Relating Cerebral Blood Flow to Structural & Functional Metrics in Typically Developing Children


Sample slices from the multivariate atlas used as a basis for neuro-anatomical comparison

Purpose: To evaluate the relationships between cerebral blood flow and other magnetic resonance (MR) imaging based measures such as fractional anisotropy, magnetic transfer ratio, cortical thickness and mean resting state BOLD signal in typically developing children.

Methods: Eighty-eight children aged 7-17 underwent pseudo-continuous arterial spin-labeled perfusion MRI (pCASL) [1] examinations along with anatomical (T1), diffusion tensor (DTI), magnetic transfer (MT) and BOLD resting state functional MRI (rs-fMRI) examinations. For each imaging modality, the ANTs [2] toolkit was used to create a modality-specific template from a subset (n=30) of the subjects. For non-scalar modalities, derived scalar images were used for template building. For pCASL the mean CBF image was used; for DTI the average diffusion weighted image was used; for rs-fMRI the mean BOLD image was used; and for MT the M0 image was used. Each modality-specific template was then registered to the T1 template to obtain a single multi-modality template (MMT). The T1 component of the MMT was then brain-masked, labeled, and three-tissue segmented using the Atropos segmentation tool [3]. For each subject, each modality was aligned to the corresponding component of the MMT for brain-masking and labeling. Intra-subject registrations were then performed to align all modalities to each subject’s T1 image. To provide a basis for comparison, a scalar metric was derived for each image modality. For pCASL the mean CBF was calculated; for T1 images, the cortical thickness was measured using the DiRECT method; fractional anisotropy was calculated from the DTI; the magnetization transfer ratio (MTR) was calculated from the MT images; and mean BOLD signal was calculated from the resting state fMRI data.

Results: Regularized canonical correlation analysis, as implemented in the sscan tool [4], was used to identify the relationship between CBF and each of the additional modalities. The analysis of each modality type is restricted to the most informative tissue type for that modality. For CBF, rs-fMRI and cortical thickness, only values in gray matter are examined, while only values in white matter are examined for FA and MTR.

Discussion: To the best of our knowledge, this is the first study to simultaneously compare CBF to cortical thickness, fractional anisotropy, magnetization transfer ratio and mean resting BOLD signal in a single population. In doing so, we hope to gain insight regarding the degree to which CBF provides statistically unique information in relation to these additional MR imaging modalities. Additionally, the development of the framework for analyzing these modalities provides a basis for future studies to explore the relationship between CBF and network based measures of both structural and functional connectivity.

Conclusion: The relationship between cortical thickness and Mean CBF (R2=0.4777) was the strongest of the metrics examined. In white matter, the MTR (R2=0.3126)  was stronger than FA (R2=0.1462). The mean BOLD (R2=0.1414) metric was the weakest.

