Author’s Accepted Manuscript Can neuroimaging disentangle bipolar disorder? Franz Hozer, Josselin Houenou

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To appear in: Journal of Affective Disorders Received date: 11 August 2015 Revised date: 2 January 2016 Accepted date: 24 January 2016 Cite this article as: Franz Hozer and Josselin Houenou, Can neuroimaging disentangle bipolar disorder?, Journal of Affective Disorders, http://dx.doi.org/10.1016/j.jad.2016.01.039 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.

Can neuroimaging disentangle bipolar disorder?

Franz Hozer1,2,3, Josselin Houenou1,2,3,4

1 Neurospin, UNIACT, Psychiatry Team, I2BM, CEA Saclay, F-91191, Gif-Sur-Yvette, France 2 INSERM U955, IMRB, Université Paris Est, Equipe 15 “Psychiatrie Translationnelle”, Créteil F94000, France 3 Fondation Fondamental, Créteil F-94010, France 4 AP-HP, Hôpitaux Universitaires Mondor, DHU PePsy, Pôle de Psychiatrie, Créteil, F-94000, France

* Corresponding author: Dr Josselin Houenou, INSERM U955, IMRB, Equipe 15 « Psychiatrie Translationnelle », 40 rue de Mesly, 94000 Créteil, France. Tel/Fax: + 33 1 49 81 30 51 / 32 90. Email: [email protected]

Abstract

Background: Bipolar disorder heterogeneity is large, leading to difficulties in identifying neuropathophysiological and etiological mechanisms and hindering the formation of clinically homogeneous patient groups in clinical trials. Identifying markers of clinically more homogeneous groups would help disentangle BD heterogeneity. Neuroimaging may aid in identifying such groups by highlighting specific biomarkers of BD subtypes or clinical dimensions.

Methods: We performed a systematic literature search of the neuroimaging literature assessing biomarkers of relevant BD phenotypes (type-I vs. II, presence vs. absence of psychotic features, suicidal behavior and impulsivity, rapid cycling, good vs. poor medication response, age at onset, cognitive performance and circadian abnormalities).

Results: Consistent biomarkers were associated with suicidal behavior, i.e. frontal/anterior alterations (prefrontal and cingulate grey matter, prefrontal white matter) in patients with a history of suicide attempts; and with cognitive performance, i.e. involvement of frontal and temporal regions, superior and inferior longitudinal fasciculus, right thalamic radiation, and

corpus callosum in executive dysfunctions. For the other dimensions and sub-types studied, no consistent biomarkers were identified.

Limitations: Studies were heterogeneous both in methodology and outcome.

Conclusions: Though theoretically promising, neuroimaging has not yet proven capable of disentangling subtypes and dimensions of bipolar disorder, due to high between-study heterogeneity. We issue recommendations for future studies.

Keywords: bipolar disorder; heterogeneity; neuroimaging; MRI; sub-types; dimension

Abbreviations

AAO: age at illness onset ACC: anterior cingulate cortex BD: bipolar disorder BP: bipolar patients BP-I/II: bipolar patients type I/II CC: corpus callosum DLPFC: dorsolateral prefrontal cortex DTI: diffusion tensor imaging EOBP: early-onset bipolar patients FA: fractional anisotropy fMRI: functional MRI GM: grey matter IOBP: intermediate-onset bipolar patients LOBP: late-onset bipolar patients LSI: local sulcal indices MPFC: medial prefrontal cortex PET: positron emission tomography PF: psychotic features PPC: posterior parietal cortex

SA: suicide attempts SPECT: single photon emission computed tomography SZ: schizophrenia sMRI: structural MRI VLPFC: ventrolateral prefrontal cortex VPFC: ventral prefrontal cortex WM: white matter WMH: white matter hyperintensities

1. Introduction

Current definitions of bipolar disorder (BD) reflect a clinically and etiologically heterogeneous entity, covering complex and heterogeneous phenotypes (Hägele et al., 2015; Hasler and Wolf, 2015). This may explain the difficulties in forming clinically homogeneous patient groups in clinical trials, leading to only partial effectiveness of current psychotropic treatments and to difficulties identifying neuropathophysiological mechanisms or genetic factors underlying BD (Cuthbert and Insel, 2013).

Although the etiology of BD remains largely uncertain, a growing body of literature seeks to highlight specific biomarkers of this disorder. A biomarker is "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention" (Frey et al., 2013). Awareness of these biomarkers would help clinicians understand underlying neuropathophysiological processes corresponding to neural circuit abnormalities (Hägele et al., 2015; Insel et al., 2010; Phillips and Kupfer, 2013).

Neuroimaging, and specifically magnetic resonance imaging (MRI) shows promise in the search for these biomarkers. However, because results of MRI studies comparing patients with BD and healthy subjects are inconsistent, they have not led to a clear picture of the neuropathophysiological processes underlying BD (Phillips and Kupfer, 2013; Selvaraj et

al., 2012). Studies including clinically more homogeneous subject groups would aid in deciphering BD heterogeneity, but require innovative approaches to identify these subgroups of patients (Houenou et al., 2015). Two approaches that would help achieve this goal are dimensional clinical assessment and clearly defined sub-typing of BD.

One approach, a dimensional clinical assessment of BD, is useful because for one given clinical syndrome, patients with the same diagnosis (e.g., bipolar depression) may have opposite symptoms (e.g., increased or decreased appetite; insomnia or hypersomnia; psychomotor retardation or agitation...). This phenomenon leads to the formation of heterogeneous groups of patients (Casey et al., 2013). A dimensional approach, such as the Research Domain Criteria (RDoC) project proposed by the US National Institute of Mental Health, may help to solve this issue by overstepping the boundaries between different diagnostic categories to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.” (http://www.nimh.nih.gov/research-priorities/rdoc/index.shtml).

Another approach is to sub-categorize BD into clinically defined sub-types. Although the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (American Psychiatric Association, 2013) defines several categories of BD (type I or II, presence or absence of psychotic features and with or without rapid cycling), many others could be identified, including the presence or absence of suicide attempts, differences in age at onset and predominant polarity (Henry and Etain, 2010; Houenou et al., 2015). However, even if these classifications have phenomenological relevance, we do not know at this time if any are biologically relevant. Nor is it clear if they could increase specificity in neuroimaging data and thus help identify the neuropathophysiological mechanisms underlying BD. Working toward answering these questions, we aim to perform a review of the current literature, which attempts to identify such neuroimaging-based potential biomarkers of relevant BD phenotype.

2. Methods One challenge we faced was to define the “core” clinical dimensions and sub-categories related to specific neuroanatomical or functional markers in BD (Houenou et al., 2015). Based on the literature and main models of BD (Phillips & Kupfer, 2013; Houenou et al, 2015), we selected sub-types and clinical dimensions commonly used in epidemiological

and clinical studies. This resulted in six sub-types: history of psychotic features (PF), type (I or II), rapid cycling, history of suicide attempts (SA), response to medication and age at illness onset (AAO - early, intermediate or late); and three dimensions: impulsivity, circadian rhythm abnormalities and cognitive performance.

