SUPPLEMENTARY METHODS
In order to assess whether our findings were of behavioral relevance, we investigated the
relationship between diffusion metrics (FA and RD) and cognitive performance and how BDNF
genotype modulated these.
Neuropsychological assessment
Cognitive test performance data was available for a subset of participants (Supplementary Table
1). As described in detail in (Dickinson et al, 2011), neuropsychological testing in the NIMH
Clinical Brain Disorders Branch “Sibling Study” protocol is performed using a test battery
including measures or variables from the Wechsler Adult Intelligence Scale (WAIS), Wechsler
Memory Scale (WMS), California Verbal Learning Test (CVLT), Wisconsin Card Sorting Test
(WCST), and Wide Range Achievement Test (WRAT), among others. Composite scores for six
cognitive domains (verbal memory, visual memory, processing speed, n-back working memory,
span working memory, and card sorting) and a general cognitive ability (“g”) composite were
derived from the battery based on exploratory factor analyses. Factor-based composites were
calculated as the average z-transformed score across contributing cognitive variables, and further
transformed (e.g., squared or cubed transformations) to normalize the data distribution (see
Supplementary Table 1 for demographic information).
Data reduction of FA and RD data for exploratory analysis of relationships to cognition and
BDNF genotype
In an exploratory analysis, we examined the relationship between diffusion metrics
derived from multiple tracts and all cognitive factors described above, with the exception of the
visual memory factor where the number of available data points (n = 40) precluded a reasonable
further analysis. In a first step, DTI estimates of brain white matter tracts were calculated and
1
reduced using factor analysis based on the full sample of 85 participants who had DTI data.
Here, average FA and RD values were extracted for the ROIs provided by the JHU white matter
atlas (Mori et al, 2008) as applied to the TBSS skeleton. AD was not explored given prior
research indicating that its association with cognition is minor compared to FA and RD (Jacobs
et al, 2011; Penke et al, 2010; Roberts et al, 2010). For each DTI metric (FA and RD), the
number of variables was subsequently reduced by dropping ROIs belonging to the cerebellar
peduncles and brain stem regions. The correlation matrix of all ROIs was then inspected and
bilateral ROIs were averaged for most remaining tracts, since correlations between left and right
hemisphere were r > 0.6. Only the ROIs for the left and right superior fronto-occipital (SFO)
fascicles for FA (r = 0.4) were used as separate variables. This yielded 21 ROI values for FA and
20 for RD. All variables were normally distributed (Kolmogoroff-Smirnoff test: P > 0.05), with
the sole exception of fornix estimates for RD. This variable was log transformed prior to entering
it into further analysis.
Factor analysis was subsequently run for FA and RD separately, and factors were
extracted with the maximum likelihood method in Statistica 7 (Statsoft, Tulsa OK). The number
of factors to be extracted were decided based on scree plots (Cattell, 1966), and factors
accounting for less than 4% of the total variance were dropped. This procedure identified four
factors for FA (see Supplementary Table 2 for details). After varimax normalized rotation, the
identified factors could be interpreted as referring to 1) midline structures such as genu and body
of the corpus callosum (11% explained variance), 2) subcortical tracts feeding into the frontal
and occipital lobes (20% explained variance), 3) fronto-parietal and projecting fibers of the
cortico-spinal tracts (18% explained variance), and 4) basal fronto-temporal tracts (4% explained
variance). For RD, this procedure identified three factors (see Supplemental Table 2 for details)
2
including a regionally more unspecific, general factor (Factor 2, 34% explained variance), a
posterior brain factor (Factor 1, 25% explained variance), and a medial structure factor (Factor
3, 10% explained variance). “Eigenvariate” values for RD and FA were calculated for each
subject by weighing the data from each contributing ROI by the loadings for each factor. We
then surveyed all the variables and dropped values that were more than 3 standard deviations
below or above the mean in order to be less sensitive to outliers. This resulted in removal of one
value for each of the following variables: card sorting, general cognitive ability, processing
speed and for FA factor 3.
The resulting individual factor diffusion values were then modeled as independent
variables (together with BDNF genotype, sex, and age) in a general multiple regression
backward stepwise analysis for association with six of the seven cognitive factor dimensions
(described above) as dependent variables. The model was constrained as follows: all main effects
were allowed into the model regardless of significance, and so were all two-way interactions
with P < 0.1. No interactions beyond the second degree were entered into the model due to the
complexity inherent in interpreting these interactions. If an independent variable was not
significant with a p<0.1 either as a main effect or in an interaction, it was removed from the
overall model, in order to obtain the simplest model possible, while explaining maximal
variance. A corrected significance threshold of P < 0.008 for six comparisons was defined for the
overall model. Univariate analyses of the models that survived this threshold were considered
statistically significant when p<0.05.
