Summary

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

Details

Page properties

Content

...	...	@@ -1,146 +1,109 @@
1		-Here is the updated **Methodology** section for the EBRAINS Wiki, incorporating the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)** for **biomarker classification across all neurodegenerative diseases**.
	1	+=== **Overview** ===
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.
	4	+
3	3	----
4	4
5		-== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
	7	+=== **1. Data Integration** ===
6	6
7		-=== **Project Overview** ===
	9	+==== **Data Sources** ====
8	8
9		-Neurodiagnoses AI implements **AI-driven diagnostic and prognostic models** for central nervous system (CNS) disorders, expanding the **Florey Dementia Index (FDI) methodology** to a broader set of neurological conditions. The approach integrates **multimodal data sources** (EEG, neuroimaging, biomarkers, and genetics) and employs machine learning models to provide **explainable, real-time diagnostic insights**. This framework now incorporates **Neuromarker**, a **generalized biomarker ontology** that categorizes biomarkers across neurodegenerative diseases, enabling **standardized, cross-disease AI training**.
	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.
10	10
11		-== **Neuromarker: Generalized Biomarker Ontology** ==
12	12
13		-Neuromarker extends the **Common Alzheimer’s Disease Research Ontology (CADRO)** into a **cross-disease biomarker categorization framework** applicable to all neurodegenerative diseases (NDDs). It allows for **standardized classification, AI-based feature extraction, and multimodal integration**.
	20	+==== **Data Preprocessing** ====
14	14
15		-=== **Core Biomarker Categories** ===
	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.
16	16
17		-The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
18		-
19		-|=**Category**|=**Description**
20		-|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
21		-|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
22		-|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
23		-|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
24		-|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
25		-|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
26		-|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
27		-|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
28		-
29	29	----
30	30
31		-== **How to Use External Databases in Neurodiagnoses** ==
	28	+=== **2. AI-Based Analysis** ===
32	32
33		-To enhance diagnostic accuracy, Neurodiagnoses AI integrates data from **multiple biomedical and neurological research databases**. Researchers can follow these steps to access, prepare, and integrate data into the Neurodiagnoses framework.
	30	+==== **Model Development** ====
34	34
35		-=== **Potential Data Sources** ===
	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.
36	36
37		-Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
	37	+==== **Dimensionality Reduction and Interpretability** ====
38	38
39		-* **ADNI**: Alzheimer's Disease Imaging & Biomarkers → [[ADNI>>url:https://adni.loni.usc.edu/]]
40		-* **PPMI**: Parkinson’s Disease Imaging & Biospecimens → [[PPMI>>url:https://www.ppmi-info.org/]]
41		-* **GP2**: Whole-Genome Sequencing for PD → [[GP2>>url:https://gp2.org/]]
42		-* **Enroll-HD**: Huntington’s Disease Clinical & Genetic Data → [[Enroll-HD>>url:https://www.enroll-hd.org/]]
43		-* **GAAIN**: Multi-Source Alzheimer’s Data Aggregation → [[GAAIN>>url:https://gaain.org/]]
44		-* **UK Biobank**: Population-Wide Genetic, Imaging & Health Records → [[UK Biobank>>url:https://www.ukbiobank.ac.uk/]]
45		-* **DPUK**: Dementia & Aging Data → [[DPUK>>url:https://www.dementiasplatform.uk/]]
46		-* **PRION Registry**: Prion Diseases Clinical & Genetic Data → [[PRION Registry>>url:https://prionregistry.org/]]
47		-* **DECIPHER**: Rare Genetic Disorder Genomic Variants → [[DECIPHER>>url:https://decipher.sanger.ac.uk/]]
	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).
48	48
49	49	----
50	50
51		-== **1. Register for Access** ==
	44	+=== **3. Diagnostic Framework** ===
52	52
53		-* Each external database requires **individual registration and access approval**.
54		-* Ensure compliance with **ethical approvals and data usage agreements** before integrating datasets into Neurodiagnoses.
55		-* Some repositories may require a **Data Usage Agreement (DUA)** for sensitive medical data.
	46	+==== **Axes of Diagnosis** ====
56	56
57		-----