After defining the sub-groups and clinical dimensions, we searched the online PubMed database for relevant literature. We reviewed English-language studies published before July 2015, using systematic combinations of the keywords “neuroimaging”, and “bipolar”, with each dimension or sub-type. Studies considered for inclusion used a neuroimaging tool, specifically structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), single photon emission computed tomography (SPECT) or positron emission tomography (PET) and compared bipolar patients (BP) according to the different sub-types or along different dimensional levels. We also checked these articles’ reference lists and considered literature reviews. Our method resulted in two types of studies considered, those using a categorical approach (e.g., brain volumes of patients with versus without rapid-cycling), and those using a dimensional approach (e.g., correlation between brain volumes and impulsivity score of patients with BD).

Once we identified the relevant literature, we checked that, in their analysis and interpretation, each took into account the variables most likely to cause bias in neuroimaging (i.e. age, sex; mood state, consumption of substances status, illness duration and psychotropic medication status). For white matter hyperintensities (WMH) and DTI studies, we specifically checked whether they controlled for cardiovascular risk factors. These risks are associated with WMH in the general population (Murray et al., 2005) and so may particularly affect DTI results (Jones et al., 1999).

3. Results

Overall, we identified 63 studies that fit our inclusion criteria. Tables 1-6 show detailed results of each study, that we also summarized in Figures 1-6. For each, we specify neuroimaging technique, neuroimaging approach (e.g. whole brain or regions of interest) when relevant, methodology and main results with effect sizes if available. We also comment on potential methodological issues. All studies adjusted their results by age and sex of patients.

Although the characteristics of the sample populations from study to study were not always the same, most excluded bipolar patients (BP) with a recent history of substance abuse or dependence. Few included BP in a euthymic state. Generally, studies using a categorical approach compared only subgroups for the variables illness duration and psychotropic medication use. Conversely, to test their potential effects, those taking a dimensional approach correlated them with dimension score or neuroimaging data. In all cases, we specify how studies took into account these different confounding variables in their results and analyses.

3.1 Psychotic features history (table 1; figure 1)

We identified 12 studies that included subjects with history of PF: 9 sMRI, 1 DTI and 2 fMRI studies. All selected studies used a categorical approach, comparing BP with and without a history of PF. One study also used a dimensional approach, correlating psychotic symptom severity with neuroimaging data. In most studies, at least one manic or one depressive episode with delusions or hallucinations, independent of mood congruence, defined patients with a history of PF. Among these studies, only two included only euthymic patients (Anticevic et al., 2013; Walterfang et al., 2009b).

BP with a history of PF showed larger corpus callosum (CC) rostrum area (Sarrazin et al., 2015) and larger right globus pallidus (Womer et al., 2014) than those without a history of PF. Six sMRI studies did not find any significant differences between these two sub-groups (Edmiston et al., 2011; Laidi et al., 2015; Radaelli et al., 2014; Strasser et al., 2005; Walterfang et al., 2009a, 2009b). Compared to controls, BP with a history of PF showed larger left ventricle (Edmiston et al., 2011) and lateral + third ventricles volumes (Strasser et al., 2005); no difference was found between BP without a history of PF and controls. Radaelli et al. (2014) showed lower dorso-lateral prefrontal cortex (DLPFC) and insula grey matter (GM) volumes in BP with a history of PF than those without, but these results lost statistical significance after correction for multiple comparisons at the voxel level.

The largest DTI study we identified (Sarrazin et al., 2014) showed lower mean generalized fractional anisotropy (FA) value along the body of the CC in BP with than without a history of PF.

Different paradigms were used in the 3 fMRI studies we selected. One used a memory-

working task (Brandt et al., 2014) that saw higher activation levels in the superior and inferior parietal regions (including postcentral gyrus, inferior and superior parietal lobules, angular gyrus, supramarginal gyrus and inferior temporal gyrus) of BP with a history of PF than in those without this history. Two of the studies used resting state fMRI: Lois et al. (2014) found no difference in functional connectivity between euthymic BP with and without a history of PF. However, using a Global Brain Connectivity method restricted to the prefrontal cortex, Anticevic et al. (2013) demonstrated that euthymic BP with a history of PF exhibited lower medial prefrontal cortex (MPFC) global brain connectivity, higher connectivity between amygdala and MPFC and more negative connectivity between amygdala and DLPFC than euthymic BP without a history of PF. In this study, dimensional approach revealed that the magnitude of observed effects correlated with lifetime psychotic symptom severity.

3.2 Bipolar type I and type II (table 2; figure 2)

We found 10 studies comparing BP type I (BP-I) and type II (BP-II). Of these studies, three included euthymic BP (Caseras et al., 2013, 2015; Li et al., 2012), three depressed BP (Ha et al., 2009; Kiesppä et al., 2014; Maller et al., 2014), one included both euthymic and depressed BP (Gutierrez-Galve et al., 2012) and three did not clearly identify patients’ mood states (Ha et al., 2011; Liu et al., 2010; Strasser et al., 2005).

Three sMRI studies showed smaller GM volumes in right medial orbito-frontal, and left superior temporal regions (Maller et al., 2014), a trend for lower GM volumes in frontal, temporal, occipital and posterior cingulate regions (Ha et al., 2009) and lower left putamen volumes (Caseras et al., 2013) in BP-I compared to BP-II. Two studies explored frontal and temporal cortical thickness and area (Gutierrez-Galve et al., 2012) and ventricular and hippocampal volumes (Strasser et al., 2005), but did not find any difference between BP-I and BP-II.

DTI data showed a trend for lower white matter integrity in BP-II than in BP-I in temporal regions (Maller et al., 2014). This is consistent with Liu et al.’s (2010) results, which demonstrated lower FA values in temporal and inferior prefrontal regions in BP-II than in BP-I. However, Ha et al. (2011) found a higher FA in temporal regions, and a lower apparent diffusion coefficient in frontal, parietal, temporal and thalamic regions in BP-II than in BP-I. Caseras et al. (2015) showed higher FA in the right uncinate fasciculus in BP-

II than in BP-I. These results are not consistent with the findings of Kieseppä et al. (2014), who reported an association between deep WMH grade and BP-I.

One of the fMRI studies used a reward paradigm (Caseras et al., 2013) while the other used an emotion regulation paradigm (Caseras et al., 2015). Respectively, results showed greater left putamen volume and greater bilateral ventral striatal activity (Caseras et al., 2013) and more efficient DLPFC downregulation of amygdala reactivity in BP-II than in BPI, with an increased emotional reactivity inefficiently downregulated in BP-I (Caseras et al., 2015).

Results of the PET study we selected (Li et al., 2012) reflected a greater fronto-limbic dysfunction in BP-I than in BP-II.

3.3 Rapid cycling (table 3)

We found 2 sMRI study comparing BP with and without rapid cycling (Blumberg et al., 2005; Narita et al., 2011). Strikingly, both studies showed smaller ventral prefrontal cortex (VPFC) volumes in rapid cyclers than in non-rapid cyclers.

3.4 Medication response (table 3; figure 3)

We retrieved 3 sMRI (lithium response) and 2 fMRI (antipsychotics and antiepileptics response) studies that included medication response as a variable. Three studies used a categorical approach, i.e. compared responders to non-responders; in these studies, response to treatment was defined by a 50% improvement on the Hamilton Rating Scale for Depression (Hamilton, 1980) and the Young Mania Rating Scale (Young et al., 1978). The two other studies used a dimensional approach, i.e. correlated improvement in symptomatic scores to neuroimaging data. All these studies included BP in various mood states.