Hypothesis driven analyses
3
We addressed four hypotheses that have been put forth in the literature. 1) That working
memory performance is predicted by the FA of the left superior longitudinal fasciculus (SLF)
(Karlsgodt et al, 2008); 2) That processing speed is predicted by a principal component derived
from the FA of eight white matter tracts (genu and splenium of the corpus callosum, bilateral
dorsal cinguli, uncinates and SLFs) (Penke et al, 2010); 3) that verbal memory is predicted by
the FA of the inferior longitudinal fasciculus (ILF), anterior and posterior cinguli (Kantarci et al,
2011); 4) that general cognitive ability is related to a principal component derived from the the
FA of 12 tracts (genu and splenium, of the corpus callosum, bilateral dorsal cinguli, uncinates,
anterior thalamic radiations, inferior longitudinal fasciculi and SLFs) (Penke et al, 2012). For all
these hypotheses, the FA data was derived from the atlas-based ROIs discussed above. The
initial models were backward stepwise multiple regressions with the cognitive factor of interest
as the dependent variable and age, sex and the appropriate FA values as independent variables.
After assessing these basic relationships, BDNF genotype was added to the model to assess
whether it contributed significantly to prediction of the cognitive variables. The models were
simplified in a second model building run by removing variables with p>0.1 as main effects or
interactions. The significance for these models was set to 0.05 uncorrected because these were
hypothesis-driven analyses with a prior basis in the literature.
SUPPLEMENTARY RESULTS
Exploratory Analysis of FA-cognition relationships and their moderation by BDNF Val/Met
genotype
Four factors were identified for FA (see Supplemental Table 2 for details). After varimax
normalized rotation, the identified factors could be interpreted as referring to 1) midline
structures such as genu and body of the corpus callosum (11% explained variance), 2)
4
subcortical tracts feeding into the frontal and occipital lobes (20% explained variance), 3) frontoparietal and projecting fibers of the cortico-spinal tracts (18% explained variance), and 4) basal
fronto-temporal tracts (4% of explained variance: Supplemental Table 2). For RD, factor rotation
identified three factors (Supplemental Table 3) including a regionally more unspecific, general
factor (Factor 2, 34% explained variance), a posterior brain factor (Factor 1, 25% explained
variance), and a medial structure factor (Factor 3, 10% explained variance).
Multiple Regression Results for FA
For FA, the results of multiple regression backward stepwise procedures are summarized
in Supplementary Table 4a. For verbal memory, age, sex Factor1 and Factor 4 were dropped
from the analysis. Univariate tests of significance are shown in Supplementary Table 4b, and the
most statistically significant univariate result (the interaction of BDNF by FA Factor 2) is plotted
in Supplementary Figure 1.
For n-back working memory performance, age, sex and factor 4 were removed from the
analysis. Results of the univariate analysis are shown in Table 4c and also in this case the most
significant effect was the interaction of BDNF and factor 2, plotted in Supplementary Figure 2.
This interaction remained significant after removing two possible outliers with the lowest
working memory performance in the BDNF Val/Val group.
For card sort performance, only age and factor 2 remained in the model, but the model
was highly significant. Univariate results are shown in Supplementary Table 4d. The most
significant effect was that of age, while factor 2 explained a smaller proportion of the variance
(the relationship is shown in Supplementary Figure 3 prior to adjustment for age).
5
For processing speed (age and Factor 3 remained in the model), span (BDNF and Factor 2
dropped from the model) and G (only Factor 3 remained in the model) statistical significance of
the overall model was not below the threshold established for multiple comparisons.
Multiple Regression Results for RD
For RD, the same procedure resulted in significant findings for n-back working memory
and card sorting performance (Supplementary Table 5a). For working memory, all variables
were retained in the model, but the role of BDNF genotype was modest and not significant in the
univariate analysis. Factor 3 for RD appeared to negatively predict working memory
performance (the higher the RD in the corpus callosum and columns of the fornix, the lower the
working memory performance: Supplementary Figure 4). There was a significant interaction of
factors 2 and 3, but this is difficult to interpret.
For card sort performance (results for univariate analysis presented in Table 5c), BDNF
genotype was dropped from the final model. The effects of RD factors were weakly significant in
the univariate analysis. The main variable predicting performance was age.
For verbal memory (BDNF, factors 1 and 3 in the final model), processing speed (age,
factor1 and BDNF in the model), span (sex, Factors 1 and 2 in the model) and general cognitive
ability (age, Factors 2 and 3 remaining in the model), results were below the threshold for
statistical significance.
Hypothesis based analyses
1) SLF FA predicts working memory performance
The model using left hemisphere SLF FA alone as a predictor of n-back working memory
was not significant, however, when we added age, sex, BDNF genotype and right hemisphere
SLF to the model, statistical significance was present (multiple R2=0.26, F=2.43, p=0.015).
Univariate results are presented in Supplementary Table 6. The most significant main effect was
6
that of sex (males>females). An interesting sex-by-right SLF FA interaction emerged, with
reductions in FA predicting working memory in females, while the directionality of the
association was the opposite in males (Supplementary Figure 5). BDNF genotype had a limited
role in predicting working memory performance in this model.
2) The first principal component derived from the FA of 8 major tracts predicts processing
speed
The first principal component accounted for 54.5% of the variance in FA of the various
tracts. Supplementary Table 7a shows the factor loadings for the first principal component. The
overall model was not significant (multiple R2=0.047, F=1.32, p=0.27) and the addition of BDNF
genotype in the model did not improve prediction of processing speed.
3) The FA of the ILF, anterior and posterior cinguli predict verbal memory
The FA of the ILF (left and right hemisphere ROIs were entered into the model) did not predict
verbal memory (multiple R2=0.016, F=0.31, p=0.87). The most parsimonious model included
BDNF and the left ILF only, yielding a trend finding (multiple R2=0.085, F=2.47, p=0.068).
Similarly, the FA of the ventral portion of the cingulum was not associated with verbal memory
(multiple R2=0.053, F=1.11, p=0.36), but when BDNF was added to the model and the left
cingulum dropped, trend significance was achieved (multiple R2=0.15, F=1.9, p=0.08). Also the
dorsal cingulum did not achieve significance with (multiple R2=0.044, F=0.71, p=0.62) or
without BDNF in the model (multiple R2=0.041, F=0.84, p=0.5).
4) The first component derived from the FA of 12 tracts predicts general intellectual ability
7
The factor loadings for the 1st principal component derived from the FA data of 12 tracts are
shown in Table 7b. The first principal component derived from 12 tracts did not achieve
significance, with or without BDNF in the model, in predicting general cognitive ability.
SUPPLEMENTARY DISCUSSION
The exploratory approach used here has the advantage of being relatively unbiased, since
it includes samples of most forebrain tracts and it minimizes assumptions on which variables will
enter the model by using stepwise regression in order to build the final model. Our analysis of
the relationship between measures of white matter tract microstructure and cognition, which
included the moderating effect of BDNF Val/Met, showed some specificity of FA factors and
BDNF genotype in predicting different cognitive dimensions. For example, BDNF genotype had
a role in predicting verbal and working memory performance by altering the relationship
between Factor 2 and these cognitive domains, but we could not demonstrate a role in predicting
performance in processing speed, card sort, span or general cognitive ability. Similarly, while
Factor 2 (including the ventral portion of the cingulum, the fornix body/stria terminalis, a
significant portion of posterior fibers and the anterior limb of the internal capsule [ALIC], and
the right superior fronto-occipital fasciculi [SFOF]) had some role in predicting verbal memory,
working memory and card sorting, only Factor 1 (midline structures such as the corpus callosum
and the columns of the fornix) seemed to play a role in working memory. Factor 4 (external
capsule and uncinate fasciculi) did not contribute to the determination of cognition alone or in
combination with BDNF genotype. This somewhat regional pattern was also confirmed when we
attempted to enter in the multiple regression analyses only one unrotated factor from the FA data,
accounting for about 38% of the total variance. This analysis was not significant for any of the
8
cognitive variables, indicating that multiple rotated factors were necessary in order to show
relationships to cognition in this sample.
A finding that was not predicted was the significant interaction of BDNF and Factor 2 in
predicting verbal and working memory performance. This is a difficult interaction to explain in
biologic terms, but it suggests that the underlying basis of FA variance in Val/Val individuals is
different than in Met carrier subjects. This may be indicative of BDNF genotype affecting the
formation of white matter tracts and consequently memory performance during development,
however, this inference is highly speculative given that this is just an association and we cannot
prove the order of causality or if an unknown variable is driving this complex association. It
should be noted that a similar finding with the same directionality was found by Chiang et al.
(Chiang et al, 2011) for object assembly (a subtest that was not available to us) in the splenium
of the corpus callosum and that further work by Chiang et al. found several SNPs that displayed
this type of interaction in predicting performance intelligence quotient (Chiang et al, 2012).
RD Factors were predictive of cognitive performance only in the case of Factor 3 and
working memory. Also card sort performance was predicted by a model that included RD
factors, but their statistical significance was limited. Also BDNF appeared to have a more limited
role when combined with RD Factors than seen for the analyses of FA above.
Although of interest, these associations suffer from multiple weaknesses that makes them
difficult to interpret. Given our sample size, a reduction of the number of ROIs derived from the
JHU atlas was necessary. We chose to drop regions associated with the brain stem and
cerebellum and to average left and right ROIs with correlation coefficients above 0.6. Even after
this data reduction, the ratio between the sample size and the number of variables entered in the
9
factor analysis (4:1, roughly) is not high enough to obtain stable solutions (Costello and Osborne,
2005). Moreover, the solutions found, even after varimax normalized rotation, contain several
crossloadings (e.g. the genu of the corpus callosum loads on both Factor 1 and Factor 2 for FA),
which complicate the biological interpretation of the data. The results are of difficult
interpretation also due to the concomitant presence of main effects and interactions in the same
model (e.g. verbal memory), which dilute the specificity of the findings. Finally, these analyses
are extremely sensitive to outliers and to the particular group composition. A larger sample size
and a completely independent replication sample are needed in order to support both the factor
structure for the diffusion metrics and their associations with cognition.
We also tried to approximate prior findings in the literature relating to prediction of
cognitive variables based on FA. For example, Karlsgodt et al. (Karlsgodt et al, 2008) had
reported in a sample of healthy volunteers (N=17) a positive correlation between the FA of the
left superior longitudinal fasciculs (SLF) and verbal working memory performance. This was not
true in our sample, although we used a transformed factor instead of simple accuracy during nback performance. Another difference from the work of Karlsgodt et al. (Karlsgodt et al, 2008)
was that our n-back was a mixed numerical-spatial task rather than a verbal task. This may be
consistent with the fact that while they found that the left SLF was predictive of performance,
only the right SLF contributed as a main effect in our models. The effect of sex (males>female)
seen here is consistent with males outperforming females in spatial tasks and with the data
collected by our group in a much larger sample deriving from the sibling study (data available on
request). Sex had not been included as a variable of interest in the analysis by Karlsgodt et al. It
should be noted, however, that these models are highly dependent on the variables included. For
example, a main effect of sex on working memory was not seen when we entered Factors 1-3
10
and BDNF in the model (Supplementary Table 4c) during our exploratory analyses of working
memory performance. Another cause for skepticism in interpreting these results is that the model
became statistically significant only when BDNF genotype was added into the model, however
this variable was not significant at the p<0.05 level in the univariate analysis as a main effect or
in interactions. The proportion of the variance explained in working memory by using the
bilateral SLF values rather than the FA Factors (exploratory analysis) was similar (multiple
R2=0.26 for SLF vs. 0.25 for Factors 1-3), an indication that it is possible to simplify the model
and pick single variables or combinations of un-weighted variables in order to achieve better
interpretability of the findings. Picking which regions of white matter should enter the prediction
scheme, however, remains problematic due to the high interconnectedness of the brain. Future
studies confirming the factor structure of the diffusion metrics in different age cohorts are
necessary.
Another issue we tried to address in this sample, is the reported relationship of processing
speed with the first principal component derived from the FA of eight white matter tracts (Penke
et al, 2010). The same group (Penke et al, 2012) also reported that the first principal component
derived from the FA of 12 tracts, predicted general cognitive ability. Both of these studies
(Penke et al, 2012; Penke et al, 2010) were conducted in healthy elderly subjects in their
seventies. Most of the literature on relationships between diffusion metrics and cognition with
large Ns has been centered on samples that include participants over 60 years of age. The first
principal component obtained in our investigation accounted for 54.5% of the variance when
derived from eight tracts and for 49% when using 12, which is roughly in accord with Penke et
al. (Penke et al, 2012; Penke et al, 2010). Supplementary Tables 7a and 7b show the factor
loadings for the first principal component derived from 8 or 12 tracts. Several differences emerge
11
from the findings of (Penke et al, 2010) (see their Table 1 in comparison with Table 7a). Our
factor loadings all have negative signs, while theirs are all positive, and the ranking of the ROIs
is different. The splenium of the corpus callosum had the lowest loading in (Penke et al, 2010),
while in our study the uncinate fasciculi have the lowest loadings.
Using these principal components, we were not able to predict either general cognitive
ability or processing speed. This difference is most likely due to the difference in age between
the two samples (~33 years old in our sample vs. ~72 in (Penke et al, 2012; Penke et al, 2010),
however there are significant differences in the way the FA values were obtained between our
study (atlas derived ROIs in the template space of the TBSS skeleton) and (Penke et al, 2012;
Penke et al, 2010) (tractography based results).
A final hypothesis that we tried to test with our data derived from a study by Kantarci et
al. (Kantarci et al, 2011) who found significant relationships between the FA or the ILF, dorsal
and ventral cinguli with memory performance in a large sample (N=220, 71 of which were
cognitively intact) of elderly individuals (~79 years old). Our data do not support that the FA of
these tracts predicts verbal memory, however their results (Kantarci et al, 2011) were based on a
larger sample with greater variance in memory performance due to the inclusion of a large group
of individuals with mild cognitive impairment. It is likely that the physiology of the aging brain
is substantially different from that of middle aged individuals. The overall reduction in
anisotropy that occurs with age could make white matter diffusion metrics much more relevant to
directly predicting cognitive function in a fairly diffuse manner, which may not be the case
earlier in life.
12
In summary, although we were not generally able to confirm some prior findings in the
literature, our exploratory analysis indicates that white matter microstructure as measured with
DTI is predictive of cognitive performance at least for card sorting, verbal and working memory
domains. More than one FA derived factor (i.e. subregions of the brain) is likely necessary to
uncover this relationship in middle age, contrary to old age, where one factor has been shown to
be sufficient (Penke et al, 2012; Penke et al, 2010) to predict processing speed and general
cognitive performance. Our data do not allow to definitively address the details of how different
white matter ROIs should be combined in order to obtain maximal predictability of cognitive
performance. Larger sample sizes and replications are needed to inform this choice. FA seems to
be more predictive of cognition than RD in our hands.
BDNF genotype does appear to play a role in predicting verbal and working memory by
interacting with white matter microstructure. This role is complex and could not have been
predicted from prior studies, although a similar finding with the same directionality was found
by Chiang et al. (Chiang et al, 2011) for object assembly (a subtest that was not available to us)
in the splenium of the corpus callosum.
13
SUPPLEMENTARY REFERENCES
Cattell RB (1966). The scree test for the number of factors. Multivar Behav Res 1: 245-276.
Chiang MC, Barysheva M, McMahon KL, de Zubicaray GI, Johnson K, Montgomery GW, et al
(2012). Gene Network Effects on Brain Microstructure and Intellectual Performance Identified in
472 Twins. J Neurosci 32(25): 8732-8745.
Chiang MC, Barysheva M, Toga AW, Medland SE, Hansell NK, James MR, et al (2011). BDNF
gene effects on brain circuitry replicated in 455 twins. NeuroImage 55(2): 448-454.
Costello AB, Osborne JW (2005). Best Practices in Exploratory Factor Analysis: Four
Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research
and Evaluation 10(7): 1-9.
Dickinson D, Goldberg TE, Gold JM, Elvevag B, Weinberger DR (2011). Cognitive factor
structure and invariance in people with schizophrenia, their unaffected siblings, and controls.
Schizophr Bull 37(6): 1157-1167.
Jacobs HI, Leritz EC, Williams VJ, Van Boxtel MP, Elst WV, Jolles J, et al (2011). Association
between white matter microstructure, executive functions, and processing speed in older adults:
The impact of vascular health. Hum Brain Mapp.
Kantarci K, Senjem ML, Avula R, Zhang B, Samikoglu AR, Weigand SD, et al (2011).
Diffusion tensor imaging and cognitive function in older adults with no dementia. Neurology
77(1): 26-34.
Karlsgodt KH, van Erp TG, Poldrack RA, Bearden CE, Nuechterlein KH, Cannon TD (2008).
Diffusion tensor imaging of the superior longitudinal fasciculus and working memory in recentonset schizophrenia. Biol Psychiatry 63(5): 512-518.
Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, et al (2008). Stereotaxic white matter atlas
based on diffusion tensor imaging in an ICBM template. NeuroImage 40(2): 570-582.
Penke L, Maniega SM, Bastin ME, Valdes Hernandez MC, Murray C, Royle NA, et al (2012).
Brain white matter tract integrity as a neural foundation for general intelligence. Mol Psychiatry.
14
Penke L, Munoz Maniega S, Murray C, Gow AJ, Hernandez MC, Clayden JD, et al (2010). A
general factor of brain white matter integrity predicts information processing speed in healthy
older people. J Neurosci 30(22): 7569-7574.
Roberts RE, Anderson EJ, Husain M (2010). Expert cognitive control and individual differences
associated with frontal and parietal white matter microstructure. J Neurosci 30(50): 1706317067.
15
SUPPLEMENTAL TABLE 1: Demographics, performance and genotype composition for
the available neurospcyhological domains
VERBAL
MEMORY
(N=84)
VISUAL
MEMORY
(N=40)
PROCESSING
SPEED
(N=84)
N-BACK
WORKING
MEMORY
(N=82)
SPAN (N=76)
CARD
SORTING
(N=76)
GENERAL
COGNITIVE
ABILITY
(N=82)
BDNF
genotype
Val/Val : Met
carriers
sex M:F
age
(mean +/- SD)
years of
education
(mean +/- SD)
performance
(z score mean
+/- SD)
50 : 34
45 : 39
33.7 +/- 9.5
16.5 +/- 2.5
0.059 +/- 0.87
50 : 34
45 : 39
33.7 +/- 9.5
16.5 +/- 2.5
0.015 +/- 0.88
48 : 34
43 : 39
33.6 +/- 9.5
16.5 +/- 2.4
0.13 +/- 0.84
45 : 31
40 : 36
33.7 +/- 9.6
16.5 +/- 2.5
0.008 +/- 0.79
43 : 33
39 : 37
33.9 +/- 9.6
16.5 +/- 2.4
0.089 +/- 0.69
48 : 34
43 : 39
33.3 +/- 9.3
16.5 +/- 2.4
0.073 +/- 0.48
omitted from
analysis due
to low N
16
SUPPLEMENTARY TABLE 2: Factor loadings of tract estimates for FA after factor
analysis (contributing factor loadings are bolded)
Tracts of interest
Corpus Callosum (genu)
Corpus Callosum (body)
Corpus Callosum (splenium)
Fornix (columns)
Superior fronto-occipital
fasciculus (right)
Factor 1
Factor 2
Factor 3
Factor 4
0.474031
0.866927
0.204244
0.720810
0.437264
0.145686
0.546239
0.377518
0.398595
0.389299
0.497539
-0.175613
0.108273
0.155405
0.162366
-0.095802
0.220221
0.420087
0.050687
0.242593
Superior fronto-occipital
fasciculus (left)
0.070015
0.135888
0.433217
0.132936
Internal capsule (anterior limb)
Internal capsule (posterior limb)
Internal capsule (retrolenticular)
Anterior corona radiata
Superior corona radiata
Posterior corona radiata
Posterior thalamic radiation
Sagittal stratum-ILF-IFO
External Capsule
Dorsal cingulum
Ventral cingulum
Fornix body-Stria Terminalis
Superior longitudinal fasciculus
Uncinate fasciculus
Tapetum
0.165516
-0.201717
-0.098229
0.431961
0.198768
0.055535
0.251759
0.229731
0.325302
0.370354
0.067500
0.370052
-0.067404
0.010986
0.138341
0.546144
0.043377
0.617341
0.562459
0.147000
0.188532
0.682061
0.779479
0.508495
0.354900
0.459553
0.555840
0.547625
0.166893
0.069813
0.189477
0.692114
0.597171
0.244418
0.775027
0.823254
0.340950
0.281091
0.275765
0.451497
0.086868
-0.049104
0.570402
0.130676
0.367790
0.358171
0.413112
0.100227
0.215644
0.189879
0.015706
-0.075663
-0.030196
0.566059
0.340426
0.155263
0.225376
0.242221
0.752925
-0.059283
ILF = Inferior Longitudinal Fasciculus; IFO = Inferior Fronto-Occipital fasciculus
17
SUPPLEMENTAL TABLE 3: Factor loadings of tract estimates for RD after factor
analysis (contributing factor loadings are bolded)
Tracts of interest
Factor 1
Factor 2
Factor 3
Corpus Callosum (genu)
Corpus Callosum (body)
Corpus Callosum (splenium)
Fornix (columns)
Internal capsule (anterior limb)
0.468993
0.494637
0.608369
0.058225
0.213091
0.535018
0.304524
0.493367
-0.050485
0.820091
0.363298
0.532952
0.088669
0.849035
-0.036011
Internal capsule (posterior limb)
0.331198
0.615010
-0.448344
Internal capsule (retrolenticular)
Anterior corona radiata
Superior corona radiata
Posterior corona radiata
Posterior thalamic radiation
Sagittal Stratum-ILF-IFO
External capsule
Dorsal cingulum
Ventral cingulum
Fornix body-Stria Terminalis
Superior longitudinal fasciculus
Superior fronto-occipital
fasciculus
Uncinate fasciculus
Tapetum
0.591401
0.511806
0.681966
0.917795
0.710428
0.613059
0.332801
0.390412
0.214099
0.160231
0.556249
0.652848
0.651463
0.589345
0.309371
0.421355
0.606843
0.808338
0.723173
0.640468
0.648441
0.674727
-0.175010
0.359966
0.051041
-0.027603
0.231541
0.202342
0.185866
0.246983
-0.016744
0.266644
-0.077087
0.432631
0.581726
0.328856
0.100376
0.695552
0.729273
0.008273
-0.088306
0.101563
18
SUPPLEMENTARY TABLE 4a: Results of overall regression predicting cognitive performance based on BDNF genotype,
age sex and four factors based on FA measures.
