Corticolimbic connectivity as a possible biomarker for bipolar disorder.

Review

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Corticolimbic connectivity as a possible biomarker for bipolar disorder Expert Rev. Neurother. 14(6), 631–650 (2014)

Benedetta Vai1–3, Irene Bollettini1,2,4 and Francesco Benedetti*1,2 1 Department of Clinical Neurosciences, Scientific Institute Ospedale San Raffaele, San Raffaele Turro, Via Stamira d’Ancona 20, Milano, Italy 2 C.E.R.M.A.C. (Centro di Eccellenza Risonanza Magnetica ad Alto Campo), University Vita-Salute San Raffaele, Milan, Italy 3 PhD Program in Evolutionary Psychopathology, Libera Universita` Maria SS. Assunta, Rome, Italy 4 PhD Program in Philosophy and Sciences of Mind, Universita` Vita-Salute San Raffaele, Milan, Italy *Author for correspondence: Tel.: +39 022 643 3156 Fax: +39 022 643 3265 [email protected]

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Bipolar disorder is a severe, disabling and life-threatening illness, which affects nearly 2% of the general population. The identification of reliable and objective biomarkers may aid early diagnosis and optimize treatment efficacy. Through a careful overview of the neuroimaging studies which investigated the structural, functional, and effective connectivity in bipolar disorder, we explored the role of a disconnected cortico-limbic circuitry in the development and maintenance of the disorder. This review offers perspectives and suggestions for future research, in order to propose the corticolimbic disconnection as a neurobiological underpinning and biomarker for bipolar psychopathology. KEYWORDS: biomarker • bipolar disorder • connectivity • corticolimbic circuitry • development

Clinically defined by an alternating pattern of recurring depressive and manic episodes, bipolar disorder (BD) is a severe, disabling and life-threatening illness that affects approximately 1–2% of the general population [1] and is one of the ten leading causes of disability in the world [2]. Aiming at the identification of reliable and objective biomarkers to guide clinical decisions, several authors focused their attention on a possible dysfunction in the frontolimbic circuitry underpinning the mood liability of the disorder. A workgroup of leading teams in BD neuroimaging recently proposed a consensus model of the frontolimbic network as a key circuitry in the pathophysiology and maintenance of the disorder [3]. Moreover, the International Society for BDs suggested a disruption brain connectivity within prefrontal–limbic system as possible biomarker for the disorder [4]. Nevertheless, further attention on connectivity and its relationship with bipolar psychopathology is required. Although in the past decade functional neuroimaging has successfully focused on functional segregation as a principle of organization in the human brain [5], the appearance of more advanced neuroimaging techniques is gradually shifting research perspective toward the investigation of how highly specialized areas are integrated in functional and structural networks. 10.1586/14737175.2014.915744

From a theoretical point of view, brain connectivity may be classified as structural, functional or effective connectivity (EC). Structural connectivity (SC) is commonly defined in terms of macrofiber pathways, especially white matter (WM) tracts. SC also theoretically includes the connectivity at synaptic level, which is however still very difficult to assess [6]. Functional connectivity (FC) indicates the temporal correlation (in terms of statistically significant dependence) among the activitions of different brain regions [7]. EC evaluates the direct or indirect influence that one neural system exerts over another and it can be estimated directly from the fMRI signals (i.e., following a data-driven approach) or based on a priori models, which allow to specify the causal relationship among regions [5]. Both EC and FC are estimated on fMRI data. The aim of the present review is to explore the role of a disconnected frontolimbic circuitry in the development and maintenance of BD by integrating functional, structural and EC findings. Connectivity in functional MRI

Aiming at identifying possible biomarkers for BD, several authors suggested that abnormalities within frontolimbic structures might provide a neurobiological basis for emotional and mood dysregulation [8–10]. fMRI studies usually describe

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631


Review

Vai, Bollettini & Benedetti

an increased ‘bottom-up’ and/or decreased ‘top-down’ inhibitory regulation of subcortical hyperactivity, leading some authors to suggest specific models of the neural circuitries that might be involved in bipolar psychopathology [3,11]. It is surmised that the mood liability in BD could be due to an overactive emotional ventral system that includes amygdalae, insula and the ventral areas of striatum, anterior cingulate cortex (ACC) and prefrontal cortex (PFC). This ventral network is involved in identifying salient emotional stimuli and in mediating autonomic responses [12]. A second network, which includes hippocampus, dorsal ACC and dorsal PFC, interacts with the previous one in contributing to BD pathophysiology. This dorsal system is usually recruited from selective attention, planning and explicit regulation of emotional states, with its hypoactivation being underpinning of the unstable cognitive control of emotions and affect in BD [11]. Recently, Strakowoski et al. focused their attention on two partially overlapping ventral prefrontal system involved in emotional regulation: one is implicated mainly in processing external emotional stimuli and recruits the ventral portion of PFC; the second one is activated by internal emotional stimuli and originates in the ventromedial orbitofrontal cortex (OFC). Both of these networks can recruit limbic and subcortical structures, such as thalamus and globus pallidus, and are engaged in amygdalae modulation. Alterations in these networks may results in a loss of homeostasis in emotional processing, which may contribute to mood liability and BD disorder [3]. Functional neuroimaging studies confirmed alterations within these frontolimbic networks in BD [3,8]. The amygdala is a critical area in this circuitry because it is necessary for perceiving stimuli with affective salience including fearful or dangerous environmental stimuli [13,14]. The activity of this structure changes across mood states in BD and defines a statedependent feature of the disorder [15]. The amygdala is hyperactivated in mania, whereas contrasting results have been found during depression [15]. Amygdala is part of a wider circuitry, which involves other regions, including dorsolateral PFC (DLPFC), ACC, posterior cingulate cortex, insula and parahippocampal gyrus [16]. These areas are involved in attributing the emotional salience of stimuli and interact to regulate affective states and define the emotional significance of the stimuli [11,17]. It is hypothesized that their reduced activity in BD could contribute to an erratic amygdala activity, with unstable emotions. In turn, an abnormal functioning of frontolimbic networks in BD might contribute to mood liability and emotional dysregulation typical of the disorder and increase the vulnerability of BD subjects to lapse into mood episodes [15,18,19]. Adding up to these abnormalities, a replicated finding is the hypoactivation of ventrolateral PFC (VLPFC) also persisting in euthymia and which might identify a trait feature of BD [20]. The VLPFC plays a key role in integrating emotional information and regulating the intensity of emotional responses [21,22]. Also the other regions involved in emotional processing showed state-dependent alterations. In particular, patients with BD showed a reduced ACC activity in mania [23–25], whereas the 632

