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1		-Here is the updated **Methodology** section for the EBRAINS Wiki, incorporating the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)** for **biomarker classification across all neurodegenerative diseases**.
	1	+==== **Overview** ====
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**.
	4	+
3	3	----
4	4
5		-== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
	7	+=== **1. Data Integration** ===
6	6
7		-=== **Project Overview** ===
	9	+==== **Data Sources** ====
8	8
9		-Neurodiagnoses AI implements **AI-driven diagnostic and prognostic models** for central nervous system (CNS) disorders, expanding the **Florey Dementia Index (FDI) methodology** to a broader set of neurological conditions. The approach integrates **multimodal data sources** (EEG, neuroimaging, biomarkers, and genetics) and employs machine learning models to provide **explainable, real-time diagnostic insights**. This framework now incorporates **Neuromarker**, a **generalized biomarker ontology** that categorizes biomarkers across neurodegenerative diseases, enabling **standardized, cross-disease AI training**.
	11	+**Biomedical Ontologies & Databases:**
10	10
11		-== **Neuromarker: Generalized Biomarker Ontology** ==
	13	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	14	+* **Gene Ontology (GO)** for molecular and cellular processes.
12	12
13		-Neuromarker extends the **Common Alzheimer’s Disease Research Ontology (CADRO)** into a **cross-disease biomarker categorization framework** applicable to all neurodegenerative diseases (NDDs). It allows for **standardized classification, AI-based feature extraction, and multimodal integration**.
	16	+**Dimensionality Reduction and Interpretability:**
14	14
15		-=== **Core Biomarker Categories** ===
	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.
16	16
17		-The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
	21	+**Neuroimaging & EEG/MEG Data:**
18	18
19		-|=**Category**|=**Description**
20		-|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
21		-|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
22		-|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
23		-|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
24		-|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
25		-|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
26		-|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
27		-|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
	23	+* **MRI volumetric measures** for brain atrophy tracking.
	24	+* **EEG functional connectivity patterns** (AI-Mind).
28	28
29		-----
	26	+**Clinical & Biomarker Data:**
30	30
31		-== **How to Use External Databases in Neurodiagnoses** ==
	28	+* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	29	+* **Sleep monitoring and actigraphy data** (ADIS).
32	32
33		-To enhance diagnostic accuracy, Neurodiagnoses AI integrates data from **multiple biomedical and neurological research databases**. Researchers can follow these steps to access, prepare, and integrate data into the Neurodiagnoses framework.
	31	+**Federated Learning Integration:**
34	34
35		-=== **Potential Data Sources** ===
	33	+* **Secure multi-center data harmonization** (PROMINENT).
36	36
37		-Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
	35	+----
38	38
39		-* **ADNI**: Alzheimer's Disease Imaging & Biomarkers → [[ADNI>>url:https://adni.loni.usc.edu/]]
40		-* **PPMI**: Parkinson’s Disease Imaging & Biospecimens → [[PPMI>>url:https://www.ppmi-info.org/]]
41		-* **GP2**: Whole-Genome Sequencing for PD → [[GP2>>url:https://gp2.org/]]
42		-* **Enroll-HD**: Huntington’s Disease Clinical & Genetic Data → [[Enroll-HD>>url:https://www.enroll-hd.org/]]
43		-* **GAAIN**: Multi-Source Alzheimer’s Data Aggregation → [[GAAIN>>url:https://gaain.org/]]
44		-* **UK Biobank**: Population-Wide Genetic, Imaging & Health Records → [[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
45		-* **DPUK**: Dementia & Aging Data → [[DPUK>>url:https://www.dementiasplatform.uk/]]
46		-* **PRION Registry**: Prion Diseases Clinical & Genetic Data → [[PRION Registry>>url:https://prionregistry.org/]]
47		-* **DECIPHER**: Rare Genetic Disorder Genomic Variants → [[DECIPHER>>url:https://decipher.sanger.ac.uk/]]
	37	+==== **Annotation System for Multi-Modal Data** ====
48	48
	39	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	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.
	44	+
49	49	----
50	50
51		-== **1. Register for Access** ==
	47	+=== **2. AI-Based Analysis** ===
52	52
53		-* Each external database requires **individual registration and access approval**.
54		-* Ensure compliance with **ethical approvals and data usage agreements** before integrating datasets into Neurodiagnoses.
55		-* Some repositories may require a **Data Usage Agreement (DUA)** for sensitive medical data.
	49	+==== **Machine Learning & Deep Learning Models** ====
56	56
57		-----
	51	+**Risk Prediction Models:**
58	58
59		-== **2. Download & Prepare Data** ==
	53	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
60	60
61		-* Download datasets while adhering to **database usage policies**.
62		-* Ensure files meet **Neurodiagnoses format requirements**:
	55	+**Biomarker Classification & Probabilistic Imputation:**
63	63
64		-|=**Data Type**|=**Accepted Formats**
65		-|**Tabular Data**|.csv, .tsv
66		-|**Neuroimaging**|.nii, .dcm
67		-|**Genomic Data**|.fasta, .vcf
68		-|**Clinical Metadata**|.json, .xml
	57	+* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
69	69
70		-* **Mandatory Fields for Integration**:
71		-** **Subject ID**: Unique patient identifier
72		-** **Diagnosis**: Standardized disease classification
73		-** **Biomarkers**: CSF, plasma, or imaging biomarkers
74		-** **Genetic Data**: Whole-genome or exome sequencing
75		-** **Neuroimaging Metadata**: MRI/PET acquisition parameters
	59	+**Neuroimaging Feature Extraction:**
76	76
	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	+
77	77	----
78	78
79		-== **3. Upload Data to Neurodiagnoses** ==
	71	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
80	80
81		-=== **Option 1: Upload to EBRAINS Bucket** ===
	73	+==== **Tridimensional Diagnostic Axes** ====
82	82
83		-* Location: **EBRAINS Neurodiagnoses Bucket**
84		-* Ensure **correct metadata tagging** before submission.
	75	+**Axis 1: Etiology (Pathogenic Mechanisms)**
85	85
86		-=== **Option 2: Contribute via GitHub Repository** ===
	77	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	78	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
87	87
88		-* Location: **GitHub Data Repository**
89		-* Create a **new folder under /data/** and include a **dataset description**.
90		-* **For large datasets**, contact project administrators before uploading.
	80	+**Axis 2: Molecular Markers & Biomarkers**
91	91
	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	+
92	92	----
93	93
94		-== **4. Integrate Data into AI Models** ==
	92	+=== **4. Computational Workflow & Annotation Pipelines** ===
95	95
96		-* Open **Jupyter Notebooks** on EBRAINS to run **preprocessing scripts**.
97		-* **Standardize neuroimaging and biomarker formats** using harmonization tools.
98		-* Use **machine learning models** to handle **missing data** and **feature extraction**.
99		-* Train AI models with **newly integrated patient cohorts**.
	94	+==== **Data Processing Steps** ====
100	100
101		-**Reference**: See docs/data_processing.md for detailed instructions.
	96	+**Data Ingestion:**
102	102
103		-----
	98	+* **Harmonized datasets** stored in **EBRAINS Bucket**.
	99	+* **Preprocessing pipelines** clean and standardize data.
104	104
105		-== **AI-Driven Biomarker Categorization** ==
	101	+**Feature Engineering:**
106	106
107		-Neurodiagnoses employs **AI models** for biomarker classification:
	103	+* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
108	108
109		-|=**Model Type**|=**Application**
110		-|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
111		-|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
112		-|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
	105	+**AI-Generated Annotations:**
113	113
	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	+
114	114	----
115	115
116		-== **Collaboration & Partnerships** ==
	117	+=== **5. Validation & Real-World Testing** ===
117	117
118		-=== **Partnering with Data Providers** ===
	119	+==== **Prospective Clinical Study** ====
119	119
120		-Neurodiagnoses seeks partnerships with data repositories to:
	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**.
121	121
122		-* Enable **API-based data integration** for real-time processing.
123		-* Co-develop **harmonized AI-ready datasets** with standardized annotations.
124		-* Secure **funding opportunities** through joint grant applications.
	125	+==== **Quality Assurance & Explainability** ====
125	125
126		-**Interested in Partnering?**
	127	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	128	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
127	127
128		-* If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
129		-* **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	130	+----
130	130
	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	+
131	131	----
132	132
133		-== **Final Notes** ==
	147	+=== **7. Tools and Technologies** ===
134	134
135		-Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
	149	+==== **Programming Languages:** ====
136	136
137		-**For additional technical documentation**:
	151	+* **Python** for AI and data processing.
138	138
139		-* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
140		-* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	153	+==== **Frameworks:** ====
141	141
142		-**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
	155	+* **TensorFlow** and **PyTorch** for machine learning.
	156	+* **Flask** or **FastAPI** for backend services.
143	143
	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	+
144	144	----
145	145
146		-This **updated methodology** now incorporates [[https:~~/~~/github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/biomarker_ontology>>https://Neuromarker]] for standardized biomarker classification, enabling **cross-disease AI training** across neurodegenerative disorders.
	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.**