title: Normalisation of neonatal brain network measures using stochastic approaches.
abstract: Diffusion tensor imaging, tractography and the subsequent derivation of network measures are becoming an established approach in the exploration of brain connectivity. However, no gold standard exists in respect to how the brain should be parcellated and therefore a variety of atlas- and random-based parcellation methods are used. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences necessitates the use of normalisation schemes to enable meaningful intra- and inter-subject comparisons. This work proposes methods for normalising brain network measures using random graphs. We show that the normalised measures are locally stable over distinct random parcellations of the same subject and, applying it to a neonatal serial diffusion MRI data set, we demonstrate their potential in characterising changes in brain connectivity during early development.
authors: Division of Imaging Sciences & Biomedical Engineering, King's College London, UK. markus.schirmer@kcl.ac.uk
authors: Markus
authors: M
authors: Schirmer
authors: Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors: Gareth
authors: G
authors: Ball
authors: Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors: Serena J
authors: SJ
authors: Counsell
authors: Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors: A David
authors: AD
authors: Edwards
authors: BioMedIA Group, Dept. of Computing, Imperial College London, UK.
authors: Daniel
authors: D
authors: Rueckert
authors: Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors: Joseph V
authors: JV
authors: Hajnal
authors: Division of Imaging Sciences & Biomedical Engineering, King's College London, UK.
authors: Paul
authors: P
authors: Aljabar
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000196:embryology*; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods / Q000592:standards; D016247:Information Storage and Retrieval / Q000379:methods*; D009415:Nerve Net / Q000033:anatomy & histology* / Q000196:embryology*; D010363:Pattern Recognition, Automated / Q000379:methods / Q000592:standards; D011296:Prenatal Diagnosis / Q000379:methods / Q000592:standards; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D014481:United States