One of the sMRI studies found that smaller right amygdala volume and a decrease in left hippocampus and right ACC volumes could reflect markers of lithium non-response after treatment (Selek et al., 2013). For lithium responders, lithium neurotrophic effect could be related to an increase of left PFC, left DLPFC (Selek et al., 2013) and PFC (Moore et al., 2009) or total GM volumes (Lyoo et al., 2010).

The 2 fMRI studies, using a color-matching task in pediatric BP, evaluated effects of antipsychotics and antiepileptics. Poor and good response to treatment could be predicted before treatment, respectively, by an increased amygdala activity (Pavuluri et al., 2011) and by a greater connectivity between left amygdala and PFC (Wegbreit et al., 2011). The latter study also showed greater connectivity between the right amygdala and PFC after treatment in responder compared to non-responder BP.

3.5 Suicide attempts history and impulsivity (table 4; figure 4)

We identified 9 studies (8 sMRI and1 DTI) that took patients’ history of SA into account. We merged SA history and impulsivity, since evidence points towards an association between them in BD (Swann et al., 2005; Mahon et al., 2012; Nery-Fernandes et al., 2012; Baldaçara et al., 2011; Matsuo et al., 2010). Two studies (Nery-Fernandes et al., 2012; Baldaçara et al., 2011) included euthymic BP. One study (Pompili et al., 2008) included BP and unipolar patients (UP) and another large one, from the B-SNIP consortium, included patients along the psychosis spectrum (Giakoumatos et al., 2013).

Studies showed that, compared to patients without a SA history those with a SA history showed lower prefrontal GM volume in the subgroup of patients with past psychiatric hospitalization, higher prefrontal GM volume in subgroup of patients not hospitalized (Lijffijt et al., 2014), smaller parietal (Giakoumatos et al., 2013) or prefrontal volumes (Benedetti et al., 2011) and more periventricular WMH (Pompili et al., 2008). Impulsivity was negatively correlated with anterior CC genu area in BP with SA history (Matsuo et al., 2010) and left rostral anterior cingulate cortex (ACC) GM volume in BP (Matsuo et al., 2009).

DTI data demonstrated that BP with SA history showed lower FA in left orbital frontal white matter (WM) than BP without SA history, and an inverse correlation between mean FA and impulsivity score in the left orbital frontal cluster (Mahon et al., 2012).

3.6 Early, intermediate and late age at illness onset (table 5; figure 5)

We found 6 sMRI studies; results are shown in Table 6. Two designs were used. The most frequent methodology was a categorical approach, comparing different subgroups of BP

based on AAO, defined as early-onset BP (EOBP), intermediate-onset BP (IOBP) and late-onset BP (LOBP). Different cut-off points were used to define these subgroups (for example, AAO cut-off for EOBP subgroup varied between 25 to 60 years, see Table 6 for details). The other design used was continuous, correlating AAO with neuroimaging data. Only Oertel-Knöchel et al. (2015b) and Tamashiro et al. (2008) studied euthymic BP.

Results suggested that LOBP could be characterized by greater left caudate nucleus and left middle frontal gyrus volumes, and smaller right posterior cingulate gyrus volumes (Huang et al., 2011), smaller total brain volume (Beyer et al., 2004) and greater WMH severity score in deep frontal and parietal regions (bilaterally) (Tamashiro et al., 2008) than in EOBP. Study of cortical folding differences showed lower local sulcal indices (LSI) in right DLPFC in IOBP than in EOBP (Penttilä et al., 2009).

Three sMRI studies considered AAO as a continuous variable. AAO was positively correlated with parietal deep WMH score (Tamashiro et al, 2008) and with cortical thickness in left medial orbitofrontal gyrus (Oertel-Knöchel et al., 2015). Frey et al. (2008) showed no correlation between AAO and GM volumes.

3.7 Circadian abnormalities (table 5)

We found only one fMRI study (McKenna et al., 2014) looking at circadian abnormalities. The authors found several positive and negative correlations between different sleep and circadian variables and BOLD signal, during a working memory task, in supramarginal gyrus and DLPFC, bilaterally. Although a small sample size and multiple comparisons groups limit this preliminary study, results suggest that variability in sleep and circadian variables could be associated with abnormalities in brain response.

3.8 Cognitive performance (table 6; figure 6)

We included 18 studies whose main objective was the investigation of neural correlates of cognitive impairment in BP. These reports (11 sMRI, 2 DTI, 2 fMRI, 1 SPECT study and 2 studies combining DTI and fMRI techniques) investigated different cognitive domains. Only one study (Shepherd et al., 2015) used a categorical approach, comparing BP with and without cognitive impairment. All other studies used a dimensional approach, looking for correlations between cognitive performance scores and neuroimaging data. Only 7 studies

included euthymic patients (Ajilore et al., 2015; McKenna et al., 2015; Oertel-Knöchel et al., 2014, 2015a,b; Rej et al., 2014; Strakowski et al., 2004). Two studies included depressed BP (Kieseppä et al., 2014; Poletti et al., 2015), 3 included BP in various mood states (Benabarre et al., 2005; Gutierrez-Galve et al., 2012; Zimmerman et al., 2006) and 6 (Coffman et al., 1990; Fears et al., 2015; Hartberg et al., 2011a,b; Killgore et al., 2009; Shepherd et al., 2015) did not clearly specify BP mood state.

Two sMRI studies assessed associations between WMH severity and cognitive performance in BP. One, a path analysis, highlighted a negative correlation between deep WMH grade and visual attention performances (Kieseppä et al., 2014). The other, Rej et al. (2014), found no correlation between various cognitive functions (i.e. information processing speed/executive, language, memory, visuospatial ability) and overall WMH burden, total GM and hippocampal volumes.

Two studies with large sample sizes highlighted a positive correlation between processing speed performance and left inferior temporal cortical surface. They also found that larger left lateral and inferior lateral ventricle volumes were related to poorer motor speed performance and interference control (Hartberg et al., 2011a,b).

Structural neural correlates of executive functions were assessed in 4 other studies. Shepherd et al. (2015) used a categorical approach, comparing two groups of BP and patients with schizophrenia-spectrum disorders, depending on the presence of executive dysfunction (defined by scoring less than 50% accuracy in a 2-back task performance). They found lower GM volumes in the right inferior frontal, precentral and postcentral gyri in patients with an executive deficit than in those without. Oertel-Knöchel et al. (2015b) showed a positive correlation between problem solving ability and cortical thickness. The 2 other sMRI studies assessing executive functions did not find a correlation between cognitive performance and frontal, temporal or ACC region volumes in BP (GutierrezGalve et al., 2012; Zimmerman et al., 2006).

Two studies correlated memory performance with sMRI data. One found that visual and verbal memory performances were positively correlated with pars orbitalis and supramarginal gyrus thickness (Fears et al., 2015), while the other found a positive correlation with left amygdala volume (Killgore et al., 2009). Using a global assessment of cognitive function (including memory and executive function performance), Coffman et al.

(1990) showed a positive correlation between cerebral and frontal areas and cognitive performance.

Four studies (Ajilore et al., 2015; McKenna et al., 2015; Oertel-Knöchel et al., 2014; Poletti et al., 2015) reported associations between DTI parameters and cognitive performance. Interestingly, they included euthymic BP, with the exception of Poletti et al. (2015) who studied depressed BP. This study showed multiple associations between cognitive performance (i.e. attention and information processing, verbal and working memory, psychomotor coordination and executive functions) and DTI measures in anterior and posterior thalamic radiations, inferior and superior longitudinal fasciculus, inferior frontooccipital fasciculus, cingulum bundle, CC, and corona radiata. However, no adjustment on depressive symptoms score was performed despite the possibility that depressive symptom severity might largely influence cognitive performance (Martínez-Arán et al., 2004).

Associations between DTI parameters (e.g. FA) in CC and cognitive performance (i.e. working memory and processing speed performances) have been reported by McKenna et al. (2015) and Ajilore et al. (2015); in this latter study, a connectome analysis highlighted negative and positive correlations between processing speed and interhemispheric path lengths and efficiencies, respectively. Another DTI study assessing specifically executive performance results showed a positive association between mean diffusivity and performance in the Tower of London test in right thalamic radiation and fornix (OertelKnöchel et al., 2014).

The fMRI studies showed that during a stroop interference task, performance was correlated with right middle temporal gyrus activation (Strakowski et al., 2004) and that bilateral DLPFC activation predicted working memory performance (McKenna et al., 2015). In a resting state fMRI study, Oertel-Knöchel et al. (2015a) showed a positive association between functional connectivity and episodic memory performance in the middle/superior frontal gyrus.

We also selected one SPECT study (Benabarre et al., 2005) which highlighted correlations between cognitive performance and cerebral blood flow in several regions. Main results showed correlations between performance in executive tasks and perfusion in striatal and cingulate regions (positive association), and in cerebellum and frontal region (negative

association), suggesting hypofrontality. Memory performance was related to lower cerebral blood flows in temporal and anterior frontal regions and to higher perfusion in striate regions.

4. Discussion

Recent meta-analyses of MRI studies comparing BP with healthy subjects highlighted some abnormalities. Structural MRI revealed alterations in volumes of prefrontal and specific limbic regions (amygdala, parahippocampal and cingulate cortices) (Houenou et al., 2012). Diffusion tensor imaging (DTI) studies identified micro-structural white matter abnormalities in prefrontal-limbic (uncinate), limbic (cingulum) and callosal tracts (Sarrazin et al., 2014). Most of the functional MRI (fMRI) studies used emotion-processing paradigms and reported hyperactivity of the subcortical limbic structures associated with prefrontal cortices hypoactivation (Delvecchio et al., 2013). Such data suggest abnormalities in neural circuits supporting emotion processing, emotion regulation and reward processing in BD (Phillips and Swartz, 2014).

One promising possible biomarker is the link found between the CC and the BD sub-type, "BP with a history of PF". Specifically, different patterns of anterior interhemispheric connectivity were found in BP, dependent on the presence or absence of a history of PF (Sarrazin et al., 2014, 2015). The authors also underlined that all regions linked by the rostral part of the CC (i.e. orbitofrontal, lateral prefrontal, and medial prefrontal cortices bilaterally) are highly involved in emotion processing and regulation (Sarrazin et al., 2015). Similar decreased callosal white matter anisotropy was previously reported in patients with schizophrenia (SZ) (Knochel et al., 2012; Patel et al., 2011), supporting interhemispheric transfer impairment in patients with psychosis, independent of diagnosis. Brandt et al. (2014) also evidenced a similar pattern of activity in parietal regions in BP with PF and in patients with SZ, suggesting that a history of psychosis could explain these similar results. Finally, results by Edmiston et al. (2011) and Strasser et al. (2005) (higher left ventricle volumes in BP with a history of PF than in those without) are a consistent finding in psychotic disorders (Wright et al., 2000). Volume reductions in DLPFC in BP with PF compared to those without PF history (Radaelli et al., 2014) could contribute to cognitive biases which influence the formation of delusion, since DLPFC is involved in monitoring introspective mental activity and in executive function (Gusnard et al., 2001). Higher basal ganglia volumes in BP with PF history could be attributed in part to typical antipsychotics

effects on the brain, which might lead to basal ganglia enlargement (Corson et al., 1999).

The comparison of BD sub-types I and II led to inconsistent results. The small sample sizes studied could explain these inconsistencies. However, results of Caseras et al. (2013) do suggest a distinction between BP-I and BP-II based on functional differences in ventral striatal activity during reward anticipation. Connectivity abnormalities could also differentiate BP-I and BP-II: more efficient DLPFC downregulation of amygdala reactivity in BP-II (Caseras et al., 2015), whereas BP-I might show increased emotional reactivity inefficiently downregulated and greater fronto-limbic dysfunction (Caseras et al., 2015; Li et al., 2012).

The two studies exploring rapid cycling found a similar reduction in VPFC volumes in rapid cyclers compared to non-rapid cyclers. One possible interpretation of these results is that the repetition of depressive or manic episodes in BP with rapid cycling illness could have neurotoxic effects leading to regional brain atrophy and consequently neurogenesis disruption (Moorhead et al., 2007). Instead of being a biomarker of rapid cycling, reduction in VPFC volumes could thus reflect only the consequences of mood episodes repetition.

The results of studies considering SA history and impulsivity are more consistent. The authors found that SA history in BP could be related to abnormalities of anterior, frontal neural circuits relevant to impulsiveness and suicidal behaviors, such as the anterior CC genu area (Matsuo et al., 2010), anterior cingulate cortex (ACC) (Matsuo et al., 2009) and to altered DLPFC-mediated regulation (Mahon et al., 2012). Moreover, periventricular WMH could lead to disruption of neuroanatomic mood regulation pathways, increasing suicidal behaviors (Pompili et al., 2008; Serafini et al., 2011). These findings parallel those found in other psychiatric conditions: higher rate of WMH in patients with major depressive disorders who attempt suicide (Grangeon et al., 2010) and alterations in structure and function of the prefrontal cortex in suicidal patients (Cox Lippard et al., 2014).

Concerning AAO sub-type, the difference in age cut-offs used to define different AAO led to difficulty in comparing results across studies. The most common definition used for early-onset BD and late-onset BD is onset before 25 years and after 45 years, respectively. Disease onset between these two extremes defines the subgroup “intermediate-onset BD” (Bellivier et al., 2003). The inconsistent application of this definition in the studies selected, resulted in clinically heterogeneous subgroups between

studies, therefore limiting comparability of the results. Moreover, cardiovascular risk factors, leading to cerebrovascular abnormalities, are more frequent in elderly people, and could thus skew the results, particularly in studies comparing late-onset BD subgroup with other subgroups. Finally, the influence of illness duration and mood episodes repetition is particularly relevant when comparing these sub-types, to avoid bias of these variables in the identification of potential biomarkers. Among studies using a categorical approach, only Penttilä et al. (2009) correlated their neuroimaging data (i.e. LSI) with illness duration. The absence of correlation between LSI and illness duration did not support hypothesis of later neurodegenerative processes to explain LSI decrease in IOBP, which could reflect a potential biomarker of this subgroup. Studies using a dimensional approach (i.e. correlating AAO with neuroimaging data) showed that late AAO could be associated with deep WMH, suggesting the role of cerebrovascular factors in the emergence of BD in late life (Tamashiro et al., 2008) whereas early AAO could be associated with cortical thickness in left medial orbitofrontal gyrus (Oertel-Knöchel et al., 2015b), reflecting effects of the disease.