[1] J. T. Duda, D. J. J. Wang, E. Kilroy, J. C. Gee, and B. B. Avants, “Relating cerebral blood flow to structural and functional metrics in typically developing children,” in Proceedings of Perfusion MRI: Standardization, Beyond CBF and Everyday Clinical Applications, International Society for Magnetic Resonance in Medicine Scientific Workshop, Amsterdam, 2012, p. 40.
author = {Duda, Jeffrey T. and Wang, Danny J.J. and Kilroy, Emily and Gee,
James C. and Avants, Brian B.},
title = {{R}elating cerebral blood flow to structural and functional metrics
in typically developing children},
booktitle = {{P}roceedings of {P}erfusion {MRI}: {S}tandardization, {B}eyond {CBF}
and {E}veryday {C}linical {A}pplications, {I}nternational {S}ociety
for {M}agnetic {R}esonance in {M}edicine {S}cientific {W}orkshop,
year = {2012},
pages = {40}
[2] [doi] B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ANTs similarity metric performance in brain image registration.,” Neuroimage, vol. 54, iss. 3, p. 2033–2044, 2011.
author = {Avants, Brian B. and Tustison, Nicholas J. and Song, Gang and Cook,
Philip A. and Klein, Arno and Gee, James C.},
title = {{A} reproducible evaluation of {ANT}s similarity metric performance
in brain image registration.},
journal = {{N}euroimage},
year = {2011},
volume = {54},
pages = {2033--2044},
number = {3},
month = {Feb},
abstract = {The United States National Institutes of Health (NIH) commit significant
support to open-source data and software resources in order to foment
reproducibility in the biomedical imaging sciences. Here, we report
and evaluate a recent product of this commitment: Advanced Neuroimaging
Tools (ANTs), which is approaching its 2.0 release. The ANTs open
source software library consists of a suite of state-of-the-art image
registration, segmentation and template building tools for quantitative
morphometric analysis. In this work, we use ANTs to quantify, for
the first time, the impact of similarity metrics on the affine and
deformable components of a template-based normalization study. We
detail the ANTs implementation of three similarity metrics: squared
intensity difference, a new and faster cross-correlation, and voxel-wise
mutual information. We then use two-fold cross-validation to compare
their performance on openly available, manually labeled, T1-weighted
MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report
evaluation results on cortical and whole brain labels for both the
affine and deformable components of the registration. Results indicate
that the best ANTs methods are competitive with existing brain extraction
results (Jaccard=0.958) and cortical labeling approaches. Mutual
information affine mapping combined with cross-correlation diffeomorphic
mapping gave the best cortical labeling results (Jaccard=0.669±0.022).
Furthermore, our two-fold cross-validation allows us to quantify
the similarity of templates derived from different subgroups. Our
open code, data and evaluation scripts set performance benchmark
parameters for this state-of-the-art toolkit. This is the first study
to use a consistent transformation framework to provide a reproducible
evaluation of the isolated effect of the similarity metric on optimal
template construction and brain labeling.},
doi = {10.1016/j.neuroimage.2010.09.025},
institution = {{P}enn {I}mage {C}omputing and {S}cience {L}aboratory, {U}niversity
of {P}ennsylvania, {P}hiladelphia, {PA} 19104, {USA}.},
keywords = {Algorithms; Brain, anatomy /&/ histology; Databases, Factual; Diagnostic
Imaging, methods; Head, anatomy /&/ histology; Humans; Image Processing,
Computer-Assisted, methods; Linear Models; Models, Anatomic; Models,
Neurological; Population; Reproducibility of Results; Software},
language = {eng},
medline-pst = {ppublish},
owner = {pcook},
pii = {S1053-8119(10)01206-1},
pmid = {20851191},
timestamp = {2013.02.19},
url = {}
[3] [doi] B. B. Avants, N. J. Tustison, J. Wu, P. A. Cook, and J. C. Gee, “An open source multivariate framework for n-tissue segmentation with evaluation on public data.,” Neuroinformatics, vol. 9, iss. 4, p. 381–400, 2011.
author = {Avants, Brian B. and Tustison, Nicholas J. and Wu, Jue and Cook,
Philip A. and Gee, James C.},
title = {{A}n open source multivariate framework for n-tissue segmentation
with evaluation on public data.},
journal = {{N}euroinformatics},
year = {2011},
volume = {9},
pages = {381--400},
number = {4},
month = {Dec},
abstract = {We introduce Atropos, an ITK-based multivariate n-class open source