FA
verbal memory
processing speed
working memory
span
card sort
G
Multiple
R
0.45
0.30
0.49
0.42
0.75
0.25
Multiple
R²
0.20
0.09
0.25
0.18
0.56
0.06
Adjusted
R²
0.15
0.07
0.19
0.09
0.36
0.05
df
Model
5
2
5
7
14
1
df Res
F
p
77
79
75
67
31
78
3.82
3.97
4.87
2.09
2.80
5.15
0.0038
0.0227
0.0007
0.0565
0.0083
0.0260
19
SUPPLEMENTARY TABLE 4b: Results for univariate tests of significance (backward
stepwise solution) for verbal memory performance and factors derived from FA.
Verbal Memory
Intercept
BDNF Genotype
Factor2
Factor3
BDNF Genotype*Factor2
Factor2*Factor3
Error
Df
1
1
1
1
1
1
77
F
882.1058
0.0089
0.0824
4.2632
13.0424
3.2566
p
0.000000
0.924927
0.774803
0.042314
0.000540
0.075045
SUPPLEMENTARY TABLE 4c: Results for univariate tests of significance (backward
stepwise solution) for working memory performance and factors derived from FA.
Working Memory
Intercept
BDNF Genotype
Factor1
Factor2
Factor3
BDNF Genotype*Factor2
Error
Df
1
1
1
1
1
1
75
F
1119.806
0.459
7.012
0.205
4.488
14.477
p
0.000000
0.500315
0.009866
0.652367
0.037438
0.000287
SUPPLEMENTARY TABLE 4d: Results for univariate tests of significance (backward
stepwise solution) for card sort performance and factors derived from FA.
Card Sorting
Intercept
AGE
Factor2
Error
Df
1
1
1
73
F
125.6947
12.6488
4.9246
p
0.000000
0.000664
0.029584
20
SUPPLEMENTARY TABLE 5a: Results of overall regression predicting cognitive
performance as a function of BDNF genotype, age sex and three factors based on RD
measures.
RD
verbal memory
processing speed
working memory
span
card sort
G
Multiple R
0.38
0.36
0.51
0.36
0.55
0.17
Multiple R²
0.15
0.13
0.26
0.13
0.30
0.03
Adjusted R²
0.09
0.08
0.17
0.08
0.23
-0.01
df Model
5
4
9
4
7
3
df Res
78
79
72
71
68
78
F
2.68
2.86
2.88
2.57
4.11
0.77
p
0.0274
0.0288
0.0059
0.0448
0.0008
0.5167
21
SUPPLEMENTARY TABLE 5b: Results for univariate tests of significance (backward
stepwise solution) for working memory performance and factors derived from RD.
N-Back Working
Memory
Intercept
SEX
BDNF Genotype
AGE
Factor1
Factor2
Factor3
BDNF Genotype*AGE
SEX*Factor2
Factor1*Factor3
Error
Df
F
p
1
1
1
1
1
1
1
1
1
1
72
57.91
1.38
2.82
0.00
0.00
0.56
6.74
3.57
4.38
8.33
0.000
0.244
0.097
0.972
0.946
0.456
0.011
0.063
0.040
0.005
SUPPLEMENTARY TABLE 5c: Results for univariate tests of significance (backward
stepwise solution) for card sort performance and factors derived from RD.
Card Sort
Intercept
SEX
AGE
Factor1
Factor2
Factor3
SEX*Factor2
Factor1*Factor3
Error
Df
1
1
1
1
1
1
1
1
68
F
104.22
0.60
10.43
0.90
3.04
0.70
3.48
4.29
p
0.0000
0.4424
0.0019
0.3452
0.0858
0.4072
0.0666
0.0420
22
Supplementary Table 6. Prediction of working memory performance by FA of the bilateral
SLF and BDNF.
Working Memory
Intercept
SEX
BDNF Genotype
AGE
SLF R FA
SLF L FA
BDNF Genotype*AGE
SEX*SLF R FA
AGE*SLF R FA
AGE*SLF L FA
SLF R FA*SLF L FA
Error
Df
1
1
1
1
1
1
1
1
1
1
1
71
F
5.40
8.71
2.03
0.09
6.65
2.73
3.28
8.95
5.39
3.34
4.73
p
0.023
0.004
0.159
0.759
0.012
0.103
0.075
0.004
0.023
0.072
0.033
23
Supplementary Table 7a. Factor loadings for the first principal component based on the
average FA of eight white matter tracts.
