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1		-==== **Overview** ====
	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.
2	2
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**.
	3	+**Neuromarker: Generalized Biomarker Ontology**
4	4
5		-=== **Workflow** ===
	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.
6	6
7		-1. (((
8		-**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
	7	+**Recommended Software**
9	9
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**
	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]].
15	15
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		-)))
	11	+**Core Biomarker Categories**
20	20
21		-----
	13	+Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
22	22
23		-=== **1. Data Integration** ===
	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
24	24
25		-==== **Data Sources** ====
	25	+**Integrating External Databases into Neurodiagnoses**
26	26
27		-**Biomedical Ontologies & Databases:**
	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:
28	28
29		-* **Human Phenotype Ontology (HPO)** for symptom annotation.
30		-* **Gene Ontology (GO)** for molecular and cellular processes.
	29	+1. (((
	30	+**Register for Access**
31	31
32		-**Dimensionality Reduction and Interpretability:**
	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**
33	33
34		-* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
35		-* **Leverage [[DEIBO>>https://github.com/Mellandd/DEIBO]] (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
	39	+* Download datasets while adhering to database usage policies.
	40	+* (((
	41	+Ensure files meet Neurodiagnoses format requirements:
36	36
37		-**Neuroimaging & EEG/MEG Data:**
	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**:
38	38
39		-* **MRI volumetric measures** for brain atrophy tracking.
40		-* **EEG functional connectivity patterns** (AI-Mind).
	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**
41	41
42		-**Clinical & Biomarker Data:**
	62	+* (((
	63	+**Option 1: Upload to EBRAINS Bucket**
43	43
44		-* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
45		-* **Sleep monitoring and actigraphy data** (ADIS).
	65	+* Location: EBRAINS Neurodiagnoses Bucket
	66	+* Ensure correct metadata tagging before submission.
	67	+)))
	68	+* (((
	69	+**Option 2: Contribute via GitHub Repository**
46	46
47		-**Federated Learning Integration:**
	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**
48	48
49		-* **Secure multi-center data harmonization** (PROMINENT).
	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.
50	50
51		-----
	84	+**Reference**: See docs/data_processing.md for detailed instructions.
	85	+)))
52	52
53		-==== **Annotation System for Multi-Modal Data** ====
	87	+**AI-Driven Biomarker Categorization**
54	54
55		-To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
	89	+Neurodiagnoses employs advanced AI models for biomarker classification:
56	56
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.
	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
60	60
61		-----
	96	+=== **Jupyter Integration with EBRAINS** ===
62	62
63		-=== **2. AI-Based Analysis** ===
	98	+=== **Overview** ===
64	64
65		-==== **Machine Learning & Deep Learning Models** ====
	100	+Neurodiagnoses Open Source leverages **Jupyter Notebooks from EBRAINS** to facilitate neurodiagnostic research, biomarker analysis, and AI-driven data processing. This integration provides an interactive and reproducible environment for developing machine learning models, analyzing neuroimaging data, and exploring multimodal biomarkers. Jupyter integration in EBRAINS empowers **Neurodiagnoses Open Source** to: ✅ **Analyze MRI, EEG, and biomarker data efficiently**. ✅ **Train machine learning models with high-performance computing**. ✅ **Ensure transparency with interactive explainability tools**. ✅ **Enable collaborative neurodiagnostic research with reproducible notebooks**.
66	66
67		-**Risk Prediction Models:**
	102	+=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
68	68
69		-* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	104	+==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
70	70
71		-**Biomarker Classification & Probabilistic Imputation:**
	106	+* **Preprocessing Pipelines:**
	107	+** Use **Nipype, NiLearn, ANTs, and FreeSurfer** for structural and functional MRI analysis.
	108	+** Skull stripping, segmentation, and registration of MRI scans.
	109	+** Entropy-based slice selection for training deep learning models.
	110	+* **Deep Learning for Neuroimaging:**
	111	+** Implement **CNN-based models (ResNet, VGG16, Autoencoders)** for biomarker extraction.
	112	+** Feature-based classification of **Alzheimer’s, Parkinson’s, and MCI** from neuroimaging data.
72	72
73		-* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
	114	+==== **2. EEG and MEG Signal Processing** ====
74	74
75		-**Neuroimaging Feature Extraction:**
	116	+* **Data Preprocessing & Artifact Removal:**
	117	+** Use **MNE-Python** for filtering, ICA-based artifact rejection, and time-series normalization.
	118	+** Extract frequency and time-domain features from EEG/MEG signals.
	119	+* **Feature Engineering & Connectivity Analysis:**
	120	+** Functional connectivity analysis using **coherence and phase synchronization metrics**.
	121	+** Graph-theory-based EEG biomarkers for neurodegenerative disease classification.
	122	+* **Deep Learning for EEG Analysis:**
	123	+** Train LSTMs and CNNs for automatic EEG-based classification of MCI and cognitive decline.
76	76
77		-* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
	125	+==== **3. Machine Learning for Biomarker Discovery** ====
78	78
79		-==== **AI-Powered Annotation System** ====
	127	+* **SHAP-based Explainability for Biomarkers:**
	128	+** Use **Random Forest + SHAP** to rank the most predictive CSF, blood, and imaging biomarkers.
	129	+** Generate SHAP summary plots to interpret the impact of individual biomarkers.
