
==== Front
Cereb Cortex
Cereb Cortex
cercor
Cerebral Cortex (New York, NY)
1047-3211
1460-2199
Oxford University Press

10.1093/cercor/bhae366
bhae366
Original Article
AcademicSubjects/MED00310
AcademicSubjects/MED00385
AcademicSubjects/SCI01870
Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder
https://orcid.org/0000-0002-9189-6687
Klahn Anna Luisa Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 413 45 Gothenburg, Sweden
Department of Chemistry and Molecular Biology, Faculty of Science, University of Gothenburg, Medicinaregatan 7B, 413 90 Gothenburg, Sweden

https://orcid.org/0000-0002-0533-6035
Thompson William Hedley Department of Applied Information Technology, Forskningsgången 6, 417 56 Gothenburg University, Gothenburg, Sweden
Centre for Cognitive and Computation Neuropsychiatry, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden

Momoh Imiele Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 413 45 Gothenburg, Sweden

Abé Christoph Centre for Cognitive and Computation Neuropsychiatry, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden
Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden

Liberg Benny Department of Clinical Neuroscience, Karolinska Institutet, Retzius väg 8, 171 65 Stockholm, Sweden

Landén Mikael Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 413 45 Gothenburg, Sweden
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, 171 65 Stockholm, Sweden

Corresponding author: Anna Luisa Klahn, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Blå Stråket 15, 431 45 Gothenburg, Sweden. Email: luisa.klahn@gu.se
Anna Luisa Klahn and William Hedley Thompson contributed equally to this work.

9 2024
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© The Author(s) 2024. Published by Oxford University Press.
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Abstract

Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.

bipolar disorder
brain network hubs
functional connectivity
network neuroscience
resting-state fMRI
Swedish Research Council 10.13039/501100004359 2022-01643 Swedish Brain foundation FO2022-0217 Swedish Foundation for Strategic Research 10.13039/501100001729 KF10-0039 Swedish Federal Government ALFGBG-716801
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pmcIntroduction

Bipolar disorder (BD) is characterized by alternating mood states, switching between depressive and (hypo) manic episodes. These recurrent illness states are usually interspersed by episodes of euthymia during which patients are clinically stable (World Health Organization 2019; McIntyre et al. 2020). However, patients with BD often experience impaired executive functions and occupational deficits despite clinical remission (Sanchez-Moreno et al. 2009). We propose that such persisting impairments result from disruptions in brain network functional connectivity, as evidenced by recent findings indicating altered functional connectivity of the somatomotor network (SMN) in clinically-remitted bipolar patients (Klahn et al. 2023). Specifically, we reported a within-network dysconnectivity of the SMN alongside stronger between-network connectivity of the somatomotor and the frontoparietal and the default mode network in BD patients. This pattern contrasts with the expected self-contained nature of the SMN (Thomas et al. 2019) and is interesting considering that sensory processing disturbances are recognized to persist during euthymic episodes of BD (Giakoumaki et al. 2007; Mrad et al. 2016). Other studies have also reported altered functional connectivity of the default mode network, the frontoparietal network (FP), SMN, and salience network (SN) in BD (Lois et al. 2014; Wessa et al. 2014; Perry et al. 2019).

Functional brain networks consist of subsets of brain regions working in unison to accomplish different tasks (Yeo et al. 2011). By applying network theory on brain imaging data, we can identify that nodes (brain regions) serve different roles within their respective community (brain network) based on their integrating to segregating qualities. Nodes characterized by numerous connections and a high topological centrality are referred to as “hubs”. Brain hubs form at an early developmental stage and serve as important relay stations in the brain by integrating information from specialized brain networks (Fransson et al. 2011; van den Heuvel and Sporns 2013; Oldham and Fornito 2019). Functional brain network hubs are crucial for efficient information integration aiming at maximizing brain network communication while minimizing connection costs. This network cost-efficiency has been shown to be under genetic control (Fornito et al. 2011) and, interestingly, several studies have revealed a substantial genetic contribution to the formation and function of brain network hubs (van den Heuvel et al. 2013; Arnatkeviciute et al. 2021). Distinct subtypes of brain network hubs can be distinguished based on their extent of integration or segregation.

