Research reportNeuroimaging-based markers of bipolar disorder: Evidence from two meta-analyses
Introduction
Bipolar disorder (BD) is the sixth largest cause of disability-adjusted life years (Murray and Lopez, 1996) and its prevalence worldwide is at least 1% (Merikangas et al., 2007). This high prevalence is associated with under-recognition (Hirschfeld et al., 2003) and delayed diagnosis leading to inadequate treatment, huge medical costs and high rates of comorbidity (Keck et al., 2008). As a consequence, there is a clear need to improve diagnostic tools and to identify objective biomarkers. Specific functional and structural brain abnormalities underlying cognitive and emotional trait impairments that are present during both acute episodes and remission have been proposed as promising candidates for biomarkers of bipolar disorders (Phillips and Vieta, 2007, Singh and Rose, 2009).
One of the characteristic impairments of BD is abnormal emotional liability reflected by extreme fluctuations in mood and emotions, and heightened emotional reactivity during all phases of the illness (Henry et al., 2003, Henry et al., 2008). The diagnostic criteria for BD, including euphoria, irritability and depression, suggest that not only is emotional reactivity increased but also that the capacity to regulate emotional states is impaired in these patients. On a neurobiological level, excessive emotional reactivity and difficulties in regulating emotions in patients with BD has been proposed by some investigators to result from an imbalance between a functionally hyperactive ventral-limbic pathway and a functionally hypoactive cortical-cognitive pathway (Blumberg et al., 2002, Phillips et al., 2003, Phillips et al., 2008). The ventral-limbic pathway involves the orbitofrontal cortex (OFC), the subgenual cingulate cortex (SGC), the amygdala and the hippocampus. By contrast, deficient emotion regulation is thought to be mediated by a network of brain regions including the dorsolateral prefrontal cortex (dlPFC), the dorsal ACC (dACC), the posterior cingulate cortex (PCC) and the precuneus. Consequently prefrontal and cingulate systems may exert weaker than normal inhibitory control of subcortical structures (Ochsner and Gross, 2005, Strakowski et al., 2005) resulting in impaired emotion regulation. As the ventrolateral prefrontal cortex (vlPFC; Brodmann Area (BA) 45/47) plays an important role in the regulation of emotional states (Kalisch, 2009, Kanske et al., 2010) it has been included in the cortical-cognitive rather than the ventral-limbic path in more recent models of BD (Phillips et al., 2008). These neurobiological models of BD overlap with models for major depression (MD) as introduced by Mayberg (1997; 2003). The models differ, however, in the assumption that the suggested neurobiological alterations mediate a general emotional hyperactivity, i.e. to positive and negative stimuli, whereas for MD this is only expected for negative stimuli. Yet, the specificity of neurobiological alterations has still to be investigated and unfortunately most studies did not use or analyze positive and negative stimuli separately.
In line with the above described neurobiological models of BD, greater than normal activation in ventral-limbic brain structures has indeed been demonstrated in symptomatic and remitted patients with BD, particularly in response to emotional stimuli (Strakowski et al., 2005, Wessa and Linke, 2009). There is also empirical evidence of hypoactivation of cortical-cognitive prefrontal and parietal regions in these patients (Wessa and Linke, 2009), although the relevant findings are inconsistent.
Interestingly, structural neural abnormalities corresponding to these observed functional neural alterations have been described. A recent review (Savitz and Drevets, 2009) and meta-analysis (Arnone et al., 2009) suggest that the prefrontal lobe in general and cortical-cognitive network components such as the dlPFC, in particular, are smaller in BD patients. Diverse findings have been reported for ventral-limbic structures with increased amygdala volume, normal size of the hippocampus and smaller than normal gray matter content in the orbitofrontal cortex, the SGC and the rostral ACC (Hajek et al., 2009, Savitz et al., 2009).
Overall, findings from functional and structural neuroimaging studies of BD seem to support the neurobiological model and the pattern of abnormalities described may thus be a core feature, or biomarker, of bipolar disorder. However, this conclusion is weakened by the inconsistency of empirical findings that may be linked to heterogeneity across studies. Furthermore, most neuroimaging studies of BD are underpowered, with a high risk of type II error (Kempton et al., 2008), leading to false negative findings. On the other hand, false positives may occur from studies undertaking too many statistical comparisons. One methodological approach to at least partly overcome these problems is to use meta-analysis, a method searching for communalities across different studies investigating patients with diverse clinical features (e.g., mood state) and using disparate methodologies. For the purpose of neuroimaging studies in BD this means to search for neurobiological abnormalities common to different samples of patients and using different methodologies.