striatum is hyperactivated during euthymia [26–30] (for a detailed review, see [15]). This dysregulation in brain activities may result from an altered connectivity between prefrontal and limbic areas. Several studies of FC evidenced a reduced negative connectivity between the VLPFC/ACC and the amygdala in manic [31,32], euthymic [33,34] and depressed patients [35]. A reduced FC in depression was also found between the amygdala and DLPFC and OFC [36]. A comparison of FC in healthy controls (HCs), BD and their first-degree relatives [37] showed that abnormalities in frontoinsular connectivity are a key correlate of disease expression for BD, whereas the reduced frontocingulate FC was associated with BD phenotype irrespective of clinical outcome. BD patients and their relatives affected by major depressive disorder showed additional abnormalities in frontal–basal ganglia connectivity, while increased coupling between the ventral and dorsal lateral PFC was observed in relatives without any Axis I disorder [37]. A way to explore the task-independent FC is to measure the resting-state fluctuations in brain activity with fMRI. Several studies focused their attention on the basal activity of the brain, unrelated to any explicit tasks [38] in order to isolate different brain networks including the primary motor network, the extrastriate visual network, the default mode network, the parietal–frontal network and other corticolimbic networks [39–41]. Eight studies explored the resting-state connectivity in BD. The heterogeneity of the samples and of the assessment methods limits noticeably the inference from the data. From an overall perspective, all the studies supported a dysregulation in corticolimbic circuitry [42]. Alterations of resting-state connectivity between PFC, ACC and mesolimbic areas including amygdala, thalamus, insula have been found in manic, depressive, mixed or euthymic patients [43–51] (for a detailed review, see [42]). In the last decade, new powerful fMRI techniques have been developed, such as Dynamic Causal Modeling [52] and Granger Causality Analysis [53], to model and estimate interactions among neuronal populations in terms of causal relationships. These techniques made it possible to infer how functional networks differ between populations. Despite the potential of these techniques, only five studies performed these analyses in BD. Their results are definitely promising. In the first study, Almeida et al. observed a reduced topdown connectivity from left orbitomedial PFC to Amy combined with an enhanced right-sided bottom-up EC in female depressed bipolar patients compared with HC during the emotional labeling of happy faces. The medication load was associated with an amelioration of this abnormal top-down EC, suggesting that EC might be proposed as a target for effective treatments. Patients also showed a reduced left-sided top-down orbitomedial PFC –Amy EC when processing sad stimuli, but this result did not survive correction for multiple tests [54]. In the second study, the same authors explored the EC between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems in remitted BD patients and HC during the viewing of mild and intense Expert Rev. Neurother. 14(6), (2014)


Corticolimbic connectivity as a possible biomarker for BD

happy and neutral faces. The abnormally increased right parahippocampal–subgenual cingulate cortex and the reduced right parahippocampal activity to emotional stimuli suggest a dysfunctional ventromedial neural system, which is implicated in early stimulus appraisal, encoding and automatic regulation of emotion [55]. Perlman and colleagues [56] confirmed alterations in the EC between Amy and dorsomedial and VLPFC in depressed and remitted bipolar patients compared with HC during the processing of happy and sad facial expressions. Furthermore, a reduced top-down EC from rostral/dACC to Amy was also confirmed in remitted and euthymic patients during a task that required the attentional control in the presence of fear distracters [57]. Recently, a study performed by Dima et al. evidenced that bipolar patients carrying the genetic risk variants of ACNA1C and ANK3 genes, which influence the neuronal firing by modulating calcium and sodium channel functions, exhibited a reduced EC in visual–prefrontal network, whereas HC with the same genetic variant showed an increased EC within this network. EC was measured during a facial affect-processing task [58]. Finally, our group detected a decreased top-down EC between DLPFC and Amy during emotion labeling in depressed patients with BD, thus confirming the hypothesis of a deficit in the inhibitory modulation of the Amy by cortical structures [59]. Interestingly, in the same study, we also observed a negative correlation between the strength of the ACC to Amy connection and the score on the suicide item of the Hamilton depression rating scale during depression, thus confirming the clinical relevance of these findings. Despite some contrasting results, fMRI studies generally detected abnormalities within the corticolimbic network in BD [3,8]. A reduced connectivity between VLPFC/ACC/OFC and limbic areas has been widely found across mood states [31–35] and also in not affected relatives [37]. These data suggest that the chronic reduced or altered top-down modulation, confirmed by EC studies, may identify an endophenotype of BD. A reduced activation of PFC [60,61], and a disrupted inhibitory top-down connectivity, also detectable in euthymia [33,34], probably co-occur in interfering with the cortical regulation of limbic structures. An impaired top-down modulation in emotional processing in response to stressful life events provides a biological underpinning of the increased bipolar vulnerability to lapse into mood episodes after stressful events. TABLE 1 summarized the main characteristics of the studies, which explored FC and EC in BD. Structural connectivity

fMRI data suggest disrupted neural connectivity as core biological correlate of BD, and it could play a role in the mechanistic explanation of its symptomatology [62]. The functional dysregulation of brain network may be due to WM pathology as suggested by an increased number of evidences [63]. In order to assess the neuroanatomical connectivity of WM fibers, nowadays, the most informahealthcare.com

