No evidence from a negative mood induction fMRI task for frontal functional asymmetry as a suitable neurofeedback target

Frontal functional asymmetry (FA) has been proposed as a potential target for neurofeedback (NFB) training for mental disorders but most FA NFB studies used electroencephalography while the investigations of FA NFB in functional magnetic resonance imaging (fMRI) are rather limited. In this study, we aimed at identifying functional asymmetry effects in fMRI and exploring its potential as a target for fMRI NFB studies by re-analyzing an existing data set containing a resting state measurement and a sad mood induction task of n = 30 participants with remitted major depressive disorder and n = 30 matched healthy controls. We applied low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity and estimated functional asymmetry in both a voxel-wise and regional manner. We assessed functional asymmetry during rest and negative mood induction as well as functional asymmetry changes between the phases, and associated the induced mood change with the change in functional asymmetry. Analyses were conducted within as well as between groups. Despite extensive analyses, we identified only very limited effects. While some tests showed nominal significance, our results did not contain any clear identifiable patterns of effects that would be expected if a true underlying effect would be present. In conclusion, we do not find evidence for FA effects related to negative mood in fMRI, which questions the usefulness of FA measures for real-time fMRI neurofeedback as a treatment approach for affective disorders.

Participants with rMDD were enrolled if they had at least two previous major depressive episodes or a previous chronic major depressive episode of at least two years duration according to DSM-IV and were in partial or full remission for at least the previous two months.Participants were excluded if they fulfilled the criteria of bipolar and psychotic disorders, substance dependence, current substance abuse, current obsessive-compulsive, posttraumatic stress, and eating disorders, have contraindications for the MRI (including hypertension, heart diseases and surgeries, and other severe illnesses).Healthy participants fulfilled the same criteria but did not have a history of depression and were enrolled if they matched the patients by age, gender, and education level.
The study was approved by the local ethics committee of the Medical Faculty Mannheim of Heidelberg University.All methods were performed in accordance with the relevant guidelines and regulations.Written informed consent was obtained from all participants.

Data acquisition
Participants went through a six-phase experiment, each phase lasts 4.5 min: two resting states, two sad mood inductions, one rumination phase and one distraction phase (the order of the rumination and distraction phases were counterbalanced).For the present paper, only the first resting state and the first mood induction phases were analyzed.The Positive and Negative Affect Scale (PANAS) 53 was measured before and after each phase with a built-in keypad to assess the positive and negative affect.In the present study, we used the initial resting state and the first sad mood induction phase, and the PANAS score after these phases.fMRI data were acquired with a 3 T Trio TIM Scanner with a 12-channel head coil (Siemens Medical Systems, Erlangen, Germany).T1-weighted structural images were obtained with repetition time (TR) = 2.3 s, flip angle (α) = 9°, echo time (TE) = 3.03 ms, 192 slices, slice thickness = 1 mm, voxel size = 1 × 1 × 1 mm 3 , FOV = 256 mm 2 .In each phase 180 T2* weighted echo planar imaging (EPI) sequences were acquired with TR = 1.5 s, α = 80°, TE = 28 ms, 24 slices, slice thickness = 4 mm, voxel size = 3 × 3 × 4 mm 3 , FOV = 192 mm 2 .Respiration and heart rate were recorded at 50 Hz with the scanner built-in equipment.

Negative mood induction task
Before the fMRI session, participants were shortly interviewed about three negative life events and provided keywords that reminded them of these events (for example 'breakup Nina' , 'death granny' , 'cellar asylum' , 'New Year's Eve 2010').In the scanner, the task was conducted using Presentation software package (version 18.1; www.neurobs.com).During the resting state, participants were asked to keep their eyes open, the keywords 'Rest 1' , 'Rest 2' , and 'Rest 3' were shown for 4.5 min (1.5 min for each), and background pink noise was presented.During the sad mood induction phase, the individual pre-recorded keywords for personal negative life events were presented (each for 1.5 min, thus 4.5 min in total) and sad background music (parts of Adagio in g-minor by Albinoni) was played.Participants were instructed to focus on the self-provided keywords and think about themselves in the situation.

