title: Ensemble average propagator-based detection of microstructural alterations after stroke.
abstract: OBJECTIVE
  New analytical reconstruction techniques of diffusion weighted signal have been proposed. A previous work evidenced the exploitability of some indices derived from the simple harmonic oscillator-based reconstruction and estimation (3D-SHORE) model as numerical biomarkers of neural plasticity after stroke. Here, the analysis is extended to two additional indices: return to the plane/origin (RTPP/RTOP) probabilities. Moreover, several motor networks were introduced and the results were analyzed at different time scales.
  
  
  METHODS
  Ten patients underwent three diffusion spectrum imaging (DSI) scans [1 week (tp1), 1 month (tp2) and 6 months (tp3) after stroke]. Ten matched controls underwent two DSI scans 1 month apart. 3D-SHORE was used for reconstructing the signal and the microstructural indices were derived. Tract-based analysis was performed along motor cortical, subcortical and transcallosal networks in the contralesional area.
  
  
  RESULTS
  The optimal intra-class correlation coefficient (ICC) was obtained in the subcortical loop for propagator anisotropy (ICC [Formula: see text] 0.96), followed by generalized fractional anisotropy (ICC [Formula: see text] 0.94). The new indices reached the highest stability in the transcallosal network and performed well in the cortical and subcortical networks with the exception of RTOP in the cortical loop (ICC [Formula: see text] 0.59). They allowed discriminating patients from controls at the majority of the timescales. Finally, the regression model using indices calculated along the subcortical loop at tp1 resulted in the best prediction of clinical outcome.
  
  
  CONCLUSIONS
  The whole set of microstructural indices provide measurements featuring high precision. The new indices allow discriminating patients from controls in all networks, except for RTPP in the cortical loop. Moreover, the 3D-SHORE indices in subcortical connections constitute a good regression model for predicting the clinical outcome at 6 months, supporting their suitability as numerical biomarkers for neuronal plasticity after stroke.
authors.affiliation: Department of Computer Science, University of Verona, Verona, Italy. lorenza.brusini@univr.it.
authors.forename: Lorenza
authors.identifier: http://orcid.org/0000-0001-5357-5180
authors.initials: L
authors.lastname: Brusini
authors.affiliation: Department of Computer Science, University of Verona, Verona, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Obertino
authors.affiliation: Institute of Nuclear Medicine, University College of London, London, UK.|Department of Neuroradiology, University Hospital Verona, Verona, Italy.
authors.forename: Ilaria Boscolo
authors.initials: IB
authors.lastname: Galazzo
authors.affiliation: Department of Computer Science, University of Verona, Verona, Italy.
authors.forename: Mauro
authors.initials: M
authors.lastname: Zucchelli
authors.affiliation: Siemens Healthcare USA, Boston, MA, USA.
authors.forename: Gunnar
authors.initials: G
authors.lastname: Krueger
authors.affiliation: Department of Clinical Neuroscience, CHUV and University of Lausanne, Lausanne, Switzerland.
authors.forename: Cristina
authors.initials: C
authors.lastname: Granziera
authors.affiliation: Department of Computer Science, University of Verona, Verona, Italy.
authors.forename: Gloria
authors.initials: G
authors.lastname: Menegaz
mesh_terms: D016880:Anisotropy; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D020521:Stroke / Q000175:diagnosis*
keywords: 3D-SHORE; Biomarker; GFA; Stroke