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OPEN

Resting‑state functional
connectivity disruption
between the left and right pallidum
as a biomarker for subthresholddepression
Yosuke Sato 1, Go Okada 1, Satoshi Yokoyama 1, Naho Ichikawa 1,2, Masahiro Takamura 3,4,
Yuki Mitsuyama 1, Ayaka Shimizu 1, Eri Itai 1, Hotaka Shinzato 1, Mitsuo Kawato 5,
Noriaki Yahata 6 & Yasumasa Okamoto 1*
Although the identification of late adolescents with subthreshold depression (StD) may provide
a basis for developing effective interventions that could lead to a reduction in the prevalence of
StD and prevent the development of major depressive disorder, knowledge about the neural basisof StD remains limited. The purpose of this study was to develop a generalizable classifier for StD
and to shed light on the underlying neural mechanisms of StD in late adolescents. Resting-statefunctional magnetic resonance imaging data of 91 individuals (30 StD subjects, 61 healthy controls)
were included to build an StD classifier, and eight functional connections were selected by using the
combination of two machine learning algorithms. We applied this biomarker to an independent cohort(n = 43) and confirmed that it showed generalization performance (area under the curve = 0.84/0.75 forthe training/test datasets). Moreover, the most important functional connection was between the left
and right pallidum, which may be related to clinically important dysfunctions in subjects with StD suchas anhedonia and hyposensitivity to rewards. Investigation of whether modulation of the identified
functional connections can be an effective treatment for StD may be an important topic of future
research.
Recently, there has been growing interest in subthreshold depression (StD)1, which is defined as clinically relevant
depressive symptoms not meeting the criteria for full-blown major depressive disorder (MDD)2. StD is highly
prevalent among university students w­ orldwide3 and its prevalence seems to be ­increasing4. Late adolescentuniversity students are reportedly vulnerable to depression and ­anxiety5–7, have a higher rate of depression compared to the general p­ opulation8, and their depressive symptoms may be related to an increased risk for suicidal
ideation and suicide a­ ttempts9. StD is associated with severe functional impairments and has a negative impact
on the quality of life. In addition, StD is a risk factor for developing M­ DD6,10. Hence, the identification of lateadolescents with StD may provide a basis for developing effective interventions that could lead to a reduction in
the prevalence of StD and prevent the development of MDD. However, knowledge about the neural basis of StD
remains limited, which makes it challenging to develop effective biomarkers and t­ reatments11.
Some studies have evaluated impaired brain regions and abnormal patterns of brain connectivity to develop
novel biomarkers for StD using resting-state functional magnetic resonance imaging (rs-fMRI)11–18. In earlystudies, it was reported that spontaneous neuronal activity, as measured by the amplitude of low-frequency
fluctuations, is altered, that resting-state functional connections (FCs) are impaired, and that subcortical degree
centrality is decreased and cortical degree centrality is increased in subjects with ­StD12,13,16. In studies pertaining

1

Department of Psychiatry and Neurosciences, Hiroshima University, 1‑2‑3 Kasumi, Minami‑ku,
Hiroshima 734‑8551, Japan. 2Deloitte Analytics R&D, Deloitte Touche Tohmatsu LLC, Tokyo, Japan. 3Departmentof Neurology, Shimane University, Matsue, Japan. 4Center for Brain, Mind and KANSEI Research Sciences,
Hiroshima University, Hiroshima, Japan. 5ATR Brain Information Communication Research Laboratory Group,
Kyoto, Japan. 6Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba,
Japan. *email: oy@hiroshima-u.ac.jp
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to brain networks, it was suggested that FCs between subregions of the anterior cingulate cortex are altered,that the FCs of the habenula within the default model network are increased, and that the FCs within salience
networks are diminished in individuals with S­ tD11,17. Recently, beyond the group-level analyses used in theseprevious studies, a data-driven approach using machine learning, which identifies phenotypes in a way that isclinically useful and can be applied to clinical diagnosis or prognosis, has gained a­ ttention19. In a recent investigation, by using the Support Vector Machine, which has been used frequently to identify imaging biomarkers
in some d­ iseases20, connectome-based biomarkers predicting StD associated with regions of the thalamus wereidentified, but with an insufficient generalization c­ apability21. If the generalization capability to independent data
is not sufficient, it is difficult to discuss the pathophysiology of the target disease through FC features selectedby machine learning. Yahata et al.22 developed an rs-fMRI-based classifier for autism spectrum disorder withgeneralization capability by combining two machine learning algorithms, L1-regularized sparse canonical correlation analysis (L1-SCCA)23 and sparse logistic regression (SLR)24, to overcome the two major difficulties
causing poor generalization performance, i.e., over-fitting and nuisance variables, and additionally by testing
the classifier in an independent validation c­ ohort22. By using the same method, we also developed a classifier
for MDD with melancholic features that were generalized to an independent cohort and extracted criticallyimportant ­FCs25. To our knowledge, no study has attempted to identify an rs-fMRI-based biomarker for StD andconfirmed its generalization capability. Therefore, the purpose of the present study was to develop a generalizableclassifier for StD in late adolescents by using the combination of two machine learning algorithms, L1-SCCAand ­SLR22,25. Another purpose of this study was to shed light on the underlying neural mechanisms of StD byusing the critically important extracted FCs. In late adolescents, the StD group has been characterized only byhyposensitivity to environmental rewards in previous studies, whereas the MDD group has been characterized
by a higher frequency of avoidance and hyposensitivity to environmental rewards compared with the nondepression ­group26. Moreover, anhedonia is one of the most common symptoms of S­ tD27 and, in adolescents,
predicts adult ­MDD28. In previous studies, reward dysfunction was observed in depression patients. Pizzagalli
et al. found that MDD patients showed significantly weaker responses to gains in the s­ triatum29. The abnormal
resting-state FCs of the ventral striatum, a key region in the reward network, was observed in StD s­ ubjects30.
Thus, we examined if the identified FCs in the StD classifier were associated with anhedonia and reward responsiveness in the present study.

Results

Highly accurate classifier for StD and generalization to an independent cohort. A classifier wasconstructed based on the FCs of each subject to distinguish StD subjects from HCs. The classification was firstcarried out by feature-selection from all 9316 FCs with L1-SCCA and SLR. Then, for this classifier, the weighted
linear summation (or linear discriminant function) of the identified FCs was computed. This algorithm wasapplied to the training dataset (n = 91, Table 1), and then a classification accuracy of 80% (area under the curve
0.84, sensitivity 70%, specificity 85%; p = 0.001 with a permutation test, Supplementary Fig. S1a,c) was revealedby the leave-one-out cross validation (LOOCV) procedure. Next, the classifier was tested on an independentvalidation cohort (n = 43), and then a classification accuracy of 79% (area under the curve 0.75, sensitivity 84%,
specificity 72%; p = 0.001 with a permutation test, Supplementary Fig. S1b,d) was revealed.

FCs in the classifier. Then, the neural basis of the StD classifier was investigated. The sparse classification
algorithm allowed the survival of eight FCs from 29 FCs that were selected at least once throughout the LOOCV
procedure based on their contribution to the classifier (Table 2 and Fig. 1a). The robustness and stability of thedetected FCs throughout the cross-validation procedure are shown in Fig. 1b. Furthermore, we confirmed thatthe weights of the eight identified FCs across the LOOCV procedure were significantly nonzero (two-sided
Wilcoxon signed rank test, p < 0.001), indicating their important contribution to the classifier. The contribution index of individual FCs to the corresponding connection, which was defined by the difference of each FC
between StD subjects and HCs multiplied by the classifier weight, is plotted in Fig. 1c. We found that FC#1 (rightpallidum to left pallidum) between inter-hemispheric regions showed the most outstanding contribution of
the eight FCs. We also found that only FC#1, out of 9316 FCs, was identified as having a significant difference
between StD and HCs by using conventional between-group comparison (two-sample t test with Bonfferoni
correction, p < 0.05/9316).

Training dataset
No. of participants

Test dataset

StD

Healthy controls

p-value

StD

Healthy controls

p-value

30

61

NA

16

27

NA

Sex, male/female

19/11

32/29

0.326

10/6

18/9

0.782

Age (years), mean (SD)18.2 (0.4)

18.4 (0.5)

0.054

18.6 (0.7)

18.5 (0.6)

0.921

BDI-II, mean (SD)

16.8 (3.5)

3.0 (1.9)0.000***

20 (5.0)

3 (2.2)0.000***

Table 1.  Demographic and clinical information of the participants used to construct the subthresholddepression (StD) classifier. The differences between StD subjects and healthy controls were tested by a twotailed t test (for age and BDI-II) or the chi-squared test (for sex). ***p < 0.001. BDI-II Beck’s Depression
Inventory-II, NA not applicable, SD standard deviation.

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ID
1
2
3
4

5

6

7

8

Name

Lat

BrainVISA Sulci Atlas (sulcus)

BA

Pallidum

R

Pallidum–

Pallidum

L

Pallidum–

Fusiform gyrus

R

Internal occipito-temporal lateral sulcus

37

CuneusR

Cuneal sulcus

18

Precuneus

R

Transverse parietal sulcus

7

Triangular part of the inferior frontal gyrus

L

Anterior inferior frontal sulcus

45

Middle occipital gyrus

L

Lobe occipital

19

Rolandic operculum

L

Anterior sub-central ramus of the lateral fissure48

CaudateR

Caudate–R

Anterior terminal ascending branch of the superior temporalsulcus40

Supramarginal gyrus
Supplementary motor area

R

Median frontal sulcus

6

Superior frontal gyrus

L

Median frontal sulcus

9Angular gyrus

L

Anterior terminal ascending branch of the superior temporalsulcus

39

Superior parietal gyrus

R

Superior parietal sulcus

7

Middle frontal gyrus

L

Intermediate frontal sulcus

46

Inferior parietal gyrus

L

Superior postcentral intraparietal superior sulcus

40

rStD

rHC

WeightContribution

0.344

0.513

− 3.41

0.578

0.147

0.341

− 1.900.370− 0.200− 0.287

2.51

0.217

− 0.149− 0.038− 1.910.213− 0.058

0.044− 1.67

0.172

0.359

0.467

− 1.460.157− 0.153− 0.297

0.88

0.127

− 0.0610.016− 1.330.102

Table 2.  Characteristics of the eight interregional functional connections (FCs) used in the subthresholddepression (StD) classifier. The mean functional correlation values for StD (rStD) and healthy controls (rHC)are presented for individual FCs. Since the StD group was the positive class in the classification of the StD andHC populations, the sign of weight was positive when rStD > rHC, whereas it was negative when rStD < rHC.
Contribution was computed as (rStD − rHC) × weight. BA Brodmann area.

Figure 1.  The eight identified functional connections (FCs) for the subthreshold depression (StD) classifier.
(a) The 16 brain regions connected by the eight FCs of the StD biomarker (rendered with: MRIcroGL 64-bit,
June 12, 2015, https://​www.​nitrc.​org/​proje​cts/​mricr​ogl). (b) Contribution of individual FCs to the StD
biomarker. The cumulative absolute weights are shown for all 9316 FCs, of which 29 FCs were selected at least
once throughout the leave-one-out cross validation process. The eight FCs identified through the StD classifier
were derived from a key subset of the 29 FCs (the red columns represent the eight identified FCs, and the graycolumns represent the remaining 21 FCs). (c) Contribution of each of the eight FCs to the StD biomarker.
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BDI anhedonic subscore

EROS

Identified FCs

r

p

r

FC#1

− 0.4690.000*

0.450

0.000*

FC#2− 0.3210.002*

0.282

0.007

FC#3

0.287

0.006

− 0.119

0.261

FC#4

− 0.264

0.011

0.183

0.083

FC#5

− 0.241

0.021

0.185

0.079

FC#6

− 0.256

0.014

0.383

0.000*

FC#7

0.199

0.059

− 0.217

0.039

FC#8

− 0.156

0.141

0.147

0.164

p

Table 3.  Relationship between each functional correlation value and clinical score. Clinical scores associatedwith anhedonia (BDI anhedonic subscore and EROS). *Bonferroni-adjusted significance level of p < 0.05/8(= 0.006). BDI Beck’s Depression Inventory, EROS Environmental Reward Observation Scale, FC functional
connection.

