Diffusion tensor-based regional gray matter tissue segmentation using the international consortium for brain mapping atlases.

Abstract:

:In this communication, we extended a previously described and validated diffusion tensor imaging (DTI) method for segmenting whole brain cerebrospinal fluid (CSF) and gray and white matter (WM) tissue to provide regional volume and DTI metrics of WM tract and cortical and subcortical gray matter. This DTI-based regional segmentation was implemented using the statistical parametric mapping (SPM) toolbox and used the international consortium for brain mapping atlases and Montreal Neurological Institute brain templates. We used our DTI-based segmentation approach to calculate the left putamen volume in a cohort of 136 healthy right-handed males and females aged 15.8-62.8 years. We validated our approach by demonstrating its sensitivity to age-related changes of the putamen. Indeed, our method found that the putamen volume decreased with age (r = -0.30; P < 0.001) while the corresponding fractional anisotropy (FA) increased with advancing age (r = 0.5; P < 0.00001). It is then demonstrated, on a subset of our cohort (n = 31), that the putamen volume obtained by our method correlated with measurements obtained from FreeSurfer (r = 0.396, P < 0.05). Our novel approach increases the information obtained with a DTI examination by providing routine volumetry measure, thereby eliminating separate scans to obtain volumetry data. In addition, the labeled volumes obtained with our method have the potential to increase the accuracy of fiber tracking. In the future, this new approach can be automated to analyze large data sets to help discover noninvasive neuroimaging markers for clinical trials and brain-function studies in both health and disease.

journal_name

Hum Brain Mapp

journal_title

Human brain mapping

authors

Hasan KM,Frye RE

doi

10.1002/hbm.21004

subject

Has Abstract

pub_date

2011-01-01 00:00:00

pages

107-17

issue

1

eissn

1065-9471

issn

1097-0193

journal_volume

32

pub_type

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