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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	+**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	3	**Neuromarker: Generalized Biomarker Ontology**
4	4
5	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	+**Recommended Software**
	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]].
	10	+
7	7	**Core Biomarker Categories**
8	8
9	9	Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
...	...	@@ -89,6 +89,101 @@
89	89	|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
90	90	|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
91	91
	96	+=== **Jupyter Integration with EBRAINS** ===
	97	+
	98	+=== **Overview** ===
	99	+
	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**.
	101	+
	102	+=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
	103	+
	104	+==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
	105	+
	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.
	113	+
	114	+==== **2. EEG and MEG Signal Processing** ====
	115	+
	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.
	124	+
	125	+==== **3. Machine Learning for Biomarker Discovery** ====
	126	+
	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.
	136	+
	137	+==== **4. Computational Simulations & Virtual Brain Models** ====
	138	+
	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.
	145	+
	146	+==== **5. Interactive Data Visualization & Reporting** ====
	147	+
	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.
	154	+
	155	+=== **How to Use Neurodiagnoses with Jupyter in EBRAINS** ===
	156	+
	157	+==== **1. Access EBRAINS Jupyter Environment** ====
	158	+
	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:
	162	+
	163	+{{{git clone https://github.com/neurodiagnoses
	164	+cd neurodiagnoses
	165	+pip install -r requirements.txt
	166	+}}}
	167	+
	168	+==== **2. Run Prebuilt Neurodiagnoses Notebooks** ====
	169	+
	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.
	176	+
	177	+==== **3. Train Custom AI Models on EBRAINS HPC Resources** ====
	178	+
	179	+* Use EBRAINS **GPU and HPC clusters** for deep learning training:
	180	+
	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:
	185	+
	186	+{{{model.save('models/neurodiagnoses_cnn.h5')
	187	+}}}
	188	+
	189	+For further developments, contribute to the **[[Neurodiagnoses GitHub Repository>>url:https://github.com/neurodiagnoses]]**.
	190	+
92	92	**Collaboration & Partnerships**
93	93
94	94	Neurodiagnoses actively seeks partnerships with data providers to: