Wiki source code of Methodology

Last modified by manuelmenendez on 2025/03/14 08:31

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27.1	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.
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22.1	3	Neuromarker: Generalized Biomarker Ontology
1.1	4
22.1	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.
1.1	6
25.1	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
22.1	11	Core Biomarker Categories
19.1	12
22.1	13	Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
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20.1	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)
23.1	18	\|Fluid Biomarkers\|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
20.1	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
19.1	24
22.1	25	Integrating External Databases into Neurodiagnoses
19.1	26
22.1	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:
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22.1	29	1. (((
	30	Register for Access
12.2	31
22.1	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
12.2	38
22.1	39	* Download datasets while adhering to database usage policies.
	40	* (((
	41	Ensure files meet Neurodiagnoses format requirements:
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20.1	43	\|=Data Type\|=Accepted Formats
	44	\|Tabular Data\|.csv, .tsv
	45	\|Neuroimaging\|.nii, .dcm
	46	\|Genomic Data\|.fasta, .vcf
	47	\|Clinical Metadata\|.json, .xml
22.1	48	)))
	49	* (((
	50	Mandatory Fields for Integration:
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22.1	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
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22.1	62	* (((
	63	Option 1: Upload to EBRAINS Bucket
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22.1	65	* Location: EBRAINS Neurodiagnoses Bucket
	66	* Ensure correct metadata tagging before submission.
	67	)))
	68	* (((
	69	Option 2: Contribute via GitHub Repository
12.2	70
22.1	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
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22.1	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.
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20.1	84	Reference: See docs/data_processing.md for detailed instructions.
22.1	85	)))
12.2	86
22.1	87	AI-Driven Biomarker Categorization
12.2	88
22.1	89	Neurodiagnoses employs advanced AI models for biomarker classification:
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20.1	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
1.1	95
28.1	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
22.1	191	Collaboration & Partnerships
1.1	192
22.1	193	Neurodiagnoses actively seeks partnerships with data providers to:
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22.1	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.
6.1	198
20.1	199	Interested in Partnering?
1.1	200
22.1	201	If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
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22.1	203	Contact: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
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24.1	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.

Wiki source code of Methodology

Neurodiagnoses