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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. 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 -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**.
3 +**Neuromarker: Generalized Biomarker Ontology**
4 4  
5 -== **Workflow** ==
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. (((
8 -**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
7 +**Core Biomarker Categories**
9 9  
10 -* Create a **GitHub repository** for AI scripts and models.
11 -* Use **GitHub Projects** to manage research milestones.
12 -)))
13 -1. (((
14 -**We Use EBRAINS for Data & Collaboration**
9 +Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
15 15  
16 -* Store **biomarker and neuroimaging data** in **EBRAINS Buckets**.
17 -* Run **Jupyter Notebooks** in **EBRAINS Lab** to test AI models.
18 -* Use **EBRAINS Wiki** for structured documentation and research discussion.
19 -)))
11 +|=**Category**|=**Description**
12 +|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
13 +|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
14 +|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL, autoantiboides
15 +|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
16 +|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
17 +|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
18 +|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
19 +|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
20 20  
21 -----
21 +**Integrating External Databases into Neurodiagnoses**
22 22  
23 -== **1. Data Integration** ==
23 +To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
24 24  
25 -=== **EBRAINS Medical Informatics Platform (MIP)**. ===
26 -
27 -Neurodiagnoses integrates clinical data via the **EBRAINS Medical Informatics Platform (MIP)**. MIP federates decentralized clinical data, allowing Neurodiagnoses to securely access and process sensitive information for AI-based diagnostics.
28 -
29 -==== How It Works ====
30 -
31 -
32 32  1. (((
33 -**Authentication & API Access:**
26 +**Register for Access**
34 34  
35 -* Users must have an **EBRAINS account**.
36 -* Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
28 +* Each external database requires individual registration and access approval.
29 +* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
30 +* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
37 37  )))
38 38  1. (((
39 -**Data Mapping & Harmonization:**
33 +**Download & Prepare Data**
40 40  
41 -* Retrieved data is **normalized** and converted to standard formats (.csv, .json).
42 -* Data from **multiple sources** is harmonized to ensure consistency for AI processing.
43 -)))
44 -1. (((
45 -**Security & Compliance:**
35 +* Download datasets while adhering to database usage policies.
36 +* (((
37 +Ensure files meet Neurodiagnoses format requirements:
46 46  
47 -* All data access is **logged and monitored**.
48 -* Data remains on **MIP servers** using **federated learning techniques** when possible.
49 -* Access is granted only after signing a **Data Usage Agreement (DUA)**.
39 +|=**Data Type**|=**Accepted Formats**
40 +|**Tabular Data**|.csv, .tsv
41 +|**Neuroimaging**|.nii, .dcm
42 +|**Genomic Data**|.fasta, .vcf
43 +|**Clinical Metadata**|.json, .xml
50 50  )))
45 +* (((
46 +**Mandatory Fields for Integration**:
51 51  
52 -==== Implementation Steps ====
53 -
54 -
55 -1. Clone the repository.
56 -1. Configure your **EBRAINS API credentials** in mip_integration.py.
57 -1. Run the script to **download and harmonize clinical data**.
58 -1. Process the data for **AI model training**.
59 -
60 -For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
61 -
62 -----
63 -
64 -=== Data Processing & Integration with Clinica.Run ===
65 -
66 -Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
67 -
68 -==== How It Works ====
69 -
70 -
48 +* Subject ID: Unique patient identifier
49 +* Diagnosis: Standardized disease classification
50 +* Biomarkers: CSF, plasma, or imaging biomarkers
51 +* Genetic Data: Whole-genome or exome sequencing
52 +* Neuroimaging Metadata: MRI/PET acquisition parameters
53 +)))
54 +)))
71 71  1. (((
72 -**Neuroimaging Preprocessing:**
56 +**Upload Data to Neurodiagnoses**
73 73  
74 -* MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
75 -* Supports **longitudinal and cross-sectional analyses**.