In sum, brain network hubs are substantially influenced by genetics, emerge early in development, and are characterized by their central position in brain networks. Given their prominent role, disrupted brain hubs can impair information integration within the brain. It thus stands to reason that brain network hub functioning is relevant for mental disorders, especially those with a significant genetic component such as schizophrenia and BD. Indeed, abnormal brain hub functioning has been demonstrated in neurodegenerative and mental disorders such as Alzheimer’s and schizophrenia (Buckner et al. 2009; Lynall et al. 2010; Fornito et al. 2012). The extant literature on brain network hubs in BD is, however, sparse.

BD has been associated with reduced network integration (Roberts et al. 2018; Perry et al. 2019) but one study investigating the structural connectome in bipolar patients did not find any alterations of hub-to-hub connection intensity (Collin et al. 2016). Investigating functional connectivity of the inferior frontal gyrus as an important hub in the executive control network, dysconnectivity of this region was revealed in bipolar patients as well as in first-degree relatives (Roberts et al. 2017).

The aim of this study was to compare brain network hub functioning between individuals with BD and healthy control (HC) individuals. Based on the previously reported alteration from within-network to between-network functional connectivity, we hypothesized to find altered hub functioning in the SMN, specifically in the primary somatosensory cortex and the supplementary motor area. Moreover, as existing findings about SMN alterations in BD are sparse, we conducted a subsequent exploratory analysis of all SMN nodes. Thus, we conducted static and time-varying functional connectivity analyses to identify hubs’ cartographic profiles as well as the trajectory of hub functioning through time. Finally, we analyzed whether nodes of the SMN follow the integration profile of another network, such as the FP or the default mode network, rather than its own assigned network.

Materials and methods

Sample and clinical measures

We studied individuals with BD and HC individuals from the St. Göran project, which is a longitudinal study at the BD outpatient clinic of the Northern Stockholm psychiatric clinic. We included individuals diagnosed with either BD type 1 or 2 along with HC individuals. Board certified specialists in psychiatry or residents in psychiatry made the diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria using the Structured Clinical Interview for DSM-IV-Axis I (SCID-I) included in the Swedish version of the Affective Disorder Evaluation (Sachs et al. 2003). The Mini-International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al. 1998) was administered to screen for co-occurring mental disorders. The final diagnoses were made by a consensus panel of experienced psychiatrists specialized in BD. Further details regarding recruitment, exclusion criteria, and diagnostic tools have been described in (Ekman et al. 2010). Age- and sex-matched HC individuals were selected randomly by Statistics Sweden, the Swedish government agency for official statistics. Control individuals were examined by a psychiatrist using the M.I.N.I. interview and parts of the Affective Disorder Evaluation. Exclusion criteria were any current mental disorder, a family history of schizophrenia or BD in first-degree relatives, drug or alcohol abuse, and neurological conditions (for details, see Rolstad et al. 2015).

All participants consented in writing to participate in the study that was conducted according to the Declaration of Helsinki. The study was approved by the regional ethics committee in Stockholm, Sweden.

We assessed manic symptoms using the Young Mania Rating Scale (YMRS), and we assessed depressive symptoms using the Montgomery-Åsberg Depression Rating Scale (MADRS). We used the illness severity scale of the Clinical Global Impressions Scales. Additionally, we assessed functional (daily life functioning) and symptom (severity of psychiatric symptoms) domains of the Global Assessment of Functioning (GAF).

Magnetic resonance imaging acquisition

Magnetic resonance imaging (MRI) scans were acquired at the MR Research Centre at the Karolinska University Hospital in Stockholm on a General Electric 1.5 T Signa Excite MRI medical scanner with an 8-channel head coil. For resting-state fMRI (rsfMRI), we acquired 180 volumes with 39 blood oxygenation level-dependent sensitive T2-weighted axial echo-planar images each (resolution of 3.79 × 3.79 mm, slice thickness 4 mm covering the whole brain, repetition time = 2.5 s, echo time = 40 ms, field of view = 24.3 cm, flip angle = 85°). The total duration of the resting-state fMRI scan was 7.5 min. The first 4 volumes were discarded due to T1 equilibration. Resting-state fMRI data was collected between September 2012 and March 2017 at the 7-yr follow-up assessment of the longitudinal study and analyzed in 2023. Additionally, we acquired T2-weighted images that were examined by radiologists to exclude any clinical pathologies. We included only individuals in the bipolar group that were in a euthymic state (as defined by MADRS < 8 and YMRS < 4) at the time of scanning.

