Summary

• Page properties (1 modified, 0 added, 0 removed)
• Attachments (0 modified, 1 added, 0 removed)
  • workflow neurodiagnoses.png

Details

Page properties

Content

...	...	@@ -1,109 +1,216 @@
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 section describes the step-by-step process used in the **Neurodiagnoses** project to develop a novel diagnostic framework for neurological diseases. The methodology integrates artificial intelligence (AI), biomedical ontologies, and computational neuroscience to create a structured, interpretable, and scalable diagnostic system.
	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		-=== **1. Data Integration** ===
	7	+**Recommended Software**
8	8
9		-==== **Data Sources** ====
	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	10
11		-* **Biomedical Ontologies**:
12		-** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
13		-** Gene Ontology (GO) for molecular and cellular processes.
14		-* **Neuroimaging Datasets**:
15		-** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
16		-* **Clinical and Biomarker Data**:
17		-** Anonymized clinical reports, molecular biomarkers, and test results.
	11	+**Core Biomarker Categories**
18	18
	13	+Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
19	19
20		-==== **Data Preprocessing** ====
	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
21	21
22		-1. **Standardization**: Ensure all data sources are normalized to a common format.
23		-1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
24		-1. **Data Cleaning**: Handle missing values and remove duplicates.
	25	+**Integrating External Databases into Neurodiagnoses**
25	25
26		-----
	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:
27	27
28		-=== **2. AI-Based Analysis** ===
	29	+1. (((
	30	+**Register for Access**
29	29
30		-==== **Model Development** ====
	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**
31	31
32		-* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
33		-* **Classification Models**:
34		-** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
35		-** Purpose: Predict the likelihood of specific neurological conditions based on input data.
	39	+* Download datasets while adhering to database usage policies.
	40	+* (((
	41	+Ensure files meet Neurodiagnoses format requirements:
36	36
37		-==== **Dimensionality Reduction and Interpretability** ====
	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		-* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
40		-* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
	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		-----
	62	+* (((
	63	+**Option 1: Upload to EBRAINS Bucket**
43	43
44		-=== **3. Diagnostic Framework** ===
	65	+* Location: EBRAINS Neurodiagnoses Bucket
	66	+* Ensure correct metadata tagging before submission.
	67	+)))
	68	+* (((
	69	+**Option 2: Contribute via GitHub Repository**
45	45
46		-==== **Axes of Diagnosis** ====
	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**
47	47
48		-The framework organizes diagnostic data into three axes:
	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.
49	49
50		-1. **Etiology**: Genetic and environmental risk factors.
51		-1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
52		-1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
	84	+**Reference**: See docs/data_processing.md for detailed instructions.
	85	+)))
53	53
54		-==== **Recommendation System** ====
	87	+**AI-Driven Biomarker Categorization**
55	55
56		-* Suggests additional tests or biomarkers if gaps are detected in the data.
57		-* Prioritizes tests based on clinical impact and cost-effectiveness.
	89	+Neurodiagnoses employs advanced AI models for biomarker classification:
58	58
59		-----
	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		-=== **4. Computational Workflow** ===
	96	+=== **Jupyter Integration with EBRAINS** ===
62	62
63		-1. **Data Loading**: Import data from storage (Drive or Bucket).
64		-1. **Feature Engineering**: Generate derived features from the raw data.
65		-1. **Model Training**:
66		-1*. Split data into training, validation, and test sets.
67		-1*. Train models with cross-validation to ensure robustness.
68		-1. **Evaluation**:
69		-1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
70		-1*. Compare against baseline models and domain benchmarks.
	98	+=== **Overview** ===
71	71
72		-----
	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**.
73	73
74		-=== **5. Validation** ===
	102	+=== **Key Capabilities of Jupyter in Neurodiagnoses** ===
75	75
76		-==== **Internal Validation** ====
	104	+==== **1. Neuroimaging Analysis (MRI, fMRI, PET)** ====
77	77
78		-* Test the system using simulated datasets and known clinical cases.
79		-* Fine-tune models based on validation results.
	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.
80	80
81		-==== **External Validation** ====
	114	+==== **2. EEG and MEG Signal Processing** ====
82	82
83		-* Collaborate with research institutions and hospitals to test the system in real-world settings.
84		-* Use anonymized patient data to ensure privacy compliance.
	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.
85	85
86		-----
	125	+==== **3. Machine Learning for Biomarker Discovery** ====
87	87
88		-=== **6. Collaborative Development** ===
	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.
89	89
90		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	137	+==== **4. Computational Simulations & Virtual Brain Models** ====
91	91
92		-* **Jupyter Notebooks**: For data analysis and pipeline development.
93		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
94		-* **Wiki Pages**: For documenting methods and results.
95		-* **Drive and Bucket**: For sharing code, data, and outputs.
96		-* **Collaboration with related projects: **For instance: [[//Beyond the hype: AI in dementia – from early risk detection to disease treatment//>>https://www.lethe-project.eu/beyond-the-hype-ai-in-dementia-from-early-risk-detection-to-disease-treatment/]]
	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.
97	97
98		-----
	146	+==== **5. Interactive Data Visualization & Reporting** ====
99	99
100		-=== **7. Tools and Technologies** ===
	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.
101	101
102		-* **Programming Languages**: Python for AI and data processing.
103		-* **Frameworks**:
104		-** TensorFlow and PyTorch for machine learning.
105		-** Flask or FastAPI for backend services.
106		-* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
107		-* **EBRAINS Services**:
108		-** Collaboratory Lab for running Notebooks.
109		-** Buckets for storing large datasets.
	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	+**Collaboration & Partnerships**
	192	+
	193	+Neurodiagnoses actively seeks partnerships with data providers to:
	194	+
	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.
	198	+
	199	+**Interested in Partnering?**
	200	+
	201	+If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	202	+
	203	+**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	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.

workflow neurodiagnoses.png

Author

...	...	@@ -1,0 +1,1 @@
	1	+XWiki.manuelmenendez

Size

...	...	@@ -1,0 +1,1 @@
	1	+157.5 KB

Content