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Biezonski et al.
Supplemental Information
EVIDENCE FOR THALAMOCORTICAL CIRCUIT ABNORMALITIES AND
ASSOCIATED COGNITIVE DYSFUNCTIONS IN UNDERWEIGHT INDIVIDUALS
WITH ANOREXIA NERVOSA
Dominik Biezonski, PhDa, Jiook Cha, PhDa,
Joanna Steinglass, MDa, Jonathan Posner, MDa
aDepartment
of Psychiatry, Columbia University College of Physicians and
Surgeons and New York State Psychiatric Institute, New York, NY 10032, USA
Contents:
- Supplemental Methods
- Supplemental Results
- Supplemental Figures
- Supplemental Tables
- Supplemental References
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SUPPLEMENTAL METHODS
Cognitive Measures
Letter-Number Sequencing (LNS) Task
The LNS is a subtest of the Wechsler Adult Intelligence Scale-III (WAIS-III, Wechsler
(1997)) that measures working memory. Participants were orally presented random
numbers and letters that they were asked to mentally organize and verbally return in
numerical and alphabetical order. The total number of correct responses was scored,
with higher scores indicating better performance.
Stroop Task
The Stroop task was used to assess cognitive control in participants and was performed
as described in Golden (1978), with modifications. Briefly, in a color naming task (Task
A), participants were asked to quickly name the color (red, green, or blue) of 126 dots,
5.6 mm in diameter, arrayed randomly in 9 columns and 14 rows on an 8.5 x 11-inch
sheet of white paper, scanning left to right and then top to bottom. In a word-reading
task (Task B), subjects were asked to read out loud an equal number of similarly
arrayed words (red, green, or blue) printed in black ink. In a final color-word naming task
(Task C), subjects were asked to name a similar array of words written in incongruent
colors. Subjects were given 45 s to complete each task, and correct number of
responses was recorded. The Stroop interference score was calculated as C − [(A ×
B)/(A + C)] (Golden, 1978), where higher scores indicate lower interference, and better
performance.
Trail-Making Test (TMT)
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The TMT was used to measure processing speed and visual attention (Bowie and
Harvey, 2006). Study participants were asked to draw a continuous line connecting
randomly placed numbers (1-25) in numerical order as quickly as possible without
making a mistake (“A”). Subjects were then asked to repeat the same task, but
connecting 25 letters and numbers in alphabetical and numerical order, respectively,
alternating between letters and numbers (i.e., A-1, 1-B, B-2, etc.) (“B”). The time to
perform each task was recorded, and the results of A were then divided by B to
compute the final score for this measure, with higher scores indicating better
performance.
Head Motion Parameters: FD and DVARS
In order to examine the confounding effects of head motion on our resting-state
functional MRI connectivity results, we used two different indices. First, based on each
individual’s head alignment parameters derived from the SPM8 realignment procedure,
we calculated framewise displacement (FD). We then differentiated the six head
realignment parameters across frames, and calculated instantaneous head motion as a
scalar in each frame using the following formula: FDi = |Δdix| + |Δdiy| + |Δdiz| + |Δαi| +
|Δβi| + |Δγi|, where Δdix = d(i − 1)x − dix; this was similarly performed for the other rigid
body parameters [dix diy diz αi βi γi]. Finally, we converted rotational displacements from
degrees to millimeters by calculating displacement on the surface of a sphere of radius
50 mm. Second, we computed DVARS (D denoting the temporal derivative of the
timeseries, and VARS denoting the root mean square variance over voxels). DVARS
represents the rate of change of BOLD signal across the brain at each frame. Initially,
we calculated the signal intensity of each individual’s resting-state functional MRI
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averaged across the whole brain. We then differentiated the timeseries relative to one
past TR and computed RMS signal change, following this equation:
,
where
is the image intensity at locus
on frame
, and angle brackets denote
the spatial average over the whole brain. Since this measure is applied to data that
have been resampled in atlas space, it is important that the spatial average include only
voxels that, during acquisition, were within the field of view at all times. We therefore
entered the four measurements of head motion (i.e., FDPEAK, FDMEAN, DVARSPEAK, and
DVARSMEAN) as nuisance variables in all relevant statistical models in our study. No
differences in any of these motion parameters were found between patients with AN
and HC in either the main group or subgroup (Table S2).
Sensitivity Analyses
Two sample t-tests were used to assess for differences between AN subtypes in
extracted thalamic deformation values, thalamo-frontal connectivity strength values, and
task performance. In addition, we used two sample t-tests to analyze group differences
in extracted thalamic deformation and thalamo-frontal connectivity strength values
between participants with AN who were free of a comorbid diagnosis and healthy
controls. We also compared AN patients with and without comorbid diagnoses on these
measures. Partial correlations controlling for age, IQ, and head motion parameters
were used to compute the association between thalamo-frontal functional connectivity
strength, and performance on all three cognitive tasks across participants with AN who
were free of a comorbid diagnosis and healthy controls. Bivariate correlations were
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used to compute associations between extracted thalamic deformation values, thalamofrontal connectivity strength values, and either current BMI, lowest lifetime BMI or illness
duration within the AN group.