Resting-state fMRI preprocessing

Preprocessing of the resting-state fMRI data was performed using fMRIPrep 22.0.0 (Esteban et al. 2019; RRID:SCR_016216) based on Nipype 1.8.3 (Gorgolewski et al. 2011; RRID:SCR_002502). See supplement for full details of preprocessing methods. We used 36 nuisance regressors (Ciric et al. 2017) to denoise the data. The data was band-passed between 0.008 and 0.1 Hz and 400 regions were extracted applying the Schaefer 400-nodes parcellation (Schaefer et al. 2018) using nilearn (Abraham et al. 2014). To scrub the data for micromovement, we removed all values with a frame-wise displacement greater than 0.5. This data was replaced with estimations using a cubic spline from the remaining data. We then assigned the 400 nodes to 7 resting-state networks according to the node network template (Yeo et al. 2011).

Connectivity estimates for functional connectivity were made using Pearson correlations. Time-varying estimates used the weighted Pearson correlation estimates where the weights were derived by using similar timepoints using 1 divided by the Euclidian distance between all timepoints including all 400 regions of interest (see Thompson et al. 2018; Thompson and Fransson 2018) using Teneto (Thompson et al. 2017).

Choice of nodes of interest

Based on the previous literature, we expected to find altered hub profiles in the primary somatosensory cortex and the supplementary motor area. We selected the nodes of interest based on the peak voxel provided by (https://neuroquery.org/) for the corresponding region. We then applied the coordinates of the respective node of interest onto the Schaefer 400-nodes parcellation (Schaefer et al. 2018) with the node indices corresponding to (Ciric et al. 2023). For the primary somatosensory cortex, we chose index node 45 for the left and index node 262 for the right hemisphere, and for the supplementary motor area, we chose index nodes 56 and 53 (see Supplementary Table S1).

Cartographic profile

We applied a dual-metric approach from graph theory to identify candidate hub nodes that play distinct roles in integrating information within or between communities. The participation coefficient (PC) quantifies the extent to which a node participates in multiple communities, thus integrating information between those communities (Guimerà and Nunes Amaral 2005). In addition, the within-module degree quantifies the degree to which the overall brain network can be partitioned into distinct modules or communities, indicating a high specialization or segregation. A node with a high within-module degree indicates strong integration of information within its own community (Guimerà and Nunes Amaral 2005).

Combining these 2 metrics, we define 4 quadrants that represent different hub qualities (see Fig. 1). The first quadrant defines “provincial hubs” that integrate information within their own community. These nodes are primarily linked to 1 single module and characterized by a high within-module degree and a low PC (van den Heuvel and Sporns 2013). The second quadrant defines “dual hubs”, which are nodes that exhibit both a high within-module degree and a high PC. Dual hubs are thought to integrate information both within and between communities. The third quadrant comprises peripheral nodes, characterized by a low within-module degree and a low PC. These nodes do not display any specific hub qualities, which is expected as only few nodes within a brain network will function as hubs. Finally, nodes with high PC but low within-module degree are defined as a “connector hubs”, linking nodes across different modules (van den Heuvel and Sporns 2013). Connector hubs adeptly integrate information between communities, which can be understood as sharing information between their own community and other brain networks.

Fig. 1 Concept of hub quality quadrants. Quadrant 1 represents a provincial hub that is characterized by a high module degree and a low PC. Quadrant 2 represents a dual hub with a high module degree and a high PC. Quadrant 3 is characterized by both low module degree and low PC and therefore represents no specific hub qualities. Quadrant 4 represents a connector hub with a high PC and a low module degree.