Studying the neural correlates of response to different medication classes (i.e lithium, antipsychotics and antiepileptics) during different time frames, and in different mood states, also limits the comparison and the interpretation of results. The most consistent data is relative to lithium response. If Selek et al. (2013) and Moore et al. (2009) did not show differences between baseline sMRI of responder and non-responder BP, results of Pavuluri et al. (2011) and Wegbreit et al. (2011) could highlight relevant biomarkers of response/non-response, suggesting that increased functional connectivity of amygdala within the fronto-limbic affective network at baseline predicts good treatment response, while increased amygdala activity at baseline predicts poor treatment response.

The influence of mood state is particularly important while identifying biomarkers of cognitive performance in BP, because depressed and manic symptoms clearly impact cognitive processing in a state-dependent manner (Martínez-Arán et al., 2004). This confounding variable was not systematically taken into account by these studies. Moreover, the exploration of different cognitive domains, with different imaging techniques, limits the comparability of results. Consistent results involve the frontal and temporal regions, superior and inferior longitudinal fasciculus, right thalamic radiation, and CC in executive dysfunctions. Poorer executive functions performances were related to smaller GM volumes (Shepherd et al., 2015), smaller cortical thickness and area (Hartberg et al.,

2011a; Oertel-Knöchel et al., 2015b), lower fMRI activation (Strakowski et al., 2004), lower cerebral blood flows (Benabarre et al., 2005) in frontal and temporal regions and larger lateral ventricle volume (Hartberg et al., 2015a,b). Executive functions were associated with DTI parameters in inferior (Poletti et al., 2015) and superior (McKenna et al., 2015) longitudinal fasciculus, right thalamic radiation (Oertel-Knöchel et al., 2014; Poletti et al., 2015) and CC (Ajilore et al., 2015; McKenna et al., 2015; Poletti et al., 2015). Neuroimaging markers of cognitive impairment may be common with other psychiatric disorders (Alexopoulos et al., 2002; Oertel-Knöchel et al., 2013; Perez-Iglesias et al., 2010; Yuan et al., 2007).

Given the heterogeneity of these findings, we considered them at a brain network level. Human brain functions require effective communication between different brain regions. By interacting with each other, key nodes form a complex network known as the human connectome, composed of a “comprehensive structural description of the network of elements and connections forming the human brain” (Sporns et al, 2005). Recent research highlighted three distinct functional networks in this connectome: the central-executive network (key nodes: posterior parietal cortex (PPC) and DLPFC), the default-mode network (key nodes: ventromedial prefrontal cortex and posterior cingulate cortex) and the salience network (key nodes: ventrolateral prefrontal cortex (VLPFC), anterior insula and ACC) (Sridharan et al, 2008). Interestingly, abnormalities in the central-executive network appear to be involved in two subtypes of BP: those with a “SA history”, Benedetti et al. (2011) highlighting lower grey matter volumes in both PPC and DLPFC in these patients, and those with “Type I or II”, Li et al. (2012) showing lower metabolism in DLPFC in BP-I than in BP-II and Liu et al. (2010) finding lower FA in precuneus in BP-II than in BP-I. Differences between BP-I and BP-II have also been found in the three key nodes of the salience network. Li et al. (2012) showed lower metabolism in ACC and insula in BP-I than in BP-II while Liu et al. (2010) found lower FA in inferior frontal gyrus (where VLPFC is located) in BP-II than in BP-I. However, these results need to be replicated. Additionally, the body of literature would benefit from studies specifically looking at connectivity between key nodes of each functional network among the different subgroups of BD.

Limitations and recommendations

Several explanations can be highlighted to explain the variability of the observed results. A first one is that selected studies used different neuroimaging approaches. In sMRI studies

for example, some groups explored WMH, others brain volumes; in fMRI studies, very different paradigms were used; finally, many studies used seed-based approaches, exploring different regions and thus restricting the possibility for meta-analyses. This lack of overall consistency among neuroimaging techniques prevents drawing firm conclusions. Small size of studied samples, and recruitment in single centers might also be involved in the lack of reproducibility and generalizability of the observed results.

Another source of heterogeneity is the large methodological variability in the consideration of confounding factors. Even when results were similarly adjusted for age and sex of patients, the studies sometimes differed in the way they took into account other potential confounders. First, the history of substance abuse: some studies excluded any patient presenting history of substance use, while the majority only excluded patients with a recent history of substance use. However, the minimum duration of abstinence was highly variable between studies, possibly influencing the results. Second, illness duration: very few studies adjusted their results for this variable. Third, the influence of current and past psychotropic medication (including mood stabilizers): these treatments may result for example in neurotrophic effects (Phillips et al., 2008), modifying neurodegenerative processes. None of our selected studies included drug-naive BP for feasibility reasons; moreover, results were very rarely adjusted on previous treatment duration, and so may have been influenced by this variable. Fourth, patient’s mood state: some studies included depressed patients, whereas others included manic or mixed patients or various mood states. Therefore, the observed results could be related to current symptom expression. Only few studies included euthymic patients or adjusted their results on symptom severity. This issue is probably more relevant for functional than structural or diffusion studies. Fifth, cross-sectional design of selected studies might have limited the identification of specific biomarkers in some subgroups (e.g. “history of PF”, “Type I/II” and “SA history”). A patient considered at baseline without PF or SA history may later experience psychotic episode or try to commit suicide; similarly, a BP-II may later experience a manic episode, leading to a BD type I diagnosis. A longitudinal design could thus be more relevant.

Most of these biases arise from the fact that the comparison of bipolar subtypes or along clinical dimensions was a secondary objective of the study in most cases. Therefore, their design was not optimal for investigation of neuroimaging abnormalities and their relationships with clinical parameters in BP. We therefore propose a few recommendations to perform research to help decipher the heterogeneity of BD:

● Perform deep phenotyping of BP in neuroimaging studies, i.e. collecting a large number of clinical variables, particularly, those known to cause bias in neuroimaging studies such as age, sex, substance dependence, cardiovascular risk factors, mood state and current and past psychotropic medication. This would allow for describing potentially relevant subgroups that may influence neuroimaging data: illness duration and age at onset, number of mood episodes, existence of suicide attempts and a history of psychotic features. ● Large-scale sample sizes are required. However, this is limited by the difficulty in obtaining large samples by individual labs. The Scientific community could ease this burden by sharing neuroimaging and phenotyping data of BP, like the Autism Brain Imaging Data Exchange (http://fcon_1000.projects.nitrc.org/indi/abide/), does for MRI data and phenotypic information of patients with autism spectrum disorders, and healthy controls, across 16 international sites. ● Very few multimodal studies have been conducted (e.g. sMRI + DTI). Such studies may help us to get better insight into the differences between BP subtypes.

