Bipolar4LifeSupport

Abstract

Introduction

Converging evidence from the fields of structural neuroimaging and cognitive neuroscience indicates that bipolar disorder impacts neural systems involved in various aspects of cognition and temperament, and highlights the importance of understanding structure–function relationships in bipolar disorder (Bearden et al., 2001; Frangou, 2009). The most replicated neuroimaging findings in bipolar disorder involve decreased global brain volume and increased ventricular volume; more recent studies investigating regionally specific changes have observed cortical thinning of greatest magnitude in the prefrontal and temporal cortices (Rimolet al., 2010; Houenou et al., 2011; Fears et al., 2014). In parallel to imaging studies, neurobehavioural analyses have shown that subjects with bipolar disorder have impairments in cognitive domains involving attention, speeded information-processing, and working and declarative memory, as well as elevated rates of impulsivity, even when in a euthymic mood state (Robinson et al., 2006; Torres et al., 2007; Arts et al., 2008; Balanzá-Martínez et al., 2008; Bora et al., 2009; Sole et al., 2012).

A limited number of brain structure–function relationships in bipolar disorder have been investigated to date, with the goal of identifying altered structure–function associations that may provide clues into the pathophysiology of the disorder. While the majority of brain–behaviour correlations appear to be of similar magnitude and direction in bipolar disorder and healthy subjects (Coffman et al., 1990; Sax et al., 1999;Killgore et al., 2009; Hartberg et al., 2011a, b; Avery et al., 2014), some studies have found that subjects with bipolar disorder have reduced or inverse relationships relative to those observed in controls, including associations between: anterior cingulate and caudate volumes and performance on executive function measures (Zimmerman et al., 2006; Kozicky et al., 2013), as well as lateral prefrontal cortex volume and increased inhibitory control (Haldane et al., 2008). Conversely, some studies have found brain–behaviour correlations in subjects with bipolar disorder that were not found in controls. Haldane et al. (2008) found that controls showed the expected positive correlation between prefrontal grey matter volume and executive function performance, whereas patients with bipolar disorder showed an abnormal association between parietal volume and executive function. Other small studies have additionally found anomalous relationships in patients with bipolar disorder between amygdala volume and long-term memory (Killgore et al., 2009), corpus callosal area and impulsivity (Matsuo et al., 2010), temporal pole thickness and working memory (Hartberg et al., 2011a), and ventricular volumes with processing speed and executive functioning (Hartberget al., 2011b). The absence of a correlation in bipolar disorder participants that is otherwise present in controls is generally interpreted as a disruption of a normal structure–function relationship, whereas the presence of a significant correlation that is not observed in controls has been interpreted as a compensatory change that may provide some resilience (e.g. involvement of parietal cortical regions to augment executive functioning; Haldane et al., 2008). However, the collective findings of previous research are difficult to interpret because, with the exception of a few recent studies (Hartberg et al., 2011a, b), previous investigations have included small sample sizes and have focused on a few select brain and behavioural measures, with little overlap between studies in choice of measures.

Additionally, a notable challenge for the investigation of brain–behaviour relationships in neuropsychiatric disorders is the effect of age, which is known to have a significant influence on both brain morphology and cognition (Caserta et al., 2009). This is of particular relevance in the study of bipolar disorder, given emerging work suggesting that some aspects of the disorder are related to alterations of neurodevelopmental processes, whereas other consequences of the disorder such as cognitive impairment may be due to progressive brain changes that become more apparent with increasing age (Fries et al., 2012; Schneider et al., 2012; Budni et al., 2013; Gama et al., 2013). Some investigators postulate that toxicity accumulates over the course of the illness, resulting in accelerated neurodegeneration, which could explain observations of increasing clinical severity with disease progression (Budni et al., 2013). Previous studies of brain–behaviour associations in bipolar disorder have not explicitly examined the effect of age on brain–behaviour relationships; therefore it is unclear whether there are differences in the magnitude of these associations across the age span.

The current study aimed to characterize brain–behaviour associations in extended families with heavy genetic loading for bipolar disorder ascertained from two closely related, genetically isolated populations in the Central Valley of Costa Rica (CVCR) and the Antioquia region of Colombia (ANT), which have been the focus of ongoing genetic investigations of bipolar disorder for several decades (Freimer et al., 1996;Carvajal-Carmona et al., 2003; Hong et al., 2004; Herzberg et al., 2006; Service et al., 2006). The disorder is highly penetrant in these families, often affecting multiple first-degree relatives. Additionally, the disorder tends to be severe, with ∼70% of subjects with bipolar disorder in our sample reporting a history of psychotic symptoms (Fears et al., 2014). In our most recent study of these families, we reported on the initial analysis of an extensive set of brain and behavioural measures acquired using an intermediate phenotype approach to genetically dissect bipolar disorder. The phenotypic assessments included high-resolution structural neuroimaging and behavioural assessments covering a range of temperament and cognitive domains. In our most recent study, we found that about two-thirds of the measures were heritable and about one-third of the traits were associated with the disease (i.e. significantly different between participants with bipolar disorder and non-bipolar disorder family members). In particular, neuroimaging analysis showed bipolar disorder-associated global volume reduction and cortical thinning in the cortico-cognitive network of the dorsolateral and ventrolateral prefrontal cortex and the ventral-limbic system, involving the hippocampus, amygdala and orbitofrontal cortex. Analysis of the behavioural phenotypes identified bipolar disorder-associated impairments in processing speed, verbal learning and memory, category fluency and inhibitory control. We also found that there was a complex network of phenotypic correlations among the trait measures (Fears et al., 2014). The current study leverages the unique opportunity this dataset presents to investigate brain structure–function relationships across a wide age range, in both affected and unaffected individuals with a homogeneous genetic and environmental background, to provide a more complete description of the brain structure–function network relative to previous studies. Due to the large number of traits investigated, we adopted a multiple-step approach to identify the strongest set of associations. Within this set we then investigated: (i) whether any of the correlations differ in bipolar disorder versus family members without bipolar disorder; (ii) whether individuals with bipolar disorder show evidence of accelerated neurodegeneration; and (iii) whether brain–behaviour relationships differ across the wide age range included in our sample (18 to 87 years). These tests were accomplished using interaction terms in linear regression models. The inclusion of a Brain × Diagnosis interaction term in a linear model was used to test whether a brain–behaviour association differed between family members with and without a bipolar disorder diagnosis, a Diagnosis × Age interaction term was used to test whether the effect of bipolar disorder diagnosis differed across the age span (i.e. accelerated ageing), and the inclusion of a Brain × Age interaction term tested for differences in brain–behaviour associations across age.

