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Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

Abstract

Background

Patients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.

Methodology

In a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.

Results

FC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.

Conclusions

Differences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Peer Review reports

Introduction

Depressive episodes (DEs) are clinical features shared by two closely related yet neurobiologically distinct mood disorders, major depressive disorder (MDD) and bipolar disorder (BD); the similarities in these episodes frequently result in diagnostic challenges (1]. MDD is defined by one DE or recurring DEs characterized by sadness, anhedonia, decreased energy, social withdrawal, low self-esteem and sleep disturbances [2] and has a twelve-month prevalence in Europe between 3% and 10%[3]. BD, with a twelve-month prevalence of 1% [4], includes BD type I (BD I), defined by the presence of mania, and BD type II (BD II), defined by the presence of hypomania and DEs [2]. Although (hypo)mania defines BD, DEs are more frequent, last longer, and disproportionately account for morbidity and mortality in BD patients [5,6,7,8,9,10,11]. Moreover, since DEs are predominantly present at the onset of the illness, up to 40–60% of BD patients are misdiagnosed with MDD, and only 20% receive the correct diagnosis within the first year [12,13,14]. The repercussions of misdiagnosis include the risk of inappropriate drug prescription and switching to manic episodes, prolonged illness duration, risk of recurrence, suicide and overall poorer treatment response [15,16,17,18,19]. Thus, it is imperative to discern the differing neurobiological mechanisms underlying DEs in MDD patients and BD patients to refine diagnostic procedures and therapeutic interventions.

Manifestations of psychiatric disorders are associated with disturbances within and between networks of interacting regions in the brain [20]. One neuroimaging method used to assess these networks is resting-state functional magnetic resonance imaging (rs-fMRI), which examines low-frequency spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals among voxels in the brain at rest [21, 22]. Temporally correlated voxels in brain regions are presumed to be functionally connected (i.e., communicating) and to form intrinsic functional networks [23]. Within this network perspective, three consistently identified networks and their subregions [24] have been related to symptoms of depression [25] and compared between BD and MDD patients [26, 27].

First, the task-free default mode network (DMN), anchored in the medial prefrontal and parietal cortices, is associated with internally focused, self-referential thought; “mind wandering”; and social cognition [24, 28]. Compared to BD patients, MDD patients have exhibited inconsistently increased, reduced or similar functional connectivity (FC) within the DMN across studies [29,30,31,32,33,34,35,36]. However, increased FC within the DMN was found to be associated with depression severity and rumination in MDD patients [36, 37]. The variability of these findings may be related to clinical heterogeneity, i.e., depression severity [36, 38], responsiveness to treatment [39], and length of the episode [40].

Second, the frontoparietal network (FPN), or central executive network, is associated with externally focused, goal-directed tasks and cognitive regulation; the core network regions include the lateral prefrontal and parietal cortices [24, 41]. Compared to MDD patients, increased within-network FC in the FPN was found in BD patients [42,43,44]. This was linked to compensatory attempts to navigate away from or to dampen distracting inner states and thoughts originating in the affective and salience networks [45, 46], increased depression severity [26], and disease progression in BD patients [47]. In contrast, reduced within-network FC in the FPN in MDD patients can be linked to cognitive symptoms.

Finally, the salience network (SN), which partially overlaps with the reward and affective networks, monitors and integrates internal and external sensory information while also eliciting FPN or DMN responses in association with internal experience and external environmental demands [24, 41, 48]. Compared to MDD patients, BD patients displayed increased connectivity among the key regions of the SN—the anterior cingulate, thalamus, striatum and insula [42, 49,50,51,52,53,54,55]. In addition, relative to MDD patients, BD patients exhibited reduced connectivity between the insula and the FPN and DMN [50, 56, 57], while other components of the SN displayed increased connectivity with the FPN and DMN [49, 54, 55]. These findings may be associated with emotional dysregulation and impaired emotional, reward and interoceptive input in regulating the FPN and DMN in BD patients [26].

The methods used to examine functional networks in rs-fMRI studies include seed-based analysis (SBA), region-of-interest (ROI)-to-ROI analysis and independent component analysis (ICA) [58, 59]. While SBA involves selecting specific seed regions and estimating their FC throughout the brain, ROI-to-ROI analysis investigates FC between predefined brain regions (ROIs), and ICA identifies spatially independent components reflecting distinct networks. However, each of these methods carries inherent limitations: SBA and ROI-to-ROI analysis overlook FC beyond the prespecified seeds and ROIs, while ICA tends to converge on large-scale networks, potentially overlooking finer-scale functional organizations. FC multivariate pattern analysis (fc-MVPA) offers an alternative approach that can be used to estimate the entire FC pattern for each voxel in the brain [60, 61]. If used in a contrast setting, such as comparing two distinct groups, it can identify clusters of voxels with the most differential between-group FC patterns at the whole-brain level [61, 62]. This approach has not yet been used for the comparison of BD and MDD patients, it is data driven and exploratory and has the ability to find the most differential FC patterns that may not have been identified previously.

While comparisons of FC between currently depressed BD patients and MDD patients converge mainly on the three aforementioned networks [26, 27], there is marked heterogeneity in the rs-fMRI FC results. The reasons for this heterogeneity may include the patient’s current clinical state, demographics, medication, and disorder subtype (BD I and II), all of which are likely related to the underlying neurobiological mechanism and result in heterogeneous FC findings.

The aim of this exploratory study was to identify the FC patterns with the greatest ability to differentiate between MDD patients and BD patients at the whole-brain level using the novel, data-driven fc-MVPA. To reduce commonly occurring clinical heterogeneity bias, we compared participants who were currently depressed; matched for age, sex, and depression severity; nonpsychotic; diagnosed with BD I and MDD; and, with the exception of participants who were treated with mood stabilizing agents, medication-free.

Methods

Participants

One hundred forty-six participants (42 BD I patients, 40 MDD patients, and 64 controls) were initially recruited for the study. Recruitment, inclusion and MRI scanning were performed at the National Institute of Mental Health, Klecany, between 2016 and 2020 by trained psychiatrists and radiologists. The included MDD and BD patients participated in two separate intervention studies with comparable inclusion criteria focused on the treatment of DEs with neurostimulation (EudraCT number 2015–001639-19 and ISRCTN77188420). MRI scans used in this study were baseline scans obtained after (1) a period of two weeks of medication washout (MDD, BD) excluding stabilizing agents (mood stabilizers or antipsychotics, BD) and (2) before therapeutic intervention. Control participants were recruited separately for this study via internet advertisement.

