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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
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		-
	116	+**Dimensionality Reduction and Interpretability:**
	117	+
	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	+
	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.

workflow neurodiagnoses.png

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