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Supplementary Material
Hyett MP, Parker GB, Guo CC, Zalesky A, Nguyen VT, Yuen T, Breakspear MJ. Scene unseen: disrupted neuronal adaptation in melancholia during emotional film viewing.
sTable 1. Symptoms and Signs Expressed by Melancholic (Mel1-Mel16) and Non-melancholic (NMel1-NMel16) Participants.
sTable 2. Demographic and Clinical Characteristics Across Melancholic, Non-melancholic, and Control Groups.
sTable 3. Prediction of Presence or Absence of Differing Drug Classes, Controlling for Clinical Group, from Interaction of Rest and Negative Film viewing Sub-Network Edge Weights.
sFigure1. a) Overall and b) Continuous Ratings of Emotional Valence for the Two Film Clips, “Bill Cosby” and “The Power of One”, Averaged across 18 Healthy Participants. Error Bars Signify Standard Error of the Mean.
sFigure2. Average Inter-Subject Correlations of Hidden Neural States (Observed = Red) and Corresponding Null Distributions. Left = Strong Effect. Middle = Marginally Significant. Right = Non-significant Examples.
sFigure 3. Intra-Class Correlations of Hidden Neural States (Observed = Red) and Corresponding Null Distributions. Left, Middle and Right Panels Derived from same Condition/Group/Mode as sFigure 2.
sFigure 4. Average Correlations of BOLD (Observed = Red) and Corresponding Null Distributions. Left = Strong Effect. Left, Middle and Right Panels Derived from same Condition/Group/Mode as sFigure 2.
sFigure 5. Group comparisons of rank-ordered distributions of all 64 edge weights across resting state and neutral film viewing. Left column shows melancholic versus healthy controls: Right column shows melancholia versus non-melancholic MDD.
Appendix I. Methods.
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Essential Symptoms (Both Required) Specifiers (Five of Nine Required)
Patient
Psychomotor Disturbance
Distinct Anhedonia
Concentration Impairment
Mood Non-reactivity
Anergia
Diurnal Mood Variation
Appetite /Weight Loss
Early Morning Wakening
Mel1 + + + + + - - -Mel2 + + + + + + - +Mel3 + + + + + + - +Mel4 + + + + + + - -Mel5 + + + + + + - -Mel6 + + + + + + - -Mel7 + + + + + - + +Mel8 + + + + + - + +Mel9 + + - - + + - -Mel10 + + + + + - + +Mel11 + + + + + + - +Mel12 + + + - + + - -Mel13 + + + - + + - +Mel14 + + + + + + - -Mel15 + + + - + - + -Mel16 + + + + + + + +NMel1 - - - - - - - -NMel2 - - - - - - - -NMel3 - - - - - - - -NMel4 - - - - - - - -NMel5 - + - - - - - -NMel6 - - - - - - - -NMel7 - + - - - - - -NMel8 - - - - - - - -NMel9 - - - - - - - -NMel10 + + + - + - + -NMel11 - - - - - - - -NMel12 + - - - - - - -NMel13 - - - - - - - -NMel14 - + - - - - - -NMel15 + + + - + + - -NMel16 - - - - - - - -
sTable 1. Symptoms and Signs Expressed by Melancholic (Mel1-Mel16) and Non-melancholic (NMel1-NMel16) Participants‘+’ Indicates presence of symptom/sign; ‘-’ Indicates absence of symptom/sign.
sTable 2. Demographic and Clinical Characteristics Across Melancholic, Non-melancholic, and Control GroupsGroup Group Comparisona
Test VariableMelancholic
Non-melancholic Control
Melancholic vsNon-melancholic
P value Melancholic vs
Control
P value
Non-melancholic vs
ControlAge, mean (SD) 38.00 (9.94) 40.44 (10.73) 43.75 (14.10) t = -0.68 .51 t = -1.33 .19 t = -0.75Female sex, No (%) 8 (50.0) 10 (62.5) 9 (56.3) χ2 = 0.51 .48 χ2 = 0.13 .72 χ2 = 0.13Years of education, mean (SD) 14.81 (3.31) 15.88 (2.44) 17.44 (3.58) t = -1.03 .31 t = -2.15 .04 t = -1.44
Estimated IQ, mean 107.93 (12.40) 108.19 (9.96) 117.94 (7.55) t = -0.06 .95 t = -2.69 .01 t = -3.12
QIDS-SR, mean 16.69 (4.22) 15.06 (4.10) 1.19 (1.51) t = 1.10 .28 t = 13.82 <.001 t = 12.68
STAI-State, mean 49.73 (16.18) 46.25 (12.37) 25.44 (6.52) t = 0.68 .50 t = 5.42 <.001 t = 5.95
STAI-Trait, mean 55.00 (13.33) 62.88 (8.43) 31.19 (6.45) t = -1.98 .58 t = 6.27 <.001 t = 11.94
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GAF, mean (SD) 58.13 (7.04) 69.38 (6.29) 95.00 (0.00) t = -4.77 <.001 t = -20.95 <.001 t = -16.29CORE, mean (SD)
Noninteractiveness 3.44 (3.10) 0.75 (1.69) NA t = 3.05 .005 NA NARetardation 4.88 (3.40) 1.06 (2.41) NA t = 3.66 <.001 NA NAAgitation 0.69 (0.00) 0.00 (0.00) NA t = 2.55 .02 NA NATotal 9.00 (6.12) 1.81 (4.02) NA t = 3.93 <.001 NA NA
Current medications, No. (%)
No medication 1 (6.3) 5 (31.3) NA χ2 = 3.28 .07 NA NASSRI 2 (12.5) 8 (50.0) NA χ2 = 5.24 .02 NA NAAny medication other than SSRI 13 (81.3) 5 (31.3) NA χ2 = 8.13 .004 NA NA
Dual-action antidepressantb 8 (50.0) 5 (31.3) NA χ2 = 1.17 .28 NA NA
Tricyclic or monoamine oxidase inhibitor
4 (25.0) 2 (12.5) NA χ2 = 0.82.36
NA NA
Mood stabiliserc 1 (6.3) 2 (12.5) NA χ2 = 2.13 .34 NA NAAntipsychotic 4 (25.0) 0 NA χ2 = 4.57 .03 NA NA
Abbreviations: GAF, Global Assessment of Functioning; NA, not applicable; QIDS-SR, Quick Inventory of Depressive Symptomatology; SSRI, selective serotonin reuptake inhibitor; STAI State-Trait Anxiety Inventory.Uncorrected P values for between-group comparisons; differences significant at P < .05. b Serotonin noradrenaline reuptake inhibitor. c Lithium or valproate.
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Demographic and Clinical Characteristics
The melancholic, non-melancholic and control groups did not differ significantly by age or
gender (see sTable 2). Age ranges for the groups were 20-52 (melancholic), 21-56 (non-
melancholic), 22-75 (controls) – with two controls aged over 60. Two non-melancholic
participants and one healthy control participant were left-handed. The control group reported
more years of education compared to the melancholic group (t = -2.15, p = 0.04), and their
estimated IQ was higher than both melancholic (t = -2.69, p = 0.01) and non-melancholic
groups (t = -3.12, p = 0.004). The depressed groups did not differ by years of education,
estimated IQ, depression severity or state and trait anxiety scores. Consistent with the
diagnostic primacy of psychomotor disturbance, the melancholic group had higher scores
compared to the non-melancholic group on all CORE sub-scales (noninteractiveness: t =
3.05, p = 0.005; retardation: t = 3.66, p < 0.001; agitation: t = 2.55, p = 0.02) and higher total
CORE scores (t = 3.93, p < 0.001). All groups differed on the GAF with the melancholic
group having the most severe functional impairment, followed by the non-melancholic and
then the control group participants. More non-melancholic participants reported receiving a
selective serotonin reuptake inhibitor (SSRI) antidepressant drug compared with the
melancholic participants (χ2 = 5.24, p = .02); a higher proportion of melancholic participants
were receiving non-SSRI medications (χ2 = 8.13, p = .004). More melancholic participants
reported being on antipsychotic medication (χ2 = 4.57, p = .03).
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sTable 3. Prediction of Presence or Absence of Differing Drug Classes, Controlling for
Clinical Group, from Interaction of Rest and Negative Film viewing Sub-Network Edge
Weights
Dependent Variables
Predictor Variables SSRI (Yes/No) Non-SSRI Drug (Yes/No)
Rest * Negative Film Viewing†
Exp (β) Wald Sig. Exp (β) Wald Sig.