To examine whether metabolic differences in AN may have influenced the results from
our functional connectivity analysis, we used the REST pipeline (Song et al, 2011) to
calculate the average BOLD timeseries as a proxy for assessing glucose metabolism
(Nugent et al, 2015; Tomasi et al, 2013) within regions of interest in our study for each
participant. Specifically, we computed the average low-frequency fluctuations (ALFF)
in BOLD signal within user-generated masks (SMP8) encompassing the Thal4, Thal5,
DLPFC, and AntPFC, regions where we found altered thalamo-frontal connectivity in the
AN group relative to HC (see Results). Two sample t-tests were then used to assess for
differences in ALFF in BOLD signal (REST analysis) within these regions of interest
between AN and HC.
For these sensitivity analyses, a p ≤ 0.05 was considered statistically significant.
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SUPPLEMENTAL RESULTS
Sensitivity Analyses
We found no difference between AN subtypes in the extent of thalamic deformations
(p’s > 0.60), the strength of thalamo-frontal connectivity (p’s > 0.26), or performance on
our measures of cognitive function (p’s > 0.29). Within the AN group, neither illness
duration, current BMI, or lowest lifetime BMI correlated with the extent of thalamic
deformation (p’s > 0.10) or Thal4-AntPFC(L) connectivity (p’s > 0.27) (Table S3).
Thal5-DLPFC(L) and Thal5-DLPFC(R) connectivity strength was not associated with
illness duration or lowest lifetime BMI (p’s > 0.25), but did significantly correlate with
current BMI within the AN group (p’s < 0.017) (Table S3). The extent of thalamic
deformation and thalamo-frontal connectivity strength between groups remained
significant after removal of AN participants with comorbid diagnoses (p’s < 0.05, Table
S4). In addition, we found no difference in either of these measures between AN
participants with or without comorbid diagnoses (p’s > 0.18, Table S4). Sensitivity
analysis revealed that partial correlations controlling for age, IQ, and head motion
parameters remained significant for Stroop vs. Thal5-DLPFC(L) (p = 0.01) and LNS vs.
Thal4-AntPFC(L) (p = 0.03) after removal of AN participants with comorbid diagnoses
(Table S4); the Stroop vs. Thal5-DLPFC(R) no longer reached significance (p = 0.08,
Table S4), likely due to lack of statistical power.
REST-based analysis of the average low frequency fluctuations (ALFF) in BOLD signal
across scan time revealed no differences between patients with AN and HC in Thal4,
Thal5, left or right DLPFC, or left AntPFC (p’s > 0.21, Table S5).
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SUPPLEMENTAL FIGURES
Figure S1. Thalamic subregions 1-7 as defined by the Oxford thalamic
connectivity atlas (OTCA). Each of the 7 thalamic subregions is represented by a
distinct color, preferentially connecting (>25% of connections) with a cortical lobe of the
same color (inset).
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SUPPLEMENTAL TABLES
Table S1: Main vs. Subgroup Comparisons on Demographics
Main Group vs. Subgroup
Comparisons
AN
HC
Age
Test
Statistic
(d.f.)
p value*
Test
Statistic
(d.f.)
p value*
t(41)=0.51
0.61
t(36)=0.19
0.85
t(39)=1.10
0.28
t(36)=0.39
0.70
t(41)=0.51
0.61
t(36)=0.54
0.59
t(41)=-0.13
0.90
t(41)=0.94
0.36
t(41)=-0.25
0.80
t(41)=-0.32
0.75
t(36)=0.20
0.84
t(41)=0.46
0.65
t(36)=0.37
0.71
(Years)
IQ
(Estimated)
BMI
(kg/m2)
Lowest
Lifetime
BMI
(kg/m2)
Age of
Onset
(Years)
Illness
Duration
(Months)
EDE
(Score)
Education
(Years)
t(38)=1.3
0.19
t(34)=0.3
0.80
SES
2
2
Χ (2)=0.70
0.70
Χ (1)=0.01
0.97
Race
2
Subtype Χ (1)=0.78 0.38
* Two-tailed in case of t-tests. Abbreviations: AN,
Anorexia Nervosa; HC, Healthy Control; BMI,
Body Mass Index; IQ, Full-scale IQ estimated by the
Wechsler Test of Adult Reading (WTAR); EDE, Eating
Disorder Examination Interview; SES, Socioeconomic
Status#
#Household
1
2
3
4
5
6
7
income (dollars)
< 10,000
10-19,999
20-34,999
35-49,999
50-99,999
100-199,999
> 200,000
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Table S2. Group Comparisons on Head Motion Parameters from Resting-State
Functional MRI
Thalamic Connectivity
(Subgroup)
FDPEAK
FDMEAN
dVARSPEAK
dVARSMEAN
AN
HC
Test
Statistic
(d.f.)