Review

advanced techniques are based on diffusion tensor imaging (DTI), which measures the microscopic diffusion of water and allows the indirect investigation of the integrity and bundle coherence of brain WM tracts. The most commonly used parameter is fractional anisotropy (FA) value that is considered to be a measure of WM integrity. Decreased FA value was often associated with demyelization, edema, gliosis or inflammation in tissues [64,65]. The other DTI measures used to study the integrity of WM fiber tracts are axial, radial and medial diffusivity (AD, RD and MD). AD provides information on axonal integrity; low AD reflects axon injury both in ischemic [66,67] and chemically induced WM lesions [68]. RD reflects possible alterations in the myelin integrity, and increases in RD have been linked to incomplete myelination in mice [69], drug-induced demyelination [67] and loss of myelin following axon injury [66,67]. Finally, MD can be a sensitive indicator of the overall developmental changes in the brain tissue [70]: increased MD was observed in conditions of reduced membrane density [71] such as tissue degeneration after injury [72,73]. In BD, the most consistent findings highlighted a decreased FA and/or increased MD in limbic–striatal, callosal and prefrontal regions in adults [63,74–88]. DTI studies, using a region of interest approach, reported reduced FA in adult patients in superior frontal WM tracts [74], anterior-middle corpus callosum (CC) [86], anterior cingulum bundle [87], anterior limb of the internal capsule, anterior thalamic radiation and of the uncinate fasciculus [82,84]. Whole-brain DTI analysis reported decreased FA in temporo-occipital regions, with increased MD in frontal and prefrontal WM [76] and reduced FA in the genu of CC, right inferior longitudinal fasciculus (ILF) and left superior longitudinal fasciculus (SLF) [77]. Significantly decreased FA and increased MD in bilateral prefrontal–limbic– striatal WM and right inferior fronto-occipital, SLF and ILF were observed in currently depressed, but not in euthymic patients [85]. However, higher FA in bilateral frontal WM with lower FA in the left cerebellar WM was also reported [89]. Benedetti et al. found a widespread lower FA in depressed bipolar patients, specifically in the genu of CC and in anterior and right superior–posterior corona radiata and higher values of RD in WM tracts of splenium, genu and body of CC, right mid-dorsal part of the cingulum bundle, left anterior and bilateral superior and posterior corona radiata, bilateral SLF and right posterior thalamic radiation [63]. Finally, recent studies have highlighted that decreased frontotemporal FA may differentiate between BD and major depressive disorder [88,90]. Only one study combined DTI and FC analyses, finding that in bipolar patients the reduction of FA in the uncinate fasciculus was significantly positive correlated with a decreased FC between ACC and amygdala [33]. On the other hand, some studies found contrasting results: Beyer et al. and Houenou et al. evidenced no differences in FA between BD patients and HC [75,91], whereas an increased FA in anterior frontal regions [92] and in CC [93] have also been found. Furthermore, Wessa et al. showed an increase of FA in medial frontal, precentral, inferior parietal and occipital WM 633

634

15 manic/Dep. BDI

9 manic BDI

33 BDI; n = 16 euthymic; n = 7 Dep.; n = 10 manic/mixed

30 euthymic BDI

31BDI; n = 17 remitted; n = 14 Dep.

21 Dep. BDII

39 euthymic BDI; 25 healthy firstdegree relative; 14 MDD firstdegree relative

10 BD, 1 BDII; n = 6 manic; n = 5 Dep.

14 manic BDI

15 BD (no clear type), n = 8 euthymic, n = 2 Dep., n = 5 maniac or mixed

68 euthymic BDI divided into two groups: without psychosis history and with history

17 manic and mixed BDI

64 BD (no clear type); n = 3 Dep.; n = 8 maniac

Cerullo et al. (2012)

Foland et al. (2008)

Wang et al. (2009)

Townsend et al. (2013)

Versace et al. (2010)

Vizueta et al. (2012)

Pompei et al. (2001)

Anand et al. (2009)

Chai et al. (2011)

Chepenik et al. (2010)

Anticevic et al. (2013)

Ongur et al. (2010)

Meda et al. (2012)

Resting state

51

118

Resting state

Resting state

Resting state

10

15

Resting state

15

Resting state

Stroop color Word task

48

15

Emotional task

Emotional task

Emotional task

Emotional task

Emotional task

Emotional task

fMRI task

21

24

26

31

9

15

Number of healthy control

Independent component analysis


Restricted global brain connectivity and ROI method

ROI method

ROI method

ROI method

PPI

Correlational analysis TS

Linear and nonlinear dependence TS

PPI

Correlational analysis TS

PPI

Regression analysis TS

Technique

HC, Healthy relative > BDI, MDD relative

VLPFC–GP; VLPFC–CN

HC > euthymic BDI Euthymic BDII > HC

Amy–DLPFC Amy–MPFC

Meso/paralimbicfrontotemporal/ paralimbic regions

MPFC–parietal cortex

[45]

HC > BDI VLPFC and dorsofrontal and parietal regions

[47]

[48]

HC > manic and mixed BDI BD > HC

[46]

[44]

[43]

[37]

[36]

[35]

[34]

[33]

[32]

[31]

Ref.

No differences

HC > BD

Healthy relative > BDI, HC, MDD relative

VLPFC–DLPFC

ACC–thalamus

HC > relative

VLPFC–ACC

HC > Dep. BDII:

For sadness: Dep./ remitted BDI > HC. For happiness: HC > Dep

Amy–OFC

Amy–OFC, Amy–DLPFC

HC > euthymic BDI

HC > BDI

HC > manic BDI

HC > manic BDI. Dep. BDI > HC

Effect

Amy–VLPFC

Amy–ACC

Amy–VLPFC

Amy–VLPFC

Connections

ACC: Anterior cingulate cortex; Amy: Amygdala; BDI: Bipolar disorder type I; BDII: Bipolar disorder type II; CN: Caudate nucleus; Dep.: Depressed; DLPFC: Dorsolateral prefrontal cortex; GB: Globus pallidus; HC: Healthy controls; IPL: Inferior parietal lobe; MFG: Middle frontal gyrus; MPFC: Medial prefrontal cortex; OFC: Orbitofrontal cortex; PFC: Prefrontal cortex; PHG: Parahippocampal gyrus; PPI: Psychophysiological interaction; ROI: Region of interest; TS: Time series; VLPFC: Ventrolateral prefrontal cortex.

Number of bipolar patients

Study (year)

Table 1. Characteristics of the studies included in review: connectivity in functional MRI.


Review Vai, Bollettini & Benedetti

Expert Rev. Neurother. 14(6), (2014)


26 Dep. BD (no clear type)

26 Dep. BDI

15 Dep. BDI; 16 MDD

21 remitted BDI

52 BDI; n = 31 remitted; n = 21 Dep.

22 remitted and euthymic BDI

52 Dep. BDI

Liu et al. (2012)

Liu et al. (2012)

Almeida et al. (2009)

Almeida et al. (2009)

Perlaman et al. (2012)

Mullin et al. (2012)

Radaelli et al. (2014)

40

19

25

25

16

26

26


Emotional task

Emotional task

Emotional task

Emotional task

Emotional task

Resting state

Resting state

fMRI task

Dynamic causal modeling

Granger causality

Granger causality



Amplitude of lowfrequency fluctuation


Technique Dep. BD > HC Dep. BDI > HC HC > Dep. BDI

For happiness: HC > BDI; BDI > HC. In BDI: OFC ! Amy positively correlated to medication load. For sadness: HC > BDI BD > HC For happiness: remitted BDI > HC. HC > Dep. BDI. For sadness: Dep. BDI > remitted BDI For happiness: HC > remitted, BDI > Dep. BDI HC > BDI HC > Dep. BDI

Insula–CN Postcentral gyrusPHG, temporal gyrus, inferior frontal gyrus, posterior lobe of cerebellum OFC ! Amy

PHG ! ACC PFC ! Amy

Amy ! PFC

ACC ! Amy DLPFC ! Amy

Effect

MFG–IPL

Connections

[58]

[57]

[56]

[55]

[54]

[50]

[49]

Ref.

