Twenty patients with BD (10 females; mean age: 40.8 years; mean education: 14.6 years; time since diagnosis: 11.5 years) and 20 healthy non-medicated control participants (NC; 10 females; mean age: 41.5; mean education: 14.5 years) without a family history of mental illness were recruited. All participants were right handed, between 18 and 60 years of age and had completed grade-school-level education. Exclusion criteria for the participants included a history of a head injury that resulted in loss of consciousness, history of treatment with electroconvulsive therapy, or a history of substance abuse. For control participants, exclusion criteria included a history of substance abuse or a diagnosis of any current or past Axis I psychiatric illness. Groups did not differ significantly from each other in terms of gender, age, or education years. The patients were recruited from Kyushu University Hospital. This study was approved by Research Ethics Committee in Kyushu University Hospital and all participants gave written informed consent. Diagnosis of BD was made using a clinical interview. DSM-IV-TR (American Psychiatry Association, 2000) diagnoses of all patients were confirmed by two experienced psychiatrists. Participants were free of any diagnosed neurological disorders and had normal or corrected-to-normal vision.

The clinical state of patients at the time of testing was assessed using the Structured Interview Guide for Hamilton Depression Rating Scale (SIGH-D) (Williams, 1988) and the Young Mania Rating Scale (YMRS) (Young et al., 1978).

All patients were taking at least one psychotropic medication. To simplify medication status, we focused on mood-stabilizer dosages (lithium and valproic acid).

Participants' demography, clinical measurements, and medication information are summarized in Table 1.

Visual stimuli, apparatus, procedures, and ERP-recording procedures were the same as in our previous studies of healthy adults (Maekawa et al., 2005, 2009) and autism spectrum disorder (Maekawa et al., 2011).

Circular black-white windmill patterns with 90% contrast were presented on a 20-inch CRT monitor and controlled using a ViSaGe graphics board (Cambridge Research Systems Ltd, Rochester, Kent, UK). The visual stimulus subtended 5.8° of visual angle in diameter at a viewing distance of 114 cm. Participants were seated comfortably in a semi-dark room. To divert attention away from the visually deviant stimuli as much as possible, participants were instructed to focus on a story delivered binaurally through earphones while fixing their gaze on the center of the monitor. Moreover, they were instructed to press a button with their right thumb as soon as they recognized a target stimulus on the monitor. Between trial blocks, they were asked to fill out a questionnaire regarding the context of the story that they had heard (story questionnaire).

Standard, deviant, and target stimuli were presented in a random order for 200 ms on the computer monitor (Figure 1). The inter-stimulus interval (ISI) was 800 ms. Stimulus probabilities were 80% (standard), 10% (deviant), and 10% (target).

ERP recordings were composed of two sessions. Standard and deviant stimuli (6-vane and 24-vane windmill patterns) were counterbalanced across sessions, while the target (non-patterned white circle) remained the same throughout the experiment. The target stimulus was the same in the both sessions. The total number of stimuli presented was 1800 (1440 standard, 180 deviant, and 180 target stimuli).

EEG was recorded from 128 scalp sites referenced to Cz, using a high-density electroencephalography (EEG) system (Net Station 4.1 Software, Electrical Geodesics, Inc., USA). The impedances of all 128 electrodes were maintained below 50 kΩ. EEG was digitized at 500 Hz and filtered online using a 0.05–200 Hz band-pass filter and stored on a computer.

To characterize each subject's degree of attention, the accuracy of answers to the story questionnaire was calculated. Questionnaires consisted of 40 questions, such as “what was the name of the hero?” or “How many persons participated in the operation?” Additionally, reaction time (RT) and accuracy for the target stimuli were also measured as indices of participants' task performance.

EEG data were filtered off-line with a bandpass of 0.05–30 Hz. Digital codes synchronized to the stimulus onset were also stored. At the end of the experiments, EEG epochs associated with each stimulus type were extracted from the continuous record. Epochs with amplitude values exceeding a threshold of ±70 μV were discarded automatically. Artifact-free epochs were then segregated by stimulus code and averaged for each subject. The amplitudes of the ERPs were measured relative to a 100-ms pre-stimulus baseline. The grand average across all subjects in each stimulus condition was also computed. To compare our findings with those of previous studies (Maekawa et al., 2005, 2009, 2011), the average of the two electrodes on either side of the nose (electrodes 126 and 127) was adopted as the reference. Eye movements and blinks were measured from bipolar electrodes above and below the eyes (right, electrodes 14 and 126; left, electrodes 21 and 127). Mean trial numbers for standard, deviant, and target stimuli were 902.7 ± 254.9, 105.1 ± 29.6, and 114.5 ± 34.2 in the BD group and were 908.3 ± 263.4, 115.3 ± 35.3, and 131.8 ± 37.3 in the NC group. There were no significant differences in the number of trials for stimulus type or between subject groups.