Meta-analytic tools are now available for functional and structural neuroimaging studies (e.g., Anatomical Likelihood Estimation (ALE)) (Turkeltaub et al., 2002, Laird et al., 2005a, Laird et al., 2005b, Eickhoff et al., 2009) and as the meta-analytic approach explores the generalizability of findings (Rosenthal and DiMatteo, 2001), meta-analyses of neuroimaging data now allow the identification of findings common to several MRI studies without subjective bias. Additionally, meta-analytic tools for neuroimaging data make brain localizations in different studies comparable by transforming them into a single common stereotactic space (Talairach) and using a single terminology. In the last few years, several studies have used the ALE approach to identify functional brain networks underlying diverse executive functions (Minzenberg et al., 2009) and diverse emotional tasks (Fusar-Poli et al., 2009) as well as aberrant neural networks in different mental disorders (obsessive compulsive disorder: Rotge et al., 2010; schizophrenia: Li et al., 2009). No meta-analysis of functional alterations associated with emotional processing has been conducted in bipolar disorder, despite abnormal emotional reactivity appearing to be a core feature of the disease.
Most meta-analyses addressing structural changes have focused on particular regions of interest (ROI) (McDonald et al., 2004, Kempton et al., 2008, Arnone et al., 2009), such as the amygdala; the ALE approach has recently been used to analyze whole-brain voxel-wise differences in gray matter volume (voxel-based morphometry (VBM)) in adult and pediatric bipolar disorder (Ellison-Wright and Bullmore, 2010).
We describe here a meta-analytic approach to looking for functional and structural cerebral biomarkers of bipolar disorder by combining results from functional magnetic resonance imaging (fMRI) studies related to emotional processing and whole-brain VBM studies of gray matter. We used the same ALE strategy for two parallel meta-analyses in adult patients with BD, one addressing functional, the other structural gray matter changes in order to ease comparison of results from both meta-analyses. For the same reason, we restricted the structural meta-analysis to the gray matter. We only included studies of adult patients as neurodevelopment during the adolescence influences the neural modifications in bipolar disorder (Blumberg et al., 2004). We hypothesized that activity in ventral-limbic brain regions implicated in increased emotional reactivity would be higher in patients with BD than controls, whereas the activity in the cortical-cognitive network, implicated in the regulation of emotional states, would be lower. We also expected that BD patients would have more than normal gray matter in these limbic-ventral regions (parahippocampal, amygdala, SGC) and less than normal gray matter in the cortical-cognitive network (DLPFC, dACC, PCC, precuneus).
Section snippets
Study selection
For both meta-analyses we conducted PubMed literature searches of English-language, peer-reviewed studies, published before September 2009 that investigated either the neural correlates of emotional processing by functional magnetic resonance imaging (fMRI) or whole-brain gray matter abnormalities by MRI in adult patients with BD (Type I and II) and matched healthy controls. We also checked reference lists of the studies identified and relevant scholarly reviews.
For both meta-analyses, studies
Studies included
A total of 13 studies identified by the literature search were judged to be eligible for inclusion according to the criteria outlined above. ALE maps for greater activation in patients with bipolar disorder as compared to healthy comparison subjects were calculated from (1) 130 patients and 141 healthy controls and 75 foci for the comparison between all patients and healthy controls, (2) 68 patients and 73 controls and 40 foci for the comparison between euthymic patients with BD and healthy
Discussion
We report the first systematic meta-analyses of functional neuroimaging studies related to emotional processing and of structural voxel-based MRI studies in adult BD. Our aim was to contribute to the identification of potential biomarkers of the disorder for diagnosis and to guide future research.
The strength of meta-analysis of neuroimaging data lies in the objectivity and clarity of its results, based both on a standardized procedure without subjective bias and on a single terminology. One
Conclusion
In conclusion, our meta-analyses of structural and functional neuroimaging studies in BD identified decreased activation and gray matter content in cortical-cognitive brain networks implicated in both voluntary regulation and cognitive control of emotion. Further, marked hyperactivity of subcortical, limbic brain regions, responsible for automatic emotion processing, was observed. These results support and refine previously proposed neurobiological models of the disorder and suggest that an
Role of funding source
The study was supported by grants from the Deutsche Forschungsgemeinschaft (DFG) to MW (We3638/3-1 and SFB 636/C6), and from the French “Agence Nationale pour la Recherche” (ANR) to JH and ML. The DFG and ANR had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
Conflict of interest
None.
Acknowledgements
We thank Wencke Brusniak for assistance with the use of BrainMap and Simon B. Eickhoff for helpful discussions and support via e-mail. We also acknowledge Andrei Szöke for helpful comments on the manuscript. None of these individuals received compensation for their support.
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