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Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

市川奈穂広島大学

2020.05.28

概要

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OPEN

Primary functional brain
connections associated with
melancholic major depressive
disorder and modulation by
antidepressants
Naho Ichikawa1, Giuseppe Lisi2, Noriaki Yahata3, Go Okada1, Masahiro Takamura1,
Ryu-ichiro Hashimoto4, Takashi Yamada2, Makiko Yamada3,5, Tetsuya Suhara3,
Sho Moriguchi6, Masaru Mimura6, Yujiro Yoshihara7, Hidehiko Takahashi7,8, Kiyoto Kasai9,
Nobumasa Kato4, Shigeto Yamawaki1, Ben Seymour2,10*, Mitsuo Kawato2, Jun Morimoto2 &
Yasumasa Okamoto1*
The limited efficacy of available antidepressant therapies may be due to how they affect the underlying
brain network. The purpose of this study was to develop a melancholic MDD biomarker to identify
critically important functional connections (FCs), and explore their association to treatments. Resting
state fMRI data of 130 individuals (65 melancholic major depressive disorder (MDD) patients, 65
healthy controls) were included to build a melancholic MDD classifier, and 10 FCs were selected by
our sparse machine learning algorithm. This biomarker generalized to a drug-free independent cohort
of melancholic MDD, and did not generalize to other MDD subtypes or other psychiatric disorders.
Moreover, we found that antidepressants had a heterogeneous effect on the identified FCs of 25
melancholic MDDs. In particular, it did impact the FC between left dorsolateral prefrontal cortex
(DLPFC)/inferior frontal gyrus (IFG) and posterior cingulate cortex (PCC)/precuneus, ranked as the
second ‘most important’ FC based on the biomarker weights, whilst other eight FCs were normalized.
Given that left DLPFC has been proposed as an explicit target of depression treatments, this suggest
that the limited efficacy of antidepressants might be compensated by combining therapies with
targeted treatment as an optimized approach in the future.
Major depressive disorder remains a major global health challenge, with substantial socio-economic
cost. The mainstay of pharmacological treatment is selective serotonin reuptake inhibitors (SSRIs) and
serotonin-norepinephrine reuptake inhibitor (SNRIs). Despite their widespread and increasing use1–4, many
patients respond little, if at all5,6. Understanding why this is the case is complicated, because of the poorly understood relationship between the regionally distributed actions of drugs and the complex underlying neurobiology
of depressive symptoms.

1

Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences, Hiroshima University,
Hiroshima, Japan. 2ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan. 3Institute
for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba,
Japan. 4Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan. 5Department
of Functional Brain Imaging Research, National Institutes for Quantum and Radiological Science and Technology,
Chiba, Japan. 6Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan. 7Department of
Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan. 8Graduate School of Medical and Dental
Sciences, Tokyo Medical and Dental University, Tokyo, Japan. 9Department of Youth Mental Health, Graduate
School of Medicine, The University of Tokyo, Tokyo, Japan. 10Computational and Biological Learning Lab, Cambridge
University, Cambridge, UK. *email: ben@atr.jp; oy@hiroshima-u.ac.jp
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1