Calculations of nodal metrics

Static nodal measures

We calculated the cartographic profile to identify the overall hub role. Thus, we calculated static PC and within-module degree z-score (z), originally defined in (Guimerà and Nunes Amaral 2005). These measures use a community parcellation as well as strength, which for node i is defined as:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {k}_i={\sum}_j^N{A}_{ij} $$\end{document}

where N is the number of nodes and A is a weighted connectivity matrix. Further, we use within-module strength with node i (kic) which is the sum of edges between node i and nodes in community c:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {k}_{ic}={\sum}_j^N\left\{\begin{array}{c}{A}_{ij}, ifj\in c\\{}0, otherwise\end{array}\right. $$\end{document}

The PC for each node, is defined as:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {P}_i=1-{\sum}_c^C\left(\frac{k_{ic}}{k_i}\right) $$\end{document}

where C is the number of communities, ki is the strength of node i, and kic is the within community-strength of node i with nodes in community c. This value is larger if a node has its connections spread out among multiple communities. The within-module degree z-score is:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {z}_i=\frac{k_i-\overline{k_{ic}}}{\sigma_{k_{ic}}}\ where\ i\in c $$\end{document}

where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\overline{k_{ic}}$\end{document} is the mean within-degree connectivity for node i’s own community c and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\mathrm{\sigma}}_{k_{ic.}}$\end{document} is the standard deviation of the within-degree connectivity for i’s own community c.

Time-varying nodal measures

We calculated the PC and within-module degree z-score for each of the 180 timepoints. For the temporal participation measure, we use the PC through time with a static community template (see Thompson et al. 2020 for motivation). For all these time-varying measures, we use the Yeo 7 networks parcellation throughout the analysis (Yeo et al. 2011).

For both PC and z, the measures are identical, except we use each time point in the time-varying connectivity, indexed by t:

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {P}_{it}=1-{\sum}_c^C\left(\frac{k_{ict}}{k_{it}}\right)\qquad $$\end{document}

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ {z}_{it}=\frac{k_{it}-\overline{k_{ict}}}{\sigma_{k_{ict}}}\ where\ i\in c $$\end{document}

With these measures, we quantify a node’s overall relationship to other nodes within this 2D space. We split this space into 4 quadrants and each node at timepoint t was assigned to 1 of 4 quadrants based on the median of each dimension. For each node, we then calculated the number of timepoints spent in each quadrant (see Fig. 1 and Supplementary Fig. S1). This yielded 4 values which detail the number of visits of a respective node to each quadrant. However, as hubs are usually defined as the most extreme nodes, we additionally calculated the number of visits based on the top and bottom 33% of the individuals’ PC and z, respectively, to achieve a more conservative definition of provincial and connector hubs (see Supplementary Fig. S1).

Node-community integration allegiance

Finally, we analyzed the trajectory of integration profiles from SMN nodes and analyzed whether they follow the general integration pattern of the SMN or another network. Therefore, we computed a PC through time representing its time series of integrating with other communities. We can derive how much each node correlates with each communities’ overall PC time series. Here, we take the median PC for each community and correlate that community time series with each node’s PC time series. This creates a 7 × 77 correlation matrix showing how much each node of the SMN displays a similar overall integration profile as the 7 resting-state networks. Thus, if a node correlated high with the SMN and low with, for instance, the default mode network, this would suggest that this node is following the integration profile of the SMN as its signal is more similar to how the SMN is communicating with the rest of the brain. However, if this node correlated low with the SMN and high with the default mode network, this would indicate that the node shows stronger allegiance to the information being sent from the default mode network. Such behavior would imply that brain network cohesion is impaired with brain regions switching allegiance or failing to belong to their brain network.

Statistical analysis

For all statistical analyses, we used SPSS version 28 (IBM). We tested group differences using 2-sided t-tests or χ2-tests as appropriate. For the static hub analysis, we used a multiple analysis of covariance with group as independent variable, age, and sex as covariates, and the PC and the module degree z-score z of the nodes as dependent variables. For the hub trajectories, we conducted a general linear model for the 4 nodes of interest with group (BD, HCs) and quadrant (provincial hub, dual hub, no hub, connector hub) as independent variables, sex, and age as covariates, and the number of visits per quadrant as dependent variable. For the hub allegiance analysis, we conducted a general linear model with group and community as independent variables, sex, and age as covariates, and the allegiance integration coefficient per node as dependent variable. We conducted these 3 models for the 4 a-priori chosen nodes of interest and conducted a subsequent exploratory analysis for the remaining 73 nodes of the SMN in a second step. For all analyses, we conducted post-hoc t-tests to depict the direction of group or interaction effects. We corrected for multiple comparisons applying Benjamini–Hochberg false discovery rate (FDR) correction. Results were only considered significant if the FDR-corrected q-value was less than 0.05. Furthermore, we conducted analyses for medication effects by adding lithium, antipsychotic, antiepileptic, and antidepressant medication as covariates to a within-group analysis in the bipolar group while correcting for age and sex.