Data analysis
Behavior Demographic characters (age and gender) were analyzed with IBM SPSS22 (SPSS Inc., Chicago, Illinois, USA).Independent t-tests and chi-square tests were conducted to compare the distribution of the categorical variable (gender) and continuous variable (age).PANAS scores were analyzed with Matlab (R2021b; MathWorks Inc., Sherborn, Massachusetts, United States).Hedge's g and 95% confidence interval (CI) were estimated based on the t-value to assess the effect size of group differences and time differences 54 .The correlation between PANAS scores and functional asymmetry values was calculated by second level SPM regression analyses.The statistical significance threshold is p < 0.05.
Imaging analyses fMRI image preprocessing and analyses were conducted with SPM8 v5236 (Wellcome Trust Centre for Neuroimaging, University College London, UK).

Preprocessing
The first 20 images of each phase were removed from analyses, resulting in 160 volumes in each task and resting state data set that was analyzed.Data were corrected for physiological artifacts using a Matlab software (Aztec, https:// www.neuro mri.nl/ 2015/ 12/ 14/ aztec-cardi oresp irato ry-corre ction-softw are-for-funct ional-mri/) 55 including a high-pass filter of 1/512 Hz.The images were motion corrected, slice time corrected (with the 13th slice as reference), normalized to an EPI template, and smoothed with a Gaussian kernel of 9 mm.Wavelet despiking 56 was applied for motion-correction.It identifies and removes non-stationary events caused by head movement in the fMRI time series without the need for data scrubbing.This approach is not limited by the temporal resolution of movement parameter information and is able to characterize non-stationary event coefficients in multiple frequency bands.This capability allows us to motion-correct low-frequency data and remove artifacts only in the frequencies in which they occur, but leaving the other frequencies intact.

ALFF and fALFF
ALFF is computed by the average square root of a power spectrum across 0.01-0.08Hz at a voxel, it represents the deviation of the spontaneous low-frequency fluctuations (LFF) of BOLD signal 50 .It is a reliable measure to index spontaneous brain activity in the resting state, and due to its character of unconstrained by experimental design and task-related performance confounds, it can identify the effects of mental conditions on spontaneous www.nature.com/scientificreports/brain activity.fALFF was introduced by Zuo et al. 51 to reduce the confounds of physiological noise in ALFF and enhance specificity and sensitivity in reflecting fMRI regional spontaneous activity by computing the ratio of the power spectrum of low-frequency to that of the entire frequency range.

ReHo
ReHo characterizes the so-called local functional connectivity (FC) between adjacent areas in a voxel-wise perspective.It measures the time series of a voxel and its nearest 26 neighbors and indexes the synchronization by Kendall's coefficient of concordance (KCC) 52 .Some researchers have proposed local FC can alter the remote FC and affect the whole brain dynamics 57 .

Asymmetry index
In all analyses, asymmetry was expressed as the asymmetry index AI = (x l − x r )/(x l + x r ) with subscripts l and r identifying the left and right instances of value x, respectively (see for example 47,58,59 .Please note that AI is bounded by 1 (all activation in the left hemisphere) and − 1 (all activation in the right hemisphere) and that its directionality is completely arbitrary.In our formulation, AI becomes positive when x l > x r and negative when x r > x l , thus a value > 0 means higher activation in the left hemisphere, and < 0 means higher activation in the right hemisphere.An increase in AI represents a shift toward left side activation and a decrease means a shift towards the right (applies throughout the manuscript).

Voxel-wise approach
For conducting voxel-wise asymmetry analyses, the individual parameter maps were mirrored at the midline (MNI x = 0), and the AI was calculated for every voxel from the original and flipped values.Second level group analyses were conducted on these images with one-sample and two-sample t-tests implemented in SPM GLMs with the covariates age and gender.A significance threshold of p < 0.05 cluster-level corr.with a cluster-defining threshold (CDT) of p = 0.001 unc.was applied in all imaging analyses.Because of the integration of lateralized information, our voxel-wise brain plots show results for both index directions (positive and negative) concurrently.Effects in a hemisphere reflect an increase in the respective voxels over the corresponding voxels in the other hemisphere.

Regional approach
To apply another approach that is not dependent on the exact correspondence of specific voxels in the hemispheres and is able to capture potentially weaker distributed effects over larger parts of the cortex, we applied a second, regional analysis strategy based on anatomical parcellation.For this, we used the 20 Parcels per hemisphere of the Neuromorphometrics (NMM) atlas distributed with SPM that are covering the frontal and anterior cingulate cortex (Fig. 1; see region abbreviations in the supplement).Imaging parameters were averaged within the parcels and the asymmetry index for each corresponding parcel pair was calculated.In addition, the average Figure 1.Regions in the regional approach.20 parcels per hemisphere from the Neuromorphometrics atlas distributed with SPM in the frontal and anterior cingulate cortex were used for the regional analyses.Imaging parameters were averaged within the parcels and the asymmetry index for each corresponding pair was calculated.In addition, an average overall parcel was used to calculate asymmetry indices over the whole frontal hemisphere.
Vol.:(0123456789) www.nature.com/scientificreports/overall parcels were used to calculate asymmetry indices over the whole frontal hemisphere.The p value threshold was corrected for multiple comparisons by Bonferroni correction, which is 0.0024 (0.05 divided by 21).The effect size (Hedge's g) and its confidence interval were reported and plotted in Figs. 3, 4 and 5.The 95% CI represents a 95% probability of containing the true parameter value (and its limits are equivalent to the significance threshold of a two-sided test with p < 0.05).

Levels of analyses
Our procedures allow us to conduct analyses at several different levels that are relevant to investigate associations of functional asymmetry with negative mood.Here, we report results from three levels: changes between rest and negative mood induction, group differences, and the relationship between changes in mood and changes in functional asymmetry values.(1) To investigate whether functional asymmetry changed over time, we analyzed the time difference between the rest phase and the induction phase per group with one sample t-tests.(2) To test the functional asymmetry differences across rMDD and healthy controls, we assessed group differences by two sample t-tests in the rest phase, the induction phase, and the change over time (induction-rest) respectively.
(3) To test for the relationship between self-reported negative mood and functional asymmetry, we conducted correlation analyses within each group, during the rest phase, mood induction phase, and for the change over time (induction-rest) respectively.All analyses were corrected for age and gender.Similar analyses for positive mood scores are further reported in the supplement (Supplementary Table S1 and Figures S5, S6 & S7).As an extension to our analyses we conducted mixed-ANOVA models and report the significant results in Table 3 and the full results in Supplementary Table S3.

Behavior
To validate the sad mood induction task, we compared the PANAS negative affect score before and after the task, and we compared it between groups (Table 1).After the sad mood induction task, both groups reported increased negative affect (rMDD: t(27) = 4.79, p < 0.001, g = 0.850; HC: t(27) = 3.88, p = 0.001, g = 0.688), and the two groups changed in varying degrees (t(56) = − 0.32, p = 0.002, g = − 0.848), that is, rMDD increased significantly more negative affect than HC.There is no significant group difference after the resting state, while rMDD and HC differed significantly after the induction task (rest state: t(56) = − 0.52, p = 0.609, g = − 0.131; induction state: t(56) = − 3.05, p = 0.004, g = − 0.778).Accordingly, the task was also able to reduce positive affect for both groups; see Supplementary Table S1 for detail.

Voxel-wise approach
In the voxel-wise analysis, we found very sparse results.We decided to report in the main text the results for the voxel-wise analyses which should theoretically show the strongest effects, namely the change in functional asymmetry over the experimental conditions in the rMDD group; and the association of the change in negative mood (PANAS negative score) with the change in functional asymmetry over the experimental conditions in the rMDD group.Full voxel-wise imaging results for all conducted analyses at a threshold of p = 0.001 unc.are reported in Supplementary Table S2.
In the rMDD group, we identified a cluster in the left motor cortex with ALFF (k = 47 voxels) and fALFF (k = 48 voxels), but not with ReHo, which showed higher asymmetric activation in the induction phase compared to rest (Fig. 2a).In addition, we found a cluster (k = 48 voxels) in the left cerebellum with ALFF and a cluster in the right occipital fusiform gyrus (k = 42 voxels) with fALFF that showed an association between the change in the PANAS negative score and the change in functional asymmetry between the rest and induction phase in the rMDD group.Again, no effect was significant in the ReHo analyses (Fig. 2b).Significant results are summarized in Table 2.

Regional approach
Regional functional asymmetry changes between the rest phase and negative mood induction phase.www.nature.com/scientificreports/ We defined the functional asymmetry change as the difference of functional asymmetry between the rest phase and the negative mood induction phase (induction-rest).A positive change reflects that functional asymmetry in the induction phase is higher than that in the resting state, that is, functional asymmetry increased after the sad mood induction task, and a negative change reflects a decreased functional asymmetry.We depicted the functional asymmetry changes (ALFF, fALFF, and ReHo respectively) of the 21 regions per group in Fig. 3.At the nominal significance level, the rMDD group showed decreased ALFF functional asymmetry in ACgG (anterior cingulate gyrus) (t(27) = − 2.10, p = 0.046, g = − 0.372) and SCA (subcallosal area) (t(27) = − 2.08, p = 0.047, g = − 0.370), and decreased fALFF functional asymmetry in ACgG (t(27) = − 3.97, p = 0.00048, g = − 0.704) after the sad mood induction task; while the HC group showed increased ALFF functional asymmetry in GRe (gyrus rectus) (t(27) = 2.17, p = 0.039, g = 0.386) and LOrG (lateral orbital gyrus) (t(27) = 2.14, p = 0.042, g = 0.380), increased fALFF functional asymmetry in SMC (supplementary motor cortex) (t(27) = 3.07, p = 0.005, g = 0.545), and increased ReHo functional asymmetry in SMC (t(27) = 3.17, p = 0.004, g = 0.563) (Fig. 3).Please find functional Table 2. Significant results of the voxel-wise functional asymmetry analysis.a ALFF functional asymmetry has higher asymmetric activation in the induction phase compared to rest.b fALFF functional asymmetry has higher asymmetric activation in the induction phase compared to rest.c The change in the PANAS negative score correlates to the change in ALFF functional asymmetry.d The change in the PANAS negative score correlates to the change in fALFF functional asymmetry.Change is defined as induction-rest.*p < 0.05.S1 and Figure S2.
The decrease in fALFF functional asymmetry in the ACgG (anterior cingulate gyrus) of rMDD was the only effect in our main regional analyses that reached a significance threshold that was Bonferroni corrected for the number of regions (p < 0.0024).Interestingly, the effect is based on an elevated functional asymmetry in the rMDD in the baseline resting state that returns exactly to the level of the HC group in the induction phase (see Supplementary Figure S8).

Group differences
We compared functional asymmetry between the rMDD group and the HC group in the rest state and the induction phase (Fig. 4), and the change over time (Fig. 5) respectively.In the resting state, compared to HC, Figure 3. Regional functional asymmetry change between the induction phase and the rest phase.Dots represent Hedge's g, the error bar represents 95% confidence interval.The effect size of the time difference between the induction phase and the rest phase was estimated based on the t-value.A positive effect size reflects that functional asymmetry in the induction phase is higher than in the resting state.See the supplement for region abbreviations, regions with significant results are described above in the text.+ p < 0.05, *p < 0.0024.rMDD showed lower ALFF functional asymmetry in FRP (frontal pole) (t(56) = 2.55, p = 0.014, g = 0.650), higher fALFF functional asymmetry in ACgG (anterior cingulate gyrus) (t(56) = − 2.01, p = 0.049, g = − 0.513) and OrlFG (orbital part inferior frontal gyrus ) (t(56) = − 2.15, p = 0.036, g = − 0.548).In the induction state, rMDD showed lower ALFF functional asymmetry in FRP (frontal pole) (t(56) = 2.19, p = 0.032, g = 0.560) and MSFG (superior frontal gyrus medial ) (t(56) = 2.85, p = 0.006, g = 0.728), and lower ReHo functional asymmetry in SMC (supplementary motor cortex) (t(56) = 2.26, p = 0.028, g = 0.577).No nominally significant group differences were identified in the functional asymmetry change (induction-rest).
An effect that might be of potential interest but is contrary to an expected stronger change in the rMDD group is the nominal group difference in the SMC (supplementary motor cortex) for ReHo functional asymmetry change.This difference is based on an increase in fALFF functional asymmetry and ReHo functional asymmetry in the HC group while these values are not changed in the rMDD group (see supplementary Figures S9 & S10).Group differences of the regional functional asymmetry between rMDD and HC in the rest and induction phase.Dots represent Hedge's g, the error bar represents 95% confidence interval.The effect size of the group difference was estimated based on the t-value.A positive effect size reflects that rMDD has lower functional asymmetry than HC.See the supplement for region abbreviations, regions with significant results are described above in the text.+ p < 0.05.
We also conducted mixed-model ANOVA as an extension to our analyses, and the results were consistent with the results reported above.There were significant main effects on several ROIs, but the 2 (group: rMDD or HC) × 2 (time: rest or induction) mix ANOVA revealed no significant group-by-time interactions.(see Table 3 for significant results and supplementary Table S3 for full results).

Relationship between changes in negative mood and changes in functional asymmetry values
To capture the association between the negative mood and the functional asymmetry, we calculated the partial correlation between the PANAS negative change (induction-rest) and the functional asymmetry change (induction-rest) corrected for age and gender (Fig. 6).No significant associations were identified in the rMDD group.In HC, PANAS negative change has a negative correlation with fALFF functional asymmetry change in LOrG (lateral orbital gyrus) (rho = − 0.39, p = 0.041); and has a positive correlation with ReHo functional asymmetry Overall, the results we obtained are very limited and do not contain a clearly identifiable pattern that corresponds to expected effects if an underlying relationship between functional asymmetry values and negative mood would be present.

Discussion
In this study, we reanalyzed an existing fMRI data set 48 on negative mood induction in participants with rMDD and HC and addressed the question of whether evidence for functional asymmetry of frontal brain regions can be found in the data.We were specifically interested in this question because of our aim to explore potential target processes for real-time fMRI neurofeedback interventions.With this analysis, we wanted to test whether a negative-mood related functional asymmetry effect, which might be a promising neurofeedback candidate process, can be identified in fMRI.While FA effects have been extensively studied especially with EEG, the fMRI literature on this topic is rather limited 37,40,[60][61][62][63][64] .
After extensively exploring our results, we came to the conclusion that we were not able to detect FA effects related to negative emotion with our fMRI analyses.While, as expected by chance, some individual tests showed nominal significance, the results as a whole are probably best characterized as a 'dance of p-values' around a zero effect and lack any clearly interpretable congruent and systematic patterns.Even the results we found at nominal significance levels turned out to be based on underlying changes and group differences that were not corresponding to patterns expected if the expected effects would really be present.
It seems important to emphasize that this failure to detect interpretable results was not our intention in this study.We conducted our analyses with the goal to obtain positive results, and were aiming to implement straightforward and meaningful analysis strategies.Although we did not pre-register the analysis plan, which retrospectively would have been preferable, we think that our procedures are comprehensible and well justified.
While the research question was not planned when the original study was set up, this study was designed in an almost optimal way to address our research question.If we would have designed a new study we would have come up with a virtually identical design.The experimental task clearly evoked negative mood in the participants (Table 1), and the study design allows to investigate the different intra-and interindividual effects that should be present if FA effects are present and detectable with fMRI.Also, while the analyzed data set is not especially large, and our analyses might therefore not have the highest power, the data set still has a decent size and is of high quality, which could for example be seen by the good matching of the rMDD and HC group.
Another limitation of our study is the use of a normalization procedure based on the functional images in the re-analyzed data set, which potentially reduces the spatial precision of the voxel-wise analyses.To alleviate these spatial precision and power problems, we conducted not only voxel-wise but also regional analyses, which can be expected to be more sensitive, and report whole-brain results for every analysis thresholded at p < 0.001 uncorrected in the supplement.
We used three different measures to assess fMRI activation, namely ALFF, fALFF, and ReHo.All of these measures were originally developed for resting state analyses, but are very well suited to analyze our mood induction data, which has very little task structure and mostly, on the temporal level, resembles a resting state measurement.The induced negative mood can be assumed to be present over the whole experimental phase, and related effects should thus theoretically be detectable by our procedures.
Obviously, we used a specific analysis pipeline on already existing data, and, given the high flexibility of fMRI analyses, other analysis strategies are possible, which might have yielded somewhat different results if they were used.It was neither feasible for us nor our goal to explore all possible analysis strategies in this single study.www.nature.com/scientificreports/However, if a FA effect plays a major role in negative mood and can be detected by fMRI we would expect it to be reflected in our results at least in some of our complex analyses.Interestingly, the putative inability of fMRI to detect FA effects in comparison to EEG has already been recognized.Kelley et al. 65 have discussed this topic and suggested that possible reasons are probably the supine position, which has been demonstrated can reduce approach motivation and decreases left frontal asymmetry 66 ; and their different physiological basis, as fMRI indirectly measures the neuronal activity with BOLD response and the hemodynamic response in fMRI measurement is dependent on action potentials and cortical stellate cells.Morys et al. 47 further discussed this incongruence may be due to alpha power and fALFF measuring different processes, and fMRI measures a different frequency range of oscillations than EEG (0.01-0.1 Hz vs. 8-12 Hz).Another possible reason could arise from the validation of the FA theory: although there is meta-analysis supporting the theory in affective disorders that depression is related to left side frontal asymmetry 67 , not all studies observed this link (e.g. 36,68,69), and a recent meta-analysis review did not find significant results but only suggested www.nature.com/scientificreports/ a slight tendency toward left lateralization in the depression group 70 .Another meta-analysis also reported nonsignificant results when exploring the diagnostic value of frontal asymmetry in depression 27 .Similarly, Kołodziej et al. 71 concluded in their meta anaylsis that treating EEG frontal alpha asymmetry as a biomarker of depressive disorders is not sufficiently empirically grounded as only 13 of their 270 analyses revealed significant results.Especially in this situation, it seems relevant that negative findings as ours are published so that other researchers addressing similar questions get a full picture of the state of knowledge.We suspect that the relative lack of studies investigating functional asymmetry effects with fMRI might hint to a file drawer problem and that probably studies like ours were conducted but also yielded negative results and were not published.
A limitation of our study is that we only investigated participants with remitted MDD.While this group clearly responded more strongly to the negative mood induction than the HC group, we can not exclude that functional asymmetry effects could play a specific role in acute MDD, although we consider this unlikely, given the assumed trait character of frontal asymmetry effects.Moreover, the limited sample size in our study in combination with a small effect size of functional asymmetry changes may have contributed to our non-significant results.However, since an effect must be observable in a rather small sample to be a reasonable target for NFB, which is conducted on the single individual level, our results suggest that FA is likely not a plausible candidate for fMRI NFB approaches.It would not make any sense to train a very small effect or an effect that can only be observed in a small subgroup of affected individuals.While we think that our results are not providing any evidence for the presence of a FA effect associated with negative mood induction in fMRI, other researchers might interpret specific findings in our results differently.If this would be the case, our standardized effect sizes for all regional analyses could be used for planning the sample size of future studies investigating these effects.
From our understanding of our results, we conclude that it seems not advisable to consider FA as a target for real-time fMRI neurofeedback interventions for mood disorders, as we were not able to systematically establish the expected underlying phenotype.In EEG NFB, frontal alpha asymmetry has been tested as a targeted intervention approach for depressive patients (see for example 29 ) and several of these investigations have reported clinically relevant improvements.However, most NFB studies published so far were either non-randomized, non-blinded, or single-armed.High quality experimental designs, as suggested by the CRED-nf checklist 72 , are urgently needed to systematically examine frontal alpha asymmetry EEG NFB approaches and testing the presence of the suspected mechanisms. https://doi.org/10.1038/s41598-023-44694-3