The inclusion criteria for the patients were age 18–70 years, met the DSM-IV (2) criteria for BD I or MDD, current DEs without psychosis as determined by the Mini-International Neuropsychiatric Interview (M.I.N.I.) [63], duration of current DE more than 1 month, Montgomery and Åsberg Depression Rating Scale (MADRS) score ≥ 20 [64] and Young Mania Rating Scale (YMRS) score < 12 in BD patients [65], the ability to provide informed written consent, and right handedness. The exclusion criteria were a history of any DSM-IV Axis I diagnosis other than BD I or MDD (excluding anxiety disorders) in the last year; personality disorders; a history of substance dependence in the last year except nicotine; pregnancy or breastfeeding; severe somatic disorders (cardiovascular disease, neoplasms, endocrine disorders, etc.) that could be associated with depression; contraindications for MRI (metal device in the head, applied pacemaker or other electronic stimulation devices, etc.); and treatment with electroconvulsive therapy less than 3 months before enrolment. The inclusion and exclusion criteria for the control subjects were identical, with the additional exclusion criterion of the presence of any psychiatric diagnosis, symptoms or medication use in their medical history.

The patients were age- and sex-matched. One BD patient and one control participant were excluded after rs-fMRI preprocessing and quality control assessment, resulting in a total of 144 (41 BD I patients, 40 MDD patients, and 63 controls) participants included in the analysis.

Data acquisition

All participants were scanned with the same protocols in a 3T Siemens Prisma MRI scanner. For anatomical reference, high-resolution 3D T1-weighted magnetization prepared by rapid acquisition gradient echo (MPRAGE) images of the whole brain were acquired with a 64-channel head/neck coil (repetition time (TR) 2400 ms, inversion time (TI) 1000 ms, echo time (TE) 2.34 ms, flip angle 8, slice thickness 0.7, acquisition matrix 320 × 320, voxel size 1 × 1 × 1 mm). Functional resting-state images covering the whole brain were obtained using a T2*-weighted 2D multiband echo planar imaging sequence with a multiband factor of 4 (MB4 EPI) with BOLD contrast (TE 30 ms, TR 700 ms, flip angle 52, slice thickness 3, acquisition matrix 74 × 74, voxel size 3 × 3 × 3 mm, 700 volumes per subject). The total resting scan duration per participant was 8.2 min.

Data preparation and statistical analysis

Preprocessing, denoising and data analysis of the neuroimaging data included in this manuscript were performed using CONN release 22.a [66, 67] and SPM release 12.7771 [68] using the default settings with author recommendations. Calculations of demographic and clinical variables were performed in IBM SPSS [69].

Data preprocessing

Default preprocessing was performed as recommended in the CONN toolbox [62]. Functional data were realigned using the SPM realign and unwarp procedure, where all scans were coregistered to a reference image (first scan of the first session) using a least squares approach and a 6-parameter (rigid body) transformation and resampled using b-spline interpolation to correct for motion and magnetic susceptibility interactions. Temporal misalignment between different slices of the functional data was corrected following SPM slice-timing correction procedure, using sinc temporal interpolation to resample each slice BOLD time-series to a common mid-acquisition time. Potential outlier scans, defined as acquisitions with framewise displacement (FD) above 0.5 mm or global BOLD signal changes above 3 standard deviations, were identified using ART (https://www.nitrc.org/projects/artifact_detect/), and a reference BOLD image was computed for each subject by averaging all scans excluding outliers. Functional and anatomical data were coregistered and normalized into standard MNI space; segmented into grey matter, white matter, and CSF tissue classes; and resampled to 2 mm isotropic voxels following an indirect normalization procedure using the SPM unified segmentation and normalization algorithm with the default IXI-549 tissue probability map template. Finally, functional data were smoothed using spatial convolution with a Gaussian kernel of 6 mm full width at half-maximum (FWHM).

Denoising

Functional data were denoised using a standard denoising pipeline [62], including the regression of potential confounding effects characterized by white matter time series (5 CompCor noise components), CSF time series (5 CompCor noise components), motion parameters and their first-order derivatives (12 factors), outlier scans, session effects and their first-order derivatives (2 factors), and linear trends (2 factors) within each functional run, followed by bandpass frequency filtering of the BOLD time series between 0.008 Hz and 0.09 Hz. The CompCor noise components within white matter and CSF were estimated by computing the average BOLD signal as well as the largest principal components orthogonal to the BOLD average, motion parameters, and outlier scans within each subject’s eroded segmentation masks. From the number of noise terms included in this denoising strategy, the effective degrees of freedom of the BOLD signal after denoising were estimated to be satisfactory, ranging from 35.4 to 77.4 (average 73.7) across all subjects.

Quality control assessment

Quality control of the T1 and MB4 EPI data was performed according to previously described methods [70]. The structural data did not exhibit any artefacts. The coregistration of T1 images to the MNI template was performed with > 80% accuracy. After visual inspection of the raw, preprocessed and denoised functional data, we excluded one BD participant after finding a systematic artefact in the right dorsal frontal area visible in all 700 scans and one HC participant due to the low number of obtained functional scans. After the ART excluded outlier scans with excessive motion and global signal changes, we evaluated the functional data using quality control measures to identify outlier participants to be excluded from the analysis (these measures are described in Supplement Fig. 1). No further participants were excluded. The quality control plots created in CONN confirmed satisfactory final preprocessing and denoising results (details in Supplement Figs. 2 and 3, and 4).

Data analysis

Primary analysis

Multivariate pattern analysis

We performed fc-MVPA using CONN following the default procedure [61, 62]. Only BD and MDD participants were included in this part of the analysis, because including controls might shift the results towards the largest differences between the control group and the two possibly more similar clinical groups.

FC multivariate pattern analyses were performed to estimate the first 8 eigenpatterns (components) characterizing the principal axes of heterogeneity in FC across subjects. The number of components was determined by a convention ratio of 10:1 (number of BD and MDD participants: number of components), as recommended by the authors of the CONN toolbox [61, 66, 67]. From these eigenpatterns, 8 associated eigenpattern score images were derived for each individual subject, characterizing their brain-wide functional connectome state. Eigenpatterns and eigenpattern scores were computed separately for each individual seed voxel as left and right singular vectors, respectively, from a group-level singular value decomposition (SVD) of the matrix of FC values between this seed voxel and the rest of the brain (a matrix with one row per target voxel and one column per subject). Individual FC values were computed from the matrices of bivariate correlation coefficients between the BOLD time series from each pair of voxels, estimated using a singular value decomposition of the z score-normalized BOLD signal (subject-level SVD) with 64 components separately for each subject. The aforementioned 8 components were retained for group-level analysis.

Group-level analyses were performed for each individual voxel, with first-level connectivity measures at each voxel as the dependent variables and groups as the independent variables. Voxel-level hypotheses were evaluated using multivariate parametric statistics with random effects across subjects and sample covariance estimation across multiple measurements. Inferences were performed at the level of individual clusters (groups of contiguous voxels). We used the F test (ANCOVA) across all eight MVPA components to test for group differences (MDD > BD) in whole-brain connectivity, comparing the component scores between the two groups for each voxel. The results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold and a familywise corrected p-FWE < 0.05 cluster-size threshold and adjusted for age, sex and subject motion (FD) as covariates.

Seed-based analysis

To characterize the FC patterns between the fc-MVPA clusters and the rest of the brain, post hoc SBA using the fc-MVPA clusters as seeds was performed [61]. FC strength was represented by Fisher-transformed bivariate correlation coefficients estimated for the seed region and each target voxel. Group-level analysis was performed using a GLM, in which a separate GLM was estimated for each voxel, with first-level connectivity at this voxel as the dependent variable, group (MDD, BD) as the independent variable, and age, sex and FD as covariates. Cluster-level inferences were based on parametric statistics from Gaussian random field theory, and the results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold and a familywise-corrected p-FWE < 0.05 cluster-size threshold. The regions identified in the fc-MVPA and subsequent SBA were assigned to specific networks using the atlases included in the CONN toolbox (Harvard-Oxford cortical atlas, https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases; Yeo 7-network parcellation atlas [71]).

Secondary analysis

The secondary analysis explored how the differences between BD patients and MDD patients identified in the fc-MVPA and subsequent SBA represent abnormal neural functioning compared to the controls, representing the neurotypical population. In a series of ROI-to-ROI analyses compared with controls, the FC values of patients with MDD and BD were compared between clusters identified by fc-MVPA and clusters identified by the subsequent SBA. FC strength was represented by Fisher-transformed bivariate correlation coefficients estimated separately for each pair of ROIs. The groups were compared pairwise and controlled for age, sex, and FD. The results were considered significant if p < 0.05 and if they survived an FDR < 0.05 correction for multiple comparisons.

Results

Participants

Demographic and clinical characteristics are displayed in Table 1. MDD patients and BD I patients did not differ in age, sex, FD, current episode duration (weeks) or depression severity (MADRS score). BD I participants had a younger age of illness onset, longer illness duration, more DEs, more overall episodes, and greater illness severity (defined as episode/illness duration) than MDD participants. The control participants were significantly younger and had lower FD than did the MDD and BD I participants.

Table 1 Demographics and clinical characteristics

Primary analysis

The fc-MVPA revealed one cluster located in the right frontal pole (RFP) with significantly different connectivity patterns between BD patients and MDD patients (Fig. 1; Table 2). A post hoc analysis explored the associations of demographic and clinical variables with the fc-MVPA results. None of the correlations between any of the eight components extracted from the fc-MVPA and clinical or demographic variables were significant (details in Supplement Table 1).

Fig. 1
figure 1

Resulting cluster from fc-MVPA analysis - right frontal pole. Lateral and anterior view. Voxel level p < 0.001, cluster level p-FWE < 0.05

Table 2 Resulting cluster from fc-MVPA using MDD > BD contrast

SBA revealed significantly lower FC between the RFP and the left lingual gyrus/occipital cortex (LLG/OCC) (t(76)=-6.8, p-FWE < 0.0001, d = 1.51), right occipital cortex (ROCC)(t(76)=-5.7, p-FWE < 0.0001, d = 1.27), and left precentral gyrus (LPCG) (t(76)=-4.97, p-FWE = 0.0421, d = 1.11) and greater FC between the RFP and the left posterior inferior/middle temporal gyrus (LI/MTG) (t(76) = 5.26, p-FWE = 0.0238, d = 1.17) and posterior cingulate cortex (PCC) (t(76) = 5.17, p-FWE = 0.0013, d = 1.15) in MDD patients than in BD patients (Table 3; Fig. 2).

Fig. 2
figure 2

Resulting clusters from seed-based analysis using the right frontal pole as seed. Red colour - higher connectivity between seed and cluster in MDD > BD. Blue colour - higher connectivity between seed and cluster in BD > MDD. Voxel level p < 0.001, cluster level p-FWE < 0.05. Abbreviations: L left, LI/MTG left inferior/middle temporal gyrus, LLG/OCC left lingual gyrus/occipital cortex, LPCG left precentral gyrus, PCC posterior cingulate cortex, R right, ROCC right occipital cortex

Table 3 Seed-based analysis results using right frontal Pole as seed

Secondary analysis

ROI-to-ROI analysis compared the FC between the RFP and the clusters found in the SBA among BD patients, MDD patients and controls. The MDD vs. BD comparison results were corresponding to the SBA results and are displayed for illustrative purposes (Figs. 3 and 4). Relative to BD patients, controls exhibited increased FC between the RFP and the PCC (t(99) = 2.88, p-FDR = 0.0081, d = 0.58) and decreased FC between the RFP and the LLG/OCC (t(99)=-2.97, p-FDR = 0.0093, d = 0.60), ROCC (t(99)=-2.47, p-FDR = 0.0189, d = 0.50) and LPCG (t(99)=-3.21, p-FDR = 0.009, d = 0.65). Conversely, relative to MDD patients, controls exhibited increased FC between the RFP and the LLG/OCC (t(98) = 2.67, p-FDR = 0.0448, d = 0.54) and ROCC (t(98) = 2.53, p-FDR = 0.0328, d = 0.51) and decreased FC between the RFP and the LI/MTG (t(98)=-2.37, p-FDR = 0.0325, d = 0.48). The comparisons are displayed in Figs. 3 and 4, and the correlation matrices are shown in Supplement Table 2.

Fig. 3
figure 3

ROI-to-ROI connectivity analyses results. MDD vs. BD comparisons of FC between FP and Left PCG, Left ITG/MTG, PCC, Left LG/OCC and Right OCC. CON vs. BD comparisons of FC between FP and Left PCG, PCC, Left LG/OCC and Right OCC. CON vs. MDD comparisons of FC between FP and Left ITG/MTG, Left LG/OCC and Right OCC. Results represent significant differences in correlation (p < 0.05, FDR < 0.05). Red colour - higher functional connectivity, blue colour - lower functional connectivity. Abbreviations: BD bipolar disorder, CON control participants, FP frontal pole, ITG/MTG inferior temporal gyrus/middle temporal gyrus, L left, LG lingual gyrus, MDD major depressive disorder, OCC occipital cortex, PCG precentral gyrus, PCC posterior cingulate cortex, R right, ROI region of interest

Fig. 4
figure 4

Frontal pole connectivity differences between BD, MDD and CON in ROI-to-ROI analyses. Abbreviations: BD bipolar disorder, CON control participants, FP frontal pole, LI/MTG left inferior/middle temporal gyrus, LLG/OCC left lingual gyrus/occipital cortex, LPCG left precentral gyrus, MDD major depressive disorder. PCC posterior cingulate cortex, ROCC right occipital cortex. * p-FDR < 0.05

Discussion

The aim of this study was to explore resting-state FC differences between currently depressed BD patients and MDD patients using the data-driven fc-MVPA method. The main finding suggested that the connectivity patterns between the right frontal pole and associated brain regions were significantly different between the two groups, indicating that different FC patterns of the RFP in BD and MDD patients should be further investigated as potential neuroimaging markers.

The frontal pole is typically divided into three subparts, and the cluster found in our study in the right ventral/lateral frontal pole is associated with the processing of goals and action plans and the processing of information about stimuli, values and emotions [72]. A lesion study linked the frontal pole to the ability to disengage executive control from the current task and redistribute it to novel sources of rewards [73]. In addition, the lateral frontal pole integrates affective information into cognitive control representations originating in the anterior cingulate cortex [74] and is functionally connected with the FPN [75]. Some functional connectivity studies considered this region part of the DMN, while others considered it part of the FPN [75, 76]. The lateral frontal pole is a highly interconnected regulatory relay that acts between several neural networks and functions and likely plays a role in mood disorders. Specific differences in the FC patterns of the RFP among BD patients, MDD patients, and controls are discussed below.

We found decreased connectivity between the RFP and the PCC in BD patients compared to MDD patients and controls. The PCC, as a core hub of the DMN [77], is mostly associated with self-referential thoughts, mind wandering [78], and rumination during depression [79, 80]. It also has a lesser-known role in regulating the focus of attention (internal vs. external) [81, 82], its activity varies with arousal state [83], and its interactions with other brain networks may be important in conscious awareness [84]. The link between the PCC and the frontal pole is less evident, although its functional link has been established [83, 85]. Contrary to common understanding, the FPN, including the frontal pole, is also activated when attention is directed inwards [86]. Reduced connectivity between the RFP and PCC might indicate an impaired or decreased ability to balance attention between external stimuli and internal states in patients with BD. On the other hand, the difference between the MDD and controls was not significant, although there was a clear trend towards an increase in MDD patients (Fig. 4), suggesting increased self-focus and rumination.

Next, we found increased connectivity between the RFP and the LI/MTG in the MDD group compared to the BD and controls. The LI/MTG is connected to the frontal pole both anatomically via the arcuate fasciculus and functionally [87]. The inferior temporal gyrus is associated with visual object recognition, decision making, and impulsivity control and is also a component of the FPN and the ventral visual pathway [88,89,90]. The middle temporal gyrus is associated with memory, emotion regulation, and multimodal sensory integration and is considered a hub of the posterior DMN [91,92,93]. Increased connectivity between the RFP and the LI/MTG in RD MDD patients may correspond to complex and sustained rumination, i.e., the retrieval of memories and imagery with a corresponding, likely negative, affect. The significantly lower connectivity in BD patients compared to that in MDD patients and the trend compared to that in controls may be associated with impaired concentration, irritability, and impaired automatic emotion regulation.

The LPCG, the LLG/OCC, and the ROCC displayed negative, anticorrelated connectivity with the RFP in the MDD patients and controls and positive connectivity in the BD patients (Fig. 4). Anticorrelation in rs-fMRI is a debated concept, but it has been assumed to be associated with inefficient synchronization in functionally connected brain regions [94]. Another study suggested that the anticorrelation between brain regions is a result of synchronized neuronal activity with heterogeneous (blood flow vs. blood volume) haemodynamic responses [95]. Therefore, we interpreted our findings as a decrease in FC coupling between functionally connected regions.

Increased connectivity between the RFP and LPCG in BD patients can be interpreted from more perspectives. The specific LPCG cluster found in our study is a part of the motor network (MN) and is associated with motor planning, i.e., the elaboration of information into a premovement plan, which can be suppressed and detached from action [29, 96]. The frontal pole mediates predictable event sequences and is involved in planning and controlling intentional motor acts [97, 98]. Increased FC between the RFP and LPCG may have a compensatory nature and account for psychomotor disturbances, most likely at the premovement planning phase [29]; soft neurological symptoms [99]; and typical BD symptoms such as psychomotor agitation, tension, and inability to relax. Consistent with this result, movement in the scanner, represented quantitatively as FD (Table 1), was significantly greater in the BD patients than in the controls and approached the significance threshold compared to that in the MDD patients. In addition, as the frontal pole and LPCG are parts of the FPN, increased FC corresponds to previous findings of overactivity in the FPN as a compensatory mechanism aimed at restoring the function of other disrupted networks [26, 45, 100]. On the other hand, lower connectivity/anticorrelation in MDD patients and controls might correspond to disengagement between the motor network (MV) and frontal pole and possibly to the ability to relax movements. Speculatively, the trend of significantly lower FC in MDD patients than in controls may suggest psychomotor slowness or delayed motor reactions.

Patients with BD had increased FC between the RFP and the left LLG/OCC and ROCC. Both regions are part of the visual network (VN) and fronto-occipital network and are connected to the prefrontal cortex anatomically and functionally [72, 75, 101]. While both regions are functionally associated with visual processing, the lingual gyrus is also associated with visual attention [89], facial emotion/empathy processing (102), visual memory and emotion [103], and even control of negative emotions associated with painful events [104]. As BD is characterized by overactivity within the SN resulting in increased input from visceral and sensory regions, causing emotional dysregulation and distractibility, increased FC between the RFP and the lingual gyrus and OCC in BD patients may have a compensatory function. This finding is also consistent with increased connectivity within the FPN in BD patients [26]. Another explanation may be the reported bias of BD patients towards external negative stimuli [35, 105]. On the other hand, lower connectivity in MDD patients than in both BD patients and controls may indicate visual attentional deficits, lower responsiveness to external stimuli, and an inability to disengage from internal focus.

Interestingly, two distinct patterns of RFP FC distributions among the three groups emerged, as shown in Fig. 4: between the RFP and the PCC and LI/MTG and between the RFP and the LPCG, LLG/OCC and ROCC. This broader view suggests a pattern in which MDD patients, compared to BD patients and controls, have increased FC between the RFP (FPN) and the DMN. This increased connectivity can be associated with increased self-focus, sustained depressive rumination, and difficulty detaching from internally focused processing. On the other hand, BD patients displayed increased connectivity between the RFP and the LPCG, LLG/OCC and ROCC, which indicates increased connectivity between the FPN and the MN and VN. This pattern can be associated with persisting psychomotor symptoms and compensatory attempts against distractibility, which are characteristic of BD.

This study did not replicate the most common findings in BD and MDD comparisons, namely, differences in SN FC [26]. One reason for this discrepancy may be the way in which the fc-MVPA calculates the differences in FC patterns, in which the differences in the SN may not be among the largest. Another possible reason, with a broader range of consequences, may lie in the clinical group, i.e., in the possible treatment resistance in MDD patients. All 40 MDD participants had at least one failed treatment attempt with an adequate dose and duration (first-stage treatment resistance according to Thase and Rush [106]); 20 patients had two failed attempts, and another 12 also had two failed attempts if neurostimulation was considered an adequate treatment [107]. Compared with BD, treatment-resistant MDD (TRD) is a poorly studied subtype of MDD, although differences in FC between TRD and MDD patients have been established mostly in the DMN [108] and may explain the heterogeneous findings in the DMN in previous studies. One branch of research suggests that TRD may fall between BD and MDD and may even be more similar to BD than to MDD from the bipolar diathesis perspective [109, 110]. Only one prior study compared currently depressed TRD patients with euthymic BD patients and euthymic MDD patients; surprisingly, this study also found differences in the FC of the frontal pole between BD patients and TRD patients [76]. If our MDD group is considered as having TRD, our findings may suggest similarities in the within- and between-network FC of the SN between patients with BD and patients with TRD. Increased FC within the SN, which has typically been found in BD patients in previous studies, is associated with emotional dysregulation and impaired emotional, reward, somatosensory and interoceptive input for appropriate switching between the DMN and FPN. We propose that in this respect, BD and TRD are similar. This similarity may correspond with the variability of symptoms observed in both disorders, such as dysregulation of emotional control, inner tension and fearfulness, feelings of inhibition, heaviness, tiredness of the body, and slower thinking [111,112,113].

