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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	+**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.
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		-
11	11	**Core Biomarker Categories**
12	12
13	13	Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
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15	15	|=**Category**|=**Description**
16	16	|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
17	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
	14	+|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
19	19	|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
20	20	|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
21	21	|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
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93	93	|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
94	94	|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
95	95
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		-
191	191	**Collaboration & Partnerships**
192	192
193	193	Neurodiagnoses actively seeks partnerships with data providers to:
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202	202
203	203	**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
204	204
205		-**Final Notes**
206		-
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.
208		-
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.
210		-
211		-**For additional technical documentation and collaboration opportunities:**
212		-
213		-* **GitHub Repository:** [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
214		-* **EBRAINS Collaboration Page:** [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
215		-
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.
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