Basic ERPs that indicate common neurophysiological information processing were assessed using the P1, N1, and P2 components at the Oz. After overviewing all ERP waveforms, P1, N1, and P2 components from each subject were clearly identifiable (see Table 2). The time windows for P1, N1, and P2 peak amplitudes for standard and deviant stimuli were set at 90 ± 30 ms, 120 ± 40 ms, and 220 ± 50 ms after stimulus onset, respectively. These time windows were considered to include P1, N1, and P2 peaks in all participants (Luck, 2005). Time windows for P1, N1, and P2 peak amplitudes for the target stimuli were set to 80–140 ms, 140–200 ms, and 200–300 ms after stimulus onset, respectively (Luck, 2005). Basic ERP measurements (P1, N1, and P2 peak amplitudes/latencies) at Oz were subjected to a repeated measure analysis of variance (ANOVA) with stimulus type (standard, deviant) as the within-subject variables and participant group (BD, NC) as the between-subject variable. We recognize that measuring basic ERP time-window amplitudes (P1-N1-P2) might be more appropriate than measuring their peak amplitudes (Picton et al., 2000). However, because most previous ERP studies regarding BD have measured peak amplitudes, choosing the same measure makes comparisons between studies more meaningful. Note stimulus type did not include the target stimulus because its pattern was distinct from that of standard and deviant stimuli (i.e., a white circle vs. a windmill pattern).

In all the participants, the response to deviant stimuli at the Oz was more negative than that to standard stimuli during the 150–350 ms following stimulus onset (combined BD and NC groups; paired t-test: t = 5.186, P < 0.001). The time window was justified by visual inspection of grand averaged waveforms and difference waveforms from each participant (see Figure 2), consistent with our previous studies (Maekawa et al., 2005, 2009, 2011). Figure 3 shows the selected electrodes of interest. Electrode numbers 65, 66, and 70 represented the left occipitotemporal region (blue circles), 62, 72, and 75 the mid-occipitoparietal region (red circles), and 83, 84, and 90 the right occipitotemporal region (green circles). Note that Oz corresponds to channel 75 in the EGI net, and this channel was included in the ROI that was later used for statistical analysis. An ANOVA was performed for vMMN mean amplitudes with electrode site (right occipitotemporal, mid-occipitoparietal, left occipitotemporal) being the within subject variable and participant group (BD, NC) the between subject variable. Bonferroni post-hoc analysis was performed when significant main effects or interactions were observed.

Attentive visual information processing was evaluated by the N2 and P3 components, which were evoked only in response to the target stimulus. N2 and P3 peak amplitudes/latencies and vMMN mean amplitude were subjected to a repeated measures ANOVA with electrode site (Fz, Cz, Pz, Oz) being the within-subject variable and participant group (BD, NC) the between-subject variable.

Questionnaire accuracy, response accuracy, and reaction time for target stimuli were evaluated and compared between BD and NC groups using a One-Way ANOVA. There was a marginally significant difference in mean accuracy rates for questions related to the story context [BD, 89.4%; NC, 96.7%; F_{(1, 19)} = 3.15, P = 0.084], which may indicate either a deficit in attention or short-term memory in BD. Regarding target-stimulus detection, accuracy did not differ between groups [BD, 84.3 ± 2.5%; NC, 89.8 ± 2.5%; F_{(1, 19)} = 2.52, P = 0.12]. However, compared with the NC group, BD patients showed significantly delayed RTs [BD, 467.4 ± 15.1 ms; NC, 402.4 ± 15.1 ms; F_{(1, 19)} = 9.19, P = 0.0044].

Grand-averaged ERP waveforms in response to the standard and deviant stimuli are shown in Figure 2. Positive (P1)-negative (N1)-positive (P2) deflections were elicited by all three stimulus type and were maximal at Oz. N2-P3 complexes only appeared with the target stimulus, and were maximal at Pz (Figure 4). Mean latency and peak amplitudes for each common ERP component (P1, N1, or P2) are shown in Table 2.