	48	+The framework organizes diagnostic data into three axes:
58	58
59		-== **2. Download & Prepare Data** ==
	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).
60	60
61		-* Download datasets while adhering to **database usage policies**.
62		-* Ensure files meet **Neurodiagnoses format requirements**:
	54	+==== **Recommendation System** ====
63	63
64		-|=**Data Type**|=**Accepted Formats**
65		-|**Tabular Data**|.csv, .tsv
66		-|**Neuroimaging**|.nii, .dcm
67		-|**Genomic Data**|.fasta, .vcf
68		-|**Clinical Metadata**|.json, .xml
	56	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	57	+* Prioritizes tests based on clinical impact and cost-effectiveness.
69	69
70		-* **Mandatory Fields for Integration**:
71		-** **Subject ID**: Unique patient identifier
72		-** **Diagnosis**: Standardized disease classification
73		-** **Biomarkers**: CSF, plasma, or imaging biomarkers
74		-** **Genetic Data**: Whole-genome or exome sequencing
75		-** **Neuroimaging Metadata**: MRI/PET acquisition parameters
76		-
77	77	----
78	78
79		-== **3. Upload Data to Neurodiagnoses** ==
	61	+=== **4. Computational Workflow** ===
80	80
81		-=== **Option 1: Upload to EBRAINS Bucket** ===
	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.
82	82
83		-* Location: **EBRAINS Neurodiagnoses Bucket**
84		-* Ensure **correct metadata tagging** before submission.
85		-
86		-=== **Option 2: Contribute via GitHub Repository** ===
87		-
88		-* Location: **GitHub Data Repository**
89		-* Create a **new folder under /data/** and include a **dataset description**.
90		-* **For large datasets**, contact project administrators before uploading.
91		-
92	92	----
93	93
94		-== **4. Integrate Data into AI Models** ==
	74	+=== **5. Validation** ===
95	95
96		-* Open **Jupyter Notebooks** on EBRAINS to run **preprocessing scripts**.
97		-* **Standardize neuroimaging and biomarker formats** using harmonization tools.
98		-* Use **machine learning models** to handle **missing data** and **feature extraction**.
99		-* Train AI models with **newly integrated patient cohorts**.
	76	+==== **Internal Validation** ====
100	100
101		-**Reference**: See docs/data_processing.md for detailed instructions.
	78	+* Test the system using simulated datasets and known clinical cases.
	79	+* Fine-tune models based on validation results.
102	102
103		-----
	81	+==== **External Validation** ====
104	104
105		-== **AI-Driven Biomarker Categorization** ==
	83	+* Collaborate with research institutions and hospitals to test the system in real-world settings.
	84	+* Use anonymized patient data to ensure privacy compliance.
106	106
107		-Neurodiagnoses employs **AI models** for biomarker classification:
108		-
109		-|=**Model Type**|=**Application**
110		-|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
111		-|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
112		-|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
113		-
114	114	----
115	115
116		-== **Collaboration & Partnerships** ==
	88	+=== **6. Collaborative Development** ===
117	117
118		-=== **Partnering with Data Providers** ===
	90	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
119	119
120		-Neurodiagnoses seeks partnerships with data repositories to:
	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	+* **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/]]
121	121
122		-* Enable **API-based data integration** for real-time processing.
123		-* Co-develop **harmonized AI-ready datasets** with standardized annotations.
124		-* Secure **funding opportunities** through joint grant applications.
125		-
126		-**Interested in Partnering?**
127		-
128		-* If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
129		-* **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
130		-
131	131	----
132	132
133		-== **Final Notes** ==
	100	+=== **7. Tools and Technologies** ===
134	134
135		-Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
136		-
137		-**For additional technical documentation**:
138		-
139		-* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
140		-* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
141		-
142		-**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
143		-
144		-----
145		-
146		-This **updated methodology** now incorporates [[https:~~/~~/github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/biomarker_ontology>>https://Neuromarker]] for standardized biomarker classification, enabling **cross-disease AI training** across neurodegenerative disorders.
	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.