Figure 2 .
Figure 2. Results of the voxel-wise functional asymmetry analysis.Results are shown for the rMDD group for (a) the change in functional asymmetry between the rest and induction phase and (b) the association between the change in PANAS negative scores and the change in functional asymmetry.All maps are thresholded at a whole-brain cluster-level threshold of p = 0.05 with a cluster-defining threshold (CDT) of p = 0.001 unc.Please note that in the voxelwise analyses, a positive value in a hemisphere reflects an increase in the respective voxels over the corresponding voxels in the other hemisphere.

Figure 4 .
Figure 4. Group differences of the regional functional asymmetry between rMDD and HC in the rest and induction phase.Dots represent Hedge's g, the error bar represents 95% confidence interval.The effect size of the group difference was estimated based on the t-value.A positive effect size reflects that rMDD has lower functional asymmetry than HC.See the supplement for region abbreviations, regions with significant results are described above in the text.+ p < 0.05.

Figure 5 .
Figure 5. Group differences of the regional functional asymmetry change (induction-rest) between rMDD and HC.Dots represent Hedge's g, the error bar represents 95% confidence interval.The effect size of the group difference was estimated based on the t-value.A positive effect size reflects that rMDD has lower functional asymmetry change than HC.No significant results were identified.See the supplement for region abbreviations.

Figure 6 .
Figure 6.Relationship between the change in PANAS negative score and the change in functional asymmetry.The change is defined as Induction-Rest.The bar chart presents the strength of the association (rho).See the supplement for region abbreviations, regions with significant results are described above in the text.+ p < 0.05.

Table 1 .
Demographic variables and PANAS negative score of both groups.a Time difference is the difference between the rest phase and the induction phase per group.*p < 0.05.