Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -1,4 +1,4 @@
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
...	...	@@ -93,101 +93,6 @@
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: