Altered prefrontal cortex activity during working memory task in Bipolar Disorder: A functional Magnetic Resonance Imaging study in euthymic bipolar I and II patients

Author’s Accepted Manuscript Altered prefrontal cortex activity during working memory task in bipolar disorder: A functional magnetic resonance imaging study in euthymic bipolar I and II patients Bernardo Dell’Osso, Claudia Cinnante, Annabella Di Giorgio, Laura Cremaschi, M. Carlotta Palazzo, Marta Cristoffanini, Leonardo Fazio, Cristina Dobrea, Sabrina Avignone, Fabio Triulzi, Alessandro Bertolino, A. Carlo Altamura

PII: DOI: Reference:

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S0165-0327(15)00326-2 http://dx.doi.org/10.1016/j.jad.2015.05.026 JAD7462

To appear in: Journal of Affective Disorders Received date: 26 January 2015 Revised date: 17 April 2015 Accepted date: 11 May 2015 Cite this article as: Bernardo Dell’Osso, Claudia Cinnante, Annabella Di Giorgio, Laura Cremaschi, M. Carlotta Palazzo, Marta Cristoffanini, Leonardo Fazio, Cristina Dobrea, Sabrina Avignone, Fabio Triulzi, Alessandro Bertolino and A. Carlo Altamura, Altered prefrontal cortex activity during working memory task in bipolar disorder: A functional magnetic resonance imaging study in euthymic bipolar I and II patients, Journal of Affective Disorders, http://dx.doi.org/10.1016/j.jad.2015.05.026 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Altered prefrontal cortex activity during working memory task in Bipolar Disorder: a functional Magnetic Resonance Imaging study in euthymic bipolar I and II patients

Bernardo Dell’Osso1,6*, MD, Claudia Cinnante2, MD, Annabella Di Giorgio3,4, MD, PhD, Laura Cremaschi1, MD, M. Carlotta Palazzo1, MD, Marta Cristoffanini2, PsyD, Leonardo Fazio4, PhD, Cristina Dobrea1, MD, Sabrina Avignone2, MD, Fabio Triulzi2, MD, Alessandro Bertolino3,5, MD, PhD and A. Carlo Altamura1, MD.

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Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università degli Studi di Milano, Dipartimento di Salute Mentale, Fondazione

IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122 Milano, Italy 2

U.O. Neuroradiologia, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Via F. Sforza 35, 20122 Milano, Italy

3

IRCCS “Casa Sollievo della Sofferenza”, Viale Cappuccini 1, 71013 San Giovanni Rotondo (FG), Italy

4

Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, “Aldo Moro” University, P.zza Giulio

Cesare 11, 70124 Bari, Italy 5

pRED, NORD DTA, F. Hoffman-La Roche Ltd., Basel, Switzerland

6

Bipolar Disorders Clinic, Stanford Medical School, Stanford University, CA, U.S.

*Corresponding Author: Dr Bernardo Dell’Osso, M.D., Assistant Prof. of Psychiatry, Department of Psychiatry, University of Milan, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico,

Text language: English Abstract: 250 words Manuscript: 5751 words References: 57 Figures: 1 (1a and b) Tables: 2 (+ 2 supplemementary)

Via Francesco Sforza 35, 20122 Milano, Italy Phone: 02-55035994; Fax: 02-503203100 Email: [email protected]

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ABSTRACT Background. Working memory (WM) deficits are among the most frequently impaired cognitive domains in patients with bipolar disorder (BD), being considered promising cognitive endophenotype of the disorder. However, the related neurobiological correlates still deserve further investigation. The present study was aimed to explore whether dorsolateral prefrontal cortex (DLPFC) activity during WM task was abnormal in euthymic bipolar patients and may represent a potential trait-related phenotype associated with the disorder. Methods. Using three Tesla functional Magnetic Resonance Imaging (3T fMRI), we studied 28 euthymic bipolar patients (15 BDI and 13 BDII), and 27 healthy controls (HCs), matched for a series of socio-demographic variables, while performing the N-back task for WM assessment. Results. We found that euthymic bipolar patients showed a pattern of increased right middle frontal gyrus engagement compared with HCs (FWE-corrected p=1x10-3), regardless of WM load, and in spite of similar behavioural performance. BDI patients had greater BOLD signal change in the same region, compared to HCs (post-hoc Tukey HSD, p=1x10-3). BDII patients expressed an intermediate, not statistically significant, pattern of activation. No other significant effects were detected in the corrected whole-brain analysis. Limitations. Sample size, cross-sectional assessment and potential influence of some clinical variables. Conclusions. Results provide direct evidence of a primary physiological abnormality in DLPFC function in BDI and II, even in the absence of behavioral differences with HCs. Such exaggerated fMRI response suggests inefficient WM processing in prefrontal circuitry, and further studies are warranted to investigate whether the dysfunction is related to the genetic risk for the disorder. Key words: Bipolar Disorder (BD), functional Magnetic Resonance Imaging (fMRI), working memory (WM), dorso-lateral prefrontal cortex (DLPFC).