0.000 1.331 0.249 0.000 1.261 0.262
† Controlling for clinical group (melancholic/non-melancholic)
We used logistic regression to test whether differing medication classes confounded our
observed sub-network interaction differences. Clinical participants were divided into two
subsets: those in receipt of an SSRI medication, versus those not on any such medication; and
those on any other non-SSRI medication (eg, antipsychotics, mood stabilisers, all broad-
action antidepressants) compared to those who were not taking non-SSRI medication. We
controlled for clinical group and showed that sub-network scores, averaged across rest and
negative film viewing, were not predictive of differing medication classes.
Emotion Ratings During Emotional Film viewing
An independent cohort of 18 healthy participants was recruited to provide emotion ratings of
the stand up comedy (“Bill Cosby – Himself”) and The Power of One films. While viewing
each film, participants provided continuous ratings of their emotion using rating software
custom-built in LabView. They were instructed to continuously report their emotion by
moving a computer mouse as they viewed the film. Participants were required to move the
mouse all the way to the left if they felt completely sad, depressed, disgusted or unpleasant;
and move the mouse all the way to the right if they felt completely happy, joyful and pleased.
A vertical bar, indicating their current rating (between -1 and 1), provided visual feedback.
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Negative ratings corresponded to values towards -1, whilst positive ratings corresponded to
values towards +1. In addition, participants provided an overall rating of the film
immediately after viewing. The order of film presentation was counterbalanced between
participants. Overall, valence ratings of the films were consistent with the choice of valence
labels in the current study (eg, ‘positive’/ Bill Cosby and ‘negative’/The Power of One).
sFigure 1. a) Overall and b) Continuous Ratings of Emotional Valence for the Two Film
Clips, “Bill Cosby” and “The Power of One”, Averaged across 18 Healthy Participants. Error
Bars Signify Standard Error of the Mean
Inter-subject correlations of BOLD fluctuations, and comparison of inferred (hidden)
neural states and BOLD.
Consistent neuronal responses to film stimuli between participants is a hallmark finding in
the use of dynamic stimuli during functional imaging experiments. Previous research has
imputed these responses from direct observation of the blood-oxygen-level-dependent
(BOLD) signal. To validate the inversion of dynamic causal models (DCMs) during film
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viewing, we examined inter-subject correlations (ISCs) of the inferred neuronal states
following model inversion, by calculating the average pair-wise correlations between all
participants (giving ISCs). The statistical significance of these neuronal ISCs was tested
using a permutation approach. In particular, for any choice of mode (auditory, visual etc.), the
time series from each subject was cycled forward in time by independent random integer
increment (modulus N=181). This ensures that correlations within each time series are
preserved, while those between subjects are destroyed. Average pair-wise correlations from
an ensemble of 1000 of these surrogate data were used to represent the null distribution that
the measured values represent trivial effects arising from serial auto-correlations and finite
sample length. Family-wise control for multiple comparisons (across all possible pairs) was
achieved using false discovery rate (FDR) correction ( = 0.0055). Examples of strong,
marginal and null observations are provided in sFigure 2.
sFigure2. Average Inter-Subject Correlations of Hidden Neural States (Observed = Red) and
Corresponding Null Distributions. Left = Strong Effect. Middle = Marginally Significant.
Right = Non-significant Examples.
We used the mean correlation coefficient, averaged over all pairs of subjects because
this measure has been used widely in the analysis of film viewing fMRI data. However, as
subjects are included more than once in all possible pair combinations, there is some
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redundancy in this approach. We thus also used an arguably more principled measure,
namely the intra-class correlation. Corresponding results for the same three modes are shown
in sFigure 3. The results are highly consistent with the averaged correlation coefficients.
sFigure 3. Intra-Class Correlations of Hidden Neural States (Observed = Red) and
Corresponding Null Distributions. Left, Middle and Right Panels Derived from same
Condition/Group/Mode as sFigure 2.
To validate the novel approach of inverting DCMs from film viewing data, we also
compared the ISCs of the inferred neuronal states to those of the observed BOLD signals,
within and between each of the three participant groups. Corresponding results for the same
three modes are shown in sFigure 4. In general, the ISCs of the raw BOLD signals are
stronger than those of the hidden neural states. However, the statistical significance of the
two data sets are strongly consistent.
