Mechanisms of white matter changes induced
Yi-Yuan Tanga,b,c,1, Qilin Lub, Ming Fand, Yihong Yange, and Michael I. Posnerc,1
aDepartment of Psychology, Texas Tech Neuroimaging Institute, Texas Tech University, Lubbock, TX 79409;bInstitute of Neuroinformatics and Laboratory for
Body and Mind, Dalian University of Technology, Dalian 116024, China;cDepartment of Psychology, University of Oregon, Eugene, OR 97403;dInstitute of
Basic Medical Sciences, Beijing 100850, China; andeNeuroimaging Research Branch, National Institute on Drug Abuse-Intramural Research Program,
Baltimore, MD 21224
Contributed by Michael I. Posner, May 9, 2012 (sent for review April 6, 2012)
Using diffusion tensor imaging, several recent studies have shown
that training results in changes in white matter efficiency as
measured by fractional anisotropy (FA). In our work, we found
that a form of mindfulness meditation, integrative body–mind
training (IBMT), improved FA in areas surrounding the anterior
cingulate cortex after 4-wk training more than controls given re-
laxation training. Reductions in radial diffusivity (RD) have been
interpreted as improved myelin but reductions in axial diffusivity
(AD) involve other mechanisms, such as axonal density. We now
report that after 4-wk training with IBMT, both RD and AD de-
crease accompanied by increased FA, indicating improved effi-
ciency of white matter involves increased myelin as well as other
axonal changes. However, 2-wk IBMT reduced AD, but not RD or
FA, and improved moods. Our results demonstrate the time-course
of white matter neuroplasticity in short-term meditation. This
dynamic pattern of white matter change involving the anterior
cingulate cortex, a part of the brain network related to self-regu-
lation, could provide a means for intervention to improve or pre-
vent mental disorders.
attention network test|anterior corona radiata|profile of mood states
iscapable ofmeasuring whitematter’s structural plasticity.Studies
indicate that training or learning alters brain white matter (1–5).
Fractional anisotropy (FA) is an important index for measuring
the integrity of white matter fibers. In general, a higher FA value
has been related to improved performance, and reduced FA has
been found in normal aging and in neurological or psychiatric
disorders (1, 6–8).
myelination, axon density, axonal membrane integrity, axon di-
ameter, and intravoxel coherence of fiber orientation and others
changes (1, 9). To understand the mechanisms of FA change,
several DTI studies have examined axial diffusivity (AD) and
FA (6–8). Usually, alterations in AD are associated with axon
morphological changes, such as changes in axonal density or
caliber (10, 11). In contrast to AD, which signifies axonal mor-
phology, RD implicates the character of the myelin. Decrease
in RD implies increased myelination, and increase represents
demyelination (2, 3, 8). This evidence in human neuroimaging
studies is consistent with animal studies examining axons and
myelination histologically and comparing them directly with DTI
results (12, 13).
To examine RD and AD it is best to have a significant change
in FA (14). Thus, in our study we investigated AD and RD al-
teration patterns only where integrity of white matter fibers are
enhanced (identified by FA increase). Numerous studies have
used AD and RD changes in the location where FA changes are
found to determine whether the FA changes are a result of
axonic morphology or myelin (1–3, 6, 8, 14, 15).
Studies of normal aging and Alzheimer’s disease have found
FA decreases, and reported different patterns of AD and RD
iffusion tensor imaging (DTI) is a noninvasive MRI-based
technique that can delineate white matter fibers in vivo. DTI
alteration. Depending upon the brain region examined, these
studies found either only RD increase, or both RD and AD in-
crease, or RD increase and AD decrease (6–8). These results
showed considerable diversity in the way in which brain regions
respond to aging or neurodegenerative diseases. In contrast to
aging, training in reading, use of the abacus, and working memory
have resulted in FA increase by decreasing RD without changing
AD. This pattern supports the notion that myelination is the
predominant process of the increased FA following training in
specific tasks (2–4). Keller and Just (3) proposed that skill
learning would increase neural firing and thus increase myelina-
tion (decrease in RD and increase in FA). The increased myeli-
nation would enhance communication among cortical areas,
resulting in better performance.