Materials and methods

Sample

Neuroimages and neurocognitive assessments were acquired from members of extended pedigrees with heavy genetic loading for bipolar disorder. Subjects were recruited from nuclear families within pedigrees that included at least one member with a confirmed bipolar disorder type I diagnosis, available parents, and at least two siblings without bipolar disorder. Diagnoses were based on DSM-IV criteria, and were established with a best estimate process using Spanish language versions of the Mini International Neuropsychiatric Interview and the Diagnostic Interview for Genetic Studies (DIGS), as described in Fears et al. (2014). The study sample included 153 subjects with severe bipolar disorder disorder (BP-1) and 374 of their non-bipolar disorder relatives, ranging in age from 18 to 87 years. The distribution of age, sex, diagnostic and education variables did not differ across sites (Table 1). Written informed consent was obtained from each participant, and the institutional review boards at participating institutions approved all study procedures.

Table 1

Study demographics

Phenotypes

Brain measures

Structural neuroanatomical measures were generated from T₁-weighted images using standard methods from the Freesurfer software package (http://surfer.nmr.mgh.harvard.edu). We implemented a quality assurance pipeline involving manual inspection of intermediate steps within the processing stream to correct any errors and ensure reliable final measures (e.g. manually correcting the white matter segmentation mask to provide a more accurate base to build the tessellated surface mesh). In the first step of the statistical analysis (described below), we adopted a strictly objective approach and included all structural MRI phenotypes derived from the image processing protocol, which included ∼90 volume, surface area and cortical thickness measures. For subsequent steps, we reduced the number of tests by focusing on a subset of 37 brain traits that were selected for their relevance to the pathophysiology of bipolar disorder based on both the existing literature and findings in our sample. First, we included all brain measures that our previous analysis showed were significantly associated with bipolar disorder, including global measures (total cortical, total white matter and third ventricle volume), regional volumes (hippocampus, cerebellum, ventral diencephalon and the corpus callosum) and thickness measures from cortical regions that we found to be significantly thinner in subjects with bipolar disorder, including the majority of prefrontal and temporal regions (Fears et al., 2014). Additionally, to provide a basis for comparison of our results to previous work, we included additional brain traits that have been reported to show different patterns of brain–behaviour correlations between subjects with bipolar disorder and healthy controls, specifically: the amygdala, caudate, cingulate gyrus and parietal association cortex (Zimmerman et al., 2006; Haldane et al., 2008; Killgore et al., 2009; Kozicky et al., 2013). The cortical regions selected for analysis are shown in Fig. 1, and include all anterior and posterior association cortices.

Figure 1

Cortical regions included in linear regression analysis. Cortical regions selected based on the relevance to bipolar disorder pathophysiology are coloured by region: red = prefrontal cortex; dark blue = temporal cortex; light blue = parietal cortex; purple …

Behavioural measures

In addition to neuroimaging, participants were assessed across dimensions of temperament and neurocognition obtained from multiple instruments, including the computerized South Texas Assessment of Neurocognition (Glahn et al., 2007), paper-and-pencil neurocognitive assessment measures and self-report questionnaires (Table 2). To reduce the overall number of tests, we applied the following strategy: (i) sets of variables that tapped into the same behavioural construct were identified; and (ii) if the variables identified in the first step were correlated >r = 0.6, we eliminated the variable that showed the weakest association with bipolar disorder. Using this strategy, 38 behavioural measures were selected for the current investigation.

Table 2

Behavioural measures

Results

Analysis of overall brain–behaviour associations

One hundred and forty-seven brain–behaviour pairs were selected in the initial step of the analysis for subsequent testing in linear regression models, and are marked with a dot in Fig. 2. Thirty-two of the 147 pairs showed a significant brain–behaviour association in the linear regression analysis in Model 1 (Fig. 2and Supplementary Table 1). Twelve of 38 behavioural traits (32%) were significantly associated with at least one brain measure. Overall, after accounting for covariates the correlation estimates were of low magnitude, ranging from 0.13 to 0.21, with a mean of 0.16 (Supplementary Table 1). The proportion of variance in the behavioural trait accounted for by the brain measures, as estimated by the R-squared statistic, is shown in Fig. 3 and was generally less than the variance accounted for by age and education, and roughly the same magnitude as bipolar disorder diagnosis.