Finally, a last approach may be proposed, that is clustering patients using their MRI data. For example, Sun et al. (2015) used DTI with cluster analysis to highlight associations between different patterns of WM abnormalities and clinical parameters in patients with first-episode SZ. This neuroimaging data-driven method identified 2 distinct patterns of WM abnormalities; one of these subgroups had more severe negative symptoms, highlighting the existence of two neurobiologically distinct subgroups of patients with SZ. Such data-driven analysis might be used to identify more homogeneous subgroups of BP, and the potential underlying neurobiological patterns.

5. Conclusions

Current neuroimaging data does not yet allow for the identification of neuroimaging-based phenotypes of BD. The lack of consistent methodologies among all studies is the main cause. However, preliminary data suggest that specific neuroimaging-based markers could characterize some subgroups of BP (mostly for suicidal behavior). Finding these biomarkers is an exciting challenge for future research. Such findings could allow a more targeted approach to disease in terms of diagnosis precision, vulnerability assessment, evolution and therapeutic response prediction and planning treatment (Insel and Cuthbert, 2015).

Currently, heterogeneity of disorders classified in DSM, including BD, might lead to the partial effectiveness of current psychotropic treatments. Within the same diagnosis are grouped very heterogeneous patients in terms of neuropathophysiological mechanisms underlying BD. Identification of specific subtypes strongly linked to dysfunction of specific neurobehavioral systems could allow the defining of more homogeneous clinical targets (Casey et al., 2013; Cuthbert and Insel, 2013). New therapeutic strategies, based on therapies specific to dysfunctional brain circuits, could then be developed, increasing their efficiency and their risk/benefit ratio (Casey et al., 2013; Hägele et al., 2015; Insel and Cuthbert, 2015).

Funding This work was supported by the Investissements d’Avenir programs managed by the ANR under reference ANR-11-IDEX-0004.

Additional Contributions Authors thank Melissa Pauling, PhD student, for her help in editing the manuscript.

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Table 1: Psychotic Features History. Study

Method/Sample

Main results

Comments

Sarrazin et al., 2015

- sMRI; ROIs approach (CC). - 172 BP-I, including 113 with history of PF; 146 HC. - Euthymic or depressed BP.

- Larger CC rostrum area in BP with than without history of PF (partial η =0.044).

- Adjustment on mood symptoms severity and medication status; positive correlation between CC rostrum area and illness duration in BP with history of PF. - Large sample size; international multicentre study.

Laidi et al., 2015

- sMRI; ROIs approach (cerebellum). - 115 BP including 53 with history of PF; 52 HC. - Euthymic or depressed BP.

- No difference between cerebellar volumes of BP with/without history of PF.

- No effect of illness duration and medication status; effects of mood symptoms severity unknown. - Large sample size.

Radaelli et al., 2014

- sMRI; ROIs approach (insula, amygdala, ACC, PFC). - 73 BP-I, including 34 with history of PF. - Depressed BP.

- No difference between insula, amygdala, ACC and PFC volumes of BP with/without history of PF.

- No effect of mood symptoms severity, illness duration and medication status.

Womer et al., 2014

- sMRI; ROIs approach (caudate, putamen, globus pallidus, accumbens, thalamus). - 33 BP-I, including 21 with history of PF; 28 patients with SZ; 4 patients with SAD; 27 HC. - Unknown mood state.

- Larger right globus pallidus in BP with than without history of PF.

- Adjustment on typical antipsychotic medication status; effects of other medication status, mood symptoms severity and illness duration unknown.

Edmiston et al., 2011

- sMRI; ROIs approach (lateral ventricles). - 84 BP, including 36 with history of PF; 79 HC. - Euthymic, manic or depressed BP.

- No difference between BP with/without history of PF.

- No effect of medication status; effects of mood symptoms severity and illness duration unknown.

Walterfang et al., 2009a

- sMRI; ROIs approach (CC). - 24 BP-I, including 10 with and 11 without history of PF; 24 HC. - Unknown mood state.

- No difference between total area, length, mean thickness and curvature of CC of BP with/without history of PF.

- Effects of mood symptoms severity, illness duration and medication status unknown.

Walterfang et al., 2009b

- sMRI; ROIs approach (CC). - 70 BP-I, including 46 with history of PF; 75 HC. - Euthymic BP.

- No difference between total area, length, and curvature of CC of BP with/without history of PF.

- No effect of illness duration; effects of mood symptoms severity and medication status unknown.

Strasser et al., 2005

- sMRI; ROIs approach (hippocampus, lateral/third ventricles). - 38 BP-I/II, including 23 with history of PF; 33 patients with SZ; 44 HC. - Unknown mood state.

- No difference between ventricular and hippocampal volumes of BP with/without history of PF.

- Effects of mood symptoms severity, illness duration and medication status unknown.

- DTI; whole brain approach. - 117 BP-I, including 57 with history of PF; - Along the body of the CC, lower mean generalized FA value in BP with than without history of PF (Cohen f=0.27). 86 HC. - Euthymic, manic or depressed BP.

- No effect of mood symptoms severity, illness duration and medication status; effects of cardiovascular risk status unknown. - Large sample size; international multicenter study.

Sarrazin et al., 2014

2

Lois et al., 2014

- fMRI (resting state); whole brain approach. - 30 BP-I, including 13 with history of PF; 35 - No difference between patients with and without history of PF. HC. - Euthymic BP.

- No effect of mood symptoms severity, number of previous episodes and medication status.

Brandt et al., 2014

- fMRI (working memory task); whole brain approach. - Higher amplitude value of superior/inferior parietal regions - 100 BP-I/II including 52 with history of PF; activation in BP with than without history of PF (small effect 100 patients with SZ; 100 HC. size). - Euthymic, manic or depressed BP.

- In BP, elevated mood associated with lower amplitude value of superior/inferior parietal regions activation; no effect of illness duration and medication status on activation of these regions. - Large sample size.

Anticevic et al., 2013

- Lower medial PFC rGBC, higher connectivity between amygdala and medial PFC, and more negative connectivity - fMRI (resting state); rGBC approach. between amygdala and DLPFC in BP with than without history of - 68 BP-I, including 34 with history of PF; 51 - Effects of mood symptoms severity, illness duration and PF. HC. medication status unknown. - Elevated amygdala-medial PFC (Spearman’s ρ=0.31) and - Euthymic BP. decreased amygdala-DLPFC (Spearman’s ρ=-0.44) coupling associated with increased lifetime psychotic symptom severity.

ACC:anterior cingulate cortex; BP: bipolar patients; BP-I/II: bipolar patients type I/II; CC: corpus callosum; DLPFC: dorsolateral prefrontal cortex; DTI: diffusion tensor imaging; fMRI: functional magnetic resonance imaging; GM: grey matter; FA: fractional anisotropy; HC: healthy controls; PF: psychotic features; PFC: prefrontal cortex; rGBC: Global Brain Connectivity restricted to prefrontal cortex; ROIs: regions of interest; SAD: schizoaffective disorder; SZ: schizophrenia; sMRI: structural magnetic resonance imaging.

Table 2: Bipolar Type I and Type II. Study

Main results

Comments

- sMRI and DTI; whole brain approach. - 16 BP-I; 15 BP-II; 31 HC. - Depressed BP.