ACC: Anterior cingulate cortex; Amy: Amygdala; BDI: Bipolar disorder type I; BDII: Bipolar disorder type II; CN: Caudate nucleus; Dep.: Depressed; DLPFC: Dorsolateral prefrontal cortex; GB: Globus pallidus; HC: Healthy controls; IPL: Inferior parietal lobe; MFG: Middle frontal gyrus; MPFC: Medial prefrontal cortex; OFC: Orbitofrontal cortex; PFC: Prefrontal cortex; PHG: Parahippocampal gyrus; PPI: Psychophysiological interaction; ROI: Region of interest; TS: Time series; VLPFC: Ventrolateral prefrontal cortex.


Study (year)

Table 1. Characteristics of the studies included in review: connectivity in functional MRI (cont.).



Review

635


Review


in euthymic patients [94]. Another DTI study showed widespread reductions of FA in the main WM tracts, including the CC, cortical and thalamic association fibers, in euthymic BD patients compared with HC [95]. Reduced FA in remitted patients in bilateral prefrontolimbic–striatal WM and right inferior fronto-occipital, superior and ILF was confirmed also in another study [85]. Moreover, depressed patients showed the same reductions of FA observed in remitted sample compared with HC [85]. In contrast with these findings, in another mixed sample, currently depressed patients showed lower FA values compared with remitted patients [96], while among clinically stable patients, subsyndromal depression ratings correlated inversely with FA which was reduced in euthymic patients compared with HC [84]. Contrasting findings may be due to major clinical changes across illness phases [88]. Not reporting the current states of patients [74,76,89] or including heterogeneous samples [75,86,87,96,97], also in terms of pharmacological treatment, may deeply affect the results, their meaning and the possibility to identify WM abnormalities as state or trait feature of the disorder. Nevertheless, it should be noted that the increase of FA values in euthymic conditions could well reflect neuroplasticity [94], and that several factors such as increases in myelination, microscopic repairs of axonal structures, increases in axonal diameter, packing density and fiber branching could cause higher directionality and contribute to higher FA in the regions that have been associated with neuropsychological deficits [98]. Other studies evaluated WM abnormalities in unaffected relatives of bipolar patients. Research at-risk subjects are quite useful for two main reasons. First, it allows reducing the effect of some confounding factors such as illness duration and exposure to previous treatments. Second, it permits to evaluate the role of WM alterations in term of hereditable tract variable and endophenotype. Recently, Esmell et al. reported evidence for a genetic liability for BD in the FA reduction in uncinate fascilus and SLF by comparing BD patients, unaffected relatives and HC [99]. Mahon et al. in a similar sample found that FA values differed significantly among the three groups within the right temporal WM [100]. In unaffected siblings, FA values correlated negatively with trait impulsivity and they showed an intermediated pattern of WM integrity reduction between HC and patients. Other authors confirmed lower FA in unaffected relatives of bipolar patients [101,102]. On the other hand, Chaddock et al. [77]. did not detect any difference comparing bipolar patients’ relatives to HC, but they found that genetic liability was associated with widespread FA reductions. Finally, another study failed in demonstrating an association between polygenic risk and FA in subjects from the same family cohort [103]. Notwithstanding some contrasting results, data suggest that WM abnormalities are shared by affected patients and their relatives and could then represent a potential endophenotype of the illness [104]. In conclusion, the results about WM abnormalities in BD, summarized in TABLE 2, prompt an impaired brain ‘communication’, generalized to limbic, frontal, parietal, fronto-occipital and 636

interhemispheric connections. An altered anatomical connectivity may affect the functional integrity of the brain. Alterations and degenerations of myelinated nerve fibers lead to disconnections, and the subsequent remyelination of axons by shorter internodes and thinner myelin sheaths might slow down the rate conduction along nerve fibers [105]. These changes affect the integrity and timing in neuronal circuits and could contribute to an impaired FC. These structural alterations in bipolar patients probably result in an abnormal corticolimbic connectivity, as observed with fMRI, and in the cognitive and emotional deficits typical of BD [106]. Compromised WM integrity in cortical and subcortical structures of the anterior limbic network [62] and anterior prefrontal network [107] is consistent with the concept of disrupted neurocircuitry involved in impulse control and emotional regulation, which are core features of BD (see a comprehensive review in [108]). Developmental perspective

The onset of BD is typically located in late adolescence and is usually anticipated by subsyndromal mood symptoms such as depression, anxiety, sleep disturbances and irritability [109,110]. A quarter of the children and adolescents, who present these subsyndromal symptoms, has a frank onset of BD throughout the two following years [111], and the prevalence of BD in firstdegree relatives of patients affected is estimated between 10 and 16% [112,113]. The study of the neural correlates of the bipolar phenotype in childhood and adolescence could help to identify reliable endophenotypes/biomarkers of the disorder. Although several fMRI studies led to consistent findings about altered activation of areas in emotional circuitry in pediatric samples [114–116], few authors explored the FC in BD in this age range. The studies of FC are summarized in TABLE 3. Wang et al. found a decreased FC in ventral anterior cingulate, orbitofrontal, insular and temporopolar cortices in adolescents with BD, compared with the HC group, during processing of emotional faces [117]. Another recent study supported these findings: a reduced VPFC–Amy connectivity in response to fearful faces and a greater DLPFC–VLPFC connectivity to happy faces differentiated bipolar patients type I from HC and bipolar patients not otherwise specified [118]. A significant reduced connectivity between the left amygdala and the right posterior cingulate/precuneus and the right fusiform gyrus/parahippocampal gyrus was found in children affected by BD compared with matched HC during face emotion identification (hostility, fearfulness) [119]. Interestingly, Wegbreit et al. showed that in maniac pediatric bipolar patients, a greater connectivity in the frontolimbic network predicted a better medication response to mood stabilizer as well as the improvement in symptomatology [120]. Finally, only one study explored the connectivity during a resting-state condition in pediatric patients, showing greater negative FC between the left DLPFC and the right superior temporal gyrus versus control subjects [121]. Furthermore, secondary analyses using partial correlation highlighted that bipolar and control youths had opposite phase relationships between spontaneous FC fluctuations in the left DLPFC and right superior Expert Rev. Neurother. 14(6), (2014)