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sFigure 4. Average Correlations of BOLD (Observed = Red) and Corresponding Null
Distributions. Left = Strong Effect. Left, Middle and Right Panels Derived from same
Condition/Group/Mode as sFigure 2.
The Influence of Neutral Film Viewing on Network Parameters
We examined effective connectivity strengths across all 64 edges during neutral film
viewing. sFigure5 compares the distribution of edge weights between melancholic and
control, and melancholic and non-melancholic groups, using the resting state condition as a
reference. In keeping with the other movie viewing conditions, edge strengths were skewed
towards the stronger tails (generally in the positive direction) in the melancholic group. The
distribution of edge strengths in the non-melancholic group during neutral film viewing was
between those of the melancholic and control groups.
Using the same approach as for the positive and negative film viewing conditions (see “DCM
Analysis of Naturalistic Film Viewing” of main text), we assessed for group differences in
sub-networks of directed edge weights between resting state and neutral film viewing using
the NBS. No significant sub-networks were identified between rest and neutral conditions for
any of the group contrasts using the same threshold (4.25) that was used to identify the
effects shown in Figure 4.
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We next analyzed whether there was an interaction between melancholic and control groups,
and condition (rest and neutral film viewing) using the same sub-network of edges as in
Figure 4. Univariate analysis of variance revealed a significant group x condition interaction
(F = 4.91, p = 0.031). Sub-network strengths increased from rest to neutral film viewing in
those with melancholia (means of 0.033 and 0.075 respectively), and decreased in controls
(means of 0.084 and 0.048 for rest and neutral film viewing respectively). The mean
differences between rest and neutral film viewing conditions were thus not as large as the
difference between rest and negative film viewing and hence do not survive family wise error
correction. Nonetheless the direction of change, whilst muted in size, was consistent with that
in the emotionally salient movies. In other words, the presence of emotionally salient content
in the films was a more effective probe in eliciting between group effects.
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sFigure 5. Group comparisons of rank-ordered distributions of all 64 edge weights across resting state and neutral film viewing. Left column
shows melancholic versus healthy controls: Right column shows melancholia versus non-melancholic MDD.
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Appendix I. Methods
Detailed Diagnostic Approach to Depression Sub-typing
Clinical diagnoses of melancholic or non-melancholic depression were made by psychiatrists
weighting previously detailed criteria.1,2 For a diagnosis of melancholia, two compulsory
criteria were required (see sTable 1): (A) psychomotor disturbance (expressed as motor
slowing and/or agitation); and (B) an anhedonic mood state. In addition, five of the following
nine clinical features were required (and met) in all assigned melancholic patients: (1)
concentration and/or decision making impairment; (2) nonreactive affect; (3) distinct anergia;
(4) diurnal mood variation – being worse in the morning; (5) appetite and/or weight loss; (6)
early morning wakening; (7) no preceding stressors accounting for the depth of the
depressive episode; (8) previous good response to adequate antidepressant therapy; and (9)
normal personality functioning. Whilst respecting the DSM diagnostic approach to
melancholia, these have been customised to take into account criteria that have historically
characterised melancholia.1,2 sTable 1 shows specific criteria for each patient, as
crosschecked against their clinical assessment material (ie, clinical notes, assessment letters
and referral material to the Black Dog Institute Depression Clinic).
fMRI Image Acquisition
All participants underwent a 6 ¼-minute resting state fMRI scan (186 volumes) and were
instructed to keep their eyes shut for the duration of the scan. Resting state fMRI was
acquired prior to three separate fMRI sequences during which participant’s viewed positive
(“Bill Cosy – Himself”), negative (“The Power of One”) and neutral films (landscape
footage). The presentation order of the films was pseudo-randomly counterbalanced across
participants. All participants explicitly reported remaining awake across the scanning
sequences. Scanning was conducted using a Philips 3.0-T scanner (Philips Medical Systems;
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Best, Netherlands). Functional data were acquired using T2*-weighted gradient echo-planar
sequences (33 axial slices; repetition time/echo time: 2000/30 msec; 76° flip angle;
reconstruction matrix size: 128 × 128; field of view (anterior-posterior): 240 mm; voxel size:
3.0 × 3.0 × 3.0 mm; no gap).