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INTRODUCTION Bipolar Disorder (BD) is a highly disabling condition with a complex gene-environment etiology (Craddock and Sklar, 2013). Phenotypic expression of the disorder includes the presence of cognitive deficits, that recently emerged as a core feature of BD (Bora et al., 2009). In addition, there is a substantial body of evidence showing the persistence of cognitive impairment after the resolution of the acute episode (Kurtz and Gerraty, 2009). Cognitive deficits have been implicated among major factors contributing to patients' impaired quality of life (Arts et al., 2008; Mackala et al., 2014) and adverse outcome (Andreou and Bozikas, 2013). In addition, it has been proposed that altered brain activity associated with cognitive deficits may represent a heritable, susceptibility-related phenotype, likely intermediate from a pathophysiological point of view between genes that increase susceptibility to BD and its symptoms (Daban et al., 2012). Deficits in working memory (WM) are among the most frequently impaired cognitive domains in patients with BD (Gruber et al., 2010; Latalova et al., 2011; Thompson et al., 2007). WM is the cognitive function responsible for temporarily storing and managing the information required to carry out complex cognitive tasks such as learning, reasoning, and comprehension: it is, therefore, a fundamental component of higher-level functions (Goldman-Rakic, 1996). WM deficits in bipolar patients seem to be relatively stable across time (Bourne et al., 2013), largely independent of mood-state (Bourne et al., 2013), severity of symptoms (Pavuluri et al., 2006), comorbidity and medication regimen (Bearden et al., 2007). According to a recent metaanalysis, a different expression of cognitive dysfunction, including WM deficits, has been reported also in first-episode patients with BD (Lee et al., 2014) and in unaffected siblings of youth with BD, providing more direct support for their potential heritability (Doyle et al., 2009). The prefrontal cortex (PFC) has been identified as a key neocortical region supporting WM (Wang et al., 2006). Previous functional imaging studies in humans have demonstrated the role of PFC activity while generating and testing neuroimaging intermediate phenotypes for complex genetic 3

brain disorders (Blokland et al, 2011). Of note, converging data from post mortem and structural imaging studies have indicated that BD is associated with abnormalities of PFC biology (Manji and Duman, 2001). More specifically, post mortem morphometric brain studies have revealed abnormal size and density of pyramidal and non pyramidal neurons in DLPFC of patients with BD, as well as unexpected reductions in glia cell number and density (Ongür et al., 1998; Rajkowska et al., 2001; Savitz and Drevets, 2011; Konopaske et al., 2014). Moreover, in vivo studies with structural Magnetic Resonance Imaging (MRI) have demonstrated reduced volume of DLPFC in patients with BD (Selvaraj et al., 2012; Soares, 2003; Soares et al., 2005). In two recent voxelwise meta-analyses focused on gray matter abnormalities in BD, reductions in anterior cingulate and insular cortex were identified as the most consistent alterations in bipolar patients, potentially related to functional deficits (Bora et al., 2010; Ellison-Wright and Bullmore, 2010). Consistently with post mortem and structural imaging studies, functional imaging studies have reported both increased and decreased activation in PFC during cognitive and/or emotional tasks in euthymic bipolar patients (Chen et al., 2011). However, clinical and biological correlates of the aforementioned alterations are still under investigation. For instance, Favre and colleagues, using resting state fMRI, have recently demonstrated the lack of anti-correlated-decoupling-activity between the medial PFC and right DLPFC in bipolar euthymic patients compared to healthy subjects, as well as hyperconnectivity between medial PFC and amygdala (Favre et al., 2014). In a diffusion MRI tractography study, Emsell and coworkers showed abnormal diffusivity in the corpus callosum, cingulum bundle and fornix in euthymic BD I patients vs controls (Emsell et al., 2013). Similarly, Canales-Rodriguez and colleagues reported an altered microstructural organization at the level of perigenual areas of the frontomedial, anterior cingulate and anterior insular cortex of euthymic bipolar patients; these results, along with abnormalities in prefrontal white matter that connect the anterior cingulate to other structures, may account for some cognitive deficits observed during euthymic phases of BD (Canales-Rodriguez et al., 2014). 4