40 Dep. BDI

9 BDI

14 BD

36 BDI

19 psychotic BDI 21 unaffected firstdegree relatives

18 Manic BD

12 BDI 12 BDII

18 BD

28 BD

40 DBI 25 SZ

117 healthy firstdegree relatives

Benedetti et al. (2011)

Adler et al. (2004)

Beyer et al. (2005)

Bruno et al. (2008)

Chaddock et al. (2009)

Chen et al. (2012)

Ha et al. (2011)

Lin et al. (2011)

Macritchie et al. (2010)

McIntosh et al. (2008)

Sprooten et al. (2011)

79

49

28

16

22

27

18

28

21

9

21

Number of healthy controls

MD: BD > HC

CC, right PC, right ACR, SCR, right SLF, right PTR

TBSS

Probabilistic tractography

ROIs analysis of DTI

Tractography

Voxel-based analysis of DTI


ADC: BDI, BDII > HC FA: BDII > BDI ADC: BDI > BDII

AC Left frontal, right parietal and temporal WM Frontal, temporal, parietal and thalamic WM

CC, IC, EC, ATR, ILF, SLF, IFO, UF, CST

UF, ATR

CC

FA: HC > BD relative

FA: HC > BD, SZ

FA: HC > BD MD: BD > HC

FA: HC > BD

FA: HC > BDII

CC, AC, PC

ATR, UF, SLF, CB

FA: HC > BDI

FA: HC > BD

Increased genetic liability for BD associated with reduced FA

CC, AC, PC, SLF, ILF, IFO, UF

Left PCR

ILF, SLF, UF, CC, IFO, PCR

Genetic liability scale correlation to FA

FA: HC > BDI

FA: HC > BD

IFO CC, IC, ILF, SLF, ACR, IFO

MD: BD > HC

ADC: BD > HC

IFO, SLF, CC

OFC

FA: HC > BD

RD: BD > HC

CC, PC, left ACR, SCR, PCR, SLF, PTR

WM above the anterior commissure

FA: HC > BD

Main findings

CC, ACR, right SCR, right PCR, PC

WM tracts



ROIs analysis of ADC and FA maps


TBSS

Technique

[83]

[82]

[81]

[80]

[79]

[78]

[77]

[76]

[75]

[74]

[63]

Ref.

AC: Anterior cingulum bundle; ACR: Anterior corona radiata; AD: Axial diffusivity; ADC: Apparent Diffusion Coefficient; ATR: Anterior thalamic radiation; BDI: Bipolar disorder type I; BDII: Bipolar disorder type II; BD-NOS: Not otherwise specified; CB: Cingulum bundle; CC: Corpus Callosum; CPT: Corticopontine tract; CST: Corticospinal tract; Dep.: Depressed; EC: External capsule; FA: Fractional anisotropy; HC: Healthy controls; IC: Internal capsule; IFO: Inferior fronto-occipital fasciculus; ILF: Inferior longitudinal fasciculus; MD: Medial diffusivity; OFC: Orbito frontal cortex; OR: Optic radiation; PC: Posterior cingulum bundle; PCR: Posterior corona radiata; PTR: Posterior thalamic radiation; RD: Radial diffusivity; ROI: Region of interest; SCR: Superior corona radiata; SLF: Superior longitudinal fasciculus; SZ: Schizophrenic patients; TADC: Trace apparent diffusion coefficient; UD: Unipolar depression; UF: Uncinate fasciculus; WMH: WM hyperintensity.


Study (year)

Table 2. Characteristics of the studies included in review: structural connectivity.



Review

637

638 26

40

37 BDI (16 Dep., 21 remitted)

33 BD

42 BD

15 BDI 15 MDD

30 BD (25 BDI, 2 BDII, 3 BD-NOS)

15 BDI 16 UD

Zanetti et al. (2009)

Wang et al. (2008)

Wang et al. (2008)

Benedetti et al. (2011)

Mahon et al. (2009)


TBSS

Voxelwise analysis of DTI and tractography



ROIs analysis of DTI and voxel-based analysis of DTI



VBM

Technique

MD: Dep. BD > HC FA: remitted BD > Dep. BD MD: Dep. BD > remitted BD

UF, IFO, IC, ATR, SLF, ILF PC, IC, EC, SLF UF, ATR, PC, EC, IFO, SLF, ILF

FA: UD > BD RD: BD > RD FA: HC > BD AD: HC > BD RD: BD > HC FA: HC > UD

SLF SLF, UF

ILF

FA: HC > BD

Fibers of the pontine crossing tract

MD: BD > HC

Prefrontal WM

FA: BD > HC

MD: BD > HC AD: BD > HC RD: BD > HC

PC

CPT, CST, ATR, SLF

FA: HC > BD AD: BD > HC MD: BD > HC AD: BD > HC RD: BD > HC

FA: HC > BD

AC

AC

FA: HC > BD

MD: BD > HC

SLF, ILF

CC

FA: HC > BD

FA: HC > SZ

EC, SLF, ILF

FA: HC > BD

IC

FA: HC > SZ

IC, ATR, UF, IFO IC, UF

FA: HC > BD

Main findings

IC, ATR, UF

WM tracts

[90]

[89]

[88]

[87]

[86]

[85]

[84]

Ref.


24

38

21

42

38

42 BDI 28 SZ

Sussmann et al. (2009)



Study (year)

Table 2. Characteristics of the studies included in review: structural connectivity (cont.).





16 euthymic BD

17 BDI 7 BDII 16 cyclothymia

11 BDI

22 BD

23 euthymic BDI

31 BDI

19 BDI 21 healthy firstdegree relatives

26 BD 15 unaffected siblings

45 psychotic BDI 41 not psychotic BDI or BDII 7 healthy firstdegree relatives

70 healthy firstdegree relatives

Houenou et al. (2007)

Haznedar et al. (2005)

YurgelunTodd et al. (2007)

Wessa et al. (2009)

Barysheva et al. (2013)


Emsell et al. (2013)

Mahon et al. (2013)

Tighe et al. (2012)

Whalley et al. (2013)

60

32

15

18

25

19

21

10

36

16


TBSS

WMH volume analysis


Tractography

TBSS

Whole brain voxel-based analysis

Whole brain voxel-based analysis



Tractography

Technique

–

–

No significant correlation between FA and BD polygene

Positive relationship between increasing WMH volume and diagnostic category severity

FA: HC > unaffected siblings > BD

Increased genetic liability for BD associated with reduced FA

UF, SLF IFO

FA: HC > BD RD: BD > HC

FA: HC > BD RD: BD > HC

Right UF AC, PC, SLF, CC, ILF

FA: BD > HC AD: BD > HC

MD: BD > HC RD: BD > HC

CC, ILF, IFO, SLF, PC, PTR, IC Left UF, OR, ATR

FA: HC > BD

FA: BD > HC

FA: HC > BD

FA: HC > BD

Increased number of reconstructed fibers in BD

Main findings

CC, ILF, IFO, SLF, PC, PTR, PCR, ACR, SCR

Medial frontal, precentral, inferior parietal and occipital WM

Genu and splenium of CC

IC, frontal WM

AC, PC

WM tracts

[103]

[101]

[100]

[99]

[96]

[95]

[94]

[93]

[92]

[91]

Ref.