- sMRI: smaller grey matter volume within the right medial orbitofrontal region; thickener clusters in the right medial orbitofrontal region and in the left superior temporal region in BP-I than in BP-II. - DTI: no difference between BP-I and BP-II.

- No effect of mood symptoms severity and medication status; effects of illness duration and cardiovascular risk status unknown.

Kieseppä et al., 2014

- sMRI. - 13 BP-I; 15 BP-II; 16 UP; 21 HC. - Depressed BP. - Path analysis.

- Belonging in BP-I subgroup as opposed to HC subgroup predicted higher deep WMH grade.

- Higher depressive symptoms severity in BP-II than in BP-II (variable included in statistical analysis); no effect of illness duration; effects of medication status and cardiovascular risk status unknown.

Caseras et al., 2013

- sMRI and fMRI (reward paradigm); whole brain and ROIs (ventral striatum) approaches. - 17 BP-I; 15 BP-II. - Euthymic BP.

- sMRI: larger left putamen volume in BP-II than in BP-I. - fMRI: larger bilateral ventral striatal activity during reward anticipation in BP-II than in BP-I.

- No effect of mood symptoms severity and illness duration; fMRI but not sMRI results remained after controlling for AP status.

- sMRI; ROIs approach (frontal temporal regions). - 25 BP-I, 11 BP-II. - Euthymic or depressed BP.

- No difference in frontal or temporal cortical thickness or area between BP-I and BP-II.

- Higher number of previous hospitalizations in BP-I than in BP-II; no effect of depressive symptoms severity, illness duration and lithium status.

- sMRI; whole brain approach. - 23 BP-I; 23 BP-II; 23 HC. - Depressed BP.

- No difference between BP-I and BP-II.

- No effect of mood symptoms severity and illness duration; effects of medication status unknown.

Strasser et al., 2005

- sMRI; ROIs approach (hippocampus, lateral/third ventricles). - 8 BP-I; 8 BP-II; 16 patients with SZ; 16 HC. - Unknown mood state.

- No difference between ventricular and hippocampal volumes of BP-I/II.

- Effects of mood symptoms severity, illness duration and medication status unknown.

Caseras et al., 2015

- DTI and fMRI (emotion regulation paradigm); ROIs approach (DLPFC, amygdala, accumbens). - 16 BP-I; 19 BP-II; 20 HC. - Euthymic BP.

- DTI: lower FA in the right uncinate fasciculus in BP-I than in BP-II. - fMRI: during the presence of fear distractors, higher activity in the DLPFC and amygdala, and greater negative correlation between the DLPFC and amygdala, bilaterally, in BP-II than in BP-I; during the presence of happy distractors, higher activity in DLPFC, amygdala, and nucleus accumbens in BP-I than in BP-II.

- No effect of mood symptoms severity, illness duration and medication status; effects of cardiovascular risk status unknown.

Ha et al., 2011

- DTI; whole brain approach. - 12 BP-I; 12 BP-II; 22 HC. - Unknown mood state.

- Lower FA in the right temporal WM constituting inferior longitudinal fasciculus in BP-I than in BP-II (Cohen’s d=1.85). - Higher ADC in the left frontal, right parietal, temporal regions and in the right thalamus in BP-I than in BP-II (Cohen’s d ranging from 1.47 to 2.35).

- No effect of mood symptoms severity, illness duration and medication status; effects of cardiovascular risk status unknown. - No Bonferroni correction.

Liu et al., 2010

- DTI; whole brain approach. - 14 BP-I; 13 BP-II. - Unknown mood state.

- Lower FA values in right precuneus, right inferior frontal gyrus, and left inferior prefrontal areas in BP-II than in BP-I.

- No effect of mood symptoms severity, illness duration and medication status; higher anxiety score in BP-II than in BP-I; effects of cardiovascular risk status unknown.

Li et al., 2012

- PET; ROIs approach (DLPFC, PFC, ACC, striatum, insula, thalamus, parahippocampus, temporal cortex). - 17 BP-I; 17 BP-II; 17 HC. - Euthymic BP.

- Hypometabolism in bilateral ACC, insula, striatum, and right DLPFC, and hypermetabolism in left parahippocampus and in left middle temporal gyrus in BP-I relative to BP-II.

- No effect of mood symptoms severity, illness duration and medication status

Maller et al., 2014

Gutierrez-Galve et al., 2012

Ha et al., 2009

Method/Sample

and

ACC:anterior cingulate cortex; ADC: apparent diffusion coefficient; AP: antipsychotic; BP: bipolar patients; BP-I/II: bipolar patients type I/II; DLPFC: dorsolateral prefrontal cortex; DTI: diffusion tensor imaging; FA: fractional anisotropy; fMRI: functional magnetic resonance imaging; HC: healthy controls; PET: positron emission tomography; PFC: prefrontal cortex; ROIs: regions of interest; sMRI: structural magnetic resonance imaging; UP: unipolar patients; WM: white matter; WMH: white matter hyperintensities.

Table 3: Rapid Cycling; Medication Response. Study

Method/Sample

Main results

Comments

Rapid cycling

Narita et al., 2011

- sMRI; whole brain and ROIs (ventromedial PFC) approaches. - 31 BP-II, including 14 patients with rapid cycling; 84 controls. - Euthymic, manic or depressed BP.

- Smaller ventromedial PFC volume in rapid cycler than in non-rapid cycler BP.

- No effect of mood symptoms severity, illness duration and medication status; lifetime number of previous episodes negatively correlated with ventral PFC volume (no adjustment on this variable).

Blumberg et al., 2006

- sMRI; ROIs approach (ventral PFC). - 37 BP, including 16 rapid cycler BP; 56 HC. - Euthymic, manic or depressed BP.

- Smaller ventral PFC volume in rapid cycler than in non-rapid cycler BP (effect size=0.73).

- No effect of mood symptoms severity and illness duration; results attenuated when adjustment on medication use.

Medication response

Selek et al., 2013

- sMRI; ROIs approach (hippocampus, amygdala, PFC, DLPFC, ACC). - 24 BP-I: 6 euthymic patients, 18 noneuthymic, treated 4 weeks with lithium; 11 HC; 12 responder and 6 non-responder. - 2 sMRI: before and after treatment.

- Decreased left hippocampus and right ACC volumes in nonresponder BP after treatment compared to baseline. - Increased left PFC and left DLPFC volumes in responder BP after treatment compared to baseline. - Smaller right amygdala volume in non-responder BP than in euthymic BP and HC.

- No difference between baseline and post-treatment sMRI of responder and non-responder BP. - Effects of illness duration unknown.

Lyoo et al., 2010

- sMRI; whole brain approach. - 22 drug-naïve BP (9 BP-I, 13 BP-II), randomly assigned to lithium or valproate. - Euthymic or depressed BP. - 2 sMRI: before and after treatment.

- GM volume increase correlated with improvement in depressive symptoms in lithium-treated BP (Spearman’s ρ=0.59); no such relationship in the valproate-treated patients.

- No effect of illness duration. - Randomized controlled trial.

Moore et al., 2009

- sMRI; ROIs approach (PFC, subgenual PFC). - 28 depressed BP-I/II, treated 4 weeks with lithium; 11 responder and 17 non-responder. - 2 sMRI: before and after treatment.