Results

Demographic data

The final sample consisted of 96 euthymic patients with BD and 61 HC individuals. The groups did not differ regarding age and sex. Patients had lower levels of functioning and higher symptom severity as measured with GAF scores. Even though patients scored higher on MADRS and YMRS compared with controls, their MADRS (<8) and YMRS (<4) scores were below clinical significance. Supplementary Table S2 shows demographic and clinical information along with medication intake.

Static hubs

For the 4 nodes of interest, we found a significant group effect for 1 node in the primary somatosensory cortex (index node 45; F(1) = 14.72, p < 0.001, ηρ2 = 0.09). Bipolar patients had a higher PC than controls (t(155) = 3.75, p < 0.001). This effect persisted after FDR-correction for the nodes of interest, and when testing for the impact of medication.

In the explorative analysis of all nodes of the SMN, we found a group effect for the static PC of 13 additional nodes of the parietal and central operculum, and of the postcentral gyrus (all p < 0.001 and FDR-corrected q < 0.05; see Supplementary Table S3.1 for detailed list of nodes and statistical values). Post-hoc t-tests revealed higher PC values in bipolar patients compared with controls. For z, a significant group effect was shown for 1 node in the primary somatosensory cortex (index node 246; F(1) = 13.62, p < 0.001, ηρ2 = 0.08) where bipolar patients had lower values compared with controls (t(149) = −3.69, p < 0.001). Figure 2 shows the distribution of the aberrant SMN nodes for bipolar patients and HC individuals. Supplementary Fig. S2 shows all SMN nodes for both groups. To investigate potential medication effects, we added lithium, antipsychotics, antiepileptics, and antidepressants to the within-group analysis of bipolar patients. We found no effect of medication intake on the static hubs.

Fig. 2 Distribution of altered SMN nodes. Scatterplot depicts those nodes that showed alterations in bipolar patients compared to healthy control individuals based on the static analysis. The colored nodes (red, solid fill = BD, green, pattern fill = HC) represent the nodes that significantly differed in their integrative function indicated by PC (participation coefficient, x-axis) between groups after FDR-correction. The y-axis represents the module degree z-score (z). Numbers represent the node labels according to the Schaefer 400-nodes parcellation. The left bar-plots show the amount of the 77 nodes of the SMN per hub quality quadrant for bipolar patients (BD, red, solid fill) and healthy controls (HC, green, pattern fill) based on the median split. The right bar-plot shows the amount hub nodes from the SMN per hub quality based on the top and bottom 33% split. The error bars represent standard error. *p < 0.05, **p < 0.01, ***p < 0.001.

When comparing the number of candidate hubs between the groups, individuals with BD displayed significantly fewer nodes in the provincial hub quadrant and in the no hub quadrant, but more nodes in the dual hub quadrant. We subsequently applied a more conservative definition of hubs by looking only at the 33% most extreme nodes in a hub quadrant (see Supplementary Fig. S1). Based on the static PC and z, the number of nodes in the respective hub quadrants did not differ between the groups (see bar plots in Fig. 2 and Supplementary Table S3.2).

Hub trajectories

The general linear model for the nodes of interest revealed a quadrant × group interaction effect for 1 node in the postcentral gyrus (index node 45; F(3) = 6.71, p < 0.001, ηρ2 = 0.04; see Fig. 3A). Bipolar patients had fewer number of visits in the provincial hub quadrant (t(120) = −3.24, p = 0.002) and more visits in the connector hub quadrant (t(151) = 3.22, p = 0.002). We found no effect of medication on the hub trajectory within the BD group.

Fig. 3 Number of visits per hub quality quadrant. A) Trajectory of node 45 (left postcentral gyrus) through the hub quality quadrants over the course of the 180 timepoints during the 7.5 min rsfMRI scan. B) Location of the altered nodes in bipolar patients in the postcentral gyrus according to the Schaefer 400-nodes parcellation, right hemisphere. C and D) Bar plots for the number of visits per hub quality quadrant for 3 of the significant SMN nodes. Green (pattern fill) bars depict healthy control individuals (HC) and red (solid fill) bars represent individuals with bipolar disorder (BD). The error bars represent standard error. *p < 0.05, **p < 0.01, ***p < 0.001.

