Cognitive variability in bipolar I disorder: A cluster-analytic approach informed by resting-state data

Neuropharmacology 156 (2019) 107585

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Cognitive variability in bipolar I disorder: A cluster-analytic approach informed by resting-state data

T

Bianca Kollmanna,b, Kenneth Yuenc, Vanessa Scholzb,d, Michèle Wessab,∗ a

Emotion Regulation and Impulse Control Group, Department of Psychiatry and Psychotherapy, Johannes Gutenberg-University Mainz, Germany Department of Clinical Psychology and Neuropsychology, Institute for Psychology, Johannes Gutenberg- University Mainz, Germany c Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg-University Mainz, Germany d Radboud University, Donders Institute of Brain, Cognition and Behaviour, Nijmegen, the Netherlands b

HIGHLIGHTS

distinct clusters among BD-I patients based on cognitive performance. • 3Clusters differing cognitive profiles, irrespective of performance deficits. • Clusters show differ in network connectivity in regions of interest related to cognitive tasks investigated. • Clusters also show distinct connectivity hubs within the brain. • Results ameliorate the understanding of the different phenotypes observed for BD-I. • ARTICLE INFO

ABSTRACT

Keywords: Bipolar I disorder Cognitive profile Cluster analysis Executive functions Resting-state

Background: While the presence of cognitive performance deficits in bipolar disorder I (BD-I) is well established, there is no consensus about which cognitive abilities are affected. Heterogeneous phenotypes displayed in BD-I further suggest the existence of subgroups among the disorder. The present study sought to identify different cognitive profiles among BD-I patients as well as potentially underlying neuronal network changes. Methods: 54 euthymic BD-I patients underwent cognitive testing and resting state neuroimaging. Hierarchical clusteranalysis was performed on executive function scores of bipolar patients. The derived clusters were compared against 54 age-, gender- and IQ-matched healthy controls (HC) to facilitate the interpretation of results. Further, resting state network properties were compared to identify differences probably underlying cognitive profiles. Results: A three-cluster solution emerged. Cluster 1 (n = 22) was characterized by deficits in cognitive flexibility and motor inhibition, cluster 2 (n = 12) displayed impulsive decision-making, while cluster 3 (n = 20) showed good visuospatial planning. Weaker connections in cluster 1 compared to cluster 2 were found between regions activated during tasks cluster 1 showed deficits on. Cluster 3 had a higher modularity than cluster 2, which correlated positively with problem solving performance and risk-taking in this cluster. Conclusion: Obtained clusters showed distinct cognitive profiles, characterized by deficits and strengths, most of which remained precluded in a general comparison. Weaker interregional connections and separated subnetworks might underly behavioral deficits and strengths, respectively. The findings help explain the phenotypic heterogeneity observed in BD-I. This article is part of the Special Issue entitled ‘Current status of the neurobiology of aggression and impulsivity’.

1. Introduction Over the last decade, several studies have found deficits in cognitive performance in bipolar disorder (BD) patients (Bortolato et al., 2015). These deficits were observed in a wide range of cognitive domains,

including executive functions, attention, working memory, verbal and visual memory and psychomotor speed (for a review, see e.g. Torres et al., 2007). They were not only present in symptomatic phases, but persisted into euthymia (Martínez-Arán et al., 2004). Moreover, cognitive deficits were shown to negatively affect functional and

∗ Corresponding author. Michèle Wessa (PhD), Johannes Gutenberg-University Mainz, Institute for Psychology, Department of Clinical Psychology and Neuropsychology, Wallstraße 3, 55122 Mainz, Germany. E-mail address: [email protected] (M. Wessa).

https://doi.org/10.1016/j.neuropharm.2019.03.028 Received 19 October 2018; Received in revised form 21 March 2019; Accepted 22 March 2019 Available online 23 March 2019 0028-3908/ © 2019 Published by Elsevier Ltd.

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psychosocial outcome in these patients, which was found not only in cross-sectional (Depp et al., 2012), but also longitudinal studies, indicating the persistence of this negative influence (Mora et al., 2013). However, while the presence of persistent cognitive deficits in bipolar disorder is well established, there is great heterogeneity in the literature with regard to the specific domains of cognition being affected. Some studies found cognitive deficits to be most pronounced in verbal memory and executive functions (Martínez-Arán et al., 2004; Robinson et al., 2006), while other studies described a higher prevalence of deficits in attention and working memory, together with executive function deficits (Cullen et al., 2016). Also, not all patients seem to be equally affected. For instance, a study by Martino et al. (2008) showed that 40% of bipolar patients had cognitive deficits in one to two domains, 22% were affected in three to five domains, and 38% of patients did not display any cognitive deficits (Martino et al., 2008). Taken together, these studies indicate very heterogeneous cognitive deficits in some, but not all BD patients, pointing towards the possible existence of subgroups among BD patients, based on their cognitive profile. Up to date, eight studies have tried to shed light on the cognitive profiles existing in BD, using data-driven approaches by means of cluster analyses. Mostly, these studies found three to four clusters, with a globally impaired group underperforming on all cognitive tests administered compared to HC, an intact group performing at the level of HC and one to two selectively impaired groups showing cognitive deficits in specific, but not all, cognitive domains. However, the results from these studies do not allow for generalizability to the population of BD patients. Two of these studies included bipolar patients, who were still symptomatic (Burdick et al., 2014; Jensen et al., 2016), with one of these studies even including a subsample of patients recruited based on the presence of severe cognitive impairment (Jensen et al., 2016), thereby possibly overestimating the cognitive deficits found. Further, a study by Lewandowski et al. (2014) not only investigated BD patients, but also patients with schizophrenia and schizoaffective disorder to determine cognitive subgroups among patients suffering from psychotic episodes. This study also found four clusters ranging from normal to globally impaired cognitive performance (Lewandowski et al., 2014). However, the specificity of these found clusters for BD remains unknown, since not all BD patients suffer from lifetime psychotic symptoms (American Psychiatric Association, 2000). Another study clustering bipolar and schizophrenia patients found three clusters, ranging in level of impairment (Van Rheenen et al., 2017). Moreover, clustering bipolar patients, schizophrenia patients and healthy controls on tasks testing social and nonsocial cognition and perception identified three clusters across diagnostic groups varying in performance from high to low (Lee et al., 2017). Still, the specificity of performance differences among clusters for bipolar disorder remains precluded. As such, there are only three studies investigating cognitive subgroups in fully euthymic bipolar patients to date. The first study by Bora et al. (2016) investigated a large sample of Bipolar-I (BD-I) and -II disorder (BD-II) patients assessed on only two tests of executive functioning. It extracted four different clusters, which varied in their level of impairment, with patients being overrepresented in the globally impaired cluster, while healthy controls (HC) were overrepresented in the cognitively unimpaired cluster (Bora et al., 2016). Study two clustered bipolar patients on a cognitive test battery, identifying three clusters varying in their degree of impairment. However, the specific kind of bipolar disorder (i.e. type I or II) remains unknown (Lima et al., 2019). The third study solely investigated BD-II patients on a large cognitive test battery specifically recommended for obtaining deficits in bipolar disorder. It found three clusters, an intact, selectively impaired, and globally impaired one (Solé et al., 2016). Nonetheless, these studies aimed at separating those patients that were impaired on certain cognitive domains from those who were intact, rather than shedding light on the possible existence of different cognitive profiles within bipolar disorder. However, it might be the existence of different cognitive profiles among

BD patients, which could explain the vast phenotypic heterogeneity observed in this patient population. Moreover, to our knowledge, there is no study investigating possible subgroups with regard to cognitive performance exclusively in BD-I patients. While BD-I and -II patients both suffer from recurrent mood episodes, characterized by phases of elevated mood alternating with episodes of depressed affect (American Psychiatric Association, 2000), cognitive deficits might vary between the two disorders. Studies on the neurocognitive profiles of the two disorders displayed contradictory results. Some studies found that BD-I patients were generally more impaired in executive functions and memory than BD-II patients (Hsiao et al., 2009), while others showed that BD-II patients were more impaired in these domains (Summers et al., 2006). Further, there are also studies supporting completely different cognitive profiles for the two disorders, with BD-I patients displaying more widespread deficits and suffering from more clinically significant cognitive impairments compared to BD-II patients (Cotrena et al., 2016; Simonsen et al., 2008). In sum, studies using mixed samples of BD might over- or underestimate cognitive deficits in certain domains based on clinical diagnosis. Thus, there is a need for studies on the existence of possible cognitive subgroups among patients, solely investigating BD-I disorder. While classifying BD-I patients based on cognitive performance differences into potential subgroups is a first step towards a better understanding of the heterogeneity in BD-I disorder, our understanding can be further corroborated by investigating the underlying functional brain architecture that supports the respective cognitive performance groups. By employing resting state functional magnetic resonance imaging (rsfMRI), intrinsic functional connectivity between different brain regions can be examined in a task-free, emotionally unbiased brain state. A number of research papers have already shown significant differences in terms of specific functional connectivity strengths between bipolar patients and healthy controls (e.g. Lois et al., 2014; Magioncalda et al., 2015; Syan et al., 2017; see Vargas et al., 2013 for a review). Yet, studies investigating possible differences in functional connectivity among BD-I patients displaying a distinct cognitive performance are still scarce. Therefore, the aim of the present study was to investigate the potential existence of distinct cognitive subgroups among euthymic BD-I patients, using computerized tests assessing patients' performance on various executive functions. Based on the aforementioned literature, three distinct clusters were expected, representing a globally impaired group performing worse on all cognitive tests compared to the other two clusters, a selectively impaired group with deficits in certain, but not all cognitive domains, and an intact group, outperforming the other two groups on all tests. To improve interpretability of the distinct clusters’ cognitive performance, obtained clusters were compared against a healthy control group. Also, to determine whether BD-I patients generally differed from HC on the assessed tests, the entire sample of BD-I patients was compared against HC participants. Further, exploratory network analyses were conducted using rsfMRI data to identify possible differences in brain network configurations that might underlie the distinct behavioral performance types expected among BDI patients. 2. Materials and methods 2.1. Participants A total of 54 euthymic BD-I patients were investigated. The inclusion criteria for patients were: (i) a diagnosis of BD-I, assessed using the German version of the SCID-I (Wittchen et al., 1997), (ii) no comorbid axis I or II disorders, assessed on the same instrument in combination with the SCID-II (Wittchen et al., 1997), (iii) age between 18 and 60 years, (iv) euthymic for at least 2 months prior to testing, (v) stability on medication type and dosage for at least 2 months prior to testing, (vi) absence of substance abuse or dependence for at least 3 months 2