Study (year)

Table 2. Characteristics of the studies included in review: structural connectivity (cont.).



Review

639

640

13–18

8–17

7–18

12–18

7–17

Wang et al. (2012)

Ladouceur et.al. (2012)

Rich et al. (2008)

Wegbreit et al. (2011)

Dickstein et al. (2010)

15 Euthymic BDI/BDII

24 Manic BDI; n = 22 responder to pharmacotherapy; n = 12 nonresponders

30 BDI, 3 BDII; n = 18 euthymic; n = 11 hypomanic; n = 3 Dep.; n = 1 mixed

18 BDI, 18 BD-NOS

21 BDI


Emotional task

14

Resting state

Emotional task

24

15

Emotional task

Emotional task

fMRI task

18

36


Partial cross Correlation and Multivariate Autoregressive Modeling

Independent Component Analysis

Correlational analyses TS

PPI

Correlational analyses TS

Technique

DLPFC–STG

Amy–PHG–HIP– VLPFC–OFC– Insula–TP–CV

Amy–FSG/PHG

Amy–PCC

BD > HC

Predict response to medication in BDI responders Predict the reduction of symptoms

BDI responders > BDI non responders. BDI responders more similar to HC than nonresponders

HC > BD

For happiness: BD-NOS >HC, BDI

For happiness, fearful and neutral faces: HC > BDI

Amy–TP

VMPFC–DLPFC

For neutral faces: HC > BDI

Amy–Insula

For fear: HC, BD-NOS > BDI

For happiness and neutral faces: HC > BDI

Amy–ACC

Amy–VMPFC

For happiness, fear and neutral faces: HC > BDI

Effect

Amy–OFC

Connections

[121]

[120]

[119]

[118]

[117]

Ref.

ACC: Anterior cingulate cortex; Amy: Amygdala; BDI: Bipolar disorder type I; BDII: Bipolar disorder Type II; BD-NOS: Not otherwise specified; CV: Cerebellar vermis; Dep.: Depressed; DLPFC: Dorsolateral prefrontal cortex; FSG: Fusiform gyrus; HC: Healthy controls; HIP: Hyppocampus; OFC: Orbitofrontal cortex; PHG: Parahippocampal gyrus; PPI: Psychophysiological interaction; ROI: Region of interest; STG: Superior temporal gyrus; TP: Temporal pole; TS: Time series; VLPFC: Ventrolateral prefrontal cortex, VMPFC: Ventromedial prefrontal cortex.

Age

Study (year)

Table 3. Characteristics of the studies included in review: functional connectivity in developmental age.






temporal gyrus [121]. To our knowledge, no study explored the EC in children and adolescents with BD. Despite the exiguity of studies based on fMRI data, an abundant literature can be found concerning SC, especially using DTI. These findings have been summarized in TABLE 4. Studies in affected children and adolescents confirmed a reduction of FA in CC, SLF, cingulate–paracingulate WM tracts, orbital frontal region WM, anterior corona radiata and temporo-occipital WM tracts [97,122–126]. As we previously said, these tracts have a crucial role in connecting brain regions involved in emotional regulation, and their alteration may underlie or contribute to BD symptomatology and functional disconnection. Moreover, very interesting come from two studies, which explored the SC in high-risk children and adolescents. Versace and colleagues investigated the relationship between age and FA values in CC, ILF in a cross-sectional study, by comparing HC and subjects at-risk for BD [127]. Interestingly, in HC, age correlated positively with FA values in both CC and ILF, whereas these correlations have not been found in at-risk subjects: age correlated negatively with FA in CC, and no correlation has been observed in the right ILF. Another interesting DTI study compared HC with high risk (with familiarity for BD) and affected youth: both the bipolar and high-risk groups showed decreased FA values in bilateral SLF compared with HC. However, the high-risk subjects exhibited intermediate values between bipolar patients and HC [122]. Despite the reduced number of EC/FC studies, results suggest that some of the alterations in WM tracts are detectable not only from the early stages of the disorder, but also precede the onset and are present also in at-risk subjects. These data point out that alterations in myelination or in tract development may represent aberrant developmental patterns associated with BD [128] and may play a role in the pathophysiology of bipolar illness [129]. A disruption of SC, probably expression of complex epigenetic processes that are associated with the genetic load [122], evolves throughout the development into a progressive disconnection, which may be gradually paralleled by functional disconnectivity. Future researches should implement a longitudinal perspective in order to explore relationship between WM abnormalities in the etiopathogenesis of the disorder. Finally, as for DTI studies, the current state of patients is often heterogeneous or not given; more attention should be paid on this aspect to allow a correct inference from the data. Comparison with other psychiatric disorders

BD patients have to wait in average between 5 and 10 years from onset to receive a correct diagnosis and treatment [130,131]. The identification of reliable markers for the disorder may increase the timeliness and accuracy of diagnosis and prognosis. One of the most problematic issue is the early differential diagnosis between bipolar depression and unipolar depression: around 60% of BD individuals are initially diagnosed as unipolar depressed (UD) patients [132]. A misdiagnosis between bipolar and unipolar depression may have considerable informahealthcare.com