Data Preprocessing
Resting state and film viewing fMRI images were preprocessed using statistical parametric
mapping (SPM8) software (http://www.fil.ion.ucl.ac.uk/spm/).3 For each participant, each
image was realigned to the first acquired session-specific image, normalised (unwarped) to
standard Montreal Neurological Institute (MNI) space and smoothed with a full-width half-
maximum (FWHM) kernel of 4 mm. All preprocessed functional data (resting state and film
viewing) were then used as inputs for probabilistic concatenated independent component
analysis (ICA) using the MELODIC (Multivariate Exploratory Linear Decomposition into
Independent Components) toolbox in the FMRIB Software Library (FSL)
(http://www.fmrib.ox.ac.uk/fsl/).4 For the ICA, non-brain voxels were masked with voxel-
wise demeaning of the data and normalisation of the voxel-wise variance. Preprocessed data
were next whitened and projected into a 70-dimensional subspace using Principle
Components Analysis, providing for a reasonably fine-grained decomposition of functionally
relevant brain regions.5 These whitened observations were decomposed into sets of vectors
that describe signal variation across the temporal domain (giving time courses), the
session/subject domain, and across the spatial domain (giving spatial maps). This was
implemented through a non-Gaussian spatial source distribution using a fixed-point iteration
technique.6 Estimated component maps were divided by the standard deviation of the residual
noise, with a threshold of 0.5 set (the probability that needed to be exceeded by a voxel to be
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considered ‘active’ in the component of interest) by fitting a mixture model to the histogram
of intensity values.4
Node Selection
We selected eight ‘modes’ from the group-level spatial maps as generated by ICA, with the
aim of capturing emotional, cognitive and perceptual systems relating to attention and
interoception. The components selected were: (A) Auditory (AUD); (B) Default mode
network (DMN); (C) Executive control (EXC); (D) Left insula (L-INS); (E) Right insula (R-
INS); (F) Left frontoparietal attention (LFP); (G) Medial visual pole (MVP); and (H) Right
frontoparietal attention (RFP; see Figure 1). All components were checked for
correspondence with previously identified cognitive and sensory networks using spatial
cross-correlation,5 except for the L-INS and R-INS modes. These components were identified
from the ICA maps by, i) first obtaining the centre coordinates of the bilateral anterior insula
using PickAtlas, and then, ii) using these coordinates to identify the spatial maps with the
most specificity from the 70 components of the ICA. These maps were then used in
specifying and estimating stochastic dynamic causal models (sDCMs) across all conditions
for all study participants.
DCM Specification
Dynamic causal modelling infers effective connectivity amongst neuronal populations by
combining dynamic models of neuronal states and detailed biophysical models of
haemodynamics. Traditionally, DCM has been employed to provide generative models of
task-related data, where stimulus or task manipulations are introduced as known inputs to
regions identified through use of the general linear model.3 With stochastic DCM (sDCM),
the system perturbations are modelled as unknown system fluctuations arising
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endogenously.7,8 Broadly speaking, sDCM otherwise proceeds in a similar vein to classic
DCM, namely: 1. The user specifies a model (or models) through the choice of nodes and
inputs; 2. The empirical data are introduced as time series of each node; 3. The model
evidence (the probability of observing the data given the model) is maximised using a
variational scheme to minimise an objective function (the free energy). This also yields
posterior model parameter estimates as well as estimates of the unknown state fluctuations;8,9
and 4. If more than one model is specified, model comparison is performed using the
evidence for each model. This Bayesian model evidence (aka marginal likelihood) penalises
the accuracy of each model by a measure of its complexity.10 In the present setting, we
implemented a single, fully connected bilinear DCM with unknown fluctuations at every
node. No external inputs were specified. This model was estimated in all participants.
For each of the components, for each condition, the peak activation voxel was
identified, with MNI coordinates of these peaks used to define a regionally specific voxel of
interest to allow initial estimation of the DCMs. For visualisation purposes (eg, see Figure 1),
a 6 mm sphere was used to represent the spatial location of the peak weight of the
corresponding ICA mode. Dual regression was used to extract participant-specific time series
from condition-specific, group-level spatial maps (representing an average of the voxels
within each map, weighted by their relative expression in that map), with the corresponding
component time series used as inputs for the sDCMs.8,9 In specifying the DCMs, no inputs
were selected for the first and second levels, and a fully connected model was chosen for the
search space.
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