- Increase of GM volume in PFC in responder BP after treatment compared to baseline.

- Effects of illness duration unknown.

Wegbreit et al., 2011

- fMRI (color-matching task); ROIs approach (amygdala, ventrolateral PFC). - 34 manic/mixed pediatric BP, treated 6 weeks with risperidone or lamotrigine or valproate; 14 HC; 22 responder and 12 nonresponder. - 2 fMRI: before and after treatment.

- Higher connectivity between left amygdala and PFC before treatment, and between right amygdala and PFC after treatment, in responder than in non-responder BP.

- Effects of illness duration unknown. - Different treatments pooled.

Pavuluri et al., 2011

- fMRI (color-matching task); whole brain approach. - 24 manic/mixed pediatric BP, treated 6 weeks with risperidone or divalproex; 14 HC. - 2 fMRI: before and after treatment.

- Negative correlation between improvement in Y-MRS scores and baseline activation in right amygdala in risperidone subgroup (Pearson‘s r=-0.75), and in left amygdala in divalproex subgroup (Pearson‘s r=-0.61).

- Effects of illness duration unknown. - Double blind randomized trial.

ACC: anterior cingular cortex; BP: bipolar patients; BP-I/II: bipolar patients type I/II; DLPFC: dorsolateral prefrontal cortex; fMRI: functional magnetic resonance imaging; GM: grey matter; HC: healthy controls; PFC: prefrontal cortex; ROIs: regions of interest; sMRI: structural magnetic resonance imaging; Y-MRS: Young Mania Rating Scale.

Table 4: Suicide Attempts History and Impulsivity. Study

Method/Sample

Main results

Lijffijt et al., 2014

- sMRI; ROIs approach (PFC). - 93 BP-I/II, including 51 with SA history. - Euthymic, manic, depressed or mixed BP.

- Smaller PFC GM volume history in subgroup of hospitalization. - Larger PFC GM volume without SA history in hospitalization.

Giakoumatos et al., 2013

- sMRI; whole brain approach. - 172 BP-I with PF, including 51 with SA history; 317 patients with SZ-spectrum disorder; 262 HC. - Unknown mood state.

- Smaller left supramarginal (Cohen’s d=0.34) and right fusiform (Cohen’s d=0.42) gyri volumes in BP with than without SA.

- Effects of mood symptoms severity, illness duration and medication status unknown. - Large sample size; only BP with PF included.

Nery-Fernandes et al., 2012

- sMRI; ROIs approach (CC). - 30 BP-I, including 19 with SA history; 22 HC - Euthymic BP.

- No difference in CC subregions areas between BP with and without SA history. - Higher impulsivity scores in patients with than without SA history, although not correlated with CC areas.

- No effect of illness duration; effects of mood symptoms severity and medication status unknown. - More lifetime psychiatric comorbidities in BP with than without SA history.

Benedetti et al., 2011

- sMRI; whole brain approach. - 57 BP, including 19 with SA history. - Depressed BP.

- Smaller GM volumes in DLPFC, OFC, ACC, STC, parieto-occipital cortex, and basal ganglia in BP with than without SA.

- No effect of illness duration; lithium medication and lower depressive symptomatology associated respectively with increased GM volumes in previous areas and increased total GM volume. - Higher levels of early life stress in BP with SA history.

Baldaçara et al., 2011

- sMRI; ROIs approach (cerebellum). - 40 BP-I, including 20 with SA history; 22 HC. - Euthymic BP.

- No difference in cerebellar volumes between BP with and without SA history. - Higher impulsivity score in patients with than without SA history, although not correlated with cerebellar volumes.

- Effects of mood symptoms severity, illness duration and medication status unknown; higher number of mood episodes in BP with than without SA history.

Matsuo et al. 2010

- sMRI; ROIs approach (CC). - 20 BP, including 10 with SA history; 27 HC. - Unknown mood state.

- Anterior CC genu area inversely correlated with impulsivity in BP with SA history (Pearson‘s r ranging from -0.79 to -0.75). - Higher motor impulsivity score in BP with than without SA history.

- Longer illness duration in BP with than without SA history; effects of mood symptoms severity and medication status unknown. - Only women included.

Matsuo et al. 2009

- sMRI; ROIs approach (OFC, medial PFC, ACC, amygdala). - 63 BP-I/II. - Euthymic, manic, depressed or mixed BP.

- Left rostral ACC GM volume inversely correlated with impulsivity (Pearson‘s r ranging from -0.36 to -0.27). - Higher impulsivity scores in BP with than without SA history.

- No correlation between impulsivity severity and mood symptoms severity, illness duration and medication status.

Pompili et al., 2008

- sMRI. - 44 patients with SA history (28 BPI/II, 16 UP); 55 patients without SA history (33 BP type I-II, 22 UP). - Unknown mood state.

- More periventricular WMH in patients with than without SA history (OR [95%CI]=8.08 [2.67-24.51]).

- No effect of medication status; effects of mood symptoms severity and illness duration unknown; no effect of cardiovascular risk status except trend-level significance for higher total cholesterolemia in patients with SA history. - UP and BP included; no study of the BP subgroup.

Mahon et al., 2012

- DTI; whole brain approach. - 29 BP, including 14 BP with SA history; 15 HC. - Majority of euthymic BP.

- Lower FA in left orbital frontal white matter in BP with than without SA history. - Inverse correlation between FA and motor impulsivity score in orbital frontal white matter region in BP with SA

- No effect of mood symptoms severity and medication status; AAO negatively associated with the attention impulsivity domain; effects of cardiovascular risk status unknown.

in BP with than without SA BP with past psychiatric in BP in patients with than subgroup of BP without

Comments

- No effect of mood symptoms severity, illness duration or medication status. - Only women included; MRI scanner strength differed across subjects.

history (Spearman’s ρ=-0.69). - Higher overall impulsivity score in BP with than without SA history. 95%CI: 95% confidence interval; AAO: age at illness onset; ACC: anterior cingulate cortex; AP: antipsychotic; BP: bipolar patients; BP-I/II: bipolar patients type I/II; CC: corpus callosum; DLPFC: dorsolateral prefrontal cortex; DTI: diffusion tensor imaging; FA: fractional anisotropy; fMRI: functional magnetic resonance imaging; GM: grey matter; HC: healthy controls; OFC: orbitofrontal cortex; OR: odds risk; PF: psychotic features; PFC: prefrontal cortex; ROIs: regions of interest; SA: suicide attempt; STC: superior temporal cortex; SZ: schizophrenia; sMRI: structural magnetic resonance imaging; UP: unipolar patients; WMH: white matter hyperintensities.

Table 5: Early, Intermediate and Late Age at Illness Onset; Circadian Abnormalities. Study

Method/Sample

Main results

Comments

Early, Intermediate and Late Age at Illness Onset - sMRI; whole brain approach. - 32 BP-I; 35 HC. - Euthymic BP.

- Cortical thickness in left medial orbitofrontal gyrus positively correlated with AAO in BP (Spearman’s ρ=0.53).

- No effect of mood symptoms severity, illness duration and medication status.

Huang et al., 2011

- sMRI; whole brain approach. - 19 BP with LOM (AAO>45); 25 BP with EOM (AAO

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