We conducted a multiple regression analysis to examine the relationship between ERP-component amplitudes (MMN2 and P3) and demographic and clinical variables among BD patients. Because mean amplitudes collected from some electrodes might reduce the statistical power, we adopted the amplitude of MMN2 at Oz and that of P3 at Pz for this analysis. The demographic and clinical variables tested with the model were age, sex, years of education, symptom score (YMRS and SIGH-D), mood-stabilizer dosage (lithium or valproic acid), and illness onset age. Figure 6 shows scatterplots of ERP amplitudes and mood-stabilizer dosage. Among BD patients, Spearman's rank correlation analysis revealed that there was a significant relationship between lithium dosage and MMN2 mean amplitude (R = 0.48, P = 0.043). A significant effect of dosage on P3 amplitude was also observed (R = −0.42, P = 0.043). Because the number of patients taking valproic acid was small (i.e., five), valproic acid dosage was removed from the correlation analysis. P3 peak amplitude was also significantly correlated with age (R = −0.43, P = 0.036). Other demographic and clinical variables were not found to be related to MMN2 mean-amplitude or P3 peak-amplitude.

We found that compared with controls, the early visual potential (P1) in BD patients was altered, with a significantly shorter latency and smaller amplitude. Most previous studies have not found any significant difference of P1 latency in BD. We presume that other components (such as C1) may overlap the P1 period in the present study. Because the signal to noise ratio can increase because of enormous P1-amplitude reduction, a hidden C1 can emerge that may be mistaken for P1. Even so, the P1-amplitude reduction seen here is consistent with reduction observed in ERP studies of endogenous neuropsychiatric disorders such as schizophrenia (Yeap et al., 2008). Moreover, the reduction seen here was very similar to deficits we reported in patients with schizophrenia using the identical paradigm (Maekawa et al., 2008). This suggests that visual sensory-processing deficits are common to both conditions. Strikingly, these findings are fairly consistent with results from another study in which reduced P1 amplitude to a geometric stimulus (isolated-check image) was demonstrated in BD patients (Yeap et al., 2009). Because the weight of evidence suggests that the P1 deficit is endophenotypic for schizophrenia (Hirano et al., 2010), it will be important for future investigations to establish whether this marker of visual dysfunction indexes shared genetic liability between schizophrenia and BD.

Contrary to our expectation, N1 latency was significantly faster in the BD group than in controls. Whereas ERP studies in BD patients often do not focus on the N1 component, abnormal N1 latency to auditory and/or visual stimuli in BD has been reported (Andersson et al., 2008; Fridberg et al., 2009; Lijffijt et al., 2009). Auditory and visual N1 may share different neurophysiological roles because they are generated from different structures in the brain (supratemporal and extrastriate cortices, respectively). Even so, it is well known that both are modulated by attention (for a review, see Näätänen, 1988). Because the N1 latency and amplitude depend on stimulus conditions (e.g., stimulus type, ISI, intensity, arousal, or attention), sometimes interpreting it in terms of a mechanism for illness is difficult (Rosburg et al., 2008).

The P3, including P3a and P3b, is the most-tested ERP component in patients with BD. Although most studies show abnormal P3 amplitude and/or latency in the grand averaged waveforms in BD patients, whether real statistical differences exist has been controversial (significant: Andersson et al., 2008; Schulze et al., 2008; Fridberg et al., 2009; Hall et al., 2009; Ryu et al., 2010; Jahshan et al., 2012; Johannesen et al., 2012, insignificant: Salisbury et al., 1998, 1999; Bestelmeyer, 2012; Domján et al., 2012). Generally, the group differences and effect size found in ERP measures are not as convincing as the neurophysiological differences, allowing no firm conclusions. However, we found a significantly delayed N2 latency and smaller P3 amplitude. The N2-P3 complex in response to target stimuli is usually called the N2b-complex, and underlies attentional processing for target detection (Näätänen, 1990). The peak-P3 latency is believed to show that stimulus-evaluation speed is independent of reaction time, whereas its amplitude may represent neural activity underlying attention and memory processes involved in updating stimulus representation (Polich, 2004). Despite significantly delayed reaction time, prolonged N2 latency, and reduced P300 amplitude in our BD group, patients followed the contexts of the stories during the examination as well as the control group. Behavioral and neural results indicate that BD patients here likely had a deficit in attention that was obvious behaviorally and neurally, but not clinically.

