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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.
	1	+=== **Overview** ===
2	2
3		-**Neuromarker: Generalized Biomarker Ontology**
	3	+This section describes the step-by-step process used in the **Neurodiagnoses** project to develop a novel diagnostic framework for neurological diseases. The methodology integrates artificial intelligence (AI), biomedical ontologies, and computational neuroscience to create a structured, interpretable, and scalable diagnostic system.
4	4
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.
	5	+----
6	6
7		-**Core Biomarker Categories**
	7	+=== **1. Data Integration** ===
8	8
9		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	9	+==== **Data Sources** ====
10	10
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
	11	+* **Biomedical Ontologies**:
	12	+** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
	13	+** Gene Ontology (GO) for molecular and cellular processes.
	14	+* **Neuroimaging Datasets**:
	15	+** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
	16	+* **Clinical and Biomarker Data**:
	17	+** Anonymized clinical reports, molecular biomarkers, and test results.
20	20
21		-**Integrating External Databases into Neurodiagnoses**
	19	+==== **Data Preprocessing** ====
22	22
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:
	21	+1. **Standardization**: Ensure all data sources are normalized to a common format.
	22	+1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
	23	+1. **Data Cleaning**: Handle missing values and remove duplicates.
24	24
25		-1. (((
26		-**Register for Access**
	25	+----
27	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.
31		-)))
32		-1. (((
33		-**Download & Prepare Data**
	27	+=== **2. AI-Based Analysis** ===
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
	29	+==== **Model Development** ====
38	38
39		-|=**Data Type**|=**Accepted Formats**
40		-|**Tabular Data**|.csv, .tsv
41		-|**Neuroimaging**|.nii, .dcm
42		-|**Genomic Data**|.fasta, .vcf
43		-|**Clinical Metadata**|.json, .xml
44		-)))
45		-* (((
46		-**Mandatory Fields for Integration**:
	31	+* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
	32	+* **Classification Models**:
	33	+** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
	34	+** Purpose: Predict the likelihood of specific neurological conditions based on input data.
47	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
53		-)))
54		-)))
55		-1. (((
56		-**Upload Data to Neurodiagnoses**
	36	+==== **Dimensionality Reduction and Interpretability** ====
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	38	+* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
	39	+* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
63		-)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	41	+----
66	66
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**
	43	+=== **3. Diagnostic Framework** ===
74	74
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.
	45	+==== **Axes of Diagnosis** ====
79	79
80		-**Reference**: See docs/data_processing.md for detailed instructions.
81		-)))
	47	+The framework organizes diagnostic data into three axes:
82	82
83		-**AI-Driven Biomarker Categorization**
	49	+1. **Etiology**: Genetic and environmental risk factors.
	50	+1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
	51	+1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
	53	+==== **Recommendation System** ====
86	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
	55	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	56	+* Prioritizes tests based on clinical impact and cost-effectiveness.
91	91
92		-**Collaboration & Partnerships**
	58	+----
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	60	+=== **4. Computational Workflow** ===
95	95
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.
	62	+1. **Data Loading**: Import data from storage (Drive or Bucket).
	63	+1. **Feature Engineering**: Generate derived features from the raw data.
	64	+1. **Model Training**:
	65	+1*. Split data into training, validation, and test sets.
	66	+1*. Train models with cross-validation to ensure robustness.
	67	+1. **Evaluation**:
	68	+1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
	69	+1*. Compare against baseline models and domain benchmarks.
99	99
100		-**Interested in Partnering?**
	71	+----
101	101
102		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	73	+=== **5. Validation** ===
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	75	+==== **Internal Validation** ====
105	105
106		-**Final Notes**
	77	+* Test the system using simulated datasets and known clinical cases.
	78	+* Fine-tune models based on validation results.
107	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.
	80	+==== **External Validation** ====
109	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.
	82	+* Collaborate with research institutions and hospitals to test the system in real-world settings.
	83	+* Use anonymized patient data to ensure privacy compliance.
111	111
112		-**For additional technical documentation and collaboration opportunities:**
	85	+----
113	113
114		-* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
115		-* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	87	+=== **6. Collaborative Development** ===
116	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.
	89	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	90	+
	91	+* **Jupyter Notebooks**: For data analysis and pipeline development.
	92	+** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
	93	+* **Wiki Pages**: For documenting methods and results.
	94	+* **Drive and Bucket**: For sharing code, data, and outputs.
	95	+
	96	+----
	97	+
	98	+=== **7. Tools and Technologies** ===
	99	+
	100	+* **Programming Languages**: Python for AI and data processing.
	101	+* **Frameworks**:
	102	+** TensorFlow and PyTorch for machine learning.
	103	+** Flask or FastAPI for backend services.
	104	+* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
	105	+* **EBRAINS Services**:
	106	+** Collaboratory Lab for running Notebooks.
	107	+** Buckets for storing large datasets.

workflow neurodiagnoses.png

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