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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/users/manuelmenendezgonzalez/projects/1/views/1]]**
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		-1. (((
26		-**Register for Access**
	25	+==== **Data Sources** ====
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.
31		-)))
32		-1. (((
33		-**Download & Prepare Data**
	27	+**Biomedical Ontologies & Databases:**
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
	29	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	30	+* **Gene Ontology (GO)** for molecular and cellular processes.
38	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
44		-)))
45		-* (((
46		-**Mandatory Fields for Integration**:
	32	+**Dimensionality Reduction and Interpretability:**
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
53		-)))
54		-)))
55		-1. (((
56		-**Upload Data to Neurodiagnoses**
	34	+* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
	35	+* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	37	+**Neuroimaging & EEG/MEG Data:**
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
63		-)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	39	+* **MRI volumetric measures** for brain atrophy tracking.
	40	+* **EEG functional connectivity patterns** (AI-Mind).
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.
70		-)))
71		-)))
72		-1. (((
73		-**Integrate Data into AI Models**
	42	+**Clinical & Biomarker Data:**
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.
	44	+* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
	45	+* **Sleep monitoring and actigraphy data** (ADIS).
79	79
80		-**Reference**: See docs/data_processing.md for detailed instructions.
81		-)))
	47	+**Federated Learning Integration:**
82	82
83		-**AI-Driven Biomarker Categorization**
	49	+* **Secure multi-center data harmonization** (PROMINENT).
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
	51	+----
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
	53	+==== **Annotation System for Multi-Modal Data** ====
91	91
92		-**Collaboration & Partnerships**
	55	+To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	57	+* **Assign standardized metadata tags** to diagnostic features.
	58	+* **Provide contextual explanations** for AI-based classifications.
	59	+* **Track temporal disease progression annotations** to identify long-term trends.
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.
	61	+----
99	99
100		-**Interested in Partnering?**
	63	+=== **2. AI-Based Analysis** ===
101	101
102		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	65	+==== **Machine Learning & Deep Learning Models** ====
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	67	+**Risk Prediction Models:**
105	105
106		-
	69	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	70	+
	71	+**Biomarker Classification & Probabilistic Imputation:**
	72	+
	73	+* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
	74	+
	75	+**Neuroimaging Feature Extraction:**
	76	+
	77	+* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
	78	+
	79	+==== **AI-Powered Annotation System** ====
	80	+
	81	+* Uses **SHAP-based interpretability tools** to explain model decisions.
	82	+* Generates **automated clinical annotations** in structured reports.
	83	+* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
	84	+
	85	+----
	86	+
	87	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
	88	+
	89	+==== **Tridimensional Diagnostic Axes** ====
	90	+
	91	+**Axis 1: Etiology (Pathogenic Mechanisms)**
	92	+
	93	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	94	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	95	+
	96	+**Axis 2: Molecular Markers & Biomarkers**
	97	+
	98	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	99	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	100	+
	101	+**Axis 3: Neuroanatomoclinical Correlations**
	102	+
	103	+* **MRI and EEG data** provide anatomical and functional insights.
	104	+* **AI-generated progression maps** annotate **brain structure-function relationships**.
	105	+
	106	+----
	107	+
	108	+=== **4. Computational Workflow & Annotation Pipelines** ===
	109	+
	110	+==== **Data Processing Steps** ====
	111	+
	112	+**Data Ingestion:**
	113	+
	114	+* **Harmonized datasets** stored in **EBRAINS Bucket**.
	115	+* **Preprocessing pipelines** clean and standardize data.
	116	+
	117	+**Feature Engineering:**
	118	+
	119	+* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	120	+
	121	+**AI-Generated Annotations:**
	122	+
	123	+* **Automated tagging** of diagnostic features in **structured reports**.
	124	+* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
	125	+
	126	+**Clinical Decision Support Integration:**
	127	+
	128	+* **AI-annotated findings** fed into **interactive dashboards**.
	129	+* **Clinicians can adjust, validate, and modify annotations**.
	130	+
	131	+----
	132	+
	133	+=== **5. Validation & Real-World Testing** ===
	134	+
	135	+==== **Prospective Clinical Study** ====
	136	+
	137	+* **Multi-center validation** of AI-based **annotations & risk stratifications**.
	138	+* **Benchmarking against clinician-based diagnoses**.
	139	+* **Real-world testing** of AI-powered **structured reporting**.
	140	+
	141	+==== **Quality Assurance & Explainability** ====
	142	+
	143	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	144	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
	145	+
	146	+----
	147	+
	148	+=== **6. Collaborative Development** ===
	149	+
	150	+The project is **open to contributions** from **researchers, clinicians, and developers**.
	151	+
	152	+**Key tools include:**
	153	+
	154	+* **Jupyter Notebooks**: For data analysis and pipeline development.
	155	+** Example: **probabilistic imputation**
	156	+* **Wiki Pages**: For documenting methods and results.
	157	+* **Drive and Bucket**: For sharing code, data, and outputs.
	158	+* **Collaboration with related projects**:
	159	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
	160	+
	161	+----
	162	+
	163	+=== **7. Tools and Technologies** ===
	164	+
	165	+==== **Programming Languages:** ====
	166	+
	167	+* **Python** for AI and data processing.
	168	+
	169	+==== **Frameworks:** ====
	170	+
	171	+* **TensorFlow** and **PyTorch** for machine learning.
	172	+* **Flask** or **FastAPI** for backend services.
	173	+
	174	+==== **Visualization:** ====
	175	+
	176	+* **Plotly** and **Matplotlib** for interactive and static visualizations.
	177	+
	178	+==== **EBRAINS Services:** ====
	179	+
	180	+* **Collaboratory Lab** for running Notebooks.
	181	+* **Buckets** for storing large datasets.
	182	+
	183	+----
	184	+
	185	+=== **Why This Matters** ===
	186	+
	187	+* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
	188	+* **It enables real-time tracking of disease progression across the three diagnostic axes.**
	189	+* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**

workflow neurodiagnoses.png

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