This is the first report regarding vMMN in patients with BD. Although the existence of a visual analogue of auditory MMN (aMMN) has long been debated, some studies (Pazo-Alvarez et al., 2003; Maekawa et al., 2005, 2009) have demonstrated genuine vMMN that meets the MMN criteria. vMMN is often described as a negativity measured at the occipital electrodes between 150 and 350 ms after the onset of an infrequent (deviant) visual stimulus inserted in a sequence of frequently presented (standard) visual stimuli (Pazo-Alvarez et al., 2003; Czigler, 2007). vMMN is assumed to have similar properties to aMMN, but in the visual modality. Moreover, it can be evoked pre-attentively, which reflects the memory representation of visual-stimulation regularity (Czigler, 2007). Although a number of studies have found converging evidence for the existence of vMMN, there has been little vMMN research related to neuropsychiatric disorders (for a review, see Maekawa et al., 2012). Because there have been few BD reports regarding neurocognitive dysfunction in areas such as sustained attention, selective attention, or visual working memory (Balanzá-Martínez et al., 2008), we hypothesized that vMMN could be a sensitive biomarker for detecting deficits of pre-attentive (automatic) information processing in BD patients. As expected, results in this study demonstrated that vMMN was evoked in the parieto-occipito-temporal area in both subject groups (see topographical maps in Figure 2). Moreover, vMMN comprised an early phase (100–150 ms, MMN1) and a later one (200–350 ms, MMN2), identical to our previous findings (Maekawa et al., 2005), and MMN2 in the BD group was significantly smaller than that in the NC group. Several studies have demonstrated the existence of two vMMN components (e.g., Astikainen and Hietanen, 2009; Kimura et al., 2009) and investigated the sources of vMMN for motion (Pazo-Alzarez et al., 2004; Yucel et al., 2007; Cléy et al., 2013), direction (Kimura et al., 2010), face (Kimura et al., 2012), color (Urakawa et al., 2010a,b; Müller et al., 2012), shape (Kecskés-Kovács et al., 2013), and handedness (Stefanics and Czigler, 2012). While neural activation in the occipital lobe was commonly observed in these studies, several activations in other regions were reported (for instance, posterior parietal cortex, anterior premotor cortex, orbitofrontal cortex, and temporal cortex). Two recent studies (Kecskés-Kovács et al., 2013; Müller et al., 2012) suggest that the earlier component is localized to retinotopically organized regions of the visual cortex and that the later one is generated from the middle occipital gyrus. The early phase has been characterized by deviance-related low-level activation (not allowing for memory issues) while the later one corresponds to the detection of changes based on memory comparisons. Therefore, our vMMN findings, especially MMN2, suggest that patients with BD have limited visual information processing that underlies deficits in pre-attentive memory-based detection of changes in the visual world.

Regarding vMMN laterality between BD and NC groups, MMN2 amplitude in the right occipitotemporal area in the BD group was smaller than that in the NC group. Two reports of vMMN in patients with major depressive disorder (MDD) have been published (Chang et al., 2010; Qiu et al., 2011). Chang et al. (2010) showed that expression-related vMMN at the P8 electrode was smaller in MDD patients compared with normal controls, suggesting a dysfunction in pre-attentive processing of emotional faces. Qiu et al. tested vMMN for duration-deviant stimuli in MDD and found that patients had dysfunctional visual-duration processing in the pre-attentive stage (2011). Although vMMN for short-duration deviants did not differ across groups, vMMN for long-duration deviants was significantly smaller in the right occipitotemporal area of MDD patients. Thus, vMMN is smaller in both MDD and BD patients compared with healthy controls. Although BD and MDD are considered to be different types of illnesses, finding that both conditions are associated with altered vMMN in the right occipitotemporal area implies a common abnormality in visual information.

To date, only two studies have examined the association between vMMN amplitude and behaviorally relevant factors. Stefanics and Czigler (2012) showed that vMMN amplitude to deviant right-hand stimuli correlated with behavioral preference to use the right hand. They concluded that continuously monitoring the identity of the left or right hand is a prerequisite for the ability to automatically transform observed actions into an observer's egocentric spatial reference frame. Gayle et al. (2012) demonstrated that vMMN in individuals with autism-spectrum personality traits were less sensitive to happy emotional expressions, and correlated well with Adult Autism Spectrum Quotient scores. They suggested that vMMN elicited by deviant emotional social expressions may be a useful indicator of affective reactivity and may thus be related to social competency in autism spectrum disorder. These reports indicate that the vMMN is not only an epiphenomenon but also a pre-attentive measurement relevant to behavior.

