#### Filter Results:

- Full text PDF available (22)

#### Publication Year

1995

2017

- This year (1)
- Last 5 years (10)
- Last 10 years (27)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Brain Region

#### Data Set Used

#### Key Phrases

#### Organism

Learn More

SUMMARY A general framework for a novel non-geodesic decomposition of high dimensional spheres or high dimensional shape spaces for planar landmarks is discussed. The decomposition, Principal Nested Spheres, finds a sequence of submanifolds with decreasing intrinsic dimensions, which can be interpreted as an analogue of Principal Component Analysis (PCA).… (More)

The statistical analysis of covariance matrix data is considered, and in particular methodology is discussed which takes into account the non-Euclidean nature of the space of positive semi-definite symmetric matrices. The main motivation for the work is the analysis of diffusion tensors in medical image analysis. The primary focus is on estimation of a mean… (More)

- Ian L Dryden, Jonathan D Hirst, James L Melville
- Biometrics
- 2007

We consider Bayesian methodology for comparing two or more unlabeled point sets. Application of the technique to a set of steroid molecules illustrates its potential utility involving the comparison of molecules in chemoinformatics and bioinformatics. We initially match a pair of molecules, where one molecule is regarded as random and the other fixed. A… (More)

- S L Free, P O'Higgins, +4 authors S D Shorvon
- NeuroImage
- 2001

We describe the application of statistical shape analysis to homologous landmarks on the cortical surface of the adult human brain. Statistical shape analysis has a sound theoretical basis. Landmarks are identified on the surface of a 3-D reconstruction of the segmented cortical surface from magnetic resonance image (MRI) data. Using publicly available… (More)

- Jingyong Su, Ian L. Dryden, Eric Klassen, Huiling Le, Anuj Srivastava
- Image Vision Comput.
- 2012

Practical statistical analysis of diffusion tensor images is considered, and we focus primarily on methods that use metrics based on Euclidean distances between powers of diffusion tensors. First we describe a family of anisotropy measures based on a scale invariant power-Euclidean metric, which are useful for visualisation. Some properties of the measures… (More)

- BY DAVIDE PIGOLI, JOHN A. D. ASTON, IAN L. DRYDEN, PIERCESARE SECCHI
- 2012

A framework is developed for inference concerning the covariance operator of a functional random process, where the covariance operator itself is an object of interest for the statistical analysis. Distances for comparing positive definite covariance matrices are either extended or shown to be inapplicable for functional data. In particular, an infinite… (More)

- Natalia Petridou, César Caballero Gaudes, Ian L Dryden, Susan T Francis, Penny A Gowland
- Human brain mapping
- 2013

fMRI studies of brain activity at rest study slow (<0.1 Hz) intrinsic fluctuations in the blood-oxygenation-level-dependent (BOLD) signal that are observed in a temporal scale of several minutes. The origin of these fluctuations is not clear but has previously been associated with slow changes in rhythmic neuronal activity resulting from changes in cortical… (More)

- Ian L. Dryden, Li Bai, Christopher J. Brignell, LinLin Shen
- Statistics and Computing
- 2009

- César Caballero Gaudes, Natalia Petridou, Ian L Dryden, Li Bai, Susan T Francis, Penny A Gowland
- Human brain mapping
- 2011

This work presents a novel method of mapping the brain's response to single stimuli in space and time without prior knowledge of the paradigm timing: paradigm free mapping (PFM). This method is based on deconvolution of the hemodynamic response from the voxel time series assuming a linear response and using a ridge-regression algorithm. Statistical… (More)