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prior to testing, and (vii) euthymic mood state at the day of testing, verified with a Hamilton depression rating scale (HAM-D) score of ≤8 (Hamilton, 1960) and a Young mania rating scale (YMRS) score of ≤8 (Young et al., 1978). Further, patients were excluded, if they ever underwent an electroconvulsive therapy or had an intelligence quotient (IQ) < 75. Additionally, 54 HC participants, matched for age, gender, and IQ were recruited, in order to ameliorate interpretability of the clustering results obtained for the BD-I patient sample. Control participants were free of any psychiatric or neurological disorders, the former being confirmed by means of the SCID-I and -II (Wittchen et al., 1997). In addition, they did not have a first-degree relative with BD. All participants were assessed at the Central Institute of Mental Health (CIMH) in Mannheim or at the University Hospital Heidelberg. They were recruited through announcements on webpages, public places and via the local registration offices of the two cities. Resting-state data were acquired from 49 BD-I patients. However, 7 patients were excluded due to excessive movement during data acquisition, resulting in a final sample of 42 BD-I patients and 41 matched controls (resting-state data was not acquired for one matching control). All participants received a complete description of the study procedures, after which they gave written informed consent. Upon study completion, all participants received a monetary compensation. The study adhered to the declaration of Helsinki and was approved by the ethical committee of the University of Heidelberg, Germany.

computer counts up the amount of points that can be bet, starting at 5% of the current budget counting up in steps of 1/3 of the budget and the participant stops this counter at the amount of points, he wants to bet. In descending trials, this counter starts at 95% of the current budget to be placed at stake and counts down to the option of betting 5%. Delay aversion is the difference between the amount bet on ascending and descending trials. A high delay aversion score, meaning low bets in ascending and high bets in descending trials, thereby indicates an impulsive betting strategy, in which participants are unwilling to wait for the counter to reach an appropriate amount of points to place at bet (impulsivity). On the SST, participants are presented with an arrow pointing either left or right. They have to press one of two response pads with their left or right index finger as fast as possible, indicating the arrow's pointing direction, but need to withhold their prepared motor response when an auditory signal is presented. The stop signal response time (SSRT) is derived from the proportion of successful stops and the reaction time on GO trials and reflects motor impulsivity. The IED is a task estimating cognitive flexibility and rule reversal learning. Initially, participants are presented with two color-filled shapes. They have to learn which of the two shapes is the better one by receiving visual and auditory feedback after choosing one of the shapes. The rule is being changed following a correct response, so that the other shape is the correct stimulus (rule reversal). These shapes are a pair of pink color-filled geometric figures. This is followed by introducing another shape (i.e. white stick-shaped figures) overlaid on the pair of pink figures. Hence, subjects are now presented with two stimuli, each entailing two shapes overlaid on each other, and they have to determine which of the two stimuli is the correct one. Either the colorfilled or the white stick-shaped figure determines the correctness of the stimulus. Once more, after the rule has been learned, it is changed either by an intra-dimensional shift (i.e. other shape of the same entity is correct), or by an extra-dimensional shift (i.e. shape of the other entity is correct). Pre-EDS errors are those made before an extra-dimensional shift takes place and as such indicate rule learning, while extra-dimensional shift (EDS) errors are those mistakes made at the extra-dimensional shift stage and point towards problems with rule reversal and thus also with global cognitive flexibility. The SOC is a task of spatial planning and working memory, in which participants are presented with two assortments of colored beads. Each assortment entails three colored beads and stacks that can fit one, two, or three of these beads at the same time. Participants are required to copy the upper display in the lower display by moving the beads with their hand on the touch-screen. Since participants may only move one ball at a time and only have a certain amount of movements per trial announced beforehand, participants have to plan their moves before execution. The amount of problems solved in minimum moves hereby describes the overall planning ability, with higher scores indicating a better planning performance. Further, the initial thinking time (5 moves) is the time from problem presentation to movement of the first bead on the most difficult trials, in which participants need five moves to copy the presented assortment of beads. As such, this score reflects planning time and working memory, while the subsequent thinking time (5 moves) reflects the time from first movement to trial completion. Thus, this score infers visuomotor speed and reflects the quality of planning made before movement initiation (see Supplement Figure 4). Moreover, participants' IQ was estimated using the Multiple Choice Word Vocabulary Test (Lehrl, 2005; German: Mehrfach-Wortschatz Intelligenztest (MWT-B)). Also, all participants underwent a structured clinical interview based on the German version of the SCID-I and II (Wittchen et al., 1997) conducted by one of six trained clinicians (VS, SS, JH, AK, JL, BK). By means of this interview and the use of a life chart designed by our workgroup, several clinical variables were assessed. Those were amount and quality of lifetime episodes, age of onset, number of hospitalizations, time in remission, presence of psychotic symptoms in any of the experienced episodes, as well as

2.2. Neuropsychological and clinical assessment All participants were assessed on the Cambridge Neuropsychological Test Automated Battery (CANTAB, version 3.0.0, Cambridge Cognition Ltd., Cambridge, UK). Participants performed the Cambridge Gambling Task (CGT), Stop Signal Task (SST), Intra-Extra Dimensional Set Shift (IED), Stockings of Cambridge (SOC), Paired Associates Learning (PAL), and Delayed Matching to Sample (DMS). However, since more than 2/3 of the patients did not have complete data on the PAL and DMS, these latter two tests of memory had to be excluded from further analyses, since cluster analyses do not tolerate missing values. The most commonly used outcome variables of each task were selected for further analyses, if they were statistically independent from each other. As such, the tests and variables chosen covered several executive function domains: response impulsivity (CGT), motor inhibition (SST), decision-making (CGT), cognitive flexibility (IED), and planning (SOC). All tests were administered on a highresolution touch-screen monitor fixed on a laptop. Participant's responses were either collected via the touch-screen (CGT, IED, SOC) or through a two-button response pad attached to the laptop (SST). The CGT variables investigated provide information on the decision-making quality, risk-taking, impulsivity, and decision-making speed. On each trial, participants are presented with ten boxes, some of which are blue others are red. There is a yellow token hidden under one of the boxes. Participants have an initial budget of 100 points and their task is to bet a self-chosen amount of these points on the color of the box, which is precluding the token. First, they choose the color the box hiding the token has, followed by a bet placement on this decision. Upon a correct bet, they will earn the points bet, while those points will be lost in the face of a wrong bet. Deliberation time, in this sense, describes the time passed before participants choose a color to bet on, indicating the speed of the decision-making process. The overall proportion bet reflects the average proportion of total points the participant chooses to risk on each trial. Higher bets indicate a riskier way of playing the CGT (risky decision-making). The quality of decision making is estimated by calculating the proportion of trials, on which the participant bet on the more likely color, based on the proportion of red and blue boxes presented on the screen. Delay aversion, lastly, represents the participant's willingness to wait with his bet placement. Bet placement takes place in ascending and descending trials. In ascending trials, the 3

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medication and dosage taken. From the latter variable, a total medication load was computed, adhering to the method described by Sackheim (2001). Lastly, all participants filled out a scale referring to general functioning (Gauggel, 2006; Marburger Kompetenz Skala (MKS)). It is a thirty-item questionnaire, assessing a person's competency regarding daily activities on a five-point Likert scale, ranging from 4 (no difficulties) to 0 (large difficulties). Apart from a total score, two scale scores, entailing cognitive and motoric competencies, can be computed (Gauggel, 2006).

pairwise comparison tests were described as most reliable with regard to type I error protection in the face of unequal sample sizes, which can be expected when performing cluster analyses (Field, 2009). All tests were significant at p < .05. 2.3.4. Cluster comparisons on sociodemographic and clinical variables Moreover, the obtained clusters were compared against each other regarding demographic and clinical variables and their type and amount of medication used, applying one-way ANOVAs for continuous variables with group (the different clusters) as fixed factor and the different demographic, clinical, and medication variables as dependent variables. Significant ANOVAs were followed up with post-hoc pairwise comparisons, using GT2 by Hochberg or Games-Howell method as described above to correct for multiple testing. Alternatively, χ2 – tests were used for categorical variables.

2.3. Statistical analyses of behavioral data Data analyses were performed in SPSS (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp). 2.3.1. General group differences between BD-I patients and healthy controls Initial analyses determined possible overall group differences between BD-I patients and HC on demographic variables or cognitive test performances, using independent-samples t-tests for continuous and normally distributed data, Mann-Whitney U-tests for non-normally distributed continuous data, and χ2 – tests for categorical variables, respectively. All tests were considered significant at p < .05. General comparisons between BD-I patients and healthy controls on cognitive test performance were Bonferroni-corrected with a critical p-value of p < .005 (10 outcome variables/.05).

2.4. Resting-state data analysis 2.4.1. FMRI data acquisition Functional images were acquired on a 3T Siemens Tim Trio scanner using a 32-channel head coil. A T2*-weighted gradient echo planar imaging sequence was used to assess 120 images per participant with two different scanning parameters. One sample was measured using the following parameters: 40 slices measured with an interleaved slice-acquisition in descending order, repetition time (TR) = 2150 ms, echo time (TE) = 22 ms, flip angle = 90°, field of view (FOV) = 220 × 220mm2, matrix size = 96 × 96, and slice thickness = 2.3 mm with 0.7 mm gap. The other sample was measured using the same parameters except for a difference in TR = 2700 ms, TE = 27 ms and a voxel size of 2.3 × 2.3 × 2.3 without a gap. Participants were instructed to lie still and not to fall asleep in a darkened room with their eyes closed.