	130	+* **Multi-Modal Feature Selection:**
	131	+** Implement **Anchor-Graph Feature Selection** to combine MRI, EEG, and CSF data.
	132	+** PCA and autoencoders for dimensionality reduction and feature extraction.
	133	+* **Automated Risk Prediction Models:**
	134	+** Train ensemble models combining **deep learning and classical ML algorithms**.
	135	+** Apply **subject-level cross-validation** to prevent data leakage and ensure reproducibility.
80	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**).
	137	+==== **4. Computational Simulations & Virtual Brain Models** ====
84	84
85		-----
	139	+* **Integration with The Virtual Brain (TVB):**
	140	+** Simulate large-scale brain networks based on individual neuroimaging data.
	141	+** Model the effect of neurodegenerative progression on brain activity.
	142	+* **Cortical and Subcortical Connectivity Analysis:**
	143	+** Generate connectivity matrices using diffusion MRI and functional MRI correlations.
	144	+** Validate computational simulations with real patient data from EBRAINS datasets.
86	86
87		-=== **3. Diagnostic Framework & Clinical Decision Support** ===
	146	+==== **5. Interactive Data Visualization & Reporting** ====
88	88
89		-==== **Tridimensional Diagnostic Axes** ====
	148	+* **Dynamic Plots & Dashboards:**
	149	+** Use **Matplotlib, Seaborn, Plotly** for interactive visualizations of biomarkers.
	150	+** Implement real-time MRI slice rendering and EEG signal visualization.
	151	+* **Automated Report Generation:**
	152	+** Generate **Jupyter-based PDF reports** summarizing key findings.
	153	+** Export analysis results in JSON, CSV, and interactive web dashboards.
90	90
91		-**Axis 1: Etiology (Pathogenic Mechanisms)**
	155	+=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
92	92
93		-* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
94		-* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	157	+==== **1. Access EBRAINS Jupyter Environment** ====
95	95
96		-**Axis 2: Molecular Markers & Biomarkers**
	159	+1. Create an **EBRAINS account** at [[EBRAINS.eu>>url:https://ebrains.eu/]].
	160	+1. Navigate to the **Collaboratory** and open the Jupyter Lab interface.
	161	+1. Clone the Neurodiagnoses repository:
97	97
98		-* **Integration of CSF, blood, and neuroimaging biomarkers**.
99		-* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	163	+{{{git clone https://github.com/neurodiagnoses
	164	+cd neurodiagnoses
	165	+pip install -r requirements.txt
	166	+}}}
100	100
101		-**Axis 3: Neuroanatomoclinical Correlations**
	168	+==== **2. Run Prebuilt Neurodiagnoses Notebooks** ====
102	102
103		-* **MRI and EEG data** provide anatomical and functional insights.
104		-* **AI-generated progression maps** annotate **brain structure-function relationships**.
	170	+1. Open the **notebooks/** directory inside Jupyter.
	171	+1. Run any of the available notebooks:
	172	+1*. mri_biomarker_analysis.ipynb → Extracts MRI-based biomarkers.
	173	+1*. eeg_preprocessing.ipynb → Cleans and processes EEG signals.
	174	+1*. shap_biomarker_explainability.ipynb → Visualizes biomarker importance.
	175	+1*. disease_risk_prediction.ipynb → Runs ML models for disease classification.
105	105
106		-----
	177	+==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
107	107
108		-=== **4. Computational Workflow & Annotation Pipelines** ===
	179	+* Use EBRAINS **GPU and HPC clusters** for deep learning training:
109	109
110		-==== **Data Processing Steps** ====
	181	+{{{from neurodiagnoses.models import train_cnn_model
	182	+train_cnn_model(data_path='data/mri/', model_type='ResNet50')
	183	+}}}
	184	+* Save trained models for deployment:
111	111
112		-**Data Ingestion:**
	186	+{{{model.save('models/neurodiagnoses_cnn.h5')
	187	+}}}
113	113
114		-* **Harmonized datasets** stored in **EBRAINS Bucket**.
115		-* **Preprocessing pipelines** clean and standardize data.
	189	+For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
116	116
117		-**Feature Engineering:**
	191	+**Collaboration & Partnerships**
118	118
119		-* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	193	+Neurodiagnoses actively seeks partnerships with data providers to:
120	120
121		-**AI-Generated Annotations:**
	195	+* Enable API-based data integration for real-time processing.
	196	+* Co-develop harmonized AI-ready datasets with standardized annotations.
	197	+* Secure funding opportunities through joint grant applications.
122	122
123		-* **Automated tagging** of diagnostic features in **structured reports**.
124		-* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
	199	+**Interested in Partnering?**
125	125
126		-**Clinical Decision Support Integration:**
	201	+If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
127	127
128		-* **AI-annotated findings** fed into **interactive dashboards**.
129		-* **Clinicians can adjust, validate, and modify annotations**.
	203	+**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
130	130
131		-----
	205	+**Final Notes**
132	132
133		-=== **5. Validation & Real-World Testing** ===
	207	+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.
134	134
135		-==== **Prospective Clinical Study** ====
	209	+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.
136	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**.
	211	+**For additional technical documentation and collaboration opportunities:**
140	140
141		-==== **Quality Assurance & Explainability** ====
	213	+* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	214	+* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
142	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.
	216	+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.

workflow neurodiagnoses.png

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