Figure 2

Heat map of brain–behaviour associations. Correlation coefficient estimates of each brain–behaviour association are represented as coloured squares in the heat map. Green indicates positive correlations and red indicates negative correlations. …

Figure 3

Proportion of variances in neurobehavioural measures accounted for by covariates. The proportion of variance accounted for by each covariate for the 12 behavioural measures that were significantly associated with at least one brain measure in the linear …

For brain measures, greater volume/thickness predicted better performance on all behavioural tasks, whereas increased CSF space (i.e. greater lateral and third ventricle volume) predicted poorer performance. There was some overlap in the pattern of behavioural associations with the cerebral cortex and ventricles on measures of verbal fluency and sustained attention/working memory, but in general these two indices were correlated with measures from distinct cognitive domains. Cerebral cortical volume was more associated with long-term memory tasks, whereas ventricular volumes were inversely correlated with performance on working memory and executive function tasks. Hippocampal volume was associated with declarative memory measures, as well as verbal IQ (Wechsler Abbreviated Scale of Intelligence).

In general, cortical thickness measures showed a more restricted pattern of behavioural correlations, specifically with: verbal learning and delayed recall as assessed by the California Verbal Learning Test (CVLT), immediate visual reproduction, as assessed by the Wechsler Memory Scale, face memory, verbal letter fluency and processing speed, as measured by the Digit Symbol test. The declarative memory and verbal fluency measures appeared to associate with both anterior and posterior association regions, although the behavioural correlations appeared more robust in the posterior regions, as evidenced by a greater number of strong associations in the linear regression tests. These data suggest that processing speed is more specifically associated with variation in posterior association cortex volume (temporal and parietal regions).

The initial correlation analysis provided suggestive evidence that greater total cortical volume was inversely correlated with self-reported depressive symptoms on the TEMPS-A (Temperament Evaluation of Memphis, Pisa, Paris and San Diego-autoquestionnaire version) and reduced thickness in the superior and middle frontal gyri, posterior cingulate, posterior temporal sulcus, and all parietal regions were associated with higher TEMPS-A depression scores. Additionally, thinner cortex in adjacent temporal and parietal cortex (transverse temporal and inferior parietal regions) was suggestively associated with higher trait impulsivity on the BIS. However, these relationships did not survive correction for multiple comparisons at the more stringent P =0.01 level.

Four behavioural traits were significantly associated with three or more brain measures in the linear regression tests (Fig. 2). Three of these highly connected behavioural measures were indices of long-term memory, specifically California Verbal Learning Test immediate and delayed recall, and Wechsler Memory Scale immediate visual reproduction. Verbal letter fluency was also associated with multiple neuroanatomical measures, including thickness from anterior and posterior association cortex.

Brain × Bipolar disorder interaction analysis

The 32 significant brain–behaviour associations were tested for differences in magnitude and direction between bipolar disorder and non-bipolar disorder family members using a linear regression, as described by Model 2 which included a Brain × Diagnosis interaction term (Supplementary Table 1). After Bonferroni correction, there were significant Brain × Diagnosis interactions for two pairs of traits: the association between thickness of the pars orbitalis in the inferior frontal gyrus and Wechsler Memory Scale immediate visual memory (P = 1.6 × 10⁻⁴) and the association between thickness of the supramarginal gyrus and Wechsler Memory Scale immediate visual memory (P = 1.5 × 10⁻³). For these pairs of traits, the correlation among participants without bipolar disorder was low, whereas the correlation for individuals with bipolar disorder was of greater magnitude. Details of the chi-square test are presented in Table 3, and the interaction is plotted in Fig. 4. We determined the effect sizes for the significant interaction terms by estimating the proportion of variance explained by each interaction term. The pars orbitalis × diagnosis term accounted for 1.9% and the supramarginal gyrus × diagnosis term accounted for 1.5% of the variance in the Wechsler Memory Scale immediate visual reproduction trait. We undertook a follow-up analysis to disentangle region-specific effects from a global thickness effect. A mean cortical thickness value was derived for each individual subject by averaging the thickness measures from all 33 cortical regions obtained from the Freesurfer package. The two thickness measures were regressed on the mean cortical thickness and the residualized trait was retested with the same linear model (Model 2). The P-value for the Brain × Diagnosis interaction was attenuated for both pairs, but these regions still showed a strong signal (Table 3). These findings indicate that although global thickness accounted for some of the associations, both cortical regions seem to have a specific association with visual memory independent of the global signal.

Figure 4

Scatterplots of Brain × Diagnosis and Brain × Age interactions.The upper two panels shows the difference in magnitude of the correlation between bipolar disorder (BP) and non-bipolar disorder (non-BP) family members for Wechsler Memory …

Table 3

Brain × Diagnosis interaction analysis for the association between immediate visual memory and thickness measures from the pars orbitalis and supramarginal gryus

Age × Diagnosis and Brain × Age interaction analysis

As a main effect, age accounted for a significant proportion of the variance for many of the behavioural traits (Fig. 3). Model 3 extended the investigation of the age effect by including two interaction terms, Age × Diagnosis and Brain × Age. None of the behavioural measures included in this analysis showed significant or suggestive evidence for Age × Diagnosis interactions (Supplementary Table 1), indicating that the effect of bipolar disorder diagnosis on each of the traits did not differ across the age range in this sample. Similarly, we tested the neuroanatomical measures for evidence of accelerated decline using linear regression models, but none of the brain measures showed suggestive evidence for Age × Diagnosis interactions (data not shown).