p
value
0.51 ± 0.21
0.07 ± 0.03
22.37 ± 8.05
7.29 ± 0.43
0.64 ± 0.20
0.08 ± 0.06
25.60 ± 7.72
7.64 ± 0.52
t(29)=0.45
t(29)=0.21
t(29)=0.29
t(29)=0.52
0.66
0.83
0.77
0.61
Table S3. Correlational Comparisons Between Illness Duration, Lowest Lifetime
BMI, Current BMI, and Extracted Values from the Structural and Functional
Analyses in the AN Group
Thalamic Morphology
(Main Group)
Left
Thalamic
Deformation
Illness
Duration
(Months)
Lifetime
Lowest
BMI
(kg/m2)
Current
BMI
(kg/m2)
Right
Thalamic
Deformation
Thalamic Connectivity
(Subgroup)
ThalDLPFC(L)
ThalDLPFC(R)
ThalAntPFC(L)
r(15)=-0.33 r(15)=-0.24, r(15)=-0.06 r(15)=-0.04 r(15)=0.10
p=0.87
p=0.23
p=0.38
p=0.82
p=0.71
r(15)=0.44
p=0.10
r(15)=0.21
p=0.44
r(15)=-0.29
p=0.30
r(15)=0.27
p=0.33
r(15)=0.17
p=0.55
r(15)=-0.70 r(15)=-0.60 r(15)=0.02
p=0.02
p=0.004
p=0.95
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r(15)=-0.31 r(15)=0.31
p=0.25
p=0.27
Manuscript submitted to Neuropsychopharmacology
Biezonski et al.
Table S4. Sensitivity Analyses Comparing Study Findings Before and After
Removal of Subjects with Comorbid Diagnoses
Thalamic Morphology
(Main Group)
Thalamic Connectivity
(Subgroup)
Left
Thalamic
Deformation
Right
Thalamic
Deformation
ThalDLPFC(L)
All AN vs.
HC
t(48)=3.50,
p=0.001
t(48)=3.03,
p=0.004
NonComorbid
AN vs. HC
t(40)=0.42,
p<0.000
t(40)=3.80,
p<0.000
Comorbid
AN vs. NonComorbid
AN
t(26)=0.86,
p=0.40
t(26)=1.39,
p=0.18
t(29)=
-3.94,
p<0.000
t(22)=
-2.12,
p=0.05
t(13)=
2.15,
p=0.051
ThalThalDLPFC(R) AntPFC(L)
t(29)=
-3.85,
p=0.001
t(22)=
-2.78,
p=0.01
t(13)=
1.22,
p=0.25
t(29)=
3.78,
p=0.001
t(22)=
2.50,
p=0.02
t(13)=
-1.02,
p=0.33
Cognitive Performance vs.
Thalamic Connectivity
(Partial Correlations Controlling for
Age, IQ, and Head Motion Parameters)
All AN vs.
HC
NonComorbid
AN vs. HC
ThalDLPFC(L)
vs. Stroop
ThalDLPFC(R)
vs. Stroop
ThalAntPFC(L)
vs. LNS
r(21)=-0.45
p=0.02
r(16)=-0.61
p=0.01
r(21)=-0.38
p=0.04
r(16)=-0.42
p=0.08
r(22)=0.58
p=0.002
r(15)=0.54
p=0.025
Table S5. REST-Based Estimation of Average Low Frequency Fluctuations
(ALFF) in the BOLD Timeseries Within Thal4, Thal5, AntPFC, and DLPFC.
Thal4
HC
AN
Thal5
DLPFC
AntPFC
Mean ±
SEM
Test
Statistic
(d.f.)
Mean ±
SEM
Test
Statistic
(d.f.)
Mean ±
SEM
Test
Statistic
(d.f.)
Mean ±
SEM
Test
Statistic
(d.f.)
0.47 ±
0.01
0.49 ±
0.01
t(29)=
-1.29,
p=0.21
0.42 ±
0.01
0.42 ±
0.01
t(29)=
-0.80,
p=0.43
0.39 ±
0.01
0.41 ±
0.02
t(29)=
-0.57,
p=0.58
0.57 ±
0.05
0.56 ±
0.03
t(29)=0.38,
p=0.82
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Biezonski et al.
SUPPLEMENTAL REFERENCES
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Nugent AC, Martinez A, D'Alfonso A, Zarate CA, Theodore WH (2015). The relationship
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Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, et al (2011). REST: a toolkit for
resting-state functional magnetic resonance imaging data processing. PloS one 6(9):
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Tomasi D, Wang GJ, Volkow ND (2013). Energetic cost of brain functional connectivity.
Proceedings of the National Academy of Sciences of the United States of America
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Wechsler D (1997). WAIS-III administration and scoring manual. The Psychological
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