Our previous study showed that 4 wk of integrative body–mind
involved in communication to and from the anterior cingulate
cortex (ACC), including the corpus callosum and anterior and
superior corona radiata (5). However, whether the FA increase is
a result of changes in AD or RD in our study is unknown. We
proposed that IBMT improves attention and self-regulation via
a change in brain state (16, 17) rather than directly training an
attentional network. Thus, it is possible that IBMT may not work
in a way exactly similar to general skill training. In this article, we
first investigate mechanisms of meditation-induced white matter
changes by examining AD and RD alterations in brain areas
where we reported FA changes after 4 wk of IBMT. We then
examined white matter changes after 2 or 4 wk of training to
determine which index ofwhite matter changeis more sensitive to
the different amounts of training.
Our previously reported study (5) randomized 45 University of
Oregon undergraduates to 4-wk IBMT or relaxation training
(RT) and used MRI to assess white matter integrity before and
after training. After 4-wk training, the IBMT group increased FA
in several brain regions but the RT group did not (5). We then
analyzed changes of AD and RD in those areas where the FA
value was increased by 4-wk IBMT. We found two patterns of FA
increase were statistically significant after correction for multiple
comparisons (all PFWE< 0.05). The first pattern was FA increase
involving simultaneous decreases of AD and RD. The second
pattern was FA increase accompanied by only a decrease in RD.
The first pattern (labeled as FA↑AD↓RD↓, pattern 1) occurred in
all six brain regions in which FA was found to increase. The sec-
ond pattern (labeled as FA↑RD↓, pattern 2) occurred in parts of
four of the brain regions in which FA increased. Results are
Author contributions: Y.-Y.T., M.F., and M.I.P. designed research; Y.-Y.T. and Q.L.
performed research; Y.-Y.T., Q.L., and Y.Y. analyzed data; and Y.-Y.T., Q.L., M.F., Y.Y.,
and M.I.P. wrote the paper.
The authors declare no conflict of interest.
1To whom correspondence may be addressed. E-mail: firstname.lastname@example.org or mposner@
www.pnas.org/cgi/doi/10.1073/pnas.1207817109PNAS Early Edition
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summarized in Table1. For example, the left anterior corona
radiata showed patterns 1 and 2; the genu of corpus callosum only
showed pattern 1. Generally, each area has one subarea with
pattern 1 and a nonoverlapping subarea with pattern 2. No areas
showed greater change with 4 wk of RT than with IBMT (all P >
0.05). All six regions had voxels showing pattern 1, but four of
these areas also had voxels showing pattern 2. It should be noted
that when two patterns were found they were in contiguous areas
within the general brain region. Except at the boundary, no voxel
showed pattern 1 when an adjacent voxel showed pattern 2.
In Fig. 1, we demonstrate four regions on the Johns Hopkins
University Atlas (18) showing FA increase, and AD and RD
decrease at the sagittal section after 4-wk IBMT. These regions
were: body of corpus callosum, genu of corpus callosum, anterior
corona radiata, and superior corona radiata.
To examine how these changes in white matter arose, we
compared FA, RD, and AD after 2-wk training. In study 2, we
randomly assigned 68 Chinese undergraduates to an IBMT group
or to an RT group (34 each group). Before training, no significant
difference in FA, AD, or RD was detected between the two
groups. After 2-wk IBMT (5 h total), we found a significant de-
crease of AD in the corpus callosum, corona radiata, superior
longitudinal fasciculus, posterior thalamic radiation, and sagittal
stratum using a whole-brain analysis with a correction for multi-
ple comparisons (all PFWE< 0.05) (Fig. 2), but changes in FA and
RD did not reach significance. Meanwhile, the same amount of
RT did not show any significant change for any white matter in-
dex of FA, AD, or RD.
Our previous studies indicated 1-wk IBMT improved attention
and self-regulation using an attentional network test and a profile
of mood states (POMS) (19). We found significant behavioral
changes following 2-wk IBMT in the POMS, an index of mood.
Before training, there was no significant difference between the
IBMT and RT groups (P > 0.05). After training, t tests showed
significant reductions in anger-hostility (A), confusion-bewilder-
ment (C), depression-dejection (D), fatigue-inertia (F), and total
mood disturbance (TMD) in POMS (all P < 0.05) in the IBMT
group (but not the relaxation group). After training, correlations
between TMD change and AD decrease at the left posterior
corona radiata (r = 0.409) and TMD change and AD decrease at
the left sagittal stratum (r = 0.447) were significant (all P < 0.05),
indicating the training-induced change in mood was correlated
with the brain changes in these areas (Figs. 3 and 4).
Our studies have shown that short-term meditation training
increases the ability to resolve conflict in a cognitive task, altered
neural activity in the ACC, and improved connectivity of the ACC
to other brain regions (5, 16, 17, 19, 20). The ACC has been as-
sociated with the ability to resolve conflict and to exercise control
of cognition and emotion (21). One study found a correlation
between the ability to resolve conflict and FA in the anterior co-
rona radiata, a major pathway connecting the ACC to other brain
areas (22). Thus, the improved self-regulation following IBMT
may be mediated by the increase of communication efficiency
between the ACC and other brain areas (5, 16).
In the present study we examined the pattern of AD and RD
changes as a result of IBMT in brain areas where FA value in-
creased. The pattern of FA increase with only RD decrease has
been found in reading, working memory, and abacus training
studies (2–4), but 11 h of IBMT improves FA in a different way.
With IBMT we typically found two patterns of change: in pattern
1 both AD and RD decrease, and in pattern 2 only RD decreases.
AD decrease has also been found in early brain development and
is interpreted as reduced interaxonal space caused by increasing
axonal density or caliber (10, 11). The present results imply that
enhanced integrity of white matter fibers by IBMT may be caused
by increased numbers of brain fibers or increased axonal caliber.
Decrease of RD value was another important character of effects
of 11 h of IBMT. Several studies have indicated that RD decrease
is related to increased myelination (2–4, 23, 24). Myelination has
been found in animal and human studies to be modifiable by ex-
perience, and affects information processing by regulating the
velocity and synchrony of impulse conduction between distant
cortical regions (23, 24). Increased myelination could occur be-
cause of increased neural firing in brain areas active during
training (2–4). Changes in the ACC activation and its connectivity
have been found in both meditation (5, 16, 25) and other forms of
training (26, 27). However, IBMT differs from other forms of
cognitive training in showing significant decreases in both AD and
RD, suggesting that IBMT may have a different mechanism from
skill training or learning with specific tasks for which only RD
changes have been reported (16, 17). Other plausible explan-
ations of differences might be because of different methods of
analysis or power used so that further, more direct comparisons
are needed to clarify this.
Generally, FA value has shown less sensitivity than its com-
ponents, with AD reflecting axonal morphological changes and
RD indexing myelination (28). We found that after 2 wk of
Table 1.Different patterns of FA increase after 4-wk IBMT
Brain regionsFA↑with AD↓RD↓ (Pattern 1) (mm3) FA↑ with RD↓ (Pattern 2) (mm3)
Genu of corpus callosum
Body of corpus callosum
Anterior corona radiata L
Superior corona radiata R
Superior corona radiata L
Superior longitudinal fasciculus L
4-wk IBMT. Statistical images are shown on the Johns Hopkins University
Atlas (18) at PFWE< 0.05 corrected for multiple comparisons at sagittal sec-
tion x = −13, x = −17, x = −21, and x = −25.
FA increase and AD and RD decrease in different brain regions after
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IBMT, there was only a decrease in AD but no significant change
in FA or RD. Only after 4 wk of training did we find a change in
RD and FA. These findings, together with the early changes of
AD in development (10), suggest that axonal morphology might
be an early biomarker of white matter change. The different
results at 2 and 4 wk could be a difference between Chinese and
United States populations; however, in previous studies of the
mechanism of IBMT we have found undergraduates in the two
countries to have similar brain activity and white matter changes
by IBMT (5, 16, 20). The average age in Chinese and American
groups is 20-y old, and these undergraduates share similar
interests, such as use of iPhones, computers, and the internet.