Taken as a whole, the aforementioned findings provide further support to the existence of a dysfunctional interaction between the prefrontal-subcortical and limbic network in BD (Strakowski et al., 2005), as reported by recent meta-analyses, combining functional and structural neuroimaging studies (Houenou et al., 2011). A common experimental task to assess WM is represented by the N-back test (Karlsgodt et al., 2011). To date, fMRI investigation assessing WM through N-back task in euthymic bipolar patients has given mixed results, including hypo- and hyper-activation of PFC relative to controls (Cremaschi et al., 2013; Dell’Osso et al., 2014; Fusar-Poli et al., 2012). Heterogeneity in the results may depend on multiple factors, primarily including the limited sample size and the effects of concomitant pharmacological treatments and other clinical variables (Cremaschi et al., 2013). Interestingly, the only two studies performed in individuals at high genetic risk for BD have shown exaggerated prefrontal activity during WM, suggesting that prefrontal dysfunction may be associated with genetic liability for the disorder (Drapier et al., 2008; Thermenos et al., 2010). Finally, studied samples of bipolar patients mostly consisted of BD I subjects; therefore, whether patients with BD I and II differ in terms of WM-related brain activity remains to be clarified. In the present study, we investigated PFC activity, assessed with 3T blood oxygenation leveldependent (BOLD) fMRI in euthymic patients with BD I and II and healthy subjects, during N-back task. In light of the aforementioned studies (Drapier et al., 2008; Savitz et al., 2014; Thermenos et al., 2010), we hypothesized that PFC activity during WM may be abnormal in BD patients compared to healthy controls, and that the degree of such dysfunction may help to differentiate BD I vs BD II.

MATERIALS AND METHODS Participants Twenty eight bipolar patients, 15 with BD I (53% male; mean age 34.3 ± 10.3 years), 13 with BD II (54% male; mean age 37.3 ± 8 years) and 27 healthy subjects (55.5% male; mean age 29.4 ± 10.6 5

years) participated to the study out of an original sample size of thirty bipolar individuals (2 subjects were excluded form the analysis due to excessive head motion during the task). All participants were white Caucasians, who provided written informed consent, after receiving a complete description of the study, in accordance with the Declaration of Helsinki. The Structured Clinical Interviews for DSM-IV-TR (American Psychiatric Association, 2000) - (SCID) (First et al., 2002a, 2002b, 1997) - were used to confirm patients’ diagnosis and to exclude any psychiatric Axis I and II disorder for healthy subjects. Additional exclusion criteria were: inability to provide a valid informed consent, mental retardation, a history of substance abuse within the past six months, and the presence of any significant neurological and medical conditions revealed by clinical and MRI evaluation. Lifetime psychiatric comorbidities were accepted, while cross-sectional ones were not. When considering comorbidity, BD had to be the primary disorder, i.e. the one representing the main motivation to seek help and responsible for the most significant distress. All patients were on stable pharmacological treatment with mood stabilizers (lithium, anticonvulsants and atypical antipsychotics) and, in some cases, antidepressants, for at least 8 weeks before entering the study. The severity of depressive and manic residual symptoms was evaluated by a certified psychiatrist through the 21-item Hamilton Depression Rating Scale (HAM-D) (Hamilton, 1960) and the Young Mania Rating Scale (YMRS) (Young et al., 1970). All patients were required to be euthymic for at least 4 weeks prior to the fMRI scan session, showing a HAM-D score ≤ 7 and a YMRS score ≤ 12.

