NeuroSignature
A computational platform for computing brain-connectome signatures, neuroimaging data analysis and graph-theoretic network metrics, built for neuroscience researchers and clinical neuroimaging teams.
Brain Connectivity Analysis, Simplified
NeuroSignature takes neuroimaging data and returns the graph-theoretic metrics that characterise how a brain is connected, without requiring local HPC infrastructure or bespoke Python pipelines.
Connectome Analysis
Upload a connectivity matrix derived from resting-state fMRI or structural DTI tractography. NeuroSignature computes whole-brain and regional connectivity signatures.
Graph Theory Metrics
Computes global efficiency (GE), local efficiency (LE), clustering coefficient (CC), modularity (Q), characteristic path length, betweenness centrality and more from your connectivity data.
Batch Processing
Process multiple subjects in a single batch, upload a cohort of connectivity matrices and receive a summary output table ready for statistical analysis and group comparisons.
fMRI & DTI Support
Accepts both functional (resting-state fMRI) and structural (DTI-derived) connectivity inputs. Compatible with standard parcellation atlases including AAL, Schaefer and HarvardOxford.
Network Visualisation
Interactive brain network graphs rendered in the platform, view hub nodes, high-degree connections and community structure with colour-coded module assignments.
Export & Reports
Download results as CSV, JSON or PDF summary report, with metric tables, connectivity visualisations and ready-to-use outputs for manuscript preparation.
Who Uses NeuroSignature
Clinical Neuroimaging Researchers
Researchers studying neurological and psychiatric conditions, Alzheimer's disease, schizophrenia, depression, epilepsy, use NeuroSignature to compare connectivity metrics between patient groups and healthy controls, without needing bespoke analysis scripts.
Typical use: upload preprocessed connectivity matrices from patient and control cohorts; download a group-comparison table of graph-theoretic metrics for statistical analysis in R.
Neuroscience PhD Researchers
Graduate researchers who need reliable graph-theoretic analysis without building their own NetworkX pipelines from scratch. NeuroSignature handles the computation; researchers focus on the biology and interpretation.
Typical use: upload single-subject or group-level connectivity matrices to obtain publication-ready metrics and figures.
Neurology & Psychiatry Clinicians (Research)
Clinical teams conducting neuroimaging research alongside patient care, who need accessible brain-connectivity analysis without deep computational biology expertise on the team.
Typical use: hospital-based study with DTI data; use NeuroSignature to extract structural connectivity metrics for a paper on white matter alterations.
Pharmaceutical Neuroscience Teams
Pharma teams developing CNS drugs who need biomarkers of brain connectivity as secondary endpoints or exploratory measures in clinical trials, requiring computational analysis of neuroimaging data.
Typical use: fMRI connectivity analysis for a Phase II CNS trial to evaluate network-level treatment effects.
Under the Hood
NeuroSignature is built on validated scientific software and peer-reviewed methodology, the same computational backbone used in neuroimaging research labs worldwide.
Core Technologies
- NetworkX, graph computation and metrics
- nilearn / nibabel, neuroimaging data I/O
- NumPy / SciPy, numerical computation
- Python, analysis backend
- Django REST Framework, API layer
Supported Input Formats
- CSV connectivity matrices (N×N)
- NumPy .npy array files
- FSL-format text matrices
- Standard parcellation schemes (AAL, Schaefer, HO)
- Batch ZIP uploads for cohort processing
Ready to analyse your neuroimaging data?
NeuroSignature is live and accessible. Launch the platform or contact us for research support.