2.3.2. Hierarchical cluster and discriminant function analyses A hierarchical cluster analyses was conducted on BD-I patients' data, entering the aforementioned variables, in order to identify homogeneous subsamples of BD-I patients with regard to their cognitive test performance. Thus, ten variables were entered into the cluster analysis: quality of decision-making, delay aversion, deliberation time, and overall proportion bet from the CGT, EDS errors and pre-EDS errors from the IED, initial thinking time, subsequent thinking time, and amount of problems solved in minimum moves (5 moves) from the SOC, and the SSRT score from the SST. Squared Euclidian distance was chosen to determine the similarity between cases and the Ward linkage method was used as agglomeration procedure. Squared Euclidian distance is a widely used method, which has mostly been chosen in previous studies clustering patients in bipolar disorder. For reasons of comparability with previous studies, the present study used the same technique. Further, the Ward linkage method is among the most powerful methods for clustering variables, generating highly homogeneous clusters (Backhaus et al., 2018). Variables were pre-standardized to zscores in the process of cluster analysis. Nonetheless, for reasons of interpretability, raw scores were subsequently used to make comparisons among clusters and between participant groups (i.e. BD-I vs. HC). The appropriate number of clusters was determined based on visual inspection of the obtained dendrogram (see Supplement Figure 5). Moreover, a subsequent discriminant function analysis (DFA) was performed, in order to determine the validity of the obtained clusters. Regarding the present study, a DFA estimates the probability for a participant's membership in a certain cluster, using the above described outcome variables as predictors.

2.4.2. Preprocessing Images were preprocessed using SPM8 (http://www.fil.ion.ucl.ak. uk/spm/software/spm8) running on MATLAB2012b (The MathWorks Inc., Natick, MA). Participants were excluded from further analyses if their head movement exceeded 3 mm translation or 3° rotation in any direction. The first four volumes of each participant's scan were being discarded to allow for T1-saturation. Preprocessing steps consisted of realignment to the middle slice of each run, slice-timing, normalization, and smoothing using a 9 × 9 × 9 mm Gaussian kernel. Images were resampled to a voxel size of 2.3 × 2.3 × 2.3 during the normalization step to have the same spatial resolution for all images. Participant's mean functional image, generated after realignment, was being segmented and the resulting white matter (WM) and cerebrospinal fluid (CSF) tissue probability maps were binarized with a threshold of ≥80% probability of belonging to this tissue type to create WM and CSF masks. The average signal time-courses for WM and CSF were extracted and, together with 6 motion parameters, regressed out of fMRI timecourse signals as nuisance signals. Voxel-wise fMRI time-courses were further bandpass filtered (0.01–0.08 Hz) to examine the spontaneous low frequency coupling between different brain regions. 2.4.3. A priori regions-of-interest (ROIs) exploration 2.4.3.1. ROI generation. In a first exploratory analysis, regions-ofinterest (ROIs) were chosen based on a theory-driven approach. Previous task-based functional imaging studies were scanned to determine ROIs activated during the cognitive tasks tested in the present study. As such, ROIs important for cognitive flexibility (Lie et al., 2006; Monchi et al., 2001; Graham et al., 2009) as on the IED, motor inhibition (Aron and Poldrack, 2006; Sebastian et al., 2013) as on the SST, impulsive decision-making (Boettiger et al., 2007; Li et al., 2013; Wang et al., 2016) and risk-seeking (Dinu-Biringer et al., 2016; Huettel et al., 2006; Fukui et al., 2005) as on the CGT, and visuospatial planning (Wagner et al., 2006; Newman et al., 2003) as on the SOC

2.3.3. Comparisons of derived clusters against each other and HC Further, derived clusters were compared against each other on their cognitive test performances, to characterize the obtained clusters. Additionally, patient clusters were compared against HC to alleviate the interpretation of test performance quality in patients. Group comparisons were calculated using one-way ANOVAs with group (the different clusters and HC) as fixed factors and the test scores as dependent variables. All significant tests were followed up by post-hoc pairwise comparisons using GT2 by Hochberg for data that adhered to the homogeneity of variance assumption and Games-Howell as correction method for those tests violating this assumption. These two post-hoc 4

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Table 1 Apriori regions created. Region

vlPFC vlPFC Caudate Caudate Putamen pPFC aPFC aPFC IFG SFG MFG Frontal pole dPFC PPC rlPFC SPL (BA 5 and 7) mPFC MFG PPC STN STN IFG Pre-SMA Insula Insula DLPFC DLPFC IFG IPL IPL

MNI peak-coordinates

L R L R L L L R L R R R L R L R L L L L R L L R L R R L R

Size of sphere/box in mm

Reported for

Source

Flexibility Flexibility Flexibility Flexibility Flexibility Flexibility Impulsive decision-making Impulsive decision-making Impulsive decision-making Impulsive decision-making Impulsive decision-making Impulsive decision-making Impulsive decision-making Impulsive decision-making Planning Planning Risk-seeking Risk-seeking Risk-seeking Stopping Stopping Stopping Stopping Stopping Stopping Planning/impulsive decision-making Planning/impulsive decision-making Impulsive decision-making/stopping Impulsive decision-making/stopping Impulsive decision-making/stopping

Monchi et al. (2001) Monchi et al. (2001) Graham et al. (2009) Graham et al. (2009) Monchi et al. (2001) Monchi et al. (2001) Lie et al., 2006 Lie et al., 2006 Li et al. (2013) Li et al. (2013) Li et al. (2013) Wang et al. (2016) Boettiger et al. (2007) Boettiger et al. (2007) Wagner et al. (2006) Newman et al. (2003) Dinu-Biringer et al. (2016) Fukui et al. (2005) Huettel et al. (2006) Aron & Poldrack (2006) Aron & Poldrack (2006) Sebastian et al. (2013) Sebastian et al. (2013) Sebastian et al. (2013) Sebastian et al. (2013) Newman et al. (2003)/Li et al. (2013) Newman et al. (2003)/Li et al. (2013) Li et al. (2013)/Sebastian et al. (2013) Li et al. (2013)/Sebastian et al. (2013) Li et al. (2013)/Sebastian et al. (2013)

x

y

z

−39 34 −13 13 −26 −36 −32 30 −43 23 37 16 −22 66 −36

20 22 9 9 −10 14 47 51 5 13 5 40 50 −42 49

−2 2 10 10 4 26 8 7 30 50 49 38 44 44 7

10 10 10 10 7 10 12 12 8 10 10 5 8 10 10

−9 −2 −36 −10 10 −45 0 −33 45 −44 39 44 −34 41

17 57 −57 −15 −14 20 14 8 7 16 26 6 54 −55

46 21 50 −5 −4 1 57 10 −2 39 38 28 41 42

8 10 12 10 × 10 × 10 10 × 10 × 10 8 8

L = left; R = right; vlPFC = ventrolateral prefrontal cortex; pPFC = posterior prefrontal cortex; aPFC = anterior prefrontal cortex; IFG = inferior frontal gyrus; SFG = superior frontal gyrus; MFG = medial frontal gyrus; dPFC = dorsal prefrontal cortex; PPC = posterior parietal cortex; rlPFC = rostrolateral prefrontal cortex; SPL = superior parietal lobule; mPFC = middle prefrontal cortex; STN = subthalamic nucleus; Pre.-SMA = pre-supplementary motor area; DLPFC = dorsolateral prefrontal cortex; IPL = inferior parietal lobule.

were chosen (see Table 1). Subsequently, ROI masks of these regions were being created. The ROI masks of the right superior parietal lobule reported for planning (Newman et al., 2003), and the left and right insula reported for stopping (Sebastian et al., 2013) were created using the Wake Forest PickAtlas toolbox v2.3 (Lancaster et al., 1997; TzourioMazoyer et al., 2002; Maldjian et al., 2003), while all other ROI masks were created in the MarsBaR (Brett et al., 2002) toolbox. Several regions overlapped anatomically and were reported for more than one function. In this case, the overlap between the masks was being computed and used as ROI. This was the case for the left and right dorsolateral prefrontal cortex (DLPFC) mask reported for planning (Newman et al., 2003) and impulsive decision-making (Li et al., 2013), for the inferior frontal gyrus (IFG) right reported for impulsive decision-making (Li et al., 2013) and stopping on the SST (Sebastian et al., 2013), and the left and right inferior parietal lobule (IPL) for impulsive decision-making (Li et al., 2013) and stopping (Sebastian et al., 2013).

the numerator and denumerator of the F-test and choosing the appropriate F-value for a significance level of p = .05, which gives F (3,79) = 2.72. After thresholding the connectivity matrix with this Fstatistic at each cell to eliminate uninformative connections between brain regions, the overall significance of the network topology was determined using 5000 permutations to obtain a null network and using FWE correction for multiple comparisons. The significance of the whole network configuration against chance was determined by comparing the thresholded test network to the null network. Post-hoc t-tests were computed to test for the directionality of differences between groups. Further, all bipolar patients, irrespective of their cluster assignment, were compared against healthy controls, using an independent-samples t-test with a critical t-value of 1.99. All tests were computed using a significance level of p < .05, two-sided t-tests, and FWE-correction based on the extend of network topography. As was mentioned above, the two scanning sites used slightly different scanning parameters. To regress out any possible influences of this difference on the results, analyses were repeated with scanning sequence included in the model as covariate of no interest. Since inclusion of a covariate is not possible in NBS and statistically not valid, F-tests were being used for these follow-up analyses.

2.4.3.2. Time-signal extraction and network-based statistics. ignal time courses were extracted for each ROI mask in each participant. Pairwise Pearson's correlation coefficients were computed and Fisher-Z transformed, resulting in a 30 × 30 connectivity matrix per subject. Participant's connectivity matrices were being compared using network-based statistics in NBS v1.2 (Zalesky et al., 2010). An F-test was calculated, comparing the three patient clusters and healthy controls against each other. For this F-test, the primary test statistic to threshold the connectivity matrix against was determined. This threshold is arbitrary in nature and conventionally uses a F-statistic at p < .05 uncorrected at each cell of the connectivity matrix. In the current analysis, the critical F-statistic was calculated by determining

2.4.4. Connectomic characterization using graph theory Additionally, a second exploratory approach was chosen to investigate the cross-regional connections in the three clusters in a more data-driven approach, rather than based on previously defined ROIs. 2.4.4.1. Whole-brain parcellation. sing the AAL atlas, the whole brain was parcellated into 116 non-overlapping ROIs. For each ROI, voxelwise rsfMRI time-courses were averaged and cross-correlated with all 5

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Table 2 List of graph theoretical indices examined. Graph index

Description

Assortativity Betweenness Cluster Coefficient Degree Eccentricity Modularity Small Worldness Transitivity

A correlation coefficient between the degrees of all nodes on two opposite ends of a link The fraction of all shortest paths in the network that contain a given node The fraction of triangles around a node; fraction of node's neighbor that are neighbors of each other Number of links connected to a node The maximal shortest path length between a node and any other node A statistic that quantifies how much a network can be divided into non-overlapping sub-networks The degree in which functionally specialized modules are integrated by a number of intermodular links The ratio of triangles to triplets in the network

Adapted from Rubinov and Sporns (2010); also available at http://sites.google.com/site/bctnet/measures.

other ROIs, building a 116 × 116 connectivity matrix per subject. For each connectivity matrix several graph theoretical indices were calculated on the entire matrix, rather than on individual nodes or edges within the matrix. The obtained graph theoretical indices served to characterize the individual's network properties.