Experimental paradigm We administered a verbal variant of the N-back, consisting of 3 subtasks with different levels of cognitive load, arranged as a block design (Kirkner, 1958). In each block, single white letters were sequentially presented on a black background, in the liquid-crystal display (LCD) goggles that participants wore during the scanning session. In the baseline condition (0-back), subjects responded as soon as the “X” letter appeared. In the other conditions, the target was defined as any letter that was presented 2 (2-back) or 3 (3-back) items before, respectively. Each block consisted of 6

10 letters presented for 500 ms each, with an inter-stimulus-interval of 2000 ms. Blocks were further separated from one another by an 8000 ms interval, equivalent to the rest condition, during which no stimuli were presented and subjects looked at the default crosshair. Blocks were further separated from one another by an 8000 ms interval. All conditions were repeated 4 times in a pseudo-randomized order for a total duration of 9 minutes and 12 seconds. Subjects were informed about the upcoming condition, through an instruction slide followed by a fixation cross. During the fMRI scan, participants responded by pressing a button on an electronic response grip (NordicNeuroLab Inc., Norway), held in their right hand, allowing determination of accuracy and reaction time. Accuracy was calculated as the number of correct responses over the single subtask. The block design paradigm was implemented with nordicAktiva, a stimulus presentation software (NordicNeuroLab Inc., Norway, www.nordicneurolab.no).

fMRI data acquisition Functional images were collected by a 3T Philips Achieva MRI scanner, using T2*-weighted gradient echo, echo-planar imaging sequence sensitive to BOLD contrast (repetition time 2000-ms, echo time 35 ms, flip angle 90°, voxel size 1.8 x 1.8 x 5 mm), with 25 contiguous transversal slices of 5 mm thickness (parallel to the inter-commissural plane AC-PC), covering the whole brain. The matrix size was 96 x 96 pixels and the field of view was of 230 mm. A sense head 8 coil was used for radio frequency reception. Each functional run consisted of 276 volumes, for an overall duration of the task 9 minutes and 12 seconds. Additionally, high resolution T1*-weighted images were acquired for anatomical localization.

fMRI data analysis Data processing and analysis were performed with freely available Statistical Parametric Mapping software

(SPM8;

Wellcome

Trust

Centre

for

Neuroimaging,

London,

UK;

http://www.fil.ion.ucl.ac.uk/spm). Images, for each subject, were realigned to the first volume in the 7

time series and movement parameters were extracted to exclude subjects with excessive head motion (> 3.5 mm of translation, > 3° rotation). Images were then re-sampled to a 2 mm isotropic voxel size, spatially normalized into a standard stereotactic space (Montreal Institute on Neurology, MNI template) and smoothed using a 6 mm full-width half-maximum isotropic Gaussian kernel, to minimize noise and to account for residual inter-subject differences. A box car model convolved with the hemodynamic response function at each voxel was modeled. Subject-specific movement parameters, obtained from the realignment procedure, were included in the model as covariates, taking into account the effects of subject motion. In the first level analyses, predetermined condition effects at each voxel were calculated using a t statistic, producing a statistical image for the contrasts of 0-back, 2-back and 3-back vs rest for each subject. All the individual contrast images were entered in a second level random effects analysis. Analysis of covariance (covarying for age) was then performed, with diagnosis as the between-subjects factor and WM load as the withinsubject factor. Because of our a-priori hypothesis based on previous post mortem and imaging studies (Drapier et al., 2008; Thermenos et al. 2010; Savitz et al., 2014)we used a family-wise error (FWE) correction for multiple comparisons at p=0.05 within a region of interest (ROI) comprehensive of middle frontal gyrus as defined by the Wake Forest University PickAtlas 1.04 (WFU_PickAtlas) (www.fmri.wfubmc.edu/cms/software#PickAtlas). Furthermore, because we did not have other a priori hypotheses regarding brain activity outside the ROI above mentioned, we e used a statistical threshold of p=0.05 FWE-corrected for whole-brain comparisons. Finally, to further explore differences between subgroups, post-hoc analysis outside of SPM8 was also performed on BOLD responses extracted from the cluster, showing the main effect of diagnosis using MarsBar (http://marsbar.sourceforge.net/). All coordinates are reported in MNI system.