In addition, our findings correspond with the close to “normal” connectivity in the DMN in TRD patients compared to controls and lower connectivity in TRD patients compared to MDD patients. This finding is also consistent with studies indicating lower connectivity within the DMN and between the DMN and other brain regions in individuals with TRD than in those with MDD [108].

However, this study was not designed to assess TRD a priori, and this interpretation must be considered with caution. Moreover, there are several treatment resistance criteria, and a clear consensus is still being reached [114].

The study has several limitations related to the analysed samples. The sample sizes of the BD and MDD groups were too small to draw broader conclusions.

Next, the MDD and BD participants differed in several characteristics that might have affected the results, but these characteristics are difficult to control. While the clinical groups were well matched for sex, age, and depression severity, participants in the BD group had a younger age of onset, more episodes, longer illness duration, and greater illness severity and displayed a trend towards greater movement in the scanner. Additionally, participants in the control group, which was larger in size than the clinical groups, were younger and displayed significantly less in-scanner movement.

Another limitation is the selection process of the clinical groups. Since the rs-fMRI data were derived from subjects participating in two separate randomized controlled trials (RCTs), selection criteria were not defined prospectively. Both RCTs applied very similar inclusion and exclusion criteria, enhancing the comparability of participants and the robustness of the findings. However, while the good comparability of the clinical samples enhanced internal validity, it also introduced potential limitations in external validity. Specifically, the use of identical criteria may limit the generalizability of the results to a broader clinical population, as the selected participants might not fully represent the diversity seen in real-world settings. Additionally, the retrospective definition of the clinical groups could lead to selection bias, potentially impacting the applicability of the findings to wider clinical and community samples. These considerations are essential for interpreting the results and designing future studies to ensure a more inclusive and representative sample.

Another limitation to be considered is the effect of different medications on MDD and BD patients. While both groups underwent medication washout prior to scanning, participants from the BD group were still on mood-stabilizing agents (AP or MS) for medical reasons.

In conclusion, this exploratory study revealed that FC patterns of the right frontal pole differed between patients with BD and patients with MDD using fc-MVPA. The emerging patterns suggested that compared to MDD patients, BD patients had greater FC between the RFP and the MN and VN, which is likely associated with psychomotor symptoms and a compensatory function against increased distractibility from the inner states. Conversely, MDD patients displayed greater FC between the RFP and the DMN, which corresponds to sustained inner self-focus and depressive rumination. This study suggested that the RFP and its FC patterns should be further examined and possibly utilized as targets of treatment for depression. Given the exploratory nature of this study and considering its limitations, future studies are needed to confirm the findings in larger and more heterogeneous samples with prospectively defined study groups.

Data availability

The data that support the findings of this study are available on reasonable request from the corresponding author.

References

  1. Stiles BM, Fish AF, Vandermause R, Malik AM. The Compelling and Persistent Problem of Bipolar Disorder Disguised as Major Depression Disorder: An Integrative Review. J Am Psychiatr Nurses Assoc. 2018 Sep 28;24(5):415–25.

  2. First MB, France A, Pincus HA. DSM-IV-TR guidebook. American Psychiatric Publishing, Inc.; 2004.

  3. Arias-de la Torre J, Vilagut G, Ronaldson A, Serrano-Blanco A, Martín V, Peters M, et al. Prevalence and variability of current depressive disorder in 27 European countries: a population-based study. Lancet Public Health. 2021 Oct;6(10):e729–38.

  4. Merikangas KR, Jin R, He JP, Kessler RC, Lee S, Sampson NA, et al. Prevalence and Correlates of Bipolar Spectrum Disorder in the World Mental Health Survey Initiative. Arch Gen Psychiatry. 2011 Mar 7;68(3):241.

  5. Baldessarini RJ, Vázquez GH, Tondo L. Bipolar depression: a major unsolved challenge. Int J Bipolar Disord. 2020 Dec 6;8(1):1.

  6. Baldessarini RJ, Tondo L, Visioli C. First-episode types in bipolar disorder: predictive associations with later illness. Acta Psychiatr Scand. 2014 May 24;129(5):383–92.

  7. Perlis RH, Miyahara S, Marangell LB, Wisniewski SR, Ostacher M, DelBello MP, et al. Long-Term implications of early onset in bipolar disorder: data from the first 1000 participants in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Biol Psychiatry. 2004 May;55(9):875–81.

  8. Hui S, Zhang L, Chuchen XU, Jinling ZHU, Meijuan C, Yiru F. Analysis of misdiagnosis of bipolar disorder in an outpatient setting. Shanghai Arch Psychiatry. 2018;30(2):93.

  9. Kupka RW, Altshuler LL, Nolen WA, Suppes T, Luckenbaugh DA, Leverich GS, et al. Three times more days depressed than manic or hypomanic in both bipolar I and bipolar II disorder 1. Bipolar Disord. 2007 Aug 2;9(5):531–5.

  10. De Dios C, Ezquiaga E, Garcia A, Soler B, Vieta E. Time spent with symptoms in a cohort of bipolar disorder outpatients in Spain: A prospective, 18-month follow-up study. J Affect Disord. 2010 Sep;125(1–3):74–81.

  11. Forte A, Baldessarini RJ, Tondo L, Vázquez GH, Pompili M, Girardi P. Long-term morbidity in bipolar-I, bipolar-II, and unipolar major depressive disorders. J Affect Disord. 2015 Jun;178:71–8.

  12. Hirschfeld RMA, Lewis L, Vornik LA. Perceptions and Impact of Bipolar Disorder. J Clin Psychiatry. 2003 Feb 15;64(2):161–74.

  13. Ghaemi SN, Sachs GS, M. Chiou A, Pandurangi AK, Goodwin FK. Is bipolar disorder still underdiagnosed? Are antidepressants overutilized? J Affect Disord. 1999 Jan;52(1–3):135–44.

  14. Baca-Garcia E, Perez‐Rodriguez MM, Basurte‐Villamor I, López‐Castromán J, Fernandez del Moral AL, Jimenez‐Arriero MA, et al. Diagnostic stability and evolution of bipolar disorder in clinical practice: a prospective cohort study. Acta Psychiatr Scand. 2007 Jun 5;115(6):473–80.

  15. Baldessarini RJ, Faedda GL, Offidani E, Vázquez GH, Marangoni C, Serra G, et al. Antidepressant-associated mood-switching and transition from unipolar major depression to bipolar disorder: A review. J Affect Disord. 2013 May;148(1):129–35.

