Summary

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

Details

Page properties

Content

...	...	@@ -1,173 +1,148 @@
1		-==== **Overview** ====
	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**.
2	2
3		-This project develops a **tridimensional diagnostic framework** for **CNS diseases**, incorporating **AI-powered annotation tools** to improve **interpretability, standardization, and clinical utility**. The methodology integrates **multi-modal data**, including **genetic, neuroimaging, neurophysiological, and biomarker datasets**, and applies **machine learning models** to generate **structured, explainable diagnostic outputs**.
4		-
5	5	----
6	6
7		-=== **1. Data Integration** ===
	5	+== **Neurodiagnoses AI: Multimodal AI for Neurodiagnostic Predictions** ==
8	8
9		-==== **Data Sources** ====
	7	+=== **Project Overview** ===
10	10
11		-**Biomedical Ontologies & Databases:**
	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**.
12	12
13		-* **Human Phenotype Ontology (HPO)** for symptom annotation.
14		-* **Gene Ontology (GO)** for molecular and cellular processes.
	11	+== **Neuromarker: Generalized Biomarker Ontology** ==
15	15
16		-**Dimensionality Reduction and Interpretability:**
	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**.
17	17
18		-* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
19		-* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
	15	+=== **Core Biomarker Categories** ===
20	20
21		-**Neuroimaging & EEG/MEG Data:**
	17	+The following ontology is used within **Neurodiagnoses AI** for biomarker categorization:
22	22
23		-* **MRI volumetric measures** for brain atrophy tracking.
24		-* **EEG functional connectivity patterns** (AI-Mind).
	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
25	25
26		-**Clinical & Biomarker Data:**
	29	+----
27	27
28		-* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
29		-* **Sleep monitoring and actigraphy data** (ADIS).
	31	+== **How to Use External Databases in Neurodiagnoses** ==
30	30
31		-**Federated Learning Integration:**
	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.
32	32
33		-* **Secure multi-center data harmonization** (PROMINENT).
	35	+=== **Potential Data Sources** ===
34	34
35		-----
	37	+Neurodiagnoses maintains an **updated list** of biomedical datasets relevant to neurodegenerative diseases:
36	36
37		-==== **Annotation System for Multi-Modal Data** ====
	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/]]
38	38
39		-To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
40		-
41		-* **Assign standardized metadata tags** to diagnostic features.
42		-* **Provide contextual explanations** for AI-based classifications.
43		-* **Track temporal disease progression annotations** to identify long-term trends.
44		-
45	45	----
46	46
47		-=== **2. AI-Based Analysis** ===
	51	+== **1. Register for Access** ==
48	48
49		-==== **Machine Learning & Deep Learning Models** ====
	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.
50	50
51		-**Risk Prediction Models:**
	57	+----
52	52
53		-* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	59	+== **2. Download & Prepare Data** ==
54	54
55		-**Biomarker Classification & Probabilistic Imputation:**
	61	+* Download datasets while adhering to **database usage policies**.
	62	+* Ensure files meet **Neurodiagnoses format requirements**:
56	56
57		-* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
	64	+|=**Data Type**|=**Accepted Formats**
	65	+|**Tabular Data**|.csv, .tsv
	66	+|**Neuroimaging**|.nii, .dcm
	67	+|**Genomic Data**|.fasta, .vcf
	68	+|**Clinical Metadata**|.json, .xml
58	58
59		-**Neuroimaging Feature Extraction:**
	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
60	60
61		-* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
62		-
63		-==== **AI-Powered Annotation System** ====
64		-
65		-* Uses **SHAP-based interpretability tools** to explain model decisions.
66		-* Generates **automated clinical annotations** in structured reports.
67		-* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
68		-
69	69	----
70	70
71		-=== **3. Diagnostic Framework & Clinical Decision Support** ===
	79	+== **3. Upload Data to Neurodiagnoses** ==
72	72
73		-==== **Tridimensional Diagnostic Axes** ====
	81	+=== **Option 1: Upload to EBRAINS Bucket** ===
74	74
75		-**Axis 1: Etiology (Pathogenic Mechanisms)**
	83	+* Location: **EBRAINS Neurodiagnoses Bucket**
	84	+* Ensure **correct metadata tagging** before submission.
76	76
77		-* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
78		-* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	86	+=== **Option 2: Contribute via GitHub Repository** ===
79	79
80		-**Axis 2: Molecular Markers & Biomarkers**
	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.
81	81
82		-* **Integration of CSF, blood, and neuroimaging biomarkers**.
83		-* **Structured annotation** highlights **biological pathways linked to diagnosis**.
84		-
85		-**Axis 3: Neuroanatomoclinical Correlations**