segmentation algorithm distributed with ANTs (
The Bayesian formulation of the segmentation problem is solved using
the Expectation Maximization (EM) algorithm with the modeling of
the class intensities based on either parametric or non-parametric
finite mixtures. Atropos is capable of incorporating spatial prior
probability maps (sparse), prior label maps and/or Markov Random
Field (MRF) modeling. Atropos has also been efficiently implemented
to handle large quantities of possible labelings (in the experimental
section, we use up to 69 classes) with a minimal memory footprint.
This work describes the technical and implementation aspects of Atropos
and evaluates its performance on two different ground-truth datasets.
First, we use the BrainWeb dataset from Montreal Neurological Institute
to evaluate three-tissue segmentation performance via (1) K-means
segmentation without use of template data; (2) MRF segmentation with
initialization by prior probability maps derived from a group template;
(3) Prior-based segmentation with use of spatial prior probability
maps derived from a group template. We also evaluate Atropos performance
by using spatial priors to drive a 69-class EM segmentation problem
derived from the Hammers atlas from University College London. These
evaluation studies, combined with illustrative examples that exercise
Atropos options, demonstrate both performance and wide applicability
of this new platform-independent open source segmentation tool.},
doi = {10.1007/s12021-011-9109-y},
institution = {{P}enn {I}mage {C}omputing and {S}cience {L}aboratory, {U}niversity
of {P}ennsylvania, 3600 {M}arket {S}treet, {S}uite 370, {P}hiladelphia,
{PA} 19104, {USA}.},
keywords = {Access to Information; Algorithms; Bayes Theorem; Databases, Factual,
standards; Humans; Image Processing, Computer-Assisted, methods;
Internet, standards; Magnetic Resonance Imaging, methods; Models,
Statistical; Pattern Recognition, Automated, methods; Software, standards},
language = {eng},
medline-pst = {ppublish},
owner = {pcook},
pmid = {21373993},
timestamp = {2013.02.19},
url = {}
[4] B. Avants, P. Dhillon, B. M. Kandel, P. A. Cook, C. T. McMillan, M. Grossman, and J. C. Gee, “Eigenanatomy improves detection power for longitudinal cortical change.,” Med Image Comput Comput Assist Interv, vol. 15, iss. Pt 3, p. 206–213, 2012.
author = {Avants, Brian and Dhillon, Paramveer and Kandel, Benjamin M. and
Cook, Philip A. and McMillan, Corey T. and Grossman, Murray and Gee,
James C.},
title = {{E}igenanatomy improves detection power for longitudinal cortical
journal = {{M}ed {I}mage {C}omput {C}omput {A}ssist {I}nterv},
year = {2012},
volume = {15},
pages = {206--213},
number = {Pt 3},
abstract = {We contribute a novel and interpretable dimensionality reduction strategy,
eigenanatomy, that is tuned for neuroimaging data. The method approximates
the eigendecomposition of an image set with basis functions (the
eigenanatomy vectors) that are sparse, unsigned and are anatomically
clustered. We employ the eigenanatomy vectors as anatomical predictors
to improve detection power in morphometry. Standard voxel-based morphometry
(VBM) analyzes imaging data voxel-by-voxel--and follows this with
cluster-based or voxel-wise multiple comparisons correction methods
to determine significance. Eigenanatomy reverses the standard order
of operations by first clustering the voxel data and then using standard
linear regression in this reduced dimensionality space. As with traditional
region-of-interest (ROI) analysis, this strategy can greatly improve
detection power. Our results show that eigenanatomy provides a principled
objective function that leads to localized, data-driven regions of
interest. These regions improve our ability to quantify biologically
plausible rates of cortical change in two distinct forms of neurodegeneration.
We detail the algorithm and show experimental evidence of its efficacy.},
institution = {{P}hiladelphia, {PA} 19104, {USA}.},
keywords = {Aging, physiology; Algorithms; Brain, anatomy /&/ histology/physiology;
Humans; Image Enhancement, methods; Image Interpretation, Computer-Assisted,
methods; Information Storage and Retrieval, methods; Longitudinal
Studies; Magnetic Resonance Imaging, methods; Pattern Recognition,
Automated, methods; Reproducibility of Results; Sensitivity and Specificity},
language = {eng},
medline-pst = {ppublish},
owner = {pcook},
pmid = {23286132},
timestamp = {2013.02.19}