Figure 2.  A scatter plot of correlations between the identified functional connections (FCs) and clinical scores.
(a) FC#1 shows a negative correlation with the BDI anhedonic subscore (r = − 0.469, p < 0.006*). (b) FC#1
shows a positive correlation with EROS score (r = 0.450, p < 0.006*). *Bonferroni-adjusted significance level ofp < 0.05/8 (= 0.006). BDI Beck’s Depression Inventory, EROS Environmental Reward Observation Scale.

Association of important FCs with anhedonia and reward responsiveness. We evaluated the rel-evance of the important FCs to anhedonia and reward responsiveness, which are features of S­ tD31. FC#1 andFC#2 had significant negative correlations with the BDI anhedonic subscore. FC#1 and FC#6 had significant
positive correlations with the EROS score. Of the eight FCs, only FC#1 had significant correlations with the BDIanhedonic subscore and EROS score (Table 3, Fig. 2a,b). Also, similar results were obtained in the sum of training and test dataset (Supplementary Table S3).

Discussion

Herein, we generated a neuroimaging-based biomarker for StD of which the generalization capability was confirmed in an independent validation cohort. Furthermore, our findings suggest that the FC between the left andright pallidum, which was the most important of the eight FCs identified for this classifier, may be associatedwith clinically important dysfunctions in subjects with StD such as hyposensitivity to rewards and anhedonia.
In recent years, some impaired brain regions and abnormal patterns of brain connectivity have been evaluated in subjects with StD by using rs-fMRI11–18. Unfortunately, these findings could not be applied to direct
diagnosis, but they are useful for the identification of disease biomarkers associated with the dysfunctions
observed in StD. This is because the individual predictive ability of these biomarkers was not evaluated, eventhough they were statistically significant at the group level. Following this, there has been increasing interest in
using machine learning algorithms in order to predict phenotypes in a way that allows characterization at thelevel of the individual beyond group level ­analyses19. However, also in these studies, there were some limitations
regarding the generalizability and feasibility of the identified markers, for example, there were too many predictive FCs to explain StD ­individually21. Yahata et al.22 overcame these limitations by combining two machinelearning algorithms, L1-SCCA and SLR, and identified rs-fMRI-based classifiers for autism spectrum disorderwith high g­ eneralization22. This technique was also used successfully to extract a few FCs related only to the corecharacteristics of melancholic M­ DD25. By using the same approach, we developed a generalizable rs-fMRI-based
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Content courtesy of Springer Nature, terms of use apply. Rights reserved4www.nature.com/scientificreports/biomarker for StD tested on an independent validation cohort. The test data were acquired using a Siemens Verioscanner that was upgraded to a Skyra scanner including a retrofit of hardware and software. Imaging was also
performed with a new protocol that had higher spatial and temporal resolution than used for the discovery data
(Supplementary Table S1). Scanner ­specifications32 and imaging ­parameters33 are known to affect MRI data.
Therefore, it is considered to be a great advantage of our biomarker that it showed generalization performancefor different machines and different protocols without special harmonization.
Our findings demonstrated that FC#1 (right pallidum to left pallidum), which was the most importantof the eight FCs identified for this classifier, may be associated with the clinically important dysfunctions insubjects with StD such as hyposensitivity to rewards and anhedonia. Of the eight identified FCs, FC#1 showed
the strongest negative correlation with the BDI anhedonic subscore and strongest positive correlation with the
EROS score. In addition, both of these correlations were still significant after multiple comparison corrections
only for FC#1. These results suggest that the lower FC between the right and left pallidum may be relevant tohyposensitivity to rewards and anhedonia in subjects with StD. It is difficult to interpret that the FC between
bilateral pallidum was not selected as an important feature to discriminate between MDD and healthy subjects
in our previous s­ tudy25. To verify whether the key findings of this study targeting StD also apply in MDD, weanalyzed previous data (92 patients with MDD vs. 92 HCs from Ichikawa et al.25) focusing on the FC between