Socio-demographics, clinical ratings and behavioural data analysis One-way ANOVAs, t-test for independent samples and χ2 analyses were used to compare demographic and clinical data across diagnostic groups and healthy controls (SPSS Statistics 20.0). 8

General linear model with repeated measures for task conditions (0-back, 2-back and 3-back) and with diagnosis as predictor was used to evaluate behavioural differences across diagnostic groups. Tukey HSD test and t-tests for dependent samples were used for all post-hoc analyses.

RESULTS Socio-demographic data There were no significant differences among the three diagnostic groups (BD I, BD II and healthy controls) in terms of gender, handedness and years of education, except for age (F(2,52)=3.06, p=0.05), that was included as covariate in the imaging analyses. Socio-demographic data of the sample are provided in Table 1.

Clinical data: BD I vs BD II patients With respect to clinical features, no statistically significant differences between diagnostic subgroups were found. Diagnostic subgroups were matched in terms of gender, mean age, family history for psychiatric disorders, lifetime psychiatric comorbidity, age of onset and total duration of illness, duration of untreated illness (DUI), polarity/mean duration of last episode and type of pharmacological treatment (mono- vs poly-therapy as well as pharmacological class) to reduce possible bias. Considering the number of lifetime episodes, no statistically significant difference was found between BD I and II, in terms of depressive and hypo/manic episodes. In relation to the polarity of last episode, BD I patients showed a depressive episode in 3/4 of the cases (73.3%), while BD II patients were as likely to have experienced a hypomanic or depressive episode (46.2%). It is worth noting that, among the aforementioned clinical variables, comorbidity, DUI, number of depressive episodes, duration and polarity of last episode were not found to show any statistically significant difference, despite revealing an appreciable variety, likely due to the small sample size. Clinical data of the diagnostic subgroups are reported in Table 1. 9

Behavioural data Repeated measure ANOVA on WM load revealed no effect of diagnosis, either in terms of accuracy (F(4,92)=0.52, p=0.71) or reaction time (F(4,92)=0.58, p=0.67), thus allowing to compare brain responses (fMRI) in the absence of behavioural differences among diagnostic groups. Results of behavioural performances of diagnostic and control groups are provided in Table 2.

Imaging data ANCOVA of imaging data indicated: 1) a main effect of WM load in right (x, y, z, MNI coordinates) [(32, 0, 60), k=2136, Z=Infinite, FWE-corrected p=1x10-4)] and left middle frontal gyri [(-28, 4, 60), k=1890, Z=Infinite, p=1x10-4)]; 2) a main effect of diagnosis in the right middle frontal gyrus [(34, 42, 10), k=282, Z=4.79, FWE-corrected p=1x10-3)] (Figure 1a); 3) no significant interaction between WM load and diagnosis in bilateral middle frontal gyrus. Inspection of the BOLD signal change, extracted from the cluster with the main effect of diagnosis, allowed further characterization of the directionality of the results. In more detail, BD I patients had greater BOLD signal change compared to healthy subjects (post-hoc Tukey HSD, p=1x10-3), in the right middle frontal gyrus (Figure 2). No statistical difference was found between BD I and BD II or BD II and healthy controls in terms of BOLD signal change in the same brain region (post hoc p=0.17 and p=0.31, respectively). However, it is worth noting that BD II patients expressed an intermediate pattern of activation between BD I subjects and healthy controls, as shown in Figure 1b. No significant effects were detected in the corrected whole-brain analysis. For the sake of completeness, results of the uncorrected whole brain exploratory analysis (p=0.001, k=30) are reported in Supplementary Table 1.

