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1		-==== **Overview** ====
	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.
2	2
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**.
	3	+**Neuromarker: Generalized Biomarker Ontology**
4	4
5		-----
	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	6
7		-=== **1. Data Integration** ===
	7	+**Core Biomarker Categories**
8	8
9		-==== **Data Sources** ====
	9	+Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
10	10
11		-**Biomedical Ontologies & Databases:**
	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
	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
12	12
13		-* **Human Phenotype Ontology (HPO)** for symptom annotation.
14		-* **Gene Ontology (GO)** for molecular and cellular processes.
	21	+**Integrating External Databases into Neurodiagnoses**
15	15
16		-**Dimensionality Reduction and Interpretability:**
	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:
17	17
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.
	25	+1. (((
	26	+**Register for Access**
20	20
21		-**Neuroimaging & EEG/MEG Data:**
	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**
22	22
23		-* **MRI volumetric measures** for brain atrophy tracking.
24		-* **EEG functional connectivity patterns** (AI-Mind).
	35	+* Download datasets while adhering to database usage policies.
	36	+* (((
	37	+Ensure files meet Neurodiagnoses format requirements:
25	25
26		-**Clinical & Biomarker Data:**
	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**:
27	27
28		-* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
29		-* **Sleep monitoring and actigraphy data** (ADIS).
	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**
30	30
31		-**Federated Learning Integration:**
	58	+* (((
	59	+**Option 1: Upload to EBRAINS Bucket**
32	32
33		-* **Secure multi-center data harmonization** (PROMINENT).
	61	+* Location: EBRAINS Neurodiagnoses Bucket
	62	+* Ensure correct metadata tagging before submission.
	63	+)))
	64	+* (((
	65	+**Option 2: Contribute via GitHub Repository**
34	34
35		-----
	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**
36	36
37		-==== **Annotation System for Multi-Modal Data** ====
	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.
38	38
39		-To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	80	+**Reference**: See docs/data_processing.md for detailed instructions.
	81	+)))
40	40
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.
	83	+**AI-Driven Biomarker Categorization**
44	44
45		-----
	85	+Neurodiagnoses employs advanced AI models for biomarker classification:
46	46
47		-=== **2. AI-Based Analysis** ===
	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
48	48
49		-==== **Machine Learning & Deep Learning Models** ====
	92	+**Collaboration & Partnerships**
50	50
51		-**Risk Prediction Models:**
	94	+Neurodiagnoses actively seeks partnerships with data providers to:
52	52
53		-* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	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.
54	54
55		-**Biomarker Classification & Probabilistic Imputation:**
	100	+**Interested in Partnering?**
56	56
57		-* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
	102	+If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
58	58
59		-**Neuroimaging Feature Extraction:**
	104	+**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
60	60
61		-* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
62		-
63		-==== **AI-Powered Annotation System** ====
64		-
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**).
68		-
69		-----
70		-
71		-=== **3. Diagnostic Framework & Clinical Decision Support** ===
72		-
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.**
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workflow neurodiagnoses.png

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