R&D Discipline 05

Neuroinformatics

fMRI and DTI data processing, brain connectivity analysis, graph-theoretic network metrics, connectomics and atlas-based parcellation, the computational backbone of modern computational neuroscience. Also powering the NeuroSignature platform.

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Capabilities

Neuroinformatics Services

Analysis of neuroimaging data from preprocessing through connectivity modelling and group-level statistics. Engagements cover fMRI, DTI, and connectome-level outputs.

01 fMRI

Resting-State fMRI Analysis

Input: NIfTI / DICOM

Standard preprocessing pipeline via fMRIPrep: motion correction, slice-timing correction, spatial normalisation to MNI space, temporal filtering, and confound regression. Functional connectivity matrices are derived using atlas-based parcellation (AAL, Schaefer, Harvard-Oxford). Outputs include seed-based correlation maps, ICA decomposition, and full connectivity matrices in CSV/MAT format for downstream graph analysis.

fMRIPrepFSLSPM12nilearnAFNIMELODIC ICA
02 DTI

DTI & White Matter Tractography

Input: DWI NIfTI + bvec/bval

Diffusion-weighted image preprocessing (denoising, Gibbs ringing correction, eddy current correction), tensor estimation, and FA/MD/RD/AD scalar map computation. Probabilistic or deterministic tractography for tract delineation and structural connectivity matrix construction using atlas-based ROI masking. Outputs include per-tract FA/MD values, connectivity matrices, and 3D tract visualisations.

MRtrix3FSL FDTDSI StudioTrackVisDIPY
03 NET

Brain Connectivity & Graph Theory

Input: connectivity matrix

Construction of weighted brain graphs from functional or structural connectivity matrices. Graph-theoretic metrics computed across a range of proportional thresholds: global efficiency, local efficiency, clustering coefficient, characteristic path length, modularity (Q), betweenness centrality, degree distribution, and rich-club coefficient. Outputs are per-subject metric tables and group-level summary statistics ready for statistical testing.

NetworkXigraphBCT (Brain Connectivity Toolbox)nilearnPython
04 ATL

Atlas-Based Parcellation

Input: preprocessed fMRI / DWI

Parcellation of brain volumes using standard anatomical and functional atlases: AAL3 (166 regions), Schaefer (100-400 parcels at 7 or 17 networks), Harvard-Oxford cortical and subcortical, Brainnetome, Gordon (333 parcels), and Destrieux. Atlas images are registered to subject space and regional time-series extracted for connectivity estimation. Atlas choice is matched to the research question and cohort characteristics.

AAL3Schaefer 2018Harvard-OxfordBrainnetomenilearnFSL
05 STAT

Group-Level Neuroimaging Statistics

Input: per-subject metrics

Between-group comparison of connectivity and graph metrics using parametric and non-parametric tests, corrected for multiple comparisons (FDR, FWE, permutation). Network-Based Statistics (NBS) for edge-level inference on whole-brain connectivity matrices without mass-univariate correction. GLM-based analysis with nuisance covariate modelling (age, sex, motion, site). Results reported with effect sizes and confidence intervals.

NBSFSL randomiseR (lme4)Permutation testingFDR / FWE
NeuroSignature

Neuroinformatics in a Platform

The NeuroSignature platform makes 7DNA's neuroinformatics computation accessible without requiring local software installation or bespoke analysis pipelines. Upload your connectivity data; receive graph-theoretic metrics, visualisations and downloadable results.

  • ✓Accepts CSV matrices, NumPy arrays and FSL-format inputs
  • ✓Graph metrics: GE, LE, CC, modularity, path length
  • ✓Batch processing for cohort-level analysis
  • ✓Export as CSV/PDF for statistical downstream analysis
Learn About NeuroSignature Launch Platform
GE
0.72
LE
0.61
CC
0.44
Tools

Neuroinformatics Software Stack

FSL

Oxford's neuroimaging library, preprocessing, tractography, group stats

SPM12

Statistical parametric mapping for neuroimaging mass-univariate analyses

nilearn

Python neuroimaging, connectivity, ICA, decoding and visualisation

MRtrix3

Advanced diffusion MRI and structural tractography

Neuroinformatics R&D

Have neuroimaging data that needs computational analysis?

fMRI, DTI, or connectivity matrices, tell us about your data and we'll scope an approach.

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