Differences in brain–behaviour associations as a function of age were tested in linear regressions including a Brain × Age interaction term in Model 3. Six pairs of traits showed a significant Brain × Age interaction (Supplementary Table 1). Details of the Chi-square test for three examples are shown in Table 4. Cortical volume showed a weak correlation with verbal letter and category fluency in younger individuals, but was positively correlated with performance with increasing age (P = 2.5 × 10⁻⁴ and 1.3 × 10⁻³, respectively). Third ventricle volume showed significant Brain × Age interactions for two measures of inhibitory control; the Stop Signal Task correct Go trials (P = 4.0 × 10⁻⁴) and Stroop Colour-Word Test Errors (P = 4.7 × 10⁻⁴). Lateral ventricle volume also showed a significant Brain × Age interaction with Stroop Colour-Word Test errors (P = 1.2 × 10⁻⁵). For these three pairs, the magnitude of the brain–behaviour correlation was low in younger patients but increased with age, such that in older patients, greater ventricular volume predicted poorer performance on these inhibitory control tasks. Cortical thickness in the lingual gyrus also showed a significant Brain × Age interaction with verbal letter fluency, with younger participants showing weak correlations that increased in older patients (P = 8.6 × 10⁻⁴). The effect size of interaction terms as measured by the R-squared estimate for each model ranged between 1.2% and 3.3% of the behavioural variance. To demonstrate the difference in these relationships between ages, the brain–behaviour correlations are plotted separately for younger (<55 years) and older (>55 years) subjects in Fig. 4. Note that this representation of the data is not derived from the linear regression model, which treated age as a continuous variable, but provides a heuristic visualization of the difference in brain–behaviour correlation as a function of age. For these six pairs, a secondary analysis was performed to test whether the Brain × Age interaction differed for bipolar disorder and non-bipolar disorder groups by repeating the linear regression including a three-way interaction term, Brain × Age × Diagnosis, none of which were significant.

Table 4

Estimates and summary statistics of the Brain × Age interaction analysis

Discussion

Here, in a large sample (n = 527) ascertained from 26 families with heavy genetic loading for bipolar illness, we found an extensive network of behavioural correlates of brain traits relevant to the pathophysiology of bipolar disorder, which includes a broad range of neurocognitive and temperament traits. The network of structure–function associations in the bipolar disorder pedigrees supports the emerging picture of a distributed set of brain regions contributing to complex behaviour. Many brain areas predicted multiple behaviours across several domains; conversely, many behavioural measures were influenced by multiple brain regions. Global measures of cortical and ventricular volume had more robust associations with behavioural traits relative to local volume and thickness measures, and had distinct cognitive correlates. Specifically, whereas cortical volume was (positively) associated with declarative memory measures, ventricular volume was (inversely) associated with working memory and executive function. Prefrontal, temporal, and parietal grey matter thickness measures had fewer behavioural correlates compared to volume indices, and were more specific to declarative memory, letter fluency, and processing speed. The majority of the behavioural correlates were similar between diagnostic groups, with the exception of ventrolateral prefrontal and supramarginal gyrus cortical thickness, which showed correlations with visual memory that were specific to subjects with bipolar disorder. Our analysis of the impact of age across the broad age range represented in our sample showed that, while age had the expected large effect on all behavioural measures (Fig. 3), there was no evidence that effects of age were different in the participants with bipolar disorder compared to non-bipolar disorder family members. Our analysis of brain–behaviour associations as a function of age (Brain × Age interaction) showed that most brain–behaviour correlations were similar across age groups, with the exception of associations between cortical and ventricular volume and lingual thickness with several measures of executive functioning, which had low correlations in the younger subjects that increased with age.

The results support previous findings in both healthy and clinical populations, indicating that greater grey matter volume and thickness predict better performance, whereas greater ventricular volumes predict poorer performance (Sullivan et al., 1996; Gur et al., 2000; Antonova et al., 2004; McDaniel, 2005; Hartberg et al., 2010). Our investigation also supports previous studies that have shown that most brain–behaviour correlations are similar between bipolar disorder and non-bipolar disorder subjects, suggesting that overall brain structure–function relationships are similar between bipolar disorder and non-bipolar disorder individuals (Coffman et al., 1990; Hartberg et al., 2011a, b; Avery et al., 2014). We found little support for prior findings of differences in brain–behaviour correlations between bipolar disorder and healthy controls. Additionally, although our study provides support for the previously identified association between third ventricle volume and inhibitory control measures (Hartberg et al., 2011b), we did not find any evidence that the correlation differed between subjects with and without bipolar disorder. However, the discrepancy may be explained by our finding of a significant Brain × Age interaction for these associations, which was not explicitly analysed in the Hartberg et al. (2011b) study. Finally, in contrast to some previous studies, we did not identify any correlations that were present in non-bipolar disorder individuals, but were absent or reduced in bipolar disorder participants (Zimmerman et al., 2006; Haldane et al., 2008; Kozicky et al., 2013).