Table 3 Demographic, clinical, and cognitive variables for the full sample of participants.

2.4.4.2. Graph theoretical analysis. We used the Brain Connectivity Toolbox (BCT, Rubinov and Sporns, 2010) to compute several graph theoretical indices, namely assortativity, betweenness, clustering coefficient, degree, eccentricity, modularity, small worldness, and transitivity. A definition of these indices can be found in Table 2. To compare the three bipolar clusters, a one-way ANOVA was performed on these graph indices in SPSS (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp) using Group as the independent variable. Bonferroni correction was applied to posthoc group comparisons to adjust for multiple testing, resulting in a critical p < .00625. This analysis was also repeated using an ANCOVA, with scanning sequence as nuisance covariate.

mean (SD)

BD-I patients (n = 54)

HC (n = 54)

test statistic

p-value

Age in years Gender, no. females (%) IQ

42.63 (9.18) 25 (46.3) 100.43 (11.77) 15.75 (2.23) 1.17 (1.70) .96 (1.63) 103.51 (13.09) 47.87 (9.16) 55.64 (5.07) 25.04 (9.23) 2.41 (2.05) 3.15 (2.19) 4.60 (4.76) 2.85 (2.46) 3.17 (4.77)

43.07 (9.21) 25 (46.3) 103.42 (11.14) 15.46 (2.25) .13 (0.44) .13 (0.44) 113.74 (6.81)

t = .251 χ2 = .000 U = 1245.5

.802 1.000 .247

U = 1289.0 U = 808.5 U = 985.5 U = 518.0

.454 < .001 .001 < .001

54.75 (5.22) 58.98 (2.60) – – – – – –

U = 652.5 U = 598.0 – – – – – –

< .001 < .001 – – – – – –

.92 (.12)

.92 (.12)

U = 1398.0

.705

2669.80 (768.55) .23 (.17) .50 (.14)

2539.95 (807.71) .21 (.18) .46 (.14)

U = 1238.0

.176

t = - .784 t = −1.721

.435 .088

12.98 (11.18) 7.32 (4.34) 14,456.20 (11,914.60) 1104.39 (1273.96) 9.00 (1.83)

8.98 (10.14) 7.07 (3.90) 16,241.10 (12,656.05) 850.96 (1145.03) 9.72 (2.11)

U = 1079.0 U = 1455.5 U = 1255.5

.019 .988 .213

U = 1219.0

.138

U = 1082.0

.019

197.60 (62.05)

180.11 (41.27)

U = 1255.0

.212

Educational level, years HAM-D score YMRS score MKS total score MKS cognitive score MKS body score Age of onset, years Total medication load No. of hospitalizations No. depressions lifetime No. manias lifetime Time in remission, years Cognitive testsc CGT-quality decision makinga CGT-deliberation timeb

2.4.5. Backtranslation of imaging findings onto behavioral profiles To be able to link the graph theoretical results to the executive function profiles found in the cluster analysis, Pearson's correlation coefficients were computed between CANTAB outcome variables and significant graph theoretical indices. Additionally, partial correlations with scanning sequence regressed out were computed between CANTAB outcome variables and graph theoretical indices.

CGT-delay aversion CGT-overall proportion beta IED-EDS errors IED-pre-EDS errors SOC -initial thinking timeb SOC-subsequent thinkíng timeb SOC-problems solved in minimum moves SST-SSRTb

3. Results 3.1. Behavioral results 3.1.1. General group differences between BD-I patients and healthy controls BD-I patients and HC did not differ in age, IQ or educational level. However, groups differed significantly on the MKS total score and the two subscale scores. Here, BD-I patients displayed significantly smaller values on all three test scores, indicating a lower level of overall (U = 518, p < .01, r = −0.55), cognitive (U = 652.5, p < .01, r = −0.46), and bodily (U = 598, p < .01, r = −0.52) functioning, respectively (see Table 3 for details). With regard to subclinical symptoms on the HAM-D (U = 808.5, p < .01, r = −0.43) and YMRS (U = 985.5, p < .01, r = −0.32), BD-I patients had significantly higher scores on both measures, from which a higher level of subclinical symptoms can be inferred. Comparing BD-I patients to HC in their cognitive test performance, BD-I patients made significantly more EDS errors on the IED (U = 1079, p = .019, r = −0.23) and solved less problems in the minimum number of moves required on the SOC compared to HC (U = 1082, p < .05, r = −0.23). However, these differences were not significant anymore after multiple testing correction. Since there was a significant difference in the level of subclinical symptoms, group comparisons for the cognitive tests were repeated, entering HAM-D and YMRS scores as covariates into an analysis of covariance (ANCOVA). Subclinical

SD: standard deviation; IQ: intelligence quotient; HAM-D: Hamilton Depression Rating Scale; YMRS: Young Mania Rating Scale; MKS: Marburger Kompetenz Skala; CGT: Cambridge Gambling Task; IED: Intra-Extra Dimensional Set Shift task; EDS: Extra-dimensional shift; SOC: Stockings of Cambridge; SST: Stop Signal Task; SSRT: Stop signal reaction time. Bold p-values indicate significant differences. a Scores in percent. b Scores in milliseconds (ms). c For cognitive tests, a Bonferroni corrected p-value of p < .005 (.05/10 cognitive tests) was applied.

symptoms did not have a significant effect on any of the cognitive tests investigated and as such, group comparison results did not differ from those obtained without correcting for the effect of subclinical symptoms. 3.1.2. Hierarchical cluster and discriminant function analyses Visual inspection of the dendrogram inferred the existence of three 6

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Fig. 1. Z-scores of the three obtained clusters and healthy controls on the cognitive tests. The depiction shows mean z-scores of each outcome variable per participant group. Z-scores were calculated based on the entire sample of participants. Error bars indicate standard errors of the mean. Abbreviations: CGT: Cambridge gambling task; IED: Intra Extra Dimensional Set Shift task; EDS: Extra-dimensional shift; SOC: Stockings of Cambridge; SST: Stop Signal Task; SSRT: Stop signal reaction time.

clusters. A subsequent DFA confirmed the presence of three clusters, finding two discriminant functions explaining 57.9% and 42.1% of the variance, respectively (Wilks' λ = 0.075, χ2 = 120.362, p < .01 and Wilks' λ = 0.308, χ2 = 54.810, p < .01, respectively). The overall proportion bet and the quality of decision-making had the highest standardized coefficients (0.738 and 0.757, respectively), indicating that these two scores had the highest contribution in classifying the patients into the three found clusters. Notably though, also other scores had quite high standardized coefficients, such as SSRT (−0.687), delay aversion (−0.590), EDS errors (−0.577), and problems solved in minimum moves (0.535). As such, patients were not grouped solely on the basis of the two scores with the highest standardized coefficients. The amount of correctly classified patients in the DFA was 100% (see Fig. 6 in the Supplement for a graphical illustration of the subgroups' agglomeration). Nonetheless, the results of the DFA have to be interpreted with caution, since the Box's test was significant, indicating a violation of the assumption of equal population covariance matrices. The first cluster included 22 (40.7%), the second one entailed 12 (22.2%), and the third cluster consisted of 20 (37.1%) BD-I patients.

cluster 1 vs. cluster 3: p = .002) and HC (p < .001). It was the only cluster differing from HC in EDS errors and SSRT scores, indicating a characteristic imminent to this subgroup. In addition, patients in cluster 1 had a significantly longer mean subsequent thinking time (p = .003) and a lower amount of problems solved in minimum moves (p = .008) on the SOC than patients in cluster 3, while they did not differ from patients in cluster 2 on these variables (mean subsequent thinking time: p = .926; amount of problems solved in minimum moves: p = .181). A difference between cluster 1 and HC in the initial thinking time of the SOC failed to reach significance after multiple testing correction (p = .061). Cluster 2 had the highest mean delay aversion score of all clusters on the CGT, differing significantly from the other two clusters (cluster 2 vs. 1: p = .004; cluster 2 vs. 3: p < .001) and HC (p = .001). Moreover, patients in cluster 2 displayed a significantly longer subsequent thinking time on the SOC compared to patients in cluster 3 (p = .007), while there was no difference on this variable between cluster 2 and cluster 1 or HC (both p > .05). In addition, cluster 2 also had a significantly lower amount of problems solved in minimum moves compared to cluster 3 and HC (both p < .001) on the SOC. Cluster 3 had the shortest mean subsequent thinking time on the SOC of all clusters (cluster 3 vs. 1: p = .003; cluster 3 vs. 2: p = .007) and was also significantly faster than HC (p = .022). The other clusters scored within the range of HC on the mean subsequent thinking time (pcluster1 vs. HC = 0.139; pcluster 2 vs. HC = 0.291). It also solved the most problems in minimum moves compared to the other two clusters (cluster 3 vs. 1: p = .008; cluster 3 vs. 2: p < .001), but scored within the range of HC on this variable (p = .54). In addition, cluster comparisons revealed a significantly lower delay aversion score for this cluster than for cluster 2 (p < .001) and a significantly higher proportion of points bet than for HC (p =.043) on the CGT (see also Fig. 1, Table 4).

3.1.3. Comparisons of derived clusters against each other and HC Overall group comparisons between the patient clusters and HC showed that groups differed significantly in their quality of decisionmaking [F(3,104) = 5.295, p = .002], delay aversion [F (3,104) = 6.824, p < .001] and overall proportion bet [F (3,104) = 2.875, p = .040] on the CGT, in the EDS errors on the IED [F (3,104) = 7.115, p < .001], in the initial thinking time [F (3,104) = 2.964, p = .036], subsequent thinking time [F (3,104) = 5.915, p = .001] and problems solved in minimum moves [F (3,104) = 9.969, p < .001] on the SOC and in SSRT scores on the SST [F(3,104) = 7.356, p < .001] (see also Table 4 for details). Post-hoc pairwise comparisons using GT2 by Hochberg or Games-Howell for tests adhering to the assumption of heterogeneity of variance or violating it, respectively, revealed the following cluster characteristics regarding cognitive test performance: Patients in cluster 1 showed a significantly higher amount of EDS errors than the other two clusters (cluster 1 vs. 2: p = .020; cluster 1 vs. 3: p = .001) and HC (p < .001). It also had significantly longer SSRT scores compared to the other two clusters (cluster 1 vs. 2: p = .004;

3.1.4. Cluster comparisons on sociodemographic and clinical variables There were no significant differences between the clusters with regard to age, gender, current status of employment or IQ. However, there was a significant difference in educational level [F(2,50) = 5.285, p = .008] with cluster 2 having a significantly lower educational level compared to clusters 1 and 3 (p = .033 and p = .008, respectively). 7

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Table 4 Cluster comparisons and comparisons between clusters and healthy controls on cognitive test performance. mean (SD)

quality d.-m. delay aversion deliberation t. overall bet EDS errors pre-EDS errors init. think. time sub. think. time problems SSRT

Rigid (R) (n = 22)

Impulsive (I) (n = 12)

Strategic (S) (n = 20)

HC (n = 54)

Test statistics

Post-hoc comparisons

F

p-values

R vs. I

R vs. S

I vs. S

R vs. HC

I vs. HC

S vs. HC

.96 (.05) .20 (.12) 2728.87 (743.38) .45 (.12) 19.73 (10.19) 6.14 (2.66) 10,864.53 (5727.11) 1671.01 (1566.41) 8.64 (1.56) 232.20 (66.48)

.81 (.20) .41 (.18) 2231.25 (575.33)

.95 (.08) .15 (.14) 2867.94 (823.66)

.92 (.12) .21 (.18) 2539.95 (807.71)

5.295 6.824 2.004

.002 < .001 .118

.093 .004

.961 .896

.131 < .001

.174 1.000

.284 .001

.569 .791

.50 (.14) 9.00 (10.31) 7.08 (2.02) 10,874.33 (8914.45) 1409.50 (910.61)

.56 (.15) 7.95 (9.11) 8.75 (6.21) 20,556.15 (15,859.38) 298.05 (469.36)

.46 (.14) 8.98 (10.14) 7.07 (3.90) 16,241.10 (12,656.05) 850.96 (1145.03)

2.875 7.115 1.479 2.964

.040 < .001 .224 .036

.894 .020

.114 .001

.895 1.000

1.000 < .001

.854 1.000

.043 .999

1.000

.073

.144

.061

.331

.696

5.915

.001

.926

.003

.007

.139

.291

.022

7.25 (1.49) 170.00 (44.07)

10.45 (1.05) 176.11 (48.76)

9.72 (2.11) 180.11 (41.27)

9.969 7.356

< .001 < .001

.181 .004

.008 .002

< .001 1.000

.104 < .001

< .001 .987

.540 1.000

SD: Standard deviation; HC: healthy controls; quality d.-m.: quality of decision making on the Cambridge Gambling Task; deliberation t.: deliberation time on the Cambridge Gambling Task; overall bet: overall proportion bet on the Cambridge Gambling Task; init. think. time: mean initial thinking time for problems to be solved in 5 moves on the Stockings of Cambridge task; sub. think. time: mean subsequent thinking for problems to be solved in 5 moves on the Stockings of Cambridge task; problems: Amount of problems solved in the minimal number of moves on the Stockings of Cambridge task; SSRT: stop signal reaction time. Bold p-values indicate significant differences. Table 5 Demographic and clinical variables of the three obtained clusters. means (SD)

Age, years Gender, female (%) IQ Educational level, years Occupation affected, yes (%)a HAM-D score YMRS score MKS total score MKS cognitive score MKS body score Age of onset, years Years in remission Lifetime psychotic symptoms, yes (%) Total medication load No. of hospitalizations No. lifetime depressions No. lifetime manias No. lifetime hypomanias Total no. of episodes First episode depression (%) Last episode depression (%) Current medication Antipsychotics, no. (%) Anticonvulsants, no. (%) Antidepressants, no. (%) Lithium, no. (%)

Clusters

Test statistic

Rigid (R) (n = 22)

Impulsive (I) (n = 12)

Strategic (S) (n = 20)

F or χ2

p-value

44.09 (9.22) 13 (59.1) 99.18 (12.28) 16.05 (2.38) 10 (45.5) 1.67 (2.01) 1.05 (2.06) 102.55 (14.89) 47.45 (10.14) 55.09 (5.98) 24.14 (10.76) 3.79 (5.59) 7 (35.0) 2.32 (2.23) 4.05 (2.36) 4.00 (2.75) 3.40 (2.35) .79 (2.28) 8.15 (5.08) 12 (60.0) 13 (72.2)

43.33 (10.01) 5 (41.7) 98.83 (11.33) 14.08 (1.73) 3 (25.0) 1.00 (1.54) .67 (1.37) 100.25 (17.04) 45.08 (11.29) 55.17 (6.66) 26.83 (10.79) 2.06 (2.14) 5 (41.7) 2.00 (2.09) 2.17 (1.90) 4.20 (2.53) 2.64 (2.87) .71 (1.50) 17.40 (30.17) 6 (54.5) 6 (50.0)

40.60 (8.71) 7 (35.0) 102.75 (11.68) 16.45 (1.88) 4 (20.0) .74 (1.33) 1.05 (1.27) 106.68 (6.35) 50.11 (5.81) 56.58 (1.98) 24.95 (6.22) 3.32 (5.27) 5 (26.3) 2.75 (1.86) 2.74 (1.82) 5.53 (7.14) 2.24 (2.25) 2.43 (6.06) 9.76 (12.62) 13 (72.2) 11 (61.1)

F = .797 χ2 = 2.578 F = .614 F = 5.285 χ2 = 4.369 F = 1.610 F = .247 F = .990 F = 1.151 F = .498 F = .321 F = .469 χ2 = .821 F = .527 F = 3.376 F = .508 F = 1.036 F = .795 F = 1.163 χ2 = 2.258 χ2 = 6.194

.456 .275 .545 .008 .358 .210 .782 .379 .325 .611 .727 .629 .663 .594 .030 .605 .363 .459 .322 .688 .402

5 8 9 9

2 6 2 1

2 (10.0) 9 (45.0) 5 (25.0) 10 (50.0)

χ2 = 1.222 χ2 = 1.565 χ2 = 2.896 χ2 = 4.603

.543 .457 .235 .100

(22.7) (36.4) (42.9) (40.9)

(16.7) (60.0) (16.7) (10.0)

Post-hoc tests

R vs. I: p = .033 I vs. S: p = .008

R vs. I: p = .044

HAM-D: Hamilton Depression Rating Scale, YMRS: Young Mania Rating Scale, MKS: Marburger Kompetenz Skala, No.: number. a The occupational level is affected by the bipolar I disorder illness.

Clusters did not differ on subclinical symptoms on the HAM-D or YMRS, nor did they differ in overall daily functioning on the MKS total score or its subscale scores or on other clinical variables, except for the number of lifetime hospitalizations [F(2,50) = 3.755, p = .030]. Post-hoc tests revealed that patients in cluster 1 displayed a significantly higher amount of hospitalizations throughout their lifetime compared to patients in cluster 2 (p = .044), which might indicate a more severe or chronic form of the disorder. Further, there was no difference between the clusters with regard to total medication load or the amount of patients taking a certain medication type (see Table 5 for details). Since the three clusters differed significantly in their level of

education, post-hoc ANCOVAs with Bonferroni corrections were being computed, comparing the obtained clusters on their cognitive test scores corrected for the effect of educational level. Results did not differ from those obtained without correction for educational level (significant tests in ANOVAs had a p-value of p ≤ .04 when corrected for educational level; non-significant tests in ANOVAs had a p-value of p ≥ .066 when corrected for educational level). An exception was the delay aversion score on the CGT. Educational level was significantly related to this score [F(1,102) = 4.342, p = .040]. However, while educational level was related to delay aversion, its influence did not change overall significance with regard to group comparisons. The 8

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Fig. 2. Significant network statistical differences using predefined ROIs. Panel a: Cluster 2 > Cluster 1. Panel b: Cluster 1 < Controls. Abbreviations. vlPFC.L: left ventrolateral prefrontal cortex; vlPFC.R: right ventrolateral prefrontal cortex; Caudate.L: left caudate; Caudate.R: right caudate; Putamen.L: left putamen; posteriorPFC.L: left posterior prefrontal cortex; anteriorPFC.L: left anterior prefrontal cortex; anteriorPFC.R: right anterior prefrontal cortex; DLPFC.L: left dorsolateral prefrontal cortex; DLPFC.R: right dorsolateral prefrontal cortex; IFG.L: left inferior frontal gyrus; IFG.R: right inferior frontal gyrus; SFG.R: right superior frontal gyurs; MFG.L: left medial frontal gyrus; MFG.R: right medial frontal gyrus; FrontalPole.R: right frontal pole; dPFC.L: left dorsal prefrontal cortex; PPC.L: left posterior parietal cortex; PPC.R: right posterior parietal cortex; SPL.R: right superior parietal lobule; RostrolateralPFC.L: left rostrolateral prefrontal cortex; dmPFC.L: left dorsomedial prefrontal cortex; Insula.L: left insula; IPL.L: left inferior parietal lobule; IPL.R: right inferior parietal lobule; pre-SMA: pre-supplementary motor area; STN.L: left subthalamic nucleus; STN.R: right subthalamic nucleus.

also Fig. 3). The clusters did not differ on any of the other investigated graph indices (all p > .13). Rerunning the analysis with an ANCOVA, correcting for possible influences of scanning sequence, did not change the results ([F(2,38) = 6.23, p = .005]. Correlations between CANTAB outcome variables and the cluster's modularity were performed per cluster. Significant correlations only emerged for cluster 3. Here, a positive correlation was observed between modularity and delay aversion on the CGT (r = 0.633, p = .006), and problems solved in minimum moves on the SOC (r = 0.567, p = .018) (see Fig. 3 for a graphical overview of the modular brain networks at the group level for all three clusters). Partial correlations, controlling for the influence of scanning sequence, also showed a positive correlation between modularity and delay aversion (r = .622, p = .010) and between modularity and problems solved in minimum moves (r = 0.587, p = .017) for cluster 3.

same significant group differences emerged as were observed without this covariate. 3.2. Imaging results 3.2.1. Network statistics results Resting-state data was eligible for analyses from 15 patients in cluster 1, 10 patients in cluster 2, 17 patients in cluster 3 and for 41 HC. There was a general difference between the groups on the omnibus test [F(3,79) = 2.72, p = .021, FWE-corrected at network level]. Follow-up t-tests revealed a significant difference between cluster 1 and 2, with cluster 2 showing stronger network connections (t(23) = 2.07, p = .048, FWE-corrected). A more detailed examination of significant group differences (cluster 2 > cluster 1) in ROI-ROI coupling revealed enhanced connections between right anterior prefrontal cortex (aPFC) and left ventrolateral prefrontal cortex (vlPFC), left aPFC and left vlPFC, left aPFC and left putamen, left rostrolateral PFC (rlPFC) and left vlPFC, and between left insula and right aPFC in cluster 2 compared to cluster 1 (see Fig. 2a for details). This difference remained significant when corrected for scanning sequence [F(3,78) = 2.72, p = .028, FWEcorrected at network level]. In addition to the above mentioned comparison, cluster 1 differed also significantly from healthy controls (t (54) = 2.01, p = .036), showing a weaker connectivity than controls for the connections between right MFG and left IFG, right DLPFC and right insula, left DLPFC and left insula, and presupplementary motor area (pre-SMA) and left IFG (see Fig. 2b). However, when entering scanning sequence as covariate into the model, this comparison failed to reach significance [F(3,78) = 2.72, p = .09, FWE-corrected at network level]. Thus, the scanning sequence applied does explain some variance in the model, resulting in only a trend for significance for the comparison between cluster 1 and HC. In line with the behavioral analyses, a comparison between all bipolar patients, irrespective of cluster assignment, and controls was computed. This comparison was not significant (t(81) = 1.99, p > .05).

4. Discussion To the best of the authors’ knowledge, this is the first study assessing the existence of cognitive subgroups in a sample, consisting exclusively of euthymic BD-I patients, using a data-driven approach by means of a hierarchical cluster analysis. A general comparison between BD-I patients and HC found a lower performance of BD-I patients on the IED with regard to EDS errors committed and a lower amount of problems solved in minimum moves on the SOC. However, these differences failed to reach significance after correction for multiple testing. As such, they need to be interpreted with caution. While patients and controls differed on subclinical symptoms on the HAM-D and YMRS, analyzing cognitive performance differences between the groups controlling for the effects of subclinical symptoms, resulted in the same effects found. Thus, a greater level of subclinical symptoms cannot explain the reported results. Moreover, BD-I patients differed significantly from HC in their level of daily functioning, with HC showing better functioning than patients. Therefore, although their educational and intelligence levels were similar, HC managed requirements of daily life better than BD-I patients. When cognitive performances in BD-I patients were looked at in more detail, three subgroups of patients could be identified in the present study, based on their cognitive performance. The first cluster displayed the highest amount of EDS errors on the IED and the longest SSRT scores on the SST of all clusters. Further, it was the only cluster also differing significantly from HC on these measures, indicating a

3.2.2. Graph theoretical results and backtranslation onto behavioral performance A comparison of graph indices between the three clusters, resulted in a significant difference in modularity between the clusters [F (2,39) = 7.43, p = .002]. Post-hoc t-tests revealed a significant difference between clusters 2 and 3 on this index (p = .002), while cluster 1 did not differ from the other two clusters in terms of modularity (see 9

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Fig. 3. Modularities of the obtained bipolar-I clusters. Upper left panel: Cluster 1; upper right panel: Cluster 2; lower panel: Cluster 3. Distinct colors indicate different modules. Regions were derived from a whole brain parcellation based on the AAL atlas. Abbreviations. PreCG.L: left precentral gyrus; PreCG.R: right precentral gyrus; SFGdor.L: left superior frontal gyrus; SFGdor.R: right superior frontal gyrus; ORBsup.L: left superior frontal gyrus pars orbitalis; ORBsup.R: right superior frontal gyrus pars orbitalis; MFG.L: left middle frontal gyrus; MFG.R: right middle frontal gyrus; ORBmid.L: left middle frontal gyrus pars orbitalis; ORBmid.R: right middle frontal gyrus pars orbitalis; IFGoperc.L: left inferior frontal gyrus pars opercularis; IFGoperc.R: right inferior frontal gyrus pars opercularis; IFGtriang.L: left inferior frontal gyrus pars triangularis; IFGtriang.R: right inferior frontal gyrus pars triangularis; ORBinf.L: left inferior frontal gyrus pars orbitalis; ORBinf.R: right inferior frontal gyrus pars orbitalis; ROL.L: left Rolandic operculum; ROL.R: right Rolandic operculum; SMA.L: left supplementary motor area; SMA.R: right supplementary motor area; OLF.L: left olfactory cortex; OLF.R: right olfactory cortex; SFGmed.L: left superior medial frontal; SFGmed.R: right superior medial frontal; ORBsupmed.L: left medial orbitofrontal cortex; ORBsupmed.R: right medial orbitofrontal cortex; REC.L left gyrus rectus; REC.R: right gyrus rectus; INS.L: left insula; INS.R: right insula; ACG.L: left anterior cingulum; ACG.R: right anterior cingulum; DCG.L: left midcingulate area; DCG.R: right midcingulate area; PCG.L: left posterior cingulate gyrus; PCG.R: right posterior cingulate gyrus; HIP.L: left hippocampus; HIP.R: right hippocampus; PHG.L: left parahippocampal gyrus; PHG.R: right parahippocampal gyrus; AMYG.L: left amygdala; AMYG.R: right amygdala; CAL.L: left calcarine sulcus; CAL.R: right calcarine sulcus; CUN.L: left cuneus; CUN.R: right cuneus; LING.L: left lingual gyrus; LING.R: right lingual gyrus; SOG.L: left superior occipital; SOG.R: right superior occipital; MOG.L: left middle occipital; MOG.R: right middle occipital; IOG.L: left inferior occipital; IOG.R: right inferior occipital; FFG.L: left fusiform gyrus; FFG.R: right fusiform gyrus; PoCG.L: left postcentral gyrus; PoCG.R: right postcentral gyrus; SPG.L: left superior parietal lobule; SPG.R: right superior parietal lobule; IPL.L: left inferior parietal lobule; IPL.R: right inferior parietal lobule; SMG.L: left supramarginal gyrus; SMG.R: right supramarginal gyrus; ANG.L: left angular gyrus; ANG.R: right angular gyrus; PCUN.L: left precuneus; PCUN.R: right precuneus; PCL.L: left paracentral lobule; PCL.R: right paracentral lobule; CAU.L: left caudate; CAU.R: right caudate; PUT.L: left putamen; PUT.R: right putamen; PAL.L: left pallidum; PAL.R: right pallidum; THA.L: left thalamus; THA.R: right thalamus; HES.L: left Heschl gyrus; HES.R: right Heschl gyrus; STG.L: left superior temporal gyrus; STG.R: right superior temporal gyrus; TPOsup.L: left superior temporal pole; TPOsup.R: right superior temporal pole; MTG.L: left middle temporal gyrus; MTG.R: right middle temporal gyrus; TPOmid.L: left middle temporal pole; TPOmid.R: right middle temporal pole; ITG.L: left inferior temporal gyrus; ITG.R: right inferior temporal gyrus; CRBLCrus1.L: left cerebellum crus1; CRBLCrus1.R: right cerebellum crus1; CRBLCrus2.L: left cerebellum crus2; CRBLCrus2.R: right cerebellum crus2; CRBL3.L: left cerebellum 3; CRBL3.R: right cerebellum 3; CRBL45.L: left cerebellum 4_5; CRBL45.R: right cerebellum 4_5; CRBL6.L: left cerebellum 6; CRBL6.R: right cerebellum 6; CRBL7b.L: left cerebellum 7b; CRBL7b.R: right cerebellum 7b; CRBL8.L: left cerebellum 8; CRBL8.R: right cerebellum 8; CRBL9.L: left cerebellum 9; CRBL9.R: right cerebellum 9; CRBL10.L: left cerebellum 10; CRBL10.R: right cerebellum 10; Vermis 12: vermis 1_2; Vermis 3: vermis 3; Vermis 45: vermis 4_5; Vermis 6: vermis 6; Vermis 7: vermis 7; Vermis 8: vermis 8; Vermis 9: vermis 9; Vermis 10: vermis 10.

visuospatial planning, resulting in longer times to complete the task and more moves required to complete the task correctly. However, patients in this cluster did not differ in their planning skills from patients in cluster 2. Therefore, planning difficulties might be present in this cluster, but do not characterize it. The second cluster can best be separated from the other clusters by a high mean delay aversion score on the CGT. It differed significantly from all other clusters and HC. Delay aversion encompasses an unwillingness to wait with placing one's bet, resulting in immediate betting on ascending and descending trials, possibly leading to low wins and high losses, respectively. Whether this strategy payed off can be quantified with the variable quality of decision-making on the CGT. Albeit not significant anymore after multiple

characteristic imminent to this subgroup of BD-I patients and not to BDI per se. Higher amounts of EDS errors indicate problems with set shifting underlying cognitive flexibility, while longer SSRT scores point towards problems to inhibit an initiated motor response. Hence, patients in this cluster have problems to adapt their behavior to changing demands and were therefore labelled “the rigid ones”. This cluster also showed problems in planning compared to cluster 3, supported by a longer mean subsequent thinking time and a lower amount of problems solved in minimum moves on the SOC. Interestingly, cluster 1 also displayed a trend for a faster initial thinking time compared to HC, which failed to reach significance after correction for multiple testing (p = .061). This hints towards a rather sloppy and less efficient way of 10

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testing correction, cluster 2 had the lowest decision-making quality score of all clusters and lower than HC, pointing towards an impulsive and less beneficial betting strategy. Notably, all patients in the current study were euthymic. Still patients in cluster 2 made impulsive choices, resembling behavior in (hypo-) manic episodes. A persistence of impulsivity into euthymia might make these patients more vulnerable for future (hypo-)manic episodes. While this notion cannot be deduced directly from the findings of the current study, evidence comes from a study in participants at risk for the disorder, displaying a shorter time to (hypo-)mania onset in those individuals with heightened levels of impulsivity (Ng et al., 2016). Cluster 2 was called “the impulsive ones”. In contrast to cluster 1, which is characterized by motor impulsivity, this cluster displayed impulsivity with respect to making decisions. Further, just like cluster 1, this cluster also showed difficulties on the SOC, having a longer subsequent thinking time than patients in cluster 3 and a lower amount of problems solved than patients in cluster 3 and HC. Notably though, this cluster did not differ from HC or the other clusters in its initial thinking time on this task. Thus, patients in cluster 2 might have planned their moves rather inefficiently, possibly using a trialand-error fashion of moving the beads on the SOC, resulting in a longer task completion time and more required moves for task completion than the minimum number of moves. Cluster 3 on the other hand, was characterized by a very fast mean subsequent thinking time on the SOC, outperforming the other clusters and even HC on this measure and solved the most problems in minimum moves on this task of all clusters. Therefore, patients in this cluster seem to be very efficient visuospatial planners, planning the required steps in a way that they can be executed fast and without mistakes, resulting in a short execution time and a high amount of problems solved with the minimum number of moves required. Further, this cluster also had a lower delay aversion score than patients in cluster 2 and bet significantly more of their points than HC on the CGT. On an observational note, though not significant, this cluster had the lowest mean delay aversion score of all clusters and the highest overall proportion bet of all clusters. Hence, this cluster might have played the CGT with the highest chance for possible wins, but also possible losses, since they would wait for the high offers on the ascending trials and choose rather high offers on the descending trials. Together with their efficient visuospatial planning skills, this cluster was therefore labelled “the strategic ones”. The interpretation of the observed behavioral performance differences in cluster 1 can be further informed by the rsfMRI results. When comparing clusters 1 and 2 behaviorally, a lack in cognitive flexibility, represented by EDS errors, and a higher motor impulsivity, represented by a longer SSRT score on the SST, was found in cluster 1 compared to cluster 2. The apriori created ROIs related to these cognitive functions showed stronger network connectivities for cluster 2 than cluster 1 in regions such as caudate, putamen, insula, IFG, and PPC (see also Fig. 2a). As such, the observed behavioral deficits in cluster 1 might stem from a lower connectivity between regions supporting these behavioral functions. On a cautious note, network connectivity comparisons between cluster 1 and HC also pointed towards a better connectivity of regions related to action cancellation, such as the IFG, medial frontal gyrus, pre-SMA, and insula in HC (see also Fig. 2b for a graphical depiction). A worse interregional connectivity in cluster 1 between these regions might be at the core of the observed performance deficits on the SST in this cluster. However, this group comparison failed to reach significance after correcting for the scanning sequence used. Hence, this interpretation is highly speculative, and the results can only serve as a first starting point for future explorations at best. Regarding the graph theory indices investigated, cluster 1 did not differ in any index from the other clusters. However, when depicting the clusters' modularities (see Fig. 3a), cluster 1 seemed to have a distinct parcellation of the brain topologically. While all other clusters showed a parcellation into 4 modules, cluster 1 had 5. Thus, while the amount of segregation between existing modules did not differ (i.e. modularity), the number of modules did in this cluster. As can be seen in Fig. 3a,

cluster 1 has a very prominent dorsal network, entailing some, but not all regions activated on the SST. From the depiction it could be hypothesized that this dorsal network might lack efficient connections to striatal regions, thereby likely hampering the fronto-striatal connectivity necessary for successful action cancellation. Also, top-down regulation from frontal regions onto more subcortical regions might be less efficient in this cluster. This regulation, however, is necessary for successful stopping on the SST (Sebastian et al., 2013) and cognitive flexibility on the IED (Lie et al., 2006). This might be at the core of the observed behavioral deficits in this cluster. However, since these results are purely observational, they need to be interpreted with caution. In cluster 2, the four found sub-networks are much more spread out across the brain, encompassing cortical and subcortical regions (Fig. 3b). This should enable a better cortico-cortical and cortico-subcortical information transfer between the different regions. However, the ROIs related to impulsive decision-making, which distinguished this cluster from the other clusters, belong to different modules, thereby possibly hampering communication between these regions. This might be reflected in a behaviorally observed high delay aversion score on the CGT in this cluster and worse planning ability on the SOC compared to the other clusters. If decisions are made fast without considering the different options, the SOC should be executed in a trial and error fashion, resulting in a lower amount of problems solved in minimum moves, which is exactly what is being observed in this cluster. In cluster 3, on the other hand, the regions shown to be activated during a planning task are all entailed within the same module (Fig. 3c). In addition, a significantly higher modularity was observed for cluster 3 compared to cluster 2. As such, sub-networks in cluster 3 were more separated from each other than in cluster 2, with a higher amount of links among regions within the same module. This should enable a better cross-regional communication within the same module, possibly resulting in a better behavioral performance. This is exactly what was observed for this cluster on the SOC. There was a significant positive correlation between modularity and the amount of problems solved in minimum moves on the SOC in this cluster. Further, the regions underlying risky decision-making, which was also observed in cluster 2 as compared to cluster 2, belonged to the very same module as the regions activated during tasks requiring planning. Indeed, cluster 2 individuals showed a risky gambling strategy in the CGT that was not characterized by impulsive behavior but strategic planning, which optimized their gain and thus resulted in efficient gambling. It can be deduced from a significant positive correlation between modularity and mean delay aversion score on the CGT in cluster 3, that a better connection between the ROIs related to planning and decision-making might result in less risky but more impulsive decision-making behavior. Since delay aversion cannot be classified as advantageous or disadvantageous without taking the context of its appearance into account, and since this variable is interpreted on a continuum from low delay aversion, indicating risky decision-making, to high delay aversion, indicating impulsive decisionmaking, a positive correlation between delay aversion and modularity cannot be fully interpreted in this context. However, as can be seen from this cluster's cognitive behavioral profile, patients in this cluster seemed to be rather risk-seeking on the CGT compared to patients in cluster 2, but still showed a good performance on this task. Cluster 2 had a significantly lower level of education, compared to the other two subgroups. However, the reason for this finding remains unknown. The patients in this cluster did not differ in their IQ score or age of onset from the other clusters. Therefore, a lower level of intelligence, which might in general lead to limited access to higher education, as well as an interrupted trajectory during the obtainment of education due to the disorder, are both not a likely cause for the found differences in educational level in our sample. Nonetheless, this difference had no significant influence on test performance after correction for multiple testing. As such, it can be neglected when evaluating the obtained subgroups. There were no differences between clusters in terms of general 11

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functioning or the number of patients being occupationally affected by the disorder. This is in contrast to previous studies (e.g. Burdick et al., 2014), which found that the more impaired clusters displayed worse every day functioning and had a higher occupational disability. The absence of such differences in the present study points towards the existence of qualitatively distinct cognitive profiles, rather than distinct degrees of impairment among BD-I patients. Importantly, patients in the distinct subgroups did not differ in terms of subclinical symptoms present at the time of testing. Consequently, residual symptoms cannot account for the reported differences. Also, patients did not differ in the medication taken or the total medication load. There was, however, a difference in the number of hospitalizations, with patients in the rigid group reporting more hospital admissions due to the disorder compared to patients in the impulsive group. It could be speculated that the rigid group might have had more severe affective episodes, which made a hospitalization necessary, since the number of affective episodes per polarity and the general number of episodes did not differ between clusters. Their rigid behavioral style in euthymia might be even more pronounced in affective episodes. This would possibly prolong the process of recovery, since these patients might have difficulties in adapting their behavior to changing requirements, as was observed on the IED. Alternatively, the exposure to several severe affective episodes might have led to this behavioral style, similarly to the phenomenon of learned helplessness (Seligman, 1972). However, based on the cross-sectional design of the present study, no direct inferences of the influence of this behavioral style on the course of the disorder can be drawn. Future studies, using a longitudinal approach, are needed in order to elucidate the relationship between clinical symptoms and cognitive performance. The differences that characterized the subgroups on a cluster analytical level were also those that remained significant when the obtained clusters were compared against HC. As such, these differences, which represented mostly deficits in certain executive functions, were qualitatively different not only from the other patient subgroups, but also from the control group. Therefore, deficits were not due to the disorder per se, but reflected mostly impairments in some, but not all, BD-I patients, indicating the existence of distinct BD-I subgroups regarding executive functions. Noteworthy, none of the clusters performed significantly worse than all other clusters or HC on all applied tests and there was also no preserved cluster, which scored in the range of HC on all tests. Rather, differences among clusters and between clusters and HC displayed a diverse pattern of executive function deficits and strengths, with the specific abilities affected depending on subgroup membership. This is in contrast to the expected outcome of the present study and to results from previous studies, which found clusters differing in the amount of impairment, ranging from no impairment to global impairment on all tests administered (Bora et al., 2016; Burdick et al., 2014; Solé et al., 2016). One possible reason for this discrepancy might be the different cognitive tests applied. Since cluster analysis categorizes participants into homogeneous groups, based on the content entered into the analysis, the cognitive tests and respective outcome variables used in cluster analyses vastly determine the output of the analysis (Yim and Ramdeen, 2015). Previous studies, while also using tests assessing executive functions, did not administer the exact same tests. Some studies additionally assessed other cognitive abilities, such as verbal memory, possibly leading to different results. Also, while they could classify patients into good and bad performers on the cognitive tests obtained, the tests included in the present study do not always allow for this kind of differentiation. Some of the scores, e.g. delay aversion, do not necessarily describe a good or bad performance, but just two different approaches to solving the task at hand. While participants with a high delay aversion are more impulsive, those with a low delay aversion are rather risk-seeking (Cambridge Cognition Ltd., Cambridge, UK), both of which cannot be classified as good or bad per se, since both approaches might be useful, depending on the context. Another possible reason for the discrepant findings might be the slightly