From classical selective-attention capacity-model theory (Kahneman et al., 1992), attention resources in humans are finite and cognitive processing can work successfully only when they are shared correctly. Under a task condition that overloads attention processing, operating efficiency is apparently decreased. A well-known working memory model (Baddeley, 2001), developed from a dual storage model (Atkinson and Shiffrin, 1971), suggests that sensory information (stimulus) automatically enters into sensory registers and is kept as a sensory memory (~500 ms). If selective attention is directed to the sensory memory, intentional processing can work. The sensory register consists of the phonological loop, visuospatial sketchpad, and central executive. The phonological loop and visuospatial sketchpad are controlled and integrated by the central executive system. Therefore, vMMN underlying sensory memory and/or a prediction system (Stefanics et al., 2012a) could reflect bottom-up visual processing (Winkler and Czigler, 2012), while P3 underlying the central executive system could represent top-down visual processing (Saida et al., 2013). According to a more recent model (Friston, 2005), auditory MMN emerges when the incoming stimulus is incongruent with events that are predicted on the basis of learned statistical regularities of the stimulus properties. In line with this, vMMN emerges when a current visual event is incongruent with visual events that are predicted on the basis of extracted sequential rules (i.e., prediction error account of vMMN). Moreover, in this model, forward, backward, and lateral neural connections in the human brain underlie the vMMN. Predictive memory representations of environmental regularities are generated by interactions between multiple levels of a hierarchical system in the brain. Therefore, from these models, our results here can be interpreted as showing abnormal visual working memory systems in BD patients, including both bottom-up and top-down processing.

Correlation analysis revealed significant mutual relationships between age and P3 that were consistent with several previous studies in healthy subjects (Polich, 1991, 2007; Juckel et al., 2012), but are beyond the scope of the current report. Both MMN2 and P3 amplitudes were negatively correlated with lithium dosage (Figure 6) and to the best of our knowledge this is the first report of such correlation.

Several researchers have reported effects of lithium on neuropeptides, cognition, attention, and verbal memory (Bell et al., 2005; Senturk et al., 2007; Nishino et al., 2012; Chiu et al., 2013). For example, brain-derived neurotropic factor (BDNF), which is an important neurotrophin for learning and memory via neurogenesis (Nishino et al., 2012) is itself affected by lithium and the relationship may be explained by the glutamatergic system present in BD patients. In rats, phosphorylation of the NMDA receptor NR2B subunit at Tyr1472 is reduced, suggesting that lithium works to protect against glutamate excitotoxicity in cerebral neurons (Hashimoto et al., 2007). Thus, lithium can affect neurons through its interactions with BDNF, and increasing evidence establishes correlations between BDNF secretion and the glutamate system. In contrast, there is little evidence about its neurophysiological effect on vMMN and P3. One auditory MMN report (Jahshan et al., 2012) showed that there were no significant differences in auditory MMN or P3a between BD patients taking lithium and those that were not. Mood stabilizers such as lithium are given to BD patients to control their affective state, and the effects on attention and cognitive function is therefore very important for their quality of life. More meticulous investigation and prudent interpretations of this issue are therefore needed.

Essentially, vision is always accompanied by attention, which is a basic difference from audition. Therefore, we took scrupulous care of controlling attention in the present experimental design. Participants listened to a story throughout the experiments, and the accuracy of their answers to a questionnaire related to the story was 89.4% and 96.7% in the BD and NC groups, respectively. In addition, accuracy for detecting the correct target was 84.3% and 89.8% in the BD and NC groups, respectively. The high behavioral accuracies assured us that the subjects divided their attention between the auditory task and visual target identification. However, we could not really measure how much attention was diverted toward the deviant stimuli. Even so, the attention specific N2b-P3 complex was activated only by the target stimuli for each condition but not by the deviant stimuli. This result supports the idea that attention was shifted away from the deviant stimulus. Accordingly, we have already tested for attentional leak to the deviant stimulus and concluded our vMMN satisfies the definition of MMN (Maekawa et al., 2005). Moreover, there have been several studies that carefully controlled the direction of attention (e.g., Czigler et al., 2002, 2004; Heslenfeld, 2003; Kimura et al., 2009, 2010; Stefanics et al., 2012a, b) that reported vMMNs similar to the ones we present here. Therefore, even if attention leaked toward the deviant stimulus, we believe that it would not significantly alter our present results.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.