Recent theories regarding the pathophysiology of bipolar disorder suggest that the disorder may involve accelerated neurodegenerative processes, highlighting the importance of characterizing the pattern of structure–function relationships across the age span (Fries et al., 2012; Schneider et al., 2012; Budni et al., 2013; Gama et al., 2013). These theories postulate that the cyclic repetition of mood episodes during the course of the illness results in an increasing toxic burden (e.g. oxidative stress) that causes accelerated neurodegeneration. Such theories predict that some cognitive functions would show increased rate of decline with increasing age in subjects with bipolar disorder relative to those without. We tested this hypothesis by including a Diagnosis × Age interaction term in Model 3; bearing in mind the limitations of our cross-sectional design (discussed below), we found no evidence that the effect of the disorder on cognition or brain measures (data not shown) increased in older ages.

It is well established that ageing has a strong effect on brain measures (DeCarli et al., 2005; McDaniel, 2005;Narr et al., 2007; Luders et al., 2009). In contrast to the view espoused by some investigators that bipolar disorder may be associated with accelerated neurodegeneration (Fries et al., 2012; Schneider et al., 2012;Budni et al., 2013; Gama et al., 2013), however, we did not detect evidence of accelerated ageing in individuals with bipolar disorder. We found instead that there is a changing relationship between brain volume/thickness and some aspects of cognitive functioning in different age groups, supporting findings from previous work (Zimmerman et al., 2006; Gautam et al., 2011). Greater total cortical volume, greater lingual thickness, and smaller ventricular volumes predicted better executive functioning in older subjects, regardless of diagnosis. These results are consistent with the hypothesis that greater volumes and/or less atrophy are associated with greater functional capacity, which may provide a buffer against cognitive decline during ageing (Brickman et al., 2006; Zimmerman et al., 2006; Gautam et al., 2011). The Brain × Age interaction appeared similar in participants regardless of bipolar disorder diagnosis, suggesting that the advantages of greater brain volume on executive function are not specific to the disorder. However, in our sample, participants with bipolar disorder tended to have lower cortical and larger ventricular volume compared to non-bipolar disorder family members, suggesting that, on average, individuals with bipolar disorder will have a lower level of functioning.

Previous studies have demonstrated significant variability in functioning of bipolar disorder patients, with a substantial proportion (30–40%) of patients showing little or no evidence of neurocognitive impairment (Altshuler et al., 2004; Burdick et al., 2014). This finding is reflected in our study by the fact that the distributions of neurocognitive measures for bipolar disorder participants show considerable overlap with the distributions from non-bipolar disorder subjects (e.g. overlap of red and blue points for visual memory along the y-axis of the upper two panels in Fig. 4). Additionally, the distribution of brain measures for many of the individuals with bipolar disorder fell within the same range as the non-bipolar disorder individuals (e.g. overlap of red and blue points along the x-axis for pars orbitalis thickness and supramarginal gyrus in the upper two panels of Fig. 4). Thus, although on average some brain and behavioural measures differed between diagnostic groups, our study demonstrates the variability of brain structure and behavioural functioning within bipolar disorder subjects that may contribute to the diversity of functional outcomes in individuals with the disorder. An additional factor that may be relevant to the functional heterogeneity within individuals with bipolar disorder is the finding that a relatively small proportion of variance in neurobehavioural measures was accounted for by bipolar disorder diagnosis and neuroanatomical traits (Fig. 3). This finding may not be surprising given that individuals with bipolar disorder show less cognitive impairment and brain volume reduction relative to other neuropsychiatric disorders like schizophrenia (Rimolet al., 2010, 2012; De Peri et al., 2012; Ivleva et al., 2013; Anderson et al., 2013). Our findings also highlight the possibility that factors like education, which explain more variance in cognitive functioning in our analysis than do brain and diagnostic measures, may have the potential to compensate for any functional deficits due to bipolar disorder-associated brain changes.

Novel aspects of this study include the largest sample size to date for the analysis of brain–behaviour correlations in bipolar disorder. The multidimensional assessment we employed is the broadest range of temperament and neurocognitive measures ever analysed, to our knowledge, for brain–behaviour associations for any psychiatric disorder. We identified substantially more correlations compared to previous studies, although the magnitude of correlations identified in this study (once adjusted for confounding variables) was generally lower compared to previous studies. Additionally, the current design is unique relative to previous studies because it was explicitly designed for genetic studies, and will allow for future investigations of the genetic factors that contribute to the brain–behaviour associations. It has been notoriously difficult to identify correlations between behavioural measures and MRI morphometric measures (Boekel et al., 2015). Previous studies have been limited by small sample sizes and generally focused on a limited set of brain regions and behavioural associations. In contrast, the current study leveraged our relatively large sample size and adopted a more agnostic approach to characterize a large matrix of brain–behaviour pairs (Fig. 2). Although the large number of tests inherently limits the power of our approach (as discussed in more detail below), the consistent patterns of associations we identified (e.g. multiple assessments of long-term memory have similar patterns of correlation among cortical regions) provide confidence that we have identified biologically relevant structure–function associations.