Exploring all nodes of the SMN, we found a quadrant × group interaction effect for 7 further index nodes located in the precentral and postcentral gyrus (see Supplementary Table S4). Results remained after FDR-correction. In the BD group, all nodes were less in the provincial hub quadrant and more in the connector hub quadrant (see Fig. 3 and Supplementary Table S4). No effect of medication intake was found.

We then, again, defined provincial hubs as located in the top left corner of the provincial hub quadrant and connector hubs as located in the bottom right corner of the connector hub quadrant (see Supplementary Fig. S3). Significant quadrant × group interaction effects and post-hoc t-tests revealed that, among bipolar patients, nodes of the primary somatosensory cortex still displayed fewer number of visits in the provincial hub quadrant while displaying a greater number of visits in the no hub and the connector hub quadrant compared with controls (Supplementary Table S5).

Node-community integration allegiance

For the nodes of interest, the general linear model revealed a community × group interaction effect for 2 nodes of the primary somatosensory cortex and for 1 node of the supplementary motor area. Post-hoc t-tests indicated weaker allegiance with the SMN but stronger allegiances with the SN, limbic network (Lim), and FP in BD (Table 1) which survived FDR-correction. We found no effect of medication within the BD group.

Table 1 Results of the general linear model testing effects of community × group for the allegiance integration coefficient with age and sex as covariates.

			Interaction effect community × group		t-test	
Index node	Brain region	df	F	p	ηρ2	FDR-q	Community	df	t	p	
44	Postcentral gyrus	4.37	7.45	< 0.001	0.046	< 0.001	DAN	155	3.32	0.002	
		SN	155	2.49	0.014	
		Lim	155	3.20	0.002	
		FP	155	2.67	0.008	
45	Postcentral gyrus	4.57	4.88	< 0.001	0.031	< 0.001	SMN	155	−2.44	0.016	
		Lim	155	2.53	0.013	
47	Postcentral gyrus	5.32	3.25	0.005	0.021	0.028	SMN	155	−2.11	0.036	
		Lim	155	2.16	0.032	
48	Postcentral gyrus	4.60	6.74	< 0.001	0.042	< 0.001	SMN	155	−2.21	0.029	
		SN	155	2.18	0.031	
53	Postcentral gyrus	4.08	3.45	0.005	0.022	0.028	SN	155	2.38	0.019	
		Lim	155	3.15	0.002	
		FP	155	2.13	0.035	
59	Precentral gyrus	4.74	3.87	0.002	0.025	0.019	DAN	155	2.05	0.042	
		Lim	155	2.76	0.006	
64	Paracentral lobe	4.92	3.67	0.003	0.023	0.021	DAN	155	2.44	0.016	
		SN	155	2.66	0.009	
		Lim	155	2.41	0.017	
240	Parietal operculum	4.84	3.77	0.002	0.024	0.019	Lim	155	3.44	< 0.001	
246	Postcentral gyrus	4.68	8.23	< 0.001	0.051	< 0.001	DAN	155	2.40	0.018	
		Lim	155	2.33	0.021	
		FP	155	2.47	0.015	
262	Paracentral lobe	4.80	3.58	0.004	0.023	0.024	Lim	155	2.43	0.016	
265	Postcentral gyrus	4.91	3.62	0.003	0.023	0.021	DAN	155	2.99	0.003	
		Lim	155	2.14	0.034	
		FP	155	3.44	< 0.001	
269	Precentral gyrus	4.79	3.67	0.003	0.023	0.021	Lim	155	2.06	0.041	
270	Postcentral gyrus	4.84	4.54	< 0.001	0.029	< 0.001	Lim	155	3.23	0.002	

When exploring all nodes of the SMN, we found a significant community × group interaction effect for an additional 10 nodes located in the postcentral gyrus. These results persisted after correcting for multiple comparisons and accounting for medication effects. In the BD group, those nodes followed more the trajectories of the dorsal attention network (DAN), SN, Lim, and FP (see Fig. 4 and Table 1).

Fig. 4 Node-to-community integration allegiance. A) The concept of nodal recruitment. B) Integration trajectory of 2 example nodes from the somatosensory cortex in comparison with the allegiance integration of the DAN and SMN. C) Correlation between 2 example communities and 2 example nodes. D) Spider web plots showing that nodes of the primary somatosensory cortex follow less the trajectory of the SMN but more the trajectory of the Lim, FP, SN, and DAN. *p < 0.05, **p < 0.01, ***p < 0.001.

Discussion

Brain network hubs play a central role within brain networks and can be distinguished by their extent of within-network or between-network information integration. The present study investigated hub characteristics within the SMN, focusing on the primary somatosensory cortex and the supplementary motor area in euthymic BD patients and HC individuals. Our main findings were that nodes of the primary somatosensory cortex displayed less within-network and more between-network integration in BD. Furthermore, we observed that nodes of the SMN, which were identified as provincial hubs in HCs, displayed less provincial hub function in bipolar patients and some to these nodes even partially transitioned to connector hubs. Additionally, some somatomotor nodes displayed characteristics less typical of the SMN and more akin to those of the limbic, salience, dorsal attention, and FP.

The primary somatosensory cortex, located in the postcentral gyrus, processes bodily sensations, and sends signals to the premotor and motor cortex that execute motor reactions (ten Donkelaar et al. 2020). The SMN has been described as predominantly provincial, implying that it primarily integrates information within the own community rather than between brain networks. Our findings in HCs support this characteristic. Interestingly, however, we show that, in BD, nodes of the primary somatosensory cortex have less provincial and more connector hub qualities.

The loss of provincial hub function in BD was evidenced by a higher PC and fewer visits in the provincial hub quadrant, alongside a higher number of visits in the connector hub quadrant. This finding aligns with previous studies by us and others, which have reported dysconnectivity and reduced cohesiveness of the SMN in BD, indicating disrupted processing of sensorimotor information (Doucet et al. 2017; Klahn et al. 2023). In one study investigating static hub qualities across various disorders, one notable finding was a reduced percentage of provincial hubs as well as an increased percentage of connector hubs in BD, along with general disorder-related disruptions in the SMN (Sha et al. 2018).

Intriguingly, primary sensory regions were shown to primarily engage in a single functional network (i.e. the SMN), whereas recognized hub regions, such as the medial superior frontal cortex or the ventromedial prefrontal cortex, were found to engage in multiple functional networks (van den Heuvel and Sporns 2013). Alterations in hub quality at a fundamental level of sensory processing, as we observed in primary sensory regions, may represent a core feature of psychopathology, persisting even during episodes of clinical remission. Developmental studies have shown that brain hubs form at an early, immature stage with functional hubs being mainly located in primary visual and motor regions (Fransson et al. 2011). Over the course of development, there is an increase in functional interactions between frontal hubs and parietal and temporal regions, implicating greater cognitive control through the FP (Hwang et al. 2013). We observed hub alteration primarily in brain regions implicated in the initial stages of somatosensory processing. This observation leads us to speculate that these aberrations in brain network function may emerge at an early stage, possibly preceding the onset of BD. These alterations might thus potentially serve as early biomarkers for illness.

In a second step, we investigated the node-to-community integration allegiance of SMN nodes to address the question if SMN nodes behave like their assigned brain network (i.e. community) or start following other resting-state networks. We found that nodes of the primary and the secondary somatosensory cortex, as well as the primary and the supplementary motor cortex, connected less with their own community in BD. Instead, these nodes formed stronger allegiances with the Lim, SN, FP, and DAN. This finding indicates potential implications for sensory integration processes in BD.

Increased functional connectivity of the somatosensory cortex with the inferior prefrontal gyrus and the frontal orbital cortex has been reported in euthymic bipolar patients (Minuzzi et al. 2018). Both areas take part in the Lim (Yeo et al. 2011) are associated with integrating sensory information and are linked closely to the subcortical limbic regions. Task-based fMRI studies using verbal memory and attention tasks provide further evidence of altered activation of the somatosensory cortex in BD (Cerullo et al. 2014). Hyperactivation of somatosensory regions might reflect higher sensitivity to sensory stimuli and a higher amount of sensory information, which has been shown for individuals with bipolar mania (Shaffer et al. 2018; Lee et al. 2019). Hubs in the somatosensory and motor regions might be more responsive to salient or emotionally significant stimuli potentially affecting motor planning and execution (Wang et al. 2019; Bi et al. 2022). Nevertheless, increased communication of primary somatosensory regions with the salience, dorsal attention, and Lim might also reflect how sensory information is passed on to higher cortical instances of sensory integration and attention regulation.