  16. Drancourt N, Etain B, Lajnef M, Henry C, Raust A, Cochet B, et al. Duration of untreated bipolar disorder: missed opportunities on the long road to optimal treatment. Acta Psychiatr Scand. 2013 Feb 20;127(2):136–44.

  17. Hirschfeld RMA, Lewis L, Vornik LA. Perceptions and impact of bipolar disorder: how far have we really come? Results of the national depressive and manic-depressive association 2000 survey of individuals with bipolar disorder. J Clin Psychiatry [Internet]. 2003;64(2):161–74. Available from: http://europepmc.org/abstract/MED/12633125.

  18. Post RM, Leverich GS, Kupka RW, Keck PE, McElroy SL, Altshuler LL, et al. Early-Onset Bipolar Disorder and Treatment Delay Are Risk Factors for Poor Outcome in Adulthood. J Clin Psychiatry. 2010 Jul 15;71(07):864–72.

  19. Stensland MD, Schultz JF, Frytak JR. Diagnosis of Unipolar Depression Following Initial Identification of Bipolar Disorder. J Clin Psychiatry. 2008 May 15;69(5):749–58.

  20. Catani M, ffytche DH. The rises and falls of disconnection syndromes. Brain. 2005 Oct 1;128(10):2224–39.

  21. Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences. 1990 Dec;87(24):9868–72.

  22. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995 Oct 21;34(4):537–41.

  23. Friston KJ. Functional and Effective Connectivity: A Review. Brain Connect. 2011 Jan;1(1):13–36.

  24. Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011 Oct;15(10):483–506.

  25. Williams LM. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry. 2016 May;3(5):472–80.

  26. Siegel-Ramsay JE, Bertocci MA, Wu B, Phillips ML, Strakowski SM, Almeida JRC. Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review. Bipolar Disord. 2022 Aug 30;24(5):474–98.

  27. Han KM, De Berardis D, Fornaro M, Kim YK. Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuropsychopharmacol Biol Psychiatry. 2019 Apr;91:20–7.

  28. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007 Sep;8(9):700–11.

  29. Cantisani A, Stegmayer K, Bracht T, Federspiel A, Wiest R, Horn H, et al. Distinct resting-state perfusion patterns underlie psychomotor retardation in unipolar vs. bipolar depression. Acta Psychiatr Scand. 2016 Oct 6;134(4):329–38.

  30. Fateh AA, Long Z, Duan X, Cui Q, Pang Y, Farooq MU, et al. Hippocampal functional connectivity-based discrimination between bipolar and major depressive disorders. Psychiatry Res Neuroimaging. 2019 Feb;284:53–60.

  31. Han S, Cui Q, Wang X, Li L, Li D, He Z, et al. Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder. Hum Brain Mapp. 2020 Aug 15;41(12):3295–304.

  32. Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, et al. Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand. 2017 Sep 15;136(3):288–99.

  33. Liu Y, Wu X, Zhang J, Guo X, Long Z, Yao L. Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI. J Affect Disord. 2015 Aug;182:8–17.

  34. Luo Z, Chen G, Jia Y, Zhong S, Gong J, Chen F, et al. Shared and specific dynamics of brain segregation and integration in bipolar disorder and major depressive disorder: A resting-state functional magnetic resonance imaging study. J Affect Disord. 2021 Feb;280:279–86.

  35. Qiu M, Zhang H, Mellor D, Shi J, Wu C, Huang Y, et al. Aberrant Neural Activity in Patients With Bipolar Depressive Disorder Distinguishing to the Unipolar Depressive Disorder: A Resting-State Functional Magnetic Resonance Imaging Study. Front Psychiatry. 2018 Jun 5;9.

  36. Wang J, Wang Y, Wu X, Huang H, Jia Y, Zhong S, et al. Shared and specific functional connectivity alterations in unmedicated bipolar and major depressive disorders based on the triple-network model. Brain Imaging Behav. 2020 Feb 31;14(1):186–99.

  37. Davey CG, Pujol J, Harrison BJ. Mapping the self in the brain’s default mode network. Neuroimage. 2016 May;132:390–7.

  38. Marchand WR, Lee JN, Johnson S, Gale P, Thatcher J. Differences in functional connectivity in major depression versus bipolar II depression. J Affect Disord. 2013 Sep;150(2):527–32.

  39. Goldstein-Piekarski AN, Staveland BR, Ball TM, Yesavage J, Korgaonkar MS, Williams LM. Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers. Transl Psychiatry. 2018 Mar 6;8(1):57.

  40. Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB, Kenna H, et al. Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biol Psychiatry. 2007 Sep;62(5):429–37.

  41. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. The Journal of Neuroscience. 2007 Feb 28;27(9):2349–56.

  42. He H, Yu Q, Du Y, Vergara V, Victor TA, Drevets WC, et al. Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders. J Affect Disord. 2016 Jan;190:483–93.

  43. Jiang X, Fu S, Yin Z, Kang J, Wang X, Zhou Y, et al. Common and distinct neural activities in frontoparietal network in first-episode bipolar disorder and major depressive disorder: Preliminary findings from a follow-up resting state fMRI study. J Affect Disord. 2020 Jan;260:653–9.

  44. Zhong Y, Wang C, Gao W, Xiao Q, Lu D, Jiao Q, et al. Aberrant Resting-State Functional Connectivity in the Default Mode Network in Pediatric Bipolar Disorder Patients with and without Psychotic Symptoms. Neurosci Bull. 2019 Aug 4;35(4):581–90.

  45. Clark L, Kempton MJ, Scarnà A, Grasby PM, Goodwin GM. Sustained attention-deficit confirmed in euthymic bipolar disorder but not in first-degree relatives of bipolar patients or euthymic unipolar depression. Biol Psychiatry. 2005 Jan;57(2):183–7.

  46. Maalouf FT, Klein C, Clark L, Sahakian BJ, LaBarbara EJ, Versace A, et al. Impaired sustained attention and executive dysfunction: Bipolar disorder versus depression-specific markers of affective disorders. Neuropsychologia. 2010 May;48(6):1862–8.

  47. Niu M, Wang Y, Jia Y, Wang J, Zhong S, Lin J, et al. Common and Specific Abnormalities in Cortical Thickness in Patients with Major Depressive and Bipolar Disorders. EBioMedicine. 2017 Feb;16:162–71.

  48. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010 Jun 29;214(5–6):655–67.

  49. Liu CH, Li F, Li SF, Wang YJ, Tie CL, Wu HY, et al. Abnormal baseline brain activity in bipolar depression: A resting state functional magnetic resonance imaging study. Psychiatry Res Neuroimaging. 2012 Aug;203(2–3):175–9.

  50. Ambrosi E, Arciniegas DB, Madan A, Curtis KN, Patriquin MA, Jorge RE, et al. Insula and amygdala resting-state functional connectivity differentiate bipolar from unipolar depression. Acta Psychiatr Scand. 2017 Jul 28;136(1):129–39.

