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3	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		-=== **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	23	=== **1. Data Integration** ===
24	24
25		-== Overview ==
26		-
27		-
28		-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.
29		-
30		-== How It Works ==
31		-
32		-
33		-1. (((
34		-**Authentication & API Access:**
35		-
36		-* Users must have an **EBRAINS account**.
37		-* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
38		-)))
39		-1. (((
40		-**Data Mapping & Harmonization:**
41		-
42		-* Retrieved data is **normalized** and converted to standard formats (.csv, .json).
43		-* Data from **multiple sources** is harmonized to ensure consistency for AI processing.
44		-)))
45		-1. (((
46		-**Security & Compliance:**
47		-
48		-* All data access is **logged and monitored**.
49		-* Data remains on **MIP servers** using **federated learning techniques** when possible.
50		-* Access is granted only after signing a **Data Usage Agreement (DUA)**.
51		-)))
52		-
53		-== Implementation Steps ==
54		-
55		-
56		-1. Clone the repository.
57		-1. Configure your **EBRAINS API credentials** in mip_integration.py.
58		-1. Run the script to **download and harmonize clinical data**.
59		-1. Process the data for **AI model training**.
60		-
61		-For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
62		-
63		-----
64		-
65		-= Data Processing & Integration with Clinica.Run =
66		-
67		-
68		-== Overview ==
69		-
70		-
71		-Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
72		-
73		-== How It Works ==
74		-
75		-
76		-1. (((
77		-**Neuroimaging Preprocessing:**
78		-
79		-* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
80		-* Supports **longitudinal and cross-sectional analyses**.
81		-)))
82		-1. (((
83		-**Automated Biomarker Extraction:**
84		-
85		-* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
86		-* Integration with machine learning models in Neurodiagnoses.
87		-)))
88		-1. (((
89		-**Data Security & Compliance:**
90		-
91		-* Clinica.Run operates in **compliance with GDPR and HIPAA**.
92		-* Neuroimaging data remains **within the original storage environment**.
93		-)))
94		-
95		-== Implementation Steps ==
96		-
97		-
98		-1. Install **Clinica.Run** dependencies.
99		-1. Configure your **Clinica.Run pipeline** in clinica_run_config.json.
100		-1. Run the pipeline for **preprocessing and biomarker extraction**.
101		-1. Use processed neuroimaging data for **AI-driven diagnostics** in Neurodiagnoses.
102		-
103		-For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
104		-
105		-==== ====
106		-
107	107	==== **Data Sources** ====
108	108
109		-[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
110		-
111	111	**Biomedical Ontologies & Databases:**
112	112
113	113	* **Human Phenotype Ontology (HPO)** for symptom annotation.
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116	116	**Dimensionality Reduction and Interpretability:**
117	117
118	118	* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
119		-* **Leverage [[DEIBO>>https://github.com/Mellandd/DEIBO]] (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
	19	+* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
120	120
121	121	**Neuroimaging & EEG/MEG Data:**
122	122
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268	268
269	269	=== **Why This Matters** ===
270	270
271		-* The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
272		-* It enables real-time tracking of disease progression across the three diagnostic axes.
273		-* It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.
	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.**