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1		-=== **Overview** ===
	1	+==== **Overview** ====
2	2
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.
	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	+
5	5	----
6	6
7	7	=== **1. Data Integration** ===
8	8
	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	+
9	9	==== **Data Sources** ====
10	10
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.
	109	+[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
18	18
	111	+**Biomedical Ontologies & Databases:**
19	19
20		-==== **Data Preprocessing** ====
	113	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	114	+* **Gene Ontology (GO)** for molecular and cellular processes.
21	21
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.
	116	+**Dimensionality Reduction and Interpretability:**
25	25
	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.
	120	+
	121	+**Neuroimaging & EEG/MEG Data:**
	122	+
	123	+* **MRI volumetric measures** for brain atrophy tracking.
	124	+* **EEG functional connectivity patterns** (AI-Mind).
	125	+
	126	+**Clinical & Biomarker Data:**
	127	+
	128	+* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	129	+* **Sleep monitoring and actigraphy data** (ADIS).
	130	+
	131	+**Federated Learning Integration:**
	132	+
	133	+* **Secure multi-center data harmonization** (PROMINENT).
	134	+
26	26	----
27	27
	137	+==== **Annotation System for Multi-Modal Data** ====
	138	+
	139	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	140	+
	141	+* **Assign standardized metadata tags** to diagnostic features.
	142	+* **Provide contextual explanations** for AI-based classifications.
	143	+* **Track temporal disease progression annotations** to identify long-term trends.
	144	+
	145	+----
	146	+
28	28	=== **2. AI-Based Analysis** ===
29	29
30		-==== **Model Development** ====
	149	+==== **Machine Learning & Deep Learning Models** ====
31	31
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.
	151	+**Risk Prediction Models:**
36	36
37		-==== **Dimensionality Reduction and Interpretability** ====
	153	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
38	38
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).
	155	+**Biomarker Classification & Probabilistic Imputation:**
41	41
	157	+* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
	158	+
	159	+**Neuroimaging Feature Extraction:**
	160	+
	161	+* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
	162	+
	163	+==== **AI-Powered Annotation System** ====
	164	+
	165	+* Uses **SHAP-based interpretability tools** to explain model decisions.
	166	+* Generates **automated clinical annotations** in structured reports.
	167	+* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
	168	+
42	42	----
43	43
44		-=== **3. Diagnostic Framework** ===
	171	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
45	45
46		-==== **Axes of Diagnosis** ====
	173	+==== **Tridimensional Diagnostic Axes** ====
47	47
48		-The framework organizes diagnostic data into three axes:
	175	+**Axis 1: Etiology (Pathogenic Mechanisms)**
49	49
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).
	177	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	178	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
53	53
54		-==== **Recommendation System** ====
	180	+**Axis 2: Molecular Markers & Biomarkers**
55	55
56		-* Suggests additional tests or biomarkers if gaps are detected in the data.
57		-* Prioritizes tests based on clinical impact and cost-effectiveness.
	182	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	183	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
58	58
	185	+**Axis 3: Neuroanatomoclinical Correlations**
	186	+
	187	+* **MRI and EEG data** provide anatomical and functional insights.
	188	+* **AI-generated progression maps** annotate **brain structure-function relationships**.
	189	+
59	59	----
60	60
61		-=== **4. Computational Workflow** ===
	192	+=== **4. Computational Workflow & Annotation Pipelines** ===
62	62
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.
	194	+==== **Data Processing Steps** ====
71	71
	196	+**Data Ingestion:**
	197	+
	198	+* **Harmonized datasets** stored in **EBRAINS Bucket**.
	199	+* **Preprocessing pipelines** clean and standardize data.
	200	+
	201	+**Feature Engineering:**
	202	+
	203	+* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	204	+
	205	+**AI-Generated Annotations:**
	206	+
	207	+* **Automated tagging** of diagnostic features in **structured reports**.
	208	+* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
	209	+
	210	+**Clinical Decision Support Integration:**
	211	+
	212	+* **AI-annotated findings** fed into **interactive dashboards**.
	213	+* **Clinicians can adjust, validate, and modify annotations**.
	214	+
72	72	----
73	73
74		-=== **5. Validation** ===
	217	+=== **5. Validation & Real-World Testing** ===
75	75
76		-==== **Internal Validation** ====
	219	+==== **Prospective Clinical Study** ====
77	77
78		-* Test the system using simulated datasets and known clinical cases.
79		-* Fine-tune models based on validation results.
	221	+* **Multi-center validation** of AI-based **annotations & risk stratifications**.
	222	+* **Benchmarking against clinician-based diagnoses**.
	223	+* **Real-world testing** of AI-powered **structured reporting**.
80	80
81		-==== **External Validation** ====
	225	+==== **Quality Assurance & Explainability** ====
82	82
83		-* Collaborate with research institutions and hospitals to test the system in real-world settings.
84		-* Use anonymized patient data to ensure privacy compliance.
	227	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	228	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
85	85
86	86	----
87	87
88	88	=== **6. Collaborative Development** ===
89	89
90		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	234	+The project is **open to contributions** from **researchers, clinicians, and developers**.
91	91
	236	+**Key tools include:**
	237	+
92	92	* **Jupyter Notebooks**: For data analysis and pipeline development.
93		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
	239	+** Example: **probabilistic imputation**
94	94	* **Wiki Pages**: For documenting methods and results.
95	95	* **Drive and Bucket**: For sharing code, data, and outputs.
96		-* **Collaboration with 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/]]
	242	+* **Collaboration with related projects**:
	243	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
97	97
98	98	----
99	99
100	100	=== **7. Tools and Technologies** ===
101	101
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.
	249	+==== **Programming Languages:** ====
	250	+
	251	+* **Python** for AI and data processing.
	252	+
	253	+==== **Frameworks:** ====
	254	+
	255	+* **TensorFlow** and **PyTorch** for machine learning.
	256	+* **Flask** or **FastAPI** for backend services.
	257	+
	258	+==== **Visualization:** ====
	259	+
	260	+* **Plotly** and **Matplotlib** for interactive and static visualizations.
	261	+
	262	+==== **EBRAINS Services:** ====
	263	+
	264	+* **Collaboratory Lab** for running Notebooks.
	265	+* **Buckets** for storing large datasets.
	266	+
	267	+----
	268	+
	269	+=== **Why This Matters** ===
	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.