Special Issue on Mathematics in Brain Imaging

  title={Special Issue on Mathematics in Brain Imaging},
  author={Paul M. Thompson and Michael I. Miller and Russell A. Poldrack and Thomas E. Nichols and Jonathan E. Taylor and Keith J. Worsley and J. Tilak Ratnanather},
This special issue of NeuroImage, entitled “Mathematics in Brain Imaging,” consists of 18 invited papers fromsomeof thefinest research groups in brain imaging today. These articles cover in depth many of the mathematical techniques used in structural and functional neuroimaging studies, including diffusion tensor imaging. They highlight a diverse array of mathematical and statistical approaches: stateof-the-art algorithms for computational anatomy and algorithms for meta-analysis of functional… 
Digital Brain Atlases for Biomedicine [Life Sciences]
  • J. Gee
  • Computer Science
    IEEE Signal Processing Magazine
  • 2008
The technology and challenges of constructing digital brain atlases and some of their most promising applications in biomedicine are introduced.
Quantitation in MRI : application to ageing and epilepsy
An automated method of measuring intracranial volume, Reverse MNI Brain Masking (RBM), based on tissue probability maps in MNI standard space is presented, and it is shown that this is comparable to manual measurements and robust against field strength differences.
Multivariate statistical mapping of spectroscopic imaging data
The aims of this study were to develop and validate multivariate voxel‐based statistical mapping for magnetic resonance spectroscopic imaging and demonstrate that multivariate tests can be more powerful than univariate tests in identifying patterns of altered brain metabolism.
Collaborative computational anatomy: An MRI morphometry study of the human brain via diffeomorphic metric mapping
A large multi‐institutional analysis of the shape and structure of the human hippocampus in the aging brain as measured via MRI has the potential to enhance the understanding of neurodevelopmental and neuropsychiatric disorders.
Large Deformation Diffeomorphic Metric Mapping Registration of Reconstructed 3D Histological Section Images and in vivo MR Images
The utility of the multistage registration method in the transfer of cytoarchitectonic information from histological sections to identify regions of interest in MRI scans of nine adult macaque brains for morphometric analyses is demonstrated.
Alzheimer's Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition
Serial MRI scans can be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.
Comparison of distance measures for manifold learning: Application to Alzheimer’s brain scans
Application of MDS leads to a feature space, and this study compared the MDS-induced feature space with the feature space induced from hippocampus volume, a traditionally used feature for distinguishing AD/MCI patients from normal patients.
The generation of tetrahedral mesh models for neuroanatomical MRI
Tetrahedral modeling via the approach described here has applications in modeling brain structure in normal as well as diseased brain in human and non-human data and facilitates examination of 3D object deformations resulting from neurological illness.
The emerging discipline of Computational Functional Anatomy
This work examines the transfer via these bijections of functional response variables into anatomical coordinates via group action on scalars and matrices in DTI as well as parallel transport of metric information across multiple templates which preserves the inner product.
Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls
Ventricular expansion maps correlate with pathological and cognitive measures in AD, and may be useful in future imaging-based clinical trials.