bilateral pallidum. As a result, there was a trend toward decreased FC between bilateral pallidum in MDD
compared to HCs, which was not significant (p = 0.067). It is unclear why the decreased FC between bilateralpallidum is more characteristic of StD than MDD, but it is possible that the balance of FC changes after onset.
This issue needs further clarification in future longitudinal cohort studies.
The right and left pallidum are connected via the anterior commissure through the lateral nucleus of theglobus ­pallidus34,35. The ventral pallidum reportedly plays a crucial role in ­motivation36. In rats and monkeys,
electrical stimulation within the ventral pallidum supports self-stimulation behavior as a reward in and of­itself37,38. In addition, in humans, activation in the ventral pallidum is associated with receiving food andmoney as a ­reward39,40. It has been suggested that the ventral pallidum is deeply involved in the regulation of
motor action based on reward e­ xpectation41. Classically, lesions of the globus pallidus are reportedly related toanhedonia, decreased social interactions, and flattened ­affect42,43. Given that late adolescents with StD have beencharacterized by reward system ­dysfunction26, our findings suggest that the inter-hemispheric connectivity of thepallidum may be especially important in this process. Indeed, the volume of the bilateral pallidum is reportedly
decreased and has a negative association with the severity of depressive symptoms in young StD ­subjects44.
Although the FC between the left and right pallidum was identified as a particularly important biomarker of
StD, the other seven FCs contained areas in which abnormalities have been suggested in depression. In particular,
the FC between the precuneus and the left triangular part of the inferior frontal gyrus is interesting because it
is in the same direction as the connection between the precuneus and left dorsolateral prefrontal cortex, which
was identified by Ichikawa et al. as an important connection in the biomarker for melancholic M­ DD25. Further
research is needed in longitudinal studies, such as which connections are involved in the development of MDD.
There are four limitations related to the present study. First, because our study was cross-sectional, we
cannot determine if these observations are the cause or result of StD. Additionally, the relatively mild depressive
symptoms of StD subjects can change easily over time. Future longitudinal studies are required to address thisquestion and will help further our understanding of the neural basis of StD. Second, the sample size of thecurrent study was relatively small. The performance of a classifier is directly influenced by sample size, withwhich prediction accuracy may d­ ecrease45. Thus, a larger sample size is needed to validate the current findings
further. Third, there were some differences in head motion between the two groups in the training dataset(Supplementary Table S2) that may influence the classifier performance, although six head motion parameters
were linearly regressed out. Motion artifacts contribute substantially to the rs-fMRI signal and are not fully
countered by motion r­ egressions58. Some efforts may have to be directed at preventing head motion, such as
behavioral or physical ­interventions46. Moreover, attention should be paid to the neurobiological basis of headmotion as identified by Zeng et al.47. Differentiating true disease effects from the correlates of motion tendency
is critical for using connectivity markers in the clinical area because correlates of motion may reduce specificity
of biomarkers. Fourth, the cerebellum was not incorporated in the construction of the classifier because it was
truncated in many participants’ images. Cerebellar abnormalities have been the focus of increasing concern
recently in d
­ epression48. There are several studies on StD subjects from rs-fMRI that has identified aberrant
cerebellar ­activity12,13.
In summary, this study generated a generalizable rs-fMRI-based classifier for the accurate prediction of StD
for the first time. For the StD classifier, the FC between the left and right pallidum was the most important of eightFCs identified using sparse classification algorithms, which may be related to clinically important dysfunctionsof subjects with StD such as anhedonia and hyposensitivity to rewards. Investigation of whether modulation of
the identified FCs can be an effective treatment for StD may be an important future research topic.