  51. Chen G, Chen P, Gong J, Jia Y, Zhong S, Chen F, et al. Shared and specific patterns of dynamic functional connectivity variability of striato-cortical circuitry in unmedicated bipolar and major depressive disorders. Psychol Med. 2022 Mar 10;52(4):747–56.

  52. Satterthwaite TD, Kable JW, Vandekar L, Katchmar N, Bassett DS, Baldassano CF, et al. Common and Dissociable Dysfunction of the Reward System in Bipolar and Unipolar Depression. Neuropsychopharmacology. 2015 Aug 13;40(9):2258–68.

  53. Wang Y, Wang J, Jia Y, Zhong S, Niu M, Sun Y, et al. Shared and Specific Intrinsic Functional Connectivity Patterns in Unmedicated Bipolar Disorder and Major Depressive Disorder. Sci Rep. 2017 Jun 15;7(1):3570.

  54. Yu H, Li ML, Li YF, Li XJ, Meng Y, Liang S, et al. Anterior cingulate cortex, insula and amygdala seed-based whole brain resting-state functional connectivity differentiates bipolar from unipolar depression. J Affect Disord. 2020 Sep;274:38–47.

  55. Zeng C, Xue Z, Ross B, Zhang M, Liu Z, Wu G, et al. Salience-thalamic circuit uncouples in major depressive disorder, but not in bipolar depression. J Affect Disord. 2020 May;269:43–50.

  56. Ellard KK, Zimmerman JP, Kaur N, Van Dijk KRA, Roffman JL, Nierenberg AA, et al. Functional Connectivity Between Anterior Insula and Key Nodes of Frontoparietal Executive Control and Salience Networks Distinguish Bipolar Depression From Unipolar Depression and Healthy Control Subjects. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 May;3(5):473–84.

  57. Yin Z, Chang M, Wei S, Jiang X, Zhou Y, Cui L, et al. Decreased Functional Connectivity in Insular Subregions in Depressive Episodes of Bipolar Disorder and Major Depressive Disorder. Front Neurosci. 2018 Nov 14;12.

  58. Cole. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci. 2010;

  59. Smitha K, Akhil Raja K, Arun K, Rajesh P, Thomas B, Kapilamoorthy T, et al. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J. 2017 Aug 29;30(4):305–17.

  60. Kriegeskorte N, Goebel R, Bandettini P. Information-based functional brain mapping. Proceedings of the National Academy of Sciences. 2006 Mar 7;103(10):3863–8.

  61. Nieto-Castanon A. Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA). PLoS Comput Biol. 2022 Nov 15;18(11):e1010634.

  62. Nieto-Castanon A. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press; 2020.

  63. Sheehan D V, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry [Internet]. 1998;59 Suppl 20:22–33;quiz 34–57. Available from: http://europepmc.org/abstract/MED/9881538.

  64. Montgomery SA, Åsberg M. A New Depression Scale Designed to be Sensitive to Change. British Journal of Psychiatry. 1979 Apr 29;134(4):382–9.

  65. Young RC, Biggs JT, Ziegler VE, Meyer DA. A Rating Scale for Mania: Reliability, Validity and Sensitivity. British Journal of Psychiatry. 1978 Nov 29;133(5):429–35.

  66. Whitfield-Gabrieli S, Nieto-Castanon A. Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connect. 2012 Jun;2(3):125–41.

  67. Nieto-Castanon A, Whitfield-Gabrieli S. CONN functional connectivity toolbox: release 21. Hilbert Press; 2021.

  68. Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE. Statistical parametric mapping: the analysis of functional brain images. Elsevier; 2011.

  69. IBM Corp. IBM SPSS Statistics for Windows, Version 23.0. Armonk, New York: IBM Corp.; 2015.

  70. Morfini F, Whitfield-Gabrieli S, Nieto-Castañón A. Functional connectivity MRI quality control procedures in CONN. Front Neurosci. 2023 Mar 23;17.

  71. Thomas Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011 Sep;106(3):1125–65.

  72. Orr JM, Smolker HR, Banich MT. Organization of the Human Frontal Pole Revealed by Large-Scale DTI-Based Connectivity: Implications for Control of Behavior. PLoS One. 2015 May 6;10(5):e0124797.

  73. Mansouri FA, Buckley MJ, Mahboubi M, Tanaka K. Behavioral consequences of selective damage to frontal pole and posterior cingulate cortices. Proceedings of the National Academy of Sciences. 2015 Jul 21;112(29).

  74. Lapate RC, Ballard IC, Heckner MK, D’Esposito M. Emotional Context Sculpts Action Goal Representations in the Lateral Frontal Pole. The Journal of Neuroscience. 2022 Feb 23;42(8):1529–41.

  75. Moayedi M, Salomons T V., Dunlop KAM, Downar J, Davis KD. Connectivity-based parcellation of the human frontal polar cortex. Brain Struct Funct. 2015 Sep 14;220(5):2603–16.

  76. Rai S, Griffiths KR, Breukelaar IA, Barreiros AR, Boyce P, Hazell P, et al. Common and differential neural mechanisms underlying mood disorders. Bipolar Disord. 2022 Dec 25;24(8):795–805.

  77. Buckner RL, Andrews-Hanna JR, Schacter DL. The Brain’s Default Network. Ann N Y Acad Sci. 2008 Mar 3;1124(1):1–38.

  78. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology. 2010 Aug;20(8):519–34.

  79. Claeys EHI, Mantingh T, Morrens M, Yalin N, Stokes PRA. Resting-state fMRI in depressive and (hypo)manic mood states in bipolar disorders: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry. 2022 Mar;113:110465.

  80. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014 Jan;137(1):12–32.

  81. Gusnard DA, Raichle ME. Searching for a baseline: Functional imaging and the resting human brain. Nat Rev Neurosci. 2001 Oct;2(10):685–94.

  82. Hahn B, Ross TJ, Stein EA. Cingulate Activation Increases Dynamically with Response Speed under Stimulus Unpredictability. Cerebral Cortex. 2007 Jul;17(7):1664–71.

  83. Leech R, Kamourieh S, Beckmann CF, Sharp DJ. Fractionating the Default Mode Network: Distinct Contributions of the Ventral and Dorsal Posterior Cingulate Cortex to Cognitive Control. The Journal of Neuroscience. 2011 Mar 2;31(9):3217–24.

  84. Vogt BA, Laureys S. Posterior cingulate, precuneal and retrosplenial cortices: cytology and components of the neural network correlates of consciousness. In 2005. p. 205–17.

  85. Parvizi J, Van Hoesen GW, Buckwalter J, Damasio A. Neural connections of the posteromedial cortex in the macaque. Proceedings of the National Academy of Sciences. 2006 Jan 31;103(5):1563–8.

  86. Gerlach KD, Spreng RN, Gilmore AW, Schacter DL. Solving future problems: Default network and executive activity associated with goal-directed mental simulations. Neuroimage. 2011 Apr;55(4):1816–24.

  87. Lin YH, Young IM, Conner AK, Glenn CA, Chakraborty AR, Nix CE, et al. Anatomy and White Matter Connections of the Inferior Temporal Gyrus. World Neurosurg. 2020 Nov;143:e656–66.