It is important to underscore that our study examined a large number of possible interactions in an attempt to follow an objective, unbiased approach. While, for the primary analysis, we selected 147 brain–behaviour pairs that appear to be strongly correlated in our data, we did not use any other prior information to restrict our domain of analysis. In identifying statistically significant results, we increased the power of our approach by using a multi-step design to reduce the number of tests at each subsequent step. This should be taken into account when comparing our results with those of other studies, which focused on a smaller subset of brain and behavioural measures and did not have to contend with similar multiple testing problems. For example, here we did not find a significant association between whole brain volume and IQ, which prior studies have found to be correlated with a magnitude of 0.10 to 0.35 (Frangou et al., 2004; McDaniel, 2005; Narr et al., 2007; Luders et al., 2009). Our analysis tested total cerebral volume for association with two measures representative of IQ; the Matrix Reasoning and Vocabulary scores from the Wechsler Abbreviated Scale of Intelligence. Neither association was identified as significant in the Model 1 linear regressions; however, both of these associations passed the initial filter steps with correlation estimates of ∼ 0.13 (P-values of 0.003 and 0.007, respectively) and would have been considered significant had we focused specifically on these pairs.

A potential limitation of our study is that the ascertainment strategy may have enriched for correlations that are more specific to these bipolar disorder families relative to the general population, and may therefore not generalize to other samples. This concern is attenuated by the fact that the pattern of bipolar disorder-associated brain and behavioural differences in these families is very similar to case-control investigations of independent subjects (Fears et al., 2014). An additional issue that must be considered regarding our ascertainment strategy is that some non-bipolar disorder family members meet criteria for disorders other than bipolar disorder. The most common non-bipolar disorder diagnosis in the families is major depressive disorder, and in the current sample, 73 of 374 non-bipolar disorder family members meet criteria for major depressive disorder. Analysis of the data set excluding the 73 individuals with major depressive disorder (data not shown) was essentially identical to the complete data set indicating that the presence of these family members did not substantially influence the investigation. Compared to case-control designs, the enrichment of other psychiatric disorders in our sample likely reduces power to identify differences between the bipolar disorder and non-bipolar disorder individuals. At the same time, we can have increased confidence that the identified differences are bipolar disorder-specific (and not reflective of more general psychopathology).

Additionally, the large number of potential brain–behaviour pairs in our sample may have limited power to identify Brain × Diagnosis interactions. Nevertheless, by restricting the analysis of interaction effects to the subset of pairs showing the strongest associations (n = 37), we were able to identify significant interactions of moderate effect (i.e. accounting for 1.2–3.3% of the model variance). The multi-step approach we used may have eliminated brain–behaviour pairs in the initial steps of the analysis that may have shown a different pattern of correlation between diagnostic groups in subsequent steps. However, given the relatively large number of traits that did survive the initial steps, our results suggest that altered brain structure–function associations are not a prominent feature of bipolar disorder.

It is also important to note that inferences regarding age-associated changes are limited by the cross-sectional study design. To confirm our finding that there is no evidence of accelerated ageing in subjects with bipolar disorder would require a prospective longitudinal design. Additionally, we identified brain–behaviour correlations that varied as a function of age; however, these differences may not be related to age per se, but rather other environmental factors that were shared between cohorts of similarly aged individuals. Similarly, our study design does not allow us to draw inferences regarding causal relationships. Although neuroanatomical measures are generally considered to be ‘upstream’ of behaviours, these relationships are likely bidirectional. For example, behaviours associated with the disorder (e.g. impulsiveness or social isolation) may lead to environmental exposures that may in turn impact brain measures.

Taken as a whole, and keeping in mind the limitations of our study design, our findings indicate that typical brain structure–function relationships are largely preserved in individuals with bipolar disorder, suggesting that efforts to characterize the pathophysiology of the disorder should focus on delineating impairment of typical brain functions, rather than identifying anomalous processes unique to bipolar disorder individuals. Furthermore, despite a body of research speculating on differential effects of ageing on the brain in individuals with bipolar disorder (Fries et al., 2012; Schneider et al., 2012; Budni et al., 2013; Gama et al., 2013), within this large sample we did not find evidence for such effects. These findings suggest that, from a clinical staging perspective (Frank et al., 2014; Kapczinski et al., 2014) factors other than chronological age may be more relevant to real-world functioning for patients with bipolar disorder. Ultimately, our aim is to use genetic methods to elucidate the causal biological connections between brain structure and behaviour. In ongoing work we are investigating genotypes and whole genome sequence information to identify both common and rare genetic variants associated with the brain and behavioural phenotypes. Once identified, this information can be used to begin disentangling the complex causal pathways that contribute to the development and manifestations of bipolar disorder (Didelez and Sheehan, 2007; Ebrahim and Davey Smith, 2008).

Abstract

INTRODUCTION

Although less extensively researched than schizophrenia, bipolar disorder has been linked to a range of neuro-anatomical abnormalities compared with healthy volunteers in cross-sectional imaging studies to date. One theme to emerge from these studies, which is highlighted in meta-analyses and systematic reviews, is the marked heterogeneity of findings. Potential sources of this heterogeneity include methodological variation in imaging acquisition and analysis, and clinical variation in illness presentation, risk factor exposure and the use of psychotropic medication. In this article I selectively review neuroanatomical imaging studies of bipolar disorder, with a focus upon the findings from meta-analyses, discuss evidence that psychotropic medications used in bipolar disorder are associated with variation in neuroanatomy from complimentary research areas such as animal studies, review in detail those individual studies assessing psychotropic medication effects and discuss the design of future studies which may serve to better elucidate the relationship between the use of psychotropic medication and brain structural variation in bipolar disorder.

