1	19	\|=Category\|=Description
	20	\|Molecular Biomarkers\|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
	21	\|Neuroimaging Biomarkers\|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
	22	\|Fluid Biomarkers\|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
	23	\|Neurophysiological Biomarkers\|EEG, MEG, evoked potentials (ERP), sleep-related markers
	24	\|Digital Biomarkers\|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
	25	\|Clinical Phenotypic Markers\|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
	26	\|Genetic Biomarkers\|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
	27	\|Environmental & Lifestyle Factors\|Toxins, infections, diet, microbiome, comorbidities
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18.1	31	== How to Use External Databases in Neurodiagnoses ==
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20.1	33	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.
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18.1	35	=== Potential Data Sources ===
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20.1	37	Neurodiagnoses maintains an updated list of biomedical datasets relevant to neurodegenerative diseases:
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20.1	39	* ADNI: Alzheimer's Disease Imaging & Biomarkers → [[ADNI>>url:https://adni.loni.usc.edu/]]
	40	* PPMI: Parkinson’s Disease Imaging & Biospecimens → [[PPMI>>url:https://www.ppmi-info.org/]]
	41	* GP2: Whole-Genome Sequencing for PD → [[GP2>>url:https://gp2.org/]]
	42	* Enroll-HD: Huntington’s Disease Clinical & Genetic Data → [[Enroll-HD>>url:https://www.enroll-hd.org/]]
	43	* GAAIN: Multi-Source Alzheimer’s Data Aggregation → [[GAAIN>>url:https://gaain.org/]]
	44	* UK Biobank: Population-Wide Genetic, Imaging & Health Records → [[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
	45	* DPUK: Dementia & Aging Data → [[DPUK>>url:https://www.dementiasplatform.uk/]]
	46	* PRION Registry: Prion Diseases Clinical & Genetic Data → [[PRION Registry>>url:https://prionregistry.org/]]
	47	* DECIPHER: Rare Genetic Disorder Genomic Variants → [[DECIPHER>>url:https://decipher.sanger.ac.uk/]]
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20.1	51	== 1. Register for Access ==
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20.1	53	* Each external database requires individual registration and access approval.
	54	* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
	55	* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
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20.1	59	== 2. Download & Prepare Data ==
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20.1	61	* Download datasets while adhering to database usage policies.
	62	* Ensure files meet Neurodiagnoses format requirements:
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20.1	64	\|=Data Type\|=Accepted Formats
	65	\|Tabular Data\|.csv, .tsv
	66	\|Neuroimaging\|.nii, .dcm
	67	\|Genomic Data\|.fasta, .vcf
	68	\|Clinical Metadata\|.json, .xml
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20.1	70	* Mandatory Fields for Integration:
	71	Subject ID**: Unique patient identifier
	72	Diagnosis**: Standardized disease classification
	73	Biomarkers**: CSF, plasma, or imaging biomarkers
	74	Genetic Data**: Whole-genome or exome sequencing
	75	Neuroimaging Metadata**: MRI/PET acquisition parameters
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20.1	79	== 3. Upload Data to Neurodiagnoses ==
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20.1	81	=== Option 1: Upload to EBRAINS Bucket ===
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20.1	83	* Location: EBRAINS Neurodiagnoses Bucket
	84	* Ensure correct metadata tagging before submission.
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20.1	86	=== Option 2: Contribute via GitHub Repository ===
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20.1	88	* Location: GitHub Data Repository
	89	* Create a new folder under /data/ and include a dataset description.
	90	* For large datasets, contact project administrators before uploading.
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20.1	94	== 4. Integrate Data into AI Models ==
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20.1	96	* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
	97	* Standardize neuroimaging and biomarker formats using harmonization tools.
	98	* Use machine learning models to handle missing data and feature extraction.
	99	* Train AI models with newly integrated patient cohorts.
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20.1	101	Reference: See docs/data_processing.md for detailed instructions.
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20.1	105	== AI-Driven Biomarker Categorization ==
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20.1	107	Neurodiagnoses employs AI models for biomarker classification:
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20.1	109	\|=Model Type\|=Application
	110	\|Graph Neural Networks (GNNs)\|Identify shared biomarker pathways across diseases
	111	\|Contrastive Learning\|Distinguish overlapping vs. unique biomarkers
	112	\|Multimodal Transformer Models\|Integrate imaging, omics, and clinical data
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21.1	116	== [[image:workflow neurodiagnoses.png]] ==
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18.1	118	== Collaboration & Partnerships ==
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18.1	120	=== Partnering with Data Providers ===
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20.1	122	Neurodiagnoses seeks partnerships with data repositories to:
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20.1	124	* Enable API-based data integration for real-time processing.
	125	* Co-develop harmonized AI-ready datasets with standardized annotations.
	126	* Secure funding opportunities through joint grant applications.
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20.1	128	Interested in Partnering?
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20.1	130	* If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	131	* Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
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18.1	135	== Final Notes ==
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20.1	137	Neurodiagnoses continuously expands its data ecosystem to support AI-driven clinical decision-making. Researchers and institutions are encouraged to contribute new datasets and methodologies.
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20.1	139	For additional technical documentation:
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20.1	141	* GitHub Repository: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	142	* EBRAINS Collaboration Page: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
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20.1	144	If you experience issues integrating data, open a GitHub Issue or consult the EBRAINS Neurodiagnoses Forum.
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	148	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.

Wiki source code of Methodology

Neurodiagnoses