Methods

Participants. Participants were enrolled from 18 to 19-year-old first-year students attending Hiroshima
University, Japan. Beck’s Depression Inventory-II (BDI-II)49 was administered to the participants to scoredepressive severity. A BDI-II score of 13 is a suggested cutoff point to detect depression in college ­students50,51;
therefore, in the present study, StD was defined as a score of ≥ 13 points on the BDI-II. Participants that met thefollowing criteria after completing the Japanese version of the Composite International Diagnostic ­Interview52
were excluded from this study: major depressive episode during the past year, a lifetime history of bipolar disorder, taking psychopharmacological or psychological treatment within the past year, possibility of acute suicide
attempts, difficulty in understanding the purpose of the study, or difficulty in completing the self-report scales
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due to a serious mental condition or severe physical disease. In this way, a total of 91 subjects were enlisted in the
rs-fMRI experiments, including 30 StD subjects with BDI-II scores from 13 to 27 (19 men and 11 women) and
61 age-, sex-, and education-matched healthy controls (HCs) with BDI-II scores < 8 (32 men and 29 women).
Demographic data for the participants are presented in Table 1. This study was approved by the ethics committeeof Hiroshima University, and all participants provided their written informed consent after the study was completely described to each participant. In addition, we confirm that all procedures were carried out in accordancewith relevant guidelines and regulations.

BDI‑II. The BDI-II is a widely-used instrument for measuring depression and depressive symptoms and consists of 21 self-report items that are rated on a 4-point scale ranging from 0 to 3. The Japanese version of the
BDI-II has shown good validity and ­reliability48. Additionally, anhedonia was assessed by the BDI-II anhedonic
subscore: item #4—loss of pleasure, item #12—loss of interest, item # 15—loss of energy, and item #21—loss ofsex ­drive53–55.
Environmental Reward Observation Scale (EROS). The ­EROS56 is used to assess environmental
reward and response-contingent positive reinforcement. The original version of this assessment is a 10-item
scale measuring agreement (1: strongly disagree to 4: strongly agree). The Japanese version of the EROS has
shown good validity and ­reliability57. Additionally, this scale shows a negative correlation with depressive symptoms, especially anhedonia ­symptoms57.
fMRI data acquisition. In a darkened scanning room, the participants were required to maintain theirgaze on a fixation point in the center of a monitor screen, not to think of anything specific, and to stay awake.
Details of the imaging protocols for fMRI data acquisition and the procedure in each dataset are shown in Supplementary Table S1.
fMRI data preprocessing. T1-weighted structural images and resting-state functional images were pre-processed with SPM8 (Wellcome Trust Centre for Neuroimaging, University College London, UK) running on
MATLAB R2019a (MathWorks, Inc., Natick, MA, USA). The functional images were corrected for slice-timing
and subsequently realigned to the mean image. Next, using the associated parameters obtained through the segmentation of the T1-weighted structural images coregistered to the mean functional image, the fMRI data werenormalized and resampled in 2 × 2 × 2 ­mm3 voxels. All functional images were then smoothed using an isotropic
6-mm full-width half-maximum Gaussian kernel. Finally, a scrubbing ­procedure58 was used to compensate
further for motion, removing any volume (i.e., functional images) with excessive movement (frame displacement > 0.5 mm), based on the relative changes from frame to frame in the fMRI time-series (Supplementary
Table S2 for a summary of head motion).

Interregional correlations. For each participant, pairwise interregional FCs were evaluated between 137

regions of interest, which were defined anatomically using the BrainVISA Sulci ­Atlas59,60 covering the entire
cerebral cortex and subcortical regions. Then, the time course of fMRI data in each region was extracted. In thisstudy, the cerebellum was not incorporated in the construction of the classifier because it was truncated in many
participants’ images. After applying a band-pass filter (transmission range, 0.008–0.1 Hz), the temporal fluctuations of the white matter, cerebrospinal fluid, and entire brain as well as six head motion parameters were linearly
regressed out. Then, for each participant, we calculated pairwise Pearson’s correlations among 137 regions ofinterest in order to generate a matrix of 9316 FCs, which were normalized using Z-score transformation. The
scrubbing procedure was employed to remove any frames exhibiting abrupt head motions in the filtered timecourse.

