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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 project develops a **tridimensional diagnostic framework** for **CNS diseases**, incorporating **AI-powered annotation tools** to improve **interpretability, standardization, and clinical utility**. The methodology integrates **multi-modal data**, including **genetic, neuroimaging, neurophysiological, and biomarker datasets**, and applies **machine learning models** to generate **structured, explainable diagnostic outputs**.
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 & Databases:**
20	20
21		-**Integrating External Databases into Neurodiagnoses**
	13	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	14	+* **Gene Ontology (GO)** for molecular and cellular processes.
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:
	16	+**Dimensionality Reduction and Interpretability:**
24	24
25		-1. (((
26		-**Register for Access**
	18	+* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
	19	+* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
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**
	21	+**Neuroimaging & EEG/MEG Data:**
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
	23	+* **MRI volumetric measures** for brain atrophy tracking.
	24	+* **EEG functional connectivity patterns** (AI-Mind).
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**:
	26	+**Clinical & Biomarker 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**
	28	+* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	29	+* **Sleep monitoring and actigraphy data** (ADIS).
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	31	+**Federated Learning Integration:**
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
63		-)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	33	+* **Secure multi-center data harmonization** (PROMINENT).
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**
	35	+----
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.
	37	+==== **Annotation System for Multi-Modal Data** ====
79	79
80		-**Reference**: See docs/data_processing.md for detailed instructions.
81		-)))
	39	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
82	82
83		-**AI-Driven Biomarker Categorization**
	41	+* **Assign standardized metadata tags** to diagnostic features.
	42	+* **Provide contextual explanations** for AI-based classifications.
	43	+* **Track temporal disease progression annotations** to identify long-term trends.
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
	45	+----
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
	47	+=== **2. AI-Based Analysis** ===
91	91
92		-**Collaboration & Partnerships**
	49	+==== **Machine Learning & Deep Learning Models** ====
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	51	+**Risk Prediction Models:**
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.
	53	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
99	99
100		-**Interested in Partnering?**
	55	+**Biomarker Classification & Probabilistic Imputation:**
101	101
102		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	57	+* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	59	+**Neuroimaging Feature Extraction:**
105	105
106		-**Final Notes**
	61	+* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
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.
	63	+==== **AI-Powered Annotation System** ====
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.
	65	+* Uses **SHAP-based interpretability tools** to explain model decisions.
	66	+* Generates **automated clinical annotations** in structured reports.
	67	+* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
111	111
112		-**For additional technical documentation and collaboration opportunities:**
	69	+----
113	113
114		-* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
115		-* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	71	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
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.
	73	+==== **Tridimensional Diagnostic Axes** ====
	74	+
	75	+**Axis 1: Etiology (Pathogenic Mechanisms)**
	76	+
	77	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	78	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	79	+
	80	+**Axis 2: Molecular Markers & Biomarkers**
	81	+
	82	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	83	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	84	+
	85	+**Axis 3: Neuroanatomoclinical Correlations**
	86	+
	87	+* **MRI and EEG data** provide anatomical and functional insights.
	88	+* **AI-generated progression maps** annotate **brain structure-function relationships**.
	89	+
	90	+----
	91	+
	92	+=== **4. Computational Workflow & Annotation Pipelines** ===
	93	+
	94	+==== **Data Processing Steps** ====
	95	+
	96	+**Data Ingestion:**
	97	+
	98	+* **Harmonized datasets** stored in **EBRAINS Bucket**.
	99	+* **Preprocessing pipelines** clean and standardize data.
	100	+
	101	+**Feature Engineering:**
	102	+
	103	+* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	104	+
	105	+**AI-Generated Annotations:**
	106	+
	107	+* **Automated tagging** of diagnostic features in **structured reports**.
	108	+* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
	109	+
	110	+**Clinical Decision Support Integration:**
	111	+
	112	+* **AI-annotated findings** fed into **interactive dashboards**.
	113	+* **Clinicians can adjust, validate, and modify annotations**.
	114	+
	115	+----
	116	+
	117	+=== **5. Validation & Real-World Testing** ===
	118	+
	119	+==== **Prospective Clinical Study** ====
	120	+
	121	+* **Multi-center validation** of AI-based **annotations & risk stratifications**.
	122	+* **Benchmarking against clinician-based diagnoses**.
	123	+* **Real-world testing** of AI-powered **structured reporting**.
	124	+
	125	+==== **Quality Assurance & Explainability** ====
	126	+
	127	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	128	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
	129	+
	130	+----
	131	+
	132	+=== **6. Collaborative Development** ===
	133	+
	134	+The project is **open to contributions** from **researchers, clinicians, and developers**.
	135	+
	136	+**Key tools include:**
	137	+
	138	+* **Jupyter Notebooks**: For data analysis and pipeline development.
	139	+** Example: **probabilistic imputation**
	140	+* **Wiki Pages**: For documenting methods and results.
	141	+* **Drive and Bucket**: For sharing code, data, and outputs.
	142	+* **Collaboration with related projects**:
	143	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
	144	+
	145	+----
	146	+
	147	+=== **7. Tools and Technologies** ===
	148	+
	149	+==== **Programming Languages:** ====
	150	+
	151	+* **Python** for AI and data processing.
	152	+
	153	+==== **Frameworks:** ====
	154	+
	155	+* **TensorFlow** and **PyTorch** for machine learning.
	156	+* **Flask** or **FastAPI** for backend services.
	157	+
	158	+==== **Visualization:** ====
	159	+
	160	+* **Plotly** and **Matplotlib** for interactive and static visualizations.
	161	+
	162	+==== **EBRAINS Services:** ====
	163	+
	164	+* **Collaboratory Lab** for running Notebooks.
	165	+* **Buckets** for storing large datasets.
	166	+
	167	+----
	168	+
	169	+=== **Why This Matters** ===
	170	+
	171	+* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
	172	+* **It enables real-time tracking of disease progression across the three diagnostic axes.**
	173	+* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**

workflow neurodiagnoses.png

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