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DISCUSSION To authors’ knowledge, the present study represents the first evidence specifically showing that abnormal PFC activity during WM may be a potential imaging phenotype associated with diagnosis of BD. In particular, we have found that euthymic patients with BD I and II showed abnormal PFC engagement during WM (fMRI), compared with healthy subjects. Furthermore, an increased BOLD signal change was found in BD I vs BD II patients in the right middle frontal gyrus, regardless of WM load. Interestingly, BD II patients activated the target area in an intermediate level between BD I and healthy controls. At the neurobiological level, previous imaging studies have demonstrated reduced neuronal integrity, as indicated by reduced N-acetylaspartate levels in the DLPFC (Winsberg et al., 2000), in addition to volume reductions, that seem to be correlated with WM deficits, at least in part (Savitz et al., 2014). To date, only eight functional imaging studies have investigated brain activity during the N-back WM task in bipolar patients, and none of them has included both BD I and II patients. Only five of these studies have found differences in terms of PFC activity between BD patients and controls. More precisely, the PFC was found to be more activated in one study (Adler et al., 2004) and decreased in two studies (Hamilton et al., 2009; Townsend et al., 2010). Finally, two other studies have shown different results in specific sub-areas of the PFC (Frangou et al., 2008; Monks et al., 2004), one of them showing, indeed, a similar increased activation in the right medial frontal gyrus (Monks et al., 2004). In such regard, it may be hypothesized a higher effort of bipolar patients in recruiting WM networks and/or the involvement of alternative neural circuits, in order to overcome possible deficits as a compensatory effect. Nonetheless, such hypothesis remains to be further investigated. Differences in the design of the study, strength of the magnetic field and also in the experimental paradigm used, which may affect modulatory effects of other brain regions on PFC, may be invoked to explain inconsistencies across studies. Of note, the increased PFC activity of BD patients observed in the present study could not be ascribed to deficits in WM accuracy or reaction time, since there were no differences in terms of 11

behavioural performance among groups. Such finding is consistent with five out of eight studies reported in literature, assessing WM through N-back task in euthymic BD I patients (Frangou et al., 2008; Hamilton et al., 2009; Jogia et al., 2011; Monks et al., 2004; Townsend et al., 2010). Among the aforementioned studies, only one reported an increased reaction time for patients vs controls (Thermenos et al., 2010), whereas a reduced accuracy was found for bipolar patients by some groups (Adler et al., 2004; Drapier et al., 2008), mostly when WM load was increased. Taken as a whole, results of the present study seem to establish a specific link between the diagnosis of BD in euthymic patients and the increased activation of the dorsolateral PFC during WM task. In addition, they support the hypothesis that bipolar I euthymic patients may exhibit an increased engagement of the middle frontal gyrus in order to maintain a normal behavioural response while performing such task. Bipolar II patients, in turn, showed a normal behavioural performance with an increased pattern of activation in the target area that, however, did not reach the threshold for statistical significance, compared to healthy controls. Such finding may be interpreted on the basis that euthymic BD II compared to euthymic BD I patients may need a minor effort in recruiting neuronal resources to behaviourally perform as efficiently as healthy controls, showing less cognitive residual impairment, compared to BD I subjects. Nonetheless, they may still need a higher effort in such regard, compared to healthy controls. Given that bipolar II patients may not be regarded as simply less severely ill subjects, from a clinical point of view, compared to BD I, present findings represent a first attempt to better characterize these patients on the basis of their imaging and behavioural profile, while performing a cognitive task of WM. The following limitations need to be taken into account in the interpretation of the aforementioned results. Even though sample size was adequate and, on average, larger than that reported in previous neuroimaging studies assessing WM through N-back task (Adler et al., 2004; Drapier et al., 2008; Frangou et al., 2008; Hamilton et al., 2009; Monks et al., 2004; Thermenos et al., 2010; Townsend et al., 2010), replication of our results is necessary. The study had a cross-sectional nature. In addition, it needs to be taken into consideration that the observed findings may reflect the effect of 12

pharmacological treatment and other clinical variables. As all patients recruited were on stable psychotropic medications, concomitant pharmaco-therapy may have influenced their behavioural performance and brain activity, increasing variance in the sample. To date, available data on the cognitive effects of medications are inconsistent, with some studies reporting no relevant consequences on performance (Phillips et al., 2008; Silverstone et al., 2005) and others underlining a potential interference on executive functioning (Adler et al., 2004). Further comparisons between larger samples of medicated and un-medicated patients are, therefore, encouraged to better clarify the potential influence of treatment on cognition and, in particular, on WM processing. In addition, even though clinical variables were not found to be statistically different in BD I versus II, we could not completely exclude their potential influence over reported fMRI results. In particular, variables like lifetime comorbidity, DUI and lifetime number of depressive episodes (higher in BD II patients), as well as mean duration of last episode and depressive polarity of last episode (longer and more frequent, respectively, in BD I patients) may have differently influenced the activity of PFC in bipolar patients, although no statistically significant difference was found in this region between BD I and II in terms of BOLD signal change. Therefore, further studies with larger samples are necessary to confirm our results and additionally investigate behavioural and imaging patterns of neural correlates of WM in BD I and II, in particular, taking into account potential influence of specific clinical variables.