Review

consequences, one of the most significant is the inappropriate treatment, which is associated with a poor prognosis and increased risk of suicide and switching to mania [132–138]. Several studies explored the neural correlates of UD, showing similar pattern of functional and structural alterations, such as an abnormally elevated subcortical and reduced prefrontal cortical activity during emotion processing and a reduced FA in WM tracts (for detailed review, see [9,90,139–142]). Nevertheless, few studies explored the connectivity comparing directly BD, UD and HC (for a detailed review, see [138]). Two studies investigated the EC in these groups [54,143], highlighting a reduced bottom-up Amy–VMPFC connectivity in BD in comparison to other groups, but only UD showed a reduced topdown Amy–VMPFC connectivity compared with HC. Considering SC, two studies can be detected [90,144]. Findings suggest that the BD is characterized by a more global and deeper WM abnormalities and these may underlie to the greater mood liability observed in BD [138], but no qualitative difference has yet been described. A clinically challenging differential diagnosis is that between BD and borderline personality disorder (BPD). The similar symptomatology, with affective instability and impulsivity, and the subsyndromal symptoms that can occur between frank episodes in BD may contribute to the diagnostic confusion [145]. In terms of neural correlates, to our knowledge, no studies investigated the connectivity by directly comparing BD and BDP. SC studies highlighted that BPD patients have a reduced FA in frontal WM [146–149]. In terms of FC, an altered connectivity has been found between ACC, PFC and Amy during emotional processing of neutral faces [150,151]. BPD subjects showed an increased FC between the amygdalae, subgenual ACC and medial PFC, but a reduced connectivity between subgenual ACC and dorsal ACC compared with HC during the processing of negative emotions [150,151]. Although data suggest that BD is generally more characterized by a reduced FC connectivity than BPD, future studies may be aimed at exploring the EC in BPD and explicit comparing BD and BPD to clarify how connectivity can help in characterizing these two disorders. The study of connectivity may provide a new insight also on the relationship between BD and the other major psychosis: schizophrenia (SZ). BD and SZ are genetically related [152,153] and present some overlapping clinical phenomenology such as depression, mania, hallucinations, delusions and disorganization [154–156]. Recently, Frangou [157] has proposed a synthesis of the evidences from the relevant neuroimaging literature about the structural, functional and connectivity alterations in these two disorders. Studies that explored the connectivity during the processing of emotional stimuli and resting stare found an absent [158], reduced [159] or reversed [160] FC between PFC and Amy. A study of EC during emotional processing in SZ highlighted a reduced Amy connectivity with other areas such as fusiform gyri, cerebellum and superior frontal cortex, and insula [161]. A reduced PFC–amygdala coupling was also associated with psychosis proneness in the general population [162]. 641

642 21 BDI; n = 11 euthymic; n = 6 manic or mixed; n = 4 Dep.

9–18

4–16

Adolescents

Children

10–18

7–17

8–17

BarneaGoraly et al. (2009)

Frazier et al. (2007)

Kafantaris et al. (2009)

Pavaluri et al. (2009)

Adler et al. (2006)

Saxena et al. (2012)


25

10

17

TBSS

TBSS

Multiple analysis of variance on FA


Voxelwise analysis of DTI

26

15


TBSS

18

8

Technique


HC and age FA: linear increase; RD: linear decrease First-degree relatives and age FA: linear decrease; RD: linear increase

CC

FA : HC > BD FA values in the AC were negatively correlated with a life history of aggression in the BD group

FA : HC > BDI

FA: HC > BDI

CC, ILF

CC, AC

Superior frontal regions

ACR

ADC: BDI > HC

CC

FA: HC > BD, AR-BD

SLF

FA: HC > BDI

FA: AR-BD > BD

CG-PAC

OFC WM

FA: HC > BD

FA: HC > BDI

Effect

SLF, CG-PAC, CC, OFC WM

CC, PCR, PC, Fornix

WM tracts

[127]

[126]

[125]

[123]

[122]

[97]

Ref.

AC: Anterior commissure; ACR: Anterior corona radiata; ADC: Apparent diffusion coefficient; AR-BD: At risk of bipolar disorder; BDI: Bipolar disorder type I; BDII: Bipolar disorder type II; BD-NOS: Not otherwise specified; CC: Corpus callosum; CG–PAC: Cingulate–paracingulate white matter; Dep.: Depressed; FA: Fractional anisotropy; HC: Healthy controls; IFL: Inferior longitudinal fasciculus; OFC WM: Orbitofrontal white matter; PC: Posterior cingulate bundle; PCR: Posterior corona radiata; RD: Radial diffusivity ; SLF: Superior longitudinal fasciculus; TBSS: Tract-based spatial statistics.

20 first-degree relative BDI

8 BDI; 2 BD-NOS

11 manic BDI

14 euthymic BDI

26 BDI (in at least partial remission to manic or mixed states)

10 BD; 7 AR-BD


Age

Study (year)

Table 4. Characteristics of the studies included in review: structural connectivity in developmental age.






Remarkably, behavioral measures of emotional regulation in SZ correlated with the FC between corticolimbic structures including PFC, temporal lobe, parahippocampal cortex and amygdala [163]. Resting-state studies found that both SZ and BD disorders are mainly associated with dysfunctional connectivity within prefrontal networks [42,44,46,164–168]. Current findings implicated that a disruption in frontoparietal connectivity primarily in SZ [164,165] reduced fronto-occipital connectivity in SZ and BD [164–166] and increased fronto-Amy connectivity primarily in BD [42,46,157,167,168]. Major DTI findings highlighted a decreased FA in the cingulate, CC and frontal lobes in chronic SZ [169,170], whereas patients at the first episodes showed a decreased MD in the left parahippocampal gyrus, left insula and right anterior cingulate gyrus without any reduction of brain volume or abnormalities of FA [171–173]. A meta-analysis of the DTI studies conducted SZ highlighted a significant WM reductions in two regions: the left frontal deep WM and the left temporal deep WM [174]. The first region, in the left frontal lobe, is traversed by WM tracts interconnecting the frontal lobe, thalamus and cingulate gyrus. The second region, in the temporal lobe, is traversed by WM tracts interconnecting the frontal lobe, insula, hippocampus–amygdala, temporal and occipital lobe [174]. Interestingly, McIntosh et al. [82] found that a decreased mean FA in the uncinate fasciculus, present in both the clinical groups, may be a neural basis underlying both the psychotic disorders. Nearly 50% of patients with BD have an additional diagnosis [175], one of the most difficult to manage being obsessivecompulsive disorder (OCD) [176]. A recent review summarized the findings of DTI studies in OCD [177]. Although findings are extremely heterogeneous [178–191], the cingulate bundle, the CC and the anterior limb of the internal capsule are most commonly affected by decreased WM integrity in adult OCD. This heterogeneity may rely on the clinical and treatment variability of the patients. In pediatric and adolescent patients, initial evidence points more toward increased WM connectivity [192–195], potentially due to a premature myelination. Results of an altered WM structure of areas, such as the cingulate bundle, are in line with the hypothesis that one of the major neural correlates of OCD is the alteration of fronto-striato-thalamocortical circuitries [177]. An alteration in these circuits might underlie the repetitive behavior and cognitive inflexibility that characterize the disorder of OCD [196,197]. A study of EC found a significantly stronger task-related modulation (Stroop ColorWord task) on the connection from the dorsal ACC to left DLPFC in OCD patients [198]. These findings are consistent with an overactive error control system in OCD subserving suppression of prepotent responses during decision-making. Several studies of FC connectivity confirmed alteration in the fronto-striato-thalamo-cortical (i.e., [199–207]). The dysregulation of the fronto-striato-thalamo-cortical circuit may be one of the major findings, which can help to differentiate the two disorders, as the presence of an increased WM integrity during in pediatric and adolescent patients affected by OCD. Future studies that investigate the connectivity in the comorbidity between OCD and BD are required. informahealthcare.com