58 +* (((
59 +**Option 1: Upload to EBRAINS Bucket**
60 +
61 +* Location: EBRAINS Neurodiagnoses Bucket
62 +* Ensure correct metadata tagging before submission.
76 76  )))
77 -1. (((
78 -**Automated Biomarker Extraction:**
64 +* (((
65 +**Option 2: Contribute via GitHub Repository**
79 79  
80 -* Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
81 -* Integration with machine learning models in Neurodiagnoses.
67 +* Location: GitHub Data Repository
68 +* Create a new folder under /data/ and include a dataset description.
69 +* For large datasets, contact project administrators before uploading.
82 82  )))
71 +)))
83 83  1. (((
84 -**Data Security & Compliance:**
73 +**Integrate Data into AI Models**
85 85  
86 -* Clinica.Run operates in **compliance with GDPR and HIPAA**.
87 -* Neuroimaging data remains **within the original storage environment**.
75 +* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
76 +* Standardize neuroimaging and biomarker formats using harmonization tools.
77 +* Utilize machine learning models to handle missing data and feature extraction.
78 +* Train AI models with newly integrated patient cohorts.
79 +
80 +**Reference**: See docs/data_processing.md for detailed instructions.
88 88  )))
89 89  
90 -==== Implementation Steps ====
83 +**AI-Driven Biomarker Categorization**
91 91  
85 +Neurodiagnoses employs advanced AI models for biomarker classification:
92 92  
93 -1. Install **Clinica.Run** dependencies.
94 -1. Configure your **Clinica.Run pipeline** in clinica_run_config.json.
95 -1. Run the pipeline for **preprocessing and biomarker extraction**.
96 -1. Use processed neuroimaging data for **AI-driven diagnostics** in Neurodiagnoses.
87 +|=**Model Type**|=**Application**
88 +|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
89 +|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
90 +|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
97 97  
98 -For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
92 +**Collaboration & Partnerships**
99 99  
100 -==== ====
94 +Neurodiagnoses actively seeks partnerships with data providers to:
101 101  
102 -==== **Data Sources** ====
96 +* Enable API-based data integration for real-time processing.
97 +* Co-develop harmonized AI-ready datasets with standardized annotations.
98 +* Secure funding opportunities through joint grant applications.
103 103  
104 -[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
100 +**Interested in Partnering?**
105 105  
106 -**Biomedical Ontologies & Databases:**
102 +If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
107 107  
108 -* **Human Phenotype Ontology (HPO)** for symptom annotation.
109 -* **Gene Ontology (GO)** for molecular and cellular processes.
104 +**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
110 110  
111 -**Dimensionality Reduction and Interpretability:**
112 -
113 -* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
114 -* **Leverage [[DEIBO>>https://github.com/Mellandd/DEIBO]] (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
115 -
116 -**Neuroimaging & EEG/MEG Data:**
117 -
118 -* **MRI volumetric measures** for brain atrophy tracking.
119 -* **EEG functional connectivity patterns** (AI-Mind).
120 -
121 -**Clinical & Biomarker Data:**
122 -
123 -* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
124 -* **Sleep monitoring and actigraphy data** (ADIS).
125 -
126 -**Federated Learning Integration:**
127 -
128 -* **Secure multi-center data harmonization** (PROMINENT).
129 -
130 -----
131 -
132 -==== **Annotation System for Multi-Modal Data** ====
133 -
134 -To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
135 -
136 -* **Assign standardized metadata tags** to diagnostic features.
137 -* **Provide contextual explanations** for AI-based classifications.
138 -* **Track temporal disease progression annotations** to identify long-term trends.
139 -
140 -----
141 -
142 -== **2. AI-Based Analysis** ==
143 -
144 -==== **Machine Learning & Deep Learning Models** ====
145 -
146 -**Risk Prediction Models:**
147 -
148 -* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
149 -
150 -**Biomarker Classification & Probabilistic Imputation:**
151 -
152 -* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
153 -
154 -**Neuroimaging Feature Extraction:**
155 -
156 -* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
157 -
158 -==== **AI-Powered Annotation System** ====
159 -
160 -* Uses **SHAP-based interpretability tools** to explain model decisions.