GREY MATTER

WHITE MATTER

SUMMARY AND FUTURE DIRECTIONS

The vast majority of reports assessing the impact of psychotropic medications on brain anatomy in vivo are from neuroimaging studies which were not specifically designed to address this question, and most studies are unsurprisingly negative in terms of not detecting statistically significant results since they comprise small, cross-sectional samples, commonly heterogenous in both clinical features and medication exposure. That said, there is arguably considerable consistency in the positive studies to date in terms of the directionality of findings, whereby almost all studies which report significant neuroanatomical differences find an apparent ameliorative effect for the use of psychotropic medications in bipolar disorder. A summary of the significant neuroanatomical changes reported is provided in Table 11.

Table 1

Summary of the anatomical regions reported to significantly vary with psychotropic medication use in bipolar disorder*

The most substantial evidence is for the most frequently investigated compound and with the most statistical power, i.e. an effect of lithium use on grey matter volume, whereby lithium use in bipolar disorder patients is repeatedly associated with increased grey matter volume globally and in the medial temporal lobe, limbic and prefrontal regions. The likelihood that this is a true effect is enhanced by its support beyond small cross-sectional studies into those more methodologically robust designs which carry substantial statistical power such as meta-regression analyses, which include large numbers of patients, and prospective studies, which include intra-individual rescanning. The potentially important role that white matter pathophysiology plays in the aetiopathogenesis of bipolar disorder is underpinned by multiple recent diffusion tensor imaging studies with some evidence that genetic liability contributes to white matter microstructural abnormalities. Nevertheless there is also evidence that lithium treatment may have ameliorative effects on diffusion tensor imaging metrics.

It remains unclear at present whether these apparently ameliorative effects of lithium on the neuroanatomy of grey and white matter are related to its therapeutic effect or reflect epiphenomena unrelated to the clinical efficacy of lithium. The possibility of the former is supported by the extensive preclinical literature identifying neurotrophic and neuro-protective effects associated with lithium use [17]. However the clinical literature provides only sparse support for this hypothesis since the studies are overwhelmingly cross-sectional in design and thus cannot relate symptomatic or functional improvement to neuroanatomical change over time. Furthermore some longitudinal studies do not include analyses of symptomatic improvement with neuroanatomical variation [59, 62, 63, 95]. However it is notable that the longitudinal studies to date which have included such analyses reported that clinical responders to treatment with lithium have the greatest volume increases in grey matter and prefrontal grey matter [53, 60, 86]. These studies do suggest that the clinical efficacy of lithium is mediated through neurotrophic effects in regions of affect regulation detectable through structural neuroimaging.

An apparently weaker effect on grey matter is reported for the antiepileptic mood stabilisers, with some studies finding more prominent grey matter increases for lithium compared with valproate including one study which used a randomised design [53]. However there is also consistent directionality of the findings, with most studies which had significant findings for mood stabilisers reporting that patients taking these medications have less substantial grey matter deficits than those who do not, again including limbic regions.

The literature is much more sparse for the effects of antiepileptic mood stabilisers on white matter microstructure and for the effects of antipsychotic and antidepressant medications on brain grey and white matter structure. However these studies have even more methodological difficulties than the studies on lithium, with different medications often grouped into a single class, polypharmacy with concomitant mood stabilisers, intermittent use of such medications, and lack of studies employing large patient numbers and prospective designs. It is nevertheless reassuring that the few studies reporting statistically significant findings with the use of these medications were again in the same direction as mood stabilisers towards an ameliorative effect.

The generally normalising effect of psychotropic medications upon brain neuroanatomy is mirrored by similar findings from the functional neuroimaging literature and consistent with the results of preclinical studies which indicate that medications such as lithium have neuroprotective properties [13, 54, 106].

As further structural and diffusion tensor imaging studies emerge on bipolar disorder, they are likely to be accompanied by many more posthoc analyses to assess for the potential impact of psychotropic medications upon the authors’ findings. This will incrementally move the field forward and add to the literature. However to make more substantial progress and lead to more robust findings, structural neuroimaging research needs to move beyond the small scale cross-sectional studies which have characterised the field to date towards designs that will have enough statistical power to more emphatically address questions regarding the drivers of neuroanatomical variation in bipolar disorder. On the one hand this can be progressed by large scale collaborations between research groups where the processed or raw imaging data across many hundreds or thousands of patients with accompanying detailed information on clinical features and medication usage can be analysed using multivariate approaches to assess the impact of medication use when controlling as much as possible for clinical confounds. However there is also a need for large scale longitudinal studies ideally commencing with medication naïve individuals and with repeated multimodal imaging over time to chart the probable dynamic changes of brain structure in the course of bipolar disorder in order to tease apart the effects of clinical course variation and of psychotropic treatment. Ideally more clinically homogenous samples should be employed including monotherapy treatment to simplify the design and analyses. The effects of clinical confounding however can only be optimally removed by including repeated scanning in prospective studies that also include randomized use of psychotropic medications, which could potentially isolate the effect of particular medications upon brain anatomy in bipolar disorder. Firmly pinning down the effects of psychotropic medication upon neuroanatomy in bipolar disorder will boost other aspects of neuroimaging research into the illness by facilitating the parsing out of medication effects from those of illness course and risk factors, such as genotypic variation or exposure to environmental precipitants, and enhance the prospects of identifying reliable neuroimaging biomarkers in the illness.

