Wiki source code of Methodology

Version 24.1 by manuelmenendez on 2025/02/15 12:57

version	line-number	content
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.
1.1	2
22.1	3	Neuromarker: Generalized Biomarker Ontology
1.1	4
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.
1.1	6
22.1	7	Core Biomarker Categories
19.1	8
22.1	9	Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
19.1	10
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
19.1	20
22.1	21	Integrating External Databases into Neurodiagnoses
19.1	22
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:
1.1	24
22.1	25	1. (((
	26	Register for Access
12.2	27
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
12.2	34
22.1	35	* Download datasets while adhering to database usage policies.
	36	* (((
	37	Ensure files meet Neurodiagnoses format requirements:
12.2	38
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:
12.2	47
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
12.2	57
22.1	58	* (((
	59	Option 1: Upload to EBRAINS Bucket
12.2	60
22.1	61	* Location: EBRAINS Neurodiagnoses Bucket
	62	* Ensure correct metadata tagging before submission.
	63	)))
	64	* (((
	65	Option 2: Contribute via GitHub Repository
12.2	66
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
12.2	74
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.
12.2	79
20.1	80	Reference: See docs/data_processing.md for detailed instructions.
22.1	81	)))
12.2	82
22.1	83	AI-Driven Biomarker Categorization
12.2	84
22.1	85	Neurodiagnoses employs advanced AI models for biomarker classification:
12.2	86
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
1.1	91
22.1	92	Collaboration & Partnerships
1.1	93
22.1	94	Neurodiagnoses actively seeks partnerships with data providers to:
21.1	95
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.
6.1	99
20.1	100	Interested in Partnering?
1.1	101
22.1	102	If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
1.1	103
22.1	104	Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
1.1	105
24.1	106	Final Notes
	107
	108	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.
	109
	110	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.
	111
	112	For additional technical documentation and collaboration opportunities:
	113
	114	* GitHub Repository: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	115	* EBRAINS Collaboration Page: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	116
	117	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.

Wiki source code of Methodology

Neurodiagnoses