We thought the Chinese students may be more sensitive to the
meditation because of cultural influences, but did not find evi-
dence of this. Until new studies provide a direct comparison,
cultural or genetic differences between the two populations
remain possible explanations of the differences found between
the 2- and 4-wk studies.
In a recent review, Zatorre et al. (9) proposed that myelina-
tion is regulated by axon diameter, and changes in axon diameter
during learning could in turn cause oligodendrocytes to alter the
thickness of the myelin sheath. Conversely, myelinating glia can
regulate axon diameter and even the survival of axons (9). This
evidence indicates that changes of myelination (indexed by RD)
and axon diameter (indexed by AD) interact, and thus do not
represent independent components of FA. However, this finding
does not explain differences between training methods.
In animal studies, changes in central serotonin levels influence
axonal morphology, suggesting emotions, such as stress and de-
pression, have a negative effect on the axonal morphology (29,
30). Although few human experiments focus on the influence of
emotions on axonal morphology, several studies have demon-
strated that emotions and stress can change white matter integrity
section x = −13, x = −27, x = −35, and x = −41.
Decrease of AD in different brain regions after 2-wk IBMT. Statistical images are shown at PFWE< 0.05 corrected for multiple comparisons at sagittal
rior corona radiata after 2-wk IBMT. The horizontal axis indicates the
POMS total score change and the vertical axis indicates the AD change at
left posterior corona radiata. A positive Pearson’s correlation was observed
(r = 0.409, P = 0.016).
Correlation between TMD change and AD decrease at left poste-Fig. 4.
stratum (r = 0.409) after 2-wk IBMT. The horizontal axis indicates the
POMS total score change and the vertical axis indicates the AD change at
left sagittal stratum. A positive Pearson’s correlation was observed (r =
0.447, P = 0.008).
Correlation between TMD change and AD decrease at left sagittal
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(31, 32). For example, the remission process of depression can
enhance the FA near the anterior cingulate (32). After 1-wk
IBMT, mood and positive emotion are enhanced (16, 19). More-
over,the FAincreasesfoundafter4wkarein thecorpuscallosum,
anterior corona radiata, and superior corona radiata (5), similar
brain areas to those found in studies of white matter fibers influ-
enced by emotion (31, 32). After 2-wk IBMT, positive correlations
between POMS and AD changes are consistent with an important
role for emotion. Thus, one possible explanation of AD changes
might be that the training also has impacts on the autonomic
nervoussystem andchangingemotional state. Thechange of brain
state and mood may be one reason for FA increase following
IBMT practice (16, 17, 19, 20).
RD may be problematic when either of the following conditions
make eigenvalue directions between subjects uncertain. We rule
out these two conditions as follows: First, lower FA brain tissues,
especially gray matter, have been removed from skeletonized FA,
AD, and RD maps generating by the tract-based spatial statistics
(TBSS) method so that the noise from low FA can be removed.
Second, the longitudinal study design ensures each single voxel’s
eigenvalue directions are congruent in the pre- and posttraining
scans because they came from same subject. Thus, it is unlikely the
current results are noise or artifacts.
It should be noted that despite the controversy over the in-
terpretation of AD and RD measures (14), diffusion-imaging
measures are sensitive to many tissue properties (33), including
variation in myelin (34), axon diameter and packing density (35),
axon permeability (33), and fiber geometry (36). Diffusion im-
aging can be adapted to generate axon diameter distributions (37)
or estimates of myelin microstructure (38). Such advances offer
great potential to further our understanding of brain structural
variation with learning and behavior (9).
