Wiki source code of Methodology

Version 23.1 by manuelmenendez on 2025/02/15 12:55

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22.1	1	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.
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22.1	3	Neuromarker: Generalized Biomarker Ontology
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22.1	5	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.
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22.1	7	Core Biomarker Categories
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22.1	9	Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
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20.1	11	\|=Category\|=Description
	12	\|Molecular Biomarkers\|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
	13	\|Neuroimaging Biomarkers\|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
23.1	14	\|Fluid Biomarkers\|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
20.1	15	\|Neurophysiological Biomarkers\|EEG, MEG, evoked potentials (ERP), sleep-related markers
	16	\|Digital Biomarkers\|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
	17	\|Clinical Phenotypic Markers\|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
	18	\|Genetic Biomarkers\|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
	19	\|Environmental & Lifestyle Factors\|Toxins, infections, diet, microbiome, comorbidities
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22.1	21	Integrating External Databases into Neurodiagnoses
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22.1	23	To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
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22.1	25	1. (((
	26	Register for Access
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22.1	28	* Each external database requires individual registration and access approval.
	29	* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
	30	* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
	31	)))
	32	1. (((
	33	Download & Prepare Data
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22.1	35	* Download datasets while adhering to database usage policies.
	36	* (((
	37	Ensure files meet Neurodiagnoses format requirements:
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20.1	39	\|=Data Type\|=Accepted Formats
	40	\|Tabular Data\|.csv, .tsv
	41	\|Neuroimaging\|.nii, .dcm
	42	\|Genomic Data\|.fasta, .vcf
	43	\|Clinical Metadata\|.json, .xml
22.1	44	)))
	45	* (((
	46	Mandatory Fields for Integration:
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22.1	48	* Subject ID: Unique patient identifier
	49	* Diagnosis: Standardized disease classification
	50	* Biomarkers: CSF, plasma, or imaging biomarkers
	51	* Genetic Data: Whole-genome or exome sequencing
	52	* Neuroimaging Metadata: MRI/PET acquisition parameters
	53	)))
	54	)))
	55	1. (((
	56	Upload Data to Neurodiagnoses
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22.1	58	* (((
	59	Option 1: Upload to EBRAINS Bucket
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22.1	61	* Location: EBRAINS Neurodiagnoses Bucket
	62	* Ensure correct metadata tagging before submission.
	63	)))
	64	* (((
	65	Option 2: Contribute via GitHub Repository
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22.1	67	* Location: GitHub Data Repository
	68	* Create a new folder under /data/ and include a dataset description.
	69	* For large datasets, contact project administrators before uploading.
	70	)))
	71	)))
	72	1. (((
	73	Integrate Data into AI Models
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22.1	75	* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
	76	* Standardize neuroimaging and biomarker formats using harmonization tools.
	77	* Utilize machine learning models to handle missing data and feature extraction.
	78	* Train AI models with newly integrated patient cohorts.
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20.1	80	Reference: See docs/data_processing.md for detailed instructions.
22.1	81	)))
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22.1	83	AI-Driven Biomarker Categorization
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22.1	85	Neurodiagnoses employs advanced AI models for biomarker classification:
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20.1	87	\|=Model Type\|=Application
	88	\|Graph Neural Networks (GNNs)\|Identify shared biomarker pathways across diseases
	89	\|Contrastive Learning\|Distinguish overlapping vs. unique biomarkers
	90	\|Multimodal Transformer Models\|Integrate imaging, omics, and clinical data
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22.1	92	Collaboration & Partnerships
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22.1	94	Neurodiagnoses actively seeks partnerships with data providers to:
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22.1	96	* Enable API-based data integration for real-time processing.
	97	* Co-develop harmonized AI-ready datasets with standardized annotations.
	98	* Secure funding opportunities through joint grant applications.
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20.1	100	Interested in Partnering?
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22.1	102	If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
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22.1	104	Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
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Wiki source code of Methodology

Neurodiagnoses