161 -* Generates **automated clinical annotations** in structured reports.
162 -* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
163 -
164 -----
165 -
166 -== **3. Diagnostic Framework & Clinical Decision Support** ==
167 -
168 -==== **Tridimensional Diagnostic Axes** ====
169 -
170 -**Axis 1: Etiology (Pathogenic Mechanisms)**
171 -
172 -* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
173 -* **AI-assisted annotation** provides **causal interpretations** for clinical use.
174 -
175 -**Axis 2: Molecular Markers & Biomarkers**
176 -
177 -* **Integration of CSF, blood, and neuroimaging biomarkers**.
178 -* **Structured annotation** highlights **biological pathways linked to diagnosis**.
179 -
180 -**Axis 3: Neuroanatomoclinical Correlations**
181 -
182 -* **MRI and EEG data** provide anatomical and functional insights.
183 -* **AI-generated progression maps** annotate **brain structure-function relationships**.
184 -
185 -----
186 -
187 -== **4. Computational Workflow & Annotation Pipelines** ==
188 -
189 -==== **Data Processing Steps** ====
190 -
191 -**Data Ingestion:**
192 -
193 -* **Harmonized datasets** stored in **EBRAINS Bucket**.
194 -* **Preprocessing pipelines** clean and standardize data.
195 -
196 -**Feature Engineering:**
197 -
198 -* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
199 -
200 -**AI-Generated Annotations:**
201 -
202 -* **Automated tagging** of diagnostic features in **structured reports**.
203 -* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
204 -
205 -**Clinical Decision Support Integration:**
206 -
207 -* **AI-annotated findings** fed into **interactive dashboards**.
208 -* **Clinicians can adjust, validate, and modify annotations**.
209 -
210 -----
211 -
212 -== **5. Validation & Real-World Testing** ==
213 -
214 -==== **Prospective Clinical Study** ====
215 -
216 -* **Multi-center validation** of AI-based **annotations & risk stratifications**.
217 -* **Benchmarking against clinician-based diagnoses**.
218 -* **Real-world testing** of AI-powered **structured reporting**.
219 -
220 -==== **Quality Assurance & Explainability** ====
221 -
222 -* **Annotations linked to structured knowledge graphs** for improved transparency.
223 -* **Interactive annotation editor** allows clinicians to validate AI outputs.
224 -
225 -----
226 -
227 -== **6. Collaborative Development** ==
228 -
229 -The project is **open to contributions** from **researchers, clinicians, and developers**.
230 -
231 -**Key tools include:**
232 -
233 -* **Jupyter Notebooks**: For data analysis and pipeline development.
234 -** Example: **probabilistic imputation**
235 -* **Wiki Pages**: For documenting methods and results.
236 -* **Drive and Bucket**: For sharing code, data, and outputs.
237 -* **Collaboration with related projects**:
238 -** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
239 -
240 -----
241 -
242 -== **7. Tools and Technologies** ==
243 -
244 -==== **Programming Languages:** ====
245 -
246 -* **Python** for AI and data processing.
247 -
248 -==== **Frameworks:** ====
249 -
250 -* **TensorFlow** and **PyTorch** for machine learning.
251 -* **Flask** or **FastAPI** for backend services.
252 -
253 -==== **Visualization:** ====
254 -
255 -* **Plotly** and **Matplotlib** for interactive and static visualizations.
256 -
257 -==== **EBRAINS Services:** ====
258 -
259 -* **Collaboratory Lab** for running Notebooks.
260 -* **Buckets** for storing large datasets.
261 -
262 -----
263 -
264 -=== **Why This Matters** ===
265 -
266 -* The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
267 -* It enables real-time tracking of disease progression across the three diagnostic axes.
268 -* It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.
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