86		-
87		-* **MRI and EEG data** provide anatomical and functional insights.
88		-* **AI-generated progression maps** annotate **brain structure-function relationships**.
89		-
90	90	----
91	91
92		-=== **4. Computational Workflow & Annotation Pipelines** ===
	94	+== **4. Integrate Data into AI Models** ==
93	93
94		-==== **Data Processing Steps** ====
	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**.
95	95
96		-**Data Ingestion:**
	101	+**Reference**: See docs/data_processing.md for detailed instructions.
97	97
98		-* **Harmonized datasets** stored in **EBRAINS Bucket**.
99		-* **Preprocessing pipelines** clean and standardize data.
	103	+----
100	100
101		-**Feature Engineering:**
	105	+== **AI-Driven Biomarker Categorization** ==
102	102
103		-* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
	107	+Neurodiagnoses employs **AI models** for biomarker classification:
104	104
105		-**AI-Generated Annotations:**
	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
106	106
107		-* **Automated tagging** of diagnostic features in **structured reports**.
108		-* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
109		-
110		-**Clinical Decision Support Integration:**
111		-
112		-* **AI-annotated findings** fed into **interactive dashboards**.
113		-* **Clinicians can adjust, validate, and modify annotations**.
114		-
115	115	----
116	116
117		-=== **5. Validation & Real-World Testing** ===
	116	+== [[image:workflow neurodiagnoses.png]] ==
118	118
119		-==== **Prospective Clinical Study** ====
	118	+== **Collaboration & Partnerships** ==
120	120
121		-* **Multi-center validation** of AI-based **annotations & risk stratifications**.
122		-* **Benchmarking against clinician-based diagnoses**.
123		-* **Real-world testing** of AI-powered **structured reporting**.
	120	+=== **Partnering with Data Providers** ===
124	124
125		-==== **Quality Assurance & Explainability** ====
	122	+Neurodiagnoses seeks partnerships with data repositories to:
126	126
127		-* **Annotations linked to structured knowledge graphs** for improved transparency.
128		-* **Interactive annotation editor** allows clinicians to validate AI outputs.
	124	+* Enable **API-based data integration** for real-time processing.
	125	+* Co-develop **harmonized AI-ready datasets** with standardized annotations.
	126	+* Secure **funding opportunities** through joint grant applications.
129	129
130		-----
	128	+**Interested in Partnering?**
131	131
132		-=== **6. Collaborative Development** ===
	130	+* If you represent a **research consortium or database provider**, reach out to explore **data-sharing agreements**.
	131	+* **Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
133	133
134		-The project is **open to contributions** from **researchers, clinicians, and developers**.
135		-
136		-**Key tools include:**
137		-
138		-* **Jupyter Notebooks**: For data analysis and pipeline development.
139		-** Example: **probabilistic imputation**
140		-* **Wiki Pages**: For documenting methods and results.
141		-* **Drive and Bucket**: For sharing code, data, and outputs.
142		-* **Collaboration with related projects**:
143		-** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
144		-
145	145	----
146	146
147		-=== **7. Tools and Technologies** ===
	135	+== **Final Notes** ==
148	148
149		-==== **Programming Languages:** ====
	137	+Neurodiagnoses continuously expands its **data ecosystem** to support **AI-driven clinical decision-making**. Researchers and institutions are encouraged to **contribute new datasets and methodologies**.
150	150
151		-* **Python** for AI and data processing.
	139	+**For additional technical documentation**:
152	152
153		-==== **Frameworks:** ====
	141	+* **GitHub Repository**: [[Neurodiagnoses GitHub>>url:https://github.com/neurodiagnoses]]
	142	+* **EBRAINS Collaboration Page**: [[EBRAINS Neurodiagnoses>>url:https://ebrains.eu/collabs/neurodiagnoses]]
154	154
155		-* **TensorFlow** and **PyTorch** for machine learning.
156		-* **Flask** or **FastAPI** for backend services.
	144	+**If you experience issues integrating data**, open a **GitHub Issue** or consult the **EBRAINS Neurodiagnoses Forum**.
157	157
158		-==== **Visualization:** ====
159		-
160		-* **Plotly** and **Matplotlib** for interactive and static visualizations.
161		-
162		-==== **EBRAINS Services:** ====
163		-
164		-* **Collaboratory Lab** for running Notebooks.
165		-* **Buckets** for storing large datasets.
166		-
167	167	----
168	168
169		-=== **Why This Matters** ===
170		-
171		-* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
172		-* **It enables real-time tracking of disease progression across the three diagnostic axes.**
173		-* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**
	148	+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.

workflow neurodiagnoses.png

Author

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

Size

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

Content