INTRODUCTION

Bipolar disorder is a persistent, episodic and debilitating condition with an estimated lifetime prevalence of over 2.0%, including both types I (with mania) and II (with hypomania) [1, 2]. Bipolar disorder is associated with recurring episodes of mania, hypomania, mixed manic-depressive states, or psychosis, as well as prominent major depression and dysthymia, as well as prevalent anxiety symptoms—all leading to high risks of potentially severe functional impairment, substance abuse, and high rates of suicide, accidents, and increased mortality from co-occurring medical illnesses—all despite use of available pharmacological and psychosocial treatments [1, 3–8]. The depressive components of the disorder have been especially difficult to treat successfully and they account for three-quarters of the nearly 50% of weeks of follow-up with treatment that include clinically significant residual morbidity [3, 9].

Consensus guidelines and expert recommendations usually advocate use of monotherapy in the treatment of bipolar disorder patients whenever possible, with adjunctive therapy indicated when a patient relapses on maintenance treatment [5, 10, 11]. In reality, unsatisfactory responses to available treatments for bipolar disorder are very prevalent, especially for bipolar depression, and empirical use of various, largely untested, combinations of treatments is the rule [5, 12–14]. Clinical responses that are particularly poor are often labeled as evidence of “treatment resistance,” although the term is defined, imprecisely, by varied numbers and types of treatment trials, responses, and periods of observation [9, 15–18].

We have proposed a working definition of treatment resistance as involving responses considered clinically unsatisfactory following at least two trials of dissimilar medicinal treatments in presumably adequate doses and durations, within a specific phase of bipolar illness (manic, depressive, or mixed), or for “breakthrough” symptoms that emerge despite previous apparently effective maintenance treatment, and excluding patients who are intolerant of a treatment regimen and, to the extent possible, those who are not adherent to recommended treatment [19]. The present overview considers experimental interventions for treatment resistance found in any phase of bipolar disorder, as indicated by clinically unsatisfactory responses to current treatments based on accepted community standards and on expert guidelines and recommendations, as cited above.

METHODS

We searched the digitized medical research literature for reports related to pharmacological treatments of treatment-resistance in bipolar disorder patients using the MedLine/PubMed database of the U.S. National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/entrez/query.fcgi), and limiting the search to reports in English. We used the following search terms in various combinations: bipolar, treatment, drug or medication resistant, resistance, or refractory, and difficult to treat. We initially considered reports of meta-analyses, systematic reviews, randomized controlled trials (RCTs), naturalistic and retrospective studies, case series, and case reports. Hand-searching further considered references cited in reports initially identified by computer-searching. Authors reviewed the abstracts of identified reports, and full reports of articles that met entry criteria were reviewed in detail by at least two authors, who extracted relevant details and resolved disagreements by consensus. Minimal entry criteria included study subjects diagnosed with a bipolar disorder based on an international diagnostic standard, usually the American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders (DSM, editions III, IV, or -5), or the World Health Organization’s International Classification of Diseases (ICD, editions 9 or 10). In view of the paucity of reports on this topic, their consideration did not require specific numbers of subjects, randomization, or controls. Table 11 includes studies that specified trials in treatment-resistant subjects, but the text includes some additional studies with interesting leads developed among bipolar disorder patients who were not necessarily treatment-resistant.

Table 1.

Therapeutic trials for treatment-resistant bipolar disorder.

RESULTS

The search process considered a total of 100 potentially useful reports, of which 38 satisfied entry criteria and provided data reported here. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) flowchart for this review is shown in Fig. 11. These reports are characterized by their design, demographic and clinical characteristics of subjects, and their main findings (Table 11). Several types of psychotropic drug treatments were considered, as follows.

Fig. (1)

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flowchart illustrating methodological steps in identifying empirical studies to be included in this systematic review.

CONCLUSIONS

Bipolar disorder is a prevalent condition with a very large disease burden that includes high social and economic costs, substance abuse, disability, high suicidal risks and increased all-cause mortality rates, incomplete control of long-term morbidity, and especially poor control of depressive components of the disorder. Despite its high prevalence of treatment-resistance, studies of pharmacological treatment options in bipolar disorder remain remarkably scarce, highly variable in the quality of their designs, and largely inconclusive. Most studies reviewed involved relatively small numbers of patients, often admixtures of bipolar (I, II, or unspecified) with unipolar and schizoaffective disorder diagnoses, varying definitions of treatment-resistance, inconsistent definition of and selection by initial clinical states, imprecisely defined aims, and complex treatment regimens to which test agents were added. Encouraging results in apparent treatment-resistant bipolar disorder have been reported by adding clozapine, aripiprazole, pregabalin, bupropion, ketamine, memantine, pramipexole, and perhaps tri-iodothyronine to ongoing, sometimes already complex, regimens. The high prevalence of unresolved morbidity, especially of depressive components, in bipolar disorder requires far more experimental therapeutic trials of consistently better quality, involving coherent sampling, randomization, placebo controls, and simpler treatment regimens. Promising extant short-term findings should be pursued with trials continued for at least a year.

ACKNOWLEDGEMENTS