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Recently, brain imaging has provided important insights into the underlying neural mechanisms of depression. In particular, resting-state connectivity studies in humans have identified a number of potentially important abnormal functional connections that may play a role in disorder symptoms7–14. However, according to the
meta-analysis paper of MRI-based neuroimaging biomarkers in depressive disorders15, around 30% of reviewed
papers were using resting-state fMRI (rsfMRI) as modality, and only one-third of them were using functional
connections (FCs) among region of interests (ROIs) as features. As most of those biomarker studies applied the
algorithm of support vector machine (SVM) to achieve high accuracy with many features, it has not yet been clear
about which are the most critical FCs in depression with whole-brain analysis.
In order to determine target FCs and investigate their modulation by antidepressants, it would be important
to focus on a specific subtype of depression, because of heterogeneity of depression. Melancholic major depressive
disorder (MDD) is a subtype of MDD that is traditionally considered to be the most drug-responsive, and so provides an ideal target to probe the effect of drugs on abnormal connectivity16–20. Reliably identifying drug effects
on connectivity requires a robust biomarker that directly maps connectivity patterns to depressive symptoms.
As hypotheses on modulation by antidepressants, one possibility is that abnormal connectivity in MDD is uniformly but only partially resolved following treatment. On the other hand, it may be that whilst some functional
connections fully resolve, others do not, or are even worsened by treatment. This latter possibility is particularly
intriguing since it would suggest that the relative effect on different functional connections determines treatment
response, as well as identifying specific targets for future combined treatment approaches. Therefore, the purpose
of this study is to develop a melancholic MDD biomarker based on functional connections and explore the sensitivity to treatment.
In theory, this could identify functional connections which would be potential targets of depression treatment.
This is because, first, the resting state functional connectivity is temporal correlations between two brain regions
of interests (ROIs), and it is flexibly changed based on the type of cognitive tasks and easily can be targeted in
training or intervention treatment studies in a short period of time. Second, there have been more and more
interests in studying the network brain activity in fMRI data. The main functional networks are default mode
network (DMN), executive control network (ECN), and salience network (SN). Specifically, DMN is observed as
the network of regions functionally connected with each other during rest (i.e., with correlations of spontaneous
temporal fluctuations of BOLD signals), and it is known to be correlated with depression symptom severity in
recurrent MDDs21. So, some critical FCs in DMN could be both a good diagnostic measure and a good treatment target. Third, it has been reported that the change of within-DMN functional connectivity extends to other
regions in the default mode modules, and also associated with FCs in the fronto-parietal module22. This suggests
the possibility that we may be able to target and focus on only a critically abnormal FC to normalize, in order to
affect the whole DMN to reduce depressive symptoms. At the same time, however, it is important to point out
that functional connectivity studies are purely correlative even for the predictive diagnosis classification, and it
should be difficult to disentangle causal versus consequential changes which track behavior and symptoms. To the
best of our knowledge, there is no answer yet to the question if abnormality of functional connection is a cause of
depression or an epiphenomenon caused by depression. However, based on the following reasons, we think that it
would be appropriate to target the functional connection which is diagnostically most reliable. Although there is a
limitation that we cannot really know if the correlational relationship could be causal, more and more researchers
are focusing on the brain networks and abnormality of neural circuit dynamics, as a key for successful analysis to
integrate different levels of knowledge into a comprehensive system23–25. Before such prospects, much more work
is needed in fact, and we hope our work can contribute to the movement.
Our aims in this study were therefore two-fold: first, to extract critically important functional connections
when building a classifier of melancholic MDD; and second, to use it to test the uniformity versus heterogeneity
connectivity hypotheses of the effect of antidepressants on melancholic MDD patients.

Methods

Participants and clinical measures. The overview of depression biomarker development is shown in

Fig. 1. 177 patients were recruited at the Hiroshima University Hospital and local clinics (in Hiroshima, Japan)
and screened using the M.I.N.I.26,27 for a MDD diagnosis with the DSM-IV criteria. Out of them, 118 patients
participated in the MRI experiments. Exclusion criteria included current or past manic episodes; psychotic episodes; alcohol dependence or/and abuse; substance dependence or/and abuse; and antisocial personality disorder
based on M.I.N.I., change of diagnosis (from unipolar to bipolar depression), MRI scan after more than 2 weeks
of medication, fMRI data with excessive head motions based on scrubbing results (see the following section of
Neuroimaging data preprocessing and interregional correlations). In addition, as the exclusion criteria at the
moment of recruitment at local clinics included: already enough amount of dose and time of one type of antidepressant was administered, more than 2 types of antidepressants were administered for the current episode,
had electroconvulsive therapy (ECT), physical disorders which may have any negative effects with SSRI treatments, current pregnancy or breast-feeding, high risk of suicidality judged by the doctor, those who needed to
be hospitalized, and those who were not able to understand Japanese expressions. ...

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Acknowledgements

This research was conducted as a contract project supported by AMED under Grant Number JP18dm0307008,

and under the “Application of DecNef for development of diagnostic and cure system for mental disorders and

construction of clinical application bases” of the Strategic Research Program for Brain Sciences from Japan

Agency for Medical Research and development, AMED. N.I., G.O., M.T., S.Y., Y.O. are also partially supported

by “Integrated research on neuropsychiatric disorders”, “the Integrated Research on Depression, Dementia and

Development Disorders”, and Brain/MINDS Beyond (19dm0307002 h0002) by AMED. B.S. is partially supported

by Versus Arthritis (21537).

