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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. It 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) **and** Disease Knowledge Transfer (DKT)**, which standardizes disease and biomarker classification across all CNS 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	+----
6	6
7		-**Recommended Software**
	7	+=== **1. Data Integration** ===
8	8
9		-There is a suite of software that can help implement the workflow needed in Neurodiagnoses. Find a list of recommendations [[here>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/recommended_software]].
	9	+==== **Data Sources** ====
10	10
11		-**Core Biomarker Categories**
	11	+**Biomedical Ontologies & Databases:**
12	12
13		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	13	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	14	+* **Gene Ontology (GO)** for molecular and cellular processes.
14	14
15		-|=**Category**|=**Description**
16		-|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
17		-|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
18		-|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
19		-|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
20		-|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
21		-|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
22		-|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
23		-|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
	16	+**Dimensionality Reduction and Interpretability:**
24	24
25		-**Integrating External Databases into Neurodiagnoses**
	18	+* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
	19	+* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
26	26
27		-To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
	21	+**Neuroimaging & EEG/MEG Data:**
28	28
29		-1. (((
30		-**Register for Access**
	23	+* **MRI volumetric measures** for brain atrophy tracking.
	24	+* **EEG functional connectivity patterns** (AI-Mind).
31	31
32		-* Each external database requires individual registration and access approval.
33		-* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
34		-* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
35		-)))
36		-1. (((
37		-**Download & Prepare Data**
	26	+**Clinical & Biomarker Data:**
38	38
39		-* Download datasets while adhering to database usage policies.
40		-* (((
41		-Ensure files meet Neurodiagnoses format requirements:
	28	+* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	29	+* **Sleep monitoring and actigraphy data** (ADIS).
42	42
43		-|=**Data Type**|=**Accepted Formats**
44		-|**Tabular Data**|.csv, .tsv
45		-|**Neuroimaging**|.nii, .dcm
46		-|**Genomic Data**|.fasta, .vcf
47		-|**Clinical Metadata**|.json, .xml
48		-)))
49		-* (((
50		-**Mandatory Fields for Integration**:
	31	+**Federated Learning Integration:**
51	51
52		-* Subject ID: Unique patient identifier
53		-* Diagnosis: Standardized disease classification
54		-* Biomarkers: CSF, plasma, or imaging biomarkers
55		-* Genetic Data: Whole-genome or exome sequencing
56		-* Neuroimaging Metadata: MRI/PET acquisition parameters
57		-)))
58		-)))
59		-1. (((
60		-**Upload Data to Neurodiagnoses**
	33	+* **Secure multi-center data harmonization** (PROMINENT).
61	61
62		-* (((
63		-**Option 1: Upload to EBRAINS Bucket**
	35	+----
64	64
65		-* Location: EBRAINS Neurodiagnoses Bucket
66		-* Ensure correct metadata tagging before submission.
67		-)))
68		-* (((
69		-**Option 2: Contribute via GitHub Repository**
	37	+==== **Annotation System for Multi-Modal Data** ====
70	70
71		-* Location: GitHub Data Repository
72		-* Create a new folder under /data/ and include a dataset description.
73		-* For large datasets, contact project administrators before uploading.
74		-)))
75		-)))
76		-1. (((
77		-**Integrate Data into AI Models**
	39	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
78	78
79		-* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
80		-* Standardize neuroimaging and biomarker formats using harmonization tools.
81		-* Utilize machine learning models to handle missing data and feature extraction.
82		-* Train AI models with newly integrated patient cohorts.
	41	+* **Assign standardized metadata tags** to diagnostic features.
	42	+* **Provide contextual explanations** for AI-based classifications.
	43	+* **Track temporal disease progression annotations** to identify long-term trends.
83	83
84		-**Reference**: See docs/data_processing.md for detailed instructions.
85		-)))
	45	+----
86	86
87		-**AI-Driven Biomarker Categorization**
	47	+=== **2. AI-Based Analysis** ===
88	88
89		-Neurodiagnoses employs advanced AI models for biomarker classification:
	49	+==== **Machine Learning & Deep Learning Models** ====
90	90
91		-|=**Model Type**|=**Application**
92		-|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
93		-|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
94		-|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
	51	+**Risk Prediction Models:**
95	95
96		-**Collaboration & Partnerships**
	53	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
97	97
98		-Neurodiagnoses actively seeks partnerships with data providers to:
	55	+**Biomarker Classification & Probabilistic Imputation:**
99	99
100		-* Enable API-based data integration for real-time processing.
101		-* Co-develop harmonized AI-ready datasets with standardized annotations.
102		-* Secure funding opportunities through joint grant applications.
	57	+* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
103	103
104		-**Interested in Partnering?**
	59	+**Neuroimaging Feature Extraction:**
105	105
106		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	61	+* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
107	107
108		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	63	+==== **AI-Powered Annotation System** ====
109	109
110		-**Final Notes**
	65	+* Uses **SHAP-based interpretability tools** to explain model decisions.
	66	+* Generates **automated clinical annotations** in structured reports.
	67	+* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
111	111
112		-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.
	69	+----
113	113
114		-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.
	71	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
115	115
116		-**For additional technical documentation and collaboration opportunities:**
	73	+==== **Tridimensional Diagnostic Axes** ====
117	117
118		-* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
119		-* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
	75	+**Axis 1: Etiology (Pathogenic Mechanisms)**
120	120
121		-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.
	77	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	78	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	79	+
	80	+**Axis 2: Molecular Markers & Biomarkers**
	81	+
	82	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	83	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	84	+
	85	+**Axis 3: Neuroanatomoclinical Correlations**
	86	+
	87	+* **MRI and EEG data** provide anatomical and functional insights.
	88	+* **AI-generated progression maps** annotate **brain structure-function relationships**.
	89	+
	90	+----
	91	+
	92	+=== **4. Computational Workflow & Annotation Pipelines** ===
	93	+
	94	+==== **Data Processing Steps** ====
	95	+
	96	+**Data Ingestion:**
	97	+
	98	+* **Harmonized datasets** stored in **EBRAINS Bucket**.
	99	+* **Preprocessing pipelines** clean and standardize data.
	100	+
	101	+**Feature Engineering:**
	102	+
	103	+* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	104	+
	105	+**AI-Generated Annotations:**
	106	+
	107	+* **Automated tagging** of diagnostic features in **structured reports**.
	108	+* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
	109	+
	110	+**Clinical Decision Support Integration:**
	111	+
	112	+* **AI-annotated findings** fed into **interactive dashboards**.
	113	+* **Clinicians can adjust, validate, and modify annotations**.
	114	+
	115	+----
	116	+
	117	+=== **5. Validation & Real-World Testing** ===
	118	+
	119	+==== **Prospective Clinical Study** ====
	120	+
	121	+* **Multi-center validation** of AI-based **annotations & risk stratifications**.
	122	+* **Benchmarking against clinician-based diagnoses**.
	123	+* **Real-world testing** of AI-powered **structured reporting**.
	124	+
	125	+==== **Quality Assurance & Explainability** ====
	126	+
	127	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	128	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
	129	+
	130	+----
	131	+
	132	+=== **6. Collaborative Development** ===
	133	+
	134	+The project is **open to contributions** from **researchers, clinicians, and developers**.
	135	+
	136	+**Key tools include:**
	137	+
	138	+* **Jupyter Notebooks**: For data analysis and pipeline development.
	139	+** Example: **probabilistic imputation**
	140	+* **Wiki Pages**: For documenting methods and results.
	141	+* **Drive and Bucket**: For sharing code, data, and outputs.
	142	+* **Collaboration with related projects**:
	143	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
	144	+
	145	+----
	146	+
	147	+=== **7. Tools and Technologies** ===
	148	+
	149	+==== **Programming Languages:** ====
	150	+
	151	+* **Python** for AI and data processing.
	152	+
	153	+==== **Frameworks:** ====
	154	+
	155	+* **TensorFlow** and **PyTorch** for machine learning.
	156	+* **Flask** or **FastAPI** for backend services.
	157	+
	158	+==== **Visualization:** ====
	159	+
	160	+* **Plotly** and **Matplotlib** for interactive and static visualizations.
	161	+
	162	+==== **EBRAINS Services:** ====
	163	+
	164	+* **Collaboratory Lab** for running Notebooks.
	165	+* **Buckets** for storing large datasets.
	166	+
	167	+----
	168	+
	169	+=== **Why This Matters** ===
	170	+
	171	+* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
	172	+* **It enables real-time tracking of disease progression across the three diagnostic axes.**
	173	+* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**

workflow neurodiagnoses.png

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