In summary, our results demonstrate the mechanism of white
matter neuroplasticity during short-term meditation. These find-
ings might serve as a vehicle for examining the behavioral con-
sequences of different indices of white matter integrity, such as
functional connectivity, FA, RD, and AD that occur both dur-
ing learning, training, and development. Moreover, a number of
problems, including addiction and mental disorders such as at-
tention-deficit hyperactivity disorder, anxiety, depression, schizo-
phrenia, and borderline personality disorder, involve problems of
self-regulation (39). Thus, the dynamic pattern of white matter
change involving the ACC, a part of the brain network related to
self-regulation, could provide a means for intervention to improve
or prevent mental disorders.
Materials and Methods
1.57 (SD) y] at the University of Oregon were recruited and randomly
assigned to an IBMT group or a relaxation group. Each group had no pre-
vious training experience and received 30 min of IBMT or RT for 1 mo, with
a total of 11 h of training (5). In study 2, 68 healthy Chinese undergraduates
[36 male, mean age 20.52 ± 1.36 (SD) y] at Dalian University of Technology
were recruited and randomly assigned to an IBMT group or a relaxation
group (34:34, 18 males in each group). The participants had no previous
training experience and received 30 min of IBMT or RT for 2 wk, with a total
of 5 h of training. The experiment was approved by the Institutional Review
Board at University of Oregon and Dalian University of Technology and in-
formed consent was obtained from each participant.
Training Methods. IBMT involves body relaxation, mental imagery, and
mindfulness training, accompanied by selected music background. Co-
operation between the body and the mind is emphasized in facilitating and
achieving a meditative state. The trainees concentrated on achieving a bal-
anced state of body and mind guided by an IBMT coach and the compact disk.
alertness that allows a high degree of awareness of body, mind, and external
instructions (5, 16, 19). RT involves the relaxing of different muscle groups
over the face, head, shoulders, arms, legs, chest, back, and abdomen, guided
by a tutor and compact disk. With eyes closed and in a sequential pattern,
one is forced to concentrate on the sensation of relaxation, such as the
feelings of warmth and heaviness. This progressive training helps the par-
ticipant achieve physical and mental relaxation and calmness.
Data Acquisition and Analysis. Instudy1,diffusiontensorimageswerecollected
were as follows: TR/TE = 10,900/113 ms, diffusion-weighting gradients applied
in 60 directions (b = 700 s/mm2), combined 10 volumes without diffusion
weighting (b = 0 s/mm2).
training with a Philips 3T Achieva at Dalian Municipal Central Hospital. DTI
acquisition parameters were as follow: TR/TE = 10,815/62 ms, diffusion sen-
sitizing gradient was applied along 29 directions (b = 1,000 s/mm2) with one
volume without diffusion weighting (b = 0 s/mm2).
DTI data were processed with the FSL 4.1 Diffusion Toolbox (FDT, http://
www.fmrib.ox.ac.uk/fsl/fdt/). A standard FDT multistep procedure was
adopted including: (i) motion and eddy current correction; (ii) removal of
skull and nonbrain tissue using the Brain Extraction Tool; and (iii) voxel-by-
voxel calculation of the diffusion tensors. FA and AD maps calculated di-
rectly using DTIFit within FDT. The RD map was computed as the mean of the
second and third eigenvalue with an in-house program. TBSS was carried out
for voxelwise statistical analysis and included: (i) nonlinear alignment of
each participant’s FA volume to the standard Montreal Neurological In-
stitute (MNI152) space template; (ii) calculation of the mean of all aligned
FA images; (iii) creation of a mean FA skeleton that represents the centers of
all tracts common to all subjects; and (iv) projection of each subject’s aligned
FA image onto the mean FA skeleton. The tbss_non_FA script was used to
obtain: (i) the individual’s projected template; (ii) calculation of the mean of
all aligned FA images; (iii) creation of a mean FA skeleton that represents
the centers of all tracts common to all subjects; and (iv) projection of each
subject’s aligned FA image onto the mean FA skeleton. The tbss_non_FA
script was used to obtain the individual’s projected AD and RD maps. Per-
mutation-based nonparametric inference (http://www.fmrib.ox.ac.uk/fsl/
randomise/) was adopted to perform statistical analyses on FA (n = 5,000),
AD and RD in 2- and 4-wk IBMT and RT groups.