  88. Herath P, Kinomura S, Roland PE. Visual recognition: Evidence for two distinctive mechanisms from a PET study. Hum Brain Mapp. 2001 Feb;12(2):110–9.

  89. Ishai A, Ungerleider LG, Martin A, Schouten JL, Haxby J V. Distributed representation of objects in the human ventral visual pathway. Proceedings of the National Academy of Sciences. 1999 Aug 3;96(16):9379–84.

  90. Li J, Kong XZ. Morphological connectivity correlates with trait impulsivity in healthy adults. PeerJ. 2017 Jul 6;5:e3533.

  91. Christian KM, Song H, Ming G li. Functions and Dysfunctions of Adult Hippocampal Neurogenesis. Annu Rev Neurosci. 2014 Jul 8;37(1):243–62.

  92. Phillips ML, Ladouceur CD, Drevets WC. A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry. 2008 Sep 24;13(9):833–57.

  93. Xu J, Lyu H, Li T, Xu Z, Fu X, Jia F, et al. Delineating functional segregations of the human middle temporal gyrus with resting-state functional connectivity and coactivation patterns. Hum Brain Mapp. 2019 Dec 15;40(18):5159–71.

  94. Chen G, Chen G, Xie C, Li SJ. Negative Functional Connectivity and Its Dependence on the Shortest Path Length of Positive Network in the Resting-State Human Brain. Brain Connect. 2011 Sep;1(3):195–206.

  95. Goelman G, Gordon N, Bonne O. Maximizing Negative Correlations in Resting-State Functional Connectivity MRI by Time-Lag. PLoS One. 2014 Nov 14;9(11):e111554.

  96. Abe M, Hanakawa T. Functional coupling underlying motor and cognitive functions of the dorsal premotor cortex. Behavioural Brain Research. 2009 Mar;198(1):13–23.

  97. Babiloni C, Vecchio F, Bares M, Brazdil M, Nestrasil I, Eusebi F, et al. Functional coupling between anterior prefrontal cortex (BA10) and hand muscle contraction during intentional and imitative motor acts. Neuroimage. 2008 Feb;39(3):1314–23.

  98. Krueger F, Moll J, Zahn R, Heinecke A, Grafman J. Event Frequency Modulates the Processing of Daily Life Activities in Human Medial Prefrontal Cortex. Cerebral Cortex. 2007 Oct;17(10):2346–53.

  99. Negash A, Kebede D, Alem A, Melaku Z, Deyessa N, Shibire T, et al. Neurological soft signs in bipolar I disorder patients. J Affect Disord. 2004 Jun;80(2–3):221–30.

  100. Harmer CJ, Clark L, Grayson L, Goodwin GM. Sustained attention deficit in bipolar disorder is not a working memory impairment in disguise. Neuropsychologia. 2002 Jan;40(9):1586–90.

  101. Palejwala AH, Dadario NB, Young IM, O’Connor K, Briggs RG, Conner AK, et al. Anatomy and White Matter Connections of the Lingual Gyrus and Cuneus. World Neurosurg. 2021 Jul;151:e426–37.

  102. Nomi JS, Scherfeld D, Friederichs S, Schäfer R, Franz M, Wittsack HJ, et al. On the neural networks of empathy: A principal component analysis of an fMRI study. Behavioral and Brain Functions. 2008 Dec 17;4(1):41.

  103. Zhang L, Qiao L, Chen Q, Yang W, Xu M, Yao X, et al. Gray Matter Volume of the Lingual Gyrus Mediates the Relationship between Inhibition Function and Divergent Thinking. Front Psychol. 2016 Oct 3;7.

  104. Wu SN, Zhang MY, Shu HY, Liang RB, Ge QM, Pan YC, et al. Changes in Functional Connectivity of Specific Cerebral Regions in Patients with Toothache: A Resting-State Functional Magnetic Resonance Imaging Study. Dis Markers. 2020 Dec 28;2020:1–9.

  105. Yeap S, Kelly SP, Reilly RB, Thakore JH, Foxe JJ. Visual sensory processing deficits in patients with bipolar disorder revealed through high-density electrical mapping. Journal of Psychiatry and Neuroscience. 2009;34(6):459–64.

  106. Thase ME, Rush AJ. When at first you don’t succeed: sequential strategies for antidepressant nonresponders. Journal of Clinical Psychiatry. 1997;58(13):23–9.

  107. Gaynes BN, Asher G, Gartlehner G, Hoffman V, Green J, Boland E, et al. Definition of treatment-resistant depression in the Medicare population. 2018;

  108. Runia N, Yücel DE, Lok A, de Jong K, Denys DAJP, van Wingen GA, et al. The neurobiology of treatment-resistant depression: A systematic review of neuroimaging studies. Neurosci Biobehav Rev. 2022 Jan;132:433–48.

  109. Sharma V, Khan M, Smith A. A closer look at treatment resistant depression: is it due to a bipolar diathesis? J Affect Disord. 2005 Feb;84(2–3):251–7.

  110. Perugi G, Pacchiarotti I, Mainardi C, Verdolini N, Menculini G, Barbuti M, et al. Patterns of response to antidepressants in major depressive disorder: Drug resistance or worsening of depression are associated with a bipolar diathesis. European Neuropsychopharmacology. 2019 Jul;29(7):825–34.

  111. Fierro M, Bustos A, Molina C. Diferencias en la experiencia subjetiva entre depresión unipolar y bipolar. Rev Colomb Psiquiatr. 2016 Jul;45(3):162–9.

  112. Perlis RH, Brown E, Baker RW, Nierenberg AA. Clinical Features of Bipolar Depression Versus Major Depressive Disorder in Large Multicenter Trials. American Journal of Psychiatry. 2006 Feb;163(2):225–31.

  113. Yang T, Frangou S, Lam RW, Huang J, Su Y, Zhao G, et al. Probing the clinical and brain structural boundaries of bipolar and major depressive disorder. Transl Psychiatry. 2021 Jan 14;11(1):48.

  114. Howes OD, Thase ME, Pillinger T. Treatment resistance in psychiatry: state of the art and new directions. Mol Psychiatry. 2022 Jan 13;27(1):58–72.

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Funding

This study was funded by the grant No. NU22-04-00192 of the MH CR, the Charles University research program Cooperatio-Neurosciences, and the ERDF-Project Brain Dynamics, No. CZ.02.01.01/00/22_008/0004643.

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M.P. and T.N. conceived and designed the research. M.K., M.B. and T.N. recruited and assessed participants. M.P. and T.N. analyzed and interpreted the data, provided visualizations. M.P. wrote the first draft. All authors reviewed the manuscript.

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Correspondence to Martin Pastrnak.

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Pastrnak, M., Klirova, M., Bares, M. et al. Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study. BMC Neurosci 25, 46 (2024). https://doi.org/10.1186/s12868-024-00895-8

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  • DOI: https://doi.org/10.1186/s12868-024-00895-8

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