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1		-==== **Overview** ====
	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**.
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		-
5		-=== **Workflow** ===
6		-
7		-1. (((
8		-**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
9		-
10		-* Create a **GitHub repository** for AI scripts and models.
11		-* Use **GitHub Projects** to manage research milestones.
12		-)))
13		-1. (((
14		-**We Use EBRAINS for Data & Collaboration**
15		-
16		-* Store **biomarker and neuroimaging data** in **EBRAINS Buckets**.
17		-* Run **Jupyter Notebooks** in **EBRAINS Lab** to test AI models.
18		-* Use **EBRAINS Wiki** for structured documentation and research discussion.
19		-)))
20		-
21	21	----
22	22
23		-=== **1. Data Integration** ===
	5	+== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
24	24
25		-=== **EBRAINS Medical Informatics Platform (MIP)**. ===
	7	+=== **Project Overview** ===
26	26
27		-Neurodiagnoses integrates clinical data via the **EBRAINS Medical Informatics Platform (MIP)**. MIP federates decentralized clinical data, allowing Neurodiagnoses to securely access and process sensitive information for AI-based diagnostics.
	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**.
28	28
29		-==== How It Works ====
	11	+== **Neuromarker: Generalized Biomarker Ontology** ==
30	30
	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**.
31	31
32		-1. (((
33		-**Authentication & API Access:**
	15	+=== **Core Biomarker Categories** ===
34	34
35		-* Users must have an **EBRAINS account**.
36		-* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
37		-)))
38		-1. (((
39		-**Data Mapping & Harmonization:**
	17	+The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
40	40
41		-* Retrieved data is **normalized** and converted to standard formats (.csv, .json).
42		-* Data from **multiple sources** is harmonized to ensure consistency for AI processing.
43		-)))
44		-1. (((
45		-**Security & Compliance:**
	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
46	46
47		-* All data access is **logged and monitored**.
48		-* Data remains on **MIP servers** using **federated learning techniques** when possible.
49		-* Access is granted only after signing a **Data Usage Agreement (DUA)**.
50		-)))
51		-
52		-==== Implementation Steps ====
53		-
54		-
55		-1. Clone the repository.
56		-1. Configure your **EBRAINS API credentials** in mip_integration.py.
57		-1. Run the script to **download and harmonize clinical data**.
58		-1. Process the data for **AI model training**.
59		-
60		-For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
61		-
62	62	----
63	63
64		-=== Data Processing & Integration with Clinica.Run ===
	31	+== **How to Use External Databases in Neurodiagnoses** ==
65	65
66		-Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
	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.
67	67
68		-==== How It Works ====
	35	+=== **Potential Data Sources** ===
69	69
	37	+Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
70	70
71		-1. (((
72		-**Neuroimaging Preprocessing:**
	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/]]
73	73
74		-* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
75		-* Supports **longitudinal and cross-sectional analyses**.
76		-)))
77		-1. (((
78		-**Automated Biomarker Extraction:**
79		-
80		-* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
81		-* Integration with machine learning models in Neurodiagnoses.
82		-)))
83		-1. (((
84		-**Data Security & Compliance:**
85		-
86		-* Clinica.Run operates in **compliance with GDPR and HIPAA**.
87		-* Neuroimaging data remains **within the original storage environment**.
88		-)))
89		-
90		-==== Implementation Steps ====
91		-
92		-
93		-1. Install **Clinica.Run** dependencies.
94		-1. Configure your **Clinica.Run pipeline** in clinica_run_config.json.
95		-1. Run the pipeline for **preprocessing and biomarker extraction**.
96		-1. Use processed neuroimaging data for **AI-driven diagnostics** in Neurodiagnoses.
97		-
98		-For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
99		-
100		-==== ====
101		-
102		-==== **Data Sources** ====
103		-
104		-[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
105		-
106		-**Biomedical Ontologies & Databases:**
107		-
108		-* **Human Phenotype Ontology (HPO)** for symptom annotation.
109		-* **Gene Ontology (GO)** for molecular and cellular processes.
110		-
111		-**Dimensionality Reduction and Interpretability:**
112		-
113		-* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
114		-* **Leverage [[DEIBO>>https://github.com/Mellandd/DEIBO]] (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
115		-
116		-**Neuroimaging & EEG/MEG Data:**
117		-
118		-* **MRI volumetric measures** for brain atrophy tracking.
119		-* **EEG functional connectivity patterns** (AI-Mind).
120		-
121		-**Clinical & Biomarker Data:**
122		-
123		-* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
124		-* **Sleep monitoring and actigraphy data** (ADIS).
125		-
126		-**Federated Learning Integration:**
127		-
128		-* **Secure multi-center data harmonization** (PROMINENT).
129		-
130	130	----
131	131
132		-==== **Annotation System for Multi-Modal Data** ====
	51	+== **1. Register for Access** ==
133	133
134		-To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	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.
135	135
136		-* **Assign standardized metadata tags** to diagnostic features.
137		-* **Provide contextual explanations** for AI-based classifications.
138		-* **Track temporal disease progression annotations** to identify long-term trends.
139		-
140	140	----
141	141
142		-=== **2. AI-Based Analysis** ===
	59	+== **2. Download & Prepare Data** ==
143	143
144		-==== **Machine Learning & Deep Learning Models** ====
	61	+* Download datasets while adhering to **database usage policies**.