Author contributions

Y.O. had full access to all of the data in the study and takes responsibility for the integrity of the data and the

accuracy of the data analysis. N.I. and G.L. contributed equally as first authors. G.O., M.T, N.I., N.Y., R.H., T.Y.,

M.Y., T.S., S.M., M.M., Y.Y., H.T., K.K., N.K., S.Y. and Y.O. involved in collecting the data. N.I. and Y.O. discussed

the design of clinical data and ran the analyses. G.L. and N.Y. designed the preprocessing pipeline and prepared

the scripts for the Biomarker Toolbox. N.I. and G.L. created tables and figures. G.L., J.M., N.Y. and M.K. consulted

on all statistical analyses. N.I., G.L., N.Y., B. S., M.K. and Y.O. wrote the manuscript. All authors discussed the

results and conclusions for editing the manuscript.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information is available for this paper at https://doi.org/10.1038/s41598-020-60527-z.

Correspondence and requests for materials should be addressed to B.S. or Y.O.

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Supplementary information for “Primary functional brain connections

associated with melancholic major depressive disorder and modulation by

antidepressants”

Naho Ichikawa1, Giuseppe Lisi2, Noriaki Yahata3, Go Okada1, Masahiro Takamura1, Ryu-ichiro

Hashimoto4, Takashi Yamada2, Makiko Yamada3,5, Tetsuya Suhara3, Sho Moriguchi6, Masaru

Mimura6, Yujiro Yoshihara7, Hidehiko Takahashi7,8, Kiyoto Kasai9, Nobumasa Kato4, Shigeto

Yamawaki1, Ben Seymour2,10*, Mitsuo Kawato2, Jun Morimoto2, & Yasumasa Okamoto1*

*oy@hiroshima-u.ac.jp

Department of Psychiatry and Neurosciences, Graduate School of Biomedical Sciences,

Hiroshima University, Hiroshima, JAPAN; 2ATR Brain Information Communication Research

Laboratory Group, Kyoto, JAPAN; 3Institute for Quantum Life Science, National Institute of

Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology,

Chiba, JAPAN; 4Medical Institute of Developmental Disabilities Research, Showa University,

Tokyo, JAPAN; 5Department of Functional Brain Imaging Research, National Institutes for

Quantum and Radiological Science and Technology, Chiba, JAPAN;

Department of

Neuropsychiatry, Keio University School of Medicine, Tokyo, JAPAN; 7Department of

Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, JAPAN; 8Graduate School of

Medical and Dental Sciences Tokyo Medical and Dental University, Tokyo, JAPAN 9Department

of Youth Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, JAPAN;

Computational and Biological Learning Lab, Cambridge University, Cambridge, UK.

Supplementary Information

Supplementary Methods.

Feature selection with reduction of nuisance variable effects

Application to non-melancholic MDD, treatment-resistant MDD.

Application to other psychiatric disorders.

Application to euthymic MDD.

Changes of each FC: post minus pre antidepressant treatments.

Supplementary Results.

Tables S1: Demographic information of the participants

Tables S2: Scanner information and resting-state fMRI protocols

Tables S3: Head motion during resting-state

Figure S1: Generalization to an independent cohort assessed by permutation tests

Supplementary Methods.

Feature selection with reduction of nuisance variable effects

As specific steps, in the first data matrix X1 of demographic information, the column of diagnosis

contained either 1 or 0 (i.e., MDD or HC). The next four columns of scanner/site contained either

[1 0 0 0] for the Site1, [0 1 0 0] for the Site2, [0 0 1 0] for the Site3, or [0 0 0 1] for the Site4. The

sixth column was for age, and the seventh column was for sex (i.e., 1 for male and 0 for female).

The second data matrix X2 pools the off-diagonal lower-triangular elements of correlation matrix

as the FC of a single subject. By applying L1-SCCA to these data, the sparse projection matrices

of V1 and V2 were derived from the pair of data matrices (i.e., X1 and X2). We here defined the

diagnostic canonical variables and the diagnostic canonical constraint, and which should be

associated only with the diagnostic label. We used the columns of V1 that have non-zero elements

only in the row corresponding to the diagnostic label, and only the columns of V2 corresponding

to the diagnostic canonical variables based on the sum of absolute value across columns as union

of features across diagnostic canonical variables. This means, the columns which had non-zero

values in the rows corresponding to the scanner/site, age, sex were disregarded. In this way,

identifying the FCs corresponding to the diagnostic canonical variables enabled us to select only

essential FCs for MDD/HC classification, and simultaneously reduced undesirable effects of

nuisance variables (i.e., scanner/site, age, sex).

Application to non-melancholic MDD, treatment-resistant MDD.

In order to test if the classifier was specific to the characteristics of melancholic MDD, we

applied the same classifier to the datasets of non-melancholic and treatment-resistant MDD

(Demographics are shown in Supplementary Table S1b, with two MDD patients lacking MINI

scores of previous episodes, melancholia, and comorbidity). Non-melancholic MDDs are from

the all MDD dataset with more than mild depressive state (BDI score > = 17).

Application to other psychiatric disorders.

Autism spectrum disorder (ASD) dataset was adopted from our previous investigation. In

order to minimize any effect from comorbidity of depression, the autism spectrum disorder dataset

was limited to the data with no active antidepressant medication. A total of 110 participants

included 74 ASD patients (No. of male/female = 58/16, Age (year): mean (SD) = 31.5 (8.5)) and

36 healthy controls (No. of male/female = 30/6, Age (year): mean (SD) = 30.9 (6.9)).

For the schizophrenia spectrum disorder (SSD) data (Yoshihara et al., in submission), all

participants provided written informed consent that was approved by the Committee on Medical

Ethics of Kyoto University. A total of 170 participants included 68 SSD patients (No. of

male/female = 33/35, Age (year): mean (SD) = 38.4 (9.1), 64 Schizophrenia and 4 Schizoaffective

disorder, Duration of illness (year): mean (SD) = 12.8 (7.8)) and 102 healthy controls (No. of

male/female = 62/40, Age (year): mean (SD) = 31.1 (9.6)). See the Supplementary Table S1 for

details of MRI experimental settings for each site.

Application to euthymic MDD.

Thirty-six patients who are in euthymic states for more than two months were recruited, and

their demographic information was shown in Supplementary Table S1c. Their resting state

fMRI and T1 structural data were scanned using the identical scan protocols at the Site 4, and

went through the identical preprocessing steps.

Changes of each FC: post minus pre antidepressant treatments.

For the analysis of pre-post treatment effects, we computed contribution scores of each FC

using the following equations:

Equation (1)

where N is the number of patients who underwent the treatment and

is the classifier weight.

In order to have a point of reference, we computed the same contribution score, for the MDD and

healthy control populations:

Equation (2)

where M is the total number of MDD patients, and N is the number of healthy control subjects.

The

represents the difference in average

between healthy controls and MDD, weighted

by the classifier’s weight. It should be noted that the

the positive class in the classifier, and

subtraction) in Formula 2. The

is the subtrahend (i.e. negative sign in the

represents the difference in average

pre- antidepressant treatments. Negative value of

the post-treatment

treatment

should always be negative since MDD is

between post- and

(i.e. post- minus pre- treatment) means that

is closer to healthy controls, while positive value means that post-

is closer to MDD. For each

, we compared

and

using the Welch’s t-

test, due to the different sample sizes between HCs, MDDs, and the MDD patients who underwent

treatment:

Equation (3)

where

is the sample variance of the

the sum of the variances of

the sum of variances of

, and it is obtained using the variance sum law as

and

. Similarly,

and

is computed as

. The sample sizes are computes as

and

. The P value of each test was corrected for multiple

comparisons by the Benjamini–Hochberg procedure.

Supplementary Results.

Table S1: Demographic information of the melancholic MDD classifier.

Table S2: Scanner information and resting-state fMRI protocols of melancholic MDD, healthy

control, other subtypes of MDD, autism spectrum disorder, and schizophrenia. All the euthymic

MDD data shown in Table S1c was collected at the Site 4 with identical settings.

Table S3: Head motion of MDD and healthy control in the training dataset and test dataset.

Figure S1. Classification results of the melancholic MDD biomarker and its generalization

performance to other subtypes and psychiatric disorders. Density of the weighted linear sum

computed based on the melancholic MDD biomarker for a.) training dataset (melancholic MDD

n = 65, healthy control: n = 65) and b.) test dataset from an independent site (melancholic MDD:

n = 11, healthy control: n = 40). Permutation tests show the histogram of the permutation test

(1,000 repetitions) for c) the training dataset LOOCV, and show d.) a completely independent test

dataset accuracies, and the binomial distribution is shown as a green curve. The accuracies of the

melancholic MDD classifier trained and tested without permutation were shown as red vertical

lines. The results of permutation test were significant for LOOCV (p = .002) and for the

independent test dataset (p = .040). * p < .05, *** p < .005.

...

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Primary functional brain connections associated with melancholic major depressive disorder and modulation by antidepressants

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