Review

Expert commentary

Neuroscience has provided new insights into the neurobiological underpinnings of psychiatric disorders. Different neuroimaging techniques have been applied in BD, providing strong evidence of brain abnormalities in this disorder. Brain imaging studies mainly highlight functional and structural alterations within prefrontal cortical areas and the limbic system. A reduced FC between VLPFC/ACC/OFC and limbic areas, especially amygdala, has been widely showed in BD and detected across mood states [31–35], in not affected relatives [37], and in pediatric patients [117–119]. Moreover, an increased FC within this network was also related to a better clinical response to mood stabilizer [120], and some neuroimaging studies comparing bipolar to UD patients suggested that the amygdala–prefrontal disconnectivity is potentially able to distinguish bipolar and unipolar depression [88,90]. These data point out that the chronic reduced or altered top-down modulation, confirmed by EC studies [54–57], may identify a biomarker of the disorder. Both the hypoactivation of prefrontal areas [60,61] and a disrupted top-down connectivity may interact and result in an ineffective inhibitory regulation of limbic responses. This may provide a neurobiological underpinning of the emotional and cognitive deficits underlying the mood liability and reactivity typical of the disorder. The altered functional top-down connectivity is most likely influenced or even preceded by structural impairments in frontolimbic WM tracts. These alterations are detectable in pediatric patients [97,122–126] and in at-risk subjects [99–102,122,127], and probably underlie to subsyndromal symptomatology that precedes the onset of the disorder [109,110], and that results from epigenetic abnormal neurodevelopmental processes. These multimodal data clearly prompt the hypothesis that the frontolimbic disconnection could help to identify a plausible reliable biomarker of the disorder, mediating the relationship between the underlying susceptibility genes and the clinical expression of BD. Five-year view

Although in the past decade most studies have aimed at associating specific functions and specialized brain regions by applying a segregational approach to the brain, the research perspective is now changing by focusing on how these specialized areas work together and how they are integrated in functional and structural networks. Future research is required to consolidate the role of the corticolimbic network as a neurobiological underpinning and a biomarker in bipolar psychopathology. Longitudinal designs, with larger and more homogenous samples [104], will help to distinguish neurobiological abnormalities that underlie traits of the illness from those related to psychopathological states such as episodes of mood exacerbation or pharmacological treatment [104]. Furthermore, studies in at-risk subjects will improve our knowledge about different developmental trajectories, and possibly help to predict the outcome of the disease. Finally, the explicit statistical integration between different structural and functional neuroimaging techniques (i.e., fMRI, voxel-based morphometry and techniques of functional, effective and SC) will provide a more complex and deeper understanding of the disorder. 643

Review


Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This

includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.

Key issues • The frontolimbic network has been proposed as a key circuitry in the etiophatology and maintenance of bipolar disorder (BD). A disrupted structural, functional and effective brain connectivity within this network may identify a possible biomarker for the disorder. • A reduced functional and effective connectivity within the frontolimbic network has been widely displayed in BD and detected across Expert Review of Neurotherapeutics Downloaded from informahealthcare.com by CDL-UC San Diego on 06/11/15 For personal use only.

mood states in not affected relatives and in pediatric patients. Furthermore, the amygdala–prefrontal disconnectivity is potentially able to distinguish bipolar and unipolar depression. • An increased functional connectivity within this network was also related to a better clinical response to mood stabilizer. • Alterations in structural connectivity have also been confirmed in this network and are detectable in childhood and at-risk subjects, and probably underlie to subsyndromal symptomatology that precedes the onset of the disorder. • The multimodal data clearly prompt that the frontolimbic disconnection, probably the result of epigenetic abnormal neurodevelopmental processes, identifies a plausible reliable biomarker of the disorder, mediator of the relationship between the underlying susceptibility genes and the clinical expression of BD.

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Further evidence for plasma progranulin as a biomarker in bipolar disorder.

ECT as prophylactic treatment for bipolar disorder.

Intrinsic Amygdala Functional Connectivity in Youth With Bipolar I Disorder.

Evaluating aripiprazole as a potential bipolar disorder therapy for adults.

Lurasidone as a potential therapy for bipolar disorder.

Perturbational Profiling of Metabolites in Patient Fibroblasts Implicates α-Aminoadipate as a Potential Biomarker for Bipolar Disorder.

Ventral anterior cingulate connectivity distinguished nonpsychotic bipolar illness from psychotic bipolar disorder and schizophrenia.

Bipolar disorder in idiopathic dystonia: clinical features and possible neurobiology.

A Possible Common Neurophysiologic Basis for MDD, Bipolar Disorder, and Schizophrenia: Lessons from Electrophysiology.

Lithium response viewed as a biomarker to predict developmental psychopathology in offspring with bipolar disorder: a commentary.

Peripheral brain-derived neurotrophic factor (BDNF) as a biomarker in bipolar disorder: a meta-analysis of 52 studies.

Asenapine for bipolar disorder.

BDNF and proBDNF as biomarkers for bipolar disorder.

Metachromatic leukodystrophy presenting as bipolar disorder.

Brain lactate as a potential biomarker for comorbid anxiety disorder in autism spectrum disorder-reply.

Brain lactate as a potential biomarker for comorbid anxiety disorder in autism spectrum disorder.

Elucidating neural network functional connectivity abnormalities in bipolar disorder: toward a harmonized methodological approach.

Cognition as a target in schizophrenia, bipolar disorder and depression.

Oxidized DJ-1 as a possible biomarker of Parkinson's disease.

Mean platelet volume in bipolar disorder: the search for an ideal biomarker.

Age as a predictor of cognitive decline in bipolar disorder.

Allostasis as a conceptual framework linking bipolar disorder and addiction.

Big data for bipolar disorder.

Clinical endophenotypes for bipolar disorder.