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ACKNOWLEDGEMENTS The authors would like to thank all the technician support of the Neuroradiology Unit and Dr. Alessandro Sillani for organizing all the scan procedures. Authors would thank also Dr. Beatrice Penzo, involved in the elaboration of data.

CONTRIBUTORS The authors declare they have participated in the study and article preparation. The final article has been approved by all of them.

DISCLOSURES The authors do not have any affiliation or financial interest in any organization that might pose a conflict of interest with the content of the present article. Figure 1a

FIGURES Figure 1. Effect of diagnosis on prefrontal activity during working memory

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1a) 3 Dimensional rendering showing blood oxygenation level–dependent (BOLD) signal difference across diagnostic groups [Bipolar Type I patients, BD I=15, Bipolar Type II patients, BD II=13, and Normal Control subjects, NC=27] in right middle frontal gyrus [MNI, x=34, y=42, z=10; k=282, Z=4.79]. Images thresholded at P < 0.05, FWE corrected

1b) Graph illustrating mean ± SD of parameter estimates extracted from the significant cluster in right middle frontal gyrus [Bipolar Type I patients=BD I, Bipolar Type II patients=BD II, Normal Control subjects=NC]

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HIGHLIGHTS

1. Persistence of cognitive impairment is reported in BD patients even in euthymia 2. Working memory is among the most frequently impaired cognitive domains in BD 3. Increased middle frontal gyrus activation was found in BD patients vs controls during WM task, in spite of similar behavioural performance 4. Intermediate, not statistically significant, pattern of activation was reported in BD II patients vs other subgroups

ROLE OF THE FUNDING SOURCE The study was partially supported by a research grant (scientific productivity fund), annually provided by the Fondazione IRCCS Ca’ Granda.

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TABLES Table 1. Socio-demographic and clinical features of total sample, diagnostic subgroups and healthy controls Total sample (n =28)

BD I patients (n =15)

BD II patients (n =13)

Controls (n = 27)

Age (years ± SD) a

35.7 ± 9.2

34.3 ± 10.3

37.3 ± 8

29.4 ± 10.6

Gender Male Female

15 (53.6%) 13 (46.4%)

8 (53.4%) 7 (46.6%)

7 (53.8%) 6 (46.2%)

15 (55.5%) 12 (44.5%)

Education (years ± SD )

14.3 ± 1.9

14.7 ± 2

13.8 ± 1.6

15 ± 2.18

50

46.6

53.8

24.3 ± 7

23.7 ± 5.6

25.1 ± 8.5

35.6

26.7

46.2

7.1 10.7 7.1 10.7

0 6.7 6.7 13.3

15.4 15.4 7.7 7.7

Duration of illness (years ± SD)

11.4 ± 9.1

10.7 ± 8.3

12.2 ± 10.1

Duration of untreated illness (months ± SD)

20.7 ± 36.1

10.1 ± 19.5

33.8 ± 47.5

5.3 ± 5

4.2 ± 5.1

6.5 ± 4.9

Lifetime Hypo/manic episodes (n° ± SD)

5.7 ± 5.2

5.2 ± 5.4

6.3 ± 5.1

Mean duration of last episode (days ± SD)

39 ± 38

49.4 ± 45.6

25.9 ± 20.9

60.7 35.7

73.3 26.7

46.2 46.2

17.9 82.1

13.3 86.7

23.1 76.9

53.6 10.7 14.3 14.3 7.1

66.7 6.7 6.7 6.7 13.3

38.5 15.4 23.1 23.1 0

2.7 ± 2.7 2.1 ± 2.4

2.6 ± 2.7 2.3 ± 3

2.9 ± 2.7 1.8 ± 1.4

Family history for psychiatric disorders (%) Age at onset (years ± SD) Psychiatric comorbidity (%) Generalized Anxiety Disorder (GAD) Panic Disorder (PD) Personality Disorders Alcohol/Substance Abuse

Lifetime depressive episodes (n° ± SD)

