Poster from 2011 Annual Meeting of the Organization for Human Brain Mapping. Support vector regression trained to predict intrinsic brain activity from one individual, applied to their twin, works better for identical twins than fraternal twins.

1. Genetics Influence Inter-subject Brain State Prediction
R. Cameron Craddock1
, Stephen M. LaConte1
, F. Xavier Castellanos2,3
, Xi-Nian Zuo4
, Paul Thompson5
, Greig de Zubicaray6
, Katie
McMahon7
, Ian Hickie8
, Nicholas Martin9
, Margaret Wright9
, Michael Milham2,3
1
Virginia Tech Carilion Research Institute, Roanoke, Virginia, 2
Phyllis Green and Randolph Cowen Institute for Pediatric Neurocience, NYU Langone Medical
Center, New York, NY, 3
Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, 4
Institute of Psychology, Chinese Academy of Sciences, Beijing, China,
5
Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, 6
School of Psychology, University of Queensland, Brisbane, Australia, 7
Centre for
Advanced Imaging, University of Queensland, Brisbane, Australia, 8
Brain and Mind Research Insitute, University of Sydney, Sydney, Australia, 9
Queensland
Institute of Medical Research, Brisbane, Australia 3dsvm Powered
Introduction
The utility of applying multi-voxel pattern analysis regression techniques (MVPA-R) to the intra-subject prediction of
time-courses of intrinsic brain activity has been shown1,2
. We extend this research by evaluating the ability of MVPA-R
classifiers trained on a single subject to predict intrinsic brain activity of a different subject. Familial and genetic
influences on inter-subject prediction are evaluated by comparing prediction accuracies derived from non-sibling pairs to
those from monozygotic (MZ) and dizygotic (DZ) twin pairs.
Subjects and Scanning
◮ 460 subjects participated in accordance with instituitional policy. The sample included 96 MZ twin pairs (62 F, age
21.85 +/- 3.56), 65 DZ twin pairs (43 F, age 19.92 +/- 3.9), and 148 age and gender matched non-siblings (97F, age
21.22+/- 3.8).
◮ 5 minute resting state scans acquired for each subject on a 4-T Bruker Medspec MRI
⊲ EPI: TE/TR/FA = 30 ms/2100 ms/90◦
, 3.6 mm x 3.6 mm in-plane resolution, 36 3-mm axial slices
Preprocessing / RSN Time Course Extraction
◮ T1 processing was performed using FSL3
and included skull-stripping, segmentation into white matter (WM), CSF, and
gray matter, and nonlinear spatial normalization to MNI space using fnirt
◮ fMRI preprocessing was performed using AFNI4
and included removing the first 4 TRs, slice timing correction, motion
correction, regressing out CSF, WM, and 6 motion parameters, coregistration to T1 (in FSL), and 6-mm FWHM blur
◮ 179 ROIs were specified by spatially constrained functional parcellation of the cerebral grey-matter of an independent
41-subject dataset5
. ROI time-courses were extracted for each subject by averaging the in-ROI voxel time-courses
(observed time-courses).
MVPA Regression
◮ MVPA-R was performed using the 3dsvm6
program distributed with AFNI (linear kernel, ǫ = 0.001, max iterations =
4,000)
◮ MVPA-R training was performed using the first 100 TRs for each ROI time-course and the corresponding resting state
data (intra-ROI voxels excluded) for each subject.
◮ The resulting spatial patterns were applied to the last 41 TRs of the subject’s data, the subject’s sibling and matched
non-sibling to generate predicted ROI time-courses.
◮ Prediction accuracy was measured as the concordance correlation coefficient7
between predicted and observed ROI
time-courses.
◮ Chance prediction accuracy was estimated by a permutation test.
⊲ For each subject a model was trained using a timecourse from their siblings’ brain data.
⊲ This classifier was applied to predict sibling and non-sibling data, and prediction accuracy was computed from the
results.
Results
◮ MVPA-R models of intrinsic brain activity achieved better-than-chance inter-subject prediction accuracy, improved
substantially for siblings and is worse than intra-subject prediction accuracy (figure 1).
◮ Median prediction accuracy was slightly better for MZ than DZ twins, suggesting that genetics play a role in the
between subject generalizability of the network models (figure 2).
◮ Non-sibling prediction accuracy was highest in the parietal lobe, cingulate cortex, motor and visual areas, and medial
frontal cortex. Lateral frontal cortex and temporal lobe were the worse (figure 3 top).
◮ All but two ROIs achieved higher intersubject prediction accuracy for siblings than non-siblings. Areas that show the
greatest improvement include lateral frontal cortex, temporal lobe, and cingulate cortex. Motor areas and visual cortex
show the least different between sibling and non-siblings (figure 3, middle).
◮ A few regions show differences in inter-subject prediction accuracy between MZ and DZ twin pairs. These include
posterior cingulate, rostral and subgenual anterior cingulate, lateral parietal and temporal lobes (figure 3, bottom).
Conclusions
MVPA regression models were successfully able to predict intrinsic activity between subjects. Prediction was
dramatically better for siblings than non-siblings. Prediction between MZ and DZ twin pairs differed robustly and
significantly, largely only in the default network, suggesting that its universality reveals the substantial influence of
additive genetic factors. Other intrinsic networks presumably reflect the role of experience in determining patterns of
spontaneous brain activity.
Figures
Median
Prediction
Accuracy
−0.2
0.0
0.2
0.4
0.6
0.8
Self Sibling Non-sibling Chance
p<2.2e-16
Figure 1: Median prediction accuracy for training on a subject and using
the resulting model to predict intrinsic brain activity of the same subject,
that subject’s sibling, and an age and gender matched non-sibling. Sibling
prediction accuracy was compared to non-sibling prediction accuracy
using a one-sided paired Wilcoxon rank-sum test.
Median
Prediction
Accuracy
0.0
0.2
0.4
0.6
0.8
MZ DZ
p<0.01
Figure 2: Sibling median prediction accuracy broken out by zygosity. MZ
prediction accuracy was compared to DZ prediction accuracy using a
one-sided Wilcoxon rank-sum test.
Figure 3: ROI specific inter-subject prediction accuracy. Median prediction accuracy was calculated for non-sibling pairs and ranked (top). Sibling vs
non-sibling prediction accuracy was compared using one-sided paired Wilcoxon rank-sum tests, FDR corrected, and thresholded at p < 0.05.
Superthreshold regions were ranked by the average improvement between sibling and non-sibling (middle). Similarly MZ prediction accuracy was
compared to DZ prediction accuracy using unpaired Wilcoxon rank-sum tests, FDR corrected, and threshold at p < 0.05. Superthreshold regions
were ranked based on the average improvement between MZ and DZ (bottom).
References
1
Craddock, R. C., et. al. OHBM 2010, 2
Chu, C., et. al. PRNI 2011, 3
Smith, S.M., et. al. NeuroImage 23(S1) 2004, 4
Cox, R.W. Computers and
Biomedical Research 29 1996, 5
Craddock, R. C., et. al. HBM in press, 6
LaConte, S.M., et. al. NeuroImage 26 2005, 7
Lange, N., et. al.
NeuroImage 10 1999
Acknowledgements: We would like to thank Jonathan Lisinski for programming support.
cameron.craddock@vt.edu