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1		-== **Overview** ==
	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**.
	3	+This section describes the step-by-step process used in the **Neurodiagnoses** project to develop a novel diagnostic framework for neurological diseases. The methodology integrates artificial intelligence (AI), biomedical ontologies, and computational neuroscience to create a structured, interpretable, and scalable diagnostic system.
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		-== **1. Data Integration** ==
	7	+=== **1. Data Integration** ===
24	24
25		-=== **EBRAINS Medical Informatics Platform (MIP)**. ===
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.
28		-
29		-==== How It Works ====
30		-
31		-
32		-1. (((
33		-**Authentication & API Access:**
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:**
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:**
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		-----
63		-
64		-=== Data Processing & Integration with Clinica.Run ===
65		-
66		-Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
67		-
68		-==== How It Works ====
69		-
70		-
71		-1. (((
72		-**Neuroimaging Preprocessing:**
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	102	==== **Data Sources** ====
103	103
104		-[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
	11	+* **Biomedical Ontologies**:
	12	+** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
	13	+** Gene Ontology (GO) for molecular and cellular processes.
	14	+* **Neuroimaging Datasets**:
	15	+** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
	16	+* **Clinical and Biomarker Data**:
	17	+** Anonymized clinical reports, molecular biomarkers, and test results.
105	105
106		-**Biomedical Ontologies & Databases:**
107	107
108		-* **Human Phenotype Ontology (HPO)** for symptom annotation.
109		-* **Gene Ontology (GO)** for molecular and cellular processes.
	20	+==== **Data Preprocessing** ====
110	110
111		-**Dimensionality Reduction and Interpretability:**
	22	+1. **Standardization**: Ensure all data sources are normalized to a common format.
	23	+1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
	24	+1. **Data Cleaning**: Handle missing values and remove duplicates.
112	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** ====
	28	+=== **2. AI-Based Analysis** ===
133	133
134		-To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	30	+==== **Model Development** ====
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.
	32	+* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
	33	+* **Classification Models**:
	34	+** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
	35	+** Purpose: Predict the likelihood of specific neurological conditions based on input data.
139	139
140		-----
	37	+==== **Dimensionality Reduction and Interpretability** ====
141	141
142		-== **2. AI-Based Analysis** ==
	39	+* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
	40	+* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
143	143
144		-==== **Machine Learning & Deep Learning Models** ====
145		-
146		-**Risk Prediction Models:**
147		-
148		-* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
149		-
150		-**Biomarker Classification & Probabilistic Imputation:**
151		-
152		-* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
153		-
154		-**Neuroimaging Feature Extraction:**
155		-
156		-* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
157		-
158		-==== **AI-Powered Annotation System** ====
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**).
163		-
164	164	----
165	165
166		-== **3. Diagnostic Framework & Clinical Decision Support** ==
	44	+=== **3. Diagnostic Framework** ===
167	167
168		-==== **Tridimensional Diagnostic Axes** ====
	46	+==== **Axes of Diagnosis** ====
169	169
170		-**Axis 1: Etiology (Pathogenic Mechanisms)**
	48	+The framework organizes diagnostic data into three axes:
171	171
172		-* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
173		-* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	50	+1. **Etiology**: Genetic and environmental risk factors.
	51	+1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
	52	+1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
174	174
175		-**Axis 2: Molecular Markers & Biomarkers**
	54	+==== **Recommendation System** ====
176	176
177		-* **Integration of CSF, blood, and neuroimaging biomarkers**.
178		-* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	56	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	57	+* Prioritizes tests based on clinical impact and cost-effectiveness.
179	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** ==
	61	+=== **4. Computational Workflow** ===
188	188
189		-==== **Data Processing Steps** ====
	63	+1. **Data Loading**: Import data from storage (Drive or Bucket).
	64	+1. **Feature Engineering**: Generate derived features from the raw data.
	65	+1. **Model Training**:
	66	+1*. Split data into training, validation, and test sets.
	67	+1*. Train models with cross-validation to ensure robustness.
	68	+1. **Evaluation**:
	69	+1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
	70	+1*. Compare against baseline models and domain benchmarks.
190	190
191		-**Data Ingestion:**
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** ==
	74	+=== **5. Validation** ===
213	213
214		-==== **Prospective Clinical Study** ====
	76	+==== **Internal Validation** ====
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**.
	78	+* Test the system using simulated datasets and known clinical cases.
	79	+* Fine-tune models based on validation results.
219	219
220		-==== **Quality Assurance & Explainability** ====
	81	+==== **External Validation** ====
221	221
222		-* **Annotations linked to structured knowledge graphs** for improved transparency.
223		-* **Interactive annotation editor** allows clinicians to validate AI outputs.
	83	+* Collaborate with research institutions and hospitals to test the system in real-world settings.
	84	+* Use anonymized patient data to ensure privacy compliance.
224	224
225	225	----
226	226
227		-== **6. Collaborative Development** ==
	88	+=== **6. Collaborative Development** ===
228	228
229		-The project is **open to contributions** from **researchers, clinicians, and developers**.
	90	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
230	230
231		-**Key tools include:**
232		-
233	233	* **Jupyter Notebooks**: For data analysis and pipeline development.
234		-** Example: **probabilistic imputation**
	93	+** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
235	235	* **Wiki Pages**: For documenting methods and results.
236	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**
	96	+* **Related projects: **For instance: [[//Beyond the hype: AI in dementia – from early risk detection to disease treatment//>>https://www.lethe-project.eu/beyond-the-hype-ai-in-dementia-from-early-risk-detection-to-disease-treatment/]]
239	239
240	240	----
241	241
242		-== **7. Tools and Technologies** ==
	100	+=== **7. Tools and Technologies** ===
243	243
244		-==== **Programming Languages:** ====
245		-
246		-* **Python** for AI and data processing.
247		-
248		-==== **Frameworks:** ====
249		-
250		-* **TensorFlow** and **PyTorch** for machine learning.
251		-* **Flask** or **FastAPI** for backend services.
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		-----
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.
	102	+* **Programming Languages**: Python for AI and data processing.
	103	+* **Frameworks**:
	104	+** TensorFlow and PyTorch for machine learning.
	105	+** Flask or FastAPI for backend services.
	106	+* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
	107	+* **EBRAINS Services**:
	108	+** Collaboratory Lab for running Notebooks.
	109	+** Buckets for storing large datasets.