The statistical threshold was established as PFWE< 0.05 with multiple
comparison correction using threshold-free cluster enhancement (http://
www.fmrib.ox.ac.uk/analysis/research/tfce). An in-house program was used
to calculate the volume of AD or RD alterations within the regions where FA
changes (40–43). Using the FSL Toolbox in all studies, we conducted t tests
for pre- and posttraining differences with a correction for multiple com-
parisons (PFWE< 0.05). Pearson correlation was also conducted to analyze
correlation between imaging metrics and behavioral assessments.
ACKNOWLEDGMENTS. We thank the Institute of Neuroinformatics and
the Lewis Center for Neuroimaging for data collection and Rongxiang Tang
for manuscript preparation. This study is supported by 973 Program
2012CB518200, R21DA030066, the Office of Naval Research, and the Intra-
mural Research Program of the National Institute on Drug Abuse, National
Institutes of Health.
1. Taubert M, Villringer A, Ragert P (2011) Learning-related gray and white matter
changes in humans: An update. Neuroscientist, 10.1177/1073858411419048.
2. Hu Y, et al. (2011) Enhanced white matter tracts integrity in children with abacus
training. Hum Brain Mapp 32:10–21.
3. Keller TA, Just MA (2009) Altering cortical connectivity: Remediation-induced changes
in the white matter of poor readers. Neuron 64:624–631.
4. Engvig A, et al. (2011) Memory training impacts short-term changes in aging white
matter: A longitudinal diffusion tensor imaging study. Hum Brain Mapp, 10.1002/
5. Tang YY, et al. (2010) Short-term meditation induces white matter changes in the
anterior cingulate. Proc Natl Acad Sci USA 107:15649–15652.
6. Burzynska AZ, et al. (2010) Age-related differences in white matter microstructure:
Region-specific patterns of diffusivity. Neuroimage 49:2104–2112.
7. Bosch B, et al. (2012) Multiple DTI index analysis in normal aging, amnestic MCI and
AD. Relationship with neuropsychological performance. Neurobiol Aging 33:61–74.
8. Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH, Jr. (2010) Age-related
differences in multiple measures of white matter integrity: A diffusion tensor imaging
study of healthy aging. Hum Brain Mapp 31:378–390.
4 of 5
| www.pnas.org/cgi/doi/10.1073/pnas.1207817109 Tang et al.
9. Zatorre RJ, Fields RD, Johansen-Berg H (2012) Plasticity in gray and white: Neuro- Download full-text
imaging changes in brain structure during learning. Nat Neurosci 15:528–536.
10. Kumar R, Nguyen HD, Macey PM, Woo MA, Harper RM (2012) Regional brain axial
and radial diffusivity changes during development. J Neurosci Res 90:346–355.
11. Kumar R, Macey PM, Woo MA, Harper RM (2010) Rostral brain axonal injury in
congenital central hypoventilation syndrome. J Neurosci Res 88:2146–2154.
12. Song SK, et al. (2002) Dysmyelination revealed through MRI as increased radial (but
unchanged axial) diffusion of water. Neuroimage 17:1429–1436.
13. Song SK, et al. (2003) Diffusion tensor imaging detects and differentiates axon and
myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage 20:
14. Wheeler-Kingshott CAM, Cercignani M (2009) About “axial” and “radial” diffusivities.
Magn Reson Med 61:1255–1260.
15. Metwalli NS, et al. (2010) Utility of axial and radial diffusivity from diffusion tensor
MRI as markers of neurodegeneration in amyotrophic lateral sclerosis. Brain Res 1348:
16. Tang YY (2011) Mechanism of integrative body-mind training. Neurosci Bull 27:
17. Tang YY, Posner MI (2009) Attention training and attention state training. Trends
Cogn Sci 13:222–227.
18. Mori S, Wakana S, van Zijl PC, Nagae-Poetscher LM (2005) MRI Atlas of Human White
Matter (Elsevier Science, Amsterdam, The Netherlands).
