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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.
	1	+=== **Overview** ===
2	2
3		-**Neuromarker: Generalized Biomarker Ontology**
	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	4
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.
	5	+----
6	6
7		-**Core Biomarker Categories**
	7	+=== **1. Data Integration** ===
8	8
9		-Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
	9	+==== **Data Sources** ====
10	10
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
	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.
20	20
21		-**Integrating External Databases into Neurodiagnoses**
22	22
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:
	20	+==== **Data Preprocessing** ====
24	24
25		-1. (((
26		-**Register for Access**
	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.
27	27
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.
31		-)))
32		-1. (((
33		-**Download & Prepare Data**
	26	+----
34	34
35		-* Download datasets while adhering to database usage policies.
36		-* (((
37		-Ensure files meet Neurodiagnoses format requirements:
	28	+=== **2. AI-Based Analysis** ===
38	38
39		-|=**Data Type**|=**Accepted Formats**
40		-|**Tabular Data**|.csv, .tsv
41		-|**Neuroimaging**|.nii, .dcm
42		-|**Genomic Data**|.fasta, .vcf
43		-|**Clinical Metadata**|.json, .xml
44		-)))
45		-* (((
46		-**Mandatory Fields for Integration**:
	30	+==== **Model Development** ====
47	47
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		-)))
55		-1. (((
56		-**Upload Data to Neurodiagnoses**
	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.
57	57
58		-* (((
59		-**Option 1: Upload to EBRAINS Bucket**
	37	+==== **Dimensionality Reduction and Interpretability** ====
60	60
61		-* Location: EBRAINS Neurodiagnoses Bucket
62		-* Ensure correct metadata tagging before submission.
63		-)))
64		-* (((
65		-**Option 2: Contribute via GitHub Repository**
	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).
66	66
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.
70		-)))
71		-)))
72		-1. (((
73		-**Integrate Data into AI Models**
	42	+----
74	74
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.
	44	+=== **3. Diagnostic Framework** ===
79	79
80		-**Reference**: See docs/data_processing.md for detailed instructions.
81		-)))
	46	+==== **Axes of Diagnosis** ====
82	82
83		-**AI-Driven Biomarker Categorization**
	48	+The framework organizes diagnostic data into three axes:
84	84
85		-Neurodiagnoses employs advanced AI models for biomarker classification:
	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).
86	86
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
	54	+==== **Recommendation System** ====
91	91
92		-**Collaboration & Partnerships**
	56	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	57	+* Prioritizes tests based on clinical impact and cost-effectiveness.
93	93
94		-Neurodiagnoses actively seeks partnerships with data providers to:
	59	+----
95	95
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.
	61	+=== **4. Computational Workflow** ===
99	99
100		-**Interested in Partnering?**
	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.
101	101
102		-If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
	72	+----
103	103
104		-**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
	74	+=== **5. Validation** ===
105	105
106		-
	76	+==== **Internal Validation** ====
	77	+
	78	+* Test the system using simulated datasets and known clinical cases.
	79	+* Fine-tune models based on validation results.
	80	+
	81	+==== **External Validation** ====
	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.
	85	+
	86	+----
	87	+
	88	+=== **6. Collaborative Development** ===
	89	+
	90	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	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	+* **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/]]
	97	+
	98	+----
	99	+
	100	+=== **7. Tools and Technologies** ===
	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.

workflow neurodiagnoses.png

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