different approach chosen in the present study. While previous studies tried to determine the amount of impairment in BD patients compared to HC (e.g. Jensen et al., 2016), the present study aimed at determining different cognitive profiles among BD-I patients. Therefore, rather than using scores in the cluster analyses that were standardized based on the performance of a HC group (Jensen et al., 2016), scores standardized within the sample of BD-I patients were used in the current study. Further, previous studies often entailed patients with different types of diagnoses (e.g. Lewandowski et al., 2014). Resulting cognitive profiles might differ due to the diagnosis prevailing in these analyses. The existence of distinct subgroups among BD-I patients observed in the present study, which are characterized by their executive function abilities, fits with the research domain criteria (RDoC; Insel et al., 2010). These were put forth by the National Institute of Mental Health (NIMH) and adopt a dimensional approach to classifying mental disorders (Insel et al., 2010). As such, the discovery of cognitive subgroups, lying on the continuum of bipolar disorder, ameliorates the understanding of the full clinical spectrum existing within this disorder. Importantly, when the entire sample of BD-I patients was compared to HC, most of the reported differences in cognitive abilities between clusters and HC were absent or became nonsignificant after multiple testing correction. Also, for the resting-state data, a general comparison between patients and controls did not find any significant differences in network connections. Thus, the methodological approach of this study is important in order to reveal the different cognitive profiles and network connectivity profiles that might be present in BD-I, as they would most likely remain precluded in simple group comparisons between patients and HC. Further, the presence of distinct cognitive profiles in BD-I might explain the heterogeneous and sometimes even contradictory findings regarding cognitive deficits reported in these patients (Cullen et al., 2016; Martínez-Arán et al., 2004). This also points to the importance of tailored treatments for the various subgroups, based on the cognitive deficits observed, rather than a one-sizefits-all approach. Some limitations of the present study have to be taken into account. First, sample sizes were rather small for a cluster analysis, with one of the clusters entailing only 12 patients. The recruitment of clinical populations is difficult and usually requires several study sites to obtain a high number of participants. In the present study, though, patients from only two study sites were pooled. However, sample sizes in the current study were still within the range of those reported for some previous studies investigating cognitive performance subgroups in BD (e.g. Solé et al., 2016). Second, while the present study assessed a broad range of executive functions, it did not exhaustively cover the whole spectrum of cognitive abilities, thereby possibly missing certain subgroups or characteristics among the identified clusters. Previous studies on the cognitive deficits observed in BD-I, e.g. did find deficits in verbal memory in BD-I patients compared to HC (e.g. Robinson et al., 2006). However, since memory scores were only obtained for a small subsample of patients in the present study, these scores were excluded from the cluster analysis, since this method does not allow for missing values. Therefore, the presented findings need to be interpreted with caution, but can serve as a first hint at possible cognitive profiles that might exist among BD-I patients. However, future studies should also take cognitive tests other than executive functions into account, to gain a broader understanding of the distinct cognitive profiles there might be in BD-I. In addition, while there is no general rule stating the required sample size needed for a certain number of variables entered in cluster analyses, a rule of thumb (Formann, 1984) proposes a sample size of 2m, with m being the number of variables entered in the analysis. This would result in a required sample size of over 2000 bipolar I patients for the current study. As was mentioned above, the range of cognitive tests included in the present study is limited to executive function tests and future studies should cover a wider range of cognitive abilities. However, this would require an even larger patient sample, which could only, if ever, be recruited by a large, international multi-center study 12

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requiring the availability of identical cognitive measures over all centers. Indeed, this would be a desirable goal for the future. There is always a trade-off between the feasibility of patient recruitment and the amount of information that can be deduced from the variables entered into the analysis. Third, no inferences on patients' premorbid intelligence can be made based on the IQ test applied in the present study. However, this might be an important factor, since previous studies did find differences in patients' premorbid IQ and claimed the existence of a link between a higher premorbid IQ and the assignment to a cognitively unimpaired cluster, due to a cognitive reserve present in these patients (Burdick et al., 2014; Solé et al., 2016). Having such a reserve might result in less pronounced cognitive deficits at the onset of the disorder (Forcada et al., 2015). Still, as there was no subgroup in the present study, which outperformed the other two clusters or underperformed on all tests, differences in premorbid IQ between the patients of the different clusters seem rather unlikely. Nonetheless, future studies are needed, assessing premorbid IQ in BD-I patients, in addition to their cognitive performance, to shed light on the possible existence of a cognitive reserve in BD-I subgroups. Fourth, patients in the present study were generally rather high functioning, which becomes evident from the low amount of patients, whose occupational status was being affected by the disorder. Therefore, the generalizability of the findings is somewhat limited. Still, there was a fair amount of variability among BD-I patients with regard to clinical variables, covering a wide range of the BD-I spectrum. Future studies should use a longitudinal approach to investigate the stability of the found clusters over time. Lastly, the resting-state analyses presented here were exploratory to aid interpretation of the found clusters. With five to 6 min of acquisition time, resting-state recording times in the present study were rather short. It has been shown, tough, that a scanning time of 5 min is sufficiently long for a stabilization of connectivity strength estimates (VanDijk et al., 2010). However, a study has also shown that a longer acquisition time and a higher number of acquired volumes both increase the reliability of the observed network connections, proposing a scan length of 8–12 min as optimal (Birn et al., 2013). Additionally, network indices were found to be most stable after about 20–30 min of recording time, speaking in favor of even longer scanning periods (Gordon et al., 2017). Notably though, longer scanning times in clinical populations always come at the cost of an increased chance for movement during scanning, possibly resulting in the loss of data. As such, many studies investigating bipolar patients using rsfMRI have used recording times of about 5 min, even in euthymia (e.g. Ambrosi et al., 2017; Argyelan et al., 2014), including a study conducted in our lab (Lois et al., 2014). Further, a recent simulation study, investigating the variance related to network structure in rsfMRI data, showed that even with an acquisition time of 1 min the major brain networks (e.g. visual, sensorimotor, DMN network) could be identified (Bright and Murphy, 2015). Nonetheless, future studies, using a longer acquisition time and additional analyses, are inevitable to get a full picture of the distinct network connectivity patterns there are among BD-I patients. Future studies might also benefit from the application of an even more data-driven approach with regard to the rsfMRI data analyses than the parcellation approach used in the present study. One possibility would be to classify patients’ resting-state connectivity by means of a support vector machine, thereby possibly reproducing the obtained cognitive profiles. The current paper can mainly give a descriptive, rather than an explanatory, insight into the relationship between resting-state connectivity and behavioral outcomes in BD-I patients. In conclusion, the present study suggests the existence of distinct executive function profiles among BD-I patients, thereby shedding light on the observed phenotypic heterogeneity of the disorder. It demonstrates that these profiles are not solely characterized by deficits in executive functions, but also strengths compared to healthy controls. Further, it also gives a first insight into distinct functional network connectivities that might exist among BD-I patients, possibly underlying the discovered behavioral performance differences between the

clusters. The existence of distinct cognitive profiles, displaying different deficits but also strengths in executive function abilities, could foster the development of tailored treatment approaches specifically designed to alleviate the respective deficits displayed. This, in turn, might result in a more efficient treatment plan, ultimately resulting in less severe episodes and a lower amount of lifetime affective episodes. While the present study gives first insights into the existence of separate subgroups among BD-I patients, future studies in larger samples investigating a broader range of cognitive abilities are needed. Conflicts of interest This work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Contributors MW designed the study. BK and KY analysed and interpreted the data and BK wrote the first draft of the manuscript. VS and BK managed participant recruitment and data acquisition and VS and KY edited portions of the manuscript. All authors reviewed and revised drafts of the manuscript. All authors have approved the final article. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.neuropharm.2019.03.028. Role of funding source This research was supported by the Deutsche Forschungsgemeinschaft (SFB636/C6 and WE3638/3-1). The DFG had no further role in the study design, the collection, analysis or interpretation of the data; in the writing process; and in the decision to submit the paper for publication. References Ambrosi, E., Arciniegas, D.B., Madan, A., Curtis, K.N., Patriquin, M.A., Jorge, R.E., Spaletta, G., Fowler, J.C., Frueh, B.C., Salas, R., 2017. Insula and amygdala restingstate functional connectivity differentiate bipolar from unipolar depression. Acta Psychiatr. Scand. 136 (1), 129–139. American Psychiatric Association, 2000. In: Diagnostic and Statistical Manual of Mental Health Disorders. American Psychiatric Press, Washington, DC. Argyelan, M., Ikuta, T., DeRosse, P., Braga, R.J., Burdick, K.E., John, M., Kingsley, P.B., Malhotra, A.K., Szeszko, P.R., 2014. Resting-state fMRI connectivity impairment in schizophrenia and bipolar disorder. Schizophr. Bull. 40 (1), 100–110. Aron, A.R., Poldrack, R.A., 2006. Cortical and subcortical contributions to stop signal response inhibition: role of the subthalamic nucleus. J. Neurosci. 26 (9), 2424–2433. Backhaus, K., Erichson, B., Plinke, W., Weiber, R., 2018. Clusteranalyse. In: Multivariate Analysemethoden. Springer-Verlag, Berlin, Heidelberg, pp. 435–496. Birn, R.M., Molloy, E.K., Patriat, R., Parker, T., Meier, T.B., Kirk, G.R., Nair, V.A., Meyerand, M.E., Prabhakaran, V., 2013. The effect of scan length on the reliability of resting-state fMRI conectivity estimates. Neuroimage 83, 550–558. Boettiger, C.A., Mitchell, J.M., Tavares, V.C., Robertson, M., Joslyn, G., D'Esposito, M., Fields, H.L., 2007. Immediate reward bias in humans: fronto-parietal networks and a role for the catechol-o-methyltransferase 158Val/Val Genotype. J. Neurosci. 27 (52), 14383–14391. Bora, E., Hidiroglu, C., Özerdem, A., Kacar, Ö.F., Sarisoy, G., Civil Arslan, F., et al., 2016. Executive dysfunction and cognitive subgroups in a large sample of euthymic patients with bipolar disorder. Eur. Neuropsychopharmacol. 26 (8), 1338–1347. Bortolato, B., Miskowiak, K.W., Köhler, C.A., Vieta, E., Carvalho, A.F., 2015. Cognitive dysfunction in bipolar disorder and schizophrenia: a systematic review of metaanalyses. Neuropsychiatric Dis. Treat. 11, 3111–3125. Brett, M., Anton, J.-L., Valabregue, R., Poline, J.-B., 2002. Region of Interest Analysis Using an SPM Toolbox [abstract]. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan Available on CD-Rom in NeuroImage, 16(2). Bright, M.G., Murphy, K., 2015. Is fMRI “noise” really noise? Resting state nuisance regressors remove variance with network structure. Neuroimage 114, 158–169. Burdick, K.E., Russo, M., Frangou, S., Mahon, K., Braga, R.J., Shanahan, M., Malhotra, A.K., 2014. Empirical evidence for discrete neurocognitive subgroups in bipolar disorder: clinical implications. Psychol. Med. 44 (14), 3083–3096.

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