Classification algorithm for FC selection. An StD classifier with the identified primary FCs was devel-

oped through the combined use of two machine learning algorithms. The procedure for selecting characteristic
FCs, training a classifier prediction model, and evaluating its generalizability was performed as a sequential
process of 5 × 5 nested feature-selection and leave-one-out cross validation (see the schematic flowchart in Supplementary Fig. S2). As the first algorithm, in order to reduce the number of features to remove the effects of
nuisance variables (NVs) including sex and age that may result in catastrophic overfitting, L1-SCCA​23 was used.
L1-SCCA retained FCs that have an association with a canonical variable related only to the Diagnosis label
and not to NVs (details are provided in the Supplementary Information of our previous ­study25). As the second
algorithm, in order to identify a small number of features with a high contribution to the classifier, S­ LR24 wasused. SLR is capable of training a logistic regression model, while objectively deleting FCs that are useless for the
purpose of identifying StD. In SLR, the final number of features according to the principle of automatic relevance­determination61 is decided automatically without manually adjusting the hyper-parameters. On this process
approach, SLR classifier was trained by using all-but-one subjects in each leave-one-out (LOO) cross-validation(CV). A sequential process of nested-feature selection and LOOCV is applied to prevent information leakage
and over-optimistic ­results62. Nested cross validation and LOOCV were combined in our machine learningalgorithm. In order to have more than 20 subjects per fold, we used 5-folds CV for StD classifier development.
Classification accuracy was evaluated using the output of the logistic regression classifier generated at the end of
the LOOCV procedure. In conclusion, through the combined use of two machine learning algorithms, prior totraining SLR, it is essential to reduce the input dimension to a certain extent and remove the effects of NVs that
may result in catastrophic overfitting. Therefore, before LOOCV, nested feature-selection was carried out using

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L1-SCCA. The robustness and stability of the identified
the LOOCV procedure were confirmed by across FCs κcomputing their cumulative absolute weights: cκ = N
i=1 Wi , where N is the number of LOOCV folds (i.e.,
the number of subjects) and Wiκ is the weight associated with the κ-th FC during the i-th LOOCV fold. A greater
magnitude of cκ indicates a more significant contribution by the κ-th FC to the classification into StD and HC
throughout the LOOCV procedure. More details of this algorithm were provided in a study of autism spectrum­disorder22. The original code is also accessible (please contact the server administrator of the ATR Brain Information Communication Research Laboratory: asd-classifier@atr.jp).

Generalization to an independent dataset. Participants for an independent validation dataset wererecruited from first-year students attending Hiroshima University in a different year, who were diagnosed using
the BDI-II and Mini-International Neuropsychiatric ­Interview63,64. These participants met the same inclusionand exclusion criteria as the training dataset (StD subjects: n = 16; HCs: n = 27, Table 1). Details of fMRI dataacquisition are shown in Supplementary Table S1. The MRI data were preprocessed in the same way as for the
training dataset, and then the interregional FCs for each subject were calculated. Written informed consent was
obtained from all participants before participating in the study. The ethics committee of Hiroshima University
approved this study.

Correlations between the identified FCs and clinical measures.

The association between the identified FCs in the StD classifier and clinical assessments relative to anhedonia and reward responsiveness (BDI-IIanhedonic subscore and EROS, respectively) was tested. For correlation analyses, Pearson’s and Spearman’s rankcorrelation coefficients were calculated when normality was not rejected and was rejected, respectively, using
SPSS Statistics 25 software (SPSS, Inc., Chicago, IL, USA). Bonferroni’s correction was used to adjust for multiple
comparisons, and the significance level was set as p < 0.05/8 (= 0.006).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding
author on reasonable request.
Received: 30 June 2022; Accepted: 6 April 2023

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Acknowledgements

This research was supported by AMED under Grant Numbers JP18dm0307002, JP18dm0307008; JST-Mirai
Program under Grant Number JPMJMI20D6; and JSPS Grant-in-Aid for Scientific Research (B) under Grant
Number 20H03603. The funders had no role in study design, data collection, data analysis, data interpretation,
or writing of the manuscript. A draft of this manuscript was edited by NAI, Inc. (Yokohama, Japan).

Author contributions

Y.S., G.O., S.Y., N.I. and Y.O. designed the study, collected the data, performed analysis, drafted the manuscript,
discussed the results and conclusions for editing the manuscript. All authors contributed to the article andapproved the submitted version.

Competing interests

The authors declare no competing interests.

Additional information

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