19. Tang YY, et al. (2007) Short-term meditation training improves attention and self-
regulation. Proc Natl Acad Sci USA 104:17152–17156.
20. Tang YY, et al. (2009) Central and autonomic nervous system interaction is altered by
short-term meditation. Proc Natl Acad Sci USA 106:8865–8870.
21. Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cin-
gulate cortex. Trends Cogn Sci 4:215–222.
22. Niogi S, Mukherjee P, Ghajar J, McCandliss BD (2010) Individual differences in distinct
components of attention are linked to anatomical variations in distinct white matter
tracts. Front Neuroanat 4:2.
23. Fields RD (2010) Neuroscience. Change in the brain’s white matter. Science 330:
24. Fields RD (2008) White matter in learning, cognition and psychiatric disorders. Trends
25. Holzel BK, et al. (2011) How does mindfulness meditation work? Proposing mecha-
nisms of action from a conceptual and neural perspective. Perspect Psychol Sci 6:
26. Rueda MR, Rothbart MK, McCandliss BD, Saccomanno L, Posner MI (2005) Training,
maturation, and genetic influences on the development of executive attention. Proc
Natl Acad Sci USA 102:14931–14936.
27. Rueda MR, Checa P, Combita LM (2012) Enhanced efficiency of the executive atten-
tion nentwork after training in preschool children: Immediate and after two month
effects. Developmental Cognitive Neuroscience 2:192–204.
28. Tyszka JM, Readhead C, Bearer EL, Pautler RG, Jacobs RE (2006) Statistical diffusion
tensor histology reveals regional dysmyelination effects in the shiverer mouse mu-
tant. Neuroimage 29:1058–1065.
29. Liu Y, Nakamura S (2006) Stress-induced plasticity of monoamine axons. Front Biosci
30. Kitayama I, et al. (1997) Long-term stress degenerates, but imipramine regenerates,
noradrenergic axons in the rat cerebral cortex. Biol Psychiatry 42:687–696.
31. Chen L, et al. (2011) Impact of acute stress on human brain microstructure: An MR
diffusion study of earthquake survivors. Hum Brain Mapp, 10.1002/hbm.21438.
32. Alexopoulos GS, et al. (2008) Microstructural white matter abnormalities and
remission of geriatric depression. Am J Psychiatry 165:238–244.
33. Beaulieu C (2009) The biological basis of diffusion anisotropy. Diffusion MRI: From
(Elsevier, London), pp 105–126.
34. Concha L, Livy DJ, Beaulieu C, Wheatley BM, Gross DW (2010) In vivo diffusion tensor
imaging and histopathology of the fimbria-fornix in temporal lobe epilepsy. J Neu-
35. Takahashi M, et al. (2002) Magnetic resonance microimaging of intraaxonal water
diffusion in live excised lamprey spinal cord. Proc Natl Acad Sci USA 99:16192–16196.
36. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM (2005) Mapping complex
tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson
37. Barazany D, Basser PJ, Assaf Y (2009) In vivo measurement of axon diameter distri-
bution in the corpus callosum of rat brain. Brain 132:1210–1220.
38. Avram AV, Guidon A, Song AW (2010) Myelin water weighted diffusion tensor im-
aging. Neuroimage 53:132–138.
39. Posner MI (2012) Attention in the Social World (Oxford Univ Press, New York).
40. Smith SM, et al. (2006) Tract-based spatial statistics: Voxelwise analysis of multi-sub-
ject diffusion data. Neuroimage 31:1487–1505.
41. Smith SM, et al. (2004) Advances in functional and structural MR image analysis and
implementation as FSL. Neuroimage 23(Suppl 1):S208–S219.
42. Rueckert D, et al. (1999) Nonrigid registration using free-form deformations: Appli-
cation to breast MR images. IEEE Trans Med Imaging 18:712–721.
43. Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: Addressing prob-
lems of smoothing, threshold dependence and localisation in cluster inference.
Tang et al.PNAS Early Edition
| 5 of 5