Last episode polarity (%) Depressive Hypo/manic/mixed Therapy (%) Monotherapy Politherapy Mood stabilizers SSRI Mood stabilizers + SSRI Mood stabilizers + SNRI Mood stabilizers + other ADs Psychometric scales (mean scores ± SD) HAM-D Y-MRS

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Values for categorical and continuous variables are expressed in percentages and mean ±SD, respectively. In case of missing data, total cumulative rates may be lower than 100%. Legend: SSRI: Selective serotonin reuptake inhibitors, SNRI: serotonin noradrenaline reuptake inhibitors, AD: antidepressant, HAM-D: Hamilton Depression Rating Scale, Y-MRS: Young Mania Rating Scale. Statistics: a F(2,52)=3.06, p=0.05

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Table 2. Behavioural performance data (accuracy and reaction time) in total sample, diagnostic subgroups and healthy controls

Variable

Patients (n = 28)

BD I patients (n =15)

BD II patients (n =13)

Controls (n = 27)

0-back Accuracy (n° of correct responses ± SD)

11.9 ± 0.6

12 ± 1

12 ± 0

12 ± 0

2-back Accuracy (n° of correct responses ± SD)

10.8 ± 1.4

10.6 ± 1.7

11 ± 1

11.4 ± 1.6

3-back Accuracy (n° of correct responses ± SD)

9.2 ± 2.1

8.8 ± 1.9

9.7 ± 2.3

9.5 ± 1.4

0-back Reaction time (ms ± SD)

430.6 ± 68.6

444.6 ± 74.4

415.4 ± 61

449.6 ± 107.6

2-back Reaction time (ms ± SD)

586.8 ± 163.4

576.5 ± 174.1

597.9 ± 157.2

561.4 ± 198.5

3-back Reaction time (ms ± SD)

718.5 ± 196.2

727.3 ± 231.8

709.1 ± 158.3

693.3 ± 232.2

Accuracy is expressed as mean number of correct responses ± SD, whereas reaction time is expressed as mean (milliseconds) ± SD. Statistics: accuracy: F(4,92)=0.52, p=0.71, reaction time F(4,92)=0.58, p=0.67.

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Table 1S: Results of exploratory pFWE corrected whole brain statistics (pFWE<0.05, k=30) showing the main effect of working memory load on brain physiology at N-back

Region Left Inferior Parietal Lobule Right Inferior Parietal Lobule Left Medial Frontal Gyrus Right Cerebellum, Culmen Right Sub-lobar, Extra nuclear Left Inferior Frontal Gyrus Right Middle Frontal Gyrus Left Sub-lobar, Claustrum Left Lentiform Nucleus Right Superior Temporal Gyrus Left Middle Frontal Gyrus Right Medial Frontal Gyrus Right Transverse Temporal Gyrus Left Insula

Brodmann Area BA40 BA40 BA10

k

Z score

pFWE

x

y

z

BA38

3949 3876 851 373 138 240 968 1052 293 255

Inf Inf Inf 7.53 7.19 7.14 6.80 6.78 6.73 6.21

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

-34 44 -4 34 36 -32 44 -38 -16 40

-54 -46 62 -62 22 24 38 -12 0 0

42 46 16 -34 -2 -6 26 -6 0 -18

BA10 BA6 BA41

59 197 139

6.04 5.86 5.68

0.000 0.000 0.000

-40 2 58

54 -12 -18

10 54 10

BA13

165

5.52

0.001

-42

-16

14

BA47 BA47 BA46

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Table 2S: Results of exploratory uncorrected whole brain statistics (p<0.001, k=30) showing the main effect of diagnosis on brain physiology at N-back

MNI coordinates for the main effect of diagnosis Region

Left Occipital Lobe, Cuneus

Brodmann Area

k

Z score

p

x

y

z

BA17

123

4.80

0.000

-14

-98

-10

Right Caudate

134

4.61

0.000

22

-38

10

Left Thalamus, Pulvinar

95

4.52

0.000

-18

-34

8

49

4.48

0.000

-42

38

6

52

4.46

0.000

-6

-24

14

Left Inferior Frontal Gyrus

BA46

Left Thalamus, Lateral Dorsal Nucleus Left Superior Tamporal Gyrus

BA42

71

4.13

0.000

-58

-36

12

Right Occipital Lobe, Cuneus

BA18

41

3.77

0.000

2

-80

12

27