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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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3 **Neuromarker: Generalized Biomarker Ontology**
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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.
6
7 **Core Biomarker Categories**
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9 Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
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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)
14 |**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
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
20
21 **Integrating External Databases into Neurodiagnoses**
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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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25 1. (((
26 **Register for Access**
27
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.
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32 1. (((
33 **Download & Prepare Data**
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35 * Download datasets while adhering to database usage policies.
36 * (((
37 Ensure files meet Neurodiagnoses format requirements:
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39 |=**Data Type**|=**Accepted Formats**
40 |**Tabular Data**|.csv, .tsv
41 |**Neuroimaging**|.nii, .dcm
42 |**Genomic Data**|.fasta, .vcf
43 |**Clinical Metadata**|.json, .xml
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45 * (((
46 **Mandatory Fields for Integration**:
47
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
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54 )))
55 1. (((
56 **Upload Data to Neurodiagnoses**
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58 * (((
59 **Option 1: Upload to EBRAINS Bucket**
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61 * Location: EBRAINS Neurodiagnoses Bucket
62 * Ensure correct metadata tagging before submission.
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64 * (((
65 **Option 2: Contribute via GitHub Repository**
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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.
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71 )))
72 1. (((
73 **Integrate Data into AI Models**
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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.
79
80 **Reference**: See docs/data_processing.md for detailed instructions.
81 )))
82
83 **AI-Driven Biomarker Categorization**
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85 Neurodiagnoses employs advanced AI models for biomarker classification:
86
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
91
92 **Collaboration & Partnerships**
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94 Neurodiagnoses actively seeks partnerships with data providers to:
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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.
99
100 **Interested in Partnering?**
101
102 If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
103
104 **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
105
106 **Final Notes**
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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.
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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.