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1		-**Neurodiagnoses AI** is an open-source, AI-driven framework designed to enhance the diagnosis and prognosis of central nervous system (CNS) disorders. Building upon the Florey Dementia Index (FDI) methodology, it now encompasses a broader spectrum of neurological conditions. The system integrates multimodal data sources—including EEG, neuroimaging, biomarkers, and genetics—and employs machine learning models to deliver explainable, real-time diagnostic insights. A key feature of this framework is the incorporation of the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)**, which standardizes biomarker classification across all neurodegenerative diseases, facilitating cross-disease AI training.
	1	+==== **Overview** ====
2	2
3		-**Neuromarker: Generalized Biomarker Ontology**
	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		-Neuromarker extends the Common Alzheimer’s Disease Research Ontology (CADRO) into a comprehensive biomarker categorization framework applicable to all neurodegenerative diseases (NDDs). This ontology enables standardized classification, AI-based feature extraction, and seamless multimodal data integration.
	5	+=== **Workflow** ===
6	6
7		-**Core Biomarker Categories**
	7	+1. (((
	8	+**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
8	8
9		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	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**
10	10
11		-|=**Category**|=**Description**
12		-|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
13		-|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
14		-|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
15		-|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
16		-|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
17		-|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
18		-|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
19		-|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
	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	20
21		-**Integrating External Databases into Neurodiagnoses**
	21	+----
22	22
23		-To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
	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	+
25	25	1. (((
26		-**Register for Access**
	34	+**Authentication & API Access:**
27	27
28		-* Each external database requires individual registration and access approval.
29		-* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
30		-* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
	36	+* Users must have an **EBRAINS account**.
	37	+* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
31	31	)))
32	32	1. (((
33		-**Download & Prepare Data**
	40	+**Data Mapping & Harmonization:**
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
38		-
39		-|=**Data Type**|=**Accepted Formats**
40		-|**Tabular Data**|.csv, .tsv
41		-|**Neuroimaging**|.nii, .dcm
42		-|**Genomic Data**|.fasta, .vcf
43		-|**Clinical Metadata**|.json, .xml
	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	44	)))
45		-* (((
46		-**Mandatory Fields for Integration**:
	45	+1. (((
	46	+**Security & Compliance:**
47	47
48		-* Subject ID: Unique patient identifier
49		-* Diagnosis: Standardized disease classification
50		-* Biomarkers: CSF, plasma, or imaging biomarkers
51		-* Genetic Data: Whole-genome or exome sequencing
52		-* Neuroimaging Metadata: MRI/PET acquisition parameters
	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)**.
53	53	)))
54		-)))
55		-1. (((
56		-**Upload Data to Neurodiagnoses**
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	53	+== Implementation Steps ==
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
	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**.
63	63	)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	82	+1. (((
	83	+**Automated Biomarker Extraction:**
66	66
67		-* Location: GitHub Data Repository
68		-* Create a new folder under /data/ and include a dataset description.
69		-* For large datasets, contact project administrators before uploading.
	85	+* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
	86	+* Integration with machine learning models in Neurodiagnoses.
70	70	)))
71		-)))
72	72	1. (((
73		-**Integrate Data into AI Models**
	89	+**Data Security & Compliance:**
74	74
75		-* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
76		-* Standardize neuroimaging and biomarker formats using harmonization tools.
77		-* Utilize machine learning models to handle missing data and feature extraction.
78		-* Train AI models with newly integrated patient cohorts.
79		-
80		-**Reference**: See docs/data_processing.md for detailed instructions.
	91	+* Clinica.Run operates in **compliance with GDPR and HIPAA**.
	92	+* Neuroimaging data remains **within the original storage environment**.
81	81	)))
82	82
83		-**AI-Driven Biomarker Categorization**
	95	+== Implementation Steps ==
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
86	86
87		-|=**Model Type**|=**Application**
88		-|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
89		-|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
90		-|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
	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.
91	91
92		-**Collaboration & Partnerships**
	103	+For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	105	+==== ====
95	95
96		-* Enable API-based data integration for real-time processing.
97		-* Co-develop harmonized AI-ready datasets with standardized annotations.
98		-* Secure funding opportunities through joint grant applications.
	107	+==== **Data Sources** ====
99	99
100		-**Interested in Partnering?**
	109	+[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
101	101
102		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	111	+**Biomedical Ontologies & Databases:**
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	113	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	114	+* **Gene Ontology (GO)** for molecular and cellular processes.
105	105
106		-**Final Notes**
	116	+**Dimensionality Reduction and Interpretability:**
107	107
108		-Neurodiagnoses AI is committed to advancing the integration of artificial intelligence in neurodiagnostic processes. By continuously expanding our data ecosystem and incorporating standardized biomarker classifications through the Neuromarker ontology, we aim to enhance cross-disease AI training and improve diagnostic accuracy across neurodegenerative disorders.
	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.
109	109
110		-We encourage researchers and institutions to contribute new datasets and methodologies to further enrich this collaborative platform. Your participation is vital in driving innovation and fostering a deeper understanding of complex neurological conditions.
	121	+**Neuroimaging & EEG/MEG Data:**
111	111
112		-**For additional technical documentation and collaboration opportunities:**
	123	+* **MRI volumetric measures** for brain atrophy tracking.
	124	+* **EEG functional connectivity patterns** (AI-Mind).
113	113
114		-* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
115		-* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	126	+**Clinical & Biomarker Data:**
116	116
117		-If you encounter any issues during data integration or have suggestions for improvement, please open a GitHub Issue or consult the EBRAINS Neurodiagnoses Forum. Together, we can advance the field of neurodiagnostics and contribute to better patient outcomes.
	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	+
	135	+----
	136	+
	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	+
	147	+=== **2. AI-Based Analysis** ===
	148	+
	149	+==== **Machine Learning & Deep Learning Models** ====
	150	+
	151	+**Risk Prediction Models:**
	152	+
	153	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	154	+
	155	+**Biomarker Classification & Probabilistic Imputation:**
	156	+
	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	+
	169	+----
	170	+
	171	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
	172	+
	173	+==== **Tridimensional Diagnostic Axes** ====
	174	+
	175	+**Axis 1: Etiology (Pathogenic Mechanisms)**
	176	+
	177	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	178	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	179	+
	180	+**Axis 2: Molecular Markers & Biomarkers**
	181	+
	182	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	183	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	184	+
	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	+
	190	+----
	191	+
	192	+=== **4. Computational Workflow & Annotation Pipelines** ===
	193	+
	194	+==== **Data Processing Steps** ====
	195	+
	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	+
	215	+----
	216	+
	217	+=== **5. Validation & Real-World Testing** ===
	218	+
	219	+==== **Prospective Clinical Study** ====
	220	+
	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**.
	224	+
	225	+==== **Quality Assurance & Explainability** ====
	226	+
	227	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	228	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
	229	+
	230	+----
	231	+
	232	+=== **6. Collaborative Development** ===
	233	+
	234	+The project is **open to contributions** from **researchers, clinicians, and developers**.
	235	+
	236	+**Key tools include:**
	237	+
	238	+* **Jupyter Notebooks**: For data analysis and pipeline development.
	239	+** Example: **probabilistic imputation**
	240	+* **Wiki Pages**: For documenting methods and results.
	241	+* **Drive and Bucket**: For sharing code, data, and outputs.
	242	+* **Collaboration with related projects**:
	243	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
	244	+
	245	+----
	246	+
	247	+=== **7. Tools and Technologies** ===
	248	+
	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.

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