Our finding might further suggest a reduced filtering threshold within the attention-related brain networks. This aligns with some studies suggesting dysfunctions in the dorsal and ventral attention networks in BD (Phillips and Swartz 2014; Syan et al. 2018). Insufficient suppression of irrelevant sensory information, due to altered attention allocation modulated by the DAN, combined with overactive primary somatosensory regions, may contribute to impaired executive functions even during euthymic episodes of BD. In this study, we defined the node-community integration allegiance by associating the trajectories of the time-varying PC of the communities and those of the SMN nodes. While this method allows us to assess the strength of alignment, it does not distinguish whether a strong allegiance is driven by the communities recruiting nodes from other brain network, or by aberrant nodes behavior where nodes disengage from their assigned network and instead engage with other communities.

Conclusion

In sum, our findings suggest alterations in sensory information processing and attention allocation in euthymic patients with BD. The observed disruptions in brain network hub quality may represent a neurobiological substrate for lingering functional impairments despite clinical remission in BD. Therefore, our findings have several clinical implications: identifying specific brain regions with altered functional connectivity enables targeted interventions. For instance, neurostimulation techniques like transcranial magnetic stimulation could be applied to normalize brain network connectivity and potentially improve sensory integration and motor coordination. Moreover, our findings might help to refine the criteria of clinical remission and euthymia in BD contributing to a more holistic approach to treatment that goes beyond managing mainly mood symptoms.

Limitations and outlook

This study specifically focused on hub qualities in the SMN, which inherently limits the analysis by excluding the whole-brain perspective. Hub qualities and activities are influenced by complex, dynamic processes outside their assigned network. Therefore, future studies should consider extending the present analysis to include other resting-state networks to gain a more comprehensive understanding. BD is mostly accompanied by a continuous intake of mood stabilizing medication and the complexity of pharmacotherapy often reflects the severity of illness. Our HC individuals, by definition, do not use any psychopharmacological drugs. We investigated potential medication effects within the group of bipolar patients and found no impact of medication intake, suggesting that our results are not confounded by medication effects. We acknowledge, however, that studying the impact of medication in BD is complicated as medication use is associated with illness severity (Abé et al. 2020). Therefore, future studies are needed to disentangle medication side effects from effects of psychopathology.

Supplementary Material

revised-Supplement_bhae366

Acknowledgments

We are very grateful to all our study participants and the staff at the St. Göran bipolar affective disorder unit. We specifically acknowledge our research nurses Lena Lundberg, Stina Stadler, Agneta Carlswärd-Kjellin, and Martina Wennberg.

Author contributions

Anna Luisa Klahn (Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Visualization, Writing—original draft, Writing—review & editing), William Hedley Thompson (Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing—review & editing), Imiele Momoh (Formal analysis, Writing—review & editing), Christoph Abé (Data curation, Investigation, Writing—review & editing), Benny Liberg (Data curation, Investigation, Writing—review & editing), and Mikael Landén (Funding acquisition, Investigation, Supervision, Writing—review & editing).

Funding

This research was supported by grants from the Swedish Research Council (Vetenskapsrådet, 2022-01643), the Swedish Brain foundation (Hjärnfonden, FO2022-0217), the Swedish Foundation for Strategic Research (KF10-0039), and the Swedish Federal Government under the LUA/ALF agreement (ALFGBG-716801) to ML and by a grant from the Märta-Lundqvist Foundation to ALK. The funding sources were not involved in analysis, interpretation of data, or writing of the manuscript.

 

Conflict of interest statement: ML declares that he has received lecture honoraria from Lundbeck pharmaceuticals outside the present work. All other authors declare no conflict of interest.

Data availability

Anonymized data and custom written code will be shared upon reasonable request from a qualified academic investigator for the sole purpose of replicating procedures and results presented in the article and under the condition that data transfer is in agreement with EU legislation on the general data protection regulation and decisions by the Ethical Review Board of Sweden and regulated in a data transfer agreement.
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