ROI
Genu CC FA
Splenium CC FA
Dors. Cing. R FA
Dors. Cing. L FA
SLF R FA
SLF L FA
UF R FA
UF L FA
factor
loadings
-0.721110
-0.796593
-0.813736
-0.854284
-0.834219
-0.815361
-0.483780
-0.466819
Supplementary Table 7b. Factor loadings for the first principal component based on the
average FA of twelve white matter tracts.
ROI
Factor
loadings
Genu CC FA
Splenium CC FA
ALIC R FA
ALIC L FA
SS-ILF-IFO R FA
SS-ILF-IFO L FA
Dors. Cing. R FA
Dors. Cing. L FA
SLF R FA
SLF L FA
UF R FA
UF L FA
-0.305799
-0.331982
-0.268846
-0.282537
-0.284854
-0.277110
-0.309260
-0.334474
-0.326140
-0.328967
-0.179487
-0.178663
Abbreviations: ROI=region of interest, FA=fractional anisotropy, CC=corpus callosum, Dors.=
dorsal, Cing.=cingulum, SLF=superior longitudinal fasciculus, UF=uncinate fasciculus, ALIC=
Anterior limb of the internal capsule; SS-ILF-IFO=sagittal stratum-inferior longitudinal
fasciculus-inferior fronto-occipital fasciculus.
24
Supplementary Figure 1. Interaction of BDNF genotype by FA factor 2 in predicting verbal
memory performance.
BDNF
Genotype::Trans_Factor1_VerbalMemory:
1 Trans_Factor1_VerbalMemoryr 2==26.8988+2.7714*x
BDNF Genotype:
1 FACTOR2
0.0943; r = 0.3071, p = 0.0301
BDNF
Genotype:
2 Trans_Factor1_VerbalMemoryr 2==25.6753-3.3015*x
BDNF Genotype: 2 FACTOR2 :Trans_Factor1_VerbalMemory:
0.1363; r = -0.3691, p = 0.0317
45
40
Verbal Memory Performance
35
30
25
20
15
10
5
0
-3
-2
-1
0
1
2
3 -3
-2
BDNF Val/Val
-1
0
1
2
3
BDNF Met carriers
FA Factor2
BDNF Genotype 1= Val/Val, Genotype 2 = Met carriers
25
Supplementary Figure 2. Interaction of BDNF genotype by FA factor 2 in predicting nback working memory performance.
BDNF1 Genotype:
Trans_Factor2_Nback =r226.7881+3.3461*x
BDNF Genotype:
FACTOR21:Trans_Factor2_Nback:
= 0.1481; r = 0.3848, p = 0.0069
BDNF
Genotype:
2
Trans_Factor2_Nback =r227.6547-2.8962*x
BDNF Genotype: 2 FACTOR2 :Trans_Factor2_Nback:
= 0.1331; r = -0.3648, p = 0.0339
45
40
Working Memory Performance
35
30
25
20
15
10
5
0
-3
-2
-1
0
1
2
3 -3
BDNF Val/Val
-2
-1
0
1
2
3
BDNF Met Carriers
FA Factor 2
BDNF Genotype 1= Val/Val, Genotype 2 = Met carriers
26
Supplementary Figure 3. FA Factor 2 predicts Card Sort performance
Trans_Factor5_CardSorting =r2138.4656+15.0822*x
= 0.0971; r = 0.3117, p = 0.0061
FACTOR2 :Trans_Factor5_CardSorting:
240
220
200
Card Sort Performance
180
160
140
120
100
80
60
40
20
0
-3
-2
-1
0
1
2
3
FA Factor 2
27
Supplementary Figure 4. Relationship of RD-based Factor 3 and n-back working memory
performance.
Trans_Factor2_Nback = 27.032-1.985*x
FACTOR3 :Trans_Factor2_Nback: r2 = 0.0535; r = -0.2314, p = 0.0365
45
Working Memory Performance
40
35
30
25
20
15
10
5
0
-3
-2
-1
0
1
2
3
4
RD Factor 3
28
Supplementary Figure 5. Interaction of right SLF FA by sex in predicting n-back working
memory performance.
1 Trans_Factor2_Nback
SEX: 1 SLF R SEX:
FA:Trans_Factor2_Nback:
r2 == 4.4523+47.5289*x
0.0136; r = 0.1166, p = 0.4566
SEX:
2
Trans_Factor2_Nback
SEX: 2 SLF R FA:Trans_Factor2_Nback: r2==73.5037-97.1013*x
0.1426; r = -0.3777, p = 0.0178
45
Working Memory Performance
40
35
30
25
20
15
10
5
0
0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56
Males
Females
SLF R FA
SEX: 1= Males; SEX: 2= Females
29