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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	+=== **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	+
25	25	1. (((
26		-**Register for Access**
	33	+**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.
	35	+* Users must have an **EBRAINS account**.
	36	+* 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**
	39	+**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
	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.
44	44	)))
45		-* (((
46		-**Mandatory Fields for Integration**:
	44	+1. (((
	45	+**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
	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)**.
53	53	)))
54		-)))
55		-1. (((
56		-**Upload Data to Neurodiagnoses**
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	52	+==== Implementation Steps ====
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
	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**.
63	63	)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	77	+1. (((
	78	+**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.
	80	+* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
	81	+* Integration with machine learning models in Neurodiagnoses.
70	70	)))
71		-)))
72	72	1. (((
73		-**Integrate Data into AI Models**
	84	+**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.
	86	+* Clinica.Run operates in **compliance with GDPR and HIPAA**.
	87	+* Neuroimaging data remains **within the original storage environment**.
81	81	)))
82	82
83		-**AI-Driven Biomarker Categorization**
	90	+==== 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
	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.
91	91
92		-**Collaboration & Partnerships**
	98	+For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	100	+==== ====
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.
	102	+==== **Data Sources** ====
99	99
100		-**Interested in Partnering?**
	104	+[[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.
	106	+**Biomedical Ontologies & Databases:**
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	108	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	109	+* **Gene Ontology (GO)** for molecular and cellular processes.
105	105
106		-
	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	+----
	131	+
	132	+==== **Annotation System for Multi-Modal Data** ====
	133	+
	134	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	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	+----
	141	+
	142	+== **2. AI-Based Analysis** ==
	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	+----
	165	+
	166	+== **3. Diagnostic Framework & Clinical Decision Support** ==
	167	+
	168	+==== **Tridimensional Diagnostic Axes** ====
	169	+
	170	+**Axis 1: Etiology (Pathogenic Mechanisms)**
	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	+----
	186	+
	187	+== **4. Computational Workflow & Annotation Pipelines** ==
	188	+
	189	+==== **Data Processing Steps** ====
	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	+----
	211	+
	212	+== **5. Validation & Real-World Testing** ==
	213	+
	214	+==== **Prospective Clinical Study** ====
	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**.
	219	+
	220	+==== **Quality Assurance & Explainability** ====
	221	+
	222	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	223	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
	224	+
	225	+----
	226	+
	227	+== **6. Collaborative Development** ==
	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	+----
	241	+
	242	+== **7. Tools and Technologies** ==
	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.

workflow neurodiagnoses.png

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