	62	+* Ensure files meet **Neurodiagnoses format requirements**:
145	145
146		-**Risk Prediction Models:**
	64	+|=**Data Type**|=**Accepted Formats**
	65	+|**Tabular Data**|.csv, .tsv
	66	+|**Neuroimaging**|.nii, .dcm
	67	+|**Genomic Data**|.fasta, .vcf
	68	+|**Clinical Metadata**|.json, .xml
147	147
148		-* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	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
149	149
150		-**Biomarker Classification & Probabilistic Imputation:**
	77	+----
151	151
152		-* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
	79	+== **3. Upload Data to Neurodiagnoses** ==
153	153
154		-**Neuroimaging Feature Extraction:**
	81	+=== **Option 1: Upload to EBRAINS Bucket** ===
155	155
156		-* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
	83	+* Location: **EBRAINS Neurodiagnoses Bucket**
	84	+* Ensure **correct metadata tagging** before submission.
157	157
158		-==== **AI-Powered Annotation System** ====
	86	+=== **Option 2: Contribute via GitHub Repository** ===
159	159
160		-* Uses **SHAP-based interpretability tools** to explain model decisions.
161		-* Generates **automated clinical annotations** in structured reports.
162		-* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
	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.
163	163
164	164	----
165	165
166		-=== **3. Diagnostic Framework & Clinical Decision Support** ===
	94	+== **4. Integrate Data into AI Models** ==
167	167
168		-==== **Tridimensional Diagnostic Axes** ====
	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**.
169	169
170		-**Axis 1: Etiology (Pathogenic Mechanisms)**
	101	+**Reference**: See docs/data_processing.md for detailed instructions.
171	171
172		-* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
173		-* **AI-assisted annotation** provides **causal interpretations** for clinical use.
174		-
175		-**Axis 2: Molecular Markers & Biomarkers**
176		-
177		-* **Integration of CSF, blood, and neuroimaging biomarkers**.
178		-* **Structured annotation** highlights **biological pathways linked to diagnosis**.
179		-
180		-**Axis 3: Neuroanatomoclinical Correlations**
181		-
182		-* **MRI and EEG data** provide anatomical and functional insights.
183		-* **AI-generated progression maps** annotate **brain structure-function relationships**.
184		-
185	185	----
186	186
187		-=== **4. Computational Workflow & Annotation Pipelines** ===
	105	+== **AI-Driven Biomarker Categorization** ==
188	188
189		-==== **Data Processing Steps** ====
	107	+Neurodiagnoses employs **AI models** for biomarker classification:
190	190
191		-**Data Ingestion:**
	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
192	192
193		-* **Harmonized datasets** stored in **EBRAINS Bucket**.
194		-* **Preprocessing pipelines** clean and standardize data.
195		-
196		-**Feature Engineering:**
197		-
198		-* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
199		-
200		-**AI-Generated Annotations:**
201		-
202		-* **Automated tagging** of diagnostic features in **structured reports**.
203		-* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
204		-
205		-**Clinical Decision Support Integration:**
206		-
207		-* **AI-annotated findings** fed into **interactive dashboards**.
208		-* **Clinicians can adjust, validate, and modify annotations**.
209		-
210	210	----
211	211
212		-=== **5. Validation & Real-World Testing** ===
	116	+== [[image:workflow neurodiagnoses.png]] ==
213	213
214		-==== **Prospective Clinical Study** ====
	118	+== **Collaboration & Partnerships** ==
215	215
216		-* **Multi-center validation** of AI-based **annotations & risk stratifications**.
217		-* **Benchmarking against clinician-based diagnoses**.
218		-* **Real-world testing** of AI-powered **structured reporting**.
	120	+=== **Partnering with Data Providers** ===
219	219
220		-==== **Quality Assurance & Explainability** ====
	122	+Neurodiagnoses seeks partnerships with data repositories to:
221	221
222		-* **Annotations linked to structured knowledge graphs** for improved transparency.
223		-* **Interactive annotation editor** allows clinicians to validate AI outputs.
	124	+* Enable **API-based data integration** for real-time processing.
	125	+* Co-develop **harmonized AI-ready datasets** with standardized annotations.
	126	+* Secure **funding opportunities** through joint grant applications.
224	224
225		-----
	128	+**Interested in Partnering?**
226	226
227		-=== **6. Collaborative Development** ===
	130	+* If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
	131	+* **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
228	228
229		-The project is **open to contributions** from **researchers, clinicians, and developers**.
230		-
231		-**Key tools include:**
232		-
233		-* **Jupyter Notebooks**: For data analysis and pipeline development.
234		-** Example: **probabilistic imputation**
235		-* **Wiki Pages**: For documenting methods and results.
236		-* **Drive and Bucket**: For sharing code, data, and outputs.
237		-* **Collaboration with related projects**:
238		-** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
239		-
240	240	----
241	241
242		-=== **7. Tools and Technologies** ===
	135	+== **Final Notes** ==
243	243
244		-==== **Programming Languages:** ====
	137	+Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
245	245
246		-* **Python** for AI and data processing.
	139	+**For additional technical documentation**:
247	247
248		-==== **Frameworks:** ====
	141	+* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	142	+* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
249	249
250		-* **TensorFlow** and **PyTorch** for machine learning.
251		-* **Flask** or **FastAPI** for backend services.
	144	+**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
252	252
253		-==== **Visualization:** ====
254		-
255		-* **Plotly** and **Matplotlib** for interactive and static visualizations.
256		-
257		-==== **EBRAINS Services:** ====
258		-
259		-* **Collaboratory Lab** for running Notebooks.
260		-* **Buckets** for storing large datasets.
261		-
262	262	----
263	263
264		-=== **Why This Matters** ===
265		-
266		-* The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
267		-* It enables real-time tracking of disease progression across the three diagnostic axes